Search Results (98)

Retinal vessel segmentation remains challenging for thin vessels and low-contrast bifurcations. We evaluate a Swin-UNet-style model family that conditions decoder features with a single-channel local fractal dimension prior refined by a short learnable anisotropic diffusion model and injected through Spatially-Adaptive Normalization (SPADE). On Fundus Image Vessel Segmentations (FIVES), the strongest no-test-time-augmentation result was obtained by OPT-I v2 at 200 epochs, reaching Dice 0.8899, clDice 0.8517, and Area Under the ROC Curve (AUC) 0.9904, compared with 0.8643, 0.8125, and 0.9856 for the matched 200-epoch baseline. In a matched Neural Cellular Automata (NCA)/no-NCA ablation using the same seed, data, 200-epoch budget, and evaluation pipeline, enabling NCA improved the test Dice from 0.8813 to 0.8907 and the test clDice from 0.8325 to 0.8518, with NCA winning on all 80 paired test images for both metrics. The results support PDE (partial differential equation)-SPADE fractal prior conditioning and NCA topology refinement as ablation-grounded improvements over the tested baseline family, while broader matched external validation requires future work. Full article

A numerical investigation of forced convection heat transfer in a three-dimensional T-shaped bifurcating channel with an upstream rotating cylinder and a downstream vibrating wavy wall is presented. The working fluid is a ternary hybrid nanofluid (Fe₂O₃, CuO, MoS₂ in water) exhibiting Casson rheology under an inclined magnetic field. The novelty of this work lies in the first integrated configuration combining these simultaneous mechanical, magnetic, and non-Newtonian effects. Using COMSOL Multiphysics, 413 parametric combinations of Reynolds number, Hartmann number, Casson parameter, nanoparticle shape and volume fraction, magnetic field angle, cylinder rotation speed, wall amplitude (Am), and period were solved. Average Nusselt and Bejan numbers quantified heat transfer enhancement and thermodynamic irreversibility. To interpret the high-dimensional parameter space and to circumvent the prohibitive computational cost of additional 3D magnetohydrodynamics simulations, machine learning (XGBoost) models were developed to rank feature importance and provide fast, accurate surrogate predictions (R² > 0.99). Cylinder rotation dominates heat transfer, increasing the Nusselt number by over 980% (feature importance 0.42) with a modest entropy penalty. Nanoparticle volume fraction reduces the Nusselt number via viscous damping. Magnetic field parameters negligibly affect heat transfer but strongly influence entropy generation; a perpendicular field recovers up to 97% thermal efficiency at high Hartmann numbers. Full article

Telmisartan (TEL) is a non-peptide, orally administered antihypertensive agent primarily known as angiotensin II type 1 (AT1) blocker. In this review, we provide a detailed overview of how TEL modulates voltage-gated Na⁺ current (I_Na) and affects action potential (AP) firing behavior. TEL exerts differential stimulatory effects on the peak and late components of I_Na when subjected to brief depolarizing pulses across a range of cell types, such as mHippoE-14 hippocampal neuron, cultured dorsal root ganglion neurons, and HL-1 atrial cardiomyocytes. TEL can augment the non-inactivating (persistent) I_Na elicited by ascending long ramp pulse in mHippoE-14 cells. By using a parvalbumin-expressing interneuron-based modeled cell combined with bifurcation analysis, it is possible to predict how applied current influences subthreshold oscillations and the generation of somatic spiking in the presence of TEL. According to the Hodgkin-Huxley model, mimicking the action of TEL—characterized by an increased peak amplitude of I_Na and a slowed inactivation time course—leads to the emergence of periodic oscillations in membrane potential. Using a Markovian process, a separate model can also be mathematically constructed, showing that changes in certain rate constants can simulate the effect of TEL on I_Na in cardiac cells. The molecular docking prediction between TEL and the Na_V1.7 channel was made by expected formation of hydrophobic interactions as well as hydrogen bonding. In addition to its antagonistic action at the AT1 receptor and its agonistic activation of peroxisome proliferator-activator-γ, TEL may also directly enhance I_Na, thereby modulating AP firing in a variety of excitable cells. Current evidence supports TEL’s modulatory impact on Na_V channel activity and cellular excitability, while also acknowledging that the mechanism—whether direct or indirect—remains under investigation. Full article

