Block Model Research Articles

Belonging in remote higher education classrooms: The dynamic interaction of intensive modes of learning and arts-based pedagogies

In this paper, the authors explore the conditions that support belonging in remote VU Block Model® teaching. They examine the role of arts-based, embodied pedagogy in promoting engagement in learning, connection between students, and between students and teachers, and in an environment in which vulnerability and risk-taking in learning is valued. A discussion of belonging in higher education and the practice of embodied learning is followed by the reflections of seven participants. These participants were students in a remotely taught, arts-based higher education block unit, which had been mindfully adapted to retain the embodied nature of delivery during the COVID-19 pandemic. After students’ participation in focus group interviews, the lead author constructed found poetry from their comments and reflections. This found poetry forms the dataset through which the questions of belonging are explored. The researchers found that when explored through the lens of the Community of Inquiry Framework, embodied and arts-based practices provided opportunities for students to develop a sense of belonging, deepen understanding of lived experiences, and realise higher education and career goals. This study elevates the voices of students, providing opportunities for higher education teachers to consider the importance of belonging for student success in remote, intensive, and on-campus modes of delivery.

Lateral and vertical variations of seismic anisotropy in the lithosphere–asthenosphere system underneath Central Europe from long-term splitting measurements

SUMMARY Backazimuthal variations in the shear wave splitting of core-refracted shear waves (SKS, SKKS and PKS phases, jointly referred to as XKS) at the Black Forest Observatory (BFO, Southwest Germany) indicate small-scale lateral and partly vertical variations of the seismic anisotropy. However, existing anisotropy studies and models for the nearby Upper Rhine Graben (URG) area in the northern Alpine foreland are mostly based on short-term recordings and by this suffer from a limited backazimuthal coverage and averaging over a wide or the whole backazimuth range. To identify and delimit laterally confined anisotropy regimes in this region, we carry out XKS splitting measurements at six neighbouring (semi-)permanent broad-band seismological recording stations (interstation distance 10–80 km). We manually analyse long-term (partly > 20 yr) recordings to achieve a sufficient backazimuthal coverage to resolve complex anisotropy. The splitting parameters (fast polarization direction $\phi $, delay time $\delta t$) are determined in a single- and multi-event analysis. We test structural anisotropy models with one layer with horizontal or tilted symmetry axis and with two layers with horizontal symmetry axes (transverse isotropy). To account for lateral variations around a single recording site, modelling is compared for the whole and for limited backazimuth ranges. Based on this, we provide a 3-D block model with spatial variation of anisotropic properties. Based on delay times > 0.3 s and missing discrepancies between SKS and SKKS phases, which do not support lower mantle anisotropy, the found anisotropy is placed in the lithosphere and asthenosphere. The spatial distribution as well as the lateral and backazimuthal variations of the splitting parameters confirm lateral and partly vertical variations in anisotropy. On the east side of the URG, we suggest two anisotropic layers in the Moldanubian Zone (south) and one anisotropic layer in the Saxothuringian Zone (north). In the Moldanubian Zone, a change of the fast polarization directions is observed between the east and the west side of the URG, indicating different textures. At the boundary between the two terranes, an inclined anisotropy is modelled which may be related with deformation during Variscan subduction. Regarding the observation of numerous null measurements and inconsistent splitting parameters, especially (southwest of BFO) in the southern URG, different hypothesis are tested: scattering of the seismic wavefield due to small-scale lateral heterogeneities, a vertical a-axis due to a vertical mantle flow related to the Kaiserstuhl Volcanic Complex, as well as a different preferred orientation of the olivine crystals (not A-type, but C-type) due to specific ambient conditions (high temperature, water content).

