Propagation of transient explosive synchronization in a mesoscale mouse brain network model of epilepsy.

Generalized epileptic attacks, which exhibit widespread disruption of brain activity, are characterized by recurrent, spontaneous, and synchronized bursts of neural activity that self-initiate and self-terminate through critical transitions. Here we utilize the general framework of explosive synchronization (ES) from complex systems science to study the role of network structure and resource dynamics in the generation and propagation of seizures. We show that a combination of resource constraint and adaptive coupling in a Kuramoto network oscillator model can reliably generate seizure-like synchronization activity across different network topologies, including a biologically derived mesoscale mouse brain network. The model, coupled with a novel algorithm for tracking seizure propagation, provides mechanistic insight into the dynamics of transition to the synchronized state and its dependence on resources; and identifies key brain areas that may be involved in the initiation and spatial propagation of the seizure. The model, though minimal, efficiently recapitulates several experimental and theoretical predictions from more complex models and makes novel experimentally testable predictions.

Understanding seizure dynamics at the whole-brain level is crucial for controlling abnormal hypersynchronous activity. Currently, complete brain coverage recordings are lacking in both patients and animal models. We employ network science tools to investigate epileptic seizure-like synchronization in a mouse whole-brain network, leveraging network structure and supported dynamics as the basis for seizure evolution. Our results align with experimental findings, suggesting that seizure activity initiates in the cortico-thalamic circuit. Importantly, our novel analysis identifies key nodes, primarily in the cortex, driving this hypersynchronous activity. Our findings highlight the network structure's role in shaping seizure dynamics, and the techniques developed here could enhance our ability to control generalized seizures when combined with patient-specific data.

Neurosimilator for Undergraduate Biophysics and Neurophysiology Courses.

Stringent animal welfare principles are forcing undergraduate instructors to avoid the use of animals. Therefore, many hands-on lab sessions using laboratory animals are progressively replaced by computer simulations. These versatile software simulations permit the observation of the behavior of biological systems under a great variety of experimental conditions. While this versatility is important, computer simulations often work even when a student makes wrong assumptions, a situation that poses its own pedagogical problem. Hands-on learning provides pupils with the opportunity to safely make mistakes and learn organically through trial and error and should therefore still be promoted. We propose an electronic model of an excitable cell composed of different modules representing different parts of a neuron - dendrites, soma, axon and node of Ranvier. We describe a series of experiments that allow students to better understand differences between passive and active cell responses and differences between myelinated and demyelinated axons. These circuits can also be used to demonstrate temporal and spatial summation of signals coming to the neuron via dendrites, as well as the neuron coding by firing frequency. Finally, they permit experimental determination along with theoretical calculations of important biophysical properties of excitable cells, such as rheobase, chronaxie and space constant. This open-source model has been successfully integrated into an undergraduate course of the physiology of excitable cells and student feedback assessment reveals that it helped students to understand important notions of the course. Thus, this neuromorphic circuit could be a valuable tool for biophysics and neuroscience courses in other universities.

Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling.

The Discrete element method (DEM) is a robust numerical tool for simulating crack propagation and wear in granular materials. However, the computational cost associated with DEM hinders its applicability to large domains. To address this limitation, we employ DEM to model regions experiencing crack propagation and wear, and utilize the finite element method (FEM) to model regions experiencing small deformation, thus reducing the computational burden. The two domains are linked using a FEM-DEM coupling, which considers an overlapping region where the deformation of the two domains is reconciled. We employ a "strong coupling" formulation, in which each DEM particle in the overlapping region is constrained to an equivalent position obtained by nodal interpolation in the finite element. While the coupling method has been proved capable of handling propagation of small-amplitude waves between domains, we examine in this paper its accuracy to efficiently model for material failure events. We investigate two cases of material failure in the DEM region: the first one involves mode I crack propagation, and the second one focuses on rough surfaces' shearing leading to debris creation. For each, we consider several DEM domain sizes, representing different distances between the coupling region and the DEM undergoing inelasticity and fracture. The accuracy of the coupling approach is evaluated by comparing it with a pure DEM simulation, and the results demonstrate its effectiveness in accurately capturing the behavior of the pure DEM, regardless of the placement of the coupling region. Supplementary Information: The online version contains supplementary material available at 10.1007/s40571-024-00788-x.