Background/Objectives: Osteoarthritis (OA) is an age-related degenerative joint disease whose pathogenic mechanisms remain poorly understood. Experimental evidence implicates dysregulated mechanotransduction mediated by Piezo1 and TRPV4 channels, but how their interaction with inflammation may drive pathogenic state transitions remains unknown. Here, we aimed to study whether a Piezo1–TRPV4 network can intrinsically produce distinct stable physiological and pathological regimes. Methods: Based on literature data, we developed a nonlinear dynamical model describing closed-loop interactions involving Piezo1, TRPV4, and inflammation. The system was translated into a set of ordinary differential equations and studied using stability and bifurcation analysis. Results: Computational analysis revealed bistability, allowing the system to shift from a physiological to a pathogenic regime in response to specific stimuli. Critical bifurcation parameters were linked to Piezo1 and inflammation, suggesting that the bidirectional interaction between these two components represents a key node for interventions aimed at preventing or reversing transitions from non-pathogenic to pathogenic states. Conclusions: Our results suggest that OA pathogenesis may emerge from the intrinsic nonlinear dynamics of Piezo1/TRPV4/inflammation interactions. Bifurcation analysis indicates the sensitivity of TRPV4 to the inhibitory effect of Piezo1 as a key target for preventing or reversing pathogenic state transitions. Further investigations in preclinical and clinical settings are warranted to validate the model. Full article

High-temperature proton exchange membrane fuel cells (HT-PEMFCs) coupled with methanol reforming hold promise for distributed energy systems, yet channel hydrodynamics and geometry optimization remain underexplored. This study develops a 3D multiphysics model to investigate coupled behaviors in HT-PEMFCs fueled by methanol reformate. Results reveal bifurcation-induced Dean vortices have dual effects: they cause flow maldistribution (15–18% velocity deviation) and contribute 50% of inlet pressure loss, while generating a lateral pumping effect that enhances local mass transfer. A continuous parametric sweep of channel widths (0.9–1.9 mm) identifies a voltage-dependent performance crossover—narrower channels (1.3 mm) excel at high voltages by improving electronic conduction, whereas wider channels (1.5 mm) perform better at low voltages by mitigating mass transfer limitations. These findings provide quantitative design criteria for optimizing flow field geometry in HT-PEMFC stacks. Full article

This study numerically investigates the flow division at lateral diversions, focusing on the influence of the diversion angle and the ratio of channel widths on flow characteristics and discharge distribution. A total of 68 simulations were performed using FLOW-3D HYDRO 2022R1 software with a Large Eddy Simulation turbulence model. The investigation covered diversion angles of 30°, 45°, 60°, and 90°, combined with width ratios of 0.25, 0.50, and 1.00, under a wide range of upstream and downstream flow parameters. The flow fields were analyzed using cross-sections in both channels; the change in flow depths and velocity fields were evaluated together with organized flow structures. Streamline analyses were performed and three new empirical equations were proposed to predict the width of the divided flow and the discharge distribution in the bifurcation. Finally, the performance of existing equations previously proposed in the literature were assessed against the simulation results. Full article

