Space Scales Research Articles

Hypernetwork modeling and topology of high-order interactions for complex systems

Interactions among the underlying agents of a complex system are not only limited to dyads but can also occur in larger groups. Currently, no generic model has been developed to capture high-order interactions (HOI), which, along with pairwise interactions, portray a detailed landscape of complex systems. Here, we integrate evolutionary game theory and behavioral ecology into a unified statistical mechanics framework, allowing all agents (modeled as nodes) and their bidirectional, signed, and weighted interactions at various orders (modeled as links or hyperlinks) to be coded into hypernetworks. Such hypernetworks can distinguish between how pairwise interactions modulate a third agent (active HOI) and how the altered state of each agent in turn governs interactions between other agents (passive HOI). The simultaneous occurrence of active and passive HOI can drive complex systems to evolve at multiple time and space scales. We apply the model to reconstruct a hypernetwork of hexa-species microbial communities, and by dissecting the topological architecture of the hypernetwork using GLMY homology theory, we find distinct roles of pairwise interactions and HOI in shaping community behavior and dynamics. The statistical relevance of the hypernetwork model is validated using a series of in vitro mono-, co-, and tricultural experiments based on three bacterial species.

On the improvement of Hölder seminorms in superquadratic Hamilton-Jacobi equations

We show in this paper that maximal Lq-regularity for time-dependent viscous Hamilton-Jacobi equations with unbounded right-hand side and superquadratic γ-growth in the gradient holds in the full range q>(N+2)γ−1γ. Our approach is based on new γ−2γ−1-Hölder estimates, which are consequence of the decay at small scales of suitable nonlinear space and time Hölder quotients. This is obtained by proving suitable oscillation estimates, that also give in turn some Liouville type results for entire solutions.

Synsedimentary Compaction of Peat Under the Load of Alluvial System Sediments in the Most Basin (Czech Republic, Lower Miocene): From Weak Compaction to Strong Local Deformation of Clastics and Peat

Alluvial systems affected by peat layer compaction of a variable degree are studied all over the world, especially in areas of large river deltas and coastal plains. Ordinarily studied Holocene peat accumulations do not reach thicknesses of more than 6–10 m. This paper documents four ancient alluvial systems affected by peat compaction during the Lower Miocene in large exposures of open-cast lignite mines within the Most Basin, Czech Republic. Field observations and the study of 3000 borehole sections showed a clear dependence of the thickness and composition of the underlying peat on the scale of the accommodation space for loading by younger alluvial sediments. If the underlying partly compacted peat was tens to hundreds of meters in thickness, significant deformations of the peat appeared during its loading, leading up to the formation of growth faults. In such cases, missing parts of the coal seams were also observed below the alluvial systems. Especially borehole data showed that a linear correlation exists between the thickness of the alluvial sediments and the maximum instantaneous available compaction capacity of the underlying peat.

RGB-D visual odometry by constructing and matching features at superpixel level

Abstract Visual odometry (VO) is a key technology for estimating camera motion from captured images. In this paper, we propose a novel RGB-D visual odometry by constructing and matching features at the superpixel level that represents better adaptability in different environments than state-of-the-art solutions. Superpixels are content-sensitive and perform well in information aggregation. They could thus characterize the complexity of the environment. Firstly, we designed the superpixel-based feature SegPatch and its corresponding 3D representation MapPatch. By using the neighboring information, SegPatch robustly represents its distinctiveness in various environments with different texture densities. Due to the inclusion of depth measurement, the MapPatch constructs the scene structurally. Then, the distance between SegPatches is defined to characterize the regional similarity. We use the graph search method in scale space for searching and matching. As a result, the accuracy and efficiency of matching process are improved. Additionally, we minimize the reprojection error between the matched SegPatches and estimate camera poses through all these correspondences. Our proposed VO is evaluated on the TUM dataset both quantitatively and qualitatively, showing good balance to adapt to the environment under different realistic conditions.

OS-PSO: A Modified Ratio of Exponentially Weighted Averages-Based Optical and SAR Image Registration.

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.

