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In recent decades, with the rapid marketization of educational resources in K–12 education, private tutoring has become extremely popular in China, yet the previous research has not yet reached a consensus on the impact of private tutoring on academic outcomes, and has also overlooked the influence of individual choice under the exam-oriented educational system in China. By using data drawn from the China Education Panel Survey, this study examines the heterogeneous treatment effect of private tutoring on 8th graders’ academic performance from the perspective of individual choice. The results show that a propensity to attend private tutoring can differentiate the class disparities among middle school students in terms of individual, family, school, and other factors. Furthermore, the results indicate that private tutoring generally has limited impacts on academic achievement, net of all background factors. However, students whose propensity to attend private tutoring lies in the intermediate range tend to benefit the most from private tutoring. For both students from socioeconomically disadvantaged families with a low propensity and their advantaged counterparts with a high propensity, private tutoring has little effect on their academic performance. Sensitivity analysis further shows that the heterogeneous effects of private tutoring differs across cognitive ability, subject types, and tutoring periods. The finding has important implications for understanding the consequences of China's policy interventions in terms of reducing educational inequality.

Most scholars, parents, and educators agree that parental involvement is beneficial for children’s academic and developmental outcomes. However, a small but growing body of scholarship suggests that intensive parental involvement may potentially hinder children’s development. In this study, we examine the “more is less” assumption in parental involvement research and formally test the argument of parental overinvolvement. Using nationally representative data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998–1999 (ECLS-K), we examine whether high levels of involvement are associated with unintended negative child development in elementary school. Analyses based on curvilinear mixed effects models show that elevated parental expectations, intensive participation in extracurricular activities, and increased parental school involvement are associated with diminishing returns to children’s outcomes. The most meaningful parental overinvolvement pattern is found for internalizing problems. These patterns are generally consistent for children from all socioeconomic levels. We conclude with a discussion of the research and policy implications of these findings.

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 three-dimensional complex geometries of neurons. The transport is controlled to avoid traffic jams of materials by minimizing a predefined 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 the isogeometric analysis. 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 the 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 neurons.

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.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.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.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 .

Prior empirical research on the relationship between socioeconomic status (SES) and meritocratic attitudes has yielded inconsistent findings. This study contributes to the existing literature by examining the heterogeneous relationship between SES and meritocratic beliefs and perceptions across community socioeconomic contexts in China. Using nationally representative data from the China Family Panel Studies, the results show that individual SES is positively related to support for meritocratic beliefs, but negatively associated with perceptions of meritocracy. Moreover, SES disparities in meritocratic beliefs are more salient in socioeconomically disadvantaged and unequal communities, where residents with relatively higher SES are more likely to hold meritocratic beliefs. In contrast, SES gaps in meritocratic perceptions are more significant in socioeconomically advantaged and homogeneous communities, where individuals with relatively lower SES are more likely to perceive the allocation of resources as meritocratic. These findings provide theoretical insights for understanding how self-interest and system justification theories may jointly explain social origin disparities in public attitudes about meritocracy.

Safety-seeking has fueled the growth of charter schools. Descriptive evidence suggests different possible factors underlying safety in charter schools. This study investigates characteristics media...

Schools located in disadvantaged urban neighborhoods report disproportionately high rates of crime and violence. Theoretical reasoning suggests that school-based parent participation may le...

With the rapid expansion of higher education, educational meritocracy has received both applause and skepticism among scholars, citizens, and policy-makers. Focusing on China’s urban-rura...

Neurons exhibit remarkably complex geometry in their neurite networks. So far, how materials are transported in the complex geometry for survival and function of neurons remains an unanswered question. Answering this question is fundamental to understanding the physiology and disease of neurons. Here, we have developed an isogeometric analysis (IGA) based platform for material transport simulation in neurite networks. We modeled the transport process by reaction-diffusion-transport equations and represented geometry of the networks using truncated hierarchical tricubic B-splines (THB-spline3D). We solved the Navier-Stokes equations to obtain the velocity field of material transport in the networks. We then solved the transport equations using the streamline upwind/Petrov-Galerkin (SU/PG) method. Using our IGA solver, we simulated material transport in three basic models of the network geometry: a single neurite, a neurite bifurcation, and a neurite tree with three bifurcations. In addition, the robustness of our solver is illustrated by simulating material transport in three representative and complex neurite networks. From the simulation we discovered several spatial patterns of the transport process. Together, our simulation provides key insights into how material transport in neurite networks is mediated by their complex geometry.

