Search Results (5,354)

Recently, there has been significant progress in the area of semantic classification of water bodies at global scales with deep learning. For the key purposes of water inventory and change detection, advanced deep learning classifiers such as UNets and Vision Transformers have been shown to be both accurate and flexible when applied to large-scale, or even global, satellite image datasets from optical (e.g., Sentinel-2) and radar sensors (e.g., Sentinel-1). Most of this work is conducted with optical sensors, which usually have better image quality, but their obvious limitation is cloud cover, which is why radar imagery is an important complementary dataset. However, radar imagery is generally more sensitive to soil moisture than optical data. Furthermore, topography and wind-ripple effects can alter the reflected intensity of radar waves, which can induce errors in water classification models that fundamentally rely on the fact that water is darker than the surrounding landscape. In this paper, we develop a solution to the use of Sentinel-1 radar images for the semantic classification of water bodies that uses style transfer with multi-modal and multi-temporal image fusion. Instead of developing new semantic classification models that work directly on Sentinel-1 images, we develop a global style transfer model that produces synthetic Sentinel-2 images from Sentinel-1 input. The resulting synthetic Sentinel-2 imagery can then be classified with existing models. This has the advantage of obviating the need for large volumes of manually labeled Sentinel-1 water masks. Next, we show that fusing an 8-year cloud-free composite of the near-infrared band 8 of Sentinel-2 to the input Sentinel-1 image improves the classification performance. Style transfer models were trained and validated with global scale data covering the years 2017 to 2024, and include every month of the year. When tested against a global independent benchmark, S1S2-Water, the semantic classifications produced from our synthetic imagery show a marked improvement with the use of image fusion. When we use only Sentinel-1 data, we find an overall IoU (Intersection over Union) score of 0.70, but when we add image fusion, the overall IoU score rises to 0.93. Full article

The increasing reliance on lithium-ion batteries (LIBs) in electric vehicles (EVs) has intensified the need for structurally resilient and lightweight protective enclosures that can withstand mechanical abuse during crashes. This study addresses the challenge by drawing inspiration from the hierarchical geometry of bighorn sheep horns to design a bio-inspired battery frame with improved crashworthiness. A multilayered structure, replicating both the internal and external features of the horn, was fabricated using Fused Deposition Modeling (FDM) with Acrylonitrile Butadiene Styrene (ABS) and carbon fiber composite (CFC) materials. The experimental evaluation involved tensile and compression testing, Izod impact tests, digital image correlation (DIC), and acoustic emission (AE) monitoring for full-field strain mapping, aiming to assess structural performance under various loading scenarios. Results demonstrate that the bioinspired designs exhibit enhanced energy absorption, mechanical strength, and strain distribution compared to conventional configurations. The improved vibration response and damage tolerance observed in structured samples suggest their potential for application in battery protection systems. This work underscores the feasibility of leveraging natural design principles to engineer robust, lightweight enclosures for advanced energy storage systems, contributing to safer and more reliable EV technologies. Full article

This article presents the manufacturing technology and mechanical properties of innovative pre-impregnated coatings (PCs). The base materials for PC are powders of metal oxides, non-metals, minerals and thermoplastic non-wovens. PC can be used in the manufacture of composites by methods such as vacuum infusion, autoclave curing or hand lamination. This is possible due to the novel PC structure consisting of a functional layer (FL) and a backing layer (BL). PCs are flexible so that they can be used on curved surfaces. In this work, five types of PC were subjected to a uniaxial tensile test. Depending on the powder used, failure force values ranging from 24.61 N to 28.73 N were obtained. In the next step, the pre-impregnated coatings were applied as a coating in “sandwich” composites made by vacuum infusion, which were subjected to three-point bending (3-PB) and adhesion tests. 3-PB tests proved that the coating remained integral with the substrate, even under high flexural deformation, while the adhesion achieved was in the range of 0.95 MPa to 1.57 MPa. PC can be used in many engineering products, e.g., for the coating of façade panels, roof tiles, automotive parts or rail vehicles, etc. Full article

