Educational technology has played a critical role in enhancing the teaching and learning process, particularly in the digital age. This paper provides an overview of the role of educational technology in teaching and learning, including its benefits, challenges, and future trends. It examines how educational technology can support effective teaching practices, engage learners, and facilitate personalized learning experiences. The paper also explores the challenges associated with educational technology implementation, such as the digital divide, and discusses strategies for overcoming these challenges. Finally, the paper highlights emerging trends in educational technology, such as artificial intelligence and virtual and augmented reality, and their potential impact on teaching and learning. Overall, this paper demonstrates the importance of educational technology as a tool for promoting student engagement, improving learning outcomes, and preparing learners for success in a rapidly changing world.

Keywords: EdTech, Education, Modern technology, Science, Teaching, students, Digital operation, Online, Learning, Internet.

One possible technological development in solar cells is thought to be dye-sensitized solar cells (DSSCs). The energy-producing method employed by DSSCs is similar to that of plant cells and is based on the photo-electrochemical moments of electrons that are brought about by the interaction of photon energy and chemical reaction. Synthetic dyes that are employed as sensitizers in DSSCs have higher efficiency and longer endurance, but they also have drawbacks such the use of rare metallic components that are vulnerable to environmental degradation. As a result, researchers are experimenting with natural alternative sensitizers that are biocompatible. Plant pigments like carotenoids, flavonoids, anthocyanins, and chlorophyll are in charge of the chemical reactions in these natural sensitizers. Therefore, it is relatively easy to extract dyes containing this pigment from natural sources such as flowers, seeds, and leaves, among others. The structure and functioning of DSSCs are the main topics of this review. This review focuses on the structure and operations of DSSCs. Components of DSSCs photosensitizers, photo-anodes and counter electrodes and their functions have been outlined. Detailed summary of synthetic dye based DSSCs carried out by different groups, have also been reviewed. Details of numerous plant pigments found in plant-based products that are employed as sensitizers in DSSCs have been collated and explained. Open circuit voltage (V_oc), short circuit current (I_sc), fill factor (FF), and efficiency (η) based on both synthetic and natural dye have also been reported as DSSC performance metrics. Leveraging on the photosensitizers and maximizing on the working electrodes and counter electrodes, DSSCs would serve as the best alternative to meet the world energy demand.

Delivery of gentamicin sulphate through the oral route has always been difficult because the drug is not permeable in the intestinal mucosa. There is hardly any oral preparation of this valuable drug effective for many gram-negative and gram-positive organisms. This work reviewed research works to aid achieve this systemic circulation of orally-administered gentamicin sulphate prepared with a natural oil. Natural oils have not only proven a great deal of source of bioactive phytochemicals but also can be a proven vehicle for oral preparations as they can confer some desirable antimicrobial synergism with conventional therapies. Various bioavailability studies of gentamicin sulphate were reviewed which revealed a great importance of a specialized formulation need that either enhances entrapment of the drug in a mix, sustained release formulation, or as solid-lipid microparticles e.t.c. These are all pharmaceutically possible and can be achieved using a natural oil as a vehicle plus permeation enhancers for such oral drug preparation.

Due to the overwhelming number of solutions for Big Data processing during the last few years, distributed data engineering frameworks have become one of the most effective tools for real-time analyses and decision-making in numerous domains. However, these frameworks are becoming more expansive due to the sheer complexity that is driving the scale of these systems, and this is where the anomalies – or variations from the norm or anticipated patterns – are proving to be very hard to identify. Originally applied anomaly detection techniques are not effective when dealing with dynamic high dimensional and real time-based distributed environments.

Thus, the subject of this paper is the improvement of detecting anomalies in distributed data engineering frameworks using Artificial Intelligence (AI) approaches. This study aims at developing a rich framework for real-time anomaly detection with the help of the advanced machine learning techniques including deep learning-based auto encoder, generative adversarial network –GAN and recurrent neural network –RNN. It proposed solves the problem of unavailable labeled data by using unsupervised as well as semi supervised learning and targets scalability by integrating it to operate in big data ecosystems such as Apache Spark, Hadoop and Flink.

Overall performance analysis of the proposed framework is carried out by employing both synthetic and real-life datasets for accuracy, recall, F1-measure and time complexity analysis. The obtained results confirm the superiority of the proposed system over basic approaches and indicate its flexibility under changing loads and the possibility of detecting different forms of anomalous behavior such as point anomalies, contextual anomalies, collective anomalies, etc. In the same way, it clarifies how model interpretability, data privacy, and efficient resource use in a distributed environment are critical issues in this study.

These results also highlight the improvement prospects of AI-based anomaly detection in increasing the dependability, extensibility, and security of distributed data engineering platforms. It will open up new possibilities for further development of smart anomaly detection solutions, and provides the basis for further extension, possible in the form of federated learning or other combinations of AI approaches.