Spatial Network Model Research Articles

Formation and regulation of calcium sparks on a nonlinear spatial network of ryanodine receptors.

Accurate regulation of calcium release is essential for cellular signaling, with the spatial distribution of ryanodine receptors (RyRs) playing a critical role. In this study, we present a nonlinear spatial network model that simulates RyR spatial organization to investigate calcium release dynamics by integrating RyR behavior, calcium buffering, and calsequestrin (CSQ) regulation. The model successfully reproduces calcium sparks, shedding light on their initiation, duration, and termination mechanisms under clamped calcium conditions. Our simulations demonstrate that RyR clusters act as on-off switches for calcium release, producing short-lived calcium quarks and longer-lasting calcium sparks based on distinct activation patterns. Spark termination is governed by calcium gradients and stochastic RyR dynamics, with CSQ facilitating RyR closure and spark termination. We also uncover the dual role of CSQ as both a calcium buffer and a regulator of RyRs. Elevated CSQ levels prolong calcium release due to buffering effects, while CSQ-RyR interactions induce excessive refractoriness, a phenomenon linked to pathological conditions such as ventricular arrhythmias. Dysregulated CSQ function disrupts the on-off switching behavior of RyRs, impairing calcium release dynamics. These findings provide new insights into RyR-mediated calcium signaling, highlighting CSQ's pivotal role in maintaining calcium homeostasis and its implications for pathological conditions. This work advances the understanding of calcium spark regulation and underscores its significance for cardiomyocyte function.

Combining stable isotopes and spatial stream network modelling to disentangle the roles of hydrological and biogeochemical processes on riverine nitrogen dynamics

Intensive agricultural activities have significantly altered watershed hydrological and biogeochemical processes, resulting in water quality issues and loss of ecosystem functions and biodiversity. A major challenge in effectively mitigating nitrogen (N) loss from agricultural watersheds stems from the heterogeneity of N transformation and transport processes that complicates accurate quantification and modeling of N sources and sinks at the watershed scale. This study utilized stable isotopes of water and nitrate (NO3−) in conjunction with spatial stream network modeling (SSNMs) to explore watershed hydrology, N transformation, and sources within a mesoscale river network in the U.S. Corn Belt (Cannon River Watershed, Minnesota) under contrasting hydrological conditions. The results show that the wet season had elevated riverine NO3− concentration (medium: 8.4 mg N L−1), driven by high watershed wetness conditions that mobilizes NO3− from the near-surface source zone. Furthermore, the strong hydrologic connectivity also reduced the denitrification potential by shortening water travel times. In comparison, the dry season showed lower NO3− concentrations (0.9 mg N L−1) and stronger denitrification NO3− isotope signals. During this period, the decrease in hydrologic connectivity shifted the predominant water source to deep groundwater, with longer water travel time promoting denitrification. After accounting for isotopic fractionations during nitrification and denitrification, we identified fertilizer N as the main NO3− source during the wet season (98.2±1.3%), whereas the dry season showed contributions from diverse sources (64.4±11.9% fertilizer, 26.0±15.8% soil N, and 9.5±6.0% manure and sewage). During the dry season, karst regions with high hydrologic connectivity display increased shallow groundwater inputs, carrying elevated NO3− levels from leaching of applied chemical fertilizers. These findings highlight the importance of integrating drainage water management and N accumulation in groundwater into nutrient management strategies to develop adaptive measures for controlling N pollution in agricultural watersheds.

A Coupled Spatial-Network Model: A Mathematical Framework for Applications in Epidemiology.

There is extensive evidence that network structure (e.g., air transport, rivers, or roads) may significantly enhance the spread of epidemics into the surrounding geographical area. A new compartmental modeling framework is proposed which couples well-mixed (ODE in time) population centers at the vertices, 1D travel routes on the graph's edges, and a 2D continuum containing the rest of the population to simulate how an infection spreads through a population. The edge equations are coupled to the vertex ODEs through junction conditions, while the domain equations are coupled to the edges through boundary conditions. A numerical method based on spatial finite differences for the edges and finite elements in the 2D domain is described to approximate the model, and numerical verification of the method is provided. The model is illustrated on two simple and one complex example geometries, and a parameter study example is performed. The observed solutions exhibit exponential decay after a certain time has passed, and the cumulative infected population over the vertices, edges, and domain tends to a constant in time but varying in space, i.e., a steady state solution.

