Space Scales Research Articles

Of Geosocial Relations and Separations

How might an attention to the role that the geologic plays in everyday social and political formations help reveal and politicize the geographically, temporally, and stratigraphically distributed forms of violence in the Anthropocene? Building on recent work in environmental humanities, anthropology, geography, and feminist geophilosophy that aims to rethink racialized forms of violence alongside planetary forces and earthly formations, this article explores how geosocial relations and exclusions register distributed forms of violence that are often kept separate from each other. Through an ethnographic account of a state-led oil shale exploration project in southwestern Turkey during the eruption of war between Kurdish freedom fighters and the Turkish state in southeastern Turkey in the summer of 2015, the article traces the links and disjunctures between the everyday disavowal of resource exploration and colonial warfare. It explores how the disavowal of war and hydrocarbon exploration forecloses political and ethical possibilities. It further examines how emergent geosocial relations between people and rocks carry the possibility of reckoning with anti-Kurdish war and violence. In doing so, the article invites environmental humanities to rethink methodological and analytical ways of rendering violence visible. The article concludes by speculating about the possibility of geosocial solidarity, or a mode of relation with geological formations and humans that forges connections between racialized forms of othering and planetary scales of time, space, and materiality. As a mode of earthly praxis, geosocial solidarity is what might come after the unfinished task of detangling distributed forms of violence in the Anthropocene.

Automated Variational Nonlinear Chirp Mode Decomposition for Bearing Fault Diagnosis

The variational non-linear chirp mode decomposition (VNCMD) requires initialization of number of modes (NMs) and instantaneous frequency (IF). This paper proposes an automated method for NM selection and IF initialization which works on the scale-space representation based automated boundary detection in magnitude spectrum (MS). The proposed automated VNCMD (AVNCMD) method is applied for bearing fault detection in which the kurtosis based dominant mode selection method is recommended. The instantaneous amplitude (IA) and IF with spectral entropy are computed from the dominant mode. Features are given to feed forward neural network classifier. Methodology is investigated on two datasets for inner race, outer race, and ball race faults detection. The proposed method classifies inner race, outer race, and ball race bearing faults with 97.52% accuracy and classifies inner race and outer race bearing fault with 100% accuracy. Efficacy of the proposed method is compared with the existing methods to justify the superiority.

Global existence of solutions for Boussinesq system with energy dissipation

The Boussinesq system coupled by an energy dissipation brings new challenges in the study of global existence of solutions, for instance, this system does not have scale invariance which makes it difficult to show existence of mild solutions when initial data belongs to scaling invariant function spaces. In this paper we are interested to show global existence of solutions [u,θ] for Boussinesq system coupled by bilinear energy dissipation Φ(u)=2μE(u)⋅E(u) on smooth bounded domain Ω⊆Rn or whole space Rn, n⩾3, when the initial data [u0,θ0]∈X0=Wσ1,n/2(Ω)×Ln/2(Ω) is sufficiently small and the external force F(θ)=ϱfθen has low regularity in the sense t1/2−b/2nf∈L∞((0,T):Lb(Ω)) or f∈Ls((0,T):Lb(Ω)), where 2s=1−nb and b∈[n,∞). It seems that our results on the global and local well-posedness are the first to provide an Lp-approach when the initial velocity data u0 belongs to the space W˙σ1,n/2(Rn) that is scaling invariant.

On initial value problem for elliptic equation on the plane under Caputo derivative

Abstract In this article, we are interested to study the elliptic equation under the Caputo derivative. We obtain several regularity results for the mild solution based on various assumptions of the input data. In addition, we derive the lower bound of the mild solution in the appropriate space. The main tool of the analysis estimation for the mild solution is based on the bound of the Mittag-Leffler functions, combined with analysis in Hilbert scales space. Moreover, we provide a regularized solution for our problem using the Fourier truncation method. We also obtain the error estimate between the regularized solution and the mild solution. Our current article seems to be the first study to deal with elliptic equations with Caputo derivatives on the unbounded domain.

A Graphic Processing Unit–High-Order Spectral (GPU-HOS) Numerical Wave Tank for the Simulation of Directional Wave Field Evolution over a Long Time

Existing numerical models for direct simulations of stochastically directional waves are limited by their computational burden, and only a few of them can be applied to modeling directional waves on account of nonlinear evolutions on large time scales. In response to these drawbacks, this paper proposes a modified rectangular numerical wave tank based on the high-order spectral (HOS) method. Three main arrangements are responsible for the model improvement. Firstly, the relaxation technique is applied on the four sides of the tank for generating and attenuating waves, with an enhancement in the total computational efforts required of less than 5% being achieved. Secondly, for this paper, the HOS method for solving the nonlinear evolution of waves was modified to a GPU-speedup version for the first time, and the speedup increases dramatically (up to 16-fold) when the grid number is O(106). Thirdly, the higher-order Runge–Kutta method with order 8 is adopted for the purpose of suppressing cumulative temporal errors. Furthermore, several numerical results are presented for the validation of the model. With our modified numerical wave tank, the nonlinear evolutions of random directional waves can be efficiently studied over a large time and space scale.

