An efficient and positivity-preserving layer method for modeling radiation belt diffusion processes

An efficient and positivity-preserving layer method is introduced to solve the radiation belt diffusion equation and is applied to study the bounce resonance interaction between relativistic electrons and magnetosonic waves. The layer method with linear interpolation, denoted by LM-L (layer method-linear), requires the use of a large number of grid points to ensure accurate solutions. We introduce a monotonicity- and positivity-preserving cubic interpolation method to be used with the Milstein-Tretyakov layer method. The resulting method, called LM-MC (layer method-monotone cubic), can be used to solve the radiation belt diffusion equation with a much smaller number of grid points than LM-L while still being able to preserve the positivity of the solution. We suggest that LM-MC can be used to study long-term dynamics of radiation belts. We then develop a 2-D LM-MC code and use it to investigate the bounce resonance diffusion of radiation belt electrons by magnetosonic waves. Using a previously published magnetosonic wave model, we demonstrate that bounce resonance with magnetosonic waves is as important as gyroresonance; both can cause several orders of magnitude increase of MeV electron fluxes within 1ﾠday. We conclude that bounce resonance with magnetosonic waves should be taken into consideration together with gyroresonance

Chorus acceleration of radiation belt relativistic electrons during March 2013 geomagnetic storm

Abstract The recent launching of Van Allen probes provides an unprecedent opportunity to investigate variations of the radiation belt relativistic electrons. During the 17-19 March 2013 storm, the Van Allen probes simultaneously detected strong chorus waves and substantial increases in fluxes of relativistic (2 - 4.5 MeV) electrons around L = 4.5. Chorus waves occurred within the lower band 0.1-0.5fce (theelectron equatorial gyrofrequency), with a peak spectral density ∼10-4 nT 2/Hz. Correspondingly, relativistic electron fluxes increased by a factor of 102-103 during the recovery phase compared to the main phase levels. By means of a Gaussian fit to the observed chorus spectra, the drift and bounce-averaged diffusion coefficients are calculated and then used to solve a 2-D Fokker-Planck diffusion equation. Numerical simulations demonstrate that the lower-band chorus waves indeed produce such huge enhancements in relativistic electron fluxes within 15 h, fitting well with the observation. Key Points Initial RBSP correlated data of chorus waves and relativistic electron fluxes A realistic simulation to examine effect of chorus on relativistic electron flux Chorus yields huge increases inelectron flux rapidly, consistent with data

Topological fractal networks introduced by mixed degree distribution

Several fundamental properties of real complex networks, such as the small-world effect, the scale-free degree distribution, and recently discovered topological fractal structure, have presented the possibility of a unique growth mechanism and allow for uncovering universal origins of collective behaviors. However, highly clustered scale-free network, with power-law degree distribution, or small-world network models, with exponential degree distribution, are not self-similarity. We investigate networks growth mechanism of the branching-deactivated geographical attachment preference that learned from certain empirical evidence of social behaviors. It yields high clustering and spectrums of degree distribution ranging from algebraic to exponential, average shortest path length ranging from linear to logarithmic. We observe that the present networks fit well with small-world graphs and scale-free networks in both limit cases (exponential and algebraic degree distribution respectively), obviously lacking self-similar property under a length-scale transformation. Interestingly, we find perfect topological fractal structure emerges by a mixture of both algebraic and exponential degree distributions in a wide range of parameter values. The results present a reliable connection among small-world graphs, scale-free networks and topological fractal networks, and promise a natural way to investigate universal origins of collective behaviors.Comment: 14 pages, 6 figure

Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection

Blending algorithms model land cover change by using highly resolved spatial data from one sensor and highly resolved temporal data from another. Because the data are not usually observed concurrently, unaccounted spatial and temporal variances cause error in blending algorithms, yet, to date, there has been no definitive assessment of algorithm performance against spatial and temporal variances. Our objectives were to: (i) evaluate the accuracy of two advanced blending algorithms (STARFM and ESTARFM) and two simple benchmarking algorithms in two landscapes with contrasting spatial and temporal variances; and (ii) synthesise the spatial and temporal conditions under which the algorithms performed best. Landsat-like images were simulated on 27 dates in total using the nearest temporal cloud-free Landsat-MODIS pairs to the simulation date, one before and one after. RMSD, bias, and r2 estimates between simulated and observed Landsat images were calculated, and overall variance of Landsat and MODIS datasets were partitioned into spatial and temporal components. Assessment was performed over the whole study site, and for specific land covers. Results addressing objective (i) were that: ESTARFM did not always produce lower errors than STARFM; STARFM and ESTARFM did not always produce lower errors than simple benchmarking algorithms; and land cover spatial and temporal variances were strongly associated with algorithm performance. Results addressing objective (ii) indicated ESTARFM was superior where/when spatial variance was dominant; and STARFM was superior where/when temporal variance was dominant. We proposed a framework for selecting blending algorithms based on partitioning variance into the spatial and temporal components and suggested that comparing Landsat and MODIS spatial and temporal variances was a practical method to determine if, and when, MODIS could add value for blending

