Spatial Structure Research Articles

Research on urban agglomeration spatial network structure in the middle reaches of the Yangtze River based on real-time traffic accessibility scenario analysis

ABSTRACT Investigating the spatial network structure of urban agglomerations based on traffic accessibility has an important practical significance in accelerating the process of regional integration. Taking urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) as the case study, this study aims to examine the characteristics associated with the spatial network structure based on traffic accessibility and its changes under scenarios of different traffic accessibilities by adopting real-time traffic data, modified gravity model, and social network analysis (SNA). The main conclusions are as follows. Wuhan, Changsha, and Nanchang are the central cities in the spatial network; the spatial network structure of UAMRYR is imbalanced and in the agglomeration stage of regional economic development. With the development of traffic accessibility, resources first gather in the individual center cities and then spread to other cities, and the network tends toward equilibrium.

Adjustable High-Energy Soliton Fiber Laser Based on a Spatial Alignment Structure

Adjustable High-Energy Soliton Fiber Laser Based on a Spatial Alignment Structure

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or “niches” followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon’s rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

Decoding Active Sites for Highly Efficient Semihydrogenation of Acetylene in Palladium-Copper Nanoalloys.

Accurately decoding the three-dimensional atomic structure of surface active sites is essential yet challenging for a rational catalyst design. Here, we used comprehensive techniques combining the pair distribution function and reverse Monte Carlo simulation to reveal the surficial distribution of Pd active sites and adjacent coordination environment in palladium-copper nanoalloys. After the fine-tuning of the atomic arrangement, excellent catalytic performance with 98% ethylene selectivity at complete acetylene conversion was obtained in the Pd34Cu66 nanocatalysts, outperforming most of the reported advanced catalysts. The quantitative deciphering shows a large number of active sites with a Pd-Pd coordination number of 3 distributed on the surface of Pd34Cu66 nanoalloys, which play a decisive role in highly efficient semihydrogenation. This finding not only opens the way for guiding the precise design of bimetal nanocatalysts from atomic-level insight but also provides a method to resolve the spatial structure of active sites.

A Semantic Spatial Structure-Based Loop Detection Algorithm for Visual Environmental Sensing

Loop closure detection is an important component of the Simultaneous Localization and Mapping (SLAM) algorithm, which is utilized in environmental sensing. It helps to reduce drift errors during long-term operation, improving the accuracy and robustness of localization. Such improvements are sorely needed, as conventional visual-based loop detection algorithms are greatly affected by significant changes in viewpoint and lighting conditions. In this paper, we present a semantic spatial structure-based loop detection algorithm. In place of feature points, robust semantic features are used to cope with the variation in the viewpoint. In consideration of the semantic features, which are region-based, we provide a corresponding matching algorithm. Constraints on semantic information and spatial structure are used to determine the existence of loop-back. A multi-stage pipeline framework is proposed to systematically leverage semantic information at different levels, enabling efficient filtering of potential loop closure candidates. To validate the effectiveness of our algorithm, we conducted experiments using the uHumans2 dataset. Our results demonstrate that, even when there are significant changes in viewpoint, the algorithm exhibits superior robustness compared to that of traditional loop detection methods.

Crystallization-Inspired Design and Modeling of Self-Assembly Lattice-Formation Swarm Robotics

Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model’s accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability.

Exploration of morphological coherence in open clusters with a "core-shell'' structure

