Search Results (25,641)

Hierarchical functionalisation of the UiO-66(Zr)-NH₂ metal–organic framework with cysteine, poly(ethylene glycol) (PEG), and the SARS-CoV-2 spike receptor-binding domain (RBD) was developed to enable receptor-specific interaction with the angiotensin-converting enzyme 2 receptor (ACE2) in model cells. Post-synthetic modification using cysteine and heterobifunctional PEG linkers allowed controlled bioconjugation of SpyTag-labelled RBD via SpyTag/SpyCatcher chemistry, while preserving the crystallinity, microporosity, and intrinsic optical properties of the UiO-66(Zr)-NH₂ framework. Comprehensive physicochemical characterisation confirmed successful surface functionalisation, tunable aggregation behaviour, and retention of multimodal optical characteristics. Cellular studies in HEK293T and HeLa cells overexpressing EGFP-tagged ACE2 demonstrated enhanced and selective association and uptake of RBD-functionalised nanoparticles compared with non-targeted analogues. Multimodal fluorescence imaging, fluorescence lifetime imaging microscopy, flow-cytometry, and electron microscopy indicated ACE2-dependent endocytic internalisation, with predominant localisation in endosomal and autophagosomal compartments, while both amine- and cysteine-modified formulations exhibited good biocompatibility. Overall, this study establishes a virus-mimetic, ACE2-targeted UiO-66(Zr)-based nanosystem as a proof-of-concept biointerface platform for receptor-specific cellular delivery and imaging, providing a foundation for future MOF-based nanocarriers exploiting ligand–receptor interactions. Full article

This study investigates the durability of silicone-based membranes in contact with hempcrete under combined moisture and temperature exposure. Membrane specimens were aged in contact with non-treated and treated hempcrete under dry and wet conditions at temperatures up to 90 °C. The evolution of chemical, thermal, and microstructural properties was characterized using FTIR, TGA, DSC, optical microscopy, and SEM–EDS analyses. Results show that dry exposure does not induce measurable changes in membrane structure or performance, confirming that temperature alone is not a critical degradation factor. In contrast, wet exposure leads to significant chemical, thermal, and microstructural changes in the membrane, including degradation of the siloxane network, reduced polymer chain mobility, and the formation of calcium-rich mineral deposits at the interface. These results indicate that membrane degradation is governed by a coupled moisture–ion mechanism involving ion transport, mineral deposition, and hydrolysis of the polymer network. Fiber treatment slightly reduces the aggressiveness of the leachate but does not prevent degradation under wet conditions. Overall, moisture availability and leachate chemistry are identified as key factors controlling the durability of silicone membranes in contact with bio-based materials. Full article

This paper presents the process of gathering data for a flapping-wing micro air vehicle (FWMAV) using optical tracking and force sensors for subsequent dynamic modeling and simulation purposes. Tethered and clamped experiments were performed to track the vehicle’s overall motion, wing kinematic angles, and aerodynamic force patterns, while additional properties such as mass, inertia tensor, center-of-mass position, and short-period excitation frequency were also examined. The methodology includes the testing approaches, modeling choices, and error analyses applied to the measurements. The results demonstrate that both tethered and clamped configurations introduce key limitations, particularly for steady-state flight. Additional constraints include structural fragility (hindering high-frequency testing), over-simplified CAD geometry, and controller tuning issues on the tail. Based on the identified parameters and experimental datasets, a high-fidelity simulation model was developed in MATLAB to serve as a platform for future control and flight envelope studies. Overall, the combination of optical tracking and force sensing provides a structured framework for linking experimental data to physical models, laying the foundation for future improvements in ornithopter modeling and testing. Full article

Bearings in electric motors are exposed to stray currents and shaft voltages, which can accelerate surface damage and reduce service life. This study examines how pulsed direct current (DC) direction affects early-stage degradation in rolling bearings under low-speed operation. A dedicated test rig was used in which the bearing inner and outer rings were connected directly to the positive and negative terminals of a pulsed DC power supply. Unipolar excitation was applied at 20 kHz with a nominal current of 3 A and shaft-voltage peaks of about 3 V for 3 h, with current flowing in only one direction during each test. After testing, the bearings were sectioned and examined by optical microscopy, scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The results showed that when current flowed from the outer ring to the inner ring, visible electrical discharge machining (EDM)-type damage was mainly found on the outer raceway. When the current direction was reversed, the damaged region shifted to the inner raceway. The affected areas showed crater-like discharge features and surface chemical changes, while the opposite raceway showed much less change under the same test conditions. These observations indicate that current direction influences where EDM-type damage more likely forms in the bearing under the present low-speed unipolar excitation conditions. Full article

