Spatial Correlation Research Articles

Exploring the Spatial Correlation Network and Its Formation Mechanisms in Urban Land Use Performance: A Case Study of the Yangtze River Economic Belt

Urban land is the primary location for manufacturing and services, facilitating the expansion and interconnectedness of economic activities and factor flows to shape various urban land-use performances (ULUP). Exploring these spatial linkages of urban land-use performance can play a crucial role in fostering cohesive urban development. Taking 109 prefecture-level cities as research samples, this paper explores the characteristics and influencing factors of the spatial network associated with ULUP in the Yangtze River Economic Belt through modified gravity model, network analysis and QAP analysis. The analysis shows that ULUP has shown an N-shaped trend over the past two decades. It has formed a network of correlations, with Chongqing, Shanghai, and Wuhan emerging as central nodes. Notably, the correlation predominantly occurs between geographically adjacent cities, with weaker links between the Yangtze River Economic Belt’s upstream, midstream, and downstream regions. The network can be divided into four distinct types: main inflow plate, main outflow plate, agent plate, and bidirectional spillover plate. Geographic location is the most significant factor influencing network formation, followed by resource mismatch, economic development, and openness. The study provides theoretical guidance and empirical support for improving the utilisation of urban land and promoting coordinated development.

Symmetries of spatial correlators of light and heavy mesons in high temperature lattice QCD

The spatial z correlators of meson operators in Nf=2+1+1 lattice QCD with optimal domain-wall quarks at the physical point are studied for seven temperatures in the range of 190–1540 MeV. The meson operators include a complete set of Dirac bilinears (scalar, pseudoscalar, vector, axial vector, tensor vector, and axial-tensor vector), and each for six flavor combinations (u¯d, u¯s, s¯s, u¯c, s¯c, and c¯c). In a recent paper [], I focused on the meson correlators of u and d quarks, and discussed their implications for the effective restoration of SU(2)L×SU(2)R and U(1)A chiral symmetries, as well as the emergence of approximate SU(2)CS chiral spin symmetry. In this work, we extend our study to meson correlators of six flavor contents, and first observe the hierarchical restoration of chiral symmetries in QCD, from SU(2)L×SU(2)R×U(1)A to SU(3)L×SU(3)R×U(1)A, and to SU(4)L×SU(4)R×U(1)A, as the temperature is increased from 190 to 1540 MeV. Moreover, we compare the temperature windows for the emergence of the approximate SU(2)CS symmetry in light and heavy vector mesons, and find that the temperature windows are dominated by the (u¯c,s¯c,c¯c) sectors. Published by the American Physical Society 2024

Hierarchical channel-spatial interaction network for partial-to-partial point cloud registration

ABSTRACT Partial-to-partial point cloud registration is a fundamental and challenging task in the three-dimensional computer vision. In this letter, we propose a novel Hierarchical Channel-Spatial Interaction Network (HCSINet), which can hierarchically explore information interaction in terms of the channel and spatial dimensions, enabling well partial point cloud perception for partial-to-partial registration. Specifically, the Channel-wise Feature Interaction (CFI) module is regarded as the basic unit of the encoder sub-network, which achieves feature hierarchical interaction to learn the overlapping scores and intuitively improve the importance of overlapping areas. Furthermore, to improve the discrimination of the overlapping points, the Co-Attention-based Spatial Association (CA-SA) module progressively excavates multi-scale spatial correlation of overlapping points by conjointly computing collaborative attention. Experimental results verify the validity of our HCSINet, which achieves competitive partial-to-partial registration performances compared to existing traditional and deep learning-based methods. The source code is publicly available https://github.com/lxnudt/HCSINet.

