Space Scales Research Articles

Elastic deformation as a tool to investigate watershed storage connectivity

Storage-discharge relationships and dynamic changes in storage connectivity remain key unknowns in understanding and predicting watershed behavior. In this study, we use Global Positioning System measurements of load-induced Earth surface displacement as a proxy for total water storage change in four climatologically diverse mountain watersheds in the western United States. Comparing total water storage estimates with stream-connected storage derived from hydrograph analysis, we find that each of the investigated watersheds exhibits a characteristic seasonal pattern of connection and disconnection between total and stream-connected storage. We investigate how the degree and timing of watershed-scale connectivity is related to the timing of precipitation and seasonal changes in dominant hydrologic processes. Our results show that elastic deformation of the Earth due to water loading is a powerful new tool for elucidating dynamic storage connectivity and watershed discharge response across scales in space and time.

Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow Forecasting

Traffic flow prediction is a crucial research area in traffic management. Accurately predicting traffic flow in each area of the city over the long term can enable city managers to make informed decisions regarding the allocation of urban transportation resources in the future. The existing traffic flow prediction models either give insufficient attention to the interactions of long-lasting spatio-temporal regions or extract spatio-temporal features in a single scale, which ignores the identification of traffic flow patterns at various scales. In this paper, we present a multi-scale spatio-temporal information fusion model using non-local networks, which fuses traffic flow pattern features at multiple scales in space and time, complemented by non-local networks to construct the global direct dependence relationship between local areas and the entire region of the city in space and time in the past. The proposed model is evaluated through experiments and is shown to outperform existing benchmark models in terms of prediction performance.

A single-stage anchor-free rotating target visual detection algorithm suitable for flexible body vibration displacement measurement

The phenomenon of angular inclination of flexible structures during vibration poses a significant challenge to the applicability of visual vibration measurement methods because the target locked in the captured image will produce unknown geometric deformations such as scale, displacement, and angle in the time domain space, and the horizontal rectangular frame used for matching during target detection will also increase the false detection rate of the target due to the introduction of more background information. Such subtle geometric deformations and false detections can lead to severe fit errors in the displacement curves regressed by the visual vibration measurement algorithm. To effectively improve the accuracy and robustness of vibration image target recognition, this article takes the flexible body captured by a high-speed camera as the target of vibration displacement measurement. It introduces the rotating target detection method based on deep learning into the field of visual vibration measurement, which verifies the feasibility of the deep learning method in flexible body vibration measurement, and based on the deep convolutional neural network framework, a high-precision displacement measurement algorithm based on single-stage anchor-free rotating target detection is proposed. The algorithm in this article first uses the CSPDarknet backbone network to extract multi-scale features of flexible structural image sequences. It then uses PANet to fuse the top-down and bottom-up bidirectional feature maps of the four bridge target feature maps obtained through the backbone network. The shallow and deep information is used for semantic feature fusion and combined with the Coordinate Attention mechanism to achieve target finding and fine positioning on the feature map. Finally, we use the coordinates of the bounding box obtained from the test to regress the position offset of the object’s center point. To verify the accuracy of the algorithm in this article, we conducted experimental validation on the cable-stayed bridge model and the actual bridge and compared the performance with the traditional template matching algorithm, differential optical flow method, and various deep learning algorithms with different localization principles, as well as the displacement signals collected and processed by accelerometers. The experimental results of time-frequency characteristics analysis show that the vibration displacement trajectories regressed by the algorithm in this paper have the best overlap with the displacement measurements collected by the accelerometer, which verifies that the algorithm in this article has good application potential and implementation space in the field of condition monitoring of flexible structural bodies.

