Search Results (3,373)

With the upgrading of geostationary meteorological satellites, their capabilities in Convective Initiation (CI) identification have been enhanced. To improve the applicability of the ARGI-based CI algorithm in central and eastern China, this study uses Fengyun-4A data, integrates radar and precipitation data to construct a True_CI dataset, and defines False_CI events (satellite-identified events without radar or precipitation signals) for comparative analysis. The results show that True_CI events tend to have longer durations, larger cloud cluster areas, and lower central cloud-top brightness temperature (BT) during development. They exhibit distinct features such as reduced differences between water vapor and infrared channels, increased cloud optical thickness, and ice-phase transformation 30 min before CI occurrence—features absent in most False_CI events. Based on these comparative findings, a new satellite-based CI definition is proposed with a set of reference thresholds, which should be adjusted for different latitudes and seasons. The evaluation of the Defined_CI events (defined using the CI definition) via True_CI events indicates that the CI definition on satellite cloud imagery proposed in this study is reliable, and suggests that further research on the pre-CI environmental conditions of weak convection is needed. Supported by hyperspectral data or numerical model products, such research will help clarify which cloud clusters are prone to developing into convective weather. Full article

This paper proposes an adaptive layer-dependent threshold function (ALDTF) for denoising electrocardiogram (ECG) and multimode optical fiber-based cardiopulmonary signals. Based on wavelet transform, the method employs a layer-dependent threshold function strategy that utilizes the non-zero periodic peak (NZOPP) of the signal’s normalized autocorrelation function to adaptively determine the optimal threshold for each decomposition layer. The core idea applies soft thresholding at lower layers (high-frequency noise) to suppress pseudo-Gibbs oscillations, and hard thresholding at higher layers (low-frequency noise) to preserve signal amplitude and morphology. The experimental results show that for ECG signals contaminated with baseline wander (BW), electrode motion (EM) artifacts, muscle artifacts (MA), and mixed (MIX) noise, ALDTF outperforms existing methods—including SWT, DTCWT, and hybrid approaches—across multiple metrics. It achieves a ΔSNR improvement of 1.68–10.00 dB, ΔSINAD improvement of 1.68–9.98 dB, RMSE reduction of 0.02–0.56, and PRD reduction of 2.88–183.29%. The method also demonstrates excellent performance on real ECG and optical fiber cardiopulmonary signals, preserving key diagnostic features like QRS complexes and ST segments while effectively suppressing artifacts. ALDTF provides an efficient, versatile solution for physiological signal denoising with strong potential in wearable real-time monitoring systems. Full article

Background/Objectives: Disorganization of the retinal inner layers (DRIL) is an important biomarker of diabetic macular edema (DME) that has a very strong association with visual acuity (VA) in patients. But the unavailability of annotated training data from experts severely limits the adaptability of models pretrained on real-world images owing to significant variations in the domain, posing two primary challenges for the design of efficient computerized DRIL detection methods. Methods: In an attempt to address these challenges, we propose a novel, self-supervision-based learning framework that employs a huge unlabeled optical coherence tomography (OCT) dataset to learn and detect clinically applicable interpretations before fine-tuning with a small proprietary dataset of annotated OCT images. In this research, we introduce a spatial Bootstrap Your Own Latent (BYOL) with a hybrid spatial aware loss function aimed to capture anatomical representations from unlabeled OCT dataset of 108,309 images that cover various retinal abnormalities, and then adapt the learned interpretations for DRIL classification employing 823 annotated OCT images. Results: With an accuracy of 99.39%, the proposed two-stage approach substantially exceeds the direct transfer learning models pretrained on ImageNet. Conclusions: The findings demonstrate the efficacy of domain-specific self-supervised learning for rare retinal pathological detection tasks with limited annotated data. Full article

