Spatial Structure Research Articles

INTELLIGENT INFORMATION TECHNOLOGIES TO SUPPORT DECISION-MAKING WHEN APPLYING THE CAD/CAM/CAE SYSTEM OF DESIGN AND USING ADDITIVE TECHNOLOGIES

The direction of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on apatite-biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. The relevance is due to the fact that the development of an intelligent decision-making support system based on neural network modeling, the development of methods for their training, tabagato-criterion optimization of design processes, will allow the creation of three-dimensional solid models of defects taking into account their spatial structure and bone substitutes for the synthesis of biomaterials with controlled composition, porosity and mechanical strength, which are optimal for a specific area of bone replacement, which will increase the effectiveness of treatment and prosthetics in orthopedics and traumatology. A set of methods for analyzing images of bone tissue, taking into account its spatial structure, which are obtained by sensors of different physical nature, with the use of neural network models, development of methods of their design, optimization, and training is proposed. A modification of the method of learning neural networks based on gradient descent, based on the application of the theory of nonlinear dynamics, is proposed. Corresponding theoretical provisions have been developed.

YOLOv5-T: A precise real-time detection method for maize tassels based on UAV low altitude remote sensing images

Maize tassels is an important organ of maize plant, and has a very important impact on yield prediction and variety breeding. Therefore, realizing the efficient and accurate detection of maize tassels in the natural environment is the key to obtain maize phenotypes in large quantities and accurately predict maize yield. This study constructed a dataset of tassels from different growth stages, proposed a YOLOv5-Tassel (YOLOv5-T) maize tassels detection model based on UAV remote sensing platform, combined with attention mechanism, YOLOv5_l network, spatial pyramid pooling structure and multi-scale extraction advantages of Atrous convolution. The experimental results showed that the Average Precision (AP) of the model for maize tassels detection could reach 98.70 %, and the detection speed was 42.2f/s. And the method in this paper had strong robustness to changes in light intensity and maize tassels at different growth stages. It was feasible to use YOLOv5-T to detect large areas of maize tassels in real time, which provided a useful reference for the estimation of maize yield and the selection of maize varieties.

Temporal beta diversity and plant metacommunity structure dynamics over a 43-year period in an inter-tropical mexican arid region

Understanding the dynamics of metacommunity structure and temporal beta diversity is vital for comprehending ecological dynamics at local and regional scales; it is essential to evaluate impacts of factors such as climate change, dispersal, and biotic interactions. We examined changes in the composition and abundance of 199 perennial plant species in the semiarid Zapotitlán Valley, Mexico, over a 43-year period. We studied three local community types, scrubland, cardonal, and tetechera, with surveys conducted in 1980, 2003, 2018, and 2023. Shifts in species composition and abundance over time were analysed using the Temporal Beta Diversity Index distinguishing between species gains or losses. We investigated the metacommunity structure elements—coherence, turnover, and boundary clumping—in each survey. We observed pronounced temporal changes in species’ composition and abundance across all community types. These temporal changes stem from the losses and gains in species and individuals. Notably, 2023 showed significant losses in species occurrence and abundance, potentially attributable to an extended drought likely exacerbated by climate change. Surprisingly, the metacommunity structure remained Clementsian throughout our study, emphasising the stability of the cardonal in maintaining distinctions between local community types. This suggests that the metacommunity spatial structure is resistant to changes in local community compositions.

TENSOR QUANTILE REGRESSION WITH LOW-RANK TENSOR TRAIN ESTIMATION.

Neuroimaging studies often involve predicting a scalar outcome from an array of images collectively called tensor. The use of magnetic resonance imaging (MRI) provides a unique opportunity to investigate the structures of the brain. To learn the association between MRI images and human intelligence, we formulate a scalar-on-image quantile regression framework. However, the high dimensionality of the tensor makes estimating the coefficients for all elements computationally challenging. To address this, we propose a low-rank coefficient array estimation algorithm based on tensor train (TT) decomposition which we demonstrate can effectively reduce the dimensionality of the coefficient tensor to a feasible level while ensuring adequacy to the data. Our method is more stable and efficient compared to the commonly used, Canonic Polyadic rank approximation-based method. We also propose a generalized Lasso penalty on the coefficient tensor to take advantage of the spatial structure of the tensor, further reduce the dimensionality of the coefficient tensor, and improve the interpretability of the model. The consistency and asymptotic normality of the TT estimator are established under some mild conditions on the covariates and random errors in quantile regression models. The rate of convergence is obtained with regularization under the total variation penalty. Extensive numerical studies, including both synthetic and real MRI imaging data, are conducted to examine the empirical performance of the proposed method and its competitors.

Effects of theacrine on the astringency of EGCG by affecting salivary protein − EGCG interactions through different molecular mechanisms

(−)-Epigallocatechin gallate (EGCG) and theacrine are involved in imparting tea with its astringent and bitter tastes. This study investigated the effect of theacrine on the astringency of EGCG and its molecular mechanism. Sensory evaluation was used to study the astringent intensities of EGCG solutions in the presence and absence of various concentrations of theacrine. The results indicated a considerable increase in the astringency values of EGCG–theacrine solutions compared with those of EGCG solutions alone. Furthermore, dynamic light scattering (DLS) and molecular dynamics simulations (MD) were to explore the interaction mechanisms. The results revealed that theacrine increased the particle size of EGCG-proline-rich proteins (PRPs) aggregates with that of EGCG and PRPs alone. MD revealed that theacrine potentially acted as a bridge between EGCG and PRPs, promoting their interaction and intensifying the EGCG's astringency. However, theacrine could not bridge two or more mucins owing to the substantial spatial structure of mucin.

Multi-view 3D reconstruction of seedling using 2D image contour

3D reconstruction of seedling can provide comprehensive and quantitative spatial structure information, offering an effective digital tool for breeding research. However, accurate and efficient reconstruction of seedling is still a challenging work due to limited performance of depth sensor for seedling with small-size stem and unavoidable error for multi-view point cloud registration. Therefore, in this paper, we propose an accurate multi-view 3D reconstruction method for seedling using 2D image contour to constrain 3D point cloud. The rotation axis is calibrated and optimised by minimising point-to-contour distance between 2D image contour and projected exterior points from 3D point cloud. Then, to remove outliers and noise, we introduce the seedling mask of 2D image to constrained and delete projected outlier points of 3D model from corresponding view. Furthermore, we propose a residual-guided method to recognise missing region for 3D model and complete 3D model of small-size stem. Finally, we can obtain an accurate 3D model of seedling. The reconstruction accuracy is evaluated by average distance between projected contour of 3D model and 2D image contour of all views (0.3185 mm). Then, the phenotypic parameters were calculated from 3D model and the results are close to manual measurements (Plant height: R2 = 0.98, RMSE = 2.3 mm, rRMSE = 1.52%; Petioles inclination angle: R2 = 0.99, RMSE = 0.73°, rRMSE = 1.41%; Leaf area: R2 = 0.66, RMSE = 1.05 cm2, rRMSE = 7.63%; Leaf inclination angle: R2 = 0.99, RMSE = 1.01°, rRMSE = 1.72%; Stem diameter: R2 = 0.95, RMSE = 0.12 mm, rRMSE = 5.43%). Breeders can improve the selection of more resilient varieties and cultivars to different growing conditions starting from the dynamic analysis of their phenotype.

Does the universal adaptive strategy theory apply to natural regeneration in heterogeneous subtropical karst forests?

The universal adaptive strategy theory (UAST) describes how the ecological strategy of plants changes along the succession after disturbance. However, whether it applies to those off-peak successional pathways in extremely heterogeneous forests is uncertain. This study established a series of forest dynamic plots along the natural regeneration in subtropical karst forests, and aimed to verify whether UAST applied to the succession in extremely heterogeneous forests. By measuring the dominant plant functional traits (PFTs) and quantifying the plant ecological strategy spectrum, we found that the PFTs varied significantly along the natural regeneration, as did plant ecological strategies. Further results showed that the C- and S-strategy dominated the community-level ecological strategy spectrum. In addition, the C-strategy gradually increased along the natural regeneration, while the S-strategy exhibited a decreasing trend. The community spatial structure and abiotic factors including soil physicochemical properties and topographic factors jointly drove the ecological strategy spectrum of the plant community, with the community spatial structure playing a more important role. This study provided clear evidence to support Grime’s CSR theory and the UAST, which emphasized the abiotic stress conditions supported the S-strategy plants, while the C-strategy would dominate in relative fertile soil conditions.