A cognitive deep learning approach for medical image processing

In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

Long-Term Analysis of Regional Vegetation Correlation with Climate and Phenology in the Midsection of Maowusu Sandland

It is essential to monitor the dynamics of vegetation at different scales in space and time to promote the sustainable development of terrestrial ecosystems. We used the Google Earth Engine (GEE) cloud platform to perform a comprehensive analysis of the changes in normalized difference vegetation index (NDVI) Mann-Kendall (MK) + Sen trend in the hinterland region of the Maowusu sandland in China over the last two decades. We performed bias-correlation studies using soil and climate data. Furthermore, we performed a partial Mantel test to analyze the spatial and temporal fluctuations of vegetation health-related indices. Additionally, we developed a logistic dual model of the phenology index using the Lenvenberg–Marquardt technique. The objective was to uncover the factors contributing to the regional shifts in vegetation dynamics. We provide a comprehensive analytic method designed to monitor vegetation over some time and forecast its future recovery. The findings indicate that over the past 20 years, more than 90% of the regional NDVI in the study area has exhibited a consistent and significant upward trend. This trend is primarily influenced by the adverse impact of temperature and the beneficial impact of precipitation. Additionally, long-term phenological indicators in the study area reveal that the vegetation’s growth cycle commences on the 125th day of the year and concludes on the 267th day of the year. This suggests that the shorter duration of the vegetation’s growth season may be attributed to the local climate and unfavorable groundwater depth conditions. levated temperatures throughout the next spring and autumn seasons would significantly affect the wellbeing of plants, with soil moisture being a crucial determinant of plant development in the examined region. This study presents a wide range of analytical tools for monitoring vegetation over a long period and predicting its future recovery. It considers factors such as vegetation health, phenology, and climatic influences. The study establishes a solid scientific foundation for understanding the reasons behind regional vegetation changes in the future.

Three results on the energy conservation for the 3D Euler equations

We consider the 3D Euler equations for incompressible homogeneous fluids and we study the problem of energy conservation for weak solutions in the space-periodic case. First, we prove the energy conservation for a full scale of Besov spaces, by extending some classical results to a wider range of exponents. Next, we consider the energy conservation in the case of conditions on the gradient, recovering some results which were known, up to now, only for the Navier–Stokes equations and for weak solutions of the Leray-Hopf type. Finally, we make some remarks on the Onsager singularity problem, identifying conditions which allow to pass to the limit from solutions of the Navier–Stokes equations to solution of the Euler ones, producing weak solutions which are energy conserving.

Ecological niche conservatism spurs diversification in response to climate change

Lengthy debate has surrounded the theoretical and empirical science of whether climatic niche evolution is related to increased or decreased rates of biological diversification. Because species can persist for thousands to millions of years, these questions cross broad scales of time and space. Thus, short-term experiments may not provide comprehensive understanding of the system, leading to the emergence of contrasting opinions: niche evolution may increase diversity by allowing species to explore and colonize new geographic areas across which they could speciate; or, niche conservatism might augment biodiversity by supporting isolation of populations that may then undergo allopatric speciation. Here, we use a simulation approach to test how biological diversification responds to different rates and modes of niche evolution. We find that niche conservatism promotes biological diversification, whereas labile niches—whether adapting to the conditions available or changing randomly—generally led to slower diversification rates. These novel results provide a framework for understanding how Earth–life interactions produced such a diverse biota.

Existence and regularity of steady-state solutions of the Navier-Stokes equations arising from irregular data

We analyze the forced incompressible stationary Navier-Stokes flow in R+n, n>2. Existence of a unique solution satisfying a global integrability property measured in a scale of tent spaces is established for small data in homogeneous Sobolev space with s=−12 degree of smoothness. The velocity field is shown to be locally Hölder continuous while the pressure belongs to Llocp for any p∈(1,∞). Our approach is based on the analysis of the inhomogeneous Stokes system for which we derive a new solvability result involving Dirichlet data in Triebel-Lizorkin classes with negative amount of smoothness and is of independent interest.

Multiple correlation filters with gaussian constraint for fast online tracking

The correlation filter based online tracking methods usually can achieve high real-time performance due to the leverage of the well-known FFT. However, they are also apt to generate the “corrupted training samples” in scenarios with complex background, which will trigger the model drift and deteriorate the tracking accuracy rapidly. The existing methods usually consider this problem from certain aspect and none of them has mined the potential of combining multiple formulas. In this paper, we propose the Output Constraint Transfer - Discriminative Scale Space Tracking (OCT-DSST) algorithm, which has taken full consideration of multiple channel feature, multiple filters, kernel trick, memory with incremental learning, and the self-supervision mechanism. We re-formulate the online tracking process by combining all formulas above in a unified framework. The so obtained adaptive learning rate can better exploit the feedback information coming from the intermediate tracking results, and effectively mitigate the corrupted sample problem. The experimental results on the OTB-50/100 and the VOT2016 datasets reveal that the proposed method is comparative to most state-of-the-arts algorithms, and can increase the accuracy by 2% and the success rate by 1.7%, compared to the traditional DSST method.

