Network Geometry Research Articles

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%).

Impact of stress regime change on the permeability of a naturally fractured carbonate buildup (Latemar, the Dolomites, northern Italy)

Abstract. Changing stress regimes control fracture network geometry and influence porosity and permeability in carbonate reservoirs. Using outcrop data analysis and a displacement-based linear elastic finite-element method, we investigate the impact of stress regime change on fracture network permeability. The model is based on fracture networks, specifically fracture substructures. The Latemar, predominantly affected by subsidence deformation and Alpine compression, is taken as an outcrop analogue for an isolated (Mesozoic) carbonate buildup with fracture-dominated permeability. We apply a novel strategy involving two compressive boundary loading conditions constrained by the study area's NW–SE and N–S stress directions. Stress-dependent heterogeneous apertures and effective permeability were computed in the 2D domain by (i) using the local stress state within the fracture substructure and (ii) running a single-phase flow analysis considering the fracture apertures in each fracture substructure. Our results show that the impact of the modelled far-field stresses at (i) subsidence deformation from the NW–SE and (ii) Alpine deformation from N–S increased the overall fracture aperture and permeability. In each case, increasing permeability is associated with open fractures parallel to the orientation of the loading stages and with fracture densities. The anisotropy of permeability is increased by the density and connectedness of the fracture network and affected by shear dilation. The two far-field stresses simultaneously acting within the selected fracture substructure at a different magnitude and orientation do not necessarily cancel each other out in the mechanical deformation modelling. These stresses affect the overall aperture and permeability distributions and the flow patterns. These effects – potentially ignored in simpler stress-dependent permeability – can result in significant inaccuracies in permeability estimation.

Unraveling the mesoscale organization induced by network-driven processes

Complex systems are characterized by emergent patterns created by the nontrivial interplay between dynamical processes and the networks of interactions on which these processes unfold. Topological or dynamical descriptors alone are not enough to fully embrace this interplay in all its complexity, and many times one has to resort to dynamics-specific approaches that limit a comprehension of general principles. To address this challenge, we employ a metric-that we name Jacobian distance-which captures the spatiotemporal spreading of perturbations, enabling us to uncover the latent geometry inherent in network-driven processes. We compute the Jacobian distance for a broad set of nonlinear dynamical models on synthetic and real-world networks of high interest for applications from biological to ecological and social contexts. We show, analytically and computationally, that the process-driven latent geometry of a complex network is sensitive to both the specific features of the dynamics and the topological properties of the network. This translates into potential mismatches between the functional and the topological mesoscale organization, which we explain by means of the spectrum of the Jacobian matrix. Finally, we demonstrate that the Jacobian distance offers a clear advantage with respect to traditional methods when studying human brain networks. In particular, we show that it outperforms classical network communication models in explaining functional communities from structural data, therefore highlighting its potential in linking structure and function in the brain.

Quartz Dissolution Effects on Flow Channelization and Transport Behavior in Three‐Dimensional Fracture Networks

AbstractWe perform a set of reactive transport simulations in three‐dimensional fracture networks to characterize the impact of geochemical reactions on flow channelization. Flow channelization, a frequently observed phenomenon in porous and fractured subsurface rock formations, results from the spatially variable hydraulic resistance offered by a geological structure. In addition to geo‐structural features such as network connectivity, geometry, and hydraulic resistance, geochemical reactions, for example, dissolution and precipitation, can dynamically inhibit or enhance flow channelization. These geochemical processes can change the fracture permeability leading to increased flow channelization, which are localized connected regions of high volumetric flow rates that are seemingly ubiquitous in the subsurface. In our simulations, fractures partially filled with quartz are gradually dissolved until quasi‐steady state conditions are obtained. We compare the flow field's initial unreacted and final dissolved states in terms of flow and transport observations. We observe that the dissolved fracture networks provide less resistance to flow and exhibit increased flow channelization when compared to their unreacted counterparts. However, there is substantial variability in the magnitude of these changes which implies that the channelization strongly depends on the network structure. In turn, we identify the interplay between the particular network structure and the impact of geochemical dissolution on flow channelization. The presented results indicate that geological systems that have been weathering or reactive for longer times in older landscapes are likely to have increased flow channelization compared to their equivalent but younger counterparts, which implies a time dependence on flow channelization in fractured media.

