Network Geometry Research Articles

A computational algorithm for optimal design of a bioartificial organ scaffold architecture.

We develop a computational algorithm based on a diffuse interface approach to study the design of bioartificial organ scaffold architectures. These scaffolds, composed of poroelastic hydrogels housing transplanted cells, are linked to the patient's blood circulation via an anastomosis graft. Before entering the scaffold, the blood flow passes through a filter, and the resulting filtered blood plasma transports oxygen and nutrients to sustain the viability of transplanted cells over the long term. A key issue in maintaining cell viability is the design of ultrafiltrate channels within the hydrogel scaffold to facilitate advection-enhanced oxygen supply ensuring oxygen levels remain above a critical threshold to prevent hypoxia. In this manuscript, we develop a computational algorithm to analyze the plasma flow and oxygen concentration within hydrogels featuring various channel geometries. Our objective is to identify the optimal hydrogel channel architecture that sustains oxygen concentration throughout the scaffold above the critical hypoxic threshold. The computational algorithm we introduce here employs a diffuse interface approach to solve a multi-physics problem. The corresponding model couples the time-dependent Stokes equations, governing blood plasma flow through the channel network, with the time-dependent Biot equations, characterizing Darcy velocity, pressure, and displacement within the poroelastic hydrogel containing the transplanted cells. Subsequently, the calculated plasma velocity is utilized to determine oxygen concentration within the scaffold using a diffuse interface advection-reaction-diffusion model. Our investigation yields a scaffold architecture featuring a hexagonal network geometry that meets the desired oxygen concentration criteria. Unlike classical sharp interface approaches, the diffuse interface approach we employ is particularly adept at addressing problems with intricate interface geometries, such as those encountered in bioartificial organ scaffold design. This study is significant because recent developments in hydrogel fabrication make it now possible to control hydrogel rheology and utilize computational results to generate optimized scaffold architectures.

Documenting customary land boundaries using unmanned aerial vehicle imagery and artificial intelligence

AbstractThe use of computer vision and deep learning in boundary documentation for land registration stems from the ongoing demand for appropriate mapping approaches of unregistered land rights to eradicate the global challenge of tenure insecurity. Previous research has yielded promising results towards automated extraction of photo‐visible cadastral boundaries from high‐resolution imagery. Nonetheless, the extraction of invisible cadastral boundaries is still a challenge. This study investigates the place of sensor/s on‐board unmanned aerial vehicles and deep learning algorithms in detecting cadastral boundaries. It develops a participatory boundary marking procedure using low‐cost markers to bring monument to previously invisible and ill‐defined cadastral boundaries. After that, the researchers trained and tested the accuracy of a convolutional neural network, namely single shot multi‐box detector (SSD) based on Residual Neural Network (ResNet) and Visual Geometry Group (VGG) backbone networks to automatically detect cadastral boundary markers from unmanned aerial vehicle imagery. SSD based on ResNet34 performed best with 0.88 precision, 0.92 recall and 0.91 F measure or (F1) score. VGG19‐based SSD yielded a precision of 0.47, recall of 0.53 and F1 score of 0.50. The horizontal accuracy of the cadastral map generated varied from 0.089 to 0.496 m per parcel, with a standard deviation of 0.120 m. Results show that this approach is practical for cadastral mapping in rural areas.

