Social media platforms serve as avenues for individuals to express themselves and share pertinent details concerning their mental well-being through posts and comments. However, many individuals tend to overlook their mental health. This data lends itself to insightful analysis of an individual's psychological state through sentiment analysis techniques. The research explores the utilization of sentiment analysis techniques on social media data, specifically focusing on mental health discussions. Data gathered from platforms like Twitter is preprocessed and then used to train various Transformer models including DistilBERT, Albert, and a hybrid BERT-CNN model. Notably, the BERT-CNN hybrid model achieved a remarkable accuracy of 95%. This outcome underscores the effectiveness of advanced model architectures in analyzing mental health-related sentiment on social media. The significance of this research lies in its potential to offer valuable insights into individuals' mental states through computational analysis of their online expressions. The study's thorough methodology, encompassing data collection, preprocessing, and model training, sets a strong precedent for future research in this domain. Additionally, the successful performance of the BERT-CNN hybrid model highlights the importance of innovative model design in achieving accurate sentiment analysis results. Overall, this research contributes to the growing body of knowledge aimed at leveraging technology for mental health awareness and support.

The purpose of this article is to search and extract the necessary content, identifying curriculum topics. Classification and clustering of text documents are challenging artificial intelligence tasks. Therefore, an important objective of this study is to propose and implement a tool for analyzing textual information.
The study used Data Mining methods to analyze text data and generate educational content. The work used methods for classifying text information, namely, support vector machines (SVM), Naive Bayes classifier, decision tree, K-nearest neighbor (kNN) classifier.
These methods were used in developing the curriculum for the specialty “Cybersecurity” for the Faculty of Information and Telecommunication Technologies. About 48 curricula in this specialty were analyzed, topics and sections in disciplines were identified, and the content of the academic program was improved. It is expected that the results obtained can be used by specialists, managers and teachers to improve educational activities.

This paper is dedicated to a comprehensive analysis of hybrid energy options, with a specific focus on exploring their economic and environmental advantages within the context of an ice cream factory located in Fukuoka, Japan. The study takes a holistic approach, delving into various facets such as power generation, energy expenses, and related factors to uncover the potential benefits associated with specific configurations of hybrid energy solutions.  The analysis presented in this study serves as a valuable tool for assessing the impact of different power generation technologies and energy management strategies. It sheds light on how these choices can influence not only the factory's operational costs but also its environmental footprint. By quantifying these effects, the study provides critical insights  
that can guide decision-makers toward more sustainable and economically sound energy solutions. As a forward-looking application approach, this research envisions the utilization of a PV-wind-diesel-grid-electrolyzer power system. This hybrid configuration serves as a versatile platform for conducting simulation studies, allowing for the exploration of a wide spectrum of potentially viable solutions. The insights derived from these simulations not only facilitate informed decision-making but also pave the way for anticipating and strategically planning future energy implementations. In essence, this study represents a proactive and data-driven approach to energy optimization, offering the ice cream factory in Fukuoka a roadmap to harnessing the benefits of hybrid energy systems, ultimately contributing to both economic efficiency and environmental sustainability. So, at a cost of energy (COE) of 18.313¥ per kWh, this arrangement stands out as an economically advantageous and environmentally friendly solution for the electrification of the ice cream factory.

The large volume of electricity consumption data calls for the aggregation of this data. The implementation of aggregation methods is therefore a major concern to which an answer is given by presenting a case of aggregation of electricity consumption data using the jump process. A data set made it possible to carry out simulations and to present the results obtained for the daily, monthly and annual aggregations. The principle of using the jump process for the approval of these data is highlighted. This work is a concrete presentation of a simulation for the aggregation of electricity consumption data in a network of wireless sensors that can constitute a network of smart meters. The approach of this work consists in using aggregation methods to reduce the flow of data exchanges in wireless sensor networks. In fact, this work highlights several interesting properties that justify the choice of the jump process including flexibility, modeling of rare events, management of uncertainties adaptability to non-stationary data management of fluctuations in demand, consideration of volatility effects and scalability. Many significant impacts are expected, including improving network stability, optimizing resource management, reducing operational costs, integrating renewable energies, and data-driven decision-making. The jump process also presents limitations including modeling complexity, model calibration, computational complexity, interpretability of results, uncertainty management.

The Metaverse is an emerging virtual universe that combines technologies such as VR, AR, AI, and IoT, to captivate the user in an artificial universe that blends reality and technology seamlessly. The metaverse provides a plethora of opportunities to vastly expand and realize the capabilities of our imagination as technology materializes ideas into intractable objects for an enhanced experience. The metaverse can also impact the education sector offering a unique opportunity to revolutionize education by providing immersive and interactive learning experiences through 3D avatars. In traditional libraries, students read books to learn about a vast array of topics and can visualize those topics through 2D images provided in the books, but such images might not always be the best stimulus to enhance the reader’s imagination. The introduction and integration of “Digital Libraries” through the Metaverse enable school students to explore various subjects in 3D environments, providing a practical approach to knowledge and improving comprehension, resulting in enjoyable and effective learning experiences. Metaverse enables seamless embodied user communication in real-time and dynamic interactions with digital antiquities. Besides, a digital library is defined as an online database of digital collections. It contains a lot of papers and resources which are arranged digitally. Its benefits include effectiveness, accuracy, authenticity assurance, simpler plagiarism management, easy accessibility, and high convenience. However, it has its drawbacks too such as copyright issues, distractions due to notifications, and various health hazards. In response to those drawbacks, how a digital library may use the Metaverse to add visual and auditory effects to enrich information provided in books has been illustrated, and how digital libraries in the Metaverse have the potential to revolutionize education while also addressing and potentially solving the problems that afflict traditional libraries has been highlighted by suggesting a prototype of the digital library in the Metaverse.