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In the discussion of social capital and social equality, gender is still one of the most important variables that may navigate once's opportunity to pursue higher education. Despite numerous studies having examined the influence of digital literacy, socioeconomic status (SES), social capital, and their subsequent impact on access to higher education (HE), investigating the inputs and output paths of graduates using gender as a theme is yet to be done. Viewing the input path as secondary education certification and admission into university as the output path, 623 secondary school graduates for Study-1 and 635 secondary school graduates for Study-2 were examined using the quantitative method. Primary data was collected through a questionnaire to assess one independent variable (social capital), while secondary data stored in the institutions serve to measure the independent (SES), mediator, moderator and dependent variables. Multiple regression, interaction terms and KHB (Karlson-Holm-Breen method) analysis were utilized to analyse the data. Results indicated that SES-driven social capital predicts the digital literacy, and digital literacy mediates the relationship between graduates' social capital and access to HE. Furthermore, gender does not moderate the correlations between digital literacy and access to higher education. This paper contributes to the discourse on technology in societies and inequalities in higher education. The study concludes that digital discrimination increases inequality in terms of people's access to higher education. However, gender does not pose any technological discrimination for graduates in China.
PurposeIn 2021, China implemented the double reduction policy, aiming to promote more equitable and sustainable learning environments. This study aims to investigate whether the policy reshaped over time the relationship between family socioeconomic status (SES) and academic achievement and whether its effects varied when comparing public and private schools.Design/methodology/approachUsing a quasi-experimental longitudinal research design and relying on naturally occurring variations across time (before vs during policy implementation) and school type (public vs private), students' performance data were observed before 2020 and after 2022. Regression analysis, moderation models and heterogeneity tests were conducted. Academic achievement was measured using standardized composite scores in Chinese, mathematics and English; SES was constructed from household income and parental education.Findings SES remained a significant predictor of academic achievement in both public schools (beta = 0.37, R & sup2; = 0.14 in 2020) and private schools (beta = 0.42, R & sup2; = 0.18 in 2020). However, its influence weakened significantly in public schools (beta = 0.16 in 2022), while in private schools it slightly increased (beta = 0.45, R & sup2; = 0.20 in 2022).Originality/valueFrom a sustainable education perspective, the findings suggest that regulatory reforms can contribute to educational sustainability by reducing students' reliance on family background rather than by increasing short-term performance levels.
This research investigates the effect of thermal radiation on the unsteady fractional-order flow of a Casson fluid in a convectively heated Couette tube. The Caputo-Fabrizio and Atangana-Baleanu fractional derivatives are used to represent non-Newtonian behavior, whereas the Rosseland approximation describes radiative heat transfer. Exact solutions to the fractional momentum and energy equations are achieved using the Laplace transform and Riemann sum approximation techniques. The impacts of essential factors, such as fractional order, Casson parameter, Biot number, and radiation parameter, are visually and mathematically evaluated. The results reveal that increasing thermal radiation increases fluid temperature and velocity, whereas higher Casson parameters increase flow resistance and decrease velocity. The Nusselt number and wall shear stress are also evaluated, and validation against existing literature supports the model's correctness. The results apply to engineering, biomedical, and thermal energy applications.
3D concrete printing is increasingly being adopted in construction, yet limited knowledge exists on the creep behaviour of printed elements, particularly in the hardened state. This study presents an investigation into the short-term creep behaviour of 3D printed concrete (3DPC) under compressive stress. Printed samples reinforced with micro-synthetic polypropylene fibers were compared with conventionally cast samples of the same mix. A sustained load of 35
Equatorial Spread F (ESF) is a well-documented phenomenon characterised by electron density perturbations in the F-layer, significantly impacting the propagation of electromagnetic waves. The automatic identification of ESF and its statistical analysis are crucial for understanding the physical mechanisms that drive ionospheric irregularities and for improving predictive models. This study implements a convolutional neural network (CNN), a deep learning model, for the automatic detection of ESF using ionosonde data from a Digisonde (DPS-4) at Ilorin (Geographic: 8.5 degrees N, 4.5 degrees E; Geomagnetic: 4.5 degrees S), an equatorial station in the African sector. We used a dataset of 3,000 manually labelled ionograms randomly selected from the period 2019 to 2021. The dataset was categorised into clear, ESF, and unidentified, with 1,000 images per category. Two models were trained using raw ionograms (RGB-model) and gray-scale preprocessed ionograms (Gray-model). The models achieve overall accuracies of 95.17% and 99.33% for raw and preprocessed ionograms, respectively, when tested on an unseen dataset. Evaluation using three complementary skill scores: F1-score, True Skill Score (TSS) and Heidke Skill Score (HSS) indicates that while both methods enable efficient CNN-based ionogram classification tasks, the grayscale approach requires lower computational load and obtains the largest gains compared to the RGB-image approach. For the first time, we provide classification of 60,326 ionograms across diverse geophysical conditions in the African sector, using both manual classification and machine-learning methods. The main contribution lies in the systematic evaluation and application of the models for ionogram classification tasks. The improved classification performance and robustness of the models under both quiet and disturbed geomagnetic conditions demonstrate their potential for large-scale, automated ionogram analysis.
