Search Results (152)

In this work, the so-called characteristic function method is proposed as a new approach to describe and interpret the diffusion process with interacting adsorbates in terms of surface coverage. In this context, the intermediate scattering function is identified as a characteristic function that is very well defined in probability theory. From this function, the generating functions of the moments and cumulants of the jump probability distribution are straightforwardly obtained at any order. This analysis is carried out in two stages. First, the dilute limit, corresponding to non-interacting adsorbates or very low surface coverage, is briefly reviewed. Second, the method is extended to low and intermediate coverages, where adsorbate-adsorbate interactions become relevant. A further consequence of the present analysis is that the static structure factor is also a characteristic function of the adsorbate separation distance distribution. This method thus provides a compact and physically transparent route for connecting scattering observables, diffusion coefficients, and coverage-dependent structural correlations. Full article

The Korteweg–de Vries (KdV) equation is a foundational model in geophysical fluid dynamics (GFD), governing the propagation of long internal and surface gravity waves in stratified and shallow ocean environments where the interplay between nonlinear steepening and frequency-dependent dispersion gives rise to solitons. Although the analytical tractability of the KdV equation through inverse scattering is well established, systematic numerical exploration of multi-soliton interactions remains valuable for benchmarking solvers, probing conservation properties under varied oceanic initial conditions, and building intuition for more complex ocean wave phenomena. This article presents sangkuriang, an open-source Python library that solves the KdV equation using Fourier pseudo-spectral spatial discretization and adaptive eighth-order Runge–Kutta time integration. The implementation leverages just-in-time (JIT) compilation to achieve research-grade computational efficiency on standard hardware, making it readily accessible for coastal and ocean engineering applications, including idealized modeling of internal solitary waves on continental shelves, rapid parameter studies for solitary wave propagation in stratified basins, and pedagogical investigations of nonlinear dispersive wave dynamics. The solver is validated through four progressively complex idealized scenarios motivated by oceanic wave dynamics: isolated soliton propagation, symmetric interactions, overtaking collisions, and three-body interactions. High-fidelity conservation of mass, momentum, and energy is demonstrated, with relative errors remaining below $\mathcal{O}\left({10}^{-4}\right)$ across all test cases. Measured soliton velocities align with theoretical predictions within 5%, confirming the capture of the amplitude-dependent dispersion characteristic of oceanic solitary waves. Complementary diagnostics, including spectral entropy and recurrence quantification analysis (RQA), verify that the numerical solutions preserve the regular phase-space structure characteristic of integrable Hamiltonian systems. These results establish sangkuriang as a robust, lightweight platform for reproducible numerical investigation of idealized nonlinear dispersive wave dynamics relevant to coastal and ocean engineering applications. Full article

Optical attenuation caused by absorption and scattering in turbid water significantly degrades underwater image quality, making reliable underwater imaging a challenging problem. Underwater polarization imaging has attracted increasing attention because of its ability to suppress scattered light and provide additional polarization cues. However, existing polarization-based enhancement approaches often adapt conventional underwater image enhancement strategies, and the multi-dimensional characteristics of polarization information are not always fully utilized, which may limit detail restoration in complex underwater environments. To address this issue, this paper proposes a bio-inspired underwater polarization image enhancement framework motivated by the polarization vision mechanism of marine organisms. Specifically, a two-stage architecture consisting of a Polarization Adversarial Network (PAN) and a Polarization Enhancement Network (PEN) is designed. The PAN incorporates a Bionic Antagonistic Module (BAM) to exploit complementary information among polarization channels, while Salient Feature Extraction (SFE) is introduced to reduce redundant feature interference. The subsequent PEN integrates a frequency-aware Mamba-based structure to enhance feature representation and improve detail reconstruction. Experiments on simulated underwater polarization datasets indicate that the proposed framework can effectively suppress backscattering and improve structural detail visibility in challenging underwater scenes, demonstrating competitive performance compared with representative traditional and learning-based methods. Full article