Fetus descent simulation with the active uterine contraction during the vaginal delivery: MRI-based evaluation and uncertainty quantification.

Finite element models ranging from single to multiscale models have been widely used to gain valuable insights into the physiological delivery process and associated complication scenarios. However, the fetus descent simulation with the active uterine contraction is still challenging for validation and uncertainty quantification issues. The present study performed a fetus descent simulation using the active uterine contraction. Then, simulation outcomes were evaluated using theoretical and in vivo MRI childbirth data. Moreover, parameter uncertainty and propagation were also performed. A maternal pelvis model was developed. The active uterine contraction was modeled using a transversely isotropic Mooney-Rivlin material. Displacement trajectories were compared between simulation, theoretical and in vivo MRI childbirth data. Monte Carlo (M.C) and Polynomial Chaos Expansion (PCE) methods were applied to quantify uncertain parameters and their propagations. Obtained results showed that fetal descent behavior is consistent with the MRI-based observation as well as the theoretical trajectory (curve of Carus). The head downward vertical displacement ranges from 0 to approximately 47 mm. A reduction of 50% in uterine size was observed during the simulation. Three high-sensitive parameters (C1,C2,Ca0) were also identified. Our study suggested that the use of the active uterine contraction is essential for simulating vaginal delivery but the global parameter sensitivity, parameter uncertainty, and outcome evaluation should be carefully performed. As a perspective, the developed approach could be extrapolated for patient-specific modeling and associated delivery complication simulations to identify risks and potential therapeutic solutions.

Twists through turbidity: propagation of light carrying orbital angular momentum through a complex scattering medium.

We explore the propagation of structured vortex laser beams-shaped light carrying orbital angular momentum (OAM)-through complex multiple scattering medium. These structured vortex beams consist of a spin component, determined by the polarization of electromagnetic fields, and an orbital component, arising from their spatial structure. Although both spin and orbital angular momenta are conserved when shaped light propagates through a homogeneous, low-scattering medium, we investigate the conservation of these angular momenta during the propagation of Laguerre-Gaussian (LG) beams with varying topological charges through a turbid multiple scattering environment. Our findings demonstrate that the OAM of the LG beam is preserved, exhibiting a distinct phase shift indicative of the 'twist of light' through the turbid medium. This preservation of OAM within such environments is confirmed by in-house developed Monte Carlo simulations, showing strong agreement with experimental studies. Our results suggest exciting prospects for leveraging OAM in sensing applications, opening avenues for groundbreaking fundamental research and practical applications in optical communications and remote sensing.

Termination Without Global Propagation in Re-Entrant Ventricular Tachycardia: Electrophysiologic Characteristics and 3D-Electroanatomical Mapping Analysis.

BACKGROUND: Termination of ventricular tachycardia (VT) by a pacing stimulus that does not generate a QRS complex (termination without global propagation [TWGP]) can be a marker for a critical re-entry circuit isthmus. However, the electrophysiologic and anatomic features of these sites and their relation to VT substrate defined by 3-dimensional electroanatomical maps (3D-EAM) remain unknown. OBJECTIVES: This retrospective study aimed to characterize TWGP sites and their relation to VT substrate identified by 3D-EAM. METHODS: A total of 632 consecutive cases of catheter ablation for scar-related VT at 2 University medical centers were reviewed to identify TWGP. RESULTS: TWGP was observed 12 times at 11 different sites in 10 patients (5 ischemic cardiomyopathy). The TWGP stimulus fell immediately before or synchronous with the QRS in all cases, and evidence of local capture despite absence of a QRS complex was observed 6 times. In 5 sites, pacing after VT termination produced a QRS different than the VT. Four sites were in dense scar areas (<0.1 mV), and 6 in abnormal low voltage zone (0.1-1.5 mV). Additional mapping or ablation that abolished VT were consistent with the TWGP site being in a VT isthmus. A substrate marker for VT of late potentials, evoked delayed potentials, or slow conduction was present at 6 of 11 TWGP sites. CONCLUSIONS: TWGP may be a marker for detecting a re-entry circuit isthmus that has escaped detection based on electrogram or pace mapping-based substrate mapping.

