Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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The Impact of Inclusive Finance on Reducing the Urban-Rural Income Gap in Henan Province: An Analytical Study
No full textChina is undergoing a transition from being a major technology importer to becoming a leading technology innovator, with rapid digital transformation across its economy and society. The accessibility of financial services is emerging as a crucial platform that supports comprehensive economic and social development, significantly contributing to economic growth. However, during this process, Henan Province, known for its agricultural modernization initiatives and large agricultural population, has experienced a steady increase in farmers\u27 incomes and significant shifts in income structure. Nonetheless, considerable income disparities remain among different regions, and the development levels of inclusive finance vary significantly, highlighting substantial imbalances. In this context, this article examines 17 prefecture-level cities in Henan Province, calculating the inclusive finance index for each city from 2010 to 2021, and investigates the relationship between inclusive finance and the urban-rural income gap. Panel data analysis indicates that enhancing the level of inclusive finance can reduce the urban-rural income gap. Based on the findings, the article proposes policy recommendations from the perspective of inclusive finance to further narrow the urban-rural income gap in Henan Province. Rural finance is deemed the cornerstone of the modern rural economy. This study elucidates the vital role of inclusive finance in rural economic development and urban-rural income equilibrium and how fostering the development of inclusive finance can drive rural economic growth and balanced income growth between urban and rural residents
Dominance of Artificial Intelligence and Machine Learning Algorithms in Real-Time Traffic Flow prediction and Route Optimization in Autonomous Vehicles
No full textThe evolution of autonomous vehicles has elicited significant interest in understanding how real-time data may be used to provide enhanced driving experiences. This research project explored AI and Machine Learning methodologies applied for traffic forecasting and route optimization, and their implications for autonomous vehicles and urban mobility. For this project, the road traffic flow Dataset was utilized from Kaggle, containing 48,000 records of the flow of traffic, each including the following key features. In our work, we deployed some credible and well-established machine learning models: linear regression and random forest. These algorithms were separately trained by using a part of preprocessed data. The MSE for the Random Forest model was significantly lower, which means that the Random Forest Regressor had much smaller errors in estimating the volume of traffic compared to the Linear Regression model. The Random Forest Model had a high R² score, proving that this model explains a great deal of variance in the volume of traffic. This means that the \u27model of random forest regressors\u27 excellently fitted the data, snatching most of the important patterns and relationships between input features and target variables
Evaluating Self-Study Practices and Needs for Chinese Idioms among Upper-Year Students at Ho Chi Minh City University of Education
No full textThis study aims to investigate the current status and requirements concerning self-studying Chinese idioms among third- and fourth-year students at Ho Chi Minh City University of Education. The research team conducted surveys among 50 third-year students (K47) and 50 fourth-year students (K46) to gather data. The survey questions were designed to explore students\u27 self-assessment of their proficiency in using Chinese idioms, their retention and utilization levels of idioms, and their demand for an online self-study resource. The results indicate a prevailing lack of confidence among the majority of students in both cohorts regarding their ability to comprehend and apply Chinese idioms. Additionally, there is a noticeable desire for an online idiom database to facilitate their self-study endeavors. This study underscores the pressing need of students for effective resources and strategies to improve their Chinese idiom proficiency. The identification of these needs presents an opportunity for educational institutions and online learning platforms to develop tailored solutions that cater to students\u27 requirements, thereby enhancing the efficacy of Chinese idiom learning. By shedding light on the current challenges and demands of students in self-studying Chinese idioms, this research contributes to the ongoing discourse on language education and learning strategies. Furthermore, the study proposes practical solutions aimed at addressing these needs, thereby offering valuable insights for educators, curriculum developers, and educational technology providers
Behavioral Intention to Adopt Artificial Intelligence in Educational Institutions: A Hybrid Modeling Approach
