Effect Of Spatial Correlations Research Articles

Solidarity or self-interest? Carbon footprint pressure measurement and spatial correlation in the Yangtze River Delta region

The augmentation of collaborative governance, intertwining both emission mitigation and carbon sequestration enhancement, is pivotal for urban areas to manifest a leading role within the context of the “dual carbon” objectives. Notwithstanding, extant research exhibits an insufficiency in detail and a neglect of the intricate relationship and reciprocal dynamics between urban carbon emissions and ecological carbon sinks. In response to this gap, this study constructs and gauges the carbon footprint pressure (CFP) index of the Yangtze River Delta region over the period 2000–2020. Moreover, a comprehensive approach combining a modified gravity model, social network analysis, and the STIRPAT model is employed to systematically explore the evolution characteristics and effects of spatial correlations of CFP. The results show that the overall CFP predominantly displayed an oscillatory uptrend, underscored by stark spatial non-uniformity and an annually exacerbating divergence. The CFP exhibited a complex network structure, with continually intensifying stability and incremental convergence in its hierarchical framework. Nevertheless, the core-periphery attributes within this network remained pronounced, despite the low degree of network connectivity. Furthermore, the network incorporated four dynamically transmuting blocks. With the variable extent of the block's deconstruction and restructuring, the “club” spatial dispersion characteristic became increasingly evident. The degree centrality and betweenness centrality of urban nodes rendered an adverse impact on CFP, with the latter wielding a more potent effect.

Effect of spatial correlations on Hopfield Neural Network and Dense Associative Memories

Hopfield model is one of the few neural networks for which analytical results can be obtained. However, most of them are derived under the assumption of random uncorrelated patterns, while in real life applications data to be stored show non-trivial correlations. In the present paper we study how the retrieval capability of the Hopfield network at null temperature is affected by spatial correlations in the data we feed to it. In particular, we use as patterns to be stored the configurations of a linear Ising model at inverse temperature β thus limiting our analysis to exponentially decaying spatial correlations. Exploiting the signal to noise technique we obtain a phase diagram in the load of the Hopfield network and the Ising temperature where a fuzzy phase and a retrieval region can be observed. Remarkably, as the spatial correlation inside patterns is increased, the critical load of the Hopfield network diminishes, a result also confirmed by numerical simulations. The analysis is then generalized to Dense Associative Memories with arbitrary odd-body interactions, for which we obtain analogous results.

Frequency estimation under non-Markovian spatially correlated quantum noise

We study the estimation precision attainable by entanglement-enhanced Ramsey interferometry in the presence of spatiotemporally correlated non-classical noise. Our analysis relies on an exact expression of the reduced density matrix of the qubit probes under general zero-mean Gaussian stationary dephasing, which is established through cumulant-expansion techniques and may be of independent interest in the context of non-Markovian open dynamics. By continuing and expanding our previous work (Beaudoin et al 2018 Phys. Rev. A 98 020102(R)), we analyze the effects of a non-collective coupling regime between the qubit probes and their environment, focusing on two limiting scenarios where the couplings may take only two or a continuum of possible values. In the paradigmatic case of spin–boson dephasing noise from a thermal environment, we find that it is in principle possible to suppress, on average, the effect of spatial correlations by randomizing the location of the probes, as long as enough configurations are sampled where noise correlations are negative. As a result, superclassical precision scaling is asymptotically restored for initial entangled states, including experimentally accessible one-axis spin-squeezed states.

Developing Tree Mortality Models Using Bayesian Modeling Approach

The forest mortality models developed so far have ignored the effects of spatial correlations and climate, which lead to the substantial bias in the mortality prediction. This study thus developed the tree mortality models for Prince Rupprecht larch (Larix gmelinii subsp. principis-rupprechtii), one of the most important tree species in northern China, by taking those effects into account. In addition to these factors, our models include both the tree—and stand—level variables, the information of which was collated from the temporary sample plots laid out across the larch forests. We applied the Bayesian modeling, which is the novel approach to build the multi-level tree mortality models. We compared the performance of the models constructed through the combination of selected predictor variables and explored their corresponding effects on the individual tree mortality. The models precisely predicted mortality at the three ecological scales (individual, stand, and region). The model at the levels of both the sample plot and stand with different site condition (block) outperformed the other model forms (model at block level alone and fixed effects model), describing significantly larger mortality variations, and accounted for multiple sources of the unobserved heterogeneities. Results showed that the sum of the squared diameter was larger than the estimated diameter, and the mean annual precipitation significantly positively correlated with tree mortality, while the ratio of the diameter to the average of the squared diameter, the stand arithmetic mean diameter, and the mean of the difference of temperature was significantly negatively correlated. Our results will have significant implications in identifying various factors, including climate, that could have large influence on tree mortality and precisely predict tree mortality at different scales.

Nonlocal Kondo effect and quantum critical phase in heavy-fermion metals

Heavy fermion metals typically exhibit unconventional quantum critical point or quantum critical phase at zero temperature due to competition of Kondo effect and magnetism. Previous theories were often based on certain local type of assumptions and a fully consistent explanation of experiments has not been achieved. Here we develop an efficient algorithm for the Schwinger boson approach to explore the effect of spatial correlations on the Kondo lattice and introduce the concept of nonlocal Kondo effect in the quantum critical region with deconfined spinons. We predict a global phase diagram containing a non-Fermi liquid quantum critical phase with a hidden holon Fermi surface and a partially enlarged electron Fermi surface for strong quantum fluctuations while a single quantum critical point for weak quantum fluctuations. This explains the unusual metallic spin liquid recently reported in the frustrated Kondo lattice CePdAl and resolves the Fermi volume puzzle in YbRh$_2$Si$_2$. Our theory highlights the importance of nonlocal physics and provides a unified understanding of heavy fermion quantum criticality.

Effects of spatial correlations in particulate media on dependent scattering and radiative transport

Radiative transport in particulate media is a ubiquitous problem with energy, manufacturing, and catalysis applications. Despite the limited applicability of the theory of independent scattering, it continues to be widely applied to model radiative transport in an ensemble of large particles. While scale factors that correct for dependent scattering effects have been obtained as a function of the solid volume fraction, its dependencies on the spatial distributions of the particles have not been investigated. Herein, we examine the influences of the three-dimensional spatial distributions and the solid volume fraction of particles on radiative transport, and quantify the resulting deviations from the independent scattering assumption. Packed beds with Poisson, random, ordered and clustered spatial distributions of particles, and varying solid volume fractions (0.05–0.5) are computationally generated. Spatial correlations of particles are statistically quantified using an index of cluster sampling. Monte Carlo ray tracing simulations are preformed to obtain radiative transmittance, path-length distribution, angular distribution of the transmitted rays, and the scattering phase function. Results reveal that the radiative transmittance and the extinction coefficient are strongly influenced by the spatial distributions of the particles, in addition to the solid volume fraction. For the first time, we have developed a generalized correlation that predicts scale factors as a function of the index of cluster sampling, which is dictated by the spatial distributions and the solid volume fractions of the particles. Ordered beds with perfectly negative correlations in the spatial positions of the particles exhibit a more discrete interaction of radiation with the ensemble as compared to the other spatial distributions. This aspect leads to increased directionality of transmitted radiation and anisotropy in the scattering phase function for large solid volume fractions.

Analyzing the effect of anisotropic spatial correlations of earthquake intensity measures on the result of seismic risk and resilience assessment of the portfolio of buildings and infrastructure systems

Analyzing the effect of anisotropic spatial correlations of earthquake intensity measures on the result of seismic risk and resilience assessment of the portfolio of buildings and infrastructure systems

Role of Spatially Correlated Fluctuations in Photosynthetic Excitation Energy Transfer with an Equilibrium and a Nonequilibrium Initial Bath.

The transfer of excitation energy in photosynthetic light-harvesting complexes has inspired growing interest for its scientific and engineering significance. Recent experimental findings have suggested that spatially correlated environmental fluctuations may account for the existence of long-lived quantum coherent energy transfer observed even at physiological temperature. In this paper, we investigate the effects of spatial correlations on the excitation energy transfer dynamics by including a nonequilibrium initial bath in a simulated donor-acceptor model. The initial bath state, which is assumed to be either equilibrium or nonequilibrium, is expanded in powers of coupling strength within the polaron formalism of a quantum master equation. The spatial correlations of bath fluctuations strongly influence the decay of coherence in the dynamics. The role of a nonequilibrium initial bath is also influenced by spatial correlations and becomes the most conspicuous for certain degrees of spatial correlations from which we propose a picture that the spatial correlations of bath fluctuations open up new energy transfer pathways, playing a role of protecting coherence. Besides, we apply the polaron master equation approach to study the dynamics in a two-site subsystem of the FMO complex and provide a practical example that shows the versatility of this approach.