Artificial neural network based forecasting of diesel engine performance and emissions utilizing waste cooking biodiesel.

Ecological and environmental problems resulting from fossil fuels are due to the harmful emissions released into the atmosphere. The rising interest in searching for alternative fuels like biodiesel is growing to solve these problems. Waste cooking oil (WCO) is transformed into methyl ester and combined with biodiesel in percentages of 25, 50, 75, and 100%. Research is done on the impacts of methyl ester blends on engine performance and emissions. Compared to diesel, the methyl ester combination showed 25% lower brake power and 24% loss in thermal efficiency at maximum load and 1500 rpm. However, diesel fuel showed 23% lower specific fuel consumption increase than biodiesel. Compared to diesel, methyl ester exhibits 15% lower air-fuel ratio and 4% volumetric efficiency. Biodiesel lowers CO, HC, and smoke concentrations by 12, 44, and 48%, respectively, compared to diesel. Biodiesel emits 23% higher NOx at 1500 rpm and 100% engine load. To predict the emissions and performance of different percentages of biodiesel at engine speed variation, an artificial neural network (ANN) model is presented. ANN modeling minimizes labor, time, and finances and uses nonlinear data. Predictions were produced about the brake output power, specific fuel consumption, thermal efficiency, air-fuel ratio, volumetric efficiency, and emissions of smoke, CO, HC, and NOx as a function of engine speed and blend ratio. All correlation coefficients (r) over 0.99 and R 2 values were beyond 0.98 for all variables. There were low values of MSE, MAPE, and MSLE with significant predictive ability. WCO's biodiesel is a viable diesel engine replacement fuel.

Cascading spatial drought network: A complex networks approach to track propagation of meteorological droughts to agricultural droughts.

Meteorological droughts often propagate to agricultural (and other) droughts, both spatially and temporally. The present study proposes a novel complex networks-based cascading spatial drought network to examine the spatial propagation of meteorological droughts in a region to agricultural droughts in other regions. This is done through: (1) establishing stable homogeneous drought communities; (2) investigating inter-community drought propagation; (3) locating drought sources; and (4) evaluating drought connections within major crop belts. The approach is implemented to study droughts in the Indian-subcontinent during the period 1948-2022. Monthly precipitation and root-zone soil moisture data from GLDAS (Global Land Data Assimilation System) are used to compute the standardized precipitation index (SPI) for meteorological droughts and standardized soil moisture index (SSI) for agricultural droughts. Primarily, the drought network is demarcated into several subsets of network communities within which clusters of localized propagation take place. Multi-community subgraphs combining different communities are also formed to understand the long-distance inter-community drought linkages. Using network centrality measures, such as degree, closeness, and clustering coefficient, network properties of scale-freeness, small-worldness, and presence of rich-clubs are checked. Although the overall network does not exhibit any of these properties, certain subgraphs have significant small-worldness, rich-clubs, and partial scale-freeness. Some of the crucial nodes that support these network properties lie in the monsoon pathways (in the Western Ghats), and others have a strong association with El Niño Southern Oscillation (ENSO) teleconnections, thus validating the ability of the drought network to capture seasonal and climatic features. Additionally, subgraphs of nodes with high productivity of different food crops are created to study drought propagation within crop belts. Barring potential shortcomings related to data dependencies, the cascading spatial drought network helps identify an impending agricultural drought that could strengthen our ability to forecast droughts.

