Structure Of Space Research Articles

3D-3C measurements of flow reversal in small sessile drops in shear flow

Sessile drops play an important role in nature and the operation of many technical and biological systems as for instance fuel cells, cleaning processes, lab-on-a-chip devices and single-cell analysis. Nonetheless, their dynamic behaviour in a shear flow is still not fully understood (e. g. detachment mechanism). Challenges exist regarding the precise simulation of two-phase flows as well as difficulties in conducting flow measurements with sufficient temporal resolution of more than 1 kHz. In this article, we present the first three-dimensional flow measurements in strongly oscillating drops stemming from a shear flow by using a monocular 3D localization microscope based on a Double-Helix Point Spread Function combined with Particle Tracking Velocimetry. The high temporal resolution and the large measurement volume - in terms of microscopy - make it possible to measure the time- and phase-averaged flow in small drops with Bond numbers (Bo) smaller than 1. Water drops were placed in an air flow channel and measurements were conducted for Reynolds numbers (Red) from about 750 to 1700. Our measurements show that the results of previous investigations for drops with Bo>1 concerning vortex pattern and flow reversal inside the drop apply for smaller drops as well. In addition, we are able to reveal the three-dimensional flow structure and multiple vortex-pattern in time and space. We discover a periodic 3D vortical flow pattern that corresponds to the first and second eigenfrequency of the drop. Moreover, we demonstrate the potential of adaptive optics to correct measurement errors stemming from time-varying light refraction when conducting measurements through the fluctuating drop surface, in particular for opaque substrates. The results may help in understanding the coupling of inner and outer flow for sessile drops in shear flow which allows for an analysis of the onset motion of these drops, i.e. drop removal. Removal of sessile drops plays a crucial role in many applications, which includes among other things the water management of fuel cells where small drops with Bo<1 predominantly occur.

S2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI.

To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.

Simulation Analysis of Loss of Coolant Accidents Based on CNIFA Program

Abstract The loss of coolant accident in the primary pipeline of a reactor is a typical accident in the safety analysis of nuclear power plants, and it is one of the accidents with a high probability of occurrence. Due to the extremely complex structure of the lower space of the containment vessel, it increases the complexity of fluid flow during a loss of coolant accident. This article is based on the internal water flooding analysis simulation software CNIFA of a nuclear power plant to simulate the water flooding of a rupture in the main pipeline heat pipe section of a power plant, and to conduct a comprehensive water flooding scenario simulation for this working condition. The quantitative information collected during the simulation process, including dynamically displayed water volume, water level, and their changes over time, facilitate emergency response personnel to choose the correct response plan in advance, it can also provide more accurate basis for various specialties in design analysis.

Parametric design of curved hydrocyclone using data points and its separation enhancement mechanism

Based on parametric design of curves using data points, two novel hydrocyclones were designed: Spline-Curved-Cone hydrocyclone (H2) and Expassoc-Curved-Cone hydrocyclone (H3). These designs are improvements on the traditional biconical hydrocyclone (H1). Numerical simulations and experimental validation by PIV measurement were used to investigate the effects of cone section profiles on the flow characteristics and separation efficiency. The results showed both H2 and H3 achieved higher separation efficiency than H1. Specifically, the highest efficiency of H3 increased by 24.71%, and that of H2 increased by 16.22% compared with H1. It was also found the curved design of cone section profile directly affects the tangential velocity distribution and pressure distribution of the flow field inside hydrocyclones. H3 exhibited better flow field stability and highest separation efficiency due to its optimized cone section space and flow structure. This study provides scientific basis and data support for the optimization and industrial application of the cylinder-on-cone hydrocyclones.

