This study explores the impact of Project-Based Learning (PBL) and locally sourced reading materials on improving speaking proficiency in English as a Foreign Language (EFL) learners. The participants consist of college students aged 18 to 19 years. Forty-four participants from two groups—experimental and control—were evaluated using pre-and post-tests. The experimental group engaged with local cultural reading materials and followed a PBL framework, while the control group used standard commercial textbooks from Western publishers. The findings reveal that the experimental group demonstrated significantly improved fluency, vocabulary, and speaking confidence compared to the control group, with 37.04% showing improvement. PBL facilitated collaborative learning in real-life scenarios, reducing anxiety and fostering more significant participation in speaking tasks. In contrast, the control group showed minimal improvement, highlighting the limitations of traditional lecture-based methods. This study concludes that integrating PBL and locally relevant content into language instruction can enhance speaking proficiency, learner motivation, and engagement. The results suggest that PBL is a dynamic approach that supports developing linguistic and collaborative skills, providing a more holistic learning experience.

Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.

This research delves into the intricate dynamics of ethical leadership within the context of Vietnamese Small and Medium Enterprises (SMEs). By scrutinizing its impact on organizational effectiveness, the study unveils a comprehensive understanding of the interconnectedness between ethical leadership, knowledge sharing, and organizational learning. Employing a mixed-methods approach, the research investigates the mediating roles played by knowledge sharing and organizational learning in the relationship between ethical leadership and organizational effectiveness. Through empirical analysis and case studies, this study contributes valuable insights to the literature, offering practical implications for fostering ethical leadership practices in Vietnamese SMEs to enhance overall organizational effectiveness. The findings shed light on the nuanced mechanisms through which ethical leadership contributes to sustainable success, emphasizing the pivotal roles of knowledge sharing and organizational learning in this intricate relationship.

This study aims to explore the feasibility of using virtual reality technology to educate students with learning difficulties in the Asir region. To achieve the study aims, the researcher employed a descriptive design and deployed a quantitative technique, depending on the questionnaire as the main instrument for data collection. The research was carried out on a cohort of 240 educators hailing from the Asir region who were enlisted through a process of random sampling. The results of this study show that factors like infrastructure, human resources, administrative regulation, and student population have an impact on the use of virtual reality technology. The results suggest that there are no statistically significant differences in the development of using virtual reality technology among teachers of students with learning disabilities in the Asir region when taking into account factors such as experience and level of qualification.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

This research aims to develop a Synergy Learning Model in the context of science learning.  This research was conducted at Islamic Junior High School, Madrasah Tsanawiyah Negeri 2 Medan, involving 64 students of Grade 7 as the research subject. The method used in this research refers to the development research approach (R&D). In collecting the data, the research employed test and non-test techniques. The results prove that the Synergy learning model developed is effective in improving student learning outcomes. This is evident through the t-test statistical test where the t-count of 4.26 is higher than the t-table of 1.99. In addition, the level of practicality with a score of 3.39 is categorized as practical. This learning model emphasizes the learning process that supports the development of science skills and develops students' competencies in planning, collaborating, and critically reflecting. The findings of this study contribute to pedagogical practices and literature in the field of science learning.