Polybutylene succinate (PBS), as one of the most promising multi-application polymer, still suffers from low toughness, poor miscibility, and high crystallinity. Blending with starch is an effective strategy to improve the properties of PBS, but the compatibility and dispersity between starch and PBS still need to be optimized. In this study, mechanical ball milling was carried out to synthesize esterified starch and the subsequent PBS/esterified starch blend. The FT-IR and XPS analyses confirmed the existence of molecular interactions between PBS and esterified starch. SEM images showed a homogeneous surface for the PBS/esterified starch blend, highlighting the favorable compatibility and good dispersion of starch within the PBS matrix. TGA, DSC, and VSP tests indicated that the introduction of esterified starch into PBS lowered the thermal transition temperatures, thereby enhancing the processability. WCA measurements displayed that the water contact angle of the PBS/esterified starch blends gradually decreased with increasing esterified starch content, proving the improved hydrophilicity of PBS/esterified starch blends. Mechanical testing indicated that incorporating 5 wt% esterified starch into PBS significantly improved the tensile strength to 36.35 ± 2.16 MPa and the breaking elongation to 27.18 ± 5.08%, surpassing those of the pure PBS, PBS/esterified starch mixture, and PBS/starch blend. Our study indicates that mechanical ball milling is an efficient method to improve the properties of PBS composites. Full article

Accurate and detailed tissue characterization is a central goal in medical diagnostics, often requiring the combination of multiple imaging modalities. This study presents a multimodal imaging system that integrates mid-infrared (MIR) scanning with fluorescence imaging to enhance the chemical specificity and spatial resolution in biological samples. A motorized mirror allows rapid switching between MIR and fluorescence modes, enabling efficient, co-registered data acquisition. The MIR modality captures label-free chemical maps based on molecular vibrations, while the fluorescence channel records endogenous autofluorescence for additional biochemical contrast. Applied to mouse brain tissue, the system enabled the clear differentiation of gray matter and white matter, supported by the clustering analysis of spectral features. The addition of autofluorescence imaging further improved anatomical segmentation and revealed fine structural details. In mouse skin, the approach allowed the precise mapping of the layered tissue architecture. These results demonstrate that combining MIR scanning and fluorescence imaging provides complementary, label-free insights into tissue morphology and chemistry. The findings support the utility of this approach as a powerful tool for biomedical research and diagnostic applications, offering a more comprehensive understanding of tissue composition without relying on staining or external markers. Full article

This research successfully synthesized a novel n-n heterojunction Bi₂O₂CO₃/AgVO₃ nanocomposite photocatalyst via the in situ chemical deposition process. Characterization results strongly confirmed the formation of a tight heterojunction at the Bi₂O₂CO₃/AgVO₃ interface. The nanocomposite exhibited characteristic XRD peaks and FT-IR vibrational modes of both Bi₂O₂CO₃ and AgVO₃ simultaneously. Electron microscopy images revealed AgVO₃ nanorods tightly and uniformly loaded onto the surface of Bi₂O₂CO₃ nanosheets. Compared to the single-component Bi₂O₂CO₃, the composite photocatalyst exhibited a red shift in its optical absorption edge to the visible region (515 nm) and a decrease in bandgap energy to 2.382 eV. Photoluminescence (PL) spectra demonstrated the lowest fluorescence intensity for the nanocomposite, indicating that the recombination of photogenerated electron–hole pairs was suppressed. After 90 min of visible-light irradiation, the degradation efficiency of Bi₂O₂CO₃/AgVO₃ toward methylene blue (MB) reached up to 99.55%, with photodegradation rates 2.51 and 2.79 times higher than those of Bi₂O₂CO₃ and AgVO₃, respectively. Furthermore, the nanocomposite exhibited excellent cycling stability and reusability. MB degradation was gradually enhanced with increasing the photocatalyst dosage and decreasing initial MB concentration. Radical trapping experiments and absorption spectroscopy of the MB solution revealed that reactive species h⁺ and ·O₂⁻ could destroy and decompose the chromophore groups of MB molecules effectively. The possible mechanism for enhancing photocatalytic performance was suggested, elucidating the crucial roles of charge carrier transfer and active species generation. 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

This article explores the underlying ideas conveyed by the literary representations associated with Luqmān b. ʿĀd and Luqmān the Sage in classical Arabic sources. It avoids conflating them or collapsing all portrayals of Luqmān b. ʿĀd into a single composite figure. At the same time, it resists imposing a rigid dichotomy between these representations, instead examining possible mutual influences and conceptual continuities. To assess the range of divergent Luqmān images in light of the Qurʾānic tradition, the article treats them as manifestations of diverse local and regional narrative currents, woven together within a broader pan-Arabic reservoir of motifs. Full article