Gyrokinetic linear simulation of feedback instability in dipole magnetosphere

We perform a novel linear simulation of the feedback instability by applying a gyrokinetic model to the magnetosphere, where the geomagnetic field is modeled by a dipole magnetic field. In order to avoid huge numerical calculation costs, the effect of the fluctuating mirror force is neglected. It is found that kinetic effects, such as the finite Larmor radius effect and the electron Landau damping, strongly stabilize the feedback instability and form a peak in the perpendicular wave number spectrum of the growth rate. During the linear growth, it is observed that electron free energy has a much larger value than the electromagnetic field perturbation energy. Since such large electron free energy may lead to electron heating and acceleration, it is important to understand its generation mechanism. Analyses based on the energy conservation reveal that the coupling of the non-uniformity of the equilibrium fields along the geomagnetic field and the fluctuating electron distribution gives rise to thermodynamic power generating the electron free energy. A fine velocity space structure around an average Alfvén velocity of the fluctuating electron distribution mainly contributes to drive the thermodynamic power.

Stepwise Optimization of Actuator Placement for Large-Scale Space Structure During On-Orbit Assembly

Stepwise Optimization of Actuator Placement for Large-Scale Space Structure During On-Orbit Assembly

Domestic Space Structure and Abandonment Behavior: The Ye Family Complex at Baomei, Tong An District (Fujian, P.R. China)

Anthropological archaeology strives to recover, understand and explain past social dynamics. It relies on the uncovered material record to highlight past behavior patterns. For such a research agenda to be successful, one needs to fully understand all aspects of the processes involved in the formation of the archaeological record. It is undisputed that “Abandonment” is the condition _sine qua none_ for the formation of archaeological sites. Fieldwork conducted at the Ye Family high ranking Qing Dynasty office-holder housing complex, outlines the “use-history” of the investigated architectural complex and its adjustment to China recent contemporary history. The housing complex shifted from an initial prestigious imperial office-holder family to a “commune” housing for disadvantaged families during the cultural revolution, to be finally either progressively abandoned and/or re-cycled as storage complex. Combining informant input and field archaeology methodologies, the study of an abandoned elite traditional vernacular house provides the opportunity to decipher abandonment processes and contribute to the theoretical agenda of anthropological archaeology.

Историческое архитектурное наследие и современная застройка Владивостока как ресурс интеграции в АТР и формирования бренда территории в рамках политики «Разворота на Восток»: тренды и проблемы

The article examines the problems of forming a brand for the territory of Vladivostok as the largest agglomeration in the Russian Far East and a center of international cooperation with the countries of the Asia-Pacific region. The author analyzes the features of the building and territorial development of the city both in previous historical periods and in the conditions of the present moment. The period of analysis covers the entire history of the city’s development – from the second half of the 19th century to the present day, territorially – the entire urban development, taking into account port and industrial development. The article pays attention to such problems as the established fame of Vladivostok, the features of the urban environment, the specific structure of urban space, the relationship ofthese aspects with the development of international cooperation, business and economic activity, inbound tourism and the transformation of the capital of the Far Eastern Federal District into a well-known international location. The modern Russian policy of "Turn to the East" on the scale of the Far Eastern Federal District turns out to be largely connected with such locations as Vladivostok, and for this reason it is necessary to pay more and more attention to the development of such territories, harmoniously linking the historical "background" with the possibilities of modern urbanism.

Study on Self-Learning System for Milling Chatter Feature Based on Hybrid Preprocessing Model and Improved Convolutional Clustering

Milling chatter is a self-excited vibration phenomenon during the cutting process, typically occurring in machining operations with lower stiffness. It significantly impacts the machining precision and surface morphology of the workpiece. To summarize the vibration signal features under operating conditions and enhance the precision of the intelligent recognition model for milling chatter features, this paper proposes an adaptive learning model for chatter feature recognition based on the improved convolutional clustering algorithm. This study uses wavelet analysis to extract chatter features and improved convolutional clustering to identify chatter and stable cutting signals. This method selected appropriate mapping techniques based on differences in chatter signals under various conditions and uses convolution for feature extraction. Improve convolutional clustering evaluates sample similarity through energy transfer, less influenced by signal space structure compared to Euclidean or Mahalanobis distances. This method provides a unified criterion for evaluating different signal feature representation spaces, thereby improving the accuracy and efficiency of chatter sample recognition. The recognition accuracy of chatter phenomena at different spindle speed ranges reaches $95$ % and $96.3$ % respectively. The results show that this method can effectively distinguish between chatter signals and stable cutting signals.