The morphology of open clusters plays a major role in the study of their dynamic evolution. The study of their morphological coherence, namely, the three-dimensional (3D) difference between the inner and outer morphologies of open clusters, allows us to obtain a better understanding of the morphological evolution of open clusters. We aim to investigate the morphological coherence of 132 open clusters with up to 1 kpc from the Sun in the three-dimensional (3D) space within the heliocentric cartesian coordinate frame. The 132 open clusters have a 3D core-shell structure and conform to the ellipsoidal model, with all of them coming from a catalog of publicly available clusters in the literature. We employed the ellipsoid fitting method to delineate the 3D spatial structure of the sample clusters, while using the morphological dislocation (MD) defined in our previous work and the ellipticity ratio (ER) of the clusters' inner and outer structures to characterize the morphological coherence of the sample clusters. The results show an inverse correlation between the ER of the sample clusters and the number of their members, indicating that sample clusters with a much more elliptical external morphology than internal shape generally tend to host a large number of members. Meanwhile, a slight shrinking of the MD of the sample clusters with their members' number may shed light on the significant role of the gravitational binding of the sample clusters in maintaining their morphological stability. Moreover, there are no correlations between the MD and ER of the sample clusters and their age. They are also not significantly correlated with the X-axis, the Y-axis, their orbital eccentricities, and the radial and vertical forces on them. However, the ER of the sample clusters displays some fluctuations in the distributions between it and the above covariates, implying that the morphologies of the sample clusters are sensitive to the external environment if sample effects are not taken into account. Finally, the analysis of the 3D spatial shapes of sample clusters with a small ER or a large ER demonstrates that the number of members lays an important foundation for forming a dense internal system for sample clusters. At the same time, the MD of the sample clusters can serve well as an indicator of their morphological stability, which is built upon a certain amount of member stars. We present a new insight into the morphological coherence of open clusters, attributed to the combination of their gravitational binding capacity and external environmental perturbations.

The evolution of traditional ancient towns in South China: Tangjiawan Ancient Town in Zhuhai city

ABSTRACT Based on the urban morphology theory of the Conzen School and the actual situation of the Tangjiawan Ancient Town, a morphological process analysis of the Tangjiawan Ancient Town was conducted. The end result is a visualization of the process of urban form evolution. The first objective is to describe the morphological framework of Tangjiawan Ancient Town and the morphological changes that occurred during its development. The current urban landscape is divided into units by superimposing morphological units, building types, and land use. The results of the division also reflect the morphological development, structure, and compositional characteristics of the ancient city of Tangjiawan. Using Tangjiawan Ancient Town in Zhuhai city as an example, on-site research, historical evidence, urban morphology analysis methods, and interviews were used to elaborate and analyze the spatial structure and evolution process of traditional ancient towns in South China. In particular, morphological units, building types, and land use are included. The morphological map of the Tangjiawan ancient town is inconsistent with the current economic-oriented development of ancient town planning. This shows that current planning is less concerned with the physical form of architectural heritage, which may lead to further damage to the landscape of the ancient town. This study provides a strong reference for the follow-up planning of ancient town protection of the same type from the perspective of morphology and area and provides ideas for the subsequent preparation of protection plans.

Genetic connectivity of the common octopus (Octopus insularis) along the southwestern Caribbean

AbstractOctopus insularis is a shallow‐water octopus distributed throughout the western Atlantic, the Gulf of Mexico and the Caribbean. To evaluate its phylogeographic pattern, mitochondrial markers (16S rRNA and COIII) and genome‐wide nuclear markers (SNP's) were analyzed in individuals from artisanal fishing in different locations of the Colombian Caribbean coast. Mitochondrial analyzes displayed novel haplotypes (16S = H2, H3 and H4; COIII = H2–H7) and haplotypes shared with distant geographic areas (16S = H1 COIII = H1). The COIII gene did not show genetic differentiation between the analyzed localities, while the 16S gene showed significant differences between Santa Marta and Isla Fuerte. COIII's demographic analysis indicated that the species' effective population size has remained constant. Inferences were made from next‐generation genomic data with restriction site‐associated DNA (ddRAD‐seq) and 6769 polymorphic loci. The pairwise FST test indicated that there are low but significant differences between localities; however, the general molecular analysis of variance (AMOVA) and the principal coordinate analysis showed a lack of spatial structure in the populations analyzed. The Mantel test found no correlation between genetic distance and geographic distance, and the genetic structure analyses showed the presence of one genetic stock. The information obtained in this study indicates the genetic connectivity of O. insularis in the southwestern Caribbean, the absence of putative biogeographic barriers that affect its gene flow, and the crucial role played by life history strategies (planktonic paralarvae) and oceanographic factors to define the current distribution of its genetic diversity in this region.

