Space Distribution Research Articles

Urban green space exposure is low and unequally distributed in an Amazonian metropolis

AbstractUrban green spaces are important for interactions between people and non-human nature, with their associated health and well-being impacts, although their distribution is often unequal. Here, we characterize the distribution of urban green spaces in Belém, the largest city in the Amazon Delta, and relate it to levels of human development and social vulnerability across the city; this is the first such analysis to be conducted for a Brazilian Amazon city. We first conducted a supervised maximum likelihood classification of images at 5–m spatial resolution taken in 2011 by the RapidEye satellites to map the distribution of green space across the urban part of the municipality of Belém. We then calculated two measures of urban green space at the level of human development units: the proportional cover of vegetation (Vegetation Cover Index; VCI) and the area of vegetation per person (Vegetation Cover per Inhabitant; VCPI), and we used hurdle models to relate them to two measures of socioeconomic status: the Social Vulnerability Index and the Human Development Index, as well as to demographic density. We find that VCI and VCPI are higher in more socially vulnerable areas. We explain how this pattern is driven by historical and ongoing processes of urbanization, consider access to urban green space and the benefits to human health and well-being and discuss equitable planning of urban green space management in the Amazon. We conclude that the assumption that urban greening will bring health benefits risks maintaining the status quo in terms of green exclusion and repeating historical injustices via displacement of socially vulnerable residents driven by demand for access to urban green spaces.

Numerical simulations of the flow past a perforated plate enclosed by a filament using the Immersed Boundary Method

The influence of the central hole on dynamics of the coupled system including a perforated plate and a filament was numerically investigated using the immersed boundary method (IBM). Dynamics of the system were first investigated across various length ratios of filament to plate Lr and hole sizes Dh at the Reynolds number Re=100. When Dh≤0.1, the IBM was proven to be unusable due to the area leakage brought about by the smoothed approximation of the Dirac delta function. When Dh>0.1, the calculation results were reliable and showed that the key values were not significantly affected by Dh. Compared to a bare plate, the system exhibits a maximum drag reduction of about 30% at Lr=2.0, regardless of Dh. In contrast to a closed system, a hole facilitated a more harmonious filament flap by allowing fluid exchange, thereby preventing local oscillation of the filament. Additionally, the multiple frequency dominant (MFD) mode was observed and its distribution in Lr-Dh space was also presented. Finally, the simulations under various Re were conducted. The effect of Dh on the system drag was still weak regardless of Re. However, the filament motion changed from periodic to chaotic as Re increased.

A newly discovered trematode parasite infecting the bay scallop, Argopecten irradians

A novel parasite infecting the gill tissue of bay scallops (Argopecten irradians) was first observed in North Carolina in 2012 and has also been found infecting bay scallop populations along the Gulf Coast of Florida. Because the parasite infects a host of great fisheries interest and is extremely conspicuous to the naked eye, and yet was not observed before 2012, it is likely an introduced species. Preliminary morphological analyses indicate that this parasite is a trematode in the superfamily Hemiuroidea and that it uses the bay scallop as a first intermediate host. All other hosts are currently unknown. Phylogenetic analyses show that this novel parasite belongs to family Didymozoidae and forms a strongly supported clade with a recently described trematode from Australia, Saccularina magnacetabula. Histological analysis indicates that Saccularina sp. sporocysts inhabit and greatly expand the afferent vessels of the host's gills. Prevalence monitoring of Saccularina sp. in caged and wild bay scallop populations in North Carolina shows a strong pattern of seasonality that is consistent across sites, but no latitudinal gradient was found, suggesting that prevalence is likely driven by final host distribution in time and space. This research provides important baseline information on the basic biology of Saccularina sp. and impacts on its economically important host, the bay scallop.

Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification

Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled, but also unlabeled examples. While SSL for the simple tasks of classification and regression has received much attention from the research community, this is not the case for complex prediction tasks with structurally dependent variables, such as multi-label classification and hierarchical multi-label classification. These tasks may require additional information, possibly coming from the underlying distribution in the descriptive space provided by unlabeled examples, to better face the challenging task of simultaneously predicting multiple class labels. In this paper, we investigate this aspect and propose a (hierarchical) multi-label classification method based on semi-supervised learning of predictive clustering trees, which we also extend towards ensemble learning. Extensive experimental evaluation conducted on 24 datasets shows significant advantages of the proposed method and its extension with respect to their supervised counterparts. Moreover, the method preserves interpretability of classical tree-based models.

