MH Hamzah, AF Abbas, International Journal of Early Childhood Special Education, 2022

This review paper examines the crucial impact of YouTube on learning English as a Foreign Language. Recently, learners’ interaction and development of their skills have been improved due to the integration of digital platforms into language education. YouTube is regarded as one of the most prevalent platforms due to its accessibility, multimodal content, and capacity to simulate real-life communication. This study tackles thirty selected research articles from various cultural and institutional backgrounds to identify the pedagogical benefits and challenges associated with using YouTube in teaching English. Conventional methods of teaching English as a foreign language encounter difficulties in improving students’ engagement and

Assessment should give more attention on the learning achievement of the curriculum. Portfolio- based learning assessment (PBLA) is utilized in language learning materials recently .It is assessment tools to test the learners’ learning for instance open-ended problem solving, and creative and critical thinking, imaginative, reflective, have the capacity to apply their information in new issues, and to express oral and composing. This paper aims to discover the impact of utilizing (PBLA) on students’ performance at College of Education (Ibn –Rushd) in English Department the third stage. To fulfill the aim, a sample of the study is (64) EFL students of two groups. The study used a test and an instrument design .The test group ut

The objective of the study was to predict crop coefficient (K) values for cucumber inside the greenhouse during the growing season 2014, using watermarks gypsum blocks and atmometer c apparatus during the growing stages and to compare the predicted values of the crop coefficient with different methods and approaches. The study was conducted in the greenhouses field within Al-Mahawil Township, 70 km south of Baghdad, Iraq. The watermarks soil water sensors and atmometer apparatus were used to measure crop evapotranspiration and reference evapotranspiration on daily basis, respectively. The comparison and the statistical analysis between the calculated K in this study and values obtained from greenhouse gave a good agreement. The root mean

Anatomical changes in internal tissue of stem and leaf when seed and plant treated with acids to enhance growth and development in maize was studied during the spring seasons of 2019 and 2020. Randomized complete block design was used with three replications. Main plots received foliar nutrition treatments, including ascorbic acid (AA), citric acid (CA), and humic acid (HA) at concentrations of 100 mg L−1, alongside HA at 1 ml L−1, with distilled water as the control. Sub-plots underwent corresponding treatments for seed soaking. Results indicated variations in vascular bundle size among treatments, with foliar CA treatment showing superior results in both years, as well as seed soaking in CA and HA. Interaction effects were observed, n

To determine the potential of gingival crevicular fluid (GCF) volume, E‐cadherin and total antioxidant capacity (TAC) levels to predict the outcomes of nonsurgical periodontal therapy (NSPT) for periodontitis patients.

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi