Search Results (1,245)

This article develops a structural–analogical framework to investigate conceptual resonances between Qur’an 24:35—the Verse of Light—and contemporary relational models in physics, while maintaining firm epistemic boundaries between theology, philosophy, and empirical science. The Qur’anic metaphors of niche, glass, tree, oil, and layered light depict a graded ontology of manifestation in which being unfolds through ordered relations grounded in a transcendent divine command (amr). By contrast, modern physics—as represented by quantum field theory, loop quantum gravity, and cosmological models—operates entirely within immanent causality, conceiving spacetime and matter as relational, dynamic, and structurally emergent. Despite their distinct registers, both discourses converge structurally around a shared grammar of potentiality, relation, and manifestation. Drawing on classical Islamic metaphysics—especially al-Ghazālī’s Mishkāt al-Anwār—alongside contemporary relational ontologies in physics (Smolin, Rovelli, Markopoulou), the article argues that “real time” functions as an ontological choice that conditions intelligibility, agency, and novelty. The Qur’anic notion of nūr is interpreted not as physical luminosity but as the metaphysical ground of determinability, while the quantum vacuum is treated as a field of latent potential—without suggesting empirical equivalence. Rather than concordism, the comparison highlights a structural resonance (used here as a heuristic notion indicating pattern-level affinity rather than equivalence, correspondence, or empirical verification): both traditions affirm that reality is neither static nor substance-based, but arises through dynamic relational processes grounded—whether transcendently or immanently—in principled order. Full article

Recently, deep learning methods have seen major advancements and are preferred for medical image analysis. Clinically, deep learning techniques for cancer image analysis are among the main applications for early diagnosis, detection, and treatment. Consequently, segmentation of breast histology images is a key step towards diagnosing breast cancer. However, the use of deep learning methods for image analysis is constrained by challenging features in the histology images. These challenges include poor image quality, complex microscopic tissue structures, topological intricacies, and boundary/edge inhomogeneity. Furthermore, this leads to a limited number of images required for analysis. The U-Net model was introduced and gained significant traction for its ability to produce high-accuracy results with very few input images. Many modifications of the U-Net architecture exist. Therefore, this study proposes the watershed encoder–decoder neural network (WEDN) to segment cancerous lesions in supervised breast histology images. Pre-processing of supervised breast histology images via augmentation is introduced to increase the dataset size. The augmented dataset is further enhanced and segmented into the region of interest. Data enhancement methods such as thresholding, opening, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted parts from the image. Consequently, further segmentation via the connected component analysis method is used to combine image pixel components with similar intensity values and assign them their respective labeled binary masks. The watershed filling method is then applied to these labeled binary mask components to separate and identify the edges/boundaries of the regions of interest (cancerous lesions). This resultant image information is sent to the WEDN model network for feature extraction and learning via training and testing. Residual convolutional block layers of the WEDN model are the learnable layers that extract the region of interest (ROI), which is the cancerous lesion. The method was evaluated on 3000 images–watershed masks, an augmented dataset. The model was trained on 2400 training set images and tested on 600 testing set images. This proposed method produced significant results of $98.53\%$ validation accuracy, $96.98\%$ validation dice coefficient, and $97.84\%$ validation intersection over unit (IoU) metric scores. Full article

Notch signalling is a critical regulator of multiple developmental processes through its ability to control gene expression and thereby influence cell fate specification and cell proliferation through direct cell–cell communication. Although Notch signalling has been implicated in myogenesis during late embryogenesis, its role in early mesoderm development has been largely unexplored. Endocytosis of the Notch ligand Delta and the Notch receptor extracellular domain, a critical step in Notch pathway activation, has been extensively observed in the ventral mesoderm of the early Drosophila embryo, indicating a potential for Notch signalling activity in this early germ layer. Here, we present evidence that genes critical to mesoderm development require and are responsive to Notch signalling activity. Using a novel light-inducible Optogenetic variant of the Notch intracellular domain (OptoNotch), which affords precise spatial and temporal control over ectopic activation of Notch signalling, in combination with high-resolution fluorescent RNA in situ hybridization and qPCR, we identified a set of mesodermal genes whose expression is directly regulated by Notch signalling. We also provide evidence that Notch signalling indirectly regulates the dorsal–ventral patterning program mediated by the Toll signalling pathway through the Dorsal/Twist/Snail gene network. Our findings demonstrate that Notch signalling regulates ventral mesoderm patterning and is critical for establishing the mesoderm–mesectoderm–ectoderm boundary by regulating gene expression patterns and providing negative feedback on the upstream patterning network. Full article

