Complex Spatial Structure Research Articles

On the Electrochemically Active Surface Area Determination of Electrodeposited Porous Cu 3D Nanostructures

Porous 3D Cu layers with the following average parameters: thickness ~35 µm, pore density ~4.0 × 106 cm−2, and pore sizes ~25 µm were electrodeposited from an acidic sulphate electrolyte, and the suitability of different electrochemically active surface area determination methods for characterising these electrodes was assessed. Structural characterisation of the samples was conducted using SEM and an optical profiler, while electrochemical measurements were performed using cyclic voltammetry and electrochemical impedance spectroscopy. The evaluation of electrochemically active surface area involved the underpotential deposition of Tl and Pb monolayers as well as double-layer capacitance measurements. The results obtained indicate that both methods yield similar results for non-porous Cu electrodes. However, for Cu 3D nanostructures, the evaluation mode significantly influences the results. Double-layer capacitance measurements show significantly higher values for the electrochemically active surface area compared to the underpotential deposition (UPD) technique. The complex spatial structure of the Cu 3D layer hinders the formation of a continuous monolayer during the UPD process, which is the principal reason for the observed differences.

A Cross-Scale Representation of Tourist Activity Space

Destination, as a key concept in tourism geography, has largely determined the scale at which tourist activity space was modeled and studied. Existing studies usually focused on investigating tourists’ activities and movements either at the intradestination (e.g., within a city) or interdestination scale. Although useful in numerous research contexts, these models based on fixed spatial scales are incapable of portraying the complex spatial structure of tourist activity spaces, which sometimes exhibit hierarchical structures, and could span across different spatial scales. In this study, we propose a new representation of tourist activity space to bridge these gaps. The representation takes tourists’ accommodation locations as key reference points. At the macroscale, the sequence of accommodation locations forms the backbone of tourist activity space, denoted as itinerary type. At the microscale, we introduce the concept of territory to describe how individuals organize activities around these overnight “base camps” (i.e., accommodation locations). We apply this representation over a large-scale mobile phone data set of international travelers visiting South Korea to demonstrate its capability. Results show that four generic itinerary types capture the activity space structure of 89 percent of the tourists. The interrelationships of territories and their topological structures further categorize activity spaces into subtypes, leading to a new method of tourist classification based on their spatiotemporal activity patterns. We believe the proposed representation could enrich new perspectives and debates on how tourist activities can be studied. The representation can also be extended as a generic framework to delineate complex forms of human activity space.

SPATIAL STRUCTURE OF SMALL CITIES: THE APPROACHES TO TYPOLOGY (BASED ON THE MATERIALS FROM CENTRAL RUSSIA)

The article examines the spatial structure of small towns in Central Russia. The information base of the study covers satellite images of 190 small towns. The paper proposes a concept of morphological structure of the city, which is determined by the nature (shape) of external boundaries, the presence of significant internal barriers leading to the fragmentation of space, and isolated urban areas. A methodology for assessing the complexity of urban space is suggested. It is based on the scoring of the impact of various geographical barriers on the urban environment. The most significant barriers include rivers and railways; the relief features, the location of industrial enterprises and the presence of protected areas were also considered. Special emphasis was given to urban areas isolated from the main built-up territories, i. e. exclaves. Five classes of urban space complexity have been identified, from very simple to very complex. The most complex space is typical of five small towns, which were initially clusters of workers’ settlements. The combination of data on the morpho-logical structure and complexity of space made it possible to identify six final forms of the spatial structure of the city. More often than others, there are cities with a dissected morphological structure in combination with a medium-complex space. One third of the cities under consideration have such parameters. The smallest number of cities has a dissected complex space. The cities tend to increase the complexity of their space in the process of development. The study showed that despite small population, the towns under consideration have very complex spatial structure

High-precision computational modeling of soil-rock mixtures considering realistic shape of rock blocks

Soil-rock mixtures (SRMs) are heterogeneous geological bodies with complex spatial structures and peculiar mechanical properties. This paper proposes a high-precision computational modeling method to realistically simulate the shape of rock blocks in the SRMs as well as the mesostructure of SRMs. The precise control theory of the rock blocks is first described. Then, acceleration measures such as bounding volume hierarchies and position culling are introduced to increase the computational efficiency while retaining the model accuracy. Practical examples and comparisons with the existing methods are provided to demonstrate the advantageous features of this method. In the end, the modeling method is successfully integrated into the DEM platform to create engineering models involved in SRMs, which lays the foundation for further DEM analysis to understand the effects of the mesostructure on the mechanical properties of SRMs.