Existing studies have extensively investigated sand-rich shallow-water deltas. However, the sedimentary characteristics and internal architecture of mud-rich deltas remain poorly understood. In this study, two comparative flume experiments were conducted with sand–mud ratio as the key variable. High-resolution topographic data were acquired using a laser scanner to extract geometric parameters of the architectural elements. Three-dimensional architectural models were established and validated against the Ganjiang Delta (sand-rich) and the Ouchi River Delta (mud-rich) in China. The results reveal contrasting depositional styles: sand-rich deltas develop dense, laterally migrating braided channels with broad fan-shaped morphologies, forming blanket-like geometries that consist of vertically stacked and laterally amalgamated channel complexes with good connectivity; mud-rich deltas are characterized by stable channels with limited bifurcation, forming elongated finger-like morphologies with isolated, ribbon-like channel–mouth bar complexes that exhibit strong lateral heterogeneity and poor connectivity. These contrasting behaviors are governed by sediment cohesion: non-cohesive sands promote channel migration and dispersion, whereas cohesive silt and mud stabilize channels and focus sediment transport along main conduits. The experimental models successfully reproduce natural delta end-members, confirming the universal control of the sand–mud ratio. The established quantitative relationships provide a predictive basis for subsurface reservoir characterization and the formulation of differentiated development strategies. Full article

Accurate segmentation of breast cancer regions in histopathological images is critical for advancing computer-aided diagnostic systems, yet challenges persist due to heterogeneous tissue structures, staining variations, and the need to capture features across multiple scales. This study introduces MSB-UNet, a novel Multi-Scale Bifurcated U-Net architecture designed to address these challenges through a dual-pathway encoder–decoder framework that processes images at multiple resolutions simultaneously. By integrating a bifurcated encoder with a Feature Fusion Module, MSB-UNet effectively captures fine-grained cellular details and broader tissue-level patterns. MSB-UNet is formulated as a binary segmentation framework (tumor vs. outside region of interest), producing a two-channel probability map via a channel-wise Softmax output. Evaluated on a publicly available breast cancer histopathology dataset, MSB-UNet achieves a Dice Similarity Coefficient (DSC) of 91.3% and a mean Intersection over Union (mIoU) of 84.4%, outperforming state-of-the-art segmentation models. The architecture demonstrates better results compared to other baseline methods and has the potential to enhance automated diagnostic tools for breast cancer histopathology. Full article

Large-scale river modeling has transitioned toward vector-based routing, yet the global fidelity of standalone frameworks like mizuRoute remains poorly characterized due to fragmented observation networks and unquantified systematic biases. This study addresses this gap by establishing a comprehensive global benchmark using a harmonized database of 12,115 in situ gauging stations integrated with multi-dimensional catchment attributes. Simulations utilize the 5 km MERIT-Hydro network driven by ERA5-Land runoff from 1980 to 2024. Our results reveal a robust global median Pearson correlation of 0.53, though simulation efficiency is highly bifurcated with a median Kling–Gupta Efficiency (KGE) of 0.17. High fidelity is concentrated in humid temperate and cold regions, whereas performance collapses in arid zones (median KGE = −0.15) due to the structural omission of channel transmission losses. Attribution analysis identifies the aridity–moisture gradient and vegetation density as primary drivers of model skill, while topographic complexity is well-preserved by the vector framework. Furthermore, anthropogenic regulation significantly degrades accuracy; in basins with high reservoir density, naturalized routing fails to capture regulated flow signatures, leading to a sharp decline in efficiency. This work provides the first global appraisal of the mizuRoute framework and highlights that integrating dryland-specific loss functions and reservoir modules is essential for the next generation of global hydrological reconstructions. Full article

Conventional cooling schemes that rely on rigid heat-sink-to-die coupling in vertical stacks fail to track the dynamic, non-uniform heat map of high-performance artificial-intelligence (AI) chips employing chiplet-based heterogeneous integration, giving rise to local hot spots. To eliminate this mismatch, we present a leaf-vein-inspired fractal microchannel tailored for such AI processors. Its hierarchical bifurcation–confluence topology adaptively reshapes the flow field, delivering ultra-low thermal resistance, high heat-transfer coefficients, and uniform dissipation. Coupled with reconfigurable chiplet placement, the design is evaluated through FEM-based orthogonal experiments that rank the influence of coolant, channel diameter/depth, inlet/outlet position, substrate thickness, and flow rate via range analysis and Analysis of Variance (ANOVA). A machine-learned surrogate model of junction temperature is then fed to Particle Swarm Optimization (PSO) for multi-parameter optimization. When re-simulated with the optimal parameter set, the symmetric fractal network lowered the AI chip junction temperature from 127.80 °C to 30.97 °C, a 76% improvement, offering a theoretical basis for hotspot mitigation in advanced heterogeneous AI packages. Full article