This study systematically investigates the influence of key process parameters—layer thickness and substrate preheating—on solidification cracking in K418 nickel-based superalloy fabricated by laser powder bed fusion (LPBF). For a 30 μm layer, preheating to 350 °C combined with a volumetric energy density (VED) of 60–80 J/mm³ effectively suppressed hot cracking while achieving a relative density > 99%. Preheating to 200 °C showed limited effectiveness. Without preheating, increasing the layer thickness to 60 μm reduced cracking compared to 30 μm, yet preheating became counterproductive under this thicker condition due to excessive thermal accumulation and increased shrinkage stress. Microscopic analysis revealed that cracks propagated along high-angle grain boundaries accompanied by the segregation of low-melting-point elements (O, B, Si, C), with cracking attributed to thermal stress and grain boundary weakening during rapid solidification. This work establishes 350 °C preheating with moderate VED as an effective strategy for manufacturing high-density, crack-minimized K418 alloy components via LPBF. Full article

Mesh-agnostic models have advantages in processing flow field data with various topologies and densities, and they can easily incorporate partial differential equations. Beyond physics-informed neural networks, mesh-agnostic models have been studied for data-driven predictions of simple flows. In this study, a data-driven mesh-agnostic model is proposed to predict the transonic flow field of various supercritical airfoils. The model consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is an implicit neural representation used to predict spatial bases of the flow field. HyperNet is a simple neural network that determines the weights of these bases. The input of ShapeNet is extended to ensure accurate prediction for different airfoil geometries. To reduce overfitting while capturing shock waves and boundary layers, a multi-resolution ShapeNet combining two activation functions is proposed. Additionally, a physics-guided loss function is proposed to enhance accuracy. The proposed model is trained and tested on various supercritical airfoils under different free-stream conditions. Results show that the model can effectively utilize airfoil samples with different grid sizes and distributions, and it can accurately predict the shock wave and boundary layer velocity profile. The proposed mesh-agnostic model can be used as a decoder in any conventional models, contributing to their application in complex and three-dimensional geometries. Full article

In mineral processing, visual-based online particle size analysis systems depend on high-precision image segmentation to accurately quantify ore particle size distribution, thereby optimizing crushing and sorting operations. However, due to multi-scale variations, severe adhesion, and occlusion within ore particle clusters, existing segmentation models often exhibit undersegmentation and misclassification, leading to blurred boundaries and limited generalization. To address these challenges, this paper proposes a novel semantic segmentation model named VTC-Net. The model employs VGG16 as the backbone encoder, integrates Transformer modules in deeper layers to capture global contextual dependencies, and incorporates a Convolutional Block Attention Module (CBAM) at the fourth stage to enhance focus on critical regions such as adhesion edges. BatchNorm layers are used to stabilize training. Experiments on ore image datasets show that VTC-Net outperforms mainstream models such as UNet and DeepLabV3 in key metrics, including MIoU (89.90%) and pixel accuracy (96.80%). Ablation studies confirm the effectiveness and complementary role of each module. Visual analysis further demonstrates that the model identifies ore contours and adhesion areas more accurately, significantly improving segmentation robustness and precision under complex operational conditions. Full article

Wide-swath satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission provides an unprecedented opportunity to directly observe kilometer-scale ocean dynamics in two dimensions. In this study, we identify an atypical bimodal seasonal cycle of submesoscale processes in the Kuroshio Large Meander (KLM) region south of Japan using SWOT observations during 2023–2025. Submesoscale eddy kinetic energy (EKE) displays a pronounced winter maximum (December–January) as expected for midlatitude oceans, but also a distinct secondary maximum in late summer (August–September) that coincides with the Northwest Pacific typhoon season. SWOT-based eddy statistics reveal that cyclonic and anticyclonic eddies exhibit enhanced occurrence and intensity in winter and late summer. MITgcm LLC4320 outputs demonstrate that the late-summer EKE peak is primarily driven by typhoons, which rapidly deepen the mixed layer and intensify frontal gradients, leading to an intensification of submesoscale eddies. The Kuroshio path further modulates this response. During the KLM state, buoyancy gradients and mixed-layer available potential energy are amplified, allowing storm forcing to generate strong submesoscale activity. Together, typhoon forcing and current-path variability modify the traditionally winter-dominated submesoscale regime. These findings highlight the unique capability of SWOT to resolve submesoscale processes in western boundary currents during extreme weather events. Full article

