Public awareness on gender education may not be easily translated in living classrooms, which prompts new alternatives. In this study, we explored Shakespearean graphic novels in exposing Malaysian school students to gender-related issues. The two-fold research entails tracing the female presence in eight selected images from digital graphic novels that define the gender and place social expectations across the globe; and secondly, gathering teachers' perception of these selected graphic novels. The chosen images were divided into two categories-those published as the cover pages of graphic novels and those that were contested for the Graphic Shakespeare Competition established in 2016. These stereotypical images of women are inconsistent with the objective of achieving inclusivity and correct gender representations. The data analysed based on a semi-structured interview on six ESL teachers suggest that as much as the graphic novels are seen as valuable in pedagogical contexts and in exposing the students to learning English and gender representations through literature, the material selection, and pedagogical approaches, including determining the classroom activities must be cautiously considered in terms of their cultural appropriateness to ensure students' readiness and effective outcomes. Discussing these pertinent issues, especially in addressing gender (mis)representations relevant to education, helps pave a new route within the UN SDG Goal 5 where gender nuances and phrases ought to be carefully constructed in a new narrative that shapes global perception towards women.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9769572 | PMC |
http://dx.doi.org/10.3389/fpsyg.2022.874960 | DOI Listing |
J Chem Inf Model
January 2025
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
View Article and Find Full Text PDFEur Urol Open Sci
January 2025
Department of Urology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Background And Objective: Radiation-induced cystitis (RIC) is an important consequence of pelvic radiotherapy that can cause high morbidity and, in extreme cases, mortality. The lack of a widely accepted classification system makes it difficult to compare treatment regimens. Our aim was to develop a new classification system covering the RIC spectrum to improve treatment comparisons and accurate incidence estimates for systematic use in clinical and research settings.
View Article and Find Full Text PDFSci Rep
January 2025
ENET Centre, CEET, VSB-Technical University of Ostrava, 708 00, Ostrava, Czech Republic.
Load frequency control (LFC) is critical for maintaining stability in interconnected power systems, addressing frequency deviations and tie-line power fluctuations due to system disturbances. Existing methods often face challenges, including limited robustness, poor adaptability to dynamic conditions, and early convergence in optimization. This paper introduces a novel application of the sinh cosh optimizer (SCHO) to design proportional-integral (PI) controllers for a hybrid photovoltaic (PV) and thermal generator-based two-area power system.
View Article and Find Full Text PDFSci Rep
January 2025
Wollega University, Nekemte, Ethiopia.
This research paper presents an advanced AI-driven hybrid power quality management system for electrical railways that addresses critical challenges in 25 kV AC traction networks through a novel integration of single-phase PV-UPQC with ANN-Lyapunov control architecture. The system effectively manages voltage unbalance exceeding 2%, high THD, voltage variations of ± 10%, and poor power factor through a dual-approach methodology combining ANN-based reference signal generation with Lyapunov optimization, enabling dynamic parameter tuning and real-time load adaptation. MATLAB/Simulink simulations validate the system's superior performance, demonstrating significant improvements, including voltage unbalance reduction from 1.
View Article and Find Full Text PDFMethods
January 2025
School of Computer Science, Qufu Normal University, Rizhao 276826, China.
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed which can learn genotype-phenotype associations and obtain relevant biomarkers.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!