Control of spin liquid-like dynamics by geometry of 2D nanocluster networks

Network structures of magnetic molecular assemblies on a two-dimensional (2D) material are attractive platforms for molecular spintronics and for the study of 2D magnetic materials. However, it is still a challenging task to connect such assemblies with appropriate magnetic interactions. Recently, uniform nanoclusters consisting of about 100 magnetic amino-ferrocene molecules were self-organized on a graphene oxide nanosheet by on-surface synthesis. Here, the dynamics of weakly interacting molecular spins in the nanocluster networks is investigated by exploiting the tunability of the intercluster distance through the chemical reaction. The stochastic simulation shows that the entanglement of the spin orientations at the sites in the nanocluster by magnetic dipole interactions leads to a liquid-like behavior of the spins (S = 5/2) at T ≲ 15 K, generating spin correlations and slow dynamics observed in Mössbauer spectroscopy and magnetic susceptibility. The energy barrier for generating magnetic relaxation and the deviation temperature from thermally activated relaxation depend on the intercluster distance, i.e., the interactions between the nanoclusters, indicating that the relaxation can be tuned by the geometry of the nanocluster networks. The present results pave the way for the chemical design of 2D nanocluster networks and chemically functionalized 2D materials.

Semantic segmentation of optical satellite images for the illegal construction detection using transfer learning

As urban populations grow, the demand for new infrastructure intensifies. However, some builders often neglect to obtain necessary permits, resulting in unstable structures that endanger public safety and disrupt city planning. Illegal construction poses significant challenges to economic development and social harmony. To tackle this issue, there is an urgent need for an automated illegal building monitoring system that can accurately identify and notify authorities about unlawful constructions. Current methods are often inefficient due to limited data access, inadequate analysis, and lack of automation.Recent technological advancements suggest that using satellite imagery alongside semantic segmentation is a highly effective approach for detecting illegal buildings. This process not only ensures the safety and compliance of structures but also enhances overall regulatory enforcement. This research distinguishes itself from previous studies by primarily focusing on illegal construction. It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and incorporates customized evaluation metrics that are not typically found in earlier research. Additionally, it utilizes real-world data, enhancing its practical relevance, and features IoT integration for real-time monitoring capabilities.We chose U-Net as our architecture due to its symmetrical design, which facilitates effective feature extraction. Its skip connections play a crucial role in preserving important features during the segmentation process. U-Net is known for its high accuracy and adaptability across various domains, making it versatile for our needs. Additionally, it performs well even with limited data, demonstrating robustness in diverse conditions. Furthermore, its compatibility with transfer learning enhances its applicability. For all these reasons, U-Net was selected as our model.Our experiments revealed that the Unet_mini model outperformed all others, demonstrating its effectiveness in addressing illegal construction challenges in urban areas. Qualitative results further illustrate the model's robustness, showing a significant reduction in false positives and improved accuracy in identifying illegal structures. This comprehensive approach not only improves detection rates but also contributes to more efficient regulatory enforcement, thereby promoting safer urban environments.This research distinguishes itself from previous studies by primarily focusing on illegal construction detection rather than general segmentation tasks. It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and integrates customized evaluation metrics that are not typically found in earlier research. Furthermore, it utilizes real-world data, enhancing its practical relevance, and incorporates IoT integration for real-time monitoring capabilities, setting it apart from existing studies.

Quantitative Analysis of Fracture Network Geometry on Fluid Flow and Heat Transfer in Hot Dry Rock Geothermal Reservoirs

Quantitative Analysis of Fracture Network Geometry on Fluid Flow and Heat Transfer in Hot Dry Rock Geothermal Reservoirs

Peridotite Weathering and Ni Redistribution in New Caledonian Laterite Profiles: Influence of Climate, Hydrology, and Structure

The peridotite massifs of New Caledonia are characterised by complex hydrodynamics influenced by intense inherited fracturing, uplift, and erosion. Following the formation of the erosion surfaces and alteration processes, these processes drive chemical redistribution during weathering; particularly lateritisation and saprolitisation. Magnesium, silica, and trace elements such as nickel and cobalt—released as the dissolution front advances—are redistributed through the system. New observations and interpretations reveal how lateritic paleo-land surfaces evolved, and their temporal relationship with alteration processes since the Oligocene. Considering the geometry of discontinuity networks ranging from micro-fractures to faults, the transfers occur in dual-permeability environments. Olivine dissolution rates are heterogeneously due to differential solution renewal caused by erosion and valley deepening. Differential mass transfer occurs between mobile regions of highly transmissive faults, while immobile areas correspond to the rock matrix and the secondary fracture network. The progression of alteration fronts controls the formation of boulders and the distribution of nickel across multiple scales. In the saprolite, nickel reprecipitates mostly in talc-like phases, as well as minor nontronite and goethite with partial diffusion in inherited serpentine. The current nickel distribution results from a complex interplay of climatic, hydrological and structural factors integrated into a model across different scales and times.