Background/Objectives: This study aimed to investigate the effect of pancreatic ductal adenocarcinoma (PDAC) on the alterations in red blood cell aggregation related to angiogenesis and hypoxia markers. Methods: We studied 31 patients with confirmed PDAC. The aggregation of red blood cells (RBCs) was evaluated using a Laser-assisted Optical Rotational Cell Analyzer (LORCA). Serum vascular endothelial growth factor (VEGF) and hypoxia-inducible factor 1α (HIF-1α) levels were measured using ELISA. We estimated the following parameters specific to the aggregation process: the aggregation index (AI), the aggregation half-time (t_1/2), and the threshold shear rate (γ_thr). Results: All measured RBC aggregation parameters among PDAC subjects differed from those in the controls. The AI (p < 0.05) and γ_thr (p < 0.005) were significantly higher in the PDAC group, whereas t_1/2 (p < 0.01) and AMP (p < 0.001) were significantly lower compared to the control group. The levels of VEGF (p < 0.0001) and HIF-1α (p < 0.0001) were significantly higher in the PDAC group than in the control group. There were significant correlations between RBC aggregation parameters and VEGF and HIF-1α. Multivariate analyses further identified t_1/2 (p < 0.01) and γ_thr (p < 0.05) as independent predictors for VEGF. For HIF-1α, t_1/2 (p < 0.05) was confirmed as an independent predictor. Conclusions: The results suggest, but do not demonstrate, a direct pathophysiological link between PDAC-associated hypoxia/angiogenesis and erythrocyte aggregation. Further studies are needed because the relationship linking PDAC to these aggregation indices is unclear. Full article

Clear aligner therapy (CAT) increasingly relies on composite-based attachments to improve force transmission and aligner retention, yet the role of flowable composite properties in clinical performance remains poorly understood. In this study, five commercially available flowable composites used for orthodontic attachments—Aligner FLOW LC, SIMPLY SHADE, SOFT ENA Flow, TETRIC EvoFlow, and VENUS Bulk Flow One—were comparatively investigated through physicochemical, morphological, optical, thermal, and rheological characterization. Scanning electron microscopy coupled with energy-dispersive X-ray analysis, thermogravimetric analysis, UV–Vis–NIR and ATR–FTIR spectroscopy, and rheological measurements before and after curing were employed to probe composition, filler content, viscoelastic behavior, and mechanical response. The results revealed marked differences among the investigated materials, with post-curing storage modulus spanning nearly two orders of magnitude, from 0.06 MPa for SOFT ENA Flow to approximately 5 MPa for SIMPLY SHADE. Similarly, the elastic modulus ranged from about 20 MPa to nearly 1000 MPa for the softest and stiffest resins, respectively. Interestingly, SOFT ENA Flow, the softest material after curing, also exhibited the highest pre-curing viscosity, nearly one order of magnitude greater than the least viscous resin, Aligner FLOW LC. These findings highlight an intrinsic trade-off between pre-cure processability and post-cure mechanical stability, providing a rational framework for material selection in orthodontic attachments and supporting more predictable and durable CAT outcomes. Full article

The ceramic tile industry is increasingly required to reduce its environmental impact while maintaining high technological and aesthetic standards. In this context, the use of secondary raw materials (SRMs) represents a promising strategy to decrease the consumption of virgin resources and the energy demand associated with conventional frit production. At the same time, solar-reflective engobes can contribute to passive cooling by limiting solar heat absorption and mitigating the urban heat island effect. In this study, white solar-reflective engobes were developed by incorporating at least 8 wt.% of SRMs, including various recycled glass streams, ceramic wastes, and yttria-stabilized zirconia residues. The results demonstrate that optimized formulations achieve high solar reflectance values (up to 0.79) while maintaining the technological and aesthetic requirements of industrial ceramic tiles. Recycled glasses act as effective fluxing agents, whereas waste zirconia enhances optical performance due to its strong light-scattering capability. The most promising formulations were validated at the industrial scale, confirming their applicability under real production conditions. Overall, the developed engobes represent a scalable alternative to traditional frit-based systems, enabling reduced resource consumption and supporting the development of energy-efficient ceramic surfaces. Full article

Purpose: We investigated the association between age and retinal microvasculature parameters as measured by optical coherence tomography angiography (OCTA) and the modifying effect of diabetes status on this association. Methods: In this serial, cross-sectional study, 3 × 3 mm² macular OCTA images were obtained from healthy adults and adults with diabetes mellitus (DM) with no diabetic retinopathy (DR) or with mild non-proliferative DR (NPDR). The parameters analyzed included foveal avascular zone (FAZ) area and perimeter, vessel density (VD), vessel length density (VLD), and flow index (FI) of the superficial capillary plexus (SCP) and deep capillary plexus (DCP). The associations between OCTA parameters and age were explored using multivariable linear regression models. Results: For the included 1855 patients (1855 eyes) (49% male; mean age: 55 years), the results were as follows: no diabetes (N = 217), DM no DR (N = 1352), and mild NPDR (N = 286). Increasing age was significantly associated with decreased SCP and DCP VD and VLD in the diabetic and non-diabetic groups. The slope of association between SCP and DCP FI and age in the diabetic patients was significantly different than that in the control patients. Conclusions: The strength of the association between aging and OCTA parameters differed significantly between the controls and those with early retinopathy, pointing to a potentially altered retinal vascular homeostasis secondary to diabetic pathophysiology. This finding offers insight into the early pathological biomarkers of DR and may guide early DR management for patients based on personalized risk scores. Full article