Accurate co-registration between on-scalp Optically Pumped Magnetometer (OPM)–Magnetoencephalography (MEG) sensors and anatomical Magnetic Resonance Imaging (MRI) remains a critical bottleneck restricting the spatial fidelity of source localization. Optical Scanning Image (OSI) methods can provide high spatial accuracy but depend on surface visibility and cannot directly determine the internal sensitive point of each OPM sensor. Coil-based magnetic dipole localization, in contrast, targets the sensor’s internal sensitive volume and is robust to occlusion, yet its accuracy is affected by coil fabrication imperfections and the validity of the dipole approximation. To integrate the complementary advantages of both approaches, we propose a hybrid co-registration framework that combines Rigid Coil Structures (RCS), magnetic dipole-based sensor localization, and optical orientation constraints. A complete multi-stage co-registration pipeline is established through a unified mathematical formulation, including MRI–OSI alignment, OSI–RCS transformation, and final RCS–sensor localization. Systematic simulations are conducted to evaluate the accuracy of the magnetic dipole approximation for both cylindrical helical coils and planar single-turn coils. The results quantify how wire diameter, coil radius, and turn number influence dipole model fidelity and offer practical guidelines for coil design. Experiments using 18 coils and 11 single-axis OPMs demonstrate positional accuracy of a few millimeters, and optical orientation priors suppress dipole-only orientation ambiguity in unstable channels. To improve the stability of sensor orientation estimation, optical scanning of surface markers is incorporated as a soft constraint, yielding substantial improvements for channels that exhibit unstable results under dipole-only optimization. Overall, the proposed hybrid framework demonstrates the feasibility of combining magnetic and optical information for robust OPM–MEG co-registration. Full article

In this work, we study a generalised high-order nonlinear Schrödinger equation with time-dependent coefficients, embracing a wide range of physical influences. By employing the Darboux transformation, we construct explicit breather and rogue wave solutions, illustrating how the spectral parameter governs waveform transitions. In these dynamics, dispersion determines stability and symmetry, nonlinearity influences the peak amplitude and width, and third-order dispersion introduces asymmetry and drift in the wave profile. We have demonstrated that stabilization, destabilization and shifting of the centre of the localization, or drifting towards the soliton in space or even temporal directions, can be possible by manoeuvring the spectral parameter relating dispersion and nonlinearity in optical fibre. Manoeuvring the spectral parameter relates the dispersion $a_1\left(t\right)$ and nonlinearity from 100 t to 0.1 t leads to the stabilization of the soliton by a notable decrease in the amplitude for two hundred folds. The results reveal that the inclusion of higher-order term functions as a control mechanism for managing instability and localisation in nonlinear optical fibre systems, offering promising prospects for future developments in nonlinear optics. Full article

A person’s face can reveal their mood, and microexpressions, although brief and involuntary, are also authentic. People can recognize facial gestures; however, their accuracy is inconsistent, highlighting the importance of objective computational models. Various artificial intelligence models have classified microexpressions into three categories: positive, negative, and surprise. However, it is still significant to address the basic Ekman microexpressions (joy, sadness, fear, disgust, anger, and surprise). This study proposes a Transformers-based machine learning model, trained on CASME, SAMM, SMIC, and its own datasets. The model offers comparable results with other studies when working with seven classes. It applies various component-based techniques ranging from ViT to optical flow with a different perspective, with low training rates and competitive metrics comparable with other publications on a laptop. These results can serve as a basis for future research. Full article