Scenario Planning Management Actions to Restore Cold Water Stream Habitat: Comparing Mechanistic and Statistical Modeling Approaches

ABSTRACTUnder the United States Clean Water Act, states are required to periodically assess state waters to determine compliance with water quality criteria (including temperature) and then to develop total maximum daily loads (TMDLs) for impaired waters as necessary to bring them into compliance. We compared the performance of mechanistic stream temperature models (HeatSource, QUAL2K, and QUAL2Kw) applied to the mainstem of three TMDL watersheds (Middle Fork John Day, OR; Wind River, WA; South Fork Nooksack, WA) with that of spatial stream network (SSN) models applied to the full watersheds and used these to evaluate the potential effectiveness of restoration strategies. SSN models performed well with slightly lesser accuracy (RMSE = 0.47–0.87) for mainstem predictions than mechanistic models (RMSE = 0.4) but provided additional benefits to inform management, including information on spatial and temporal heterogeneity of restoration effectiveness throughout the watershed. Of the four scenarios considered (restoration of riparian zones to potential natural vegetation, channel narrowing, increasing flow by restricting irrigation withdrawals, and combined applications), riparian zone restoration was consistently the most effective in reducing temperatures at the outlet, mainstem, and throughout the watersheds. Predicted restoration effectiveness for thermal regimes varied significantly both within and among watersheds. A focus on water quality criteria exceedance only at the watershed outlet or along the mainstem reach can obscure knowledge of restoration potential for fish habitat in tributaries and headwaters, potential for creation of thermal refuge areas along the mainstem critical for maintaining migration corridors, and thermal regime heterogeneity across space and time.

A conjugate method for simulating the dynamics of stochastic urban spatial network models

Urban networks are integral components of urban systems, contributing to their functioning and shaping the overall dynamics of urban areas. They are characterized by their complexity, interdependence, and dynamic nature. The construction, analysis and understanding of urban network models is therefore essential to address complex urban challenges, fostering sustainable development, and improving the overall quality of life in systems like cities and regions. In this work, we present and analyze the properties of a stochastic spatial-interaction model of urban structures. In addition, we devise a suitable time-stepping integrator that allows analyzing the evolution of this stochastic system at large times intervals, providing information of the dynamical behavior of the system in its equilibrium state. Numerical simulation studies are carried out to illustrate the practical effectiveness of the proposed approach.

SSN2: The next generation of spatial stream network modeling in R.

The SSN2 R package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN R package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the R-spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & Gómez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN R package but leverages modern R-spatial tools like sf (Pebesma, 2018). SSN2 also provides many useful features that were not available in the SSN R package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other R generic functions.

The influence of burn severity on dissolved organic carbon concentrations across a stream network differs based on seasonal wetness conditions

Abstract. Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds. Altered sources of carbon and changes in catchment hydrology after wildfire can lead to shifts in dissolved organic carbon (DOC) concentrations in streams, which can have negative impacts on aquatic ecosystem health and downstream drinking-water treatment. Despite its importance, post-fire DOC responses remain relatively unconstrained in the literature, and we lack critical knowledge of how burn severity, landscape elements, and climate interact to affect DOC concentrations. To improve our understanding of the impact of burn severity on DOC concentrations, we measured DOC at 129 sites across a stream network extending upstream, within, and downstream of a large, high-severity wildfire in Oregon, USA. We collected samples across the study sub-basin during four distinct seasonal wetness conditions. We used our high-spatial-resolution data to develop spatial stream network (SSN) models to predict DOC across the stream network and to improve our understanding of the controls on DOC concentrations. Spatially, we found no obvious wildfire signal – instead, we observed a pattern of increasing DOC concentrations from the high-elevation headwaters to the sub-basin outlet, while the mainstem maintained consistently low DOC concentrations. This suggests that effects from large wildfires may be “averaged” out at higher stream orders and larger spatial scales. When we grouped DOC concentrations by burn severity group, we observed a significant decrease in the variability of DOC concentrations in the moderate and high burn severity sub-catchments. However, our SSN models were able to predict decreases in DOC concentrations with increases in burn severity across the stream network. Decreases in DOC concentrations were also highly variable across seasonal wetness conditions, with the greatest (−1.40 to −1.64 mg L−1) decrease occurring in the high-severity group during the wetting season. Additionally, our models indicated that in all seasons, baseflow index was more influential in predicting DOC concentrations than burn severity was, indicating that groundwater discharge can obscure the impacts of wildfire in a stream network. Overall, our results suggested that landscape characteristics can regulate the DOC response to wildfire. Moreover, our results also indicated that the seasonal timing of sampling can influence the observed response of DOC concentrations to wildfire.