A Reconstruction of May–June Mean Temperature since 1775 for Conchos River Basin, Chihuahua, Mexico, Using Tree-Ring Width

Currently there are several precipitation reconstructions for northern Mexico; however, there is a lack of temperature reconstructions to understand past climate change, the impact on ecosystems and societies, etc. The central region of Chihuahua is located in a transition zone between the Sierra Madre Occidental and the Great Northern Plain, characterized by extreme temperatures and marked seasonal variability. The objectives of this study were (1) to generate a climatic association between variables from reanalysis models and the earlywood series for the center of Chihuahua, (2) to generate a reconstruction of mean temperature, (3) to determine extreme events, and (4) to identify the influence of ocean–atmosphere phenomena. Chronologies were downloaded from the International Tree-Ring Data Bank and climate information from the NLDAS-2 and ClimateNA reanalysis models. The response function was performed using climate models and regional dendrochronological series. A reconstruction of mean temperature was generated, and extreme periods were identified. The representativeness of the reconstruction was evaluated through spatial correlation, and low-frequency events were determined through multitaper spectral analysis and wavelet analysis. The influence of ocean–atmosphere phenomena on temperature reconstruction was analyzed using Pearson correlation, and the influence of ENSO was examined through wavelet coherence analysis. Highly significant correlations were found for maximum, minimum, and mean temperature, as well as for precipitation and relative humidity, before and after the growth year. However, the seasonal period with the highest correlation was found from May to June for mean temperature, which was used to generate the reconstruction from 1775 to 2022. The most extreme periods were 1775, 1801, 1805, 1860, 1892–1894, 1951, 1953–1954, and 2011–2012. Spectral analysis showed significant frequencies of 56.53 and 2.09 years, and wavelet analysis from 0 to 2 years from 1970 to 1980, from 8 to 11 years from 1890 to 1910, and from 30 to 70 years from 1860 to 2022. A significant association was found with the Multivariate ENSO Index phenomenon (r = 0.40; p = 0.009) and Pacific Decadal Oscillation (r = −0.38; p = 0.000). Regarding the ENSO phenomenon, an antiphase association of r = −0.34; p = 0.000 was found, with significant periods of 1 to 4 years from 1770 to 1800, 1845 to 1850, and 1860 to 1900, with periods of 6 to 10 years from 1875 to 1920, and from 6 to 8 years from 1990 to 2000. This study allowed a reconstruction of mean temperature through reanalysis data, as well as a historical characterization of temperature for central Chihuahua beyond the observed records.

Wind-induced interference effect of complex building clusters on long-span roof structures

The presence of complex buildings introduces interference effects and complicates the wind field around the long-span roof structure. The complexity of the problem makes it challenging to predict wind load of long-span roof structures, resulting in a scarcity of design data available to engineers and designers. Thus, the influence of interference effect on mean and peak pressure coefficients of a long-span tri-centered cylindrical roof structure was investigated through wind tunnel tests conducted in an atmospheric boundary layer wind tunnel. The study focused on the interference effects caused by two cooling towers, chimney, and some low-rise buildings, considering all wind directions. The results show that the interference effects are the result of a combination of amplification effects from the adjacent cooling towers and chimney and shielding effects from the surrounding low-rise buildings. Due to the shielding effect of low-rise buildings, the wind suction on the roof is mitigated compared to that of a single building, while at the end of the roof, wind suction is amplified by cooling tower effects. The interference effect of the building clusters, however, amplifies the fluctuating wind pressure coefficient and the peak pressure coefficient on the roof. To accurately estimate the wind load of building components and envelopes, furthermore, this study investigates the influence mechanism of interference effect on non-Gaussian characteristics of wind pressure combining with fluctuating wind pressure spectrum, spatial correlation of fluctuating wind pressure, and probability distribution characteristics of standardized wind pressure coefficient. The spatial correlation of wind pressure in the roof interference area exhibits a strong association, and the probability density distribution characteristics of the standardized wind pressure coefficient significantly deviate from the Gaussian curve, with a peak factor exceeding 3.5. Thus, it can be concluded that the wind pressure within this region demonstrates significant non-Gaussian characteristics. Hence, the zone values of peak pressure coefficients are determined using the peak factor approach to inform the wind resistance design of the envelope structure.

TCF1-positive and TCF1-negative TRM CD8 T cell subsets and cDC1s orchestrate melanoma protection and immunotherapy response

BackgroundMelanoma, the most lethal form of skin cancer, has undergone a transformative treatment shift with the advent of checkpoint blockade immunotherapy (CBI). Understanding the intricate network of immune cells infiltrating...