Technical Project of the Ion Beam Diagnostics System in Phase Space

Increasing requirements for modern acceleration complexes require precise data on ion beams injected into the accelerator. This paper presents a system of ion beam diagnostics that implements the four-slit method and makes it possible to measure the beam current density distribution in the four-dimensional phase space (PS) with high precision. Technical solutions and design features aimed at obtaining the design parameters of the system are described: the range of investigated beam currents from 1 pA to 20 mA with energy up to 50 keV, spatial resolution from 6 mm to 50 μm.

Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler

Bayesian inference with deep generative prior has received considerable interest for solving imaging inverse problems in many scientific and engineering fields. The selection of the prior distribution is learned from, and therefore an important representation learning of, available prior measurements. The SA-Roundtrip, a novel deep generative prior, is introduced to enable controlled sampling generation and identify the data's intrinsic dimension. This prior incorporates a self-attention structure within a bidirectional generative adversarial network. Subsequently, Bayesian inference is applied to the posterior distribution in the low-dimensional latent space using the Hamiltonian Monte Carlo with preconditioned Crank-Nicolson (HMC-pCN) algorithm, which is proven to be ergodic under specific conditions. Experiments conducted on computed tomography (CT) reconstruction with the MNIST and TomoPhantom datasets reveal that the proposed method outperforms state-of-the-art comparisons, consistently yielding a robust and superior point estimator along with precise uncertainty quantification.

Incorporating suburban cropland into urban green infrastructure: A perspective of nature-based solutions in China

Urban green infrastructure (GI) addresses human social development issues and challenges through urban green space ecosystem services. However, the ecosystem service functions of suburban cropland, especially regulating services and cultural services, have been seriously underestimated. The distribution of green spaces and suburban cropland varies in different regions of China, as well as their potential development methods. This article examines the potential of integrating suburban cropland into urban green infrastructure, offering a perspective on nature-based solutions in China. We divided the suburban cropland in China into three parts: peri‑suburban, mid-suburban, and outer-suburban by the concept of 15-minute living circle based on different travel modes of people. Based on statistical yearbook data, satellite remote sensing images and the fragstats 4.2 programme, a quantitative analysis was conducted on the spatial distribution, landscape pattern and social development of suburban farmland and green space in four cities. Then, the potential for improving the exposure level of suburban cropland after incorporating suburban cropland into urban green infrastructure was quantified and compared. Finally, we explored the potential development of the three kinds of suburban cropland as green infrastructure based on the concept of NbS. We found the spatial pattern of cropland and green space in the mid-suburban is greatly affected by regional geographical conditions, the green exposure level, i.e., green space availability indicators, in Beijing, Shanghai, Guangzhou, and Xi'an increased by 83 %, 300 %, 24 %, and 612 %, respectively, after incorporating suburban cropland into urban green infrastructure. The potential development models for suburban cropland should be based on NbS and China's national conditions: peri‑suburban cropland should be fully utilized for cultural and social functions, the mid-suburban cropland should focus on multifunctional utilization of cropland, and the outer-suburban is prioritized for ecological functions. Our study provides scientific insights for deep exploration of the functions of suburban cropland in China.

Improving image segmentation with contextual and structural similarity

Deep learning models for medical image segmentation are usually trained with voxel-wise losses, e.g., cross-entropy loss, focusing on unary supervision without considering inter-voxel relationships. This oversight potentially leads to semantically inconsistent predictions. Here, we propose a contextual similarity loss (CSL) and a structural similarity loss (SSL) to explicitly and efficiently incorporate inter-voxel relationships for improved performance. The CSL promotes consistency in predicted object categories for each image sub-region compared to ground truth. The SSL enforces compatibility between the predictions of voxel pairs by computing pair-wise distances between them, ensuring that voxels of the same class are close together whereas those from different classes are separated by a wide margin in the distribution space. The effectiveness of the CSL and SSL is evaluated using a clinical cone-beam computed tomography (CBCT) dataset of patients with various craniomaxillofacial (CMF) deformities and a public pancreas dataset. Experimental results show that the CSL and SSL outperform state-of-the-art regional loss functions in preserving segmentation semantics.