Synthetic libraries of urban landslide simulations to identify slope failure hotspots and drivers across spatial scales and landscapes

AbstractRainfall-triggered landslides are most deadly in developing countries, and future urban sprawl and climate change could intensify existing risks. In these regions, enhancing efforts in urban landslide risk mitigation and climate change adaptation is crucial. Current landslide probability assessment methodologies struggle to support effective mitigation because they fail to represent local anthropogenic factors (e.g. informal housing) across space and time scales. To meet this challenge, we demonstrated in previous work that hillslope-scale mechanistic models representing such localised changes can be used to create synthetic libraries of urban landslides that account for both data and future scenario uncertainty. Here, we show how these libraries can become an explorative tool for researchers and stakeholders, allowing them to investigate slope stability variations across spatial scales and landscapes. Results highlight, for example, how the main slope instability drivers change according to the location (e.g., upper vs lower catchment), the landcover (e.g. forest vs urban) and the spatial scale analysed (e.g. at hillslope scale slope stability was mostly controlled by water table height, whereas at regional scale by slope geometry). Ultimately, we demonstrate that stochastic analyses can lead to a greater understanding of the system interactions and they can support the identification of mitigation strategies that perform well across spatial scales and uncertain scenarios. These strategies should be prioritised even if future conditions are unknown. This reasoning is shown on a data-scarce region with expanding informal housing. However, the same methodology can be applied to any urban context and with any mechanistic-based model.

A comparative study of bandpass-filter-based multi-scale methods for turbulence energy cascade

Two different bandpass filtering methods have been used in the literature to study turbulence energy cascade simultaneously in physical and scale spaces. One of them is the derivative-based filter by Leung et al. [J Fluid Mech. 2012;V710:453–481] and the other is the difference-based filter by Motoori and Goto [J Fluid Mech. 2021;V911:A27]. The differences in their definitions lead to a variability in the quantitative results across the literature. In this work, we compare the two filtering methods and show that the difference-based filter can be related to the derivative-based filter through Taylor's approximation. Within the truncation error, the deviation between the two filters depends on the ‘filter width’, ‘a’, which is an input to the difference-based filter. The apparent reason for the deviations is that for large ‘a’, the difference-based filter extracts scales in a wider bandwidth compared to the derivative-based filter, which corresponds to the limiting case of the difference-based filter as a → 1 . Thus, one must be careful while choosing a filter based on their objectives, and while comparing results based on different filter designs. It is hoped that the present work will help reconcile the quantitative variations across the relevant literature.

Generalized Hilbert operators arising from Hausdorff matrices

For a finite, positive Borel measure μ \mu on ( 0 , 1 ) (0,1) we consider an infinite matrix Γ μ \Gamma _\mu , related to the classical Hausdorff matrix defined by the same measure μ \mu , in the same algebraic way that the Hilbert matrix is related to the Cesáro matrix. When μ \mu is the Lebesgue measure, Γ μ \Gamma _\mu reduces to the classical Hilbert matrix. We prove that the matrices Γ μ \Gamma _\mu are not Hankel, unless μ \mu is a constant multiple of the Lebesgue measure, we give necessary and sufficient conditions for their boundedness on the scale of Hardy spaces H p , 1 ≤ p > ∞ H^p, \, 1 \leq p > \infty , and we study their compactness and complete continuity properties. In the case 2 ≤ p > ∞ 2\leq p>\infty , we are able to compute the exact value of the norm of the operator.

Stress sensitivity analysis of vuggy porous media based on two-scale fractal theory

Interparticle pore space and vugs are two different scales of pore space in vuggy porous media. Vuggy porous media widely exists in carbonate reservoirs, and the permeability of this porous media plays an important role in many engineering fields. It has been shown that the change of effective stress has important effects on the permeability of vuggy porous media. In this work, a fractal permeability model for vuggy porous media is developed based on the fractal theory and elastic mechanics. Besides, a Monte Carlo simulation is also implemented to obtain feasible values of permeability. The proposed model can predict the elastic deformation of the fractal vuggy porous media under loading stress, which plays a crucial role in the variations of permeability. The predicted permeability data based on the present fractal model are compared with experimental data, which verifies the validity of the present fractal permeability model for vuggy porous media. The parameter sensitivity analysis indicates that the permeability of stress-sensitivity vuggy porous media is related to the capillary fractal dimension, capillary fractal tortuosity dimension, Young’s modulus, and Poisson’s ratio.