Scaling laws for optimal power-law fluid flow within converging–diverging dendritic networks of tubes and rectangular channels

Flows in dendritic–fractal networks have garnered extensive research attention, but most studies assume a constant tube or channel cross section. In many applications, the cross section of the tube or channel changes as the flow progresses through it, such as the blood flow through the arterial system, which is a prime example of a deformable or non-uniform tree-like network. Heating, ventilation, and air conditioning ductwork also exemplify a tree-like network with varying cross sections. This research investigates power-law fluid flows in the converging–diverging tubes and rectangular channels, prevalent in engineered microfluidic devices, many industrial processes, and heat transfer applications. Power-law fluid flows through linear, parabolic, hyperbolic, hyperbolic cosine, and sinusoidal converging–diverging dendritic networks of tubes and rectangular channels are studied. The flow is assumed to be steady, incompressible, two-dimensional planar, and axisymmetric laminar flow without considering network losses. A theoretical model has been derived to evaluate the flow conductance under network volume and surface-area constraints. The flow conductance is highly sensitive to network geometry. The effective conductance of all networks increases with increasing daughter-to-parent radius ratio before eventually declining. The maximum conductance occurs when a specific radius or channel-height daughter–parent ratio β* is achieved. This value depends on the constraint and vessel geometry, such as tubes or rectangular channels. The optimal flow conditions for maximum conductance in a constrained tube volume network, βmax*=βmin*=N−1/3, while for a constrained tube's surface-area network, βmax*=βmin*=N−(n+1)/(3n+2). This scaling applies to all converging–diverging tube network profiles. Here, βmax*, βmin* are the radius ratios of the daughter–parent pair at the maximum divergent or minimum convergent part of the vessel. N represents the number of branches splitting at each junction, and n is the power-law index of the fluid. Furthermore, the optimal flow scaling for the height ratio in the rectangular channel, βmax*=βmin*=N−1/2α−1/2 for constrained channel volume and βmax*=βmin*=N−1/2α−n/(2n+2) for constrained surface area for all converging–diverging channel networks, respectively, where α is the channel-width ratio between parent and daughter branches. Additionally, at optimal conditions in both the channels and tube network, pressure drops are equally partitioned across each branching level. The results in this work are validated with experiments and existing theories for limiting conditions. This research expands existing design principles for efficient flow systems, previously in the literature developed for uniform vessels, to encompass non-uniform converging–diverging vessels. Additionally, it provides a valuable framework for studying non-Newtonian flows within complex, non-uniform tree-like networks.

Numerical modeling and simulation of microbially induced calcite precipitation on a cement surface at the pore scale

Accurate estimation of contaminant transport in cementitious material using numerical tools plays a key role in the risk assessments of nuclear waste disposal. At the pore scale, the increase of microbial activity, such as microbially induced calcite precipitation on cementitious material, causes changes in solid surface topography, pore network geometry, and pore water chemistry, which affect contaminant transport at the core scale and beyond. Consequently, a meaningful estimation of contaminant migration in the subsurface requires a pore-scale investigation of the influence of microbial activity on transport processes. In this study, a pore-scale reactive transport model is presented to simulate the physicochemical processes resulting from microbially induced calcite precipitation on a cement surface. Numerical investigations focus on modeling the reactive transport in a two-dimensional flow-through cell. The model results are validated by experimental data showing an increase in pH and a decrease in calcium concentration due to microbially induced calcite precipitation. Our results show heterogeneous calcite precipitation under transport-limited conditions and homogeneous calcite precipitation under reaction-limited conditions, resulting in non-uniform and uniform changes in the material surface topography. Moreover, power spectral density analysis of the surface data demonstrates that microbially induced calcite precipitation affects the surface topography via both general changes over the entire frequency and local modifications in the high-frequency region. The sensitivity studies provide a comprehensive understanding of the evolution of surface topography due to the microbially induced calcite precipitation at the pore scale, thus contributing to an improved predictability of contaminant transport at the core scale and beyond.