This study proposes a method to quantify uncertainty in the scattering parameter (S-parameter) measurements when using de-embedding techniques. After calibrating the measurement setup with reference standards, de-embedding algorithms are employed to extract the intrinsic S-parameter of the device under test (DUT). This process introduces additional complexity to the uncertainty analysis. This study investigates the sources of uncertainty inherent to vector network analyzer (VNA) measurements. Subsequently, a covariance matrix-based approach is employed to propagate these uncertainties, culminating in the quantification of S-parameter uncertainty. The effectiveness of the proposed is determined by comparing the measured S-parameters of power dividers and couplers to their nominal values, considering parameters such as balance, coupling, and voltage standing wave ratio (VSWR). Additionally, an uncertainty analysis is conducted for the power divider’s S-parameters, tracing the uncertainty sources back to the calibration standards. Full article

Synthetic aperture radar (SAR) imaging is highly vulnerable to radio-frequency interference (RFI) in complex electromagnetic environments, which can introduce structured artifacts and obscure targets in single-look complex (SLC) products. Most existing suppression methods rely on separability along a single dimension or require interference-specific parameter tuning, limiting robustness under multidimensional coupling and strong scatterers. We propose a range-domain sliding-window local tensorization that rearranges SLC data into localized range–azimuth–block-index tensors to better expose multi-mode correlations. On this representation, an energy-guided tensor CUR low-rank projector is embedded into an alternating-projection scheme that alternates complex-valued soft-thresholding for the sparse scene-plus-noise term and CUR-based projection for the structured RFI term. The cleaned SLC image is obtained by de-tensorizing the estimated RFI component and subtracting it from the input SLC. Experiments on semi-synthetic data, where controlled RFI is superimposed on real SLC scenes, and on real Sentinel-1 SLC data containing RFI demonstrate improved Pearson correlation coefficient (PCC) and perceptual image quality while preserving target signatures and scene textures, particularly under strong interference and strong coupling. The proposed approach provides a practical SLC-domain RFI mitigation tool for post-focusing SAR products without requiring explicit interference parameterization. Full article

Soot particle deposition is a common form of surface contamination in enclosed architectural environments and can significantly alter the optical appearance of painted surfaces. In the Zhaomiao temple halls of Inner Mongolia, long-term exposure to soot generated by butter lamps and incense burning has led to pronounced color darkening of mural pigments. To clarify the mechanisms by which soot deposition affects pigment optical behavior, this study investigates the surface deposition-induced color degradation of mineral pigment coatings, using Zhaomiao temple murals as a representative application context. Thirty-six typical mineral pigments were prepared as standardized coating specimens, and controlled soot deposition experiments were conducted to simulate progressive particulate accumulation on pigment surfaces. Variations in Commission Internationale de l’Éclairage (CIE) XYZ tristimulus values, luminance, and color difference (ΔE) were quantitatively analyzed under different soot-loading conditions. The results show systematic luminance attenuation and chromatic compression with increasing soot deposition, indicating that optical degradation is primarily governed by surface absorption and scattering effects introduced by carbonaceous particles. These results establish a quantitative framework based on measurable optical parameters—rather than a single absolute value—for evaluating particulate-induced optical degradation of pigment coatings. This study provides a quantitative basis for evaluating particulate-induced optical degradation of pigment coatings and supports surface condition assessment and digital reconstruction of soot-contaminated painted surfaces in architectural contexts such as the Zhaomiao temples. Full article