The morphospace of the brain-cognition organisation.

Over the past three decades, functional neuroimaging has amassed abundant evidence of the intricate interplay between brain structure and function. However, the potential anatomical and experimental overlap, independence, granularity, and gaps between functions remain poorly understood. Here, we show the latent structure of the current brain-cognition knowledge and its organisation. Our approach utilises the most comprehensive meta-analytic fMRI database (Neurosynth) to compute a three-dimensional embedding space-morphospacecapturing the relationship between brain functions as we currently understand them. The space structure enables us to statistically test the relationship between functions expressed as the degree to which the characteristics of each functional map can be anticipated based on its similarities with others-the predictability index. The morphospace can also predict the activation pattern of new, unseen functions and decode thoughts and inner states during movie watching. The framework defined by the morphospace will spur the investigation of novel functions and guide the exploration of the fabric of human cognition.

Spatio-Temporal Evolution and Implications of Urban Residential Space Based on the New Commercial Housing in Xi’an, China, 2006–2022

Since 1998, comprehensive housing marketization has led to a more complex urban residential space structure in China, as evidenced by the changes in new commercial housing. The current stage is critical for China in its pursuit of high-quality and sustainable urban development, with the spatial structure of housing being a key aspect in achieving this goal. In order to clarify the characteristics of the urban residential space in China and grasp the spatio-temporal changes of new commercial housing, this study takes the data of new commercial housing projects in Xi’an, northwest China, from 2006 to 2022 as the basis, and uses the GIS method to explore the characteristics of the spatio-temporal evolution of the urban residential space and to investigate the factors influencing it. The results reveal changes in the number, size, and spatial distribution of new commercial housing in Xi’an. The evolution of residential space is influenced by cultural and social changes, population, and economic factors, in addition to the main influence of national policies. The renewal of urban villages into commercial housing projects is a prominent feature of changes in urban residential space, which saves construction land and improves residents’ living conditions. Finally, the expansion of urban residential space parallels the urbanization process, showing a pattern of ‘external expansion accompanied by internal renewal’, and the evolution process and property rights management are different from those of other countries.

Gating-Enhanced Hierarchical Structure Learning in Hyperbolic Space and Multi-scale Neighbor Topology Learning in Euclidean Space for Prediction of Microbe-Drug Associations.

Identifying drug-related microbes may help us explore how the microbes affect the functions of drugs by promoting or inhibiting their effects. Most previous methods for the prediction of microbe-drug associations focused on integrating the attributes and topologies of microbe and drug nodes in Euclidean space. The heterogeneous network composed of microbes and drugs has a hierarchical structure, and the hyperbolic space is helpful for reflecting the structure. However, the previous methods did not fully exploit the structure. We propose a multi-space feature learning enhanced microbe-drug association prediction method, MFLP, to fuse the hierarchical structure of microbe and drug nodes in hyperbolic space and the multiscale neighbor topologies in Euclidean space. First, we project the nodes of the microbe-drug heterogeneous network on the sphere in hyperbolic space and then construct a topology which implies hierarchical structure and forms a hierarchical attribute embedding. The node information from multiple types of neighbor nodes with the new topological structure in the tangent plane space of a sphere is aggregated by the designed gating-enhanced hyperbolic graph neural network. Second, the gate at the node feature level is constructed to adaptively fuse the hierarchical features of microbe and drug nodes from two adjacent graph neural encoding layers. Third, multiple neighbor topological embeddings for each microbe and drug node are formed by neighborhood random walks on the microbe-drug heterogeneous network, and they cover neighborhood topologies with multiple scales, respectively. Finally, as each scale of topological embedding contains its specific neighborhood topology, we establish an independent graph convolutional neural network for the topology and form the topological representations of microbe and drug nodes in Euclidean space. The comparison experiments based on cross validation showed that MFLP outperformed several advanced prediction methods, and the ablation experiments verified the effectiveness of MFLP's major innovations. The case studies on three drugs further demonstrated MFLP's ability in being applied to discover potential candidate microbes for the given drugs.