Spatial structure could explain the maintenance of alternative reproductive tactics in tree cricket males.

Trait polymorphisms are widespread in nature, and explaining their stable co-existence is a central problem in ecology and evolution. Alternative reproductive tactics, in which individuals of one or more sex exhibit discrete, discontinuous traits in response to reproductive competition, represent a special case of trait polymorphism in which the traits are often complex, behavioural, and dynamic. Thus, studying how alternative reproductive tactics are maintained may provide general insights into how complex trait polymorphisms are maintained in populations. We construct a spatially explicit individual-based model inspired from extensively collected empirical data to address the mechanisms behind the co-existence of three behavioural alternative reproductive tactics in males of a tree cricket (Oecanthus henryi). Our results show that the co-existence of these tactics over ecological time scales is facilitated by the spatial structure of the landscape they inhabit, which serves to equalize the otherwise unequal mating benefits of the three tactics. We also show that this co-existence is unlikely if spatial aspects of the system are not considered. Our findings highlight the importance of spatial dynamics in understanding ecological and evolutionary processes and underscore the power of integrative approaches that combine models inspired from empirical data.

Local economic development strategies in a medium-sized city on the European periphery: a path to become an economic center of Eastern Central Europe importance

Large cities are becoming increasingly valuable in the spatial structure of globalized economies. Although these centers vary significantly regarding population, economic structure, and income conditions, they—compared to their surroundings—generally show a higher productivity level, a higher rate of employment, higher education, and higher innovation performance. However, this metropolization and concentration of the economy in large cities made the development of cities regarded as medium-sized on an international scale an issue of competitiveness in Europe, characterized by dense town networks, a small number of metropolises, and a significant weight of medium- and small-sized towns. However, one of the main preconditions for achieving success in this competition is to form a comprehensive and complex concept with a significant emphasis on economic development. Therefore, the aim of our study is to analyze the local economic development strategies for the last three decades, as elaborated in Debrecen. In the framework of our empirical analysis, we examined how different mainstream concepts appear in local development strategies and which development objectives are determined by the local strategies. Furthermore, we also aimed to identify possible changes in ideas about city roles by referring to the organization of economic space.

Simultaneous Biofilm Disruption, Bacterial Killing, and Inflammation Elimination for Wound Treatment Using Silver Embellished Polydopamine Nanoplatform.

Due to the presence of spatial barriers, persistent bacteria, and excessive inflammation in bacteria biofilm-infected wounds, current nanoplatforms cannot effectively address these issues simultaneously during the therapeutic process. Herein, a novel biomimetic photothermal nanoplatform integrating silver and polydopamine nanoparticles (Ag/PDAs) that can damage biofilms, kill bacterial persisters, and reduce inflammation for wound treatment is presented. These findings reveal that Ag/PDAs exhibit a broad-spectrum antimicrobial activity through direct damage to the bacterial membrane structure. Additionally, Ag/PDAs demonstrate a potent photothermal conversion efficiency. When combined with near-infrared (NIR) irradiation, Ag/PDAs effectively disrupt the spatial structure of biofilms and synergistically eradicate the resident bacteria. Furthermore, Ag/PDAs show remarkable anti-inflammatory properties in counteracting bacterium-induced macrophage polarization. The in vivo results confirm that the topical application of Ag/PDAs significantly suppress Staphylococcus aureus biofilm-infected wounds in murine models, concurrently facilitating wound healing. This research provides a promising avenue for the eradication of bacterial biofilms and the treatment of biofilm-infected wounds.