[Prediction of color simulation prescription for traditional Chinese medicine placebo solution based on whale algorithm-optimized back propagation neural network].

Traditional Chinese medicine(TCM) placebos are simulated preparations for specific objects and the color simulation in the development of TCM placebos is both crucial and challenging. Traditionally, the prescription screening and pattern exploration process involves extensive experimentation, which is both time-consuming and labor-intensive. Therefore, accurate prediction of color simulation prescriptions holds the key to the development of TCM placebos. In this study, we efficiently and precisely predict the color simulation prescriptions of placebos using an image-based approach combined with Matlab software. Firstly, images of TCM placebo solutions are captured, and 13 chromaticity space values such as the L* a* b*, RGB, HSV, and CMYK values are extracted using Photoshop software. Correlation analysis and normalization are then performed on these extracted values to construct a 13×9×3 back propagation(BP) neural network model. Subsequently, the whale optimization algorithm(WOA) is employed to optimize the initial weights and thresholds of the BP neural network. Finally, the optimized WOA-BP neural network is validated using three representative instances. The training and prediction results indicate that, compared to the BP neural network, the WOA-BP neural network demonstrates superior performance in predicting the pigment ratios of placebos. The correlation coefficients for training, validation,testing, and the overall dataset are 0. 95, 0. 87, 0. 95, and 0. 95, respectively, approaching unity. Furthermore, all error values are reduced, with the maximum reduction reaching 99. 83%. The color difference(ΔE) values for the three validation instances are all less than 3, further confirming the accuracy and practicality of the WOA-BP neural network approach.

Unraveling the occurrence of hyperhydricity in oil palm somatic embryos during somatic embryogenesis process.

The propagation of oil palm through somatic embryogenesis is the most effective method of cloning this palm tree; however, in vitro cultivation can lead to abnormalities in plant tissue, such as hyperhydricity. The present study aimed to evaluate the difference in anatomical, morphological, and histochemical characteristics, and gene expression in normal (Nm) and hyperhydric (Hh) somatic embryos of oil palm. For this purpose, Nm and Hh somatic embryos were collected from the differentiation medium and were submitted to anatomical and histochemical analyses to assess the nucleus/cytoplasm ratio (toluidine blue), starch (Lugol), and proteins (XP), as well as ultrastructural analyses via transmission electron microscopy. Additionally, gene expression analyses were performed to gain a better understanding on the molecular aspect of hyperhydric abnormality. A higher quantity of differentiated Nm somatic embryos per explant was observed, with a germination rate close to zero in Hh somatic embryos. Additionally, a higher accumulation of proteins and starch was found in Nm somatic embryos when compared to Hh embryos. It was also noted that in Nm somatic embryos, protein reserves were primarily located in the proximal region (embryonic axis), whereas starch reserves were mainly accumulated in the distal region of the somatic embryos. Hh somatic embryos exhibit insignificant starch reserves, and a greater number of intercellular spaces were observed compared to Nm somatic embryos. However, some Hh somatic embryos displayed histochemical characteristics similar to Nm, which could explain the occurrence of reversions from the Hh state to the Nm state observed in this study. Regarding molecular analyses, the gene expression results obtained showed that out of the 19 genes analyzed, 17 were upregulated in hyperhydric embryos when compared to the control condition (normal somatic embryos). Genes involved in stress response, energy metabolism, defense, membrane transport, hormonal regulation, and development were positively regulated, especially those involved in ethylene synthesis and energetic metabolism. To the best of our knowledge, this is the first in-depth study addressing hyperhydricity in oil palm during somatic embryogenesis.

