Effects of Socioeconomic Status, Parent–Child Relationship, and Learning Motivation on Reading Ability

Against the background of Chinese culture, we investigated the relationship between family socioeconomic status (SES) and children’s reading ability. Participants included 2294 middle-school students in grade 8. SES was measured by parents’ education level, parents’ occupational prestige, and family property, and children’s reading ability was estimated with item response theory. In addition, we adopted an 8-item parent–child relationship scale and a 22-item learning motivation scale that included four dimensions. We examined whether the parent–child relationship mediated the relationship between family SES and reading ability and whether this was moderated by learning motivation. The results indicated that the parent–child relationship played a mediating role in the relationship between SES and reading ability. This relationship was moderated by students’ learning motivation. The direct effects of SES on reading ability at high, medium, and low levels of learning motivation were 0.24, 0.32, and 0.40, respectively

AI Nushu: An Exploration of Language Emergence in Sisterhood -Through the Lens of Computational Linguistics

This paper presents "AI Nushu," an emerging language system inspired by Nushu (women's scripts), the unique language created and used exclusively by ancient Chinese women who were thought to be illiterate under a patriarchal society. In this interactive installation, two artificial intelligence (AI) agents are trained in the Chinese dictionary and the Nushu corpus. By continually observing their environment and communicating, these agents collaborate towards creating a standard writing system to encode Chinese. It offers an artistic interpretation of the creation of a non-western script from a computational linguistics perspective, integrating AI technology with Chinese cultural heritage and a feminist viewpoint.Comment: Accepted for publication at SIGGRAPH Asia 202

Stress CMR T1-mapping technique for assessment of coronary microvascular dysfunction in a rabbit model of type II diabetes mellitus: Validation against histopathologic changes

BackgroundCoronary microvascular dysfunction (CMD) is an early character of type 2 diabetes mellitus (T2DM), and is indicative of adverse events. The present study aimed to validate the performance of the stress T1 mapping technique on cardiac magnetic resonance (CMR) for identifying CMD from a histopathologic perspective and to establish the time course of CMD-related parameters in a rabbit model of T2DM.MethodsNew Zealand white rabbits (n = 30) were randomly divided into a control (n = 8), T2DM 5-week (n = 6), T2DM 10-week (n = 9), and T2DM 15-week (n = 7) groups. The CMR protocol included rest and adenosine triphosphate (ATP) stress T1-mapping imaging using the 5b(20b)3b-modified look-locker inversion-recovery (MOLLI) schema to quantify stress T1 response (stress ΔT1), and first-pass perfusion CMR to quantify myocardial perfusion reserve index (MPRI). After the CMR imaging, myocardial tissue was subjected to hematoxylin-eosin staining to evaluate pathological changes, Masson trichrome staining to measure collagen volume fraction (CVF), and CD31 staining to measure microvascular density (MVD). The associations between CMR parameters and pathological findings were determined using Pearson correlation analysis.ResultsThe stress ΔT1 values were 6.21 ± 0.59%, 4.88 ± 0.49%, 3.80 ± 0.40%, and 3.06 ± 0.54% in the control, T2DM 5-week, 10-week, and 15-week groups, respectively (p < 0.001) and were progressively weakened with longer duration of T2DM. Furthermore, a significant correlation was demonstrated between the stress ΔT1 vs. CVF and MVD (r = −0.562 and 0.886, respectively; p < 0.001).ConclusionThe stress T1 response correlated well with the histopathologic measures in T2DM rabbits, indicating that it may serve as a sensitive CMD-related indicator in early T2DM

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

Landslide susceptibility prediction (LSP) is the basis for risk management and plays an important role in social sustainability. However, the modeling process of LSP is constrained by various factors. This paper approaches the effect of landslide data integrity, machine-learning (ML) models, and non-landslide sample-selection methods on the accuracy of LSP, taking the Yinghu Lake Basin in Ankang City, Shaanxi Province, as an example. First, previous landslide inventory (totaling 46) and updated landslide inventory (totaling 46 + 176) were established through data collection, remote-sensing interpretation, and field investigation. With the slope unit as the mapping unit, twelve conditioning factors, including elevation, slope, aspect, topographic relief, elevation variation coefficient, slope structure, lithology, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), distance to road, distance to river, and rainfall were selected. Next, the initial landslide susceptibility mapping (LSM) was obtained using the K-means algorithm, and non-landslide samples were determined using two methods: random selection and semi-supervised machine learning (SSML). Finally, the random forest (RF) and artificial neural network (ANN) machine-learning methods were used for modeling. The research results showed the following: (1) The performance of supervised machine learning (SML) (RF, ANN) is generally superior to unsupervised machine learning (USML) (K-means). Specifically, RF in the SML model has the best prediction performance, followed by ANN. (2) The selection method of non-landslide samples has a significant impact on LSP, and the accuracy of the SSML-based non-landslide selection method is controlled by the ratio of the number of landslide samples to the number of mapping units. (3) The quantity of landslides has an impact on how reliably the results of LSM are obtained because fewer landslides result in a smaller sample size for LSM, which deviates from reality. Although the results in this dataset are satisfactory, the zoning results cannot reliably anticipate the recently added landslide data discovered by the interpretation of remote-sensing data and field research. We propose that the landslide inventory can be increased by remote sensing in order to achieve accurate and impartial LSM since the LSM of adequate landslide samples is more reasonable. The research results of this paper will provide a reference basis for uncertain analysis of LSP and regional landslide risk management