In Vivo Detection of Cucurbit[6]uril, a Hyperpolarized Xenon Contrast Agent for a Xenon Magnetic Resonance Imaging Biosensor

The Hyperpolarized gas Chemical Exchange Saturation Transfer (HyperCEST) Magnetic Resonance (MR) technique has the potential to increase the sensitivity of a hyperpolarized xenon-129 MRI contrast agent. Signal enhancement is accomplished by selectively depolarizing the xenon within a cage molecule which, upon exchange, reduces the signal in the dissolved phase pool. Herein we demonstrate the in vivodetection of the cucurbit[6]uril (CB6) contrast agent within the vasculature of a living rat. Our work may be used as a stepping stone towards using the HyperCEST technique as a molecular imaging modality

3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion

We tackle the task of text-to-3D creation with pre-trained latent-based NeRFs (NeRFs that generate 3D objects given input latent code). Recent works such as DreamFusion and Magic3D have shown great success in generating 3D content using NeRFs and text prompts, but the current approach of optimizing a NeRF for every text prompt is 1) extremely time-consuming and 2) often leads to low-resolution outputs. To address these challenges, we propose a novel method named 3D-CLFusion which leverages the pre-trained latent-based NeRFs and performs fast 3D content creation in less than a minute. In particular, we introduce a latent diffusion prior network for learning the w latent from the input CLIP text/image embeddings. This pipeline allows us to produce the w latent without further optimization during inference and the pre-trained NeRF is able to perform multi-view high-resolution 3D synthesis based on the latent. We note that the novelty of our model lies in that we introduce contrastive learning during training the diffusion prior which enables the generation of the valid view-invariant latent code. We demonstrate through experiments the effectiveness of our proposed view-invariant diffusion process for fast text-to-3D creation, e.g., 100 times faster than DreamFusion. We note that our model is able to serve as the role of a plug-and-play tool for text-to-3D with pre-trained NeRFs.Comment: 15 page

Tai chi as an alternative exercise to improve physical fitness for children and adolescents with intellectual disability

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Objective: The purpose of this study was to investigate the effects of Tai Chi (TC) on anthropometric parameters and physical fitness among children and adolescents with intellectual disabilities (ID). Methods: Sixty-six Chinese individuals engaged in sport-related extracurricular activities (TC and aerobic exercise (AE)) as exercise interventions or arts/crafts activities as a control condition (CON). The experimental protocol consisted of a baseline assessment, a 12-week intervention period, and a post-intervention assessment. Results: Significant interaction effect was only observed in the performance of a 6-min walk test. After 12 weeks of intervention, the AE group had significant changes in body mass index (p = 0.006, d = 0.11), sit-ups (p = 0.030 and d = 0.57), and 6-min walk test (p = 0.005, d = 0.89). Significant increases in vertical jump (p = 0.048, d = 0.41), lower-limb coordination (p = 0.008, d = 0.53), and upper-limb coordination (p = 0.048, d = 0.36) were observed in the TC group. Furthermore, the TC group demonstrated significantly greater improvements on balance compared to the control group (p = 0.011). Conclusions: TC may improve leg power and coordination of both lower and upper limbs, while AE may be beneficial for body mass index, sit-ups and cardiorespiratory fitness

Superior effects of modified chen-style Tai Chi versus 24-style Tai Chi on cognitive function, fitness, and balance performance in adults over 55

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Background: Cognitive decline and balance impairment are prevalent in the aging population. Previous studies investigated the beneficial effects of 24-style Tai Chi (TC-24) on either cognitive function or balance performance of older adults. It still remains largely unknown whether modified Chen-style TC (MTC) that includes 18 complex movements is more beneficial for these age-related health outcomes, as compared to TC-24. Objective: We investigated if MTC would show greater effects than TC-24 on global cognitive function and balance-related outcomes among older adults. Methods: We conducted a randomized trial where 80 eligible adults aged over 55 were allocated into two different styles of Tai Chi (TC) arms (sixty-minute session × three times per week, 12 weeks). Outcome assessments were performed at three time periods (baseline, Week 6, and Week 12) and included the Chinese Version of the Montreal Cognitive Assessment (MoCA) for overall cognitive function, One-leg Standing Test (LST) for static balance, Timed Up and Go Test (TUGT) for dynamic balance, chair Stand Test (CST) for leg power, and the six-meter Walk Test (6MWT) for aerobic exercise capacity. Results: Compared to TC-24 arm, MTC arm demonstrated significantly greater improvements in MoCA, LST, TUGT, CST, and 6MWT (all p \u3c 0.05). Conclusions: Both forms of TC were effective in enhancing global cognitive function, balance, and fitness. Furthermore, MTC was more effective than TC-24 in enhancing these health-related parameters in an aging population

Growing Scale-free Small-world Networks with Tunable Assortative Coefficient

In this paper, we propose a simple rule that generates scale-free small-world networks with tunable assortative coefficient. These networks are constructed by two-stage adding process for each new node. The model can reproduce scale-free degree distributions and small-world effect. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$ by adjusting the parameter: the preferential exponent $\beta$. The model can unify the characterization of both assortative and disassortative networks.Comment: 13 pages, and 5 figure