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 $$120 \sim 330$$ 120 ∼ 330 times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks.

While disasters like the coronavirus pandemic, hurricanes, and wildfires tend to negatively affect large swaths of people, they also bring preexisting inequalities and social vulnerabilities into sharp focus. In many cases, disasters often reinforce structural inequalities existing in society during crises moments, and then subsequently exacerbate inequalities in the aftermath through an unequal recovery process. Disasters and crises have important implications for educational inequalities and schooling. In this chapter, we provide a literature review of how preexisting inequalities by social status and where one lives often cause unequal educational effects during disasters using theoretical insights from the sociology of disasters and the sociology of education. We first summarize how preexisting social inequalities crises and disasters in the United States can lead to unequal learning outcomes during the crisis. Next, we explore how the aftermath of crises tends to exacerbate the aforementioned inequalities through an uneven recovery process, again focusing on the role of education and schooling. While disasters often draw our attention to existing injustices, they also provide us an opportunity to imagine what types of institutional changes are needed to promote a more socially just society and how education may play a role in that vision.

The governmental responses to coronavirus disease 2019 (COVID-19) pandemic, including the approach, interventions, and their associated effectiveness, vary across social, cultural, political, and i...

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

This article examines the relationship between parental networks and parental school involvement during the elementary school years. Using a large, nationally representative data set of elementary school students—the Early Childhood Longitudinal Study–Kindergarten Cohort—and contextual data from the 2000 U.S. Census, our multilevel analysis shows that higher levels of parental networks in first grade are associated with higher levels of parental school involvement in third grade after controlling for individual- and school-level characteristics. Parental networks are positively related to school involvement activities in formal organizations that consist of parents, teachers, and school staff, including participating in parent–teacher organizations and volunteering at school. Furthermore, the positive effects of parental networks on parental school involvement is stronger for families whose children attend schools in disadvantaged neighborhoods. This suggests that well-connected parental networks can serve as a buffer against school neighborhood disadvantages in encouraging parents to be actively involved in schools.

Sociological research examines the gender gap reversal in higher education and the gender division in paid and unpaid labor for adult women, especially “the second shift literature,” as two distinct topics. In this study, we extend the insights of the second shift literature to research on youth labor and adolescents’ enrollment in higher education. Using data from the Youth Development Study from 1988 to 1992, we find that the negative association of unpaid labor with adolescents’ college enrollment odds was at least as large as, if not greater than, that of paid labor. Although labor engagement had adverse impacts both for female and male adolescents during this time, the negative associations of youth labor with college enrollment were more pronounced for male students. We discuss the implications of these findings and explain their relevance to more contemporary cohorts of high school students in the conclusion.

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.

Supplemental material, DS_10.1177_0038040719867598 for Is Daily Parental Help with Homework Helpful? Reanalyzing National Data Using a Propensity Score–Based Approach by Angran Li and Daniel Hamlin in Sociology of Education

A path planning system based on the Informed RRT* path planner was developed to enable an unmanned aerial vehicle (UAV) to avoid moving obstacles in a cluttered 3D environment. For congested environments such as a construction site, path planning systems that help a UAV to safely manoeuvre around dynamic objects and potential co-workers operating within the same workspace is needed. Instead of using a general RRT* path planner approach which will generate a sinuous path, we proposed a flexible approach to increase the convergence of our path planner by re-defining the search space based on 2D Informed RRT* path planner. General RRT* has a relatively low convergence speed to optimize its original solution. By using motion tracking cameras, we obtained real-time feedback of the UAVs pose as well as map structuring and obstacle positions. With this setup, the performance of our proposed path planning approach was assessed using a set of diverse scenarios to compare against general RRT* in convergence rate, quality of solution and ability to handle multiple obstacle situation.

Neurons depend critically on active transport of cargoes throughout their complex neurite networks for their survival and function. Defects in this process have been strongly associated with many human neurodevelopmental and neurodegenerative diseases. To understand related neuronal physiology and disease mechanisms, it is essential to measure the traffic flow within the neurite networks. Currently, however, image analysis methods required for this measurement are lacking. To address this deficiency, we developed a method that could measure the flow rates of cargo traffic at any specified locations along individual branches of the neurite networks. Our method is based on detecting and counting cargo trajectories passing through the specified locations of measurement in kymographs, which are spatiotemporal maps of cargo movement within one-dimensional neurites. A main focus of our method development is robust performance, which ensures that our method works reliably and accurately under low signal-to-noise ratios. We validated and benchmarked our method using both synthetic and actual image data and found its accuracy to be >85% on average under normal conditions. Our method can be used to measure traffic flow in not just neurite networks but also other intracellular networks such as cytoskeletal filament networks.

To systematically explore the influence of aromatic polycarboxylate ligands on building Zn II –BMP (1,5-bis(2-methylbenzimidazol) pentane) coordination polymers, four coordination polymers, namely {[Zn 2 (BMP)(PHSA) 2 ]·H 2 O} n ( 1 ), [Zn 4 (BMP) 2 (BTEC) 2 (H 2 O)] n ( 2 ), [Zn 2 (BMP)(BTRC)(OH)] n ( 3 ), {[Zn 3 (BMP)(TCI) 2 (H 2 O)]·H 2 O} n ( 4 ) (H 2 PHSA = 2-phenylsuccinic acid, H 4 BTEC = benzene-1,2,4,5-tetracarboxylic acid, H 3 BTRC = benzene-1,3,5-tricarboxylic acid, H 3 TCI = Tris (2-carboxyethyl) isocyanurate) are synthesized by varying aromatic polycarboxylate ligands. Systematic structure analysis shows that 1 – 4 are comprised by one or more Zn II dimer in which carboxylate ligands bridge two metal ions forming dinuclear cores, and BMP links these cores. In 1 , PHSA 2− bridges Zn II ions to generate paddle-wheel dimer [Zn 2 ( η 1 -COO) 4 ] and these dimers are further linked by BMP to form 2D structure. 2 exhibits a layer with two different dimers: [Zn 2 ( η 1 -BTEC 4− ) 2 ] and [Zn 2 ( η 1 -BTEC 4− )( μ 2 -H 2 O)] indicating that bridge water have subtle influences on the coordination geometry. [Zn 2 ( η 1 -BTRC 3− )( μ 2 -OH)] dimers in 3 are further linked by two kind of wavelike layers constructing the 2-fold interpenetrated 3D architecture. 4 shows a 2-fold interpenetrated 3D architecture with dimeric centre. Notably, besides Zn II dimer [Zn 2 ( η 1 -TCI 3− ) 3 ], a monuclear Zn II ion exists as a terminal of ligand BMP enhancing photoluminescence intensity of 4 subtlely.

In this article, a chiral azolate-based octanuclear metallamacrocycle three-dimensional (3D) complexes {[Co2(pbt)2(H2O)2] · 4.5H2O}n (1) have been synthesized and characterized. Complex 1 crystallizes in the space group P43212 and displays a complicate homochiral three-dimensional (3D) framework containing octanuclear cobalt metallamacrocycles. The trizole-N1, N2 bridging cobalt trinuclear secondary building units (SBUs) in complex 1 provides a platform to investigate the magneto-structural correlations. The weak magnetic exchange of this complex is due to the asymmetric doubly 1,2,4-triazole-N1,N2 bridged SBUs which reduces the ineffective overlap between the orbits of paramagnetic central ions.

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 1–5 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.