Pacing Cycle Length-Dependent Electrophysiological Changes in Left Atrium: Poor Validity of Using Low Voltage Area and Slow Conduction Area under Specific Pacing Cycle Length as Absolute Substrates of Atrial Fibrillation.

BACKGROUND: Pacing cycle length (PCL)-dependent changes in left atrial (LA) electrophysiological properties have not been fully elucidated. OBJECTIVE: To elucidate these changes using a high-resolution mapping system. METHODS: Forty-eight patients underwent atrial fibrillation ablation with RHYTHMIA HDx™. Paired LA maps under a baseline PCL (600 ms) and rapid PCL (300 ms) were acquired after pulmonary vein isolation under right atrial appendage pacing. The PCL-dependent change in the low-voltage area (LVA) (area with <0.5 mV bipolar voltage), LA activation time (interval from first LA activation to wavefront collision at lateral wall), regional mean voltage, regional mean wave propagation velocity, and slow conduction area (area with <0.3-m/s wave propagation velocity) were quantitatively analyzed. RESULTS: Under the rapid PCL, the total LVA was significantly increased (7.6 ± 9.5 vs. 6.7 ± 7.6 cm2, p = 0.031), especially in patients with a ≥10 cm2 LVA on the baseline PCL map (21.5 ± 9.1 vs. 18.1 ± 6.5 cm2, p = 0.013). The LA activation time was also prolonged (87.9 ± 16.2 vs. 84.0 ± 14.0 ms, p < 0.0001). Although the rapid PCL did not decrease the regional mean voltage, it significantly decreased the regional mean wave propagation velocity and increased the slow conduction area in all measured regions. CONCLUSION: LVA and slow conduction area can be emphasized by rapid PCL LA mapping. There may be poor validity in using these areas as absolute atrial fibrillation substrates without considering the PCL-dependent changes.

Long-Range Ballistic Propagation of 80% Excitonic Fraction Polaritons in a Perovskite Metasurface at Room Temperature.

Exciton-polaritons, hybrid light-matter excitations arising from the strong coupling between excitons in semiconductors and photons in photonic nanostructures, are crucial for exploring the physics of quantum fluids of light and developing all-optical devices. Achieving room temperature propagation of polaritons with a large excitonic fraction is challenging but vital, e.g., for nonlinear light transport. We report on room temperature propagation of exciton-polaritons in a metasurface made from a subwavelength lattice of perovskite pillars. The large Rabi splitting, much greater than the optical phonon energy, decouples the lower polariton band from the phonon bath of the perovskite. These cooled polaritons, in combination with the high group velocity achieved through the metasurface design, enable long-range propagation, exceeding hundreds of micrometers even with an 80% excitonic component. Furthermore, the design of the metasurface introduces an original mechanism for unidirectional propagation through polarization control, suggesting a new avenue for the development of advanced polaritonic devices.

Holo-U2Net for High-Fidelity 3D Hologram Generation.

Traditional methods of hologram generation, such as point-, polygon-, and layer-based physical simulation approaches, suffer from substantial computational overhead and generate low-fidelity holograms. Deep learning-based computer-generated holography demonstrates effective performance in terms of speed and hologram fidelity. There is potential to enhance the network's capacity for fitting and modeling in the context of computer-generated holography utilizing deep learning methods. Specifically, the ability of the proposed network to simulate Fresnel diffraction based on the provided hologram dataset requires further improvement to meet expectations for high-fidelity holograms. We propose a neural architecture called Holo-U2Net to address the challenge of generating a high-fidelity hologram within an acceptable time frame. Holo-U2Net shows notable performance in hologram evaluation metrics, including an average structural similarity of 0.9988, an average peak signal-to-noise ratio of 46.75 dB, an enhanced correlation coefficient of 0.9996, and a learned perceptual image patch similarity of 0.0008 on the MIT-CGH-4K large-scale hologram dataset.

Mechanical Strain, Temperature, and Misalignment Effects on Data Communication between Piezoceramic Ultrasonic Transducers.