Measurement and Spatio-Temporal Characteristics of High-Quality Development Efficiency in Metropolitan Areas: A Case Study of the Changchun Metropolitan Area

The concept of high-quality development (HQD) is characterized by its emphasis on efficiency, equity, and environmental sustainability. In the context of China’s new urbanization development, the metropolitan area plays a crucial role in facilitating and sustaining HQD. This study focuses on the Changchun Metropolitan Area (CCMA) as a case study to measure the efficiency of high-quality development (HQDE) at the county level using the super-efficiency SBM model and spatial autocorrelation model. Additionally, we examine the spatio-temporal distribution characteristics of HQDE in terms of economy, innovation, coordination, greenness, openness, and sharing (EICGOS). The main findings are as follows: (1) The HQDE of the CCMA ranges from 0.7 to 0.8 with an initial rapid increase followed by a gradual decline; however, there are notable variations among different counties. (2) Regarding spatial structure within the metropolitan area, highest efficiency is observed in the half-hour living circle followed by the 2-h accessibility circle while lowest efficiency is found in the 1-h commuting circle. Over time, there is a declining trend in efficiency within core leading areas. (3) In terms of dimensions, CCMA demonstrates the highest level of economic development efficiency (EDE), whereas green development efficiency (GDE) exhibits lower levels compared to other dimensions. Furthermore, development efficiencies across all dimensions show a decline over time. (4) Spatially distributed patterns reveal significant agglomeration areas for HQDE within the CCMA region. High-high agglomeration areas are predominantly concentrated in the central region of Changchun and southern region of Liaoyuan while low-low agglomeration areas primarily exist in northwest Songyuan and specific counties within Changchun. To attain HQD of the CCMA, it is advisable to bolster the economic scale of the central city, mitigate developmental disparities between counties and cities, and expedite green transformations in old industrial cities. These findings offer a valuable point of reference for optimizing resource allocation at the metropolitan level and devising strategies to foster regional HQD.

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

Advancing rapid urban flood prediction: a spatiotemporal deep learning approach with uneven rainfall and attention mechanism

ABSTRACT Urban floods pose a significant threat to human communities, making its prediction essential for comprehensive flood risk assessment and the formulation of effective resource allocation strategies. Data-driven deep learning approaches have gained traction in urban emergency flood prediction, addressing the efficiency constraints of physical models. However, the spatial structure of rainfall, which has a profound influence on urban flooding, is often overlooked in many deep learning investigations. In this study, we introduce a novel deep learning model known as CRU-Net equipped with an attention mechanism to predict inundation depths in urban terrains based on spatiotemporal rainfall patterns. This method utilizes eight topographic parameters related to the height of urban waterlogging, combined with spatial rainfall data as inputs to the model. Comparative evaluations between the developed CRU-Net and two other deep learning models, U-Net and ResU-Net, reveal that CRU-Net adeptly interprets the spatiotemporal traits of rainfall and accurately estimates flood depths, emphasizing deep inundation and flood-vulnerable regions. The model demonstrates exceptional accuracy, evidenced by a root mean square error of 0.054 m and a Nash–Sutcliffe efficiency of 0.975. CRU-Net also accurately predicts over 80% of inundation locations with depths exceeding 0.3 m. Remarkably, CRU-Net delivers predictions for 3 million grids in 2.9 s, showcasing its efficiency.

Progress in phosphorylation of natural products.

Natural medicines are a valuable resource for the development of new drugs. However, factors such as low solubility and poor bioavailability of certain constituents have hindered their efficacy and potential as pharmaceuticals. Structural modification of natural products has emerged as an important research area for drug development. Phosphorylation groups, as crucial endogenous active groups, have been extensively utilized for structural modification and development of new drugs based on natural molecules. Incorporating phosphate groups into natural molecules not only enhances their stability, bioavailability, and pharmacological properties, but also improves their biological activity by altering their charge, hydrogen bonding, and spatial structure. This review summarizes the phosphorylation mechanism, modification approaches, and biological activity enhancement of natural medicines. Notably, compounds such as polysaccharides, flavonoids, terpenoids, anthraquinones, and coumarins exhibit increased antioxidation, anticancer, antiviral, immune regulatory, Antiaging, enzyme inhibition, bacteriostasis, liver protection, and lipid-lowering effects following phosphorylation modification.

