This study aims to use dialectical thinking to explore the impacts and responses of Artificial Intelligence (AI) empowerment on students’ personalized learning. The effect of AI empowerment on student personalization is dissected through a literature review and empirical cases. The study finds that AI plays a significant role in promoting personalized learning by enhancing students’ learning effectiveness through intelligent recommendation, automated feedback, improving students’ independent learning ability, and optimizing learning paths, however, the wide application of AI also brings problems such as technological dependence, cheating in exams, weakening of critical thinking ability, educational fairness, and data privacy protection to students. The study proposes recommendations to strengthen technology regulation, enhance the synergy between teachers and AI, and optimize the personalized learning model. AI-enabled personalized learning is expected to play a greater role in improving learning efficiency and educational fairness.

This study aimed to assess the influence of awareness and health habituation techniques, student management activities, the role of stakeholders, and the character of healthy living on health independence. The method used in this study is quantitative with descriptive test analysis techniques, partial t statistics and F test. This research was conducted in elementary schools in East Java Province, consisting of 92 elementary schools in 5 regions at East Java. Samples were taken using purposive techniques, and the number of samples was 348 people, consisting of principals, teachers and students. The results found that awareness and health habituation techniques have a significant influence on the character of healthy life of students, student management activities have a significant influence on the character of healthy life, the role of stakeholders has a significant influence on the character of healthy life, awareness and health habituation technique have a significant influence on health independence, student management activities have a significant influence on health independence, the role of stakeholders has a significant influence on health independence, the character of healthy living has a significant effect on health independence, and student management activities and the role of stakeholders have a significant effect on the character of healthy life, and have a significant impact on health independence.

Several studies have investigated Islamic endowment (Waqf), but less attention has been given to the application of legal principles of Islamic objectives in the regulation and management of Islamic endowments in Muslim communities. The primary focus of this study is to explore the legal implementation of Maqasidush-Shari’ah or otherwise known as the Objectives of Islamic Law, as evidenced in Islamic charitable endowments. This study employs an analytical research approach (ARA), systematic literature review (SLR) and content analysis (CA) to demonstrate and evaluate how the Waqf institution can be revitalized in contemporary times, drawing parallels with its effective implementation during the formative years of Islam, rooted in the principles of Maqasidush-Shari’ah. The results demonstrate that the efficacy of Waqf typically stems from the societal advantages it offers, derived from the safeguarding of faith, property, life, honour, and lineage, which are fundamental of Maqasidush-Shari’ah or objectives of Islamic law. The study further demonstrated that Islamic endowment has various benefits such as providing grant to the social development and interests to the public. However, various challenges such as knowledge deficit in the application of Shari‘ah principles in Waqf, lack of a developed framework for managing various types of Waqf among others are identified. Nonetheless, effective regulation and management of Waqf applications of Islamic objectives on Waqf. In conclusion, this study has underscored the significant contributions of the Islamic endowment system across various spheres, including social welfare, scientific advancements, economic prosperity, and healthcare, all of which align with the objectives of Islamic legal principles encapsulated in Maqasidush-Shari’ah. Hence, the research ultimately proposes several favourable elements that could bolster the resurgence of Waqf in contemporary times, reviving its significance and societal impact. It is therefore suggested that the stakeholders should enhance understanding of the policies, legal principles, and governance structures governing Waqf as an Islamic charitable foundation, substantiated by Islamic objectives (Maqasidush-Shari’ah).

the study deals with the issue of mining transport technology and its use in mines in Slovakia and Hungary at the end of the 19th and the beginning of the 20th century. It focuses on the analysis and comparison of the transport infrastructure used in these mines, either as original Slovak inventions or as products of foreign provenance. The research is based on the analysis of monographic and periodical press production from this period, where these technological achievements were presented and discussed. In addition, the study examines the media presentation of these products in the contemporary traditional periodical press. The findings of the study offer an important historical perspective on the development of mining transport technology and related industries in the region and contribute to the understanding of the media presentation and promotion of mining technology. This research is in line with the objectives of the “CultureMind” project, which focuses on the promotion and promotion of cultural heritage through media and education.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

Unmanned Aerial Vehicles (UAVs) have gained spotlighted attention in the recent past and has experienced exponential advancements. This research focuses on UAV-based data acquisition and processing to generate highly accurate outputs pertaining to orthomosaic imagery, elevation, surface and terrain models. The study addresses the challenges inherent in the generation and analysis of orthomosaic images, particularly the critical need for correction and enhancement to ensure precise application in fields like detailed mapping and continuous monitoring. To achieve superior image quality and precision, the study applies advanced image processing techniques encompassing Fuzzy Logic and edge-detection techniques. The study emphasizes on the necessity of an approach for countering the loss of information while mapping the UAV deliverables. By offering insights into both the challenges and solutions related to orthomosaic image processing, this research lays the groundwork for future applications that promise to further increase the efficiency and effectiveness of UAV-based methods in geomatics, as well as in broader fields such as engineering and environmental management.