Watershed features shape spatial patterns of fish tissue mercury in a boreal river network

Freshwater mercury (Hg) contamination is a widespread environmental concern but how proximate sources and downstream transport shape Hg spatial patterns in riverine food webs is poorly understood. We measured total Hg (THg) in slimy sculpin (Cottus cognatus) across the Kuskokwim River, a large boreal river in western Alaska and home to subsistence fishing communities which rely on fish for primary nutrition. We used spatial stream network models (SSNMs) to quantify watershed and instream conditions influencing sculpin THg. Spatial covariates for local watershed geology and slope accounted for 55 % of observed variation in sculpin THg and evidence for downstream transport of Hg in sculpins was weak. Empirical semivariograms indicated these spatial covariates accounted for most spatial autocorrelation in observed THg. Watershed geology and slope explained up to 70 % of sculpin THg variation when SSNMs accounted for instream spatial dependence. Our results provide network-wide predictions for fish tissue THg based largely on publicly available geospatial data and open-source software for SSNMs, and demonstrate how these emerging models can be used to understand contaminant behavior in spatially complex aquatic ecosystems.

Microscopic Intervention Yields Abrupt Transition in Interdependent Ferromagnetic Networks.

The study of interdependent networks has recently experienced a boost with the development of experimentally testable materials that physically realize their novel critical behaviors, calling for systematic studies that go beyond the percolation paradigm. Here we study the critical kinetics and phase transitions of a model of interdependent spatial ferromagnetic networks where dependency couplings between networks are realized by a thermal interaction having a tunable spatial range. We show how the critical phenomena and the phase diagram of this realistic model are highly affected by the range of thermal dissipation and how the latter influences the microscopic kinetics of the model. Furthermore, we show the existence of a new phase where localized microscopic interventions by heating or magnetic fields yield a macroscopic phase transition. Our results unveil rich phenomena and realistic protocols for controlling the macroscopic phases of interdependent materials by means of microscopic interventions.

Hydrologic Connectivity Regulates Riverine N2O Sources and Dynamics.

Indirect nitrous oxide (N2O) emissions from streams and rivers are a poorly constrained term in the global N2O budget. Current models of riverine N2O emissions place a strong focus on denitrification in groundwater and riverine environments as a dominant source of riverine N2O, but do not explicitly consider direct N2O input from terrestrial ecosystems. Here, we combine N2O isotope measurements and spatial stream network modeling to show that terrestrial-aquatic interactions, driven by changing hydrologic connectivity, control the sources and dynamics of riverine N2O in a mesoscale river network within the U.S. Corn Belt. We find that N2O produced from nitrification constituted a substantial fraction (i.e., >30%) of riverine N2O across the entire river network. The delivery of soil-produced N2O to streams was identified as a key mechanism for the high nitrification contribution and potentially accounted for more than 40% of the total riverine emission. This revealed large terrestrial N2O input implies an important climate-N2O feedback mechanism that may enhance riverine N2O emissions under a wetter and warmer climate. Inadequate representation of hydrologic connectivity in observations and modeling of riverine N2O emissions may result in significant underestimations.

