Search Results (223)

This study presents a comprehensive mathematical model of a semiconductor structure based on vanadium dioxide (VO₂), specifically in its conductive phase. The model was developed using the finite element method (FEM), enabling detailed simulation of the formation of a conductive channel under the influence of low-frequency alternating voltage (50 Hz). The VO₂ structure under investigation exhibits pronounced electric field concentration at the surface, where the field strength reaches approximately 5 × 10⁴ V/m, while maintaining a more uniform distribution of around 2 × 10⁴ V/m within the bulk of the material. The simulation results were validated experimentally using a test circuit. Minor deviations—no greater than 8%—were observed between the simulated and measured current values, attributed to magnetic core saturation and modeling assumptions. A distinctive feature of the model is its ability to incorporate the nonlinear dependencies of VO₂’s electrical properties on frequency. Analytical expressions were derived for the magnetic permeability and resistivity of VO₂, demonstrating excellent agreement with experimental data. The coefficients of determination (R²) for the frequency dependence of magnetic permeability and resistance were found to be 0.9976 and 0.9999, respectively. The current version of the model focuses exclusively on the conductive phase and does not include the thermally induced metal–insulator phase transition characteristic of VO₂. The study confirms that VO₂-based structures exhibit high responsiveness and nonlinear switching behavior, making them suitable for applications in electronic surge protection, current limiting, and switching elements. The developed model provides a reliable and physically grounded tool for the design and optimization components based on VO₂ in power electronics and protective circuitry. Full article

Structural health monitoring in resource-constrained environments demands crack segmentation models that match the accuracy of heavyweight convolutional networks while conforming to the power, memory, and latency limits of watt-level edge devices. This study presents a lightweight dual-attention network, which is a four-stage U-Net compressed to one-quarter of the channel depth and augmented—exclusively at the deepest layer—with a compact dual-attention block that couples channel excitation with spatial self-attention. The added mechanism increases computation by only 19%, limits the weight budget to 7.4 MB, and remains fully compatible with post-training INT8 quantization. On a pixel-labelled concrete crack benchmark, the proposed network achieves an intersection over union of 0.827 and an F1 score of 0.905, thus outperforming CrackTree, Hybrid 2020, MobileNetV3, and ESPNetv2. While refined weight initialization and Dice-augmented loss provide slight improvements, ablation experiments show that the dual-attention module is the main factor influencing accuracy. With 110 frames per second on a 10 W Jetson Nano and 220 frames per second on a 5 W Coral TPU achieved without observable accuracy loss, hardware-in-the-loop tests validate real-time viability. Thus, the proposed network offers cutting-edge crack segmentation at the kiloflop scale, thus facilitating ongoing, on-device civil infrastructure inspection. Full article

The possibility of collecting new archaeological elements useful in reconstructing the dynamics of population, production and commercial activities in the Bronze Age at the edge of the central-southern Venice Lagoon was provided between 2023 and 2024 thanks to an intervention of rescue archaeology planned during some water restoration works in the Giare–Mira area. Three small excavations revealed, approximately one meter below the current surface and covered by alluvial sediments, a rather complex palimpsest dated to the late Recent and the early Final Bronze Age. Three large circular pits containing exclusively purified grey/blue clay and very rare inclusions of vegetable fibres, and many large, fired clay vessels’ bases, walls and rims clustered in concentrated assemblages and random deposits point to potential on-site production. Two pyro-technological structures, one characterised by a sub-circular combustion chamber and a long inlet channel/praefurnium, and the second one with a sub-rectangular shape with arched niches along its southern side, complete the exceptional context here discovered. To analyse the relationship between the site and the natural sedimentary succession and to evaluate the possible extension of this site, three electrical resistivity tomography (ERT) and low-frequency electromagnetic (FDEM) measurements were collected. Several manual core drillings associated with remote sensing integrated the geophysical data in the analysis of the geomorphological evolution of this area, clearly related to different phases of fluvial activity, in a framework of continuous relative sea level rise. The typology and chronology of the archaeological structures and materials, currently undergoing further analyses, support the interpretation of the site as a late Recent/early Final Bronze Age productive site. Geophysical and geomorphological data provide information on the palaeoenvironmental setting, suggesting that the site was located on a fine-grained, stable alluvial plain at a distance of a few kilometres from the lagoon shore to the south-east and the course of the Brenta River to the north. The archaeological site was buried by fine-grained floodplain deposits attributed to the Brenta River. The good preservation of the archaeological structures buried by fluvial sediments suggests that the site was abandoned soon before sedimentation started. Full article