Product quality recognition and its industrial application based on lightweight machine learning

Quality management plays a crucial role in manufacturing industry as it directly impacts product quality and customer satisfaction. Traditional manufacturing industries have begun incorporating deep learning models to manage production quality. However, existing studies often focus on algorithmic models that require high computing power, while practical industrial applications often suffer from a lack of labelled data. This article proposes a novel and lightweight machine-learning-based quality detection model to address this issue. The proposed model tackles quality detection by efficiently detecting the local maxima of the scale space in input images using a Hessian matrix detection method, eliminating the need for continuous computation of multilayer Gaussian difference images. Additionally, image processing techniques are employed to augment the training data, enabling the model to achieve high accuracy even with limited datasets. Experimental results demonstrate that the proposed model outperforms alternative methods in terms of both accuracy and efficiency.

Probing Laser‐Driven Structure Formation at Extreme Scales in Space and Time

AbstractIrradiation of solid surfaces with high intensity, ultrashort laser pulses triggers a variety of secondary processes that can lead to the formation of transient and permanent structures over a large range of length scales from mm down to the nano‐range. One of the most prominent examples are LIPSS – Laser‐Induced Periodic Surface Structures. While LIPSS have been a scientific evergreen for of almost 60 years, experimental methods that combine ultrafast temporal with the required nm spatial resolution have become available only recently with the advent of short pulse, short wavelength free electron lasers. Here, the current status and future perspectives in this field are discussed by exploiting the unique possibilities of these 4th‐generation light sources to address by time‐domain experimental techniques the fundamental LIPSS‐question, namely why and how laser irradiation can initiate the transition of a “chaotic” (rough) surface from an aperiodic into a periodic structure.

Design and dynamic analysis of supporting mechanism for large scale space deployable membrane sunshield

AbstractStray light from the sun is one of the most significant factors affecting image quality for the optical system of a spacecraft. This paper proposes a method to design a deployable supporting mechanism for the sunshield based on origami. Firstly, a new type of space mechanism with single-closed loop was proposed according to thick-panel origami, and its mobility was analysed by using the screw theory. In order to design a deployable structure with high controllability, the tetrahedral constraint was introduced to reduce the degree of freedom (DOF), and a corresponding deployable unit named tetrahedral deployable unit (TDU) was obtained. Secondly, the process to constructing a large space deployable mechanism with infinite number of units was explained based on the characteristics of motion and planar mosaic array, and kinematics analysis and folding ratio of supporting mechanism were conducted. A physical prototype was constructed to demonstrate the mobility and deployment of the supporting mechanism. Finally, based on the Lagrange method, a dynamic model of supporting mechanism was established, and the influence of the torsion spring parameters on the deployment process was analysed.

Comparing local energy cascade rates in isotropic turbulence using structure-function and filtering formulations

Two common definitions of the spatially local rate of kinetic energy cascade at some scale $\ell$ in turbulent flows are (i) the cubic velocity difference term appearing in the ‘scale-integrated local Kolmogorov–Hill’ equation (structure-function approach), and (ii) the subfilter-scale energy flux term in the transport equation for subgrid-scale kinetic energy (filtering approach). We perform a comparative study of both quantities based on direct numerical simulation data of isotropic turbulence at Taylor-scale Reynolds number 1250. While in the past observations of negative subfilter-scale energy flux (backscatter) have led to debates regarding interpretation and relevance of such observations, we argue that the interpretation of the local structure-function-based cascade rate definition is unambiguous since it arises from a divergence term in scale space. Conditional averaging is used to explore the relationship between the local cascade rate and the local filtered viscous dissipation rate as well as filtered velocity gradient tensor properties such as its invariants. We find statistically robust evidence of inverse cascade when both the large-scale rotation rate is strong and the large-scale strain rate is weak. Even stronger net inverse cascading is observed in the ‘vortex compression’ $R>0$ , $Q>0$ quadrant, where $R$ and $Q$ are velocity gradient invariants. Qualitatively similar but quantitatively much weaker trends are observed for the conditionally averaged subfilter-scale energy flux. Flow visualizations show consistent trends, namely that spatially, the inverse cascade events appear to be located within large-scale vortices, specifically in subregions when $R$ is large.