Teacher Support, Reading Strategy and Reading Literacy: A Two-Level Mediation Model

The purpose of this study was to use the data consisted of 5,115 fifteen-year-old Shanghai students in 152 schools from the PISA 2009, by building cross-level mediation model, to explore how the influence on students’ reading literacy from teachers’ support through learning strategy. The results revealed that teacher support is positively directly related to students’ reading literacyStudents’ learning strategy (such as elaboration strategy and control strategy) and metacognition strategy were cross-level mediators between teachers’ support and their students’ reading literacy; But memory strategy in the learning strategy does not play a cross-level mediation effect.   &nbsp

Influence of Leaders' Psychological Capital on Their Followers: Multilevel Mediation Effect of Organizational Identification

We investigated the relationships between leaders' and their followers' psychological capital and organizational identification in a Chinese community. Participants included 423 followers on 34 work teams, each with its respective team leader. Hierarchical linear models (HLM) were used in the analyses to delineate the relationships among participants' demographic background (gender, age, marital status, and educational level), human capital, and tenure. The results revealed that leaders' psychological capital positively influenced their followers' psychological capital through the mediation effect of enhancing followers' organizational identification. The implications of these findings, the study's limitations, and directions for future research are discussed

Ego-Resiliency and Perceived Social Support in Late Childhood: A Latent Growth Modeling Approach

This study explored the change trajectory of schoolchildren’s ego-resiliency and perceived social support and investigated the effect of perceived social support on ego-resiliency across four time points. A sample of 437 children aged 8–13 years (M = 10.99, SD = 0.70, 51.5% boys) completed assessments at four time points. The results indicated that ego-resiliency showed an increasing linear trend and perceived social support showed a declining linear trend. Perceived social support had a positive effect on ego-resiliency over time. In addition, the initial status of perceived social support negatively predicted the growth trend of ego-resiliency, and the initial status of ego-resiliency negatively predicted the declining trend of perceived social support. The implications for theory and practice are discussed

Improving Mechanical, Electrical and Thermal Properties of Fluororubber by Constructing Interconnected Carbon Nanotube Networks with Chemical Bonds and F–H Polar Interactions

To improve the properties of fluororubber (FKM), aminated carbon nanotubes (CNTs-NH2) and acidified carbon nanotubes (CNTs-COOH) were introduced to modulate the interfacial interactions in FKM composites. The effects of chemical binding and F–H polar interactions between CNTs-NH2, CNTs-COOH, and FKM on the mechanical, electrical, thermal, and wear properties of the FKM composites were systematically investigated. Compared to the pristine FKM, the tensile strength, modulus at 100% strain, hardness, thermal conductivity, carbon residue rate, and electrical conductivity of CNTs-NH2/CNTs-COOH/FKM were increased by 112.2%, 587.5%, 44.2%, 37.0%, 293.5%, and nine orders of magnitude, respectively. In addition, the wear volume of CNTs-NH2/CNTs-COOH/FKM was reduced by 29.9%. This method provides a new and effective way to develop and design high-performance fluororubber composites

Research progress of DNA molecular markers in spinach genetic breeding

The types and characteristics of DNA molecular markers were briefly introduced in this paper,while itemphatically illustrated sex test,disease-resistant spinach breeding,high-yield and high-quality spinach breeding,genetic relationship and diversity and molecular marker-assisted selection in spinach genetic breeding.Then,the future development and application prospects of DNA molecular markers were prospected

Boosting Charge Mediation in Ferroelectric BaTiO3−x‐Based Photoanode for Efficient and Stable Photoelectrochemical Water Oxidation

Oxygen evolution reaction (OER) is a bottleneck to photoelectrochemical (PEC) water splitting; however, there remains an impressive challenge for intrinsic charge transport for the development of integrated photoanodes. Herein, covalent triazine frameworks as conjugated molecules are grafted on the surfaces of ferroelectric BaTiO3−x (CTF/BTO) nanorod array, and then oxyhydroxide oxygen evolution cocatalyst (OEC) is constructed as an integrated photoanode. The OEC/CTF/BTO array not only achieves a high photocurrent density of 0.83 mA cm−2 at 1.23 V versus reversible hydrogen electrode (vs RHE) and low onset potential of ≈0.23 VRHE, but also optimizes outstanding stability. To disclose the origin, the enhanced PEC activity can be contributed to the integration of CTF and OEC, enhancing light‐harvesting capability, boosting charge carrier mediation, and promoting water oxidation kinetics through electrochemical analysis and density functional theory calculations. This study not only provides an alternative to accelerate charge transfer, but also paves the rational design and fabrication of integrated photoanodes for boosting PEC water splitting performance