Modifying Distant Effect of High Dilutions of Inorganic and Biological Substances.

It is known that highly diluted substances can exert a modifying effect on the initial substances without direct contact with them (distant interaction). The capability of high dilutions of IFNγ and Na2SO4 for the distant modifying effect was studied by the method of terahertz spectroscopy. Statistically significant differences were shown between terahertz characteristics of the initial solution of IFNγ protein and solution that had interacted with high dilutions of IFNγ; in case of sodium sulfate, no such differences were detected. Thus, high dilutions exert a distant modifying effect on the initial substances with complex spatial structure typical of biological molecules.

Internal Variability of the Climate System Mirrored in Decadal‐Scale Trends of Surface Solar Radiation

AbstractDecadal‐scale unforced climate variability associated with low‐frequency ocean (or coupled) modes can be identified within the time‐evolving sea surface temperature (SST) pattern. We seek to find a relationship between 12 well‐known climate modes of variability (El Niño–Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, etc.), which are reflected in the SST pattern, and decadal trends in downwelling shortwave radiation at Earth's surface (surface solar radiation, surface shortwave radiation [SSR]). The analysis is performed using the pre‐industrial control runs (piControl) of 57 models from the Coupled Model Intercomparison Project—Phase 6. We find that regional SSR trends occur during periods when a climate mode is transitioning and show a mirroring pattern (opposite sign SSR trends) when the mode transitions in the opposite direction. Unforced trends in clear‐sky SSR are mostly driven by SST‐induced water vapor changes, while all‐sky SSR trends show a complex spatial structure with trends of different signs in different regions. Unforced SSR trends are generally weaker in simulations with climatological SSTs. The role of natural aerosols as another potentially relevant factor for SSR variability is briefly addressed and we find that constantly higher aerosol loads result in increased variability for clear‐sky conditions, but not for all‐sky conditions. The results from this study suggest that all‐sky dimming and brightening in different parts of the world are enhanced rather than suppressed by internal variability related to an SST‐pattern with the exception of India and China, where both effects are present. A practical application can be the planning of photovoltaic energy facilities given areas where internal variability is generally smaller or affects SSR in opposite directions.

Identifying sinks and sources of human flows: A new approach to characterizing urban structures

Human flow data are rich behavioral data relevant to people’s decision-making regarding where to live, work, go shopping, etc., and provide vital information for identifying city centers. However, it is not as easy to understand massive relational data, and datasets have often been reduced merely to the statistics of trip counts at destinations, discarding relational information from origin to destination. In this study, we propose an alternative center identification method based on human mobility data. This method extracts the scalar potential field of human trips based on combinatorial Hodge theory. It detects not only statistically significant attractive locations as the sinks of human trips but also significant origins as the sources of trips. As a case study, we identify the sinks and sources of commuting and shopping trips in the Tokyo metropolitan area. This aim-specific analysis leads to a combinatorial classification of city centers based on the distinct aspects of human mobility. The proposed method can be applied to other mobility datasets with relevant properties and helps us examine the complex spatial structures in contemporary metropolitan areas from the multiple perspectives of human mobility.

Realistic geometric modeling of the dielectric constant and its effect on the discharge with a porous dielectric

Porous dielectric discharge (PDD) is a critical phenomenon in plasma catalysis, biomedical tissue surface functionalization, and all-solid-state battery design. The dielectric constant of porous dielectric (PD) significantly impacts discharge characteristics and breakdown mechanisms across different applications. However, the complex spatial structure of porous media presents challenges in diagnosing and simulating PDD, limiting our understanding of its mechanism. In this study, the real geometric model of PD obtained from x-ray computed tomography (X-Ray-μ CT) and a two-dimensional fluid model were used to simulate and analyze the effect of dielectric constant on PDD-plasma characteristics, especially the generation and disappearance of charged particles. The simulation results reveal the following: (1) At the breakdown moment, PDD is a density-unbalanced discharge where the electron density is two orders of magnitude higher than the ion density; (2) The breakdown discharge follows the most accessible channel instead of filling the entire gap, which is guided by the electron temperature gradient; and (3) It was first discovered that the breakdown voltage exhibits a saturated growth curve under the control of the dielectric constant. By combining these mechanisms, a comprehensive explanation has been provided for this phenomenon. This study offers a robust simulation and theoretical basis for understanding the breakdown characteristics of PDD.