Spatial correlation between traditional villages and religious cultural heritage in the Hehuang region, Northwest China

ABSTRACT As a typical religious cultural heritage, Tibetan Buddhist monasteries in the Hehuang region of northwest China have strong connections with surrounding villages that practice Tibetan Buddhism. This article investigates the spatial distribution correlation between the two entities using methods such as kernel density, spatial quantity relationship model, gravity center model, and qualitative description. It analyzes the relationship between Tibetan Buddhist monasteries and traditional Tibetan villages at three levels: macro, meso, and micro, exploring the spatial correlation and influence factors. The study results are as follows: Firstly, the spatial distribution of traditional villages and Tibetan Buddhist monasteries in the Hehuang region exhibits a positive correlation with a moderate spatial correlation. Secondly, the distribution of traditional villages and Tibetan Buddhist monasteries in the Hehuang region demonstrates a high spatial balance. Yet, there are instances where certain counties have an abundance of monasteries but lack traditional villages, particularly in Minhe County, Guide County, Hualong County, and Ledu County. Various factors such as economic, policy, and cultural integration contribute to this phenomenon. Thirdly, the location relationship between traditional villages and Tibetan Buddhist monasteries in the Hehuang region can be categorized into three types: Villages separated from Tibetan Buddhist monasteries, Tibetan Buddhist monasteries in villages, and Tibetan Buddhist monasteries near villages. Finally, it is proposed to protect traditional villages in the area based on the principle of protecting cultural diversity. To safeguard Chinese traditional villages in the future, it is essential to comprehensively analyze the spatial relationship between villages and religious architectural heritage, and prioritize the preservation of significant villages located within the influence zone of these religious architectural heritage by incorporating them into the list of protected traditional villages. This study aims to enhance theoretical research on traditional villages in ethnic minority regions and provide a scientific basis for the comprehensive protection and coordinated utilization of traditional villages and religious architectural heritage in the Hehuang region and other ethnic regions.

Lightcone and quasi distribution amplitudes for light octet and decuplet baryons

We present a comprehensive investigation of leading-twist lightcone distribution amplitudes (LCDAs) and quasi distribution amplitudes (quasi-DAs) for light octet and decuplet baryons within large momentum effective theory. In LaMET, LCDAs can be factorized in terms of a hard kernel and quasi-DAs that are defined as spatial correlators and calculable on Lattice QCD. To renormalize quasi-DAs and eliminate the singular terms ln(μ2zi2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\\mu}^2{z}_i^2 $$\\end{document}) in them, which undermine the efficacy in perturbative expansion, we adopt a hybrid renormalization scheme that combines the self-renormalization and ratio scheme. Through self-renormalization, we eliminate UV divergences and linear divergences at large spatial separations in quasi distribution amplitudes without introducing additional nonperturbative effects. By taking a ratio with respect to the zero-momentum matrix element, we effectively remove UV divergences at small spatial separations. Under the hybrid renormalization scheme, we calculate the hard kernels up to one-loop accuracy. It turns out that only four different hard kernels are needed for all leading-twist LCDAs of octet and decuplet baryons. These results are crucial for the lattice-based exploration of the LCDAs of a light baryon from the first principle of QCD.

The spatial pattern of Populus euphratica competition based on competitive exclusion theory

IntroductionPopulation-level competition and spatial patterns may explain the role of competitive exclusion in communities, which is important for vegetation restoration and biodiversity conservation.MethodsWe analyzed the competitive intensity, spatial patterns, and renewal of Populus euphratica Oliv. forests in the Tarim River Basin using the Hegyi competition index and spatial point pattern analysis in a completely random model with different habitats and different forest ages. ResultsThe greatest competitive distance for P. euphratica was 10 m, and the intensity of competition steadily decreased as the diameter increased. The intensity of intraspecific and interspecific competition in young, mature, and old P. euphratica forests was as follows: riverside habitat > transitional habitat > desert margin habitat. The Simpson index values for the three habitats decreased as follows: transitional > riverside > desert margin, and the Shannon-Wiener index and Pielou index values decreased as follows: riverside > transitional > desert margin. In the riverside habitat, the young P. euphratica forest experienced the greatest competitive intensity, the mature forest in the transitional habitat was the largest, and the forest in the desert margin habitat was the oldest. Competitive intensity was greatest in the young riverside P. euphratica forest, mature P. euphratica forest in the transitional habitat, and old forest in the desert margin. Riverside P. euphratica experienced strong competition from Populus pruinosa. Competitive exclusion caused P. pruinosa to disappear from the transitional and desert margin habitats. Young, mature, and old P. euphratica forests were randomly distributed along the riverside and in the transitional habitat, while mature and old P. euphratica forests were randomly distributed in the desert margin. Populus pruinosa, Tamarix ramosissima, and Tamarix hispida were mainly randomly distributed, and T. ramosissima and T. hispida were clustered at small scales. In the riverside habitat, young, mature, and old P. euphratica had no spatial correlation, and there was a significant negative correlation at small scales in the transitional habitat. The density of P. euphratica seedlings in the riverside habitat was greater than that in the transitional habitat, and greater competitive pressures on P. euphratica tree seedlings caused a lower renewal density.ConclusionsWhen planting P. euphratica forests, spacing greater than 10 m can effectively reduce stand competition and thus promote seedling regeneration.