Percutaneous coronary intervention (PCI) has evolved significantly over the past two decades, yet challenges in achieving optimal stent deployment and long-term outcomes persist, particularly in complex coronary anatomy. Intravascular imaging (IVI) modalities such as intravascular ultrasound (IVUS), optical coherence tomography (OCT), and near-infrared spectroscopy (NIRS) have transformed the precision of PCI by providing detailed cross-sectional visualization of vessel architecture, plaque morphology, and stent apposition. Compared to angiography-guided PCI, imaging-guided PCI enables more accurate lesion assessment, appropriate stent sizing, and detection of suboptimal results including under-expansion, malapposition, and edge dissections, factors strongly linked to restenosis and stent thrombosis. Large-scale randomized trials (e.g., ULTIMATE, ILUMIEN) and meta-analyses have demonstrated that imaging-guided PCI reduces major adverse cardiovascular events (MACE) and improves long-term stent patency, particularly in left main, bifurcation, and calcified lesions. Despite these benefits, adoption remains variable due to cost, procedural complexity, and training gaps. Emerging advances, including artificial intelligence-enhanced imaging, hybrid devices, and fusion of imaging with physiologic assessments, promise to integrate imaging more seamlessly into routine practice. This review summarizes current evidence, practical applications, and future directions of IVI-guided PCI, underscoring its growing role in contemporary interventional cardiology and its potential to personalize and optimize coronary revascularization strategies. Full article

A receipt information extraction task requires both textual and spatial analyses. Early receipt analysis systems primarily relied on template matching to extract data from spatially structured documents. However, these methods lack generalizability across various document layouts and require defining the specific spatial characteristics of unseen document sources. The advent of convolutional and recurrent neural networks has led to models that generalize better over unseen document layouts, and more recently, multi-modal transformer-based models, which consider a combination of text, visual, and layout inputs, have led to an even more significant boost in document-understanding capabilities. This work focuses on the joint use of a neural multi-modal transformer and a rule-based model and studies whether this combination achieves higher performance levels than the transformer on its own. A comprehensively annotated dataset, comprising real-world and synthetic receipts, was specifically developed for this study. The open source optical character recognition model DocTR was used to textually scan receipts and, together with an image, provided input to the classifier model. The open-source pre-trained LayoutLMv3 transformer-based model was augmented with a classifier model head, which was trained for classifying textual data into 12 predefined labels, such as date, price, and shop name. The methods implemented in the rule-based model were manually designed and consisted of four types: pattern-matching rules based on regular expressions and logic, database search-based methods for named entities, spatial pattern discovery guided by statistical metrics, and error correcting mechanisms based on confidence scores and local distance metrics. Following hyperparameter tuning of the classifier head and the integration of a rule-based model, the system achieved an overall F1 score of 0.98 in classifying textual data, including line items, from receipts. Full article

Distributed Acoustic Sensing (DAS) transforms conventional optical fibres into large-scale acoustic sensor arrays. While existing telecommunication cables are increasingly considered for DAS-based monitoring, their performance depends strongly on cable construction and strain transfer efficiency. In this study, the relative DAS signal amplitudes of three commercial telecommunication optical cables were experimentally compared using a benchtop Rayleigh backscattering-based interrogator under controlled laboratory conditions. By maintaining a constant temperature and ensuring no additional strain changes from the outside environment, we guaranteed that only strain-induced variations from acoustic excitations were measured. The results show clear differences in signal amplitude and signal-to-noise ratio (SNR) among the tested cables. The Microcable consistently produced the highest spatial peak amplitude (up to 0.029 a.u.) and SNR (up to 79), while the Duct cable reached 0.00268 a.u. with mean SNR ≈ 32. The Anti-Rodent cable showed low signal amplitude (0.0018 a.u.) but exhibited a high mean SNR (≈111) driven by an exceptional low noise floor in one of the runs. These findings reflect the variations in mechanical coupling between the fibre core and external perturbations and provide practical insights into the suitability of different telecom cable types for DAS applications, supporting informed choices for future deployments. Full article

