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This action research study employed a triangulated methodology, incorporating pre- and postquestionnaires, to explore the oral fluency development of six advanced ESL students. The study compared the impact of immersion in a U.S. target language environment with learning in a home culture context. Findings indicated that students who maximized their engagement with native English speakers within the target culture demonstrated significantly greater advancements in oral fluency compared to their peers who opted not to fully utilize the linguistic and cultural environment. This study emphasizes the transformative potential of immersive language learning experiences and contributes to the understanding of best practices in second language acquisition (SLA).

Teaching English in second language contexts seems to have succeeded in implementing only target-language policy allowed during class time. However, the problem persists in teaching English in foreign language contexts where English is not the official and/or first language in those countries. Such situations kept the question unanswered; whether the first language of the target learners should be used during class times in the form of translanguaging or not. While few studies have investigated this issue thoroughly; however very a few of them examined in depth analysis from different perspectives such as: how different factors paly various roles on this issue, how those factors correlate to each other, and how they relate to translanguaging in particular. This study investigates the correlations between translanguaging practices in English as a foreign language context during class time from teachers’ perspectives. Correlation, Structural Equation Modeling (SEM) and regression analyses showed that there was significant correlation between translanguaging practices and other examined independent variables including gender, same background between teachers and learners’ and first language; whereas no significant correlation was found regarding the teaching experience. The study recommends evaluating learners’ levels even if the focus of the study was on teachers’ perceptions for future research.

Alrayes, Muhammad AbdulMohsin. PhD. The University of Memphis. May 2020. The Role of First Language Literacy on Second Language Literacy: The Perceptions of Graduate Saudi Students in US Universities. Major Professor: Dr. Emily Thrush. This study attempted to gather the perceptions of graduate Saudi students in the United States about the role of their first/second languages'(L1/L2) literacy transfer during their graduate work. The ultimate goal of this study is to reach a level of understanding about what specific role a student’s L1 literacy and educational background could play in shaping their L2 literacyduring graduate studies in an ESL context. Building on existing literature of L1/L2 literacy transfer, this study asks: How do graduate Saudi students in the US perceive their L1/L2 literacy transfer during their graduate work? The main theoretical framework this study follows is James Cummins' Common Underlying Proficiency (CUP). A mixed-methods approach is used to achieve the purpose of this study. A questionnaire was distributed among fifty participants, who were divided into two groups: 25 males and 25 females. The questionnaire was followed up by a semi-structured interview wherefour of the participants were selected using purposive sampling serving the purpose of this study. The qualitative analysis model followed in this study during the different stages of analyzing the interviews includes: organizing the data, categorizing and coding the data, proposing themes based on the categories made previously, and writing the results. The findings of this study support the existence of L1/L2 literacy transfer, whether it is negative or positive. There was emphasis on the need to improve the teaching of L1 literacy skills in Saudi Arabia, according to the participants of this study. Also, the findings revealed that participants' L2 academic writing was a challenging stage they have faced during their L2 learning journey, due to the negative L1/L2 transfer. Finally, some participants indicated the positive transfer role of their rich L1 vocabulary toward their L2 literacy success. However, further longitudinal/comparative studies are recommended to focus on specific learners with strong/limited L1 literacy skills who continue their studies in the second language.

The theoretical and methodological approaches deployed in analyzing the data were corpus linguistics (CL) and sentiment analysis approach as a way of analyzing newspapers media discourse and sentiment representations of Saudi Arabia. The use of technology in extracting data for this study was a must due the huge amount of corpora texts which were investigated in the current research. The main source of data collection and analysis was done through sketch engine (SE). Also, the R programming language was used during the analysis process which provides several libraries for coding that makes it easier to conduct this type of research including: Tidyverse, Tidytext, Syuzhet, Textstem, Ggplot2, Readxl and Writexl. The newspapers corpora were extracted from SE dataset between the period of 1993-2013. Finally, the findings were classified in two ways: one was for each newspaper for the entire time period, and the second was for the entire newspapers' corpora data together.

Sign language is the main channel for hearing-impaired people to communicate with others. It is a visual language that conveys highly structured components of manual and non-manual parameters such that it needs a lot of effort to master by hearing people. Sign language recognition aims to facilitate this mastering difficulty and bridge the communication gap between hearing-impaired people and others. This study presents an efficient architecture for sign language recognition based on a convolutional graph neural network (GCN). The presented architecture consists of a few separable 3DGCN layers, which are enhanced by a spatial attention mechanism. The limited number of layers in the proposed architecture enables it to avoid the common over-smoothing problem in deep graph neural networks. Furthermore, the attention mechanism enhances the spatial context representation of the gestures. The proposed architecture is evaluated on different datasets and shows outstanding results.