Identification of the active faults and seismotectonic zonation of Laos territory

In this study, the main active faults in the territory of Laos were identified by analyzing the spatial relationship between the distributions of neo-tectonic faults and earthquake epicenters. The map of neo-tectonic faults was built by integrating the results of neo-tectonic faults research using geological-geomorphological data together with the lineament map obtained from remote sensing analysis. Nontectonic lineaments were eliminated by correlating the spatial distribution of the lineament field with a topographic map, DEM, and geological-geomorphological data. The earthquake data, including 4416 events in Laos and its surroundings, were collected from different sources: the International Seismological Center (ISC), the earthquakes recorded by the local seismic network in Laos, the seismic data in Vietnam, and the earthquake catalog provided by the Thailand Meteorological Department (TMD). Among these, 820 events were located using the hypocenter method, and the local network recorded the data. The magnitude conversion was applied to get a unified scale Mw. The catalog of 1617 main shocks obtained after eliminating foreshocks and aftershocks using the declustering technique was used for a spatial correlation with the neotectonic fault distribution to identify active faults. A total of 14 main active fault zones in the Laos territory were defined. Most are also seismogenic faults with Mw ≥ 5.0 occurring along their trace. Considering the characteristics of seismic activity and the active and neotectonic faults, the territory of Laos can be divided into six seismotectonic zones according to the decreasing level of seismic activity: the Western, the Northeastern Samnua, the Phongsali, the South Truong Son, the North Truong Son, and the Khorat zones. Each zone is characterized by relative homogeneity in the seismic activity and the characteristics of active and neotectonic faults.

Multiscale simulation of spatially correlated microstructure via a latent space representation

When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be neglected. We propose a multiscale method that accounts for these effects using a variational autoencoder to encode the structure–property map of the stochastic volume elements making up the statistical description of the part. In this paradigm the autoencoder can be used to directly encode the microstructure or, alternatively, its latent space can be sampled to provide likely realizations. We demonstrate the method on three examples using the common additively manufactured material AlSi10Mg in: (a) a comparison with direct numerical simulation of the part microstructure, (b) a push forward of microstructural uncertainty to performance quantities of interest, and (c) a simulation of functional gradation of a part with stochastic microstructure.

Interpretable multi-graph convolution network integrating spatial-temporal attention and dynamic combination for wind power forecasting

Graph neural networks (GNN) can effectively improve wind power forecasting accuracy due to its ability to capture spatial correlations between wind farms. However, conventional GNN-based forecasting models constructing the graphs from only one specific perspective fail to characterize the spatial correlation patterns comprehensively and accurately. Furthermore, their black-box structure leads to deficient interpretability of the forecasting results, which hampers the quantification of forecasts uncertainty and reduces the credibility of applying these forecasting models in practice. To address the above problems, an interpretable ultra-short-term wind power forecasting method is proposed based on multi-graph convolution network integrating spatial–temporal attention and dynamic graph combination. Four undirected weight graphs are constructed to form a multi-graph scheme by considering various geographical and statistical aspects, and then graph convolution network (GCN) and temporal convolution network (TCN) are used to enhance the forecasting performance by leveraging information in both space and time dimensions. Meanwhile, it consists of temporal attention that deeply mines the temporal dependence information and spatial attention that interprets influence of wind farms on the forecasts. Additionally, to boost the adaptability and generalization capability of the proposed method, an improved reinforcement learning is elaborately designed to combine the results of different graphs by dynamically optimizing their weights to obtain the final forecasts. Experiment results suggest that the proposed method exhibits superior forecasting accuracy and reasonable interpretability compared with other benchmark methods.