The sea surface microlayer (SML) is a critical biogeochemical boundary, playing a key role in air–sea exchange processes, yet its sampling remains challenging due to potential dilution from subsurface water layers, susceptibility to contamination and labor- and time-consuming procedures. The design, development and operational verification of a research unmanned surface vehicle (USV), equipped with samplers for collecting both sea surface microlayer and subsurface water samples (SSW), are described in this study. The InterSeA autonomous vessel is of the catamaran type, equipped with an SML sampler consisting of rotating glass discs and a peristaltic pump for collecting SSW samples. Verification analysis with traditional manual sampling techniques (glass plate and mesh screen) revealed that the InterSeA achieved comparable results in terms of reproducibility and contamination control for both the inorganic and organic analytes examined. The results obtained highlight the effectiveness of autonomous platforms in achieving reliable, low-contamination SML sampling, emphasizing their suitability for broader use in marine biogeochemical research demanding high resolution and minimally disturbed interface measurements. InterSeA is one of the smallest and lightest USVs using rotating glass discs for SML sampling. Full article

In the process of ore drawing using a caving method under interburden conditions, the key to controlling ore dilution lies in the accurate prediction of boundary particle migration trajectories. To address the challenges of high computational costs and complex modeling in traditional numerical simulations, this study designs a dataset construction method. After calibrating parameters using the angle of repose, ore-drawing numerical simulation datasets with interburden (post-defined and pre-defined models) are established. Building upon this foundation, an improved Transformer model is proposed. The model enhances spatiotemporal representation through multi-layer feature fusion embedding, strengthens long-range dependency capture via a reinforced spatiotemporal attention backbone, improves local dynamic modeling capability through optimized decoding at the output stage, and integrates transfer learning to achieve continuous prediction of particle migration. Validation results demonstrate that the model accurately predicts the spatial distribution patterns and collective motion trends of particles, with prediction errors at critical nodes confined to within a single stage and an average estimation error of approximately 4% in interburden regions. The proposed approach effectively overcomes the timeliness bottleneck of traditional interburden ore-drawing simulations, enabling rapid and accurate prediction of boundary particle migration under interburden conditions. Full article

In the Atlantic Ocean, the Intertropical Convergence Zone (ITCZ) sustains the climate of northeastern Brazil and northwestern Africa by modulating their rainy and dry seasons. Using observational data, radiosondes and Expendable Bathythermographs (XBTs), we investigated short-term ocean–atmosphere coupling across the ITCZ region along the 38° W meridian. The data represents synchronous measurements of the marine atmospheric boundary layer (MABL) and the ocean’s mixed layer (OML) for the period between 17 October and 8 November 2018. The ITCZ demonstrated pronounced variability in position, intensity, and width, driven by the changes in the predominance of northeast and southeast trade winds. These atmospheric changes directly impacted the Equatorial Divergence (ED), which transitioned from an asymmetric structure with shallower isothermal layer depths (ILDs) (~−14 m) around 11° N to a more homogenous region between 5° N and 10° N, with an average ILD of −21.83 ± 5.23 m. A comparison with ORAS5 and WOA23 indicates that the products reproduce the vertical thermal structure of the WTAO well (r² > 0.9) but systematically overestimate the temperature at the bottom of the ILD by 3–4 °C. The difference between the ILD and the mixed layer depth (MLD) is more pronounced south of the ED due to the Amazon River salinity front, advected by the NECC, but the ILD estimated from XBT data closely matches the MLD estimated for ORAS5 and WOA23 in the ED region. These unprecedented observations showcase, for the first time, short-term ocean–atmosphere coupled variability across the WTAO ITCZ region, highlighting the importance of atmospheric synoptic-scale processes in modulating the OML and the ED. Full article

Long bone formation in vertebrates proceeds via endochondral ossification, a sequential process that begins with mesenchymal condensation, advances through cartilage anlage formation, and culminates in its replacement by mineralized bone. Recent advances in inducible lineage tracing and single-cell genomics have revealed that, rather than being a uniform event, mesenchymal condensation rapidly segregates into progenitor pools with distinct fates. Centrally located Sox9⁺/Fgfr3⁺ chondroprogenitors expand into the growth plate and metaphyseal stroma, peripheral Hes1⁺ boundary cells refine condensation via asymmetric division, and outer-layer Dlx5⁺ perichondrial cells generate the bone collar and cortical bone. Concurrently, dorsoventral polarity established by Wnt7a–Lmx1b and En1 ensures that dorsal progenitors retain positional identity throughout development. These lineage divergences integrate with signaling networks, including the Ihh–PTHrP, FGF, BMPs, and WNT/β-catenin networks, which impose temporal control over chondrocyte proliferation, hypertrophy, and vascular invasion. Perturbations in these programs, exemplified by mutations in Fgfr3, Sox9, and Dlx5, underlie region-specific skeletal dysplasias, such as achondroplasia, campomelic dysplasia, and split-hand/foot malformation, demonstrating the lasting impacts of embryonic patterning errors. Based on these insights, regenerative strategies are increasingly drawing upon developmental principles, with organoid cultures recapitulating ossification centers, biomimetic hydrogels engineered for spatiotemporal morphogen delivery, and stem cell- or exosome-based therapies harnessing developmental microRNA networks. By bridging developmental biology with biomaterials science, these approaches provide both a roadmap to unravel skeletal disorders and a blueprint for next-generation therapies to reconstruct functional bones with the precision of the embryonic blueprint. Full article