Space performance assessment of traditional houses in Akre

ABSTRACT The complex natural and geological structure of cities affects buildings’ spatial structure in terms of the forms of spatial configuration adopted in the design. Spatial structure affects buildings’ space performance in several aspects, such as privacy and accessibility, which are the most significant indicators of residential houses’ functional performance. Akre, a treasured, historical city in Iraqi Kurdistan, is characterised by a complex and distinct geological nature, and its residential part contains numerous traditional houses that deserve analysis. This study employed both a qualitative approach – via case study plan preparations and photographic documentation – and a quantitative approach, utilising space syntax tools. The results were analysed with the statistical program SPSS and show three types of spatial configuration in the residential part (linear, central and cluster); each type has a different and effective impact on the achievement of space performance in terms of privacy and accessibility. The study concludes that the linear type represents the most private typology, followed by the cluster and then the central type. The centralised typology is the most connected and accessible to different spaces within the residential unit, followed by the cluster type and the linear type, with a significant difference.

From fragments to system: a research on the mesoscale structure of vernacular landscapes in the Hang-Jia-Hu area, China

ABSTRACT Vernacular landscape incorporates spatial structures intimately associated with regional environments, agricultural production, and rural life. Analyzing and understanding these structures can help reveal spatial patterns and intrinsic mechanisms of vernacular landscapes and provide reference for contemporary rural construction. This paper aims to analyze the mesoscale structure of vernacular landscapes in the Hang-Jia-Hu area by discussing spatial relationships between four main elemental patches: water systems, farmlands, settlements and hills. Special attention is given to the correlations between landscape structure and local agriculture. Initially adopting a grid sampling approach, the paper extracts 31 fragment samples of vernacular landscape images (each 2 × 2 km) from the satellite imagery of the Hang-Jia-Hu area. Subsequently, via semantic segmentation, the four elements are identified and differentiated. Integrating related knowledge from environmental history, agricultural history, human geography, and other disciplines, the paper carries out a typological analysis on the vernacular fragment samples, deriving the latent structural types, which consist of concepts and abstract schemas. Based on the association between these structural types, establishes a structural system, as the primary integrated conclusion of this research, and looking forward to offering framework reference for contemporary rural construction.

Permutation entropy and complexity analysis of large-scale solar wind structures and streams

Abstract. In this work, we perform a statistical study of magnetic field fluctuations in the solar wind at 1 au using permutation entropy and complexity analysis and the investigation of the temporal variations of the Hurst exponents. Slow and fast wind, magnetic clouds, interplanetary coronal mass ejection (ICME)-driven sheath regions, and slow–fast stream interaction regions (SIRs) have been investigated separately. Our key finding is that there are significant differences in permutation entropy and complexity values between the solar wind types at larger timescales and little difference at small timescales. Differences become more distinct with increasing timescales, suggesting that smaller-scale turbulent features are more universal. At larger timescales, the analysis method can be used to identify localised spatial structures. We found that, except in magnetic clouds, fluctuations are largely anti-persistent and that the Hurst exponents, in particular in compressive structures (sheaths and SIRs), exhibit a clear locality. Our results shows that, in all cases apart from magnetic clouds at the largest scales, solar wind fluctuations are stochastic, with the fast wind having the highest entropies and low complexities. Magnetic clouds, in turn, exhibit the lowest entropy and highest complexity, consistent with them being coherent structures in which the magnetic field components vary in an ordered manner. SIRs, slow wind and ICME sheaths are intermediate in relation to magnetic clouds and fast wind, reflecting the increasingly ordered structure. Our results also indicate that permutation entropy–complexity analysis is a useful tool for characterising the solar wind and investigating the nature of its fluctuations.

Metropolitan spatial structure based on commuting pattern: a comparison between Jakarta and Surabaya metropolitans

ABSTRACT Different urban structures can present different problems. Therefore, it is important to organize the spatial structure of the city to ensure the movement and welfare of city residents. Metropolitan Jakarta and Surabaya are the two largest metropolitan areas in Indonesia and play a very important role as centres of national social and economic activities. This study aims to identify urban spatial structures based on commuter movement patterns in both metropolitan areas. This is done by calculating the spatial interaction and visualization of the Desire Line Map. The results of the analysis show that Jakarta and Surabaya Metropolitan Areas still tend to be monocentric because the attraction of their movements is still dominated by their respective cores.

Advances in the application of AlphaFold2: a protein structure prediction model

Protein structure prediction is an important research field in life sciences and medicine, and it is also a key application scenario of artificial intelligence in scientific research. AlphaFold2 is a protein structure prediction system developed by DeepMind based on deep learning, capable of efficiently generating the atomic-scale spatial structure of a protein from the amino acid sequence. It has demonstrated superior performance in the prediction of protein structures since its inception, thus attracting much attention and research. This paper introduces the model architecture, highlights, limitations, and application progress of AlphaFold2. Furthermore, it briefs the capabilities, highlights, and limitations of several other types of protein structure prediction models and prospects the future development direction in this field.

Application and challenge of pancreatic organoids in therapeutic research

The in-vivo non-human primate animal and in-vitro cell disease models play a crucial part in the study of the mechanisms underlying the occurrence and development of pancreatic diseases, but with increasingly prominent limitations with in-depth research. Organoids derived from human pluripotent and adult stem cells resemble human in-vivo organs in their cellular composition, spatial tissue structure and physiological function, making them as an advantageous research tool. Up until now, numerous human organoids, including pancreas, have been effectively developed, demonstrating significant potential for research in organ development, disease modeling, drug screening, and regenerative medicine. However, different from intestine, liver and other organs, the pancreas is the only special organ in the human body, consisting of an exocrine gland and an endocrine gland. Thus, the development of pancreatic organoid technology faces greater challenges, and how to construct a composite pancreatic organoid with exocrine and endocrine gland is still difficult in current research. By reviewing the fundamental architecture and physiological role of the human pancreas, along with the swiftly developing domain of pancreatic organoids, we summarize the method and characteristics of human pancreatic organoids, and its application in modeling pancreatic diseases, as a platform for individualized drug screening and in regenerative medicine study. As the first comprehensive review that focus on the pharmacological study of human pancreatic organoid, the review hopes to help scholars to have a deeper understanding in the study of pancreatic organoid.

Extreme genetic depletion upon postglacial colonization hampers determining the provenance of introduced palmate newt populations

Abstract MtDNA barcoding is regularly applied to determine the provenance of invasive species. Variation in spatial genetic structuring across a species’ range, typically high within glacial refugia and low in postglacially colonized areas, influences the precision of this approach. The palmate newt (Lissotriton helveticus) has been introduced north of its native range inside the Netherlands. We conduct mtDNA barcoding to try and retrace the origin of the introduced localities. A large increase in sample size, particularly focusing on temperate Europe, emphasizes that the palmate newt shows practically no genetic variation outside the Iberian Peninsula glacial refugium. While we find a haplotype previously only known from the Iberian Peninsula inside the native range in Belgium, the haplotype present in the introduced Dutch populations occurs widely throughout the native range north of the Iberian Peninsula. Although mtDNA barcoding can be a powerful tool in invasion biology, the palmate newt case exposes its limitations.

Identification and classification of urban employment centers based on big data: A case study of Beijing.

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules.

West African Monsoon System’s Responses to Global Ocean-Regional Atmosphere Coupling

Abstract This study explores the added value (AV) of a regional earth system model (ESM) compared to an atmosphere-only regional climate model (RCM) in simulating West African Monsoon (WAM) rainfall. The primary goals are to foster discussions on the suitability of coupled RCMs for WAM projections and deepen our understanding of ocean-atmosphere coupling’s influence on the WAM system. The study employs results from dynamical downscaling of the ERA-Interim reanalysis and Max Plank Institute ESM (MPI-ESM-LR) by two RCMs, REMO (atmosphere-only) and ROM (REMO coupled with Max Planck Institute Ocean Model; MPIOM), at ∼25-km horizontal resolution. Results show that in regions distant from coupling domain boundaries such as West Africa (WA), constraint conditions from ERA-Interim are more beneficial than coupling effects. REMO, reliant on oceanic sea surface temperatures (SSTs) from observations and influenced by ERA-Interim, is biased under coupling conditions, although coupling offers potential advantages in representing heat and mass fluxes. Contrastingly, as intended, coupling improves SSTs-monsoon fluxes’ relationships under ESM-forced conditions. In this latter case, coupling features a dipole-like spatial structure of AV, improving precipitation over the Guinean coast but degrading precipitation over half of the Sahel. Our extensive examination of physical processes and mechanisms underpinning the WAM system supports the plausibility of AV. Additionally, we found that the monsoonal dynamics over the ocean respond to convective activity, with the Sahara-Sahel surface temperature gradient serving as the maintenance mechanism. While further efforts are needed to enhance the coupled RCM, we advocate for its use in the context of WAM rainfall forecasts and projections.