Phytoplankton optical fingerprint libraries for development of phytoplankton ocean color satellite products

Phytoplankton respond to physical and hydrographic forcing on time and space scales up to and including those relevant to climate change. Quantifying changes in phytoplankton communities over these scales is essential for predicting ocean food resources, occurrences of harmful algal blooms, and carbon and other elemental cycles, among other predictions. However, one of the best tools for quantifying phytoplankton communities across relevant time and space scales, ocean color sensors, is constrained by its own spectral capabilities and availability of adequately vetted and relevant optical models. To address this later shortcoming, greater than fifty strains of phytoplankton, from a range of taxonomic lineages, geographic locations, and time in culture, alone and in mixtures, were grown to exponential and/or stationary phase for determination of hyperspectral UV-VIS absorption coefficients, multi-angle and multi-spectral backscatter coefficients, volume scattering functions, particle size distributions, pigment content, and fluorescence. The aim of this publication is to share these measurements to expedite their utilization in the development of new optical models for the next generation of ocean color satellites.

Acoustic metamaterials for realizing a scalable multiple phi-bit unitary transformation

The analogy between acoustic modes in nonlinear metamaterials and quantum computing platforms constituted of correlated two-level systems opens new frontiers in information science. We use an inductive procedure to demonstrate scalable initialization of and scalable unitary transformations on superpositions of states of multiple correlated logical phi-bits, classical nonlinear acoustic analog of qubits. A multiple phi-bit state representation as a complex vector in a high-dimensional, exponentially scaling Hilbert space is shown to correspond with the state of logical phi-bits represented in a low-dimensional linearly scaling physical space of an externally driven acoustic metamaterial. Manipulation of the phi-bits in the physical space enables the implementation of a non-trivial multiple phi-bit unitary transformation that scales exponentially. This scalable transformation operates in parallel on the components of the multiple phi-bit complex state vector, requiring only a single physical action on the metamaterial. This work demonstrates that acoustic metamaterials offer a viable path toward achieving massively parallel information processing capabilities that can challenge current quantum computing paradigms.

The Bretskyan hierarchy, multiscale allopatry, and geobiomes—on the nature of evolutionary things

AbstractThe process of evolution and the structures it produces are best understood in the light of hierarchy theory. The biota traditionally is described by either the genealogical Linnaean hierarchy or economic hierarchies of communities or ecosystems. Here we describe the Bretskyan hierarchy—a hybrid eco-genealogical hierarchy that consists of nested sets of different-sized, usually polyphyletic communities of interacting individuals separated from other such communities in space and time at multiple scales. The Bretskyan hierarchy consists of elements that have both genealogical and economic properties and functions—situated between, and connecting the elements of, the economic hierarchies (Vernadskyan) and the genealogical (Linnaean) hierarchy. The described hierarchy at lower tiers is populated by holobionts, individuals composed of multiple polyphyletic lineages integrated by functional interactions or biotically fabricated structures, such as membranes. At larger spatial tiers and longer time scales, the members of the Bretskyan hierarchy are of a more diffuse nature, partially due to the small size and relatively short duration of us as observers of larger and longer-lasting structures, here described as geobiomes. Their individuality is externally forced and directly tied to the spatial and temporal physical structures of our planet. These are sub-bioprovinces and bioprovinces—large and effectively isolated spatiotemporal structures of biota integrated internally by coevolution and individuated externally by a hierarchy of barriers. Gaia is here understood as the largest eco-genealogical individual compartmentalized by the outer space of the Earth and integrated at long time scales by biotic interactions and plate tectonic mixing of biota. The existence of a hierarchy of barriers and multilevel allopatry suggests that geographic isolation takes part not only in individuating species lineages, but also in producing coherent complexes of separate lineages forming bioprovinces at multiple space and time scales. The sizes, configurations, and durations of Bretskyan units are directly tied to geodynamics, demonstrating the central role of the physical planet in the processes of individuation and merging of geobiomes and the control of coevolution, and all its ramifications, at multiple space and time scales. The Bretskyan hierarchy also allows the integration of previously unconnected themes—“egalitarian” major transitions in individuality (e.g., eukaryogenesis) and some of the megatrajectories in the history of life—into a single theoretical framework of spatial and temporal scaling of eco-genealogy. The pervasive scaling of geodynamical processes and the direct connection of geodynamics to the dynamics of Bretskyan units allows us to formulate conjectures on the scales and limits of spatial and temporal contingency and competitiveness of biotas in evolution.