The proliferation of Internet of Things (IoT) devices and services creates not only significant benefits but also new security threats. Classical information encryption techniques are not suitable for resource-constrained edge modules, thereby generating the demand for lightweight and efficient data protection algorithms. This work presents a novel dynamical parameter estimation scheme for chaotic oscillators, applied to physical-layer authentication (PLA). The proposed approach relies on the receiver’s capability to estimate a selected parameter of the transmitter’s oscillator determined by circuit configuration from the received chaotic signal using a locally synchronized oscillator, thereby enabling secure authentication based on a hardware-encoded identifier. The scheme is intended to complement a chaos-based wireless sensor network (WSN) architecture, where sensor nodes (SNs) implement analog chaotic oscillators, and the gateway operates discrete-time models. The Vilnius chaotic oscillator was chosen to validate the proposed PLA scheme. A rigorous bifurcation analysis of analytical, SPICE and discrete oscillator models was first conducted to identify parameter regions that preserve chaotic dynamics, establishing correspondence between models to guarantee the feasibility of parameter estimation across implementations. The digital realization of the parameter estimator demonstrated accurate and stable operation, with a small and nearly constant estimation relative error not exceeding 1.01%. Key performance metrics were analyzed, including estimation time, precision, and noise robustness. A tradeoff between estimation speed and accuracy was identified, particularly under noisy channel conditions. Finally, the influence of the receiver’s native oscillator parameter on distinguishable transmitter parameter ranges was demonstrated, highlighting the configurability and security potential of the proposed system against unauthorized transmissions. Full article

River flow has historically been the primary force shaping the morphology of the Yellow River estuary. However, since the Xiaolangdi Reservoir began operating in 2000, the hydrological processes reaching the estuary have been significantly modified. To evaluate the morphological response of the estuary, we examined the evolution of the mouth channel from 1996 to 2023 using remote sensing, cartographic generalization, and hydrological analysis, supported by annual Landsat imagery, daily hydrological records, and field survey data. Our findings indicate that the channel extended slowly between 1996 and 2002, then advanced rapidly from 2003 to 2007, culminating in a natural avulsion between 2004 and 2008. Following the avulsion, the newly formed channel progressively extended (2008–2013) and, after 2014, developed into a multi-branch system. The development of this bifurcating system since 2014 is attributed to the sustained release of low-sediment-concentration flows from the Xiaolangdi Reservoir. In contrast, the earlier avulsion was triggered by the rapid discharge of a high-sediment-concentration flow in 2004. These results demonstrate that releases from the Xiaolangdi Reservoir with varying sediment concentrations at different timescales elicited distinct morphological responses in the Yellow River estuary, underscoring the need for carefully calibrated hydrological regulation. Full article

We develop a four-dimensional nonlinear model of a reflexive financial system by extending the Xin–Zhang system with a self-reinforcing sentiment channel. The model comprises four interacting variables—interest rate, investment demand, price index, and market confidence—and incorporates reflexivity to capture feedback between economic fundamentals and investor sentiment. A Lyapunov function shows that the system is well-posed and dissipative, ensuring bounded trajectories. We then analyse the dynamics using standard nonlinear-dynamics tools. Reflexive confidence sustains chaotic motion, inhibits convergence to equilibria, and produces irregular, aperiodic bifurcation patterns; sentiment-driven feedback destabilises a dissipative macroeconomic model and sustains volatility, as evidenced by a positive largest Lyapunov exponent and Kolmogorov–Sinai entropy greater than zero. Using U.S. monthly consumer sentiment and the S&P 500, we observe co-movement, a medium-horizon lead of sentiment, and a nonlinear persistence map $w_{t+1}=f\left(w_t\right)$—stylised facts consistent with the model’s self-reinforcing confidence channel. Full article