Impact of Liminality in Organic Agricultural Tourism on Well-Being: The Role of Memorable Tourism Experiences as a Mediating Variable

Organic agricultural tourism integrates organic, toxin-free natural environments with agricultural industry and cultural lifestyles, creating a liminal space and experience away from everyday life. This study explores how this environment influences tourists’ memorable tourism experiences and subjective well-being. The research employed a questionnaire survey targeting tourists engaged in organic agricultural tourism in the Hualien and Taitung regions of Taiwan. This study used convenience sampling and collected a total of 440 valid questionnaires from 1 October to 30 December 2023. SPSS and PLS-SEM were employed as the analysis tools. The findings are as follows: 1. Developed scales for organic agricultural tourism liminal space, liminal experience, liminal place, and liminal benefits, validating their reliability and validity. 2. Verified that liminality has a significant positive impact on memorable tourism experiences, which in turn positively affect subjective well-being. 3. Identified and validated the mediating role of memorable tourism experiences in the relationship between liminality in organic agricultural tourism and subjective well-being. 4. Found that liminal experience has the most significant impact on memorable tourism experiences, followed by liminal benefits, highlighting the critical role of liminal experience in tourists gaining benefits from liminal spaces. 5. Expanded the application of liminality theory in tourism-related research, providing theoretical support for how organic agricultural tourism can deepen tourists’ memorable tourism experiences and enhance their subjective well-being through liminality. This study is innovative both in its theme and theoretical contributions, offering significant academic and practical implications for the field.

Extreme fractal dimension at periodicity cascades in parameter spaces.

In the parameter spaces of nonlinear dynamical systems, we investigate the boundaries between periodicity and chaos and unveil the existence of fractal sets characterized by a singular fractal dimension that deviates greatly from the fractal sets in their vicinity. This extreme fractal dimension stands out from the typical value previously considered universal for these parameter boundaries. We show that such singular fractal sets dwell along parameter curves, called extreme curves, that intersect periodicity cascades at their centers of stability across all scales of parameter spaces. The results reported here are generally demonstrated for the class of one-dimensional maps with at least two control parameters. Generalizations to other classes of systems are possible.

Piloting: Adopting a prototype mindset for today’s workplace

This paper is designed to help corporate real estate (CRE) leaders adopt a ‘learning by doing’ approach to workplace strategy and design to improve employee experiences and organisational outcomes. While the COVID-19 pandemic has brought lingering uncertainty relative to the future of the physical office, it has also spawned a spirit of openness to change and a willingness to experiment. In this paper, we discuss ways to learn by doing at various scales, with a focus on the largest-scale methodology: piloting. We show how pilots provide a low-risk approach to introduce flexible work policies and new ways of working within a sustainable financial model. We demonstrate that by generating data and insights, pilots can drive plans to scale and inform future space types within the larger real estate portfolio. This paper draws upon years of research and exploration conducted by MillerKnoll brands and our customers on pilots of varied types and scales. It provides a roadmap to implement a pilot, including questions to ask, selection criteria for teams, and locations and methodologies to measure success. Case studies illustrate various approaches and results achieved.

Characteristics of Gravity Waves in Opposing Phases of the QBO: A Reanalysis Perspective with ERA5

Abstract Gravity waves dispersing upward through the tropical stratosphere during opposing phases of the QBO are investigated using ERA5 data for 1979–2019. Log–log plots of two-sided zonal wavenumber–frequency spectra of vertical velocity, and cospectra representing the vertical flux of zonal momentum in the tropical lower stratosphere, exhibit distinctive gravity wave signatures across space and time scales ranging over two orders of magnitude. Spectra of the vertical flux of momentum are indicative of a strong dissipation of westward-propagating gravity waves during the easterly phase and vice versa. This selective “wind filtering” of the waves as they disperse upward imprints the vertical structure of the zonal flow on the resolved wave spectra, characteristic of (re)analysis and/or free-running models. The three-dimensional structures of the gravity waves are documented in composites of the vertical velocity field relative to grid-resolved tropospheric downwelling events at individual reference grid points along the equator. In the absence of a background zonal flow, the waves radiate outward and upward from their respective reference grid points in concentric rings. When a zonal flow is present, the rings are displaced downstream relative to the source and they are amplified upstream of the source and attenuated downstream of it, such that instead of rings, they assume the form of arcs. The log–log spectral representation of wind filtering of equatorial waves by the zonal flow in this paper can be used to diagnose the performance of high-resolution models designed to simulate the circulation of the tropical stratosphere.