It has been demonstrated that a monomolecular surface film with semiconducting characteristics forms on an austenitic, corrosion- and heat-resistant chromium–nickel steel with 0.10 wt.% C, 20 wt.% Cr, 9 wt.% Ni, and 6 wt.% Mn (10Kh20N9G6), microalloyed with yttrium, in aqueous 1 M H₂SO₄. This passive layer exhibits semiconducting behavior, as confirmed by electrochemical impedance and capacitance measurements. For the first time, key electronic parameters, including the flat-band potential, the thickness of the semiconductor layer, and the Fermi energy, have been determined from experimental Mott–Schottky plots obtained for the interphase boundary between the yttrium-microalloyed austenitic Cr–Ni steel (10Kh20N9G6) and aqueous 1 M H₂SO₄. The results reveal a systematic shift in the flat-band potential toward more negative values with increasing yttrium content in the alloy, indicating a modification of the electronic structure of the passive film. Simultaneously, a decrease in the Fermi energy is observed, suggesting an increase in the work function of the metal surface due to the presence of yttrium. These findings contribute to a deeper understanding of passivation mechanisms in yttrium-containing stainless steels. The formation of a semiconducting passive film is essential for enhancing the electrochemical stability of stainless steels, and the role of rare-earth microalloying elements, such as yttrium, in this process is of both fundamental and practical interest. Full article

Wastewater treatment facilities of a diameter of approximately 15 m or more provide the opportunity to process large volumes of stormwater. The current report investigates the operation of a stormwater radial precipitator, without an impeller, working with particles of various sizes. A distinguishing feature is that the two-phase flow is solely gravity-driven, which leads to reduced energy requirements. This entails the necessity of a facility in which the linear and the local losses are minimized as much as possible. Linear losses can be reduced by decreasing the precipitator’s size. The initially proposed 15 m diameter proved to be ineffective since the sand only reached a certain zone and could not flow further to the outlet due to the insufficient energy. Therefore, it was necessary to reduce the size of the radial precipitator, which resulted in a shorter path for the sand particles and the water, which, in turn, reduced the linear resistance. As for the local losses, it turned out that many areas of the precipitator construction could be geometrically modified to significantly reduce the energy loss of the sand–water mixture. The boundary layer cannot be removed. However, it is possible the size and the number of vortex structures inside the settler to be reduced in order to create an optimal working environment. Full article

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions. Full article

Microplastics (MPs) are recognised as emerging vectors for hydrophobic organic contaminants in aquatic environments due to their relatively large surface area and the diversity of their polymer chemistries compositions. This study investigates the sorption behaviour of two priority polycyclic aromatic hydrocarbons (PAHs), pyrene (PYR) and fluoranthene (FLU), onto six common MPs: poly(m-xylene adipamide) (PA-MXD6), high- and low-density polyethylene (HDPE, LDPE), polypropylene (PP), polyethylene terephthalate (PET), and polylactic acid (PLA). Sorption isotherms and kinetics were evaluated under simulated freshwater conditions at environmentally relevant concentrations (1–50 µg·L⁻¹). Despite the low MP concentration used (0.2 g·L⁻¹), over 80% of the initial PAH content was removed by polyolefins, and more than 50% by all other MPs. Sorption capacity was strongly dependent on particle surface area. Langmuir, Henry, and Freundlich isotherms models were fitted, with linear behaviour prevailing at low concentrations. Analysis using the Dubini–-Radushkevich model confirmed that sorption involves chemisorption contributions, mainly through π–π interactions and hydrophobic interactions (polyolefins). Mechanistically, molecular diffusion within the MP matrix was not governing the sorption process, as diffusion coefficients varied with particle size instead of polymer chemistry. Instead, sorption appears to be governed by PAH diffusion through the hydrodynamic boundary layer and subsequent retention on the MP surface. Empirically, kinetic data fitted the pseudo-second-order model, further supporting that the sorption process involves chemisorption. These findings highlight the role of MPs as vectors for PAHs in freshwater systems and their potential application in contaminant removal. Expressing sorption per unit surface area is recommended for accurate assessment. This work contributes to understanding the environmental behaviour of MPs and their implications for pollutant transport and toxicity. Full article