Ports pose distinctive monitoring challenges due to harsh marine conditions, mixed construction typologies, and heterogeneous ground conditions. These factors complicate the routine use of satellite InSAR, especially when medium-resolution scatterers must be reliably attributed to specific assets for risk and asset management decisions. In current practice, persistent and distributed scatterer (PS/DS) points are often interpreted in map view without an explicit positional uncertainty model or systematic linkage to three-dimensional infrastructure geometry. We present an end-to-end Differential InSAR framework tailored to large ports that fuses medium-resolution Sentinel-1 Level 2 Co-registered Single-Look Complex (L2-CSLC) stacks with high-resolution airborne LiDAR at the post-processing stage. For the Port of Bahía de Algeciras (Spain), we process 123 Sentinel-1A/B images (2020–2022) in ascending and descending geometry using PS/DS time-series analysis with ETAD-like timing corrections and RAiDER tropospheric/ionospheric mitigation. LiDAR is then used to (i) derive look-specific shadow/layover masks and (ii) perform a whitening-transformed nearest-neighbor association that assigns PS/DS points to LiDAR points under an explicit range–azimuth–cross-range (RAC) uncertainty ellipsoid. The RAC standard deviations $({\sigma }_r,{\sigma }_a,{\sigma }_c)$ are derived from the effective CSLC range/azimuth resolution and from empirical height correction statistics, providing a geometry- and data-informed prior on positional uncertainty. Finally, we render dual-geometry red–green composites (ascending to R, descending to G; shared normalization) on the LiDAR point cloud, enabling consistent inspection in plan and elevation. Across asset types, rigid steel/concrete elements (trestles, quay faces, and dolphins) sustain high coherence, small whitened offsets, and stable backscatter in both looks; cylindrical storage tanks are bright but exhibit look-dependent visibility and larger cross-range residuals due to height and curvature; and container yards and vessels show high amplitude dispersion and lower temporal coherence driven by operations. Overall, LiDAR-assisted whitening-based linking reduces effective positional ambiguity and improves structure-specific attribution for most scatterers across the port. The fusion products, geometry-aware linking plus three-dimensional dual-geometry RGB, enhance the interpretability of medium-resolution SAR and provide a transferable, port-oriented basis for integrating deformation evidence into risk and asset management workflows. Full article

Background and Clinical Significance: Wolf–Hirschhorn syndrome (WHS, OMIM #194190) is caused by deletion of the distal short arm of chromosome 4. It is characterized by intrauterine growth restriction (IUGR), developmental delay, epilepsy, distinctive facial features, and urinary tract anomalies, particularly renal hypoplasia. However, the histological profile of renal involvement in WHS is rarely documented. Case presentation: We report a case of fetal WHS with renal hypoplasia and histological evidence of oligomeganephronia (OMN). At 21 weeks’ gestation, a prenatal ultrasound revealed oligo/anhydramnios and IUGR. Genetic testing (karyotype and CGH-array) confirmed a de novo 17.92 Mb terminal deletion from 4p16.3 to 4p15.31. The pregnancy was legally terminated at 23 weeks. The autopsy showed characteristic WHS dysmorphisms, growth restriction, and markedly small kidneys. Histology revealed OMN with a thinned renal cortex with reduced glomeruli, mainly hypoplastic, some of which were hypertrophic, and dilated proximal tubules. Scattered medullary tubules were present within the tubulointerstitial compartment, alongside thickened tubular basement membranes highlighted by Collagen IV staining. Conclusions: This case suggests that OMN may be a histological hallmark of renal hypoplasia in WHS, especially in larger 4p deletions. Recognizing this pattern may help with prenatal prognosis and clinical management. Further studies are needed to confirm this association. Full article

Nanocellulose has attracted significant attention in recent years due to its distinctive properties and vast potential applications across various fields. This study encompasses two distinct yet interconnected activities: the characterisation of eight different types of nanocellulose test materials, including crystalline, fibrillated, and bacterial nanocellulose, using a range of analytical techniques such as dynamic light scattering (DLS), asymmetric flow field-flow fractionation (AF4) coupled to multi-angle light scattering (MALS) and DLS, and transmission electron microscopy (TEM), and a focused case study employing a tiered analytical approach to identify bacterial nanocellulose in commercially available food products like pudding and drinks with nata de coco, SCOBY, and kombucha. The results demonstrate that different types of nanocellulose can be distinguished by their unique physicochemical properties using a combination of analytical techniques. This finding was used for the identification of bacterial nanocellulose in food products by combining pyGC-MS for cellulose identification, TEM for nanosize range determination, and XRD for crystallinity analysis to distinguish between bacterial and fibrillated nanocellulose. The study advances fundamental understanding of nanocellulose and provides tools to facilitate potential future regulatory compliance. Full article