Application of The Speed-Up Robust Features Method To Identify Signature Image Patterns On Single Board Computer

Through the development of a signature pattern recognition program on SBC Beagle-bone Black, this research seeks to determine how to differentiate between real and false signatures. Three techniques of gathering data were employed in this study: interviews, observations, and a review of the literature. The quick application development method is the approach that is applied. The rapid, efficient, and brief development cycle (RAD) is emphasized. This study uses a use-case diagram to illustrate the application's logic and data flow. In this study, OpenCV is used as a digital image processing library along with the C++ programming language and QT creator as an integrated development environment (IDE). This application was subjected to both accuracy and functional testing. The following conclusions are drawn from the findings of the investigation and testing that was done: Using the fast library approach for approximate nearest neighbors (FLANN) and the speeded-up robust features (SURF) feature extraction method, the signature pattern recognition program on the Beagle-bone black SBC can differentiate between real and fraudulent signatures. Through the processes of generating image scale space, feature localization, and feature description, the SURF approach extracts feature from signature images. This signature pattern recognition application is one of the digital image processing apps that can be run on the Beagle-bone Black single board computer. This indicates that the specifications of the SBC Beagle-bone Black for digital image processing are good.

Decades of eelgrass meadow dynamics across the northeast Pacific support seascape-scale conservation

Abstract Eelgrass meadows provide vital nearshore habitats and ecosystem services, but they have declined from human stressors and conservation efforts are now widespread. Dynamic ecosystems like eelgrass meadows naturally rearrange as disturbance and recruitment unfold across seascapes. However, some decisions that protect eelgrass only consider extant meadows, thus ignoring the potential for change. Here, we report decades of eelgrass dynamics observed across the northeast Pacific. Our observations support conservation expanded to the seascape scale, which includes potentially inhabitable areas along with extant meadows. We found that total seascape meadow area changed over time, and changes within seascapes were often asynchronous. Some meadows rearranged across seascapes over multiple kilometres and decades. Also, some seascapes compartmentalized meadow collapse, which enabled later recovery, or supported local recruitment that substantially increased total meadow area. These observations were consistent with hierarchical patch dynamics, which promote ecosystem persistence over larger space and time scales. Thus, to enable the dynamics that underpin eelgrass persistence, it is necessary to keep many eelgrass habitat options open across seascapes, rather than protect only extant meadows. Given that dynamic, hierarchical ecosystems are common along marine shorelines, this approach may be effective for both nearshore ecosystems in general and for eelgrass in particular.

Extended finite similitude and dimensional analysis for scaling

The theory of scaling called finite similitude does not involve dimensional analysis and is founded on a transport-equation approach that is applicable to all of classical physics. It features a countable infinite number of similitude rules and has recently been extended to other types of governing equations (e.g., differential, variational) by the introduction of a scaling space Omega _{beta }, within which all physical quantities are deemed dependent on a single dimensional parameter beta . The theory is presently limited to physical applications but the focus of this paper is its extension to other quantitative-based theories such as finance. This is achieved by connecting it to an extended form of dimensional analysis, where changes in any quantity can be associated with curves projected onto a dimensional Lie group. It is shown in the paper how differential similitude identities arising out of the finite similitude theory are universal in the sense they can be formed and applied to any quantitative-based theory. In order to illustrate its applicability outside physics the Black-Scholes equation for option valuation in finance is considered since this equation is recognised to be similar in form to an equation from thermal physics. It is demonstrated that the theory of finite similitude can be applied to the Black-Scholes equation and more widely can be used to assess observed size effects in portfolio performance.

Where and when? Combining dental wear and death seasons to improve paleoenvironmental reconstruction through ungulate diets

Dental meso- and microwear are proxies that allow the diet to be reconstructed. Applied to herbivores, these proxies provide information on vegetation structure and composition. Several studies on fossil populations have proposed environmental reconstructions based on these methods. These studies often focus on a few selected taxa and rarely consider the total variability of ungulate diets at sites in contexts where the seasonality of occupations is often not estimated. The variability of the ungulate diet associated with a better knowledge of the moment of their death can greatly improve the quality and resolution of the environmental interpretation at the scale past hominins experienced. In this paper, we propose a new approach to dental wear to reconstruct environments. We recommend including all available ungulates according to their abundance in the faunal spectrum to consider as many habitats as possible. The combination of dental meso- and microwear allows us to address two scales of time and space–the regional scale over several years and the local scale over one season, respectively. Applied to Teixoneres Cave (Units IIIa and IIIb, Spain) and Pié Lombard (Ensemble II, France), the results confirm previous microfauna analysis and make it possible to characterize the intensity of seasonal turnover.

