Spin mode reconstruction in Lagrangian space

Galaxy angular momentum directions (spins) are observable, well described by the Lagrangian tidal torque theory, and proposed to probe the primordial universe. They trace the spins of dark matter halos, and are indicators of protohalos properties in Lagrangian space. We define a Lagrangian spin parameter and tidal twist parameters and quantify their influence on the spin conservation and predictability in the spin mode reconstruction in N-body simulations. We conclude that protohalos in more tidal twisting environments are preferentially more rotation-supported, and more likely to conserve their spin direction through the cosmic evolution. These tidal environments and spin magnitudes arc predictable by a density reconstruction in Lagrangian space, and such predictions can improve the correlation between galaxy spins and the initial conditions in the study of constraining the primordial universe by spin mode reconstruction.Peer reviewe

Global attractive periodic solutions of neutral-type neural networks with delays in the leakage terms

In this paper, we introduce a class of neutral-type neural networks with delay in the leakage terms. Using coincidence degree theory, Lyapunov functional method and the properties of neutral operator, we establish some new sufficient criteria for the existence and global attractiveness of periodic solutions. Finally, an example demonstrates our findings

Constraining interacting dark energy models with the halo concentration - mass relation

The interacting dark energy (IDE) model is a promising alternative cosmological model which has the potential to solve the fine-tuning and coincidence problems by considering the interaction between dark matter and dark energy. Previous studies have shown that the energy exchange between the dark sectors in this model can significantly affect the dark matter halo properties. In this study, utilising a large set of cosmological $N$-body simulations, we analyse the redshift evolution of the halo concentration - mass ($c$ - $M$) relation in the IDE model, and show that the $c$ - $M$ relation is a sensitive proxy of the interaction strength parameter $\xi_2$, especially at lower redshifts. Furthermore, we construct parametrized formulae to quantify the dependence of the $c$ - $M$ relation on $\xi_2$ at redshifts ranging from $z=0$ to $0.6$. Our parametrized formulae provide a useful tool in constraining $\xi_2$ with the observational $c$ - $M$ relation. As a first attempt, we use the data from X-ray, gravitational lensing, and galaxy rotational curve observations and obtain a tight constraint on $\xi_2$, i.e. $\xi_2 = 0.071 \pm 0.034$. Our work demonstrates that the halo $c$ - $M$ relation, which reflects the halo assembly history, is a powerful probe to constrain the IDE model.Comment: 9 pages, 5 figures, 5 table

Association between triglyceride glucose index and H-type hypertension in postmenopausal women

BackgroundRecent studies have reported better predictive performance of triglyceride glucose (TyG) index for cardiovascular events in women, however, whether this association persists in postmenopausal women is inconclusive. We investigated the association between TyG index and H-type hypertension (HHT) in postmenopausal women.Methods1,301 eligible women with hypertension were included in this cross-sectional study. Concomitant homocysteine levels >10 μmol/L were defined as H-type hypertension. The TyG index was calculated as ln [triglycerides (mg/dl) × fasting glucose (mg/dl)/2]. Multivariable logistic regression models and restricted cubic spline models were used to assess the association between TyG index and H-type hypertension in postmenopausal women, and subgroup analyses were performed for potential confounders.ResultsOf the 1,301 hypertensive patients, 634 (48.7%) participants had H-type hypertension. In each adjusted model, TyG index was significantly associated with the risk of H-type hypertension. each 1-unit increase in TyG index was associated with an increased risk of H-type hypertension in all participants (OR = 1.6; 95% CI, 1.3–2.0; P < 0.001), and there was a linear relationship between TyG index and H-type hypertension (P for linear trend < 0.001).ConclusionTyG index is positively associated with H-type hypertension in postmenopausal women, suggesting that TyG index may be a promising marker for H-type hypertension. By controlling lipid levels and blood glucose levels, it may help prevent H-type hypertension in postmenopausal women

Association between body fat percentage and H-type hypertension in postmenopausal women

