This study intends to examine the efficiency of student-centered learning (SCL) through Google classroom in enhancing the readiness of fourth stage females’ pre-service teachers. The research employs a quasi-experimental design with a control and experimental group to compare the teaching readiness of participants before and after the intervention. The participants were 30 of fourth stage students at the University of Baghdad - College of Education for Women/the department of English and data were collected through observation checklist to assess their teaching experience and questionnaires to assess their perceptions towards using Google Classroom. Two sections were selected, C as a control group and D as the experimental one each with (15) participants. At the beginning of the experiment, both groups have been equalized in some variables, then all students have been tested by using observation checklist to measure their readiness. The researcher applied the students-centered learning in the class and electronically through google classroom on the experimental group. At the end of the experiment, the researcher retested students by observation checklist to measure their readiness to teach through structured visits at their schools observing their teaching with their real students. Then the researcher collected the students’ perceptions towards using Google Classroom. The findings elucidated the positive impact of student-centered learning that can be obtained on Google classroom in comparison with the traditional methods being used. The results showed that students are highly motivated through the student-centered approach and it enriches their knowledge, autonomy, collaboration, engagement and experiences for future teaching as well as their readiness is highly advantageous with student centered learning than with the previous methods used.
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We report here an innovative feature of green nanotechnology-focused work showing that mangiferin—a glucose functionalized xanthonoid, found in abundance in mango peels—serves dual roles of chemical reduction and in situ encapsulation, to produce gold nanoparticles with optimum in vivo stability and tumor specific characteristics. The interaction of mangiferin with a Au-198 gold precursor affords MGF-198AuNPs as the beta emissions of Au-198 provide unique advantages for tumor therapy while gamma rays are used for the quantitative estimation of gold within the tumors and various organs. The laminin receptor specificity of mangiferin affords specific accumulation of therapeutic payloads of this new therapeutic agent within prostate tumors
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In this study, thin films of pure titanium dioxide (TiO2) and titanium dioxide dual mixed with zinc oxide (ZnO) and magnesium oxide (MgO) with varying concentrations of (ZnO: MgO)x ranging from 0 to 30 wt% undoped and doped gold nanoparticles (AuNPs) were prepared on glass using the chemical spray pyrolysis (CSP) technique. The morphological, structural, and sensing properties of the prepared thin films were examined. Atomic Force Microscopy (AFM) analysis revealed that these films exhibit a consistent structure before and after doping. Initially, the roughness of these films was observed to increase upon the introduction of impurities (ZnO: MgO)x. This trend reversed at x=0.20, where a decrease in roughness occurred. Interestingly, a sub
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In this study, thin films of pure titanium dioxide (TiO 2 ) and titanium dioxide dual mixed with zinc oxide (ZnO) and magnesium oxide (MgO) with varying concentrations of (ZnO: MgO) x ranging from 0 to 30 wt% undoped and doped gold nanoparticles (AuNPs) were prepared on glass using the chemical spray pyrolysis (CSP) technique. The morphological, structural, and sensing properties of the prepared thin films were examined. Atomic Force Microscopy (AFM) analysis revealed that these films exhibit a consistent structure before and after doping. Initially, the roughness of these films was observed to increase upon the introduction of impurities (ZnO: MgO) x . This trend reversed at x = 0.20, where a decrease in roughness occurred. Interestin
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Orange peel was used as a plant-derived medium to prepare a ZnO–calcite mixed-phase nanostructured material, which was physicochemically characterized and evaluated for DPPH radical-scavenging, α-glucosidase inhibitory, and in vitro cytotoxic activities. XRD confirmed the coexistence of hexagonal wurtzite ZnO and crystalline calcite, with ZnO apparent crystallite sizes ranging from 9.2 to 28.3 nm (mean 16.4 ± 7.3 nm). FTIR identified Zn–O vibrations, carbonate-related bands, surface hydroxyl groups, and residual organic functionalities, while FESEM revealed irregular agglomerates composed of nanoscale grains. EDX showed Zn, O, C, and Ca as the principal elements, and the material exhibited a zeta potential of -14.86 ± 0.52 mV
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The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra
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Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems. This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on bo
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