No full textThe introduction and implementation of Artificial Intelligence (AI) in higher education has brought out new opportunities and obstacles. The utilization of AI will result in a significant transformation of the governance structure within global higher educational institutions. The potential use of AI involves exploring the educational implications of how teachers may enhance their teaching methods, how students can improve their learning experience, and how institutions of higher education can make more accurate and timely judgments. This is significant because the workload has significantly increased as a result of the widespread expansion of higher education. Given the circumstances, AI assistance is crucial. The implementation of artificial intelligence in higher education is a significant matter in this context. The objective of this study is to investigate the feasibility of individuals adopting it. To do this, we have formulated hypotheses and a conceptual framework, which we then validated through a survey by obtaining feedback from a total of 240 respondents. Research has discovered that the model can assist authorities in promoting the implementation of artificial intelligence in higher education. The outcome of this study will help practitioners understand the insights of people’s intentions and psychology in adopting AI in educational sectors
Sudoku Solver – A Novel Approach Using Recognition of Printed Text Image
No full textDetecting and analyzing text in multimedia is required for recognition of characters which can be used for performing multi-objectives in numerous research areas and applications. Characters or numbers in images can be hand-written, printed document, typewritten or artificially added for the purpose of searching or indexing, editing or image understanding. Various applications such as License Plate Detection, Handwritten documents recognition, Sudoku Solving, multi-oriented recognition focuses on character recognition. Image Sudoku solver works on Sudoku puzzle images. In this paper, the printed text Sudoku image is used as the input. The image is enhanced by filling the horizontal and vertical gaps in the grids that may occur due to shadow or distortion of the scanned image such that all the 81 grids are extracted. Secondly, the numbers and grids in the enhanced image are extracted and a dancing link approach using Algorithm X is adopted to solve the Sudoku. The proposed work is compared with a backtracking approach and dancing link approach without enhancing the image and its computation time for various ranks of difficulty of a puzzle taken. The Sudoku puzzle can be used to secure information stored in an image as the number of solutions to solve a Sudoku puzzle is very large, allowing it to be used as a security tool resistant to brute force attacks
Predicting Heart Failure Survival with Machine Learning: Assessing My Risk
No full textThis study investigates the application of machine learning techniques for heart disease prediction using a comprehensive dataset of 918 patients. The research employs multiple algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Networks, to develop predictive models based on 11 clinical features. The dataset, compiled from five independent sources, underwent thorough preprocessing and was split into training (70%) and test (30%) sets. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Results demonstrate consistently high performance across all models, with the SVM achieving the highest overall performance (accuracy: 88.41%, precision: 89.76%, recall: 90.85%, F1-score: 90.30%, ROC-AUC: 94.97%). Key predictors identified include age, maximum heart rate, and ST depression (Oldpeak). The study\u27s findings have significant implications for clinical practice, offering the potential for rapid, objective heart disease risk assessment. The consistent performance across different model architectures provides flexibility for implementation in various healthcare settings. Limitations include potential data collection variability and gender imbalance in the dataset. Future research directions include developing more sophisticated neural networks, incorporating additional data types, and conducting prospective studies to validate model performance in real-world clinical settings. This research contributes to the growing body of evidence supporting the use of machine learning in medical diagnostics. The developed models enhance early detection and risk stratification of heart disease, potentially improving patient outcomes through timely interventions
Current Status of Marco Polo Sheep (Ovis ammon polii) in the Pamir Mountains of Badakhshan Province, Afghanistan
No full textThe present survey was conducted to determine the abundance and population density of Marco Polo sheep in the Pamir Mountains of Badakhshan Province, Afghanistan. Marco Polo sheep inhabitants in very high mountain regions experience extremely cold winds and arid climatic conditions throughout the year. The Marco Polo sheep is listed as a critically endangered species on the IUCN Red List. Field surveys and interviews were carried out from 2022 to 2023 by using semi-structured questionnaires. 98 respondents were interviewed, and line transect walks in the field were used to observe the Marco Polo Sheep in the study region. As a result, a total of 1304 Marco Polo Sheep individual were recorded in the Pamir regions. The highest number of Marco Polo Sheep observations is related to the Tollaboy region, with 452 individuals (34.6%), and the lowest number of observations is in the Angelic region, 93(7.1%). According to the study area\u27s locality, the population density of the Marco Polo Sheep differs in each season of the year. In conclusion, the highest density was in the Tollaboy region with 125.5± 3.5 per km2, and the lowest density was in the Angelic region10.7±1.6 per km2 ware observed