BackgroundPrevious studies have explored the relationship between body fat percentage (BFP) and hypertension or homocysteine. However, evidence on the constancy of the association remains inconclusive in postmenopausal women. The aim of this study was to investigate the association between BFP and H-type hypertension in postmenopausal women.MethodsThis cross-sectional study included 1,597 eligible female patients with hypertension. Homocysteine levels ≥10 mmol/L were defined as H-type hypertension. BFP was calculated by measuring patients' physical parameters. Subjects were divided into 4 groups according to quartiles of BFP (Q1: 33.4% or lower, Q2: 33.4–36.1%, Q3: 36.1–39.1%, Q4: >39.1%). We used restricted cubic spline regression models and logistic regression analysis to assess the relationship between BFP and H-type hypertension. Additional subgroup analysis was performed for this study.ResultsAmong 1,597 hypertensive patients, 955 (59.8%) participants had H-type hypertension. There were significant differences between the two groups in age, BMI, educational background, marital status, exercise status, drinking history, WC, TG, LDL, Scr, BUN, and eGFR (P < 0.05). The prevalence of H-type hypertension in the Q1 to Q4 groups was 24.9, 25.1, 24.9, and 25.1%, respectively. After adjusting for relevant factors, we found that the risk of H-type hypertension in the Q4 group had a significantly higher than the Q1 group (OR = 3.2, 95% CI: 1.3–7.5).ConclusionBFP was positively associated with the risk of H-type hypertension in postmenopausal women. Postmenopausal women should control body fat to prevent hypertension

Underload city conceptual approach extending ghost city studies

Global population growth and land development are highly imbalanced, marked by 43% of population increase but 150% of builtup area expansion from 1990 to 2018. This results in the widely concerned ghost city phenomenon and runs against the sustainable development goals. Existing studies identify ghost cities by population densities, but ignore the spatial heterogeneity of land carrying capacities (LCC). Accordingly, this study proposes a general concept termed underload city to define cities carrying fewer people and lower economic strength than their LCC. The underload city essentially describes imbalanced human-land relationship and is understood in a broader context than the usually applied ghost city. In this study, very high-resolution satellite images are analyzed to obtain land functional structures, and further combined with population and GDP data to derive LCC. We empirically identify eight underload cities among 81 major Chinese cities, differing from previous findings of ghost cities. Accordingly, the proposed underload city considers heterogeneous human-land relationships when assessing city loads and contributes to sustainable city developments

Taking the pulse of COVID-19: A spatiotemporal perspective

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.Comment: 27 pages, 18 figures. International Journal of Digital Earth (2020

Modeling Words for Qualitative Distance Based on Interval Type-2 Fuzzy Sets

Modeling qualitative distance words is important for natural language understanding, scene reconstruction and many decision support systems (DSSs) based on a geographic information system (GIS). However, it is difficult to establish the relationship between qualitative distance words and quantitative distance for special applications since the meanings of these words are influenced by both subjective and objective factors. Some existing methods are reviewed, and the Hao–Mendel approach (HMA) is improved to model qualitative distance words for four travel modes by using interval type-2 fuzzy sets (IT2 FSs), aiming at addressing the individual and interpersonal uncertainty among qualitative distance words. The area of the footprint of uncertainty (FOU), fuzziness (entropy), and variance are adopted to measure the uncertainties of qualitative distance words. The experimental results show that the improved HMA algorithm is better than the original HMA algorithm and can be used in spatial information retrieval and GIS-based DSSs

An Encoder–Decoder with a Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images

For many urban studies it is necessary to obtain remote sensing images with high hyperspectral and spatial resolution by fusing the hyperspectral and panchromatic remote sensing images. In this article, we propose a deep learning model of an encoder–decoder with a residual network (EDRN) for remote sensing image fusion. First, we combined the hyperspectral and panchromatic remote sensing images to circumvent the independence of the hyperspectral and panchromatic image features. Second, we established an encoder–decoder network for extracting representative encoded and decoded deep features. Finally, we established residual networks between the encoder network and the decoder network to enhance the extracted deep features. We evaluated the proposed method on six groups of real-world hyperspectral and panchromatic image datasets, and the experimental results confirmed the superior performance of the proposed method versus six other methods