High-resolution remote sensing (HRRS) imagery enables the extraction of cropland information with high levels of detail, especially when combined with the impressive performance of deep convolutional neural networks (DCNNs) in understanding these images. Comprehending the factors influencing DCNNs’ performance in HRRS cropland extraction is of considerable importance for practical agricultural monitoring applications. This study investigates the impact of classifier selection and different training data characteristics on the HRRS cropland classification outcomes. Specifically, Gaofen-1 composite images with 2 m spatial resolution are employed for HRRS cropland extraction, and two county-wide regions with distinct agricultural landscapes in Shandong Province, China, are selected as the study areas. The performance of two deep learning (DL) algorithms (UNet and DeepLabv3+) and a traditional classification algorithm, Object-Based Image Analysis with Random Forest (OBIA-RF), is compared. Additionally, the effects of different band combinations, crop growth stages, and class mislabeling on the classification accuracy are evaluated. The results demonstrated that the UNet and DeepLabv3+ models outperformed OBIA-RF in both simple and complex agricultural landscapes, and were insensitive to the changes in band combinations, indicating their ability to learn abstract features and contextual semantic information for HRRS cropland extraction. Moreover, compared with the DL models, OBIA-RF was more sensitive to changes in the temporal characteristics. The performance of all three models was unaffected when the mislabeling error ratio remained below 5%. Beyond this threshold, the performance of all models decreased, with UNet and DeepLabv3+ showing similar performance decline trends and OBIA-RF suffering a more drastic reduction. Furthermore, the DL models exhibited relatively low sensitivity to the patch size of sample blocks and data augmentation. These findings can facilitate the design of operational implementations for practical applications. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) and climate change are major global health challenges. Aim: Our aim was to assess the relationship between intrahepatic fat content (IFC) and diet-related environmental impact in a Mediterranean diet (MD)-based intervention. Design: The design included a six-month longitudinal analysis within the frame of a FLIPAN randomized controlled trial, including 60 participants aged 40–60 years with MASLD, metabolic syndrome and obesity. Methods: IFC expressed as a percentage (%IFC) was measured by magnetic resonance imaging, and dietary intake was assessed via a validated food frequency questionnaire (FFQ). Environmental impacts of diets were estimated using life cycle assessment data from the Agribalyse^® database, focusing on greenhouse gas (GHG) emissions, water use, energy use and land use. A composite sustainability score was also calculated. Changes in liver fat and environmental footprints were analyzed using a general linear model (GLM) adjusted for within-subject variability and partial correlation analysis adjusted for energy intake, MD adherence and body weight. Results: The participants with the highest %IFC reduction group in the GLM showed the highest decreases in GHG emissions and land use. Water use increased in this same group. Energy use and the composite sustainability score did not differ significantly between groups. Higher %IFC reductions were also associated with higher MD adherence and lower visceral fat. When the adjusted partial correlation analysis for the environmental parameters was performed, only water use remained significant. Conclusions: Higher reductions in %IFC were linked to dietary patterns with lower GHG emissions and land use and higher water use. However, when adjusted by energy intake, MD adherence and body weight in continuous modeling, only higher water use was related to lower %IFC. These findings highlight the complexity of achieving environmentally sustainable and health-promoting diets. Full article

This study aims to evaluate the shrinkage, degree of conversion, water sorption and solubility, and mechanical properties of a newly developed one-shade universal composite and compare it with five other commercially available universal composites with one or multiple shades. Our proprietary resin and filler technologies developed the experimental one-shade universal composite (Experimental). Volumetric shrinkage was determined using the AcuVol video imaging method (n = 5). Degree of conversion was measured using FTIR (n = 5). Water sorption and solubility (15 × 1 mm, n = 5) and flexural strength and modulus (2 × 2 × 25 mm, n = 5) were measured according to ISO-4049. Diametral tensile strength (6 × 3 mm, n = 8) was tested according to ANSI/ADA-Specification #27. The data were analyzed using one-way ANOVA and post hoc Tukey tests (p ≤ 0.05). Like Clearfil Majesty ES-2, Experimental showed lower or significantly lower volumetric shrinkage than other composites. Experimental exhibited a considerably higher degree of conversion and high flexural modulus compared to the others. However, there are no significant differences in flexural strength among these universal composites except for Omnichroma. Experimental also displayed significantly higher diametral tensile strength than the others, except similar to Filtek Supreme Ultra. Experimental has the lowest values of water sorption and solubility among the composites tested. The experimental universal composite demonstrated improved or comparable physical and mechanical properties compared to commercially available one-shade universal composites or multi-shade conventional universal composites, which is of significance for the clinical performance of dental restorations. Full article