Characterization and differentiation of sublittoral sandbanks in the southeastern North Sea

Marine sublittoral sandbanks are essential offshore feeding grounds for larger crustaceans, fish and seabirds. In the southern North Sea, sandbanks are characterized by considerable natural sediment dynamics and are subject to chronic bottom trawling. However, except for the Dogger Bank, sandbanks in the southeastern North Sea have been only poorly investigated until now. We used an extensive, multi-annual dataset covering ongoing national monitoring programmes, environmental impact assessments, and basic research studies to analyse benthic communities on sublittoral sandbanks, evaluating their ecological value against the backdrop of similar seafloor habitats in this region. The analysis revealed complex spatial structuring of sandy seafloor habitats of the southeastern North Sea. Different infauna clusters were identified and could be specified by their composition of characteristic species. The sandbanks shared common structural features in their infauna community composition although they were not necessarily characterized by particularly high biodiversity compared to other sandy habitats. A close association of one of the main bioturbators in the southern North Sea, the sea urchin Echinocardium cordatum, with sandbanks was detected, which may promote the sediment-bound biogeochemical activity in this particular seafloor habitat. This would corroborate the status of sandbanks as sites of high ecological value calling for consideration in marine conservation.

Effects of casein phosphopeptides on thermal stability and sensory quality of whey protein emulsions containing calcium beta-hydroxy-beta-methylbutyrate

This study aimed to elucidate the effect of casein phosphopeptides (CPP) on the thermal stability and sensory quality of whey protein emulsions containing calcium beta-hydroxy-beta-methylbutyrate (WPEs-HMB-Ca). The interaction mechanism among CPP, HMBCa, and WP in the emulsions before and after autoclaving (121 °C, 15 min) was systematically investigated from macroscopic external and microscopic molecular perspectives. It was found that WPEs-HMB-Ca treated by autoclaving resulted in an increase in droplet size (d4,3 = 24.09 μm) due to aggregation/flocculation of proteins, along with a stronger odor with higher viscosity, compared to those without autoclaving. When CPP:HMB-Ca = 1:25 (w/w) in the emulsion, the droplets exhibited a more uniform and consistent state in the emulsion. In addition, CPP was able to inhibit the formation of complex spatial network structures of proteins during autoclaving by binding with Ca2+, thus improving the thermal stability and storage stability of WPEs-HMB-Ca. This work might provide theoretical guidance for developing functional milk drinks with good thermal stability and flavor.

Hyperspectral image classification based on residual dense and dilated convolution

The approach based on Convolutional Neural Network model has been widely employed in the field of hyperspectral image classification, demonstrating promising classification performance. However, traditional CNN methods only extract deep features from the end of the network, without considering the combination of shallow and deep features from the network. Moreover, most methods extract features using fixed convolutional kernel, which cannot be changed according to the complex spatial structure of hyperspectral image, and ignore the features of data in spatial-spectral distribution. To solve the aforementioned issues, we propose a method for classifying hyperspectral image based on residual dense and dilated convolution (RDDC-3DCNN). First, our method uses a 3D convolutional neural network as the base structure, and the raw hyperspectral data cube is used as the input of the network. Then, a residual dilation dense block incorporating dilated convolution is devised, which enabled the extraction of spatial-spectral features from hyperspectral image using 3D dilated convolution, while simultaneously fusing spatial-spectral features from different levels. After that, shortcut connections is added into the designed module to fully combine the shallow features and deep features of the network. Finally, the fused features obtained are sequentially passed through fully connected layer, dropout layer, and softmax layer to accomplish classification. The experimental results show that the method achieved classification accuracies of 98.87%, 99.33% and 99.32% on the the Indian Pines, the University of Pavia and Salinas datasets, respectively. Experimental results indicate that the proposed RDDC-3DCNN method can make full extract of the spatial and spectral information of the image, and it has better performance than some advanced algorithms in HSI classification.

Fixed-time synchronization of multiplex networks by sliding mode control

Multiplex networks involve different types of synchronization due to their complex spatial structure. How to control multiplex networks to achieve different types of synchronization is an interesting topic. This paper considers the fixed-time synchronization of multiplex networks under sliding mode control (SMC). Firstly, for realizing three types of synchronization of multiplex networks in a fixed time, a unified sliding mode surface (SMS) is established. After that, based on the theory of SMC, a sliding mode controller (SMCr) which is more intelligent and has a simpler form than those in the existing literature is put forward for multiplex networks. It can not only guarantee the emergence of sliding mode motion, but also can realize three different kinds of synchronization by adjusting some parameters or even one parameter of the controller. Based on some theories of fixed-time stability, this paper deduces several sufficient conditions for the trajectories of the system to reach the preset SMS in a fixed time, and derives some sufficient conditions for multiplex networks to realize three different types of fixed-time synchronization. At the same time, the settling time which can reveal what factors determine the fixed-time synchronization in multiplex networks is obtained. Finally, in numerical simulations, different chaotic systems are set for each layer of multiplex networks to represent the nodes of different layers, which can prove that the theoretical results are practical and effective.

Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure

The reconstruction of 3D geometries starting from reality-based data is challenging and time-consuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition.

A fast two part direct sampling method based on interpolation

Multiple-point simulation methods are effective in reproducing complex spatial structures, however intensive computations are needed in the pattern matching process. We propose a new method to alleviate the computation burden of direct sampling. This method first constructs a mixed sparse part using a regular sub grid and random cells from the target image. Next, direct sampling is only performed on this part and all other cells are estimated from neighboring conditioning cells rather than simulated. Two estimation methods are proposed for categorical and continuous variables based on the Bayes’ theorem and regression under the assumption that different conditioning cells contribute individually to the estimation of the central cell. Comparisons are made between the proposed method and direct sampling. Experiments show that the proposed method can save at most 90% and 40% computation time for the simulation of categorical and continuous variables, which is faster than not only the direct sampling but also the previously proposed two stage direct sampling method, which saves only at most 50% and 13% simulation time to generate results with similar simulation quality.

Mechanical Behavior of Hydrated-Lime–Liquid-Stabilizer-Treated Granular Lateritic Soils

Granular lateritic soil is commonly used for road construction in humid tropical and subtropical regions. However, the high plastic clay content and poor particle distribution of some laterite materials make them unsuitable for bases and subbases. Lime treatment is a widely used method for improving problematic lateritic soil, and liquid ionic stabilizers are considered an environmentally friendly solution for reinforcing such soils. However, using only lime or only stabilizers may not be optimal. This study investigated the effect of treating granular lateritic soil with hydrated lime and a new liquid stabilizer, Zhonglu-2A (ZL-2A). A series of indoor tests, including compaction, California bearing ratio, and unconfined compressive strength tests, were conducted to evaluate the effects of hydrated lime content and stabilizer content on the mechanical properties, mineralogical composition, and microstructure of the soil. The results show that an increase in hydrated lime dosage increases the optimal moisture content and decreases the maximum dry density. The CBR of lime-stabilizer-treated laterite was at least 2–3 times higher than that of the only-lime-treated soil. The highest CBR was observed in samples treated with 0.2‰ ZL-2A stabilizer. The sample with 6% lime and 0.2‰ ZL-2A stabilizer exhibited the highest unconfined compressive strength, and a nearly linear increase was observed between the unconfined compressive strength and CBR. Further investigation of the stabilization mechanism using X-ray diffraction mineralogy analysis and scanning electron microscopy revealed that the inorganic substances of the ZL-2A stabilizer and the hydrated lime provided the basic conditions for the reaction and generated cementitious hydrates on the clay particles. The mixture of granular lateritic soil and hydrated lime was wrapped by the ZL-2A stabilizer, forming a complex spatial structure and improving the strength of the soil. To improve the bearing capacity of subgrades in actual subgrade engineering, a combination of a liquid ionic stabilizer and lime should be used to treat laterite.

SPATIAL DISTRIBUTION OF AMPLITUDES OF INTERNAL TIDAL WAVES ON THE NORTH-EASTERN SHELF OF SAKHALIN

The transformation of a multicomponent barotropic tide in the Sea of Okhotsk is considered in the framework of fully nonlinear non-hydrostatic calculations. The data of the tidal model and parameterized bottom topography and vertical profiles of sea water density based on data from open international atlases are used to initialize the model. Estimates of the wave amplitudes of the diurnal and semidiurnal baroclinic tides in terms of the displacement of isopycnal surfaces at different horizons are obtained and presented in the form of geographical maps. It is shown that the distribution of amplitudes significantly depends on depth, has a complex spatial structure with a noticeable predominance of the amplitudes of baroclinic waves of the diurnal period and the main extrema located on the shelf opposite Cape Elizabeth, Okhinsky Isthmus and Cape Patience. The implemented approach to mapping the amplitudes of internal tides can be applied to other shelf areas of the seas of the Russian Federation and used for predictions of these phenomena, including engineering assessments for the design and operation of marine infrastructure.