Analysis of Connectome Graphs Based on Boundary Scale.

The purpose of this work is to advance in the computational study of connectome graphs from a topological point of view. Specifically, starting from a sequence of hypergraphs associated to a brain graph (obtained using the Boundary Scale model, BS2), we analyze the resulting scale-space representation using classical topological features, such as Betti numbers and average node and edge degrees. In this way, the topological information that can be extracted from the original graph is substantially enriched, thus providing an insightful description of the graph from a clinical perspective. To assess the qualitative and quantitative topological information gain of the BS2 model, we carried out an empirical analysis of neuroimaging data using a dataset that contains the connectomes of 96 healthy subjects, 52 women and 44 men, generated from MRI scans in the Human Connectome Project. The results obtained shed light on the differences between these two classes of subjects in terms of neural connectivity.

SPI and SPEI Drought Assessment and Prediction Using TBATS and ARIMA Models, Jordan

Drought is a complex threat where its propagation is not yet controllable, causing more environmental, social, and economic damage. This research assesses the effects of incessant warming and decrescent precipitation by calculating SPI and SPEI from 1985 to 2021 in the Amman –Zarqa Basin based on five grid points on time and space scales. The study applied the Pearson Correlation Coefficient (PCC) between each one-to-one index at different time scales and the Mann–Kendall test (MKT) to determine trends with different data sources to measure the inferential capturing of historical drought features. Machine learning algorithms are used to predict near-future droughts from 2022 to 2025. TBATS and ARIMA models run diverse input datasets, including observations, CSIC, and CMIP6-ssp126 datasets. The longest drought duration was 14 months. Drought severity and average intensity were found to be −24.64 and −1.76, −23.80 and −1.83, −23.57 and −1.96, and −23.44 and −2.0 where the corresponding drought categories were SPI-12 Sweileh, SPI-9 Sweileh, SPI-12 Wadi Dhullal, SPI-12 Amman Airport, respectively. The dominant drought event occurred between Oct 2020 and Dec 2021. CMIP6-ssp126 can capture the drought occurrence and severity by measuring SPI but did not capture the severity magnitude as the observations (SPI was −2.87 by observation and −1.77 by CMIP6). There are significant differences in drought dimensions between SPI and SPEI, where SPI was more sensitive to drought assessment than SPEI. Using CMIP6-ssp126, ARIMA was more accurate than TBATS, as well as using the observed historical SPEI and CSIC across all stations. The performance metrics ME, RMSE, MAE, and MASE implied significantly promising forecasting models with values of −0.0046, 0.278, 0.179, and 0.193, respectively, for ARIMA and −0.0181, 0.538, 0.416, and 0.466, respectively, for TBATS. The outcomes suggest an increased risk of drought incidents and, consequently, water deficits in the future. Hybrid modelling is suggested for more consistency and robustness of forecasting approaches.

Study of xenon evolution in UO[formula omitted] using multi-grain cluster dynamics modeling

The behavior of fission gas is a key issue for the performance of UO2 fuel, which is the most widely used fuel type in GEN-III reactors. Fission gas will lead to lower thermal conductivity and affect the microstructure of fuel, gradually cause swelling and other phenomena. It seriously affects the performance and safety of nuclear reactor. The numerical simulation method based on physical model is an important means to study fission gas. In this paper, in order to study the key parameters affecting the behavior and evolution of fission gas xenon in UO2 fuel, a multi-grain parallel cluster dynamics model is designed and implemented. The model can describe the multi-grain structure characteristics of UO2, and establish the correlation for the physical process of intra- and inter-granular bubbles. Furthermore, the model is extended to a larger temporal and spacial scale through parallelization, so as to simulate the fission gas behavior closer to the actual situation, and expand the applicable conditions and range of the model. Based on the modeling work, the long-term evolution processes of xenon under different initial grain radius and temperature conditions are analyzed, the results show that the initial grain radius has a much bigger effect on the inter-granular gas concentration than the intra-granular gas, while temperature has a large effect on both intra- and inter-granular gas concentrations. The model and analysis results provide theoretical support for understanding fission gas behavior and improving UO2 fuel performance.