This work explores the morphogenesis of the skeletal mineral component, with a specific emphasis on hydroxyapatite (HAp) crystal assembly. Bone is fundamentally a triphasic biomaterial, consisting of an inorganic mineral phase, an organic matrix, and an aqueous component. The inorganic phase (hydroxyapatite), is characterized by its hexagonal prismatic nanocrystalline structure. We leverage a cellular automata (CA) paradigm to computationally simulate the mineralization process, leading to the formation of the bone’s hydroxyapatite framework. This model exclusively considers the physicochemical aspects of bone formation, intentionally excluding the biological interactions that govern in vivo skeletal development. To optimize computational efficiency, a simplified anatomical segment of a long bone (e.g., the femur) is modeled. This geometric simplification encompasses an outer ellipsoidal cylindrical boundary (periosteal envelope), an inner ellipsoidal surface defining the interface between cortical and cancellous bone, and a central circular cylindrical lumen representing the medullary cavity, which accommodates the bone marrow and primary vasculature. The CA methodology is applied to generate the internal bone microarchitecture, while deliberately omitting the design of smaller, secondary vascular channels. Full article

This study presents, for the first time, a detailed linear stability analysis (LSA) of bedform evolution under low-flow conditions using a one-dimensional vertically averaged and moment (1D-VAM) approach. The analysis focuses exclusively on bedload transport. The classical Saint-Venant shallow water equations are extended to incorporate non-hydrostatic pressure terms and a modified moment-based Chézy resistance formulation is adopted that links bed shear stress to both the depth-averaged velocity and its first moment (near-bed velocity). Applying a small-amplitude perturbation analysis to an initially flat bed, while neglecting suspended load and bed slope effects, reveals two distinct modes of morphological instability under low-Froude-number conditions. The first mode, associated with ripple formation, features short wavelengths independent of flow depth, following the relation F² = 1/(kh), and varies systematically with both the Froude and Shields numbers. The second mode corresponds to dune formation, emerging within a dimensionless wavenumber range of 0.17 to 0.9 as roughness increases and the dimensionless Chézy coefficient $C^{\ast }$ decreases from 20 to 10. The resulting predictions of the dominant wavenumbers agree well with recent experimental observations. Critically, the model naturally produces a phase lag between sediment transport and bedform geometry without empirical lag terms. The 1D-VAM framework with Exner equation offers a physically consistent and computationally efficient tool for predicting bedform instabilities in erodible channels. This study advances the capability of conventional depth-averaged models to simulate complex bedform evolution processes. Full article

Quantification of optic disc microvasculature is crucial for diagnosing various ocular diseases. However, accurate quantification of the microvasculature requires the exclusion of large vessels, such as the central artery and vein, when present. To address the challenge of ineffective learning of edge information, which arises from the adhesion and transposition of large vessels in the optic disc, we developed a segmentation model that generates high-quality edge information in optic disc slices. By integrating dual-stream learning with channel-spatial attention and multi-level attention mechanisms, our model effectively learns both the target’s primary structure and fine details. Compared to state-of-the-art methods, our proposed approach demonstrates superior performance in segmentation accuracy. Superior results were obtained when the model was tested on OCTA images of the optic disc from 10 clinical patients. This underscores the significant contribution of our method in achieving clearly defined multi-task learning while substantially enhancing inference speed. Full article

It has long been recognised that airway smooth muscle cells (ASMCs) possess an abundance of M2 muscarinic receptors (M2Rs). However, the contribution of postjunctional M2Rs to contractions of airway smooth muscle (ASM) induced by the release of acetylcholine (ACh) from parasympathetic nerves was thought to be minimal. Instead, it was believed that these responses were exclusively mediated by activation of M3Rs. However, evidence is emerging that postjunctional M2Rs may have a greater role than previously realised. In this review, we discuss ACh signalling in airways, highlighting the well-established autoinhibitory role of prejunctional M2Rs and the putative roles of postjunctional M2Rs to cholinergic contractions of ASM. The cellular mechanisms that underpin M2R-dependent contractions of ASM are reviewed, with a particular emphasis on the role of ion channels in these responses. The regulation of M2R signalling pathways by β-adrenoceptor activation is also considered, along with the potential involvement of postjunctional M2Rs in airway diseases such as asthma and chronic obstructive pulmonary disease (COPD). Full article

Hans Selye’s stress concept, first introduced in the 1930s, has undergone substantial evolution, extending beyond biology and medicine to influence diverse academic disciplines. Initially, Selye’s General Adaptation Syndrome (GAS) described nonspecific physiological responses to stressors exclusively in mammals, without addressing other biological systems. Consequently, the concept of stress developed independently in biology and medicine, shaped by distinct physiological contexts. This review provides a historical overview of stress research, highlights both parallels and divergences between the stress responses of plants and animals, and integrates insights from traditional Eastern philosophies. We propose an updated GAS framework that incorporates the dynamic balance among reactive oxygen species (ROS), reactive nitrogen species (RNS), and reactive sulfur species (RSS) within the broader context of oxidative stress. We highlight the ionotropic glutamate receptor (iGluR) family and the transient receptor potential (TRP) channel superfamily as minimal molecular architectures for achieving GAS. This perspective expands the classical stress paradigm, providing new insights into redox biology, interspecies stress adaptation, and evolutionary physiology. Full article