Social media posts by individuals affected by disasters and their relatives provide a significant source of data for identifying emergencies and needs, assessing the situation, and determining affected areas. These posts often contain not only text but also text embedded within images. Therefore, focusing solely on text data may compromise the integrity of the information and lead to incomplete or limited analyses. In this study, a topic modelling-based clustering approach is proposed that accounts for the complementary nature of text and image text in social media posts, as well as the limitations of manual annotation during disasters. In this context, data pre-processing was performed on text and text extracted from images. Text extracted from images via Optical Character Recognition (OCR) was corrected using the GPT-4.0-mini model. Then, both data types were clustered separately using BERTopic with k-means, and the resulting clusters were integrated. A dictionary-based analysis was conducted to identify humanitarian relief categories and locations within the clusters. The proposed framework was applied to the social media dataset related to the Kahramanmaraş earthquakes, one of the largest disasters in recent times. The findings show that text and image text data complement each other. The resulting clusters are meaningful, with average coherence scores of 0.710 for text and 0.687 for image text. LLM-based post-OCR correction also yielded a 62.81% reduction in average character error rate and a 56.91% decrease in average word error rate compared to the normalized ground truth image text. Furthermore, the proposed approach outperformed both keyword-based filtering with k-means and BERTopic with HDBSCAN. In summary, the results demonstrate that the proposed unsupervised learning approach is effective for extracting humanitarian needs and locations from social media in disaster response. Full article

Emerging optical technologies may offer new opportunities for the non-invasive assessment of diabetic foot ulcers (DFUs), but the role of artificial intelligence (AI)-assisted autofluorescence-based approaches remains unclear. This scoping review aimed to map and summarise the published evidence on AI-assisted analysis of autofluorescence/fluorescence-based signals for DFU assessment and management. We searched Scopus, Web of Science, Embase, PubMed, CINAHL, Google Scholar, and the SPIE Digital Library, and also considered conference proceedings. We included English-language studies published between 2010 and October 2025. Of 197 records identified through database searching, 22 full-text articles were assessed for eligibility, and 5 studies met the inclusion criteria. Four studies focused on infection-related applications, specifically bacterial burden detection and Gram-type classification, whereas one study investigated tissue oxygenation estimation using a related optical imaging approach. All included studies were published between 2022 and 2025, were conducted in India, and four of the five evaluated the same device family or related variants. Overall, the evidence base was limited, geographically restricted, and technologically narrow. In addition, reporting of participant characteristics and AI methodology was often incomplete, with several studies relying on embedded proprietary or insufficiently described algorithmic components. Taken together, the available literature supports early proof-of-feasibility in restricted and largely device-specific evaluation settings rather than robust evidence of broad clinical validity, implementation readiness, or routine-care utility. Larger, more diverse, and independently validated studies with standardised acquisition procedures and more transparent AI reporting are needed before these approaches can be meaningfully evaluated for routine DFU care. Full article

Assessing methane (CH₄) emissions is critical for mitigating the environmental impact of greenhouse gases (GHGs). Optical Gas Imaging (OGI) technologies have emerged as effective tools for gas leak detection and quantification; however, their performance under varying environmental conditions and gas compositions remains insufficiently understood. In particular, previous studies have largely overlooked the combined effects of ambient temperature variations and Carbone. Dioxide (CO₂) interference on both detection sensitivity and quantification accuracy. This study addresses this gap by systematically investigating the influence of temperature and CO₂ on methane detection and quantification using an infrared OGI camera coupled with real-time analysis software. Logistic regression was employed to model detection probability under two temperature conditions at a fixed distance of 45 m. Results show that at 30 °C, the system achieved 50% and 90% detection probabilities at 5.51 and 11.30 kg/h, respectively. In contrast, at −7 °C, detection reliability decreased due to increased false positives, preventing the establishment of robust detection thresholds. However, methane quantification accuracy improved under colder conditions, with 90% of leaks correctly quantified compared to 60% in warmer environments. Furthermore, the presence of CO₂ completely inhibited methane detection and quantification. These findings demonstrate the significant and previously underexplored impact of temperature and gas composition on OGI performance, highlighting critical limitations in real-world applications and providing new insights for improving methane monitoring strategies. Full article