Real-time tracking of moving objects through efficient scale space adaptation and normalized correlation filtering

Real-time tracking of moving objects through efficient scale space adaptation and normalized correlation filtering

Measuring the cross-border spillover effects and telecoupling processes of ecosystem services in Western China

Ecosystem services provide the basic elements for global economic development. In the context of the deepening global integration process, countries and regions are increasingly connected, resulting in a series of cross-country, cross-regional, multi-scale socio-economic-environmental impacts, these new situations and changes require a multi-site, two-way, and cross-temporal and spatial scale comprehensive study of "the relationship between ecosystem services and humans". Based on this, we apply a telecoupling synthesis framework to explore the socio-economic and eco-environmental interactions of ecosystem service spillover effects. Results show that (1) the spillover effect of ecosystem services in western China is significant. More than 66% of ecosystem services spillover to eastern China provinces, and more than 40% of ecosystem services spillover to countries in Southeast Asia, South Asia, and Central Asia, resulting in a total of 679 million people benefiting from the spillover effect. (2) There are also multiple telecoupling processes between the ecosystem service spillovers in the ecologically fragile areas in western China and the rest of the world. In combination with the scenario simulation of the InVEST model, the services such as water supply, soil retention, flood mitigation, and food supply have significant impacts on sustainable development and human well-being in Asia and even the world. (3) The positive feedback effect of telecoupling is critical to the protection of the ecological environment as well as the improvement of people's livelihood and well-being in Western China's ecologically fragile areas. Therefore, we propose strengthening ecological compensation cooperation between local governments and implementing compensatory transfer payments between upstream and downstream. Simultaneously, international cooperation must be strengthened, and an ecological compensation transfer mechanism with beneficiary countries must be established, while the cross-regional flow of ecosystem services must be maintained. This study provides an example for the "ecosystem services and human relations" in multiple places, two directions and across time and space scales, and also practical reference significance for China implementation of projects such as the "the Belt and Road" initiative and transnational and cross regional resource allocation.

Curvature effects on the structure of near-wall turbulence

The interaction between near-wall turbulence and wall curvature is described for the incompressible flow in a plane channel with a small concave–convex–concave bump on the bottom wall, with height comparable to the wall-normal location of the main turbulent structures. The analysis starts from a database generated by a direct numerical simulation and hinges upon the anisotropic generalised Kolmogorov equations, i.e. the exact budget equations for the second-order structure function tensor. The influence of the bump on the wall cycle and on the energy production, redistribution and transfers is described in the physical and scale spaces. Over the upstream side of the bump, the energy drained from the mean flow to sustain the streamwise fluctuations decreases, and the streaks of high and low streamwise velocity weaken and are stretched spanwise. After the bump tip, instead, the production of streamwise fluctuations grows and the streaks intensify, progressively recovering their characteristic spanwise scale. The wall-normal fluctuations, and thus the quasi-streamwise vortices, are sustained by the mean flow over the upstream side of the bump, while energy flows from the vertical fluctuations to the mean field over the downstream side. On the concave portion of the upstream side, the near-wall fluctuations form structures of spanwise velocity which are consistent with Taylor–Görtler vortices at an early stage of development. Their evolution is described by analysing the scale-space pressure–strain term. A schematic description of the bump flow is presented, in which various regions are identified according to the signs of curvature and streamwise pressure gradient.

Stochastic Localization Methods for Convex Discrete Optimization via Simulation

Solving Convex Discrete Optimization via Simulation via Stochastic Localization Algorithms Many decision-making problems in operations research and management science require the optimization of large-scale complex stochastic systems. For a number of applications, the objective function exhibits convexity in the discrete decision variables or the problem can be transformed into a convex one. In “Stochastic Localization Methods for Convex Discrete Optimization via Simulation,” Zhang, Zheng, and Lavaei propose provably efficient simulation-optimization algorithms for general large-scale convex discrete optimization via simulation problems. By utilizing the convex structure and the idea of localization and cutting-plane methods, the developed stochastic localization algorithms demonstrate a polynomial dependence on the dimension and scale of the decision space. In addition, the simulation cost is upper bounded by a value that is independent of the objective function. The stochastic localization methods also exhibit a superior numerical performance compared with existing algorithms.