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A simple condition is given that is sufficient to determine whether a measure that is absolutely continuous with respect to a Gaußian measure on the space of distributions is reflection positive. It readily generalises conventional lattice results to an abstract setting, enabling the construction of many reflection positive measures that are not supported on lattices.

Exploring Medium and Low-Temperature Convective Geothermal Resources in Eastern China Through Acoustic-Emission Geomagnetic Bathymetry

Medium and low-temperature convective geothermal resources are a crucial component of China's geothermal wealth, primarily concentrated in the eastern coastal regions. To maximize the utilization of these resources, geophysical surveys are essential, with acoustic-emission geomagnetic bathymetry being a prominent and effective method employed in geothermal resource assessments. This study focuses on applying acoustic-emission geodetic bathymetry to survey specific geothermal sites, including the Xiu Yan Gou Tang Hot Spring, Fengcheng Gou Tang Hot Spring, and Hai Cheng Xi Huang di Geothermal Field. The results obtained through this method provide detailed insights into the distribution of both deep and shallow thermal storage spaces within these fields, shedding light on the intricate thermal circulation systems associated with them. These findings serve as a reliable foundation for planning and executing further geothermal resource development projects. Acoustic-emission geomagnetic bathymetry emerges as a comprehensive tool for understanding the nuances of medium and low-temperature convective geothermal resources.

A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance

A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance

Numerical Analysis of New PCM Thermal Storage Systems

In this paper, a thermal storage system based on a phase change material is proposed and investigated. The system is composed of several tubes that cross a phase change material mass. A fluid flowing in the tubes charges and discharges the heat storage system. A mathematical model of the system has been developed, which provides the time and space distribution of velocity, temperature, and liquid phase-changing material concentration in a non-stationary regime. A hybrid solution method based on finite volumes and finite differences techniques has been employed for the model equations in the MATLAB environment. To the tubes, a rectangular cross section has been assigned. The performance of the system in terms of accumulated energy density and accumulated power density has been investigated by varying some geometric parameters. The considered geometric parameters influence the number of tubes per unit of system width, the tube hydraulic resistance, the amount of phase change material around each tube, the heat transfer surface of the tube, and the heat storage velocity. In the parametric analysis, peaks have been evidenced in the investigated performance parameters at different instants after the beginning of the heat storage.

The hydrologic nature of swales uncovers remarkable influence of non-topographic factors on catchment-scale soil moisture variation

Concave hillslopes, also known as swales, play a pivotal role in facilitating the transfer of water and nutrients from hillslopes to riparian zones in headwater catchments by serving as pathways for surface and subsurface flow. Swales have long been recognized as hydrologic wet spots that contribute significantly to catchment runoff. However, long-term in situ observations have rarely confirmed this conventional understanding. In this study, we present a comprehensive analysis of three years of daily soil moisture measurements taken at multiple depths across 33 sites within a forested catchment to delineate key determinants of soil moisture variation at the catchment scale. Contrary to conventional understanding, our findings indicate that swales may represent areas of lower soil moisture, with those located on sunny hillslopes experiencing a higher frequency of dry conditions, amounting to 13.31% of the time during the study period, when compared to other topographical features such as slopes, valleys, and ridges. Topography alone accounted for less than half of the variation of soil moisture across the catchment. Whereas non-topographic factors (such as vegetation, soil porosity, soil temperature, and soil depth) strongly influenced soil moisture distribution in time and space. To better capture the nuances of soil moisture dynamics, we propose an enhancement to the Topographic Wetness Index (TWI) through the incorporation of non-topographic variables, which improves the model's representation of soil moisture variations. Our findings elucidate the complex interactions between topographic and non-topographic factors in shaping soil moisture dynamics at the catchment scale, offering valuable insights for refining process-based hydrological models and contributing to a deeper understanding of the spatial and temporal variability of soil moisture in headwater catchments.