The Landau–de Gennes model is one of the most significant fundamental frameworks in The Physics of Liquid Crystals and Soft Matter Physics. It is validated by the universal parameterisation of the Cotton–Mouton effect, the Kerr effect, and light scattering in the isotropic phase of nematogens. However, as early as 1974, de Gennes identified the first two puzzling problems of this model. Over the following decades, this list has expanded. This report presents the first comprehensive analysis of these issues, with the explicit experimental reference. It focuses on the hardly coherently discussed pretransitional changes in the dielectric constant and the extension in a strong electric field, specifically the nonlinear dielectric effect (NDE). Notably, there are uniquely different pretransitional forms of pretransitional effects, depending on molecular structural features such as permanent dipole moment loci or a steric hindrance. It is tested for 5CB, 5*CB, and MBBA: nematogenic liquid crystalline materials that differ in the above features. The obtained specific pretransitional effects and the evidence for the essential importance of the interplay between observation and pretransition fluctuations time scales led to a new coherent, model-based explanation of all the discussed problems, which cannot be explained within the canonical Landau–de Gennes model. Full article

Objective: The objective of this study was to identify potential robust acoustic biomarkers for functional post-thyroidectomy voice disorder (PTVD) that may support early diagnosis and personalized treatment strategies, using acoustic analysis and explainable machine learning methods. Methods: Spectral and cepstral features were extracted from /a/ and /i/ voice recordings collected preoperatively and 4–6 weeks postoperatively from a total of 126 patients. Various Support Vector Machine (SVM) and Boosting models were trained. SHapley Additive exPlanations (SHAP) analysis was applied to enhance interpretability. SHAP values from training and test sets were compared via scatter plots to identify stable candidate biomarkers with high consistency. Results: GentleBoost (AUC = 0.85) and LogitBoost (AUC = 0.81) demonstrated the highest classification performance. Performance metrics across all models were evaluated for statistical significance. DeLong’s test was conducted to assess differences between ROC curves. The features iCPP, aCPP, and aHNR were identified as stable candidate biomarkers, exhibiting consistent SHAP distributions in both training and test sets in terms of direction and magnitude. These features showed statistically significant correlations with PTVD (p < 0.05) and demonstrated strong effect sizes (Cohen’s d = −2.95, −1.13, −0.60). Their diagnostic relevance was further supported by post hoc power analyses (iCPP: 1.00; aCPP: 0.998). Conclusions: SHAP-supported machine learning models offer an objective and clinically meaningful approach for evaluating PTVD. The identified features may serve as potential biomarkers to guide individualized voice therapy decisions during the early postoperative period. Full article

In the quest for greener alternatives to conventional organic solvents, Deep Eutectic Solvents (DESs) have gained significant attention due to their sustainability, biodegradability, and tunability. The use of water as an active and genuine component has recently led to the emergence of water-based DESs (wb-DESs). Here, a careful experimental characterization was performed on choline acetate (ChAc)/water mixtures across a range of water:ChAc molar ratios (n = 2–6). Differential Scanning Calorimetry (DSC) revealed glass transitions between 150 and 180 K, with no first-order transitions, leading to a classification of these mixtures as Low Transition-Temperature Mixtures (LTTMs). Physicochemical measurements, including density, viscosity, electrical conductivity, and refractive index, were conducted over a broad temperature range. NMR analyses provided insights into dynamics and solvation environments, with ¹H T1_slow relaxation times reaching their lowest value at n = 2, consistent with the formation of a strong hydrogen-bonding network. The n = 2 mixture was further investigated using Small and Wide-Angle X-ray Scattering (S-WAXS) and ab initio molecular dynamics (AIMD). These studies, jointly with ¹H NMR choline diffusion coefficient, directly challenge previous claims of the existence of aggregation phenomena in wb-DES. The simulation revealed a well-organized solvation structure, where acetate and water synergistically stabilize the choline cation through a cooperative hydrogen-bonding network. These findings highlight the impact and significance of an integrated physicochemical study in guiding the rational development of new sustainable systems, such as wb-DESs. Full article