Graphene oxide (GO) can improve cementitious materials, but its effectiveness often differs among commercial products even at the same nominal dosage. This study proposes an optical microscopy-based framework for evaluating the initial dispersion quality of commercial GO suspensions before cement mixing. Under fixed slide preparation, imaging, and image-processing conditions, optical microscopy was used as a geometric dispersion-evaluation tool rather than a direct chemical characterization method. Three image-derived features, namely projected boundary richness, coarse-agglomerate fraction, and spatial dispersion uniformity, were integrated into an optical initial dispersion quality index, D_j. The framework was applied to five commercial GO products at a fixed dosage of 0.03 wt% of binder. The D_j-based ranking was Brand 1 > Brand 2 > Brand 3 > Brand 4 > Brand 5, and remained unchanged when the coarse-agglomerate threshold varied from 20 to 100 μm². Bootstrap resampling confirmed the robustness of the ranking. The 3-day compressive strength increased from 51.1 MPa for the control mixture to 52.6~60.8 MPa for GO-modified mortars, corresponding to enhancement ratios of 3.1~19.2%. The strength-enhancement ranking was identical to the optical dispersion ranking, with a Spearman rank correlation coefficient of ρ_s = 1.0. The proposed D_j index provides a practical pre-mixing screening tool for comparing commercial GO products before strength testing or detailed physicochemical characterization. Full article

Generative deep learning-based synthetic aperture radar (SAR)-to-optical image translation (SOIT) has been widely employed for cloud removal. However, since cloud-contaminated regions reconstructed by SOIT inevitably contain prediction errors, an additional error refinement procedure is required to achieve reliable spectral reflectance reconstruction. In this study, three machine learning-based regression models, including Random Forest (RF), eXtreme Gradient Boosting (XGB), and Natural Gradient Boosting (NGB), are comprehensively evaluated for the error refinement of optical imagery initially reconstructed by SOIT. The factors influencing refinement performance are categorized into four components: (1) the sampling strategy of training pixels from cloud-free regions (random vs. quantile-based sampling); (2) the refinement target (actual spectral reflectance vs. residual between actual and initially reconstructed reflectance); (3) SAR features (pixel-level raw SAR features vs. local spatial SAR features); and (4) the cloud fraction in the scene of interest. A systematic sensitivity analysis of their effects on error refinement performance was conducted over cropland using PlanetScope optical imagery and COSMO-SkyMed SAR imagery. The results showed that cloud fraction had the greatest impact on refinement performance. Regarding SAR features for regression, the use of local spatial SAR features improved spectral similarity by up to approximately 4.6%p compared to raw SAR features. In terms of sampling strategy, quantile-based sampling yielded better refinement performance, whereas the effect of the refinement target was less pronounced. These results suggest that local spatial SAR features and quantile-based sampling strategies are the key determinants of regression-based refinement performance in SOIT-based cloud removal. Full article

The interplay between topology and soliton is a central topic in nonlinear topological physics. So far, most studies have been confined to conservative settings. Here, we explore Thouless pumping of dissipative temporal solitons in a nonconservative one-dimensional optical system with gain and spectral filtering, described by the paradigmatic complex Ginzburg–Landau equation. Two dissipatively induced nonlinear topological phase transitions are identified. First, when varying dissipative parameters across a threshold the soliton transitions from being trapped in time to quantized drifting. This quantized temporal drift remains robust, even as the system evolves from a single-soliton state into a multi-soliton state. Second, a dynamically emergent phase transition is found: the soliton is arrested until a critical point of its evolution, where a transition to topological drift occurs. Both phenomena uniquely arise from the dynamical interplay of dissipation, nonlinearity, and topology. Full article

Orographic cirrus clouds frequently occur over mountainous regions and can influence the radiative balance of the upper troposphere, yet their characteristics and regional variability remain insufficiently understood on a global scale. In this study, we investigate the occurrence, vertical structure, and microphysical and optical properties of orographic cirrus over four major mountainous regions, namely the Rocky Mountains, the Andes, the Alps, and the Himalayas, using the Identification and Classification of Cirrus (IC-CIR) framework together with satellite observations from MODIS, CloudSat, and CALIPSO. The results reveal clear regional differences in both occurrence and structure. Cloud cover is higher over the Himalayas and the Alps and lower over the Andes, while seasonal variability is strongest over the Himalayas and the Alps and weakest over the Andes. In terms of vertical structure, cirrus over the Andes reaches higher cloud tops and exhibits a bimodal distribution. The Andes also show smaller values of ice water path, optical depth, and cirrus reflectance. These results provide a unified comparison of orographic cirrus clouds across four representative major mountainous regions and highlight substantial regional differences in their characteristics and potential radiative influence under different topographic and dynamical environments. Full article