Super-localization of spatial network models

Spatial network models are used as a simplified discrete representation in a wide range of applications, e.g., flow in blood vessels, elasticity of fiber based materials, and pore network models of porous materials. Nevertheless, the resulting linear systems are typically large and poorly conditioned and their numerical solution is challenging. This paper proposes a numerical homogenization technique for spatial network models which is based on the super-localized orthogonal decomposition (SLOD), recently introduced for elliptic multiscale partial differential equations. It provides accurate coarse solution spaces with approximation properties independent of the smoothness of the material data. A unique selling point of the SLOD is that it constructs an almost local basis of these coarse spaces, requiring less computations on the fine scale and achieving improved sparsity on the coarse scale compared to other state-of-the-art methods. We provide an a posteriori analysis of the proposed method and numerically confirm the method’s unique localization properties. In addition, we show its applicability also for high-contrast channeled material data.

Basketball Sports Posture Recognition Technology Based on Improved Graph Convolutional Neural Network

Basketball has rapidly developed in recent years. Analysis of various moves in basketball can provide technical references for professional players and assist referees in judging games. Traditional technology can no longer provide modern basketball players with theoretical support. Therefore, using intelligent methods to recognize human body postures in basketball was a relatively innovative approach. To be able to recognize the basketball sports posture of players more accurately, the experiment proposes a basketball stance recognition model based on enhanced graph convolutional networks (GCN), that is, the basketball stance recognition model based on enhanced GCN and spatial temporal graph convolutional network (ST-GCN) model. This model combines the respective advantages of the GCN and temporal convolutional network and can handle graph-structured data with time-series relationships. The ST-GCN can be further deduced by realizing the convolution operation of the graph structure and establishing a spatiotemporal graph convolution model for the posture sequence of a person’s body. A dataset of technical basketball actions is constructed to verify the effectiveness of the ST-GCN model. The final experimental findings indicated that the final recognition accuracy of the ST-GCN model for basketball postures was approximately 95.58%, whereas the final recognition accuracy of the long short term memory + multiview re-observation skeleton action recognition (LSTM+MV+AC) model was about 93.65%.

Fabrication of electrical devices on conformal surfaces based on array nozzle and five-axis motion mechanism

It has been known that conformal printing with a single nozzle has low efficiency. This study proposes a novel non-expandable curved surface printing method that could realize high-efficiency printing with array nozzles. First, based on the non-uniform rational B-splines curved surface reconstruction method, the parameter equation of the stereolithography format curved surface model is obtained. An equidistant parallel curve filling method is proposed based on the parameter equation. According to the filling accuracy, a spatial equidistant curve network model is established by fitting the surface. Using each node on the curve network as the filling pixel increases the filling rate of the non-expandable surface and improves the printing accuracy. Compared with that of the traditional equidistant filling, the droplet filling rate could be increased by 13.05 % in this study. Finally, based on the self-developed five-axis curved surface inkjet printing system and normal vector printing method, samples of concave, convex, and multi-material conformal surfaces are sequentially printed. Our research finding lays the foundation for high-efficiency integrated molding of electronic devices with complex conformal surfaces.

Statistical stream temperature modelling with SSN and INLA: an introduction for conservation practitioners

Statistical stream temperature models predicting the fine-scale spatial distribution of water temperatures (i.e., “thermalscape”) can guide aquatic species recovery and habitat restoration efforts. However, stream temperature modelling is complicated by spatial autocorrelation arising from non-independence of sampling sites within dendritic networks. We used August mean temperature data from miniature sensors deployed in Canadian Rocky Mountain streams to demonstrate two statistical stream temperature modelling techniques that account for spatial autocorrelation. The first was a spatial steam network (SSN) model specifically developed to account for spatial autocorrelation in dendritic stream networks. The second was an integrated nested Laplace approximation (INLA) model that accounts for spatial autocorrelation but was not designed to address anisotropic stream network data. We evaluated the best-fitting SSN and INLA models using leave-one-out cross-validation. Relative to INLA, SSN models had lower RMSE (1.23 vs. 1.45 C) and higher r2 (0.71 vs. 0.61); however, the SSN models required more preprocessing steps before incorporating spatially correlated random errors. We provide practical advice, an open-access r-script, and data to help non-experts develop statistical stream temperature models.