Spatial correlation network structure of carbon emission reduction capacity on urban agglomerations and its driving factors: A perspective of Sustainable Development Goals

Sustainable Development Goal 7 and Goal 13 are important for evaluating carbon emission reduction capacity in urban agglomerations. Exploring the spatial correlation network structures of carbon emission reduction capacity can help synergize carbon reduction actions between cities. Existing research regarding the urban agglomerations to address carbon emission reduction goals has neglected both coordination with Sustainable Development Goals and the impact of spatial correlation network structures. Based on Sustainable Development Goals, an evaluation system of carbon emission reduction capacity is constructed. The characteristics of spatial correlation network structure for carbon emission reduction capacity are described using the social network analysis method. Its driving factors are described using the quadratic assignment procedure method. The Guanzhong Plain urban agglomeration in China is used for validation. The spatial distribution of carbon emission reduction capacity in urban agglomerations is significantly unbalanced, and the spatial correlation network structure follows a stable core-edge pattern. Factors such as differences in technological level, economic level affect the formation of the spatial correlation, but the differences in urbanization level have no significant impact. Countermeasures are proposed to provide a scientific basis for the collaborative management of carbon emission reduction in urban agglomerations.

Assessing the forecasting power of mean-field approaches for disease spreading using active systems

The forecasting of the temporal evolution of real-world diseases is systematically performed via mean-field, compartmental models, such as the SIR model and its various variants. Here, we investigate the spreading of SIR dynamics over a system of interacting, active agents – whose dynamics generate random contacts among the agents, as well as temporal and spatial correlation and fluctuations – to assess whether mean-field-like SIR models are able to accurately describe the temporal evolution of an epidemics. We find that such models display temporal dynamics that are intrinsically and fundamentally different from the ones obtained in active agent simulations. Specifically, we numerically prove that no effective SIR model is consistent with the dynamics that emerge in agent-based simulations. Our results call into question the use of such models to forecast the evolution of real-world epidemics, independently of the method used to estimate the basic reproduction number.

SMART: Supervised Multi-class Image Retargeting Generative Model based on a Long-range Sampling Strategy

Content-aware image retargeting (CAIR) techniques are crucial in multimedia processing for displaying images on various devices while preserving visually salient contents with desirable visual effects. There are discrete and continuous algorithms. For the former, the artefacts happen when the foreground proportion is larger than the retargeting ratio; for the latter, the salient regions are prone to be squeezed. In this paper, we reformulate the retargeting process into sampling the salient signal and reconstruction under aesthetic supervision, the supervised multi-class image retargeting reconstruction (SMART) framework. The target images can be represented into complementary parts, the masked and unmasked ones, according to the saliency influences in the encoder phrase. The long-range sampling algorithm is proposed to calculate similarities through an 8-connected planar path while considering spatial distance and feature correlation. The sampled embeddings in latent space reconstruct the retargeted images under supervised signals for aesthetic quality. The semantic loss Lsem from the pretrained CLIP model can maintain consistency for both content and semantics. The supervised loss, Lir, is introduced to ensure the retargeted qualities are close to the preferred labels. Then, we release a new retargeting dataset comprising seven image classes (animal, building, car, flower, indoor, landscape and people) with supervised labels collected from designers for further aesthetic retargeting study. The ablation studies are conducted to confirm the effectiveness of the new dataset, and comparative experiments with state-of-the-art baselines demonstrate the advantages of the proposed method.

Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy

Abstract Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.

Experimental performance of a two-receivers and two-transmitters array X-ray communication system

In this work, we constructed a two-transmitter, two-receiver array based on grid-controlled modulated X-ray tubes and plastic-scintillator-coupled SiPM detectors to improve the communication rate of X-ray communication (XCOM) systems. The effects of various working parameters, and array dimensions on the bit-error-rate of the system were investigated. We derived the relationship between array dimensions and spatial correlation coefficient theoretically, and pointed out that a higher spatial correlation coefficient is the main reason affecting system performance experimentally. The array communication system achieves a communication data rate of 3.6 Mbps under a 12.5 cm laboratory atmospheric channel with a moderate spatial correlation coefficient of 0.5. The array XCOM system can effectively increase the data rate compared with previous work. This study can provide an experimental foundation and design reference for the establishment of a multiple-input, multiple-output array XCOM system.