Evaluating the Effects of Joint Set Properties on Laboratory Rockmass Analog Specimen Strength Using Bonded Block Models

Evaluating the Effects of Joint Set Properties on Laboratory Rockmass Analog Specimen Strength Using Bonded Block Models

Model selection for network data based on spectral information

In this work, we explore the extent to which the spectrum of the graph Laplacian can characterize the probability distribution of random graphs for the purpose of model evaluation and model selection for network data applications. Network data, often represented as a graph, consist of a set of pairwise observations between elements of a population of interests. The statistical network analysis literature has developed many different classes of network data models, with notable model classes including stochastic block models, latent node position models, and exponential families of random graph models. We develop a novel methodology which exploits the information contained in the spectrum of the graph Laplacian to predict the data-generating model from a set of candidate models. Through simulation studies, we explore the extent to which network data models can be differentiated by the spectrum of the graph Laplacian. We demonstrate the potential of our method through two applications to well-studied network data sets and validate our findings against existing analyses in the statistical network analysis literature.

Research on sustainable renewal of historic blocks based on urban catalyst theory

Currently, cities in China have entered the development stage of stock renewal. Urban renewal has become the focus of urban development, and optimization of stock space has become the main aspect of urban design. At the same time, applying the concept of micro-renewal of historic blocks in recent years has also put forward new requirements for the renewal mode of old blocks. This paper discusses the possibility of micro-renewal and transformation of historic Chinese blocks in the new era from the perspective of catalyst theory. Through streetscape recognition technology and crawling of Point of Information (POI) data, the existing problems of blocks and the potential vitality points of blocks are determined, which changes the traditional research that often lacks quantitative judgment and only uses qualitative judgment. Through the intervention of quantitative technology, the problems existing in the perspective rate of commercial streets and the business model of historical blocks were found. Analyzing from the perspective of space, function, and culture, and then, selecting catalyst elements, which promotes the transformation of the historic blocks from a top-down transformation pattern to a bottom-up transformation pattern.

Development and research of filter models communication and control systems configured for the required set of parameters

Problem statement. A digital filter refers to a hardware or software implementation of a mathematical algorithm that converts a digital signal in a certain way. The classification of digital filters is usually based on the functional characteristics of digital filtering algorithms. Digital filters may have parameters that cannot be implemented in analog filters, do not require periodic monitoring and calibration, and one filter can process several input channels or signals, while the accuracy is limited only by the bit depth used. The implementation of digital signal processing units using programmable logic integrated circuits (FPGAs) will significantly simplify hardware costs and make the most effective use of these digital filter capabilities. Goal. Conducting research in the field of digital signal processing in order to obtain fundamentally new software and hardware architectures, developing an RTL model of a complex functional block (SF block) of a digital signal processing system, its verification both at the logical and physical level. Results. In the course of the scientific research, a software and hardware model of the filter was developed to adjust the parameters during operation, taking into account their architectural advantages and disadvantages. In order to verify and confirm the adequacy of the methodology developed in the course of the study, an SF model was created-a digital signal processing unit. This model has been implemented and physically verified on the basis of a debugging board using FPGAs. Practical significance. The technique presented in this paper will allow the development of software and hardware devices for digital signal processing, with the possibility of their configuration at the software level for the required set of processing parameters, which in turn will significantly save resources spent on development.

Innovative ceramic membrane plate filtration system for sustainable semiconductor industry wastewater treatment: A pilot scale study