This work presents a study of copper selenite nanocrystals, obtained for the first time by chemical deposition (template synthesis) in a SiO₂/Si track template, and investigates their properties. The obtained nanostructures were subjected to structural, optical, and electrical analysis. After deposition, X-ray diffraction (XRD) analysis confirmed the formation of the orthorhombic phase CuSeO₃. Subsequent annealing in a vacuum at 800 °C and 1000 °C led to successive phase transformations: to the monoclinic phase and, finally, to the triclinic polymorph of copper selenite. Photoluminescence (PL) analysis showed that the intensity and spectral position of the emission peaks vary depending on the crystal structure, which is associated with changes in defects and bandgap width as a result of heat treatment. Current–voltage characteristic (CVC) measurements showed that the phase composition significantly affects electrical conductivity. In particular, the transition to the triclinic phase after annealing at 1000 °C led to noticeable changes in optical and electrical properties compared to the initial material. Thus, a direct relationship has been established between heat treatment conditions, crystal structure, and functional properties of CuSeO₃-based materials, opening up possibilities for their application in photonics and electronics. Full article

Mitochondrial tRNA genes are critical hotspots for pathogenic mutations and several mitochondrial diseases. They account for approximately 70–75% of disease-causing mtDNA variants despite comprising only 5–10% of the mitochondrial genome. These mutations interfere with mitochondrial translation and affect oxidative phosphorylation, resulting in remarkably heterogeneous multisystem disorders. Under this light, we systematically reviewed PubMed, Scopus, and MITOMAP databases through October 2025, indexing all clinically relevant pathogenic mt-tRNA mutations classified by affected organ systems and underlying molecular mechanisms. Approximately 500 distinct pathogenic variants were identified across all 22 mt-tRNA genes. Beyond typical syndromes like MELAS, MERRF, Leigh syndrome, and Kearns–Sayre syndrome that are linked to mt-tRNA mutations, they increasingly implicate cardiovascular diseases (cardiomyopathy, hypertension), neuromuscular disorders (myopathies, encephalopathies), sensory impairment (hearing loss, optic neuropathy), metabolic dysfunction (diabetes, polycystic ovary syndrome), renal disease, neuropsychiatric conditions, and cancer. Beyond sequence mutations, defects in post-transcriptional modification systems emerge as critical disease mechanisms affecting mt-tRNA function and stability. The mutations on tRNA genes described herein represent potential targets for emerging genome editing therapies, although several translational challenges remain. However, targeted correction of pathogenic mt-tRNA mutations holds transformative potential for precision intervention on mitochondrial diseases. Full article

This paper explores the integrability of the Akbota equation with various types of solitary wave solutions. This equation belongs to a class of Heisenberg ferromagnet-type models. The model captures the dynamics of interactions between atomic magnetic moments, as governed by Heisenberg ferromagnetism. To reveal its further physical importance, we calculate more solutions with the applications of the logarithmic transformation, the M-shaped rational solution method, the periodic cross-rational solution technique, and the periodic cross-kink wave solution approach. These methods allow us to derive new analytical families of soliton solutions, highlighting the interplay of discrete and continuous symmetries that govern soliton formation and stability in Heisenberg-type systems. In contrast to earlier studies, our findings present notable advancements. These results hold potential significance for further exploration of similar frameworks in addressing nonlinear problems across applied sciences. The results highlight the intrinsic role of symmetry in the underlying nonlinear structure of the Akbota equation, where integrability and soliton formation are governed by continuous and discrete symmetry transformations. The derived solutions provide original insights into how symmetry-breaking parameters control soliton dynamics, and their novelty is verified through analytical and computational checks. The interplay between these symmetries and the magnetic spin dynamics of the Heisenberg ferromagnet demonstrates how symmetry-breaking parameters control the diversity and stability of optical solitons. Additionally, the outcomes contribute to a deeper understanding of fluid propagation and incompressible fluid behavior. The solutions obtained for the Akbota equation are original and, to the best of our knowledge, have not been previously reported. Several of these solutions are illustrated through 3-D, contour, and 2-D plots by using Mathematica software. The validity and accuracy of all solutions we present here are thoroughly verified. Full article