Modeling segregated solutes in plastically deformed alloys using coupled Molecular Dynamics-Monte Carlo simulations

A microscopic understanding of the complex solute-defect interaction is pivotal for optimizing the alloy’s macroscopic mechanical properties. Simulating solute segregation in a plastically deformed crystalline system at atomic resolution remains challenging. The objective is to efficiently model and predict a physically informed segregated solute distribution rather than simulating a series of diffusion kinetics. To address this objective, we coupled molecular dynamics (MD) and Monte Carlo (MC) methods using a novel method based on virtual atoms technique. We applied our MD-MC coupling approach to model off-lattice carbon (C) solute segregation in nanoindented Fe-C samples containing complex dislocation networks. Our coupling framework yielded the final configuration through efficient parallelization and localized energy computations, showing C Cottrell atmospheres near dislocations. Different initial C concentrations resulted in a consistent trend of C atoms migrating from less crystalline distortion to high crystalline distortion regions. Besides unraveling the strong spatial correlation between local C concentration and defect regions, our results revealed two crucial aspects of solute segregation preferences: 1. defect energetics hierarchy and 2. tensile strain fields near dislocations. The proposed approach is generic and can be applied to other material systems as well.

Multi-site solar irradiance prediction based on hybrid spatiotemporal graph neural network

Constructing accurate spatiotemporal correlations is a challenging task in joint prediction of multiple photovoltaic sites. Some advanced algorithms for incorporating other surrounding site information have been proposed, such as graph neural network-based methods, which are usually based on static or dynamic graphs to build spatial dependencies between sites. However, the possibility of the simultaneous existence of multiple spatial dependencies is not considered. This paper establishes a spatiotemporal prediction model based on hybrid spatiotemporal graph neural network. In this model, we apply adaptive hybrid graph learning to learn composite spatial correlations among multiple sites. A temporal convolution module with multi-subsequence temporal data input is used to extract local semantic information to better predict future nonlinear temporal dependencies. A spatiotemporal adaptive fusion module is added to address the issue of integrating diverse spatiotemporal trends among multiple sites. To assess the model's predictive performance, nine solar radiation observation stations were selected in two different climatic environments. The average root mean square error (RMSE) of the constructed model was 38.51 and 49.90 W/m2, with average mean absolute error (MAE) of 14.72 and 23.06 W/m2, respectively. Single-site and multi-site prediction models were selected as baseline models. Compared with the baseline models, the RMSE and MAE reduce by 3.1%–20.8% and 8.9%–32.8%, respectively, across all sites. The proposed model demonstrates the effectiveness of improving accuracy in forecasting solar irradiance through multi-site predictions.

Turbulent flow over aligned cylindrical obstacles

Turbulent flow and boundary-layer (BL) characteristics over cylindrical obstacles have been understudied compared to the flow dynamics around cubic roughness in the urban BL literature. Using large-eddy simulation, we investigate a turbulent BL flow developed over two vertically oriented cylindrical obstacles aligned downstream. For widely separated cylinders, the wake flow undergoes periodic oscillations akin to vortex shedding behind an isolated cylinder. As the height-to-width aspect ratio (AR) of the canyon bounded by the cylinders increases, the streamline geometry exhibits a clear transition from isolated to wake interference and skimming regimes. Two-point autocorrelation functions of velocities confirm a strong coupling of canyon flow with the roughness sublayer for wider canyons, while with evident decoupling as the canyon narrows. The length scales, which measure the spatial correlation in the flow, decrease in both lateral and vertical directions with increasing AR. Turbulent kinetic energy and momentum fluxes below the roughness sublayer present pronounced monotonic scaling with AR (with R-squared values up to 0.84 and 0.98, respectively), resulting in a consistent variation in the surface roughness aerodynamic parameters, the roughness length (z0) and zero-plane displacement (d), with AR. Quantitative differences in the results with respect to those well-established for street canyons are analyzed, with the similarities highlighted. The results offer insights into boundary-layer flow parameterization concerning cylinder-occupied surface roughness.