Acoustic waves can be used for wireless telemetry as an alternative to situations where electrical or optical penetrators are unsuitable. However, the response of the ultrasonic transducer can be greatly affected by temperature variations, mechanical deformations, misalignment between transducers, and multiple layers in the propagation zone. Therefore, this work sought to quantify such influences on communication between ultrasonic transducers. The experimental measurements were performed at the frequency where power transfer is maximized. Moreover, there were four experimental models, each with its own performed setup. The ultrasonic transducers are attached to both sides of a 6 mm thick stainless-steel plate for configuring just one barrier. Multiple layers of transducers are attached to the outer side of two plates immersed in an acoustic fluid with a 100 mm thick barrier. In both cases, the S21 parameter was used to quantify the influence of the physical barrier because it correlates with the power flow between ports that return after a given excitation. The results showed that when a maximum deformation of 1250 µm/m was applied, the amplitude of the S21 parameter varied around +0.7 dB. Furthermore, increasing the temperature from 30 to 100 °C slightly affected the S21 (+0.8 dB), but the signal decayed quickly for temperatures beyond 100 °C. Additionally, the ultrasonic communication with a multiple layer was found to occur under misalignment with an intersection area of up to 40%. None of the factors evaluated resulted in insufficient power transfer, except for a large misalignment between the transducers. Such results indicate that this type of communication can be a robust alternative, with a minimum alignment of 40% between transducers and electrical penetrators.

Research on Integrated Control Strategy for Wind Turbine Blade Life.

Wind turbine blades bear the maximum cyclic load and varying self-weights in turbulent wind environments, which accelerate the propagation of cracks that ultimately progress from minor faults, resulting in blade failure and significant maintenance and shutdown costs. To address this issue, this paper proposes an adaptive control strategy for the blade's useful life. The control system is divided into the inner control loop and the outer control loop. The outer loop is based on the Paris crack propagation model combined with a particle filtering algorithm and calculates the degradation of the blade life under the crack threshold conditions provided by the operation and maintenance strategy to determine the parameter settings of the inner-loop load-shedding controller. The control strategy we propose can balance the load-shedding capability of the controller with the fatigue load of the pitch actuator while considering the predefined remaining useful blade life in the operation and maintenance strategy, avoiding unplanned downtime and reducing maintenance costs.

High-Sensitivity Janus Sensor Enabled by Multilayered Metastructure Based on the Photonic Spin Hall Effect and Its Potential Applications in Bio-Sensing.

The refractive index (RI) of biological tissues is a fundamental material parameter that characterizes how light interacts with tissues, making accurate measurement of RI crucial for biomedical diagnostics and environmental monitoring. A Janus sensor (JBS) is designed in this paper, and the photonic spin Hall effect (PSHE) is used to detect subtle changes in RI in biological tissues. The asymmetric arrangement of the dielectric layers breaks spatial parity symmetry, resulting in significantly different PSHE displacements during the forward and backward propagation of electromagnetic waves, thereby realizing the Janus effect. The designed JBS can detect the RI range of 1.3~1.55 RIU when electromagnetic waves are incident along the +z-axis, with a sensitivity of 96.29°/refractive index unit (RIU). In the reverse direction, blood glucose concentrations are identified by the JBS, achieving a sensitivity of 18.30°/RIU. Detecting different RI range from forward and backward scales not only overcomes the limitation that single-scale sensors can only detect a single RI range, but also provides new insights and applications for optical biological detection through high-sensitivity, label-free and non-contact detection.

A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models.

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as 7.3 dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 MHz, the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)-LoRa, etc.

In-situ detection of delamination reinitiation in carbon fiber reinforced polymers post barely visible impact damage.