Integrating spatial stream network models and environmental DNA to estimate current and future distributions of nonnative Smallmouth Bass

Abstract Objective Climate change is fueling the rapid range expansion of invasive species in freshwater ecosystems. This has led to mounting calls from natural resource managers for more robust predictions of invasive species distributions to anticipate threats to species of concern and implement proactive conservation and restoration actions. Here, we applied recent advances in fish sampling and statistical modeling in river networks to estimate the current and future watershed-scale spatial distribution of nonnative Smallmouth Bass Micropterus dolomieu. Methods We integrated a spatial stream network (SSN) model of stream temperature, landscape environmental covariates, and Smallmouth Bass occurrence data based on environmental DNA (eDNA) detections to develop an SSN species distribution model (SDM) representing current Smallmouth Bass distributions in the Chehalis River, Washington State, a large coastal river basin of ongoing watershed-scale restoration. The SDM was informed by spatially intensive eDNA sampling from 135 locations in the main stem and major tributaries. We then applied downscaled climate change projections to the SSN SDM to predict Smallmouth Bass range expansion in the basin by late century. Result We identified high levels of spatial autocorrelation at hydrological distances of ≤10 km in our eDNA data set, underscoring the importance of applying an SSN modeling framework. Stream temperature was identified as the most important environmental covariate explaining variability in Smallmouth Bass occurrence. Model predictions estimated that current suitable summer habitat for Smallmouth Bass habitat spans 681 km and is projected to nearly double by late century (1333 km) under a moderate climate change scenario. Current and future suitable habitat for Smallmouth Bass is prevalent in important tributaries for spring Chinook Salmon Oncorhynchus tshawytscha, a species of major conservation concern in the Chehalis River and more broadly along the Pacific coast. In both the main stem and tributaries, the SSN SDM predictions of the upstream leading edges of Smallmouth Bass closely align with (within 4.8 km) edges identified by spatially intensive eDNA sampling. Conclusion Our study highlights the value of integrating SSN models with rapidly growing eDNA data sets for accurate and precise riverine fish distribution estimation. Our application provides crucial insights for anticipating the impacts of shifting invasive species on Pacific salmon Oncorhynchus spp. in a warming world.

Supervision and early warning of abnormal data in Internet of Things based on unsupervised attention learning

With the continuous development of artificial intelligence and big data, more and more communication protocols have appeared in the network connection. Because the network protocol content specification should not need to obtain the content of the common protocol by any means, it faces different attacks, which increases the complexity of network security detection to an unlimited extent. The absolute security analysis of the content of the relevant data protocol is not too limited. Based on the content of the unknown binary number protocol in the Internet of Things, this paper does the reverse research. To address the inefficiency, learning is focused on the inverse aspect of the content processing logic of the protocol for unsupervised execution attention. This paper proposes a method based on the name of the Dirichlet process mixture model normal field and the unknown protocol of the Internet of Things. It also establishes the behavioral criteria that process the logical inference least squares method to infer the content of the target protocol. Based on the original external characteristics of abnormal data in the Internet of Things, the neural network model of attention mechanism and the model checking of variational self-encoder are chosen. An innovative multi-dimensional spatial neural network model with a centralized control mechanism is taken to extract the spatial relevant information from the original external features, which are regarded as the intermediate external features. The intermediate extrinsic features are input to a variational self-encoder for computation and reconstruction. The experimental results verify that the time required by the proposed model is 45% less than that of the traditional structure. The flow processing improves by 36.7% compared with the optimization of the parameter control function. The specific method only relies on the original external characteristics of the Internet of Things traffic and can achieve better data anomaly detection and classification under the condition of not significantly improving the detection time. The abnormal data detection scheme of the Internet of Things designed in this paper not only fills the detection gap of the current mainstream Internet of Things system which lacks an autonomous supervision mechanism, but also effectively blocks external network intrusion and further improves the management authority verification system. It plays an indispensable role in the long-term stable operation of the Internet of Things platform.

A Quantum Spatial Graph Convolutional Neural Network Model on Quantum Circuits.