Ballistic macroscopic fluctuation theory

We introduce a new universal framework describing fluctuations and correlations in quantum and classical many-body systems, at the Euler hydrodynamic scale of space and time. The framework adapts the ideas of the conventional macroscopic fluctuation theory (MFT) to systems that support ballistic transport. The resulting “ballistic MFT” (BMFT) is solely based on the Euler hydrodynamics data of the many-body system. Within this framework, mesoscopic observables are classical random variables depending only on the fluctuating conserved densities, and Euler-scale fluctuations are obtained by deterministically transporting thermodynamic fluctuations via the Euler hydrodynamics. Using the BMFT, we show that long-range correlations in space generically develop over time from long-wavelength inhomogeneous initial states in interacting models. This result, which we verify by numerical calculations, challenges the long-held paradigm that at the Euler scale, fluid cells may be considered uncorrelated. We also show that the Gallavotti-Cohen fluctuation theorem for non-equilibrium ballistic transport follows purely from time-reversal invariance of the Euler hydrodynamics. We check the validity of the BMFT by applying it to integrable systems, and in particular the hard-rod gas, with extensive simulations that confirm our analytical results.

An efficient adaptive method based on empirical wavelet transform for ultrasound tissue harmonic imaging

In this study, an efficient adaptive method based on the empirical wavelet transform (EA_EWT) was developed to separate harmonics from ultrasonic radio frequency (RF) echo signals for B-mode imaging. The spectrum for each RF scan line was calculated, and a meaningful partition based on the scale-space representation was performed on the preprocessed spectrum, from which the harmonic boundaries were automatically determined. Thereafter, the harmonic components were obtained using the empirical scaling function and empirical wavelets. Finally, harmonic signals were obtained after separating all RF scan lines in a frame for imaging. In the experiments, the EA_EWT method was used to separate the ultrasonic RF signals simulated by CREANUIS and obtained from the human carotid artery. The results were compared with separation based on bandpass filtering (BPF), pulse inversion (PI), and complete ensemble empirical mode decomposition with adaptive noise (S_CEEMDAN) methods. The EA_EWT-based simulation results were superior to the PI-based simulation results with shifted scatterer models and comparable to the BPF- and S_CEEMDAN-based results. In the in vivo measurements, the EA_EWT-based harmonic images exhibited a mean contrast, contrast-to-noise ratio, and tissue-to-clutter ratio of 0.896, 3.973, and 8.306, respectively. The processing times of the BPF, PI, and EA_EWT methods were 0.06 s, 0.01 s, and 0.214 s, respectively, which were significantly shorter than that of the S_CEEMDAN method (113.26 s). The EA_EWT approach may be used in practice to generate more precise diagnostic data since it effectively executes a better separation of harmonic components for B-mode imaging.

New physics in multi-electron muon decays

We study the exotic muon decays with five charged tracks in the final state. First, we investigate the Standard Model rate for μ+ → 3e+ 2e− 2ν (B\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{B} $$\\end{document} = 4.0 × 10−10) and find that the Mu3e experiment should have tens to hundreds of signal events per 1015μ+ decays, depending on the signal selection strategy. We then turn to a neutrinoless μ+ → 3e+ 2e− decay that may arise in new-physics models with lepton-flavor-violating effective operators involving a dark Higgs hd. Following its production in μ+ → e+hd decays, the dark Higgs can undergo a decay cascade to two e+e− pairs through two dark photons, hd→ γdγd→ 2(e+e−). We show that a μ+→ 3e+ 2e− search at the Mu3e experiment, with potential sensitivity to the branching ratio at the O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(10−12) level or below, can explore new regions of parameter space and new physics scales as high as Λ ∼ 1015 GeV.

Multiple Markov Chains for Categorial Drought Prediction on the U.S. Drought Monitor at Weekly Scale

Abstract Predicting drought severity is essential for drought management and early warning systems. Although numerous physical model-based and data-driven methods have been put forward for drought prediction, their abilities are largely constrained by data requirements and modeling complexity. There remains a challenging task to efficiently predict categorial drought, especially for the U.S. Drought Monitor (USDM). Aiming at this issue, multiple Markov chains for USDM-based categorial drought prediction are successfully proposed and evaluated in this paper. In particular, this study concentrated on how the Markov order, step size, and training set length affected prediction accuracy (PA). According to experiments from 2000 to 2021, it was found that the 1-step and first-order Markov models had the best accuracy in predicting droughts up to 4 weeks ahead. The PA steadily dropped with increasing step size, and the average accuracy at monthly scale was 88%. In terms of seasonal variability, summer (July–August) had the lowest PA while winter had the highest (January–February). In comparison with the western region, the PA in the eastern United States is 25% higher. Moreover, the length of the training set had an obvious impact on the PA of the model. The PA in 1-step prediction was 87% and 78% under 20- and 5-yr training sets, respectively. The results of the study showed that Markov models predicted categorical drought with high accuracy in the short term and showed different performances on time and space scales. Proper use of Markov models would help disaster managers and policy makers to put mitigation policies and measures into practice.