Conventional methods of coagulation and flocculation are used to treat semiconductor industry wastewater, but they often fail to recover valuable silica particles and result in resource wastage and environmental risks. Despite the potential of membrane filtration for silica recovery, limited research is available on scaling up the process beyond laboratory scale. Therefore, this study aims to develop and evaluate the feasibility of pilot-scale ceramic membrane plate filtration system for treatment of diluted back grinding wastewater (DBGW) from the semiconductor industry. Specific objectives include assessing permeate quality in terms of rejection and benchmarking it to established standards, identifying fouling mechanisms, characterising sludge constituents and conducting life cycle assessment (LCA) to evaluate the environmental impacts of the ceramic membrane plate filtration system compared with conventional treatment. The pilot membrane plate filtration system demonstrated significant reductions of 85.71 % in chemical oxygen demand, 67.31 % in total suspended solid, 75 % in nitrate–nitrogen (NO3-N), 33.33 % in ammonia nitrogen (NH3-N), 74.07 % in colour and 98.37 % in turbidity. Effective mitigation of irreversible fouling during DBGW filtration was achieved using 0.1 M NaOCl, resulting in a flux recovery ratio of 95.35 %. The retained silica on the membrane exhibited a purity of 64 wt%, as confirmed by EDX analysis. The treated wastewater met various regulatory standards including Malaysia Industrial Effluent Discharge (Standard A and Standard B), restricted USEPA standard, NWQS Class IIA, IIB, III, IV and V, and Singapore’s greywater standards. The fouling mechanism of silica on the ceramic membrane was well represented by the standard blocking model with an R2 value of 0.92. Furthermore, LCA confirmed that the utilisation of the ceramic membrane plate filtration system exhibited fewer environmental impacts than conventional coagulation–flocculation methods. This study represents a significant breakthrough in wastewater treatment in the semiconductor industry in terms of concurrent recovery and reuse of silica to address environmental and resource sustainability concerns.

Node-Level Community Detection within Edge Exchangeable Models for Interaction Processes

Scientists are increasingly interested in discovering community structure from modern relational data arising on large-scale social networks. While many methods have been proposed for learning community structure, few account for the fact that these modern networks arise from processes of interactions in the population. We introduce block edge exchangeable models (BEEM) for the study of interaction networks with latent node-level community structure. The block vertex components model (B-VCM) is derived as a canonical example. Several theoretical and practical advantages over traditional vertex-centric approaches are highlighted. In particular, BEEMs allow for sparse degree structure and power-law degree distributions within communities. Our theoretical analysis bounds the misspecification rate of block assignments while supporting simulations show the properties of the network can be recovered. A computationally tractable Gibbs algorithm is derived. We demonstrate the proposed model using post-comment interaction data from Talklife, a large-scale online peer-to-peer support network, and contrast the learned communities from those using standard algorithms including degree-corrected stochastic block models, popularity-adjusted block models, and weighted stochastic block models. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

A novel control chart scheme for online social network monitoring using multivariate nonparametric profile techniques

Online social networks have become indispensable in modern life, facilitating knowledge sharing, social communication, and business marketing. To gain a deeper understanding of individuals' behavior within social networks, researchers have undertaken essential analytical tasks such as change point detection. Recently, nonparametric change point detection methods have attracted researchers' attention due to their generality and flexibility. However, existing methods exhibit limitations, including overlooking network structure, reliance on case-based network attributes, and neglecting the dynamic nature of data, which may have correlations in evolving social networks. In this study, we propose a novel multivariate mixed-effects nonparametric profile control (MENPC) algorithm to address these limitations. The advantage of MENPC relies on its unique point of view in approaching network data, where it incorporates the dynamic nature of data into the monitoring process without assuming internal independence of networks over time. Additionally, it takes into account the network structure by considering both nodal and network-level attributes. Furthermore, by introducing an updating trick formula, the proposed algorithm simplifies computations, effectively balancing memory and speed for online monitoring. We evaluate the effectiveness of MENPC through comprehensive numerical experiments using the degree correlated stochastic block model to simulate interactions in evolving online social networks. The results demonstrate MENPC's superior performance in terms of expected detection delay, showcasing its accuracy and efficiency in comparison to competing approaches including Wilson, and eigenvalue methods. Applying MENPC to the Enron email network dataset further confirms its significant progress in social network monitoring, expanding its potential for various applications.

