Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Conversion of the ocean’s vertical thermal energy gradient to electricity via OTEC has been demonstrated at small scales over the past century. It represents one of the planet’s most significant (and growing) potential energy sources. As described here, all living organisms need to derive energy from their environment, which heretofore has been given scