High diffraction efficiency varied-line-space concave gratings for the Lyman ultraviolet explorer.

In this paper we present the design and fabrication of the reflection varied-line-space concave grating (VLSCG) for the project of CAFE (the Census of warm-hot intergalactic medium, Accretion, and Feedback Explorer), which aims to detect and map the warm-hot circumgalactic medium via OVI emission at 103.2 nm and 103.8 nm, using two off-Rowland-circle spectrograph channels. High diffraction efficiency at LUV is supposed for the VLSCG and an aperture ratio as small as $F/3.6$ is desired for a compact design. The gratings are fabricated by holographic lithography and ion beam etching techniques. We introduce an additional lens into the normal holographic exposing system to generate the varied-line-space grating patterns. Grooves with triangle profiles are obtained to increase the diffraction efficiency by oblique ion beam bombardment during the etching process. Finally, several VLSCGs with a central line density of 3300 lines/mm have been fabricated successfully. The measured results show that the groove efficiency reaches 51% at 106.4 nm and 31% at 127.4 nm. We imitated the working optical path of the spectrometer and used the ${-}{1}$ order of the VLSCG to measure the image near the exit slit. The results showed that the image of the point source has a vertical extent of 0.68 mm, and the aberrations have been corrected.

Unions, democracy, and Trump: Deconstructing the COVID-19 vaccination crisis of 2021.

The COVID-19 pandemic has posed significant challenges for U.S. workers, especially those in essential occupations. As most public health experts view vaccination as the only certain path to defeating the virus, this study examines how union membership, political participation, and support for Trump have affected adult vaccination rates. The analyses also explore how these interrelated factors intersect to either exacerbate or reduce the ongoing public health crisis. Using vaccination data from 3112 U.S. counties in July of 2021, this study finds strong support for claims that localities with high levels of support for Trump have lower percentages of adults vaccinated, while areas with higher union coverage and higher voter turnout are associated with higher rates of vaccination. Moreover, the results show that the positive effects of union density are enhanced in counties with higher rates of voter turnout and support for Trump, revealing a complex relationship between unions, democracy and partisan politics. The results suggest that workplace and political democracy can effectively facilitate individual and collective responses to large-scale collective action problems such as the COVID-19 pandemic.

Public awareness of One Health in China.

One Health is recognized as an increasingly important approach to global health. It has the potential to inform interventions and governance approaches to prevent future pandemics. Successfully implementing the One Health approach in policy will require active engagement from the public, which begs the question: how aware is the public of One Health? In this study, we examine the level and distribution of One Health awareness among the general public in China using a survey conducted in Beijing (n = 1820). We distinguish between awareness of the term of "One Health" versus awareness of the core set of ideas - the interconnection between the health of people, animals, and the environment. Our analysis shows that 40% of respondents reported that they have heard of the term, but more than double the number indicated that they recognize the core idea of interconnection between people, animals, and the environment. Specifically, about 83% of the respondents said that they believe people's health is closely connected to animal health and 86% believe people's health is closely connected to plant and environmental health. Multiple regression analysis indicates that women, younger people, and individuals with a higher level of education show higher levels of One Health awareness than their counterparts. Being aware of the term is associated with higher recognition of the core ideas. Policymakers and health practitioners should consider these findings when designing public awareness campaigns and educational initiatives to promote One Health principles.

Sociodemographic dynamics and age trajectories of depressive symptoms among adults in mid- and later life: a cohort perspective.

OBJECTIVES: This study explored the age trajectories of depressive symptoms across multiple cohort groups who were in middle and late adulthood; examined sociodemographic differences in these trajectories; and investigated how relevant factors contributed to depressive symptoms trends of different cohorts. METHODS: Drawing on data from the 1994-2016 Health and Retirement Study (HRS), we used growth curve models to examine the age patterns of depressive symptoms, changes in sociodemographic gaps in depressive symptoms trajectories, and predictors of changes in depressive symptoms. RESULTS: In general, adults' depressive symptoms started high in middle-adulthood, declined in young-old life, increased moderately in mid-old life, and peaked in old-old life; In detail, more nuanced cohort-specific age trajectories of depressive symptoms were observed, challenging the prevailing assumption of a common age trajectory of depressive symptoms. Later-born cohorts displayed higher levels of depressive symptoms than earlier-born cohorts at observed ages. Second, we found intra-cohort sociodemographic differences in levels of depressive symptoms, but these differences' growth rates varied by specific factors. Regardless of the cohort group, as people age, the gender gap in depressive symptoms persisted but the partnership gap reduced. A widening educational gap across cohorts was observed, but it declined with age in some cohorts. CONCLUSION: Results suggest more evidence for the persistent inequality and age-as-leveler hypotheses rather than the cumulative (dis-)advantage hypothesis.Supplemental data for this article can be accessed online at https://doi.org/10.1080/13607863.2021.2010182 .

Modeling material transport regulation and traffic jam in neurons using PDE-constrained optimization.

The intracellular transport process plays an important role in delivering essential materials throughout branched geometries of neurons for their survival and function. Many neurodegenerative diseases have been associated with the disruption of transport. Therefore, it is essential to study how neurons control the transport process to localize materials to necessary locations. Here, we develop a novel optimization model to simulate the traffic regulation mechanism of material transport in complex geometries of neurons. The transport is controlled to avoid traffic jam of materials by minimizing a pre-defined objective function. The optimization subjects to a set of partial differential equation (PDE) constraints that describe the material transport process based on a macroscopic molecular-motor-assisted transport model of intracellular particles. The proposed PDE-constrained optimization model is solved in complex tree structures by using isogeometric analysis (IGA). Different simulation parameters are used to introduce traffic jams and study how neurons handle the transport issue. Specifically, we successfully model and explain the traffic jam caused by reduced number of microtubules (MTs) and MT swirls. In summary, our model effectively simulates the material transport process in healthy neurons and also explains the formation of a traffic jam in abnormal neurons. Our results demonstrate that both geometry and MT structure play important roles in achieving an optimal transport process in neuron.

The Great Recession Index: A Place-based Indicator for Countries, States, and Metropolitan Areas.

The Great Recession (GR) of 2007-2009 marked the most devastating economic downturn since the Great Depression of the 1930s, and its consequences dramatically changed almost every aspect of social life. This research introduces the Great Recession Index (GRI), a place-based composite measure that captures the multidimensional nature of the GR. The GRI can be used to examine macro-level outcomes and is especially well-suited for examining the spatial variation and longterm effects of the GR. The GRI is adaptable to a variety of geospatial units of analysis, and in this article, we develop measures for countries, U.S. states, and U.S. metropolitan areas. Then, using the state-based GRI, we provide a research application to demonstrate the utility of the GRI for explaining state-level income inequality in the post-Recession period. The results show that the initial shock of the GR decreased the income share of upper-class households, but the aftershock of the Recession increased their income share, resulting in increased income inequality in the U.S. since the Recession. This paper concludes by considering the feasibility of using similar measures for evaluating the effects of catastrophic events such as wars, civil unrest, climate change, natural disasters, or pestilence on societal outcomes.

Deep learning of material transport in complex neurite networks.

Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric analysis (IGA) have been used for modeling the material transport process via solving partial differential equations (PDEs), they require long computation time and huge computation resources to ensure accurate geometry representation and solution, thus limit their biomedical application. Here we present a graph neural network (GNN)-based deep learning model to learn the IGA-based material transport simulation and provide fast material concentration prediction within neurite networks of any topology. Given input boundary conditions and geometry configurations, the well-trained model can predict the dynamical concentration change during the transport process with an average error less than 10% and [Formula: see text] times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks.

Reaction diffusion system prediction based on convolutional neural network.

The reaction-diffusion system is naturally used in chemistry to represent substances reacting and diffusing over the spatial domain. Its solution illustrates the underlying process of a chemical reaction and displays diverse spatial patterns of the substances. Numerical methods like finite element method (FEM) are widely used to derive the approximate solution for the reaction-diffusion system. However, these methods require long computation time and huge computation resources when the system becomes complex. In this paper, we study the physics of a two-dimensional one-component reaction-diffusion system by using machine learning. An encoder-decoder based convolutional neural network (CNN) is designed and trained to directly predict the concentration distribution, bypassing the expensive FEM calculation process. Different simulation parameters, boundary conditions, geometry configurations and time are considered as the input features of the proposed learning model. In particular, the trained CNN model manages to learn the time-dependent behaviour of the reaction-diffusion system through the input time feature. Thus, the model is capable of providing concentration prediction at certain time directly with high test accuracy (mean relative error <3.04%) and 300 times faster than the traditional FEM. Our CNN-based learning model provides a rapid and accurate tool for predicting the concentration distribution of the reaction-diffusion system.

Degrees of inequality: The Great Recession and the college earnings premium in U.S. metropolitan areas.

The Great Recession had devastating effects on the U.S. labor market as millions of workers lost their jobs while others faced declining earnings, erosion of job security, and loss of dignity at work. While workers of all education levels experienced rising unemployment and declining earnings, it is unclear if workers of all educational levels were equally affected. In this paper, we examine the impact of the Great Recession on variations in the college earnings premium-the ratio of earnings for workers with just four-year college degrees to those with just high school degrees-for 210 metropolitan statistical areas from 2007 to 2016. Using multilevel growth curve models, we find that the college earnings premium increased during the Great Recession and its aftermath and that metropolitan areas that experienced more severe disruptions from the Great Recession evidenced greater increases in the college earnings premium. This is mainly explained by much sharper declines in earnings of workers with high school degrees than those with college degrees.

The effect of metal ions on photocatalytic performance based on an isostructural framework.

A series of 2D layered isostructural coordination complexes {[M3(L)2(H2O)6]·H2O}n (M = Mn (1), Mn0.7Co0.3 (2), Mn0.5Co0.5 (3), Mn0.3Co0.7 (4), and Co (5), respectively, H3L = 1-aminobenzene-3,4,5-tricarboxylic acid) have been synthesized under hydrothermal conditions, and applied to catalyze the reaction of degenerating organic dyes under visible light irradiation. The photocatalytic results indicate that complex 5 exhibits good photocatalytic properties in the presence of H2O2, while 1 can restrain the photodegradation of organic dyes. Remarkably, when Mn ions are gradually replaced by Co ions in the complexes, the photocatalytic activities of 1­5 turn from inhibition to promotion, which is a controllable regulation of photocatalytic properties via changing metal ions. Moreover, by using novel magnetic analysis methods and diffuse-reflectance UV/Vis spectra analysis methods, we explain the influence of central metal ions on the photocatalytic performance.