This study develops a geomorphological identification methodology for wall reefs in the microcontinental environment of Wangiwangi Island, Indonesia, using medium-resolution Landsat 8 satellite imagery and morphological analysis based on Maxwell’s geomorphological framework. The uniqueness of the wall reef landform lies in the fact that the lagoon elongates on limestone, resulting in a habitat and ecosystem that develops differently from those of other shelf reefs, namely, platform reefs and plug reefs. Using Optimum Index Factor (OIF) optimization and RGB image composites, four reef types were successfully identified: cuspate reefs, open ring reefs, closed ring reefs, and resorbed reefs. A field check was conducted at fifteen observation sites, which included measurements of depth, turbidity, and water quality parameters, as well as an in situ benthic habitat inventory. The analysis results showed a strong correlation between image composites, geomorphological reef classes, and ecological conditions, confirming the successful adaptation of Maxwell’s classification to the Indonesian reef system. This hybrid integrated approach successfully maps the distribution of reefs on a complex continental shelf, providing an essential database for shallow-water spatial planning, ecosystem-based conservation, and sustainable management in the Coral Triangle region. Policy recommendations include zoning schemes for protected areas based on reef landform morphology, strengthening integrative monitoring systems, and utilizing high-resolution imagery and machine learning algorithms in further research. Full article

Orthopedic implants have a critical role in modern medical practice, being useful in bone regeneration, joint arthroplasty, and healing fractures. The success of osseointegration depends on implant properties (composition, stability, geometry, biocompatibility) and host factors (local reactivity, comorbidities). Preclinical evaluation in animal models is essential before clinical application. In orthopedic implantology, the selection and real utility of a range of animals are important, with an emphasis placed on bone–implant interface, biomechanical function, and long-term integration. Smaller animals such as rabbits and rats have widespread use in early biocompatibility and osseointegration testing, but larger animals such as pigs, sheep, and canines have a larger physiological bone similarity and can, therefore, be utilized for bearing loads in testing. Considering the utility and disadvantages of certain species—including suitability for new biomaterials, coatings, and biomechanical function—this article discusses testing methodologies such as push-out/pull-out tests, histomorphometry, and micro-CT and their utility in testing the integration of implants and regeneration of bone. Conclusions confirm a multi-species model in use in preclinical testing for the development of implants and improvements in clinical success. Unlike previous reviews, this article emphasizes translational strategies, integrates ethical perspectives in model selection, and discusses the synergistic use of imaging modalities with biomechanical tests for comprehensive assessment. Full article

In this paper, we propose a novel Dynamic Cipher Composition (DCC) based on multi-algorithm approach using the Library of Image Encryption Methods (LIEM). Unlike conventional static encryption schemes, the proposed DCC randomly selects and applies different encryption algorithms to spatially segmented regions of an image during each execution. To manage this process efficiently, the system employs two lightweight registers: one for configuration management and another for region-specific modality assignment, both indexed for streamlined storage and retrieval. Experimental evaluations conducted on standard test images demonstrate that the DCC achieves a near-optimal Shannon entropy, high values of Net Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI), and negligible pixel correlation coefficients. These results confirm the scheme’s strong resistance to statistical, differential, and structural attacks, while preserving computational efficiency suitable for real-time applications such as telemedicine, cloud storage, and video surveillance systems. Full article

Jets and outflows are key components of low-mass star formation, regulating accretion and shaping the surrounding molecular clouds. These flows, traced by molecular species at (sub)millimeter wavelengths (e.g., CO, SiO, SO, H₂CO, and CH₃OH) and by atomic, ionized, and molecular lines in the infrared (e.g., H₂, [Fe II], [S I]), originate from protostellar accretion disks deeply embedded within dusty envelopes. Jets play a crucial role in removing angular momentum from the disk, thereby enabling continued mass accretion, while directly preserving a record of the protostar’s outflow history and potentially providing indirect insights into its accretion history. Recent advances in high-resolution, high-sensitivity observations, particularly with the James Webb Space Telescope (JWST) in the infrared and the Atacama Large Millimeter/submillimeter Array (ALMA) at (sub)millimeter wavelengths, have revolutionized studies of protostellar jets and outflows. These instruments provide complementary views of warm, shock-excited gas and cold molecular component of the jet–outflow system. In this review, we discuss the current status of observational studies that reveal detailed structures, kinematics, and chemical compositions of protostellar jets and outflows. Recent analyses of mass-loss rates, velocities, rotation, molecular abundances, and magnetic fields provide critical insights into jet launching mechanisms, disk evolution, and the potential formation of binary systems and planets. The synergy of JWST’s infrared sensitivity and ALMA’s high-resolution imaging is advancing our understanding of jets and outflows. Future large-scale, high-resolution surveys with these facilities are expected to drive major breakthroughs in outflow research. Full article