Benthic diatoms are key primary producers in aquatic ecosystems and sensitive bioindicators for water quality monitoring; for example, the Yellow River Basin exhibits high diatom species diversity. However, traditional microscopic identification of such species remains inefficient and inaccurate. To enable automated identification, we established a benthic diatom dataset containing 3157 SEM images of 32 genera/species from the Yellow River Basin and developed a novel identification method. Specifically, the knowledge extraction module distinguishes foreground features from background noise by guiding spatial attention to focus on mutually exclusive regions within the image. This mechanism allows the network to focus more on foreground features that are useful for the classification task while significantly reducing the interference of background noise. Furthermore, a dual knowledge guidance module is designed to enhance the discriminative representation of fine-grained diatom images. This module strengthens multi-region foreground features through grouped channel attention, supplemented with contextual information through convolution-refined background features assigned low weights. Finally, the proposed method integrates multi-granularity learning, knowledge distillation, and multi-scale training strategies, further improving the classification accuracy. The experimental results demonstrate that the proposed network outperforms comparative methods on both the self-built diatom dataset and a public diatom dataset. Ablation studies and visualization further validate the efficacy of each module. Full article

Most image encryption methods based on Rubik’s cube scrambling adopt the idea of cyclic shift or map the image pixels to the cube surface, not fully considering the cube’s three-dimensional (3D) properties. In response to this defect, we propose a mixed chaotic color image encryption method based on parallel rotation scrambling in 3D Rubik’s cube space. First, a seven-dimensional hyperchaotic system is introduced to generate chaotic pseudo-random integer sequences. Then, a proven lemma is applied to preprocess the red (R), green (G), and blue (B) channels of the plain image to realize the first diffusion. Next, the chaotic integer sequence is employed to control Arnold transformation, and the scrambled two-dimensional (2D) pixel matrix is converted into a 3D matrix. Then, the 3D cube is scrambled by dynamically selecting the rotating axis, layer number, and angle through the chaotic integer sequence. The scrambled 3D matrix is converted into a 2D matrix, realizing the second diffusion via exclusive OR with the chaotic matrix generated by logistic mapping. Finally, the matrices of the R, G, and B channels are combined into an encrypted image. By performing the encryption algorithm in reverse, the encrypted image can be decrypted into the plain image. A simulation analysis shows that the proposed method has a larger key space and exhibits stronger key sensitivity than some existing methods. Full article

Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (SI) and cloud liquid water path (CLWP)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built on window channels which are not available from FY-3C/D MWTS-II. To address this limitation, this study establishes a nonlinear relationship between multispectral visible/infrared data from the FY-2F geostationary satellite and microwave sounding channels using an artificial intelligence (AI)-driven approach. The methodology involves three key steps: (1) The spatiotemporal integration of FY-2F VISSR-derived products with NOAA-19 AMSU-A microwave brightness temperatures was achieved through the GEO-LEO pixel fusion algorithm. (2) The fused observations were used as a training set and input into a random forest model. (3) The performance of the RF_SI method was evaluated by using individual cases and time series observations. Results demonstrate that the RF_SI method effectively captures the horizontal distribution of microwave scattering signals in deep convective systems. Compared with those of the NOAA-19 AMSU-A traditional SI and CLWP-based precipitation sounding algorithms, the accuracy and sounding rate of the RF_SI method exceed 94% and 92%, respectively, and the error rate is less than 3%. Also, the RF_SI method exhibits consistent performance across diverse temporal and spatial domains, highlighting its robustness for cross-platform precipitation screening in microwave data assimilation. Full article

The outflow via the weir gate in coastal estuaries is affected by factors, including channel shape, upstream inputs, sluice gate operations, and tidal variations, leading to nonlinear and transitory correlations between the water stage and discharge. The most common technique utilized to calculate discharge is the weir gate overflow equation. Nonetheless, the significant dynamic fluctuations in upstream and downstream water level differentials during the opening or closing of the gate render the exclusive use of static water level differences inadequate for formulating a connection equation that satisfies accuracy standards. This research proposes a dynamic trend prediction approach that utilizes time-series data of water levels and discharge, accounting for temporal trend variations, as input for simulation with a three-layer backpropagation neural network. In the tidal portions of the Lixia River basin, the correlation coefficients for the discharge of four harbor gates surpassed 0.8, and the mean error diminished to 3.00%. It significantly boosts the fitting accuracy of the results and improves data precision during the transition between gate opening and closure. The novel approach employs intelligent algorithm theory to analyze harbor gate flow, offering a more scientific and accurate representation of the gate’s overflow capacity. Full article