The shallow-water delta-front reservoir in Member II of the Oligocene Dongying Formation (Ed2), located in an oilfield within the Bohai Bay Basin, is a large-scale composite sedimentary system dominated by subaqueous distributary channels and mouth bars. Within this system, reservoir sand bodies exhibit significant thickness, complex internal architecture, poor injection–production correspondence during development, and an ambiguous understanding of remaining oil distribution. To enhance late-stage development efficiency, it is imperative to deepen the understanding of the genesis and evolution of the subaqueous distributary channel–mouth bar system, analyze the internal reservoir architecture, and clarify sand body connectivity relationships. Based on sedimentary physical modeling experiments, integrated with core, well logging, and seismic data, this study systematically reveals the architectural characteristics and spatial stacking patterns of the mouth bar reservoirs using Miall’s architectural element analysis method. The results indicate that the study area is dominated by sand-rich, shallow-water delta front deposits, which display a predominantly coarsening-upward character. The main reservoir units are mouth bar sand bodies (accounting for 30%), with a vertical stacking thickness ranging from 3 to 20 m, and they exhibit lobate distribution patterns in plan view. Sedimentary physical modeling reveals the formation mechanism and stacking patterns of these sand-rich, thick sand bodies. Upon entering the lake, the main distributary channel unloads its sediment, forming accretionary bodies. The main channel then bifurcates, and a new main channel forms in the subsequent unit, which transports sediment away and initiates a new phase of deposition. Multi-phase deposition ultimately builds large-scale lobate complexes composed of channel–mouth bar assemblages. These complexes exhibit internal architectural styles, including channel–channel splicing, channel–bar splicing, and bar–bar splicing. Reservoir architecture analysis demonstrates that an individual distributary channel governs the formation of an individual lobe, whereas multiple distributary channels control the development of composite lobes. These lobes are laterally spliced and vertically superimposed, exhibiting a multi-phase progradational stacking pattern. Dynamic production data analysis validates the reliability of this reservoir architecture classification. This research elucidates the genetic mechanisms of thick sand bodies in delta fronts and establishes a region-specific reservoir architecture model. This study clarifies the spatial distribution of mudstone interlayers and preferential flow pathways within the composite sand bodies. It provides a geological basis for optimizing injection–production strategies and targeting residual oil during the ultra-high water-cut stage. The findings offer critical guidance for the efficient development of shallow-water delta front reservoirs. Full article

Measuring RBC aggregation can be considered as a valuable tool for detecting pathological diseases. Most previous methods need to stop and run blood flows periodically. Thus, it is impossible to probe RBC aggregation in continuously varying infusion flow. To resolve the issues, a novel bifurcated continuous-flow mechanism is suggested to probe RBC aggregation without periodic interruption of blood flow. A microfluidic chip is then designed to split single flow into two branches (low flow rate and high flow rate). RBC aggregation occurs in the low flow-rate channel, whereas it is dispersed fully in the high flow-rate channel. Using a syringe pump, blood is infused into a microfluidic chip at constant and sinusoidal pattern. RBC aggregation index (AI) is calculated from time-lapse imaging intensity within each channel. From fluidic circuit analysis and experimental results, the optimal infusion flow rate is determined as Q_sp = 0.5~2 mL/h. The AI is higher at Hct = 30% than at Hct = 50%. The high concentration of dextran solution increases AI considerably. The period of pulsatile infusion flow rate has a strong influence on time-lapse AI. In conclusion, the present method can be capable of measuring time-lapse AI consistently, without interrupting infusion flow. Full article