Phenol Removal through Horseradish Peroxidase Immobilization on Ultrafiltration Membranes: Comparative Analysis of Immobilization Methods and Fouling Patterns

This research investigates the removal of phenol using pure peroxidase from horseradish grade I in conjunction with a dead-end ultrafiltration membrane. Various horseradish peroxidase (HRP) immobilization techniques— physical adsorption, covalent bonding, and cross-linking with glutaraldehyde—were applied to a regenerated cellulose (RC) membrane with a surface area of 44 m2 and a molecular weight cut-off of 30 kDa. The investigation examined factors influencing phenol removal, including phenol concentration, membrane fouling, and the reusability of immobilized enzymes. Results indicated that covalent bonding was the most suitable enzyme immobilization technique, achieving a remarkable 90.1% immobilization yield. Phenol removal efficiency reached 100% at 30 min under specific conditions: phenol concentration of 1 mg/L, pH 6.0, hydrogen peroxide concentration of 0.5 mM, and operating pressure set at 3 psig, with temperature maintained at 28 ± 3 °C. Membrane fouling resulted in a decrease in flux. The performance of fouling models was found to be influenced by phenol concentration, with the Cake Formation Model (CFM) proving most effective at low concentrations, while the Complete Pore Blocking Model (CBM) emerged as more suitable at higher concentrations. The immobilized enzyme exhibited reusability for five cycles, maintaining a phenol removal efficiency exceeding 50%. These findings contribute to understanding the enzymatic phenol removal process and the use of appropriate enzyme immobilization techniques for the effective and sustainable treatment of phenol-contaminated water.

Roles and Responsibilities of the Organizations Welcoming Immigrants in Remote Regions of Québec

In 1992, Québec’s government adopted a regionalization policy with the intention of enlarging the settlement possibilities for recent immigrants, thus delegating the responsibility of integrating immigrants to the regions’ local community-based organizations. These immigrants arrive in their host society needing information and services concerning their host community. Considering the supporting relationships that community-based organizations in Remote Regions cultivate with immigrants, it is relevant to address the work accomplished by those organizations’ employees. With the aim of better understanding their work, this article examines the community-based organizations responsible for the welcoming and facilitating integration of immigrants. This article proposes an ethnography based on a five blocks model of the regionalization work to take a deeper look at their daily work and gain a better understanding of their specific role in the regionalization of immigration in their community, thereby refining the initial model. It then offers a reflection on the multidimensionality of their work, which goes far beyond employability, and on the specificity of immigration in the context of remote regions.

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model.

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNAseq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.

Detecting planted partition in sparse multilayer networks

Abstract Multilayer networks are used to represent the interdependence between the relational data of individuals interacting with each other via different types of relationships. To study the information-theoretic phase transitions in detecting the presence of planted partition among the nodes of a multilayer network with additional nodewise covariate information and diverging average degree, Ma and Nandy (2023, IEEE Trans. Inf. Theory, 69, 3203–3239) introduced Multi-Layer Contextual Stochastic Block Model. In this paper, we consider the problem of detecting planted partitions in the Multi-Layer Contextual Stochastic Block Model, when the average node degrees for each network are greater than $1$. We establish the sharp phase transition threshold for detecting the planted bi-partition. Above the phase-transition threshold testing the presence of a bi-partition is possible, whereas below the threshold no procedure to identify the planted bi-partition can perform better than random guessing. We further establish that the derived detection threshold coincides with the threshold for weak recovery of the partition and provides a quasi-polynomial time algorithm to estimate it.

Metallurgical Copper Recovery Prediction Using Conditional Quantile Regression Based on a Copula Model

This article proposes a novel methodology for estimating metallurgical copper recovery, a critical feature in mining project evaluations. The complexity of modeling this nonadditive variable using geostatistical methods due to low sampling density, strong heterotopic relationships with other measurements, and nonlinearity is highlighted. As an alternative, a copula-based conditional quantile regression method is proposed, which does not rely on linearity or additivity assumptions and can fit any statistical distribution. The proposed methodology was evaluated using geochemical log data and metallurgical testing from a simulated block model of a porphyry copper deposit. A highly heterotopic sample was prepared for copper recovery, sampled at 10% with respect to other variables. A copula-based nonparametric dependence model was constructed from the sample data using a kernel smoothing method, followed by the application of a conditional quantile regression for the estimation of copper recovery with chalcocite content as secondary variable, which turned out to be the most related. The accuracy of the method was evaluated using the remaining 90% of the data not included in the model. The new methodology was compared to cokriging placed under the same conditions, using performance metrics RMSE, MAE, MAPE, and R2. The results show that the proposed methodology reproduces the spatial variability of the secondary variable without the need for a variogram model and improves all evaluation metrics compared to the geostatistical method.