Antepartum Cardiotocography (CTG) is a biomedical sensing technology widely used for fetal health monitoring. While the visual interpretation of CTG traces is highly subjective, with the inter-observer agreement as low as 29% and a false positive rate of approximately 60%, the Dawes–Redman system provides an automated approach to fetal well-being assessments. However, it is primarily designed to rule out adverse outcomes rather than detect them, resulting in a high specificity (90.7%) but low sensitivity (18.2%) in identifying fetal distress. This paper introduces PatchCTG, an AI-enabled biomedical time series transformer for CTG analysis. It employs patch-based tokenisation, instance normalisation, and channel-independent processing to capture essential local and global temporal dependencies within CTG signals. PatchCTG was evaluated on the Oxford Maternity (OXMAT) dataset, which comprises over 20,000 high-quality CTG traces from diverse clinical outcomes, after applying the inclusion and exclusion criteria. With extensive hyperparameter optimisation, PatchCTG achieved an AUC of 0.77, with a specificity of 88% and sensitivity of 57% at Youden’s index threshold, demonstrating its adaptability to various clinical needs. Its robust performance across varying temporal thresholds highlights its potential for both real-time and retrospective analysis in sensor-driven fetal monitoring. Testing across varying temporal thresholds showcased it robust predictive performance, particularly with finetuning on data closer to delivery, achieving a sensitivity of 52% and specificity of 88% for near-delivery cases. These findings suggest the potential of PatchCTG to enhance clinical decision-making in antepartum care by providing a sensor-based, AI-driven, objective tool for reliable fetal health assessment. Full article

In the context of high-quality economic development, the empowering effect of regional integration policies on urban green and low-carbon development has significantly strengthened, playing a crucial strategic role in achieving the coordinated development of the economy and ecology. This study uses the expansion of the Yangtze River Delta urban agglomeration as a quasi-natural experimental scenario, analyzing the pathways and mechanisms through which regional integration policies influence urban green and low-carbon development based on panel data from Chinese cities between 2004 and 2022, using a multi-period Difference-in-Differences (DID) model. The empirical results show the following: ① Regional integration policies significantly enhance the efficiency of urban green and low-carbon development, a conclusion that remains robust after a series of robustness tests, including PSM-DID estimation, placebo tests, instrumental variable methods, indicator reconstruction, and policy interference exclusion. ② Mechanism tests reveal that regional integration policies mainly drive the green and low-carbon transformation through three channels: innovation investment, industrial upgrading, and talent aggregation. ③ Heterogeneity analysis indicates that the positive impact of regional integration policies on the green and low-carbon development of cities is more significant in eastern regions, resource-based cities, small and medium-sized cities, and old industrial cities. Spatial effect tests show that regional integration development has a significant spatial spillover effect on urban green and low-carbon transformation. Based on these findings, it is recommended that, in the future, in global efforts should be made to continuously improve the regional collaborative governance system, strengthen multi-dimensional linkage mechanisms in urban agglomerations, and build a policy support framework that drives innovation and optimizes the allocation of factors. This study not only provides empirical support for the green efficiency enhancement mechanisms of regional integration policies but also offers decision-making references for promoting regional coordinated development and achieving green economic growth in the digital economy era. Full article

The well-known uncertainties in subsurface velocity field definition call for the integration of all the available data, including vintage seismic profiles, which, despite typically being in raster or paper format, often contain velocities derived from stacking and associated interval velocities. This study aims to build a velocity model for the time-to-depth conversion of an interpreted seismic reflection profile by using the interval velocity reported on a vintage, paper-format seismic profile and contribute to improving the subsurface geological model of the Sicily Channel, Central Mediterranean. Spline interpolation is used for velocity model building of the shallower part (3.5 sec TWT) of the seismic profile CS89-01, derived from the stacking velocities of 31 Common Depth Point (CDP) gathers. This was followed by the Gaussian convolution operator and a data exclusion filter to improve the accuracy of the velocity model. The time-to-depth-converted seismic reflection profile is a regional cross-section that covers almost the entire Sicily Channel, crossing part of the northern margin of the African Plate, from Tunisia to eastern Sicily. This study provides a new subsurface velocity field that can be applied, or taken into account, to most parts of the Sicily Channel when structural and stratigraphic interpretations are carried out at specific sites and where uncertainties in subsurface geological model exist (e.g., in the present study, the volcanic bodies in the Pantelleria Graben and Lampedusa High). Full article