Оценка влияния процесса цифровизации и новейших технологий на структуру профессионального пространства в современном информационном обществе

This study aims to examine the characteristics of the structure of professional spaces in the modern infor-mation society. Using a systematic approach, the author highlights the dynamics of professional space struc-tures influenced by the broader context of modern society and the specific process of digitalization. The analy-sis explores new algorithms of interaction among participants within the professional space, which arise as a result of adopting the latest technologies. The advent of internet technologies and mobile phones has simpli-fied communication among these participants, altering spatial and temporal barriers. The modern professional becomes more “liberated”, which often leads to reduced efficiency due to the emergence of new actors who become significant components within the system. The multiplicity of actors in the modern professional space diminishes the role of the “classical” professional, who previously served as a system-forming element of this space. The author argues that while new technologies increase the mobility of professionals, they also impose new demands. Individuals must adapt to these changes, leading to conflicts both among specialists and across different generations.

Стильно, модно, православно: опыт исследования кофейни «ANTIПА»

The article describes the experience of field research conducted during 2023–2024 on the territory of the church of the Holy Martyr Antipas of Pergamon in Kolymazhny Dvor (Moscow), where the “ANTIPA” coffee shop is located. The research addresses the issues of creation, organization, functioning and structure of the public space initiated and supervised by a religious community, as well as social agents who interact with it. The coffee shop “ANTIPA” is proposed to be examined through the lens of five aspects: 1) spatial, 2) social, 3) aesthetic, 4) organizational, 5) economic and labor. The church complex’s adjacent territory can be characterized as a secular space with religious elements, which performs the following functions: 1) a meeting place (as a point on the map where people can meet), 2) a place of communication (as a place where one can spend time socializing), 3) a mediator between the city and religion (as a place that allows residents and visitors of the capital to encounter the religious).

The primacy model and the structure of olfactory space.

Understanding sensory processing relies on the establishment of a consistent relationship between the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy model for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory.

Heating Induced Nanoparticle Migration and Enhanced Delivery in Tumor Treatment Using Nanotechnology.

Nanoparticles have been developed as imaging contrast agents, heat absorbers to confine energy into targeted tumors, and drug carriers in advanced cancer treatment. It is crucial to achieve a minimal concentration of drug-carrying nanostructures or to induce an optimized nanoparticle distribution in tumors. This review is focused on understanding how local or whole-body heating alters transport properties in tumors, therefore leading to enhanced nanoparticle delivery or optimized nanoparticle distributions in tumors. First, an overview of cancer treatment and the development of nanotechnology in cancer therapy is introduced. Second, the importance of particle distribution in one of the hyperthermia approaches using nanoparticles in damaging tumors is discussed. How intensive heating during nanoparticle hyperthermia alters interstitial space structure to induce nanoparticle migration in tumors is evaluated. The next section reviews major obstacles in the systemic delivery of therapeutic agents to targeted tumors due to unique features of tumor microenvironments. Experimental observations on how mild local or whole-body heating boosts systemic nanoparticle delivery to tumors are presented, and possible physiological mechanisms are explored. The end of this review provides the current challenges facing clinicians and researchers in designing effective and safe heating strategies to maximize the delivery of therapeutic agents to tumors.