North Africa has a poorly and unevenly known hoverfly fauna. Xanthogramma Schiner, 1861 (Syrphinae, Syrphini) is represented in this territory by some scattered records of four species, Xanthogramma dives (Rondani, 1857), Xanthogramma evanescens Becker & Stein, 1913 (endemic to North Africa), Xanthogramma marginale (Loew, 1854), and Xanthogramma pedissequum (Harris, 1776). After examination of old Xanthogramma material collected in Tanger, Morocco, from the ‘Museo Nacional de Ciencias Naturales, Madrid, Spain (MNCN)’, specimens with distinctive morphology were spotted and found to be different from a syntype of X. evanescens collected in the same locality. Consequently, we revised all the available material of Xanthogramma from North Africa, characterised a new species, proposed a lectotype for X. evanescens, and provided an identification key to the North African species of this genus. The new species is also found in Tunisia and differs from X. evanescens in facial width, colour of the thoracic pleura, length of mesonotum hairs, wing pollinosity, and shape of the yellow maculae on tergum 2. Full article

Non-invasive ventilation (NIV) is a cornerstone in the management of acute and chronic respiratory failure, offering critical support without the risks of intubation. However, successful weaning from NIV remains a complex, high-stakes process. Poorly timed or improperly executed weaning significantly increases morbidity and mortality, yet current clinical practice often relies on subjective judgment rather than evidence-based protocols. This manuscript reviews the current landscape of NIV weaning, emphasizing structured approaches, objective monitoring, and predictors of weaning success or failure. It examines guideline-based indications, monitoring strategies, and various weaning techniques—gradual and abrupt—with evidence of their efficacy across different patient populations. Predictive tools such as the Rapid Shallow Breathing Index, Lung Ultrasound Score, Diaphragm Thickening Fraction, ROX index, and HACOR score are analyzed for their diagnostic value. Additionally, this review underscores the importance of care setting—ICU, step-down unit, or general ward—and how it influences outcomes. Finally, it highlights critical gaps in research, especially around weaning in non-ICU environments. By consolidating current evidence and identifying predictors and pitfalls, this article aims to support clinicians in making safe, timely, and patient-specific NIV weaning decisions. In the current literature, there are gaps regarding patient selection and lack of universal protocolization for initiation and de-escalation of NIV as the data has been scattered. This review aims to consolidate the relevant information to be utilized by clinicians throughout multiple levels of care in all hospital systems. Full article

Salient object detection (SOD) plays a critical role in underwater exploration systems. Traditional SOD approaches encounter notable constraints in underwater image analysis, primarily stemming from light scattering and absorption effects induced by suspended particulate matter in complex underwater environments. In this work, we propose a deep learning-based multimodal method guided by multi-polarization parameters that integrates polarization de-scattering mechanisms with the powerful feature learning capability of neural networks to achieve adaptive multi-target SOD in an underwater turbid scattering environment. The proposed polarization-enhanced salient object detection network (PESODNet) employs a multi-polarization-parameter-guided, material-aware attention mechanism and a contrastive feature calibration unit, significantly enhancing its multi-material, multi-target detection capabilities in underwater scattering environments. The experimental results confirm that the proposed method achieves substantial performance improvements in multi-target underwater SOD tasks, outperforming state-of-the-art models of salient object detection in detection accuracy. Full article