Investigating the Fouling Models of the Microfiltration Mixed Matrix Membranes-Based Oxide Nanoparticles Applied for Oil-in-Water Emulsion Separation

Membrane fouling is a major problem encountered in the use of microfiltration (MF) processes to separate the emulsified oil from water. This work involves assessing the efficacy of removing oil-in-water emulsion (O/W emulsion), and evaluating fouling resistance by studying the membrane morphology before and after fouling, and after washing with different cleaning solutions via field emission scanning electron microscopy (FESEM) analysis. Also, the fundamental mechanism involved in the flux drop during crossflow MF has been assessed using models such as the Hermia blocking models and the modified model by Field. The standard and intermediate pore blocking models provided the best prediction for experimental behavior when analyzing the decay in the flux with time for the bio silicon oxide/polyvinylchloride (B-SiO2/PVC) membrane and the stannic oxide/polyvinylchloride (SnO2/PVC) membrane. This research established regression equations of the flux for both membranes in which these equations are highly correlated with R2 of 98.33% for B-SiO2/PVC and R2 of 99.52% for SnO2/PVC using the surface response methodology (RSM). The high flux recovery ratio (FRR) is indicative of the improved antifouling feature of the manufactured membranes where it was 96.8% for B-SiO2/PVC and 94.6% for SnO2/PVC. The results obtained by Hermia and Field were in good agreement with RSM analysis supporting the standard pore-blocking mechanism.

Ore dilution control when mining low-thickness ore bodies using a system of sublevel drifts

Purpose. The research is aimed at substantiating the optimal parameters of blast-hole ore breaking to reduce the rock mass destruction when mining low-thickness vein deposits using the mining system of sublevel drifts (SD). The focus is on analyzing the impact of blasting on the out-contour rock mass during the blast-hole breaking process. Methods. The three-dimensional block model is constructed using rating classifications of rocks based on studying their strength properties and structural peculiarities. The inelastic deformation zones around the stoping extraction are determined by numerical analysis using the finite element method in 2D formulation. Experimental blasts are assessed by varying the blast-hole drilling scheme depending on the stability rating. Findings. During the experimental-industrial tests, rational blast-hole drilling schemes have been substantiated, contributing to maintaining the stability of the host rocks when mining low-thickness veins. Originality. Effective methods for reducing the ore dilution have been substantiated, which take into account not only the strength properties and structural peculiarities of rocks, but also their seismic impact from blasting on the out-contour rock mass stability when mining low-thickness deposits using a system of sublevel drifts. Practical implications. Practical significance is in the possibility of minimizing the percentage of mineral dilution when mining low-thickness ore bodies using a system of sublevel drifts, which can significantly reduce the cost of mined minerals by reducing ore losses caused by the rock mass destruction during mining operations.

Dynamicity of brain network organization & their community architecture as characterizing features for classification of common mental disorders from whole-brain connectome