This article proposes a quantum spatial graph convolutional neural network (QSGCN) model that is implementable on quantum circuits, providing a novel avenue to processing non-Euclidean type data based on the state-of-the-art parameterized quantum circuit (PQC) computing platforms. Four basic blocks are constructed to formulate the whole QSGCN model, including the quantum encoding, the quantum graph convolutional layer, the quantum graph pooling layer, and the network optimization. In particular, the trainability of the QSGCN model is analyzed through discussions on the barren plateau phenomenon. Simulation results from various types of graph data are presented to demonstrate the learning, generalization, and robustness capabilities of the proposed quantum neural network (QNN) model.

Enhancing facial recognition accuracy through multi-scale feature fusion and spatial attention mechanisms

<abstract><p>Nowadays, advancements in facial recognition technology necessitate robust solutions to address challenges in real-world scenarios, including lighting variations and facial position discrepancies. We introduce a novel deep neural network framework that significantly enhances facial recognition accuracy through multi-scale feature fusion and spatial attention mechanisms. Leveraging techniques from FaceNet and incorporating atrous spatial pyramid pooling and squeeze-excitation modules, our approach achieves superior accuracy, surpassing 99% even under challenging conditions. Through meticulous experimentation and ablation studies, we demonstrate the efficacy of each component, highlighting notable improvements in noise resilience and recall rates. Moreover, the introduction of the Feature Generative Spatial Attention Adversarial Network (FFSSA-GAN) model further advances the field, exhibiting exceptional performance across various domains and datasets. Looking forward, our research emphasizes the importance of ethical considerations and transparent methodologies in facial recognition technology, paving the way for responsible deployment and widespread adoption in the security, healthcare, and retail industries.</p></abstract>

Spatial-spectral collaborative attention network for hyperspectral unmixing

In recent years, the transformer architecture has demonstrated exceptional feature extraction capabilities in the field of computer vision (CV). Building on this, our paper aims to fully exploit the potential of the attention in transformers and apply it to the task of hyperspectral unmixing (HU). We propose the Spatial Spectral Collaborative Attention Network (SSCA-Net) model. We obtain spectral information with continuous spatial attributes from HSIs in advance, and input it into SSCA-Net together with HSIs. The improved self-cross attention can collaboratively extract spatial-spectral domain information of HSIs, thereby obtaining more accurate abundance scores. In addition, we conduct ablation experiments to investigate the influence of attention with various configurations on the performance of the unmixing process. The performance of the proposed model is evaluated on three real-world datasets: Samson, Jasper, and Houston, and compared with the performance of FCLSU, GLMM, DAEU, CNNAEU, CyCU, and DHTN algorithms.

Scales of Connectivity within Stream Temperature Networks of the Clackamas River Basin, Oregon

Water quality varies along the stream network; thus, considering the directional, dendritic nature of stream networks with surrounding landscape variables is essential in explaining spatial variations of water quality. Using a spatially extensive stream temperature monitoring effort in the Clackamas River Basin in the United States, we first compare spatial scales of analysis of atmospheric, landscape, and in-stream explanatory variables through their correlation with summer stream temperatures. We then derive a predictive stream temperature model with factors representing the spatial variation of local climate, recent wildfire effects, and discharge. Finally, we compare nonspatial multiple linear regression to a spatial stream network (SSN) model to assess the combined importance of the spatial scale of analysis and flow-connected stream distance in explaining total variation in stream temperatures. Most explanatory variables show the most highly significant relationships to stream temperature when derived as a percentage of the total upstream area above observation sites. Elevation and vegetation cover, however, were most significantly correlated to stream temperature at the riparian buffer area scale and the local reach contributing area scale, respectively. Multiple regression analysis using total upstream burned area, total upstream area with underlying High Cascades geology, and the elevation within the 100-m-wide riparian area explained 81 percent of variation in stream temperature. SSN outperformed this nonspatial statistical model, however, in explaining the total variation in stream temperature. These comparisons of scaled data sets demonstrate both the local and cumulative upstream effects on stream temperature, providing a spatial network-informed framework to those prioritizing watershed restoration and wildfire recovery activities.