Three-dimensional forest structure features extraction based on TomoSAR

Abstract. Forest structure is an important indicator for evaluating forest ecosystem and an indispensable input parameter for estimating forest carbon sink. Synthetic Aperture Radar (SAR) Tomography (TomoSAR), as a novel microwave remote sensing technique with 3D imaging capability, has been demonstrated to be an valuable tool for accurate inversion of forest structure. At present, the forest vertical structure parameters inversion is almost all concentrated on the forest height estimation. However, the spatial structure of forests is complex, and it is not enough to only use the forest height to express the forest structure. This fails to identify tree canopy, branch layer and underlying vegetation layer, etc., resulting in the partial and inaccurate forest vertical structure information. To solve this problem, this paper constructs 3D structural indices from tomograms, namely Horizontal Structure Index (HS) and Vertical Structure Index (VS). The HS primarily describes canopy density, compensating for the lack of information on the horizontal dimension of forest structure provided by traditional TomoSAR techniques; while VS describes the complexity of canopy distribution in the altitude direction, further enriching information on forest structure vertically. In order to verify their feasibility and validity, HS and VS are extracted using six fully-polarized L-band SAR images covering the Krycklan watershed in Sweden. Moreover, the accuracy is verified by using external high-precision LiDAR data. The results indicate a strong correlation between the structural indices extracted from TomoSAR and those extracted from LiDAR.

Applications of Alpha Neurofeedback Processes for Enhanced Mental Manipulation of Unfamiliar Molecular and Spatial Structures.

This study explores a novel approach to enhancing cognitive proficiency by targeting neural mechanisms that facilitate science and math learning, especially mental rotation. The study specifically examines the relationship between upper alpha intensity and mental rotation skills. Although prior neurofeedback research for increasing upper alpha highlights this correlation, mostly with familiar objects, novel chemistry and math learning prompts envisioning unfamiliar objects which question the persistence of this correlation. This study revisits the upper alpha and mental rotation relationship in the context of unfamiliar objects with a single neurofeedback session and examines the efficiency of manual and automatic neurofeedback protocols. Results will provide a basis for integrating neurofeedback protocols into learning applications for enhanced learning. Our study encompassed three cohorts: Group 1 experienced an automatic neurofeedback protocol, Group 2 received a manual neurofeedback protocol, and the control group had no neurofeedback intervention. The experimental phases involved EEG measurement of individual upper alpha (frequency of maximal power + 2Hz) intensity, mental rotation tasks featuring geometric and unfamiliar molecular stimuli, one neurofeedback session for applicable groups, post-treatment upper alpha level assessments, and a mental rotation retest. The neurofeedback groups exhibited increased levels of upper alpha power, which was correlated with improved response time in mental rotation, regardless of stimulus type, compared to the control group. Both neurofeedback protocols achieved comparable results. This study advocates integrating neurofeedback into learning software for optimal learning experiences, highlighting a single session's efficacy and the substantial neurofeedback protocol's impact in enhancing upper alpha oscillations.

ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.

Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.

Effect of cold plasma treatment of sunflower seed protein modification on its structural and functional properties and its mechanism

Sunflower seed protein is a protein rich in amino acids, but its structure is more difficult to be applied due to its poor functional properties. The purpose of this study is to investigate the effect of cold plasma treatment on the structural and functional properties of sunflower seed protein. The results showed that the energetic particles and active substances generated by cold plasma treatment could improve the spatial structure of the protein by bombardment/etching and oxidation, respectively, which led to the change of its secondary structure: α-helices and random coil were transformed to β-sheet and β-turns, and the tertiary structure: the fluorescence intensity was increased, the roughness was decreased, which finally led to the increase of the zeta-potential, the solubility increased to 7.341, and the emulsion increased to 7.341, which led to the increase of the solubility and the fluorescence intensity. to 7.341, and the emulsion activity index and emulsion stability index increased from 1.842 m2/g, 107.5 min to 3.3887 m2/g, 171.4 min. These changes were attributed to the fact that the cold plasma treatment altered the structure of sunflower proteins from tightly hydrophobic to loosely hydrophilic, with the exposure of the internal hydrophobic groups and the protein aggregation from large to small. This provides a theoretical basis for its application in food.