Spatial-specific Transformer with involution for semantic segmentation of high-resolution remote sensing images

ABSTRACT High-resolution remote sensing images (HR-RSIs) have a strong dependency between geospatial objects and background. Considering the complex spatial structure and multiscale objects in HR-RSIs, how to fully mine spatial information directly determines the quality of semantic segmentation. In this paper, we focus on the Spatial-specific Transformer with involution for semantic segmentation of HR-RSIs. First, we integrate the spatial-specific involution branch with self-attention branch to form a Spatial-specific Transformer backbone to produce multilevel features with global and spatial information without additional parameters. Then, we introduce multiscale feature representation with large window attention into Swin Transformer to capture multiscale contextual information. Finally, we add a geospatial feature supplement branch in the semantic segmentation decoder to mitigate the loss of semantic information caused by down-sampling multiscale features of geospatial objects. Experimental results demonstrate that our method can achieve a competitive semantic segmentation performance of 87.61% and 80.08% mIoU on Potsdam and Vaihingen datasets, respectively.

Research on seismic analysis methods of large and complex underground pipe structures in hard rock sites

In order to study the seismic design methods of large and complex underground pipe structures (LCUP) in hard rock sites, firstly, the structural characteristics of LCUP and the calculation principles of four typical seismic analysis methods were carefully sorted out. Secondly, based on different seismic analysis methods, the deformation and internal force response of three representative pipes were studied in detail, and the calculation accuracy and error causes of each seismic analysis method were analyzed. Finally, the advantages, disadvantages and applicability of the seismic analysis methods on typical pipes were systematically explained, and the technical route for seismic design of LCUP was proposed. The results showed that both the reaction displacement method and the reaction acceleration method can effectively analyze the seismic performance of standard section structures. The three-dimensional time-history analysis method was comprehensive and highly accurate for seismic analysis of complex spatial cross-node structures. The rigid connections of different pipes leaded to serious discontinuities in stiffness at the intersection, and the deformation and bending moment distributions were highly concentrated at the height away from the intersection. The influence range of the intersection was about three times the width of the intersection, and LCUP was classified according to this influence range. For standard section structures, seismic analysis should be carried out using the reaction acceleration method or reaction displacement method, while for spatial cross-node structures, three-dimensional time-history analysis should be used for seismic analysis. This scientific seismic design method achieved multiple optimal balances of seismic safety, analysis accuracy, efficiency and economy in the seismic analysis of large and complex underground structures. A theoretical basis and useful reference for the scientific design, accurate prediction and seismic safety evaluation of seismic response of LCUP were provided in this paper.

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation area. The novel Deep Meta fusion model (DMFM) is created with a unique fusion framework of residual stacking of convolution layers with Unique covariate features (UCF), Intensity features (IF), and Colour features (CF). The overhead issues in GPU utilization during Convolution neural network (CNN) models are reduced here with a lightweight architecture. The system considers detailing feature areas to improve classification accuracy and reduce processing time. The proposed DMFM model achieved 99% accuracy, with a maximum processing time of 130 s. The training, testing, and validation losses are degraded to a significant level that shows the performance quality with the DMFM model. The system acts as a standard analysis platform for dynamic datasets since all three different features, such as Unique covariate features (UCF), Intensity features (IF), and Colour features (CF), are considered very well.

Enhancing Brain Tumor Diagnosis: A 3D Auto-Encoding Approach for Accurate Classification

The brain’s capacity to control and coordinate the body’s other organs makes it an integral part of the nervous system. Brain tumours, which form when abnormal cells in the brain grow uncontrolled, may be deadly if not diagnosed and treated promptly. The use of image processing technology is essential in the quest to identify malignancies in medical imaging. Because of this, the final depiction will be more complete. Only a tiny percentage of 3D form instances will be viable for feature learning because of its complex spatial structure. These issues have inspired the development of some potential solutions, such as automatic encoders for learning properties from 2D images and the translation of 3D shapes into 2D space. With the help of camera images and state-space structures, the suggested 3D-based Spatial Auto Encoder method may automatically learn a representation of the state. Autoencoders can be taught to use the prototypes they generate to rebuild a picture, with the resulting learned coefficients being put to use in 3D form matching and retrieval. It’s possible that the learned coefficients may serve this function. The auto-encoder’s impressive results in image retrieval have been attributed, at least in part, to the ease with which it can learn new features from existing ones.