Bias correction and ensemble techniques in statistical downscaling model for rainfall prediction using Tweedie-LASSO in West Java, Indonesia

Rainfall is a climate element with high variations in space and time scales, so it is not easy to predict. One way to predict rainfall is statistical downscaling (SD). SD can predict local rainfall based on Global Circulation Model (GCM) data. The Decadal Climate Prediction Project (DCPP), one of the GCMs, originates from adjacent grids and experiences multicollinearity problems. Rainfall as a response variable is Tweedie Compound Poisson Gamma (TCPG) distribution data because it has a discrete component (rainfall events) and a continuous component (rainfall intensity), so SD modelling will be carried out using Tweedie-LASSO. This research aims to compare the performance of bias correction and ensemble methods in SD in predicting rainfall in West Java, Indonesia. Bias correction uses Empirical Quantile Mapping (EQM) with CHIRPS data, and the ensemble method uses a stacking technique with Random Forest (Stacking-RF) due to the varied characteristics of DCPP model sources. Evaluation results using Root Mean Square Error Prediction (RMSEP) and correlation coefficient show that bias correction improves single-model performance but not ensemble models. Besides that, ensemble models outperform single models both before and after bias correction. The combination of bias correction and ensemble modelling can be recommended when conducting SD to enhance the prediction capability of rainfall at stations and other areas.

Five decades' experience of long‐term soil monitoring, and key design principles, to assist the EU soil health mission

AbstractThe European Union has a long‐term objective to achieve healthy soils by 2050. The European Commission has proposed a Directive of the European Parliament and of the Council on Soil Monitoring and Resilience (Soil Monitoring Law, SML), the first stage of which is to focus on setting up a soil monitoring framework and assessing soils throughout the EU. Situated in NW Europe, the UK has substantial experience in soil monitoring over the last half century which may usefully contribute to this wider EU effort. A set of overarching principles have and continue to guide design of national soil monitoring and may prove helpful as other European countries embark on similar monitoring programmes. Therefore, we present the principles of design from five decades of national soil monitoring. The monitoring discussed is based on a stratified‐random design, has matured in support of policy questions, and operates over space and time scales relevant to the SML. The UK Centre for Ecology & Hydrology (UKCEH) Countryside Surveys (CS) of Great Britain and Northern Ireland, Welsh Government, Environment and Rural Affairs Monitoring and Modelling Programme (ERAMMP) and the England Ecosystem Survey (EES) monitoring programme are national programmes currently operating in the UK. Some important lessons learnt include: adopting a question‐based approach; having a clear robust statistical design for the purpose; selecting indicators that address policy and underlying scientific questions; and selecting indicators that can detect change and use robust and well‐tested methodologies across a wide range of soil and land use types, remaining valid over long time scales, supporting thinking long‐term. Technical lessons learned include the proven cost effectiveness of a stratified‐random design including replication, while adopting a common stratification layer of stable environmental attributes aids comparability between monitoring programmes. Common protocols are vital for future intercomparisons, but a full ecosystem approach that includes co‐located soil and vegetation samples for interpreting a co‐evolving system has proved hugely advantageous. UK monitoring programmes offer a range of experience that may prove valuable to future soil monitoring design to address the major societal challenges of our time, such as maintaining food production and addressing climate change and biodiversity loss.

Exploring the association between openness scale of campus street spaces, street greenery, campus walkability and physical activity of college students

IntroductionResearch on the effects of campus street openness and greenery on college students' moderate to vigorous physical activity (MVPA) is relatively limited, as is research based on campus street view data. MethodsThis study utilized campus street view data to explore the relationship between objective campus environmental factors and college students' campus walkability and MVPA. The data were collected from surveys conducted at 34 universities in China involving 1173 college students. ResultsWe found that both campus walkability and MVPA of college students will not be affected by campus greenery but they are significantly positively affected by campus sky view factor (SVF) which affects college students' MVPA through partial mediation effect of campus walkability. ConclusionsSVF is the most different environmental factor in Chinese university campuses, but it is the most important factor that affects the evaluation of campus walkability and promotes MVPA of college students. Therefore, the issues of visual enclosure and spatial suppression on campus streets should be given attention. The results suggest that designers should focus on enhancing the openness of campus street spaces in campus planning, landscape design, and healthy campus construction.

Homogenization of word relationships in schizophrenia: Topological analysis of cortical semantic representations.

Patients with schizophrenia typically exhibit symptoms of disorganized thought and display concreteness and over-inclusion in verbal reports, depending on the level of abstraction. While concreteness and over-inclusion may appear contradictory, the underlying psychopathology that explains these symptoms remains unclear. In the current study, we used functional magnetic resonance imaging with an encoding modeling approach to examine how concepts of various words, represented as brain activity, are anomalously connected at different levels of abstraction in patients with schizophrenia. Fourteen individuals diagnosed with schizophrenia and 17 healthy controls underwent functional magnetic resonance imaging to measure brain activity representing concepts of various words. We used a persistent homology (PH) method to analyze the topological structures of word representations in schizophrenia patients, healthy controls, and random data, across different levels of abstraction by varying dissimilarity scales in the representation space. The results revealed that patients with schizophrenia exhibited more homogeneous word relationships across different levels of abstraction compared with healthy controls. Additionally, topological structures exhibited a shift toward a random network structure in patients with schizophrenia compared with controls. The PH method successfully distinguished semantic representations of patients with schizophrenia from those of controls. The current results provide an explanation for the mechanisms underlying the deficits in abstraction ability observed in schizophrenia. The isotopic connection of individual concepts reflects both the reduction of contextual connections at a semantically fine-grained scale and the absence of clear boundaries between related concepts at a coarse scale, which lead to concreteness and over-inclusion, respectively.

Effects of Spatial Type and Scale of Small Urban Open Spaces on Perceived Restoration: An Online Survey-Based Experiment

Small urban open spaces are critical components of high-density urban environments, and could bring multiple health benefits. However, the factors related to the restorative effect of these small urban open spaces are not well studied. This study explored how site type (lawn, water, and plaza) and spatial scales (10 × 10 m, 20 × 20 m, 30 × 30 m, and 40 × 40 m) might be associated with small urban open space’s restorative effects. We created the virtual scene images of the 12 sites and used PRS-11 to measure 1130 participants’ perceived restoration when viewing those images. The results indicated that lawn has the highest restorative effects, and are the most preferred. No significant differences in the restorative effects of sites with different spatial scales were detected, even a 10 × 10 m site has considerable restorative effects. We found high preference contributes to larger differences in the restorative effects of lawns and plazas. Those who prefer the sites may gain more restoration increases when viewing lawn scenes compared to plaza scenes. Moreover, adults over 50 years old have higher perceived restorations, and young adults aged 18–25 have a greater increase in restoration between plaza scenes and natural scenes, indicating young adults could benefit more from the building of small green spaces. These findings have direct implications for design practice. More small urban open spaces of lawn and water should be built to provide more restoration benefits, especially for young people.

Structure-Based Drug Design with a Deep Hierarchical Generative Model.

Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a notable impact on early drug design efforts. Yet screening-based methods still face scalability limits, due to computational constraints and the sheer scale of drug-like space. Machine learning approaches are overcoming these limitations by learning the fundamental intra- and intermolecular relationships in drug-target systems from existing data. Here, we introduce DrugHIVE, a deep hierarchical variational autoencoder that outperforms state-of-the-art autoregressive and diffusion-based methods in both speed and performance on common generative benchmarks. DrugHIVE's hierarchical design enables improved control over molecular generation. Its capabilities include dramatically increasing virtual screening efficiency and accelerating a wide range of common drug design tasks, including de novo generation, molecular optimization, scaffold hopping, linker design, and high-throughput pattern replacement. Our highly scalable method can even be applied to receptors with high-confidence AlphaFold-predicted structures, extending the ability to generate high-quality drug-like molecules to a majority of the unsolved human proteome.

Earth’s spheres: Conceptual and definitional debates

Eduard Suess’s invention of the word ‘biosphere’ sparked a trend of coining sphere-words, which has since evolved into something of a craze. Some of these words, such as atmosphere and hydrosphere, are simply name tags for their respective Earth spheres. Others, such as geosphere, are also used mainly as names but they carry more than one meaning. However, many sphere-words, including pedosphere, biosphere, ecosphere, anthroposphere, sociosphere, and technosphere, are far more than just names, bearing as they do, weighty conceptual connotations that fuel considerable debate. At a time when Earth and life scientists are attempting to understand the workings of the planet as a whole by modelling the interaction of its component spheres, and when they are collaborating with sociologists, anthropologists, and members of other human sciences to tackle current environmental challenges, it seems an opportune moment to probe the origin and meaning of words for the Earth spheres they investigate. To be sure, Earth System modelling is helping understand changes resulting from the interaction of all the Earth’s spheres, including the anthroposphere, over various time and space scales, while multidisciplinary, interdisciplinary, and transdisciplinary research is helping to address such global problems as sustainable development, climate change, water supply, and biodiversity loss. An awareness of the origin, development, and disputed meanings of many sphere-words that these approaches employ should prove salutary for their multifarious practitioners.