Building equity into public park and recreation service investment: A review of public agency approaches

In recent decades, academic and professional research has increased understanding of the importance of city and landscape planners engaging with social and environmental justice issues, including contemporary inequities inherent in the planning, distribution, use, and access of public green and open spaces. However, there is a gap between this research centering equity and the planning, development, and implementation rate demonstrated by public agencies. In this article, we examine examples of emerging practice in the public park and recreation sector to understand the strategies and approaches public agencies are taking to provide equitable park and recreation systems. Our research identifies and analyzes 17 examples of North American public park and open space management agencies using equity-based planning frameworks to prioritize park investment and resource distribution. Equity-focused resource analysis is distinct because while it assesses budget and project-based funding distributions, it further incorporates assessments of historical allocations to understand better areas of under-investment and the evolving needs of different communities. As economic inequities become more pronounced, local governments, and other public institutions providing services to populations, are important in helping communities navigate changes. Our findings support the ongoing advancement of equity-driven planning and implementation for public park and recreation agencies by providing practical information on existing approaches to redress the impact of unfair patterns of under-investment.

Improved Numerical Estimation Method for Surface Wave Attenuation on Metasurfaces

AbstractComprehensive numerical tools to investigate and analyze the surface wave attenuation on a metasurface waveguide that consists of periodic patches deposited on a dielectric substrate are presented. The surface wave attenuation is evaluated in both waveguide and open‐space environments at high frequencies around the 28‐GHz band. Two approaches including a driven modal approach and an integrated eigenmode approach for investigating the attenuation behaviors in a metasurface waveguide operating in transverse electromagnetic (TEM) mode are employed. These results show different attenuation values depending on the waveguide height due to an unavoidable coupling issue despite the same metasurface design. Thus, an alternative method for more accurately evaluating the frequency‐dependent attenuation characteristics through field distribution in open space with a customized horn antenna is proposed. A relationship between the surface wave attenuation and the propagation distance by analyzing the analytical electric field distribution at different points on the propagation path in the open space is established. Thus, this study provides an improved method for assessing surface wave attenuation characteristics, which contributes to designing surface wave control for wireless communications, wireless power transfer, signal processing, and electromagnetic compatibility.

Learning the long-tail distribution in latent space for Weighted Link Prediction via conditional Invertible Neural Networks

Link prediction is an important problem in dynamic systems, where the goal is to predict future unweighted or weighted link topologies using historical context. Compared with unweighted links, weighted links can preferably reveal the nature and strength of the interactions among entities. However, weighted links also bring greater challenges because they require subtle structural adjustments and numerical variations to be captured. Existing methods are primarily tailored for unweighted links and most generally suffer from low-quality performance when applied to Weighted Link Prediction (WLP) task. In this study, we propose a novel generative framework that adopts conditional Invertible Neural Networks (INNs) to achieve WLP. The proposed framework leverages the benefits of conditional INNs to exactly optimize the log-likelihood in the latent space conditioned on the historical context, which can be sensitive to minor replacements in real-world systems and derive accurate WLPs. Furthermore, to deal with the long-tail statistical phenomenon of edge weights observed in real life, a tail-adaptive distribution is learned in latent space to capture the tail properties and enhance the model’s ability. To verify the effectiveness of the proposed method, we conduct extensive experiments on four datasets from different systems. The experimental results demonstrate that our model achieves impressive results compared to state-of-the-art competitors.

Spacial distribution and suitability for Panonychus ulmi (Tetranychidae) in Brazil

Predictive studies project the geographic distribution of species and can be used to infer climatic niches. However, only a few studies have been conducted on mites. This approach helps map areas with potential for the occurrence of endemic, threatened, or potentially invasive species. Panonychus ulmi (Tetranychidae) is of global economic importance, commonly associated with apple orchards and grapevines. Potential distribution modeling is used to predict areas with environmental suitability for the distribution of a species and/or group. Considering that predictive models on national or regional scales present better data reliability, the present study aimed to analyze the distribution of P. ulmi in Brazil through bioclimatic inferences. The presence of species, bioclimatic variables, and MaxEnt algorithm were used to define a predictive model. The median performance rate of the model was 0.992, indicating its robustness. The variable that made the greatest contribution to the model was the average temperature of the coldest quarter (Bio11). The predictive model of the ecological niche indicated that the southern region of Brazil is environmentally favorable for the adaptation of this mite. The data obtained helped us understand the geographical distribution of P. ulmi in Brazil, and climatically suitable areas for its occurrence were inferred. We believe that this tool can offer indirect assistance to the agricultural sector, especially the producers of apples and grapes in Brazil regarding the presence of P. ulmi.