Straightforward fabrication of luminescent-magnetic GdF3:Yb3+, Ho3+@void@SiO2 wire-in-tube structured nanofibers

GdF3:Yb3+,Ho3+@void@SiO2 wire-in-tube structured nanofibers (WITSN) with bifunctional characteristic of up-conversion luminescence and magnetism properties are successfully obtained by self-created technique combining monoaxial electrospinning and single-crucible fluorination technology. The products are pure orthorhombic phase. GdF3:Yb3+,Ho3+@void@SiO2 WITSN exhibit large specific surface area and distinctive void space structure. The unique structure of the prepared nanofibers expands the new field of one-dimensional special morphology nanomaterials. In addition, GdF3:Yb3+,Ho3+@void@SiO2 WITSN have the bifunctionality of excellent up-conversion luminescent and paramagnetic properties. Materials have broad applications prospects in display, lighting, optical imaging, magnetic resonance imaging, and biomedicine. This work presents an innovative and high-efficient approach to construct photoluminescence-magnetism bifunctional GdF3:Yb3+,Ho3+@void@SiO2 WITSN, providing a new idea for the preparation of coated structured nanomaterials with excellent multifunctional characteristics.

Hilbert series of bipartite field theories

We study the algebraic structure of the mesonic moduli spaces of bipartite field theories by computing the Hilbert series. Bipartite field theories form a large family of 4dN\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 1 supersymmetric gauge theories that are defined by bipartite graphs on Riemann surfaces with boundaries. By calculating the Hilbert series, we are able to identify the generators and defining generator relations of the mesonic moduli spaces of these theories. Moreover, we show that certain bipartite field theories exhibit enhanced global symmetries which can be identified through the computation of the corresponding refined Hilbert series. As part of our study, we introduce two one-parameter families of bipartite field theories defined on cylinders whose mesonic moduli spaces are all complete intersection toric Calabi-Yau 3-folds.

Research progress on the role and mechanism of circular RNA in drug resistance of head and neck squamous cell carcinoma.

Drug resistance in tumors constitutes a significant obstacle to tumor therapy. Head and neck squamous cell carcinoma (HNSCC) presents a major challenge due to its deep anatomical location, limited space, and complex structure. These factors complicate surgical procedures and hinder the effectiveness of chemoradiotherapy, leading to poor prognosis and reduced quality of life. However, there is hope in the form of circular RNAs (circRNAs), non-coding RNA molecules with a closed-loop structure that exhibits superior stability and resistance to degradation compared to linear RNAs. Recent advances in high-throughput sequencing and bioinformatics technology revealed that circRNAs participate in tumor proliferation, invasion, migration, and drug resistance. This review aims to summarize current research progress on the involvement of circRNAs in drug resistance of HNSCC and provide valuable insights for the prevention and mitigation of drug resistance in HNSCC.

Characterizing the livingness of geographic space across scales using global nighttime light data

The hierarchical structure of geographic or urban space can be well-characterized by the concept of living structure, a term coined by Christopher Alexander. All spaces, regardless of their size, possess certain degrees of livingness that can be mathematically quantified. While previous studies have successfully quantified the livingness of small spaces such as images or artworks, the livingness of geographic space has not yet been characterized in a recursive manner. Zipf’s law has been observed in urban systems and intra-urban structures. However, whether Zipf’s law is applicable to the hierarchical substructures of geographic space has rarely been investigated. In this study, we recursively extract the substructures of geographic space using global nighttime light imagery. We quantify the livingness of global cities considering the number of substructures (S) and their inherent hierarchy (H). We further investigate the scaling properties of the extracted substructures across scales and the relationships between livingness and population for global cities. The results demonstrate that all substructures of global cities form a living structure that conforms to Zipf’s law. The degree of livingness better captures population distribution than nighttime light intensity values for the global cities. This study contributes in three aspects: First, it considers global cities as a whole to quantify spatial livingness. Second, it applies the concept of livingness to cities to better capture the spatial structure of the population using nighttime light data. Third, it introduces a novel method to recursively extract substructures from nighttime images, offering a valuable tool to investigate urban structures across multiple spatial scales.