Improving influenza surveillance based on multi-granularity deep spatiotemporal neural network

Influenza is a common respiratory disease that can cause human illness and death. Timely and accurate prediction of disease risk is of great importance for public health management and prevention. The influenza data belong to typical spatiotemporal data in that influenza transmission is influenced by regional and temporal interactions. Many existing methods only use the historical time series information for prediction, which ignores the effect of spatial correlations of neighboring regions and temporal correlations of different time periods. Mining spatiotemporal information for risk prediction is a significant and challenging issue. In this paper, we propose a new end-to-end spatiotemporal deep neural network structure for influenza risk prediction. The proposed model mainly consists of two parts. The first stage is the spatiotemporal feature extraction stage where two-stream convolutional and recurrent neural networks are constructed to extract the different regions and time granularity information. Then, a dynamically parametric-based fusion method is adopted to integrate the two-stream features and making predictions. In our work, we demonstrate that our method, tested on two influenza-like illness (ILI) datasets (US-HHS and SZ-HIC), achieved the best performance across all evaluation metrics. The results imply that our method has outstanding performance for spatiotemporal feature extraction and enables accurate predictions compared to other well-known influenza forecasting models.

Effect of short-ranged spatial correlations on the Anderson localization of phonons in mass-disordered systems

We investigate the effect of spatially correlated disorder on the Anderson transition of phonons in three dimensions using a Green’s function-based approach, namely, the typical medium dynamical cluster approximation, in mass-disordered systems. We numerically demonstrate that correlated disorder with pairwise correlations mitigates the localization of the vibrational modes. A correlation-driven localization–delocalization transition can emerge in a three-dimensional disordered system with an increase in the strength of correlations.

Immersive diagnostics of reservoirs under WAG injection, Part II – Effect of depositional settings and dynamic spatial correlations

Immersive visualization and diagnostics have become essential tools in the arsenal of engineers and scientists in developing innovative concepts, products, and techniques. In reservoir engineering applications, these approaches can be immensely helpful in detecting the dynamics occurring within the reservoir under complex and evolving conditions. In Part I of this study, we presented several novel immersive diagnostics quantifying displacement measures and unraveled some interesting dynamics around the injector in reservoirs undergoing Water-Alternating-Gas (WAG) injection. This article is Part II of the study on the utility of immersive diagnostics to ascertain the effect of geological formation characteristics on WAG reservoir dynamics.WAG injection can yield incremental recovery around 10% in mature oilfields. In this study, using immersive metrics, we critically investigated WAG performance of four different injection-fluid streams (pure CO2, pure methane, lean gas, and enriched gas) and three WAG injection-cycle strategies in several depositional systems. Spatial clusters of displacement behavior appeared. The relationship of oil displacement efficiency with various driving forces may depend on multiple elements. These elements include compositional variation in the residual oil close to the injector and the producers and the corresponding changes in thermophysical and multiphase flow properties. The pressure level, distance to the injector, formation permeability, and depositional units contribute to the flood performance. Over time with pressure depletion and frontal movement from the injector to producers, the distinction of these clusters dissipates.Spatial correlational analyses of displacement measures reveal interesting observations. The fluid flow of the oleic phase in channelized/turbiditic formations tends to be more inertial than diffusive. Oil displacement efficiency in the channelized/turbiditic formations depends strongly on the injector-producer connectivity via these flow conduits. In most formations, we can expect a reduction in water viscosity or an increase in the inertial force in the water, yielding more efficient oil displacement.At late times, diffusive flow dictates oil displacement in oil-water zones, wherein the advective flow of oil predominates in pockets where all three phases (oil, gas, and water) are present. In general, reservoir locations with high water-phase inertial to viscous forces wield higher oil-displacement efficiency. However, in high-permeable formations at late times, reservoir locations closer to the producers having low-permeability experience low-water inertial-to-viscous forces and relatively less oil displacement efficiency, which gets worse with the presence of all three phases.

Effect of heterogeneity and spatial correlations on the structure of a tumor invasion front in cellular environments.

Analysis of invasion front has been widely used to decipher biological properties, as well as the growth dynamics of the corresponding populations. Likewise, the invasion front of tumors has been investigated, from which insights into the biological mechanisms of tumor growth have been gained. We develop a model to study how tumors' invasion front depends on the relevant properties of a cellular environment. To do so, we develop a model based on a nonlinear reaction-diffusion equation, the Fisher-Kolmogorov-Petrovsky-Piskunov equation, to model tumor growth. Our study aims to understand how heterogeneity in the cellular environment's stiffness, as well as spatial correlations in its morphology, the existence of both of which has been demonstrated by experiments, affects the properties of tumor invasion front. It is demonstrated that three important factors affect the properties of the front, namely the spatial distribution of the local diffusion coefficients, the spatial correlations between them, and the ratio of the cells' duplication rate and their average diffusion coefficient. Analyzing the scaling properties of tumor invasion front computed by solving the governing equation, we show that, contrary to several previous claims, the invasion front of tumors and cancerous cell colonies cannot be described by the well-known models of kinetic growth, such as the Kardar-Parisi-Zhang equation.

Immiscible fluid displacement in porous media with spatially correlated particle sizes

Immiscible fluid displacement in porous media is fundamental for many environmental processes, including infiltration of water in soils, groundwater remediation, enhanced recovery of hydrocarbons and CO2 geosequestration. Microstructural heterogeneity, in particular of particle sizes, can significantly impact immiscible displacement. For instance, it may lead to unstable flow and preferential displacement patterns. We present a systematic, quantitative pore-scale study of the impact of spatial correlations in particle sizes on the drainage of a partially-wetting fluid. We perform pore-network simulations with varying flow rates and different degrees of spatial correlation, complemented with microfluidic experiments. Simulated and experimental displacement patterns show that spatial correlation leads to more preferential invasion, with reduced trapping of the defending fluid, especially at low flow rates. Numerically, we find that increasing the correlation length reduces the fluid-fluid interfacial area and the trapping of the defending fluid, and increases the invasion pattern asymmetry and selectivity. Our experiments, conducted for low capillary numbers, support these findings. Our results delineate the significant effect of spatial correlations on fluid displacement in porous media, of relevance to a wide range of natural and engineered processes.

The effects of thermal and correlated noise on magnons in a quantum ferromagnet

The dynamics and thermal equilibrium of spin waves (magnons) in a quantum ferromagnet as well as the macroscopic magnetisation are investigated. Thermal noise due to an interaction with lattice phonons and the effects of spatial correlations in the noise are considered. We first present a Markovian master equation approach with analytical solutions for any homogeneous spatial correlation function of the noise. We find that spatially correlated noise increases the decay rate of magnons with low wave vectors to their thermal equilibrium, which also leads to a faster decay of the ferromagnet’s magnetisation to its steady-state value. For long correlation lengths and higher temperature we find that additionally there is a component of the magnetisation which decays very slowly, due to a reduced decay rate of fast magnons. This effect could be useful for fast and noise-protected quantum or classical information transfer and magnonics. We further compare ferromagnetic and antiferromagnetic behaviour in noisy environments and find qualitatively similar behaviour in Ohmic but fundamentally different behaviour in super-Ohmic environments.

Mutual Information between Reflected and Transmitted Speckle Images.

We study theoretically the mutual information between reflected and transmitted speckle patterns produced by wave scattering from disordered media. The mutual information between the two speckle images recorded on an array of N detection points (pixels) takes the form of long-range intensity correlation loops that we evaluate explicitly as a function of the disorder strength and the Thouless number g. Our analysis, supported by extensive numerical simulations, reveals a competing effect of cross-sample and surface spatial correlations. An optimal distance between pixels is proven to exist that enhances the mutual information by a factor Ng compared to the single-pixel scenario.

Dark Energy Survey Year 1 results: the impact of galaxy neighbours on weak lensing cosmology with im3shape

We use a suite of simulated images based on Year 1 of the Dark Energy Survey to explore the impact of galaxy neighbours on shape measurement and shear cosmology. The hoopoe image simulations include realistic blending, galaxy positions, and spatial variations in depth and PSF properties. Using the im3shape maximum-likelihood shape measurement code, we identify four mechanisms by which neighbours can have a non-negligible influence on shear estimation. These effects, if ignored, would contribute a net multiplicative bias of $m \sim 0.03 - 0.09$ in the DES Y1 im3shape catalogue, though the precise impact will be dependent on both the measurement code and the selection cuts applied. This can be reduced to percentage level or less by removing objects with close neighbours, at a cost to the effective number density of galaxies $n_\mathrm{eff}$ of 30%. We use the cosmological inference pipeline of DES Y1 to explore the cosmological implications of neighbour bias and show that omitting blending from the calibration simulation for DES Y1 would bias the inferred clustering amplitude $S_8\equiv \sigma_8 (\Omega _\mathrm{m} /0.3)^{0.5}$ by $2 \sigma$ towards low values. Finally, we use the hoopoe simulations to test the effect of neighbour-induced spatial correlations in the multiplicative bias. We find the impact on the recovered $S_8$ of ignoring such correlations to be subdominant to statistical error at the current level of precision.

Investigating the effect of spatial and mode correlations on active transportation safety modeling

This paper describes the development of macro-level crash models for active modes of transportation incorporating spatial and mode correlation effects. The models are based on data from 134 traffic analysis zones (TAZs) in the City of Vancouver. Five years of cyclist and pedestrian crash data, as well as traffic exposure and large GIS data, were used to establish the macro-level crash models. The GIS data included land use, built environment, socioeconomic, bike network, and pedestrian network indicators. Full Bayesian multivariate models with and without spatial effects were developed and compared to the corresponding univariate models. The multivariate modeling approach allowed for including a different set of covariates for each modeled crash type. The univariate/multivariate crash models incorporating spatial effects consistently outperformed those that did not account for spatial effects. The correlation between pedestrian and cyclist crashes was found significant indicating the importance of accounting for the dependency among active commuters’ crash types. The mode and spatial correlations were affected by the number of the explanatory variables added to the model. Overall, the multivariate models outperformed the univariate ones, and the multivariate model incorporating spatial effects yielded the best fit among all the tested crash models. The associations between cyclist as well as pedestrian safety and various zones’ characteristics were also investigated in this study.

Effects of diversity schemes and correlated channels on OWC systems performance

The salient features and advantages of free-space optical (FSO) system are very appealing for different applications in a number of communication network sectors. In spite of the advantages and diverse applications of FSO communication, its extensive use is hindered by the atmospheric turbulence-induced fading in real-life scenarios. Spatial diversity technique is one of effective means of mitigating turbulence-induced fading and, consequently, improves the system performance. In this paper, we study the spatial diversity schemes for mitigating turbulence-induced fading in the FSO communication systems using the bit error rate (BER) as a performance metric. The schemes considered are based on orthogonal space–time block codes and repetition codes (RCs). We derive simple approximate closed-form expressions for the error probability of the log-normal FSO links with intensity modulation and direct detection (IM/DD). Furthermore, we also investigate the effects of spatial correlation between the transmit apertures on the system performance. We achieve this using the exponential model for determining the correlations between the apertures. We observe that the proposed BER expressions are able to quantify the effects of spatial diversity schemes and spatial correlations on the system.

Assessment of mean-field microkinetic models for CO methanation on stepped metal surfaces using accelerated kinetic Monte Carlo.

First-principles screening studies aimed at predicting the catalytic activity of transition metal (TM) catalysts have traditionally been based on mean-field (MF) microkinetic models, which neglect the effect of spatial correlations in the adsorbate layer. Here we critically assess the accuracy of such models for the specific case of CO methanation over stepped metals by comparing to spatially resolved kinetic Monte Carlo (kMC) simulations. We find that the typical low diffusion barriers offered by metal surfaces can be significantly increased at step sites, which results in persisting correlations in the adsorbate layer. As a consequence, MF models may overestimate the catalytic activity of TM catalysts by several orders of magnitude. The potential higher accuracy of kMC models comes at a higher computational cost, which can be especially challenging for surface reactions on metals due to a large disparity in the time scales of different processes. In order to overcome this issue, we implement and test a recently developed algorithm for achieving temporal acceleration of kMC simulations. While the algorithm overall performs quite well, we identify some challenging cases which may lead to a breakdown of acceleration algorithms and discuss possible directions for future algorithm development.

Correlated wounded hot spots in proton-proton interactions

We investigate the effect of non-trivial spatial correlations between proton constituents, considered in this work to be gluonic hot spots, on the initial conditions of proton-proton collisions from ISR to LHC energies, i.e. $\sqrt s\!=\!52.6,7000,13000$ GeV. The inclusion of these correlations is motivated by their fundamental role in the description of a recently observed new feature of $pp$ scattering at $\sqrt s\!=\!7$ TeV, the hollowness effect. Our analysis relies on a Monte-Carlo Glauber approach including fluctuations in the hot spot positions and their entropy deposition in the transverse plane. We explore both the energy dependence and the effect of spatial correlations on the number of wounded hot spots, their spatial distribution and the eccentricities, $\varepsilon_n$, of the initial state geometry of the collision. In minimum bias collisions we find that the inclusion of short range repulsive correlations between the hot spots reduces the value of the eccentricity ($\varepsilon_2$) and the triangularity ($\varepsilon_3$). In turn, upon considering only the events with the highest entropy deposition i.e. the ultra-central ones, the probability of having larger $\varepsilon_{2,3}$ increases significantly in the correlated scenario. Finally, the eccentricities show a quite mild energy dependence.