Spatial adequacy of afforestation in Poland: do afforestation needs and environmental preferences matter?

The article concerns the afforestation of agricultural land, especially soils of low quality classes with little suitability for agriculture. The assumption was made that in order to effectively implement afforestation and optimize spatial structures, these lands should be analyzed from the point of view of environmental and spatial conditions (including the quality of the land and its suitability for field crops, cohesion of the forest ecosystem, the presence of naturally valuable areas), according to the potential of a given area. Land that is ineffective in cultivation and does not improve the cohesion of the ecosystem should be treated as preferred for afforestation. The assessment of the spatial adequacy (optimization) of afforestation in the context of afforestation needs and predispositions was carried out for Poland in terms of municipalities using the spatial autocorrelation model and methods. The research results indicate the need to strengthen the effectiveness of solutions that take into account the endogenous potential of municipalities.

The Impact of Geographic Factors on Credit Risk: A Study of Chinese Commercial Banks

Abstract Controlling credit risk is crucial for maintaining financial stability, and the role of geographic factors in this regard is a significant concern for scholars and policymakers. Drawing on the concept of information asymmetry, we developed a theoretical model to analyze how geographic factors influence credit risk. Our theoretical proposition suggests that the spatial organization of banks affects the efficiency of collecting and processing soft information, ultimately impacting the credit risk. To test this proposition, we collected microdata from Chinese commercial banks spanning the period from 2011 to 2022. Employing a mediating effect model, we empirically examined the relationship between spatial organizational structure and credit risk. Our results indicate that the distance between bank operations and functional distance impedes the collection and processing of soft information, thereby exacerbating credit risk in banks. The study focuses on examining how the spatial organizational structure of Chinese commercial banks affects credit risk. By analyzing geographic factors and information asymmetry, the study aims to understand how the organization of banks influences the collection and processing of soft information, which in turn impacts the credit risk. Furthermore, our analysis of the sample reveals that the mediating role of soft information varies between state-owned banks and joint-stock banks due to their distinct customer profiles. On the basis of these findings, we propose several policy recommendations, including a focus on enhancing the collection and processing of soft information, promoting the growth of locally based small and medium-sized banks, and reducing information barriers within bank hierarchies.

Largely enhanced Piezoelectric-Fenton capability in rationally designed PVDF/Fe2O3 flexible porous film

The burgeoning piezocatalysts have attracted huge attentions due to their ability to convert natural mechanical energy to degrade water pollutants. However, traditional powder-based piezocatalysts still face an inevitable bottleneck of the secondary pollution because of the diffusion of nanoparticles during the piezocatalysis process. In this work, novel porous polymer-based composite films combining Fenton fillers (α-Fe2O3) and ferroelectric polymer (polyvinylidene fluoride, PVDF) are prepared by rationally designed solution-casted method with a sacrificial “hard template” of table salt. The as-prepared porous PVDF/Fe2O3 film exhibits superior decomposition efficiency of organic pollutants under ultrasonication. Particularly, the easily recyclable porous film with optimized composition and spatial structure offers an ultrahigh rate constant of 0.0498 min−1, which means it can degrade 94.8% rhodamine B (RhB) in 60 min, exceeding that of most reported polymer-based piezocatalysts. Additionally, it has a comprehensive decomposition effect on dyes, antibiotics and natural water with a good cycle performance. The outstanding degradation performance is attributed to the massive hydroxyl radical generated from the enhanced Piezoelectric-Fenton effect, which is ascribed to the synergistic effect of improved piezoelectricity in PVDF, enhanced hydrophilicity and optimized spatial structures in the porous composite films.

Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral–spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral–spatial representations for classification. In this article, the crucial factors to discriminate spectral–spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral–spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.