Gut Microbiome and Microglial Interactions in Neurodegenerative Diseases
No full textThe gut microbiome is a diverse ecosystem of trillions of microbes in the gastrointestinal tract. The microbiome has been an area of growing interest as new methods, such as sequencing and culturing techniques, have developed, shedding light on the extensive effects the gut microbiome has on various other body systems. This review focuses on the neurological system and the communication pathways between the gut and brain via the gut-brain axis. Because of the gut-brain axis, a healthy gut environment fosters increased healthiness of the brain, but when the microbiome is imbalanced - a condition called dysbiosis - brain health suffers. When dysbiosis occurs, several negative ramifications occur in various parts of the body. In the brain, microglia cells (innate immune response cells) can express a different phenotype and may be overactivated, resulting in the initiation of proinflammatory pathways. Inflammation in the brain, or neuroinflammation, is a characteristic of many neurodegenerative diseases, such as Alzheimer’s and Parkinson’s. Complex interactions between the gut microbiome and microglia exist, including how gut-derived metabolites such as trimethylamine oxide and short-chain fatty acids increase microglial activation and neuroinflammation. However, therapeutic approaches targeting microglia and the gut-brain axis through tryptophan metabolites and bile salts mitigate neuroinflammation. Understanding these mechanisms opens potential avenues for reducing neuroinflammation and treating neurodegenerative diseases through the gut microbiome and microglia relationship
Towards a Glocalized Learner Autonomy: A Systematic Review of Moroccan Higher Education
No full textThis systematic review explores the integration of learner autonomy within Moroccan Higher Education, focusing on its current state and the factors influencing its development and effectiveness. A qualitative thematic analysis was employed to review and synthesize existing literature, providing a comprehensive understanding of the topic. The review identifies a fragmented understanding of learner autonomy in Moroccan academia, with many studies relying on Western definitions and models. While theoretically sound, these models may not fully address the practical challenges in Moroccan classrooms. An emerging trend is the incorporation of self-regulated learning within autonomous learning frameworks, though its application remains underexplored. The review highlights a significant gap in research on practical strategies for fostering learner autonomy, with most studies focusing on perceptions and attitudes rather than actionable interventions. Methodologically, there is a notable dominance of quantitative approaches, which, while valuable, fail to capture the nuanced experiences of learners and educators, underscoring the need for qualitative research. The findings emphasize the necessity of a glocalized conception of autonomy, integrating local educational practices and cultural nuances to foster a more relevant approach. This review underscores the importance of engaging both teachers and students in developing and implementing autonomous learning strategies, ensuring that practices are grounded in classroom realities. By adopting a participant-centered research approach and embracing methodological diversity, more effective educational practices and policies can be developed, enhancing autonomous learning in Moroccan higher education
High-Agency Teaching Practices: The Case of English Teachers in Moroccan Secondary Education
No full textThe feeling of ownership and sense of control, often referred to as learner agency that students have over their learning, has recently garnered significant attention in English language teaching. Instructional strategies that empower learners have yielded promising results with regard to student engagement and academic outcomes. This study aimed to investigate the prevalence of high-agency practices among English teachers in Moroccan secondary education. Quantitative Data was collected using a questionnaire. The sample comprised 130 Moroccan English teachers working in public schools. The findings revealed a strong inclination among the surveyed teachers towards utilizing teaching practices that support learner agency. However, certain areas, such as providing self-access opportunities and embracing uncertainty, exhibited varying frequencies among respondents. Despite these differences, the study highlighted that Moroccan English teachers are at the frontiers of pedagogical innovation. This research pinpoints specific areas for further development to enrich the cultivation of learner agency in language education