Impact of arterial system alterations due to amputation on arterial stiffness and hemodynamics: a numerical study

Subjects with amputation of the lower limbs are at increased risk of cardiovascular mortality and morbidity. We hypothesize that amputation-induced alterations in the arterial tree negatively impact arterial biomechanics, blood pressure and flow behavior. These changes may interact with other biological factors, potentially increasing cardiovascular risk. To evaluate this hypothesis regarding the purely mechanical impact of amputation on the arterial tree, we used a simulation computer model including a detailed one-dimensional (1D) arterial network model (143 arterial segments) coupled with a zero-dimensional (0D) model of the left ventricle. Our simulations included five settings of the arterial network: (1) 4-limbs control, (2) unilateral amputee (right lower limb), (3) bilateral amputee (both lower limbs), (4) trilateral amputee (lower-limbs and right upper-limb), and (5) quadrilateral amputee (lower and upper limbs). Analysis of regional stiffness, as calculated by pulse wave velocity (PWV) for large-, medium- and small-sized arteries, showed that, while aortic stiffness did not change with increasing degree of amputation, stiffness of medium and smaller-sized arteries increased with greater amputation severity. Despite a staged decrease in cardiac output, the systolic and diastolic blood pressure values increased, resulting in an increase in both central and peripheral pulse pressures but with an attenuation of pulse pressure amplification. The most significant increase in peak systolic pressure and decrease in peak systolic blood flow was observed at the site of the abdominal aorta. Wave separation analysis indicated no changes in the shape of the forward and backward wave components. However, the results from wave intensity analysis showed that with extended amputation, there was an increase in peak forward wave intensity and a rise in the inverse peak of the backward wave intensity, suggesting potential alterations in cardiac hemodynamic load. In conclusion, this simulation study showed that biomechanical and hemodynamic changes in the arterial network geometry could interact with additional risk factors to increase the cardiovascular risk in patients with amputations.

Vibration of effects resulting from treatment selection in mixed-treatment comparisons: a multiverse analysis on network meta-analyses of antidepressants in major depressive disorder

ObjectiveIt is frequent to find overlapping network meta-analyses (NMAs) on the same topic with differences in terms of both treatments included and effect estimates. We aimed to evaluate the impact...

Neural-Network and Multivariate-Normal-Distribution Hybrid Method for Real-Time Ground-Shaking Reconstruction

ABSTRACT Ground-shaking maps provide a spatial representation of the impact of a seismic event in terms of ground-motion parameters (GMPs), especially useful in the context of seismic monitoring and civil protection operations. Algorithms used to compute these maps usually rely on seismic source parameters to steer the interpolation process and consequently are limited to operate in near-real time. The present work introduces a novel algorithm that combines neural networks with the multivariate normal distribution method to reconstruct ground-shaking maps using only data available in real time, improving on previously proposed algorithms. The core idea of the proposed algorithm is to maintain the structure proposed by ShakeMap while removing the dependence on the source parameters, imposed by the use of ground-motion prediction equations, by replacing them with an appropriate neural network working on the GMPs recorded in real time at the seismic stations. The overall workflow of the method and the details of the neural network architecture and training are described. A model trained on synthetic and recorded data to target seismic events affecting the Italian territory is tested using the 2016 Norcia, Italy, earthquake showing the method reconstruction capabilities, its robustness to noise and to network geometry changes, and its real-time potential.