The urgency of addressing common mental disorders (bipolar disorder, attention-deficit hyperactivity disorder (ADHD), and schizophrenia) arises from their significant societal impact. Developing strategies to support psychiatrists is crucial. Previous studies focused on the relationship between these disorders and changes in the resting-state functional connectome’s modularity, often using static functional connectivity (sFC) estimation. However, understanding the dynamic reconfiguration of resting-state brain networks with rich temporal structure is essential for comprehending neural activity and addressing mental health disorders. This study proposes an unsupervised approach combining spatial and temporal characterization of brain networks to classify common mental disorders using fMRI timeseries data from two cohorts (N = 408 participants). We employ the weighted stochastic block model to uncover mesoscale community architecture differences, providing insights into network organization. Our approach overcomes sFC limitations and biases in community detection algorithms by modelling the functional connectome’s temporal dynamics as a landscape, quantifying temporal stability at whole-brain and network levels. Findings reveal individuals with schizophrenia exhibit less assortative community structure and participate in multiple motif classes, indicating less specialized network organization. Patients with schizophrenia and ADHD demonstrate significantly reduced temporal stability compared to healthy controls. This study offers insights into functional connectivity (FC) patterns’ spatiotemporal organization and their alterations in common mental disorders, highlighting the potential of temporal stability as a biomarker.

Variability and ecological relevance of alternative group structures in food webs

Broad-scale interaction patterns among species in food webs can be identified using the group model, which identifies groups of species, sharing similar sets of predators and prey from other groups. These shared relationships are relevant for the functionality of species. The group model originates from stochastic block models, meaning the obtained group structures of the same food web can differ in multiple runs. A single best partition may miss relevant information, and a consensus solution may blur complementary communities. Hence, it is highly relevant to analyze the full solution landscape while searching for the optimal partitioning of species. In particular, a narrow solution landscape would highlight the reliability of the identified groups. Here, using five empirical food webs, we analyze their respective solution landscape based on multiple group model runs of the same network. By analyzing the solution landscapes, we aim to explain the differences between solutions and what they entail, structurally and ecologically. Our results show that the overall general group structures remain intact across different iterations. While some food webs vary more, differences are commonly limited to a smaller number of groups with seemingly similar species roles. Our results suggest that while the stochastic process of the group model can generate alternate solutions for the same food web, these differences generally involve weaker distinctions of species in a small number of groups rather than a large structural turnover.

Graph structure estimation neural network-based service classification

PurposeWith the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model.Design/methodology/approachFirst, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph.FindingsThe authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification.Originality/valueThis paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.

Spatiotemporal characteristics of residents' intercity travel in China under the impact of COVID-19 pandemic

Investigating the spatiotemporal characteristics of residents' intercity travel affected by the coronavirus disease (COVID-19) pandemic is of great significance to understanding and extending the impact of major public health affairs on intercity travelers, urban management policies, and intercity cooperative interactions. Taking 366 cities in China as an example, this study proposed a novel research framework for analyzing the spatiotemporal characteristics of intercity population flows. Using AutoNavi population migration big data sets from web crawling, the spatiotemporal structure patterns of intercity travel in China from January 1st to June 30th, 2020 were extracted by using the singular value decomposition (SVD) method. Then the spatial organization patterns of intercity travel networks in China and its five major urban agglomerations were identified by the weighted stochastic block model (WSBM). The results showed that the total volume of intercity travel in China and its five major urban agglomerations experienced a process of rapid decline followed by a slow recovery, presenting a “V-shaped” distribution since the outbreak of the COVID-19 pandemic. Three distinctive intercity travel patterns, namely the stable, holiday, and weekday-weekend intercity trip patterns, were extracted from the spatiotemporal OD matrix of intercity travel. Strong intercity travels were mainly distributed in eastern and central China, forming an intercity travel pattern in which core cities pushed and pulled each other within the national urban agglomerations. However, intercity migration flows were dominated by weak-intensity flows. Intercity trips during the holidays had a phenomenon of “escaping from the metropolis”. Meanwhile, it had significant spatial proximity and directionality. The daily intercity trips were characterized by a remarkable periodicity with seven days as a repeat. A more interesting finding is that when weekends overlap with holidays, intercity travel on the 1–2 days before the vacations has presented an effect of “suppression travel”. Furthermore, at the national scale, the stable intercity travel network has formed a polycentric core-periphery structure of “Three Cores-Six Semi-peripheries-Five Peripheries”. At the urban agglomeration scale, they formed respectively distinguished monocentric, bicentric, and polycentric core-periphery structures.