In this study, advancements are presented in the in-situ detection of delamination reinitiation from Barely Visible Impact Damage (BVID) in composite materials, utilizing enhancements in Digital Image Correlation (DIC) techniques during a Compression After Impact (CAI) test. The study measured strain fields in the longitudinal, transverse, and shear directions, focusing specifically on the point of highest out-of-plane displacement to identify the onset of delamination propagation from BVID sites generated at different impact energy levels. By correlating the measured strains with the peak out-of-plane displacement, a unique determination of onset damage reinitiation associated with BVID during CAI testing was achieved. This method introduces a refined in-situ assessment technique for structural integrity, allowing for the early detection of critical damage propagation in composite materials.

Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow.

BACKGROUND AND OBJECTIVE: Computational models of hemodynamics can contribute to optimizing surgical plans, and improve our understanding of cardiovascular diseases. Recently, machine learning methods have become essential to reduce the computational cost of these models. In this study, we propose a method that integrates 1-D blood flow equations with Physics-Informed Graph Neural Networks (PIGNNs) to estimate the propagation of blood flow velocity and lumen area pulse waves along arteries. METHODS: Our methodology involves the creation of a graph based on arterial topology, where each 1-D line represents edges and nodes in the blood flow analysis. The innovation lies in decoding the mathematical data connecting the nodes, where each node has velocity and lumen area pulse waveform outputs. The training protocol for PIGNNs involves measurement data, specifically velocity waves measured from inlet and outlet vessels and diastolic lumen area measurements from each vessel. To optimize the learning process, our approach incorporates fundamental physical principles directly into the loss function. This comprehensive training strategy not only harnesses the power of machine learning but also ensures that PIGNNs respect fundamental laws governing fluid dynamics. RESULTS: The accuracy was validated in silico with different arterial networks, where PIGNNs achieved a coefficient of determination (R2) consistently above 0.99, comparable to numerical methods like the discontinuous Galerkin scheme. Moreover, with in vivo data, the prediction reached R2 values greater than 0.80, demonstrating the method's effectiveness in predicting flow and lumen dynamics using minimal data. CONCLUSIONS: This study showcased the ability to calculate lumen area and blood flow rate in blood vessels within a given topology by seamlessly integrating 1-D blood flow with PIGNNs, using only blood flow velocity measurements. Moreover, this study is the first to compare the PIGNNs method with other classic Physics-Informed Neural Network (PINNs) approaches for blood flow simulation. Our findings highlight the potential to use this cost-effective and proficient tool to estimate real-time arterial pulse waves.

Influence of Natural Fractures and Laminae on Fracture Propagation and Failure Mode of Continental Shale.

Natural fractures and laminae are well-developed in continental shale, which greatly affects the fracture propagation and failure mode. Based on the natural fractures and laminae developed in the outcrops of Triassic continental shale from the southern Ordos Basin, China, four different types of shale models are constructed in this research. The CASRock software V1.0 is utilized to conduct numerical simulations to investigate the influence of natural fractures and soft-to-hard laminae on the mechanical behavior of continental shale. The results demonstrate that the uniaxial compressive strength of shale models can improve by up to 34.48% when soft-to-hard laminae are present, but it can drop by up to 18.97% when weak interfaces are present. New fractures are consistently initiated at the ends of natural fractures, with various propagation patterns in different laminae. Fractures in soft laminae usually propagate in an oblique path at an angle ß ≈ 20°-30° relative to the direction of compressive stress, manifesting as shear fractures. Fractures in medium-to-hard laminae tend to propagate parallel to compressive stress, primarily featuring tensile fractures. The ultimate fracture morphology becomes more complex as soft, medium, and hard laminae and weak interfaces occur successively. It changes from a nearly linear fracture to an echelon pattern with more secondary fractures and finally a network shape, with a total fracture area increase of up to 270.12%. This study reveals the combined effect of natural fractures, soft-to-hard laminae, and weak interfaces on the fracture propagation and failure model of continental shale, providing support for fracturing optimization based on shale's authentic structure characteristics.