Humidity monitoring is essential in industrial and scientific scenarios, yet remains challenging for compact EMI (electromagnetic interference)-immune sensors with high sensitivity and robust stability. A novel fiber Bragg grating (FBG) humidity sensor was developed, which incorporated LiCl@UIO-66 microfillers within a poly(N-isopropylacrylamide) (PNIPAM) hydrogel matrix. Structural characterization using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared (FTIR) spectroscopy confirms that LiCl is confined or nanodispersed within intact UIO-66, and that interfacial ion–dipole/hydrogen-bonding exists between the composite and water. Systematic variation in coating time (30–720 min) reveals monotonic growth of the total wavelength shift with diminishing returns. A coating time of 4 h was found to yield a wavelength shift of approximately 0.38–0.40 nm, representing about 82% of the maximum shift observed at 12 h, while maintaining good quasi-linearity and favorable kinetics. Calibration demonstrates sensitivities of 6.7 pm/%RH for LiCl@UIO-66_33 and 10.6 pm/%RH for LiCl@UIO-66_51 over ~0–95%RH. Stepwise tests show response times t90 of ≈14 min for both composites, versus ≈30 min for UIO-66 and ≈55 min for neat PNIPAM. Long-term measurements on the 51 wt.% device are stable over the first ~20 days, with only slow drift thereafter, and repeated humidity cycling is reversible. The wavelength decreases monotonically during drying while settling time increases toward low RH. The synergy of hydrogel–MOF–salt underpins high sensitivity, accelerated transport, and practical stability, offering a scalable route to high-performance optical humidity sensing. Full article

Environmentally friendly materials with superior structural, physical, optical, and shielding capabilities are of great technological importance and are continually being investigated. In this work, novel multicomponent borate glasses with the composition xTiO₂-10BaO-5Al₂O₃-5WO₃-20Bi₂O₃-(60-x) B₂O₃, where 0 ≤ x ≤ 15 mol%, were produced via the melt-quenching technique. The increase in TiO₂ content results in a decrease in molar volume and a corresponding increase in density, indicating the formation of a compact, rigid, and mechanically hard glass network. Elastic constant measurements further confirmed this behavior. FTIR analysis confirms the transformation of BO₃ to BO₄ units, signifying improved network polymerization and structural stability. The prepared glasses exhibit an optical absorption edge in the visible region, demonstrating their strong ultraviolet light blocking capability. Incorporation of TiO₂ leads to an increase in refractive index, optical basicity, and polarizability, and a decrease in the optical band gap and metallization number; all of these suggest enhanced electron density and polarizability of the glass matrix. Radiation shielding properties were evaluated using Phy-X/PSD software. The outcomes illustrate that the Mass Attenuation Coefficient (MAC), Effective Atomic Number (Z_eff), Linear Attenuation Coefficient (LAC) increase, while Mean Free Path (MFP) and Half Value Layer (HVL) decrease with increasing TiO₂ at the expense of B₂O₃, confirming superior gamma-ray attenuation capability. Additionally, both TiO₂-doped and undoped samples show higher fast neutron removal cross sections (FNRCS) compared to several commercial glasses and concrete materials. Overall, the incorporation of TiO₂ significantly enhances the optical performance and radiation-shielding efficiency of the environmentally friendly glass system, making these potential candidates for advanced photonic devices and radiation-shielding applications. Full article

Manipulating complex light fields through highly anisotropic scattering medium (HASM) remains a fundamental challenge due to the intricate underlying physics and broad application potential. We introduce a unified theoretical and experimental framework for generating and controlling arbitrarily polarized curved caustic beams using an extended polarization transfer matrix (EPTM) for the first time, enabling intuitive polarization transformation through HASM. The EPTM is experimentally measured via a four-step phase-shifting technique, and its submatrices are independently modulated with tailored caustic phase profiles. This strategy facilitates the creation of diverse high-resolution caustic beams, including Gaussian and vortex types with tunable energy distribution, polarization states, and vorticity. The achievement of polarization transformation through HASM by our approach offers versatile manipulation over optical field properties such as multiple high-resolution caustic beams, angular momentum flux, and polarization, paving the way for enhanced functionality in advanced optical systems. Full article