Fractured columnar small-world functional network organization in volumes of L2/3 of mouse auditory cortex.

The sensory cortices of the brain exhibit large-scale functional topographic organization, such as the tonotopic organization of the primary auditory cortex (A1) according to sound frequency. However, at the level of individual neurons, layer 2/3 (L2/3) A1 appears functionally heterogeneous. To identify if there exists a higher-order functional organization of meso-scale neuronal networks within L2/3 that bridges order and disorder, we used in vivo two-photon calcium imaging of pyramidal neurons to identify networks in three-dimensional volumes of L2/3 A1 in awake mice. Using tonal stimuli, we found diverse receptive fields with measurable colocalization of similarly tuned neurons across depth but less so across L2/3 sublayers. These results indicate a fractured microcolumnar organization with a column radius of ∼50 µm, with a more random organization of the receptive field over larger radii. We further characterized the functional networks formed within L2/3 by analyzing the spatial distribution of signal correlations (SCs). Networks show evidence of Rentian scaling in physical space, suggesting effective spatial embedding of subnetworks. Indeed, functional networks have characteristics of small-world topology, implying that there are clusters of functionally similar neurons with sparse connections between differently tuned neurons. These results indicate that underlying the regularity of the tonotopic map on large scales in L2/3 is significant tuning diversity arranged in a hybrid organization with microcolumnar structures and efficient network topologies.

Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm.

Feature selection plays a significant role in computer science; nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k -subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(p n2) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.

Measuring the Four Higher-Order Values in Schwartz’s Theory: Validation of a 17-Item Inventory

Schwartz’s theory of basic human values is the dominant framework for assessing values. One of its strengths is that it allows for different levels of analysis. The 10 basic values can be reliably assigned to four higher-order dimensions: Openness to Change, Conservation, Self-Transcendence, and Self-Enhancement. In this paper, we examined the psychometric properties of the Higher-Order-Value Scale-17 (HOVS17), an inventory that economically assesses these higher-order values. We analyzed data from the GESIS Panel, an ongoing large-scale probability-based panel study that fields HOVS17 annually since 2013 and for which HOVS17 was originally developed. We found HOVS17 to have satisfactory psychometric properties. The 17 items were located in the two-dimensional multi-dimensional scaling (MDS) space as hypothesized. All four subscales were unidimensional, showed good fit when modeled as reflective latent variables, and had acceptable reliabilities as well as one-year test–retest stabilities (.65 to .69). The subscales correlated in theoretically plausible ways with a wide range of correlates and criteria, such as personality traits and well-being. This demonstrates that HOVS17 provides a sound basis for studying the development, precursors, and consequences of the higher-order values in the GESIS Panel and in future surveys that adopt HOVS17. We also discuss suggestions for further improvements of the inventory.

Error analysis of inertial navigation based on quaternion approach

Abstract This research aims to establish the properties of the directional cosines and quaternion parameters to examine the proper scale, inclination, and numerical drift errors in a four-dimensional space. The research uses transformation matrices in the four-dimensional space whose elements are the directional cosines or quaternions. The kinematic equations are derived in terms of both directional cosines and quaternions. Applying the Kalman filter allows us to minimize the measurement error and refine the numerical calculation. The observation of the Euler angle covariance matrix takes on a crucial feature in the design of inertial navigation systems vital for analyzing efficient attitude.