For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically the learning rate (LR), batch size (BS), and their joint influence. In general, most of the existing research could not achieve the desired performance because the work addressed only one hyperparameter tuning. This study adopted a Cartesian product matrix-based approach, to interpret the effect of both hyperparameters and their interaction on the performance of models. To evaluate their impact, 56 two-tuple hyperparameters from the Cartesian product matrix were used as inputs to perform an extensive exercise, comprising 504 simulations for three cutting-edge architecture-based pre-trained Deep Learning (DL) models, ResNet18, ResNet50, and ResNet101. Additionally, the impact was also assessed by using three well-known optimizers (solvers): SGDM, Adam, and RMSProp. The performance assessment showed that the framework is an efficient framework to attain optimal values of two important hyperparameters (LR and BS) and consequently an optimized model with an accuracy of 99.56%. Further, our results showed that both hyperparameters have a significant impact individually as well as interactively, with a trade-off in between. Further, the evaluation space was extended by using the statistical ANOVA analysis to validate the main findings. F-test returned with p < 0.05, confirming that both hyperparameters not only have a significant impact on the model performance independently, but that there exists an interaction between the hyperparameters for a combination of their levels.

With the enhancement of the Internet of Things (IoT), smart cities have developed the idea of conventional urbanization. IoT networks permit distributed smart devices to collect and process data in smart city structures utilizing an open channel, the Internet. Accordingly, challenges like security, centralization, privacy (i.e., execution data poisoning and inference attacks), scalability, transparency, and verifiability restrict faster variations of smart cities. Detecting malicious URLs in an IoT environment is crucial to protect devices and the network from potential security threats. Malicious URL detection is an essential element of cybersecurity. It is established that malicious URL attacks mean large risks in smart cities, comprising financial damages, losses of personal identifications, online banking, losing data, and loss of user confidentiality in online businesses, namely e-commerce and employment of social media. Therefore, this paper concentrates on the proposal of a Political Optimization Algorithm by a Hybrid Deep Learning Assisted Malicious URL Detection and Classification for Cybersecurity (POAHDL-MDC) technique. The presented POAHDL-MDC technique identifies whether malicious URLs occur. To accomplish this, the POAHDL-MDC technique performs pre-processing to transform the data to a compatible format, and a Fast Text word embedding process is involved. For malicious URL recognition, a Hybrid Deep Learning (HDL) model integrates the features of stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM). Finally, POA is exploited for optimum hyperparameter tuning of the HDL technique. The simulation values of the POAHDL-MDC approach are tested on a Malicious URL database, and the outcome exhibits an improvement of the POAHDL-MDC technique with a maximal accuracy of 99.31%.

Isoflavenes have received the greatest research attention among the many groups of phytoestrogens. In this study, various isoflavene-based Mannich bases were selected for their theoretical studies. The purpose of this research was to discover the binding potential of all the designated Mannich bases acting as inhibitors against cancerous proteins EGFR, cMet, hTrkA, and HER2 (PDB codes: 5GTY, 3RHK, 6PL2, and 7JXH, respectively). For their virtual screening, DFT calculations and molecular docking studies were undertaken using in silico software. Docking studies predicted that ligands 5 and 15 exhibited the highest docking score by forming hydrogen bonds within the active pocket of protein 6PL2, ligands 1 and 15 both with protein 3RHK, and 7JXH, 12, and 17 with protein 5GTY. Rendering to the trends in polarizability and dipole moment, the energy gap values (0.2175 eV, 0.2106 eV) for the firm conformers of Mannich bases (1 and 4) replicate the increase in bioactivity and chemical reactivity. The energy gap values (0.2214 eV and 0.2172 eV) of benzoxazine-substituted isoflavene-based Mannich bases (9 and 10) reflect the increase in chemical potential due to the most stable conformational arrangements. The energy gap values (0.2188 eV and 0.2181 eV) of isoflavenes with tertiary amine-based Mannich bases (14 and 17) reflect the increase in chemical reactivity and bioactivity due to the most stable conformational arrangements. ADME was also employed to explore the pharmacokinetic properties of targeted moieties. This study revealed that these ligands have a strong potential to be used as drugs for cancer treatment.

Rock dust (RD) is a by-product of the precious metal mining industry. Some mining operations produce close to 2,000,000 Mg of RD/year, posing disposal issues. This study evaluated the physicochemical and microbial properties of RD from gold mining and its potential use in RD-based growing media. Ten media formulations were tested: Promix (Control), 100% (RD), 100% topsoil (TS), 50% RD + 50% topsoil (RDT), 25% RD + 75% topsoil (RT), 50% RD + 50% Promix (RP), 50% RD + 25% biochar + 25% Promix (RBP), 50% RD + 25% compost + 25% Promix (RCP), 50% RD + 50% biochar (RB), and Huplaso (negative control). RD particle size ranged from 0.1 to 2 mm with a bulk density of 1.5 g cm−3, while RD-based media ranged from 0.8 to 1.1 g cm−3 showing increased porosity. Nutrient content was analyzed using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), and the active microbial community assessed using PLFA biomarkers via GC-MS/FID, n = 4 and p = 0.05. Microbial analysis identified five classes (protozoa, eukaryotes, Gram-positive (G+), Gram-negative (G−), and fungi (F)), with a significant increase in G−, G+, and F in RD-based amendment RBP (28%) compared to control P (9%). G+, G−, and F showed a strong negative correlation (r = −0.98) with pH, while calcium correlated positively (r = 0.85) with eukaryotes and a strong positive correlation (r = 0.95) of cation exchange capacity with G+. This study suggests blending RD with organic amendments improves physicochemical quality and microbial activity, supporting its use in crop production over disposal.