Metrics of Astrometric Variability in the International Celestial Reference Frame. I. Statistical Analysis and Selection of the Most Variable Sources

Using very long baseline interferometry data for the sources that comprise the third International Celestial Reference Frame (ICRF3), we examine the quality of the formal source-position uncertainties of ICRF3 by determining the excess astrometric variability (unexplained variance) for each source as a function of time. We also quantify multiple qualitatively distinct aspects of astrometric variability seen in the data, using a variety of metrics. Average position offsets, statistical dispersion measures, and coherent trends over time as explored by smoothing the data are combined to characterize the most and least positionally stable ICRF3 sources. We find a notable dependence of the excess variance and statistical variability measures on decl., as is expected for unmodeled ionospheric delay errors and the Northern Hemisphere–dominated network geometries of most astrometric and geodetic observing campaigns.

A deep learning approach for white blood cells image generation and classification using SRGAN and VGG19

The classification of White Blood Cells (WBCs) is crucial for diagnosing diseases, monitoring treatment effectiveness, and understanding how the immune system functions. In this paper, we propose a deep learning approach to classify WBCs using Super Resolution Generative Adversarial Network (SRGAN) and Visual Geometry Group 19 (VGG19). Firstly, microscopic images of WBCs are generated using the SRGAN to obtain more precise and high-resolution images, which are then classified with a pretrained VGG19 classifier. Low-resolution (LR) images are inputted into the generator of SRGAN, and its discriminator compares the High-resolution (HR) image with LR, generating super-resolution images to minimize misclassification risks. A large dataset of 12,447 images containing four classes of WBCs (Eosinophil, Lymphocyte, Monocyte, and Neutrophil) is utilized to train and validate our proposed model. Following extensive experimental analysis, our proposed model achieves a test accuracy of 94.87 %, surpassing traditional Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Hybrid CNN-RNN models, and other conventional approaches. The generated images of SRGAN overcome challenges associated with misclassification due to the poor resolution of microscopic images, while the use of a pretrained model as a classifier reduces classification complexity. The source code of the entire work is available at https://github.com/Jannatul-Ferdousi/SRGAN_VGG19_WBC.git.

Symplectic geometry of electrical networks

In this paper we relate a well-known in symplectic geometry compactification of the space of symmetric bilinear forms considered as a chart of the Lagrangian Grassmannian to the specific compactifications of the space of electrical networks in the disc obtained in [11], [4] and [3]. In particular, we state an explicit connection between these works and describe some of the combinatorics developed there in the language of symplectic geometry. We also show that the combinatorics of the concordance vectors forces the uniqueness of the symplectic form, such that corresponding points of the Grassmannian are isotropic. We define a notion of Lagrangian concordance which provides a construction of the compactification of the space of electrical networks in the positive part of the Lagrangian Grassmannian bypassing the construction from [11].

Extending geodetic networks for geo-monitoring by supervised point cloud matching

Abstract In this paper we propose a monitoring method that allows the inclusion of point clouds into a geodetic monitoring network. Consequently, network adjustment and a rigorous deformation analysis can be performed allowing consistent error propagation and trustful significance calculation. We introduce a supervised pipeline based on ICP-matching of small-scale laser scan patches. It is specially designed for geo-monitoring applications and allows to improve the spatial resolution as well as the network geometry of monitoring networks in inaccessible hazardous areas in the mountains, where the installation of a sufficient number of targets is difficult. We apply our method to two datasets. The first is a monitoring setup in the laboratory, where we establish the parameters for the supervised patch selection and demonstrate how the network geometry is improved. Second is the real case study of Mt. Hochvogel, where the proposed method helps to clearly improve the spatial resolution of deformation vectors.

Local earthquake monitoring with a low-cost seismic network: a case study in Nepal

Seismic monitoring matters both for research and for populations living in areas of seismic hazard; however, it comes with a cost that is not fully affordable for developing countries. Compared to classical approaches with very quiet sites and high-quality instrumentation, it is therefore worth investigating low-cost seismic networks and how well they perform at detecting and characterizing seismicity. We analyze 1 year of seismic data from an educational seismology network in Nepal, create our own earthquake catalog, and compare it to the publicly available national observatory catalog. We find that despite the noisier seismic station sites, the overall results are comparable and all the main features relevant for seismicity are found. We present quantitative analyses of locations, magnitudes and their frequency distribution in our catalog, as well as differences with the observatory catalog. Differences between the two catalogs primarily stem from the respective network geometries and their coverage, as well as daytime noise level differences. We conclude that if properly planned and installed, low-cost seismic networks are a viable, feasible and significant complement to monitor seismic activity.Graphical

Renormalization of networks with weak geometric coupling.

The renormalization group is crucial for understanding systems across scales, including complex networks. Renormalizing networks via network geometry, a framework in which their topology is based on the location of nodes in a hidden metric space, is one of the foundational approaches. However, the current methods assume that the geometric coupling is strong, neglecting weak coupling in many real networks. This paper extends renormalization to weak geometric coupling, showing that geometric information is essential to preserve self-similarity. Our results underline the importance of geometric effects on network topology even when the coupling to the underlying space is weak.

Length control emerges from cytoskeletal network geometry

Many cytoskeletal networks consist of individual filaments that are organized into elaborate higher-order structures. While it is appreciated that the size and architecture of these networks are critical for their biological functions, much of the work investigating control over their assembly has focused on mechanisms that regulate the turnover of individual filaments through size-dependent feedback. Here, we propose a very different, feedback-independent mechanism to explain how yeast cells control the length of their actin cables. Our findings, supported by quantitative cell imaging and mathematical modeling, indicate that actin cable length control is an emergent property that arises from the cross-linked and bundled organization of the filaments within the cable. Using this model, we further dissect the mechanisms that allow cables to grow longer in larger cells and propose that cell length-dependent tuning of formin activity allows cells to scale cable length with cell length. This mechanism is a significant departure from prior models of cytoskeletal filament length control and presents a different paradigm to consider how cells control the size, shape, and dynamics of higher-order cytoskeletal structures.

Effects of adding zeolite particles on the hierarchical microstructure of zeolite-geopolymer composites and their Sr2+ adsorption properties

Strontium ions can be removed from wastewater in fixed-bed reactors by adsorption on hierarchical materials (different pore sizes and phases). A detailed understanding of the materials multiscale structure is therefore crucial to optimize their sorption properties. This article presents a multi-technique approach developed to characterize the relationship between microstructure and adsorption in a range of Linde-type A (LTA) zeolite-geopolymer composites,. Two-dimensional scanning electron microscopy and 3D X-ray tomography were used to image the porous and solid phases at different length scales. Advanced numerical methods involving machine learning were applied to segment the images and provide quantitative morphological values describing the porous network geometry and the location and accessibility of the zeolite particles in the geopolymer. These results were then correlated with the materials sorption properties, measured in batch and column processes. Increasing the materials zeolite content (from 0 to 27 wt%) slowed down their adsorption kinetics but increased their maximal adsorption capacities from 20 to 49 mg.g−1 and Sr2+-selectivity at equilibrium. In breakthrough experiments, the material with highest zeolite content had a much higher breakthrough volume passing from 169 to 478 ml in the presented experimental conditions (much higher wastewater decontamination capacity), but a shallower breakthrough curve (lower column efficiency).

Novel approach to produce 3D boron-doped diamond for pollutant removal from water

Diamond growth from Chemical Vapor Deposition (CVD) on foreign substrates can require different pretreatment not only to improve the film nucleation but also to assure its adhesion by decreasing the expected film/substrate interface stress. To improve boron-doped film nucleation, growth, and adherence, different substrate pretreatments have been used mainly from the seeding process with diamond powder at various particle sizes. Despite this, the development of diamond growth on a Ti mesh remains difficult because of the requirement of a cohesive film to cover a 3D macroporous sample with varying growth rates based on its distinct network geometry. Then, this work describes a novel approach to growing boron-doped diamond (BDD) and boron-doped ultrananocrystalline diamond (B-UNCD) on titanium dioxide nanotubes (TDNT) produced simultaneously on both sides of Ti mesh by an anodization process. The films were obtained from two-step growth processes by assuring the entire diamond overlay on both TDNT/Ti mesh sides, including their outer/inner surfaces, as a 3D sample. TiO2 - TiC conversion has dominated the renucleation process, facilitating the nanometric scale control. The film morphologies were systematically analyzed by FEG-SEM images at different sample planes and depths for both sample sides at different stages of film growth. The unique morphology of titania nanotubes associated with columnar and/or renucleation development of BDD, considering the film defects and valley, can systematically increase the electrode specific area. Raman spectra showed the film quality and its micro and/or ultrananodiamond structure and the boron doping features. Also, this growth process allowed a dopant-controlled adjustable conductivity. Then, the boron doping levels for both films were evaluated from Mott-Schottky plots at around 1019 Bcm−3, characterizing them with good conductivity. In addition, electrochemical measurements from Cyclic Voltammetry (CV) confirmed the expected diamond response on redox pair following the quasi-reversible criteria as high-performance diamond electrodes and in situ Raman spectroelectrochemical measurements assessed the stability of samples during electrochemical measurements, ensuring structural integrity. Finally, the samples were applied to the degradation of methylene blue, proving to be superior materials for electrochemical applications due to their advantages compared to those of similar 2D electrodes.

Spatial patterns of valley network erosion on early Mars

Fluvial valley networks are a key record of the geomorphic effects of past liquid water on the surface of Mars. Most valley networks formed early in Mars history (>3.5 Ga) during a period of increased surface water availability, and these landforms may preserve information on the nature and spatial heterogeneity of the ancient martian hydroclimate. Here we present work to constrain patterns of valley network geometry (length, depth, and volume), and assess how these factors are related to topography and landscape position. We find that valley depth is most strongly controlled by regional slope and landscape relief, with deeper valleys on steeper/higher relief terrains, likely driven by a slope control on the rate of fluvial erosion. In contrast, total valley length and volume are most controlled by slope orientation, with larger values on north-draining slopes, which we suggest is related to more efficient fluvial integration (i.e., inter-connection of valleys) on such slopes. At lower elevations and towards terminal basins (northern plains and Hellas), valley depth increases, while total valley length and volume decrease, consistent with the geometry expected as a branching valley system converges downstream into fewer, larger valleys. Finally, we show that patterns of total valley length and eroded volume peak about a previously proposed paleo-latitude band on a pre-Tharsis-driven true polar wander Mars. Valley depth increases from paleo-south to paleo-north in this pre-true polar wander framework, consistent with valley network development in a paleo-north direction. This is potentially consistent with syn/pre-Tharsis growth and true polar wander valley network incision; however, this signal is not unique and may be amplified by patterns of post-valley network incision resurfacing.

Weather forecast image classification based on KNN, CNN, and VGG19

Image classification is an important technology in the field of computer vision, with the main objective of categorizing input images into different classes. It is used in many areas, such as weather forecasting. The significance of weather forecast image classification spans several areas, including enhancing weather forecast accuracy, aiding agriculture, optimizing the energy sector, ensuring transportation safety, and informing urban planning and construction. In this study, the author investigates the precision and efficiency of three machine learning algorithmsConvolutional Neural Network (CNN), K-Nearest Neighbor (KNN), and Visual Geometry Group 19 (VGG19)in classifying weather forecast images. The research meticulously elucidates the methodologies underlying each algorithm, outlining their distinct architectures, training processes, and feature extraction mechanisms. The research highlights that the self-built CNN model excels in accuracy and efficiency, while VGG19 achieves higher accuracy (93%) with longer training time. In contrast, the KNN model shows shorter training time (32.50 seconds) with slightly lower accuracy (80%).