Dissimilarity functions co-symmetry property: a focus on probability distributions with involutive negation

In today’s data-rich era, there is a growing need for developing effective similarity and dissimilarity measures to compare vast datasets. It is desirable that these measures reflect the intrinsic structure of the domain of these measures. Recently, it was shown that the space of finite probability distributions has a symmetric structure generated by involutive negation mapping probability distributions into their “opposite” probability distributions and back, such that the correlation between opposite distributions equals –1. An important property of similarity and dissimilarity functions reflecting such symmetry of probability distribution space is the co-symmetry of these functions when the similarity between probability distributions is equal to the similarity between their opposite distributions. This article delves into the analysis of five well-known dissimilarity functions, used for creating new co-symmetric dissimilarity functions. To conduct this study, a random dataset of one thousand probability distributions is employed. From these distributions, dissimilarity matrices are generated that are used to determine correlations similarity between different dissimilarity functions. The hierarchical clustering is applied to better understand the relationships between the studied dissimilarity functions. This methodology aims to identify and assess the dissimilarity functions that best match the characteristics of the studied probability distribution space, enhancing our understanding of data relationships and patterns. The study of these new measures offers a valuable perspective for analyzing and interpreting complex data, with the potential to make a significant impact in various fields and applications.

Spatial identification and priority conservation areas determination of wilderness in China

In the context of an increasingly severe ecological crisis and accelerating ecological civilization construction, the specific regional differences as well as the natural environment of China restrict the uniform population distribution and the social economy's spatial layout, forming a large and widely distributed wilderness. The post-2020 Global Biodiversity Framework (GBF) takes the protection of wilderness areas as one of the key conservation goals and brings new opportunities for wilderness protection. Objective and accurate identification of the spatial range of wilderness and the determination of the focus of wilderness protection are significant for carrying out resource and environmental carrying capacity evaluations, biodiversity conservation, and national park system construction. At present, fewer studies pay attention to wilderness area identification and protection in China. In this study, we used the MaxEnt and Marxan models to establish a framework to identify the spatial distribution and conservation priorities of China's wilderness as well as guide wilderness conservation. The wilderness areas in China show a large-scale, concentrated distribution in space and a small, scattered distribution. From 2000 to 2020, the wilderness area decreased from 3.6323 million km2 in 2000 to 2.9142 million km2 in 2020, and the wilderness area reduction rate from 2000 to 2010 was greater than that in 2010–2020. From 2000 to 2020, the center of gravity of the wilderness area shifted 893.5 km to the southwest. Wilderness center of gravity relocated from Pingluo County, Ningxia Hui Autonomous Region to Delingha City, Qinghai Province. Considering conservation objectives and conservation costs, we determined that the wilderness area in urgent need of protection is 55,100 km2, accounting for 1.8 % of the total wilderness area. It is mainly distributed in Xinjiang Uygur Autonomous Region (13,000 km2), Yunnan Province (12,000 km2), and Sichuan Province (5300 km2). This methodological framework can provide an important reference for governments in dealing with global climate change, maintaining biodiversity and ecosystem diversity, and promoting sustainable human development.

Prediction of turbulent energy based on low-rank resolvent modes and machine learning

A modelling framework based on the resolvent analysis and machine learning is proposed to predict the turbulent energy in incompressible channel flows. In the framework, the optimal resolvent response modes are selected as the basis functions modelling the low-rank behaviour of high-dimensional nonlinear turbulent flow-fields, and the corresponding weight functions are determined by data-driven neural networks. Turbulent-energy distribution in space and scales, at the friction Reynolds number 1000, is predicted and compared to the data of direct numerical simulation. Close agreement is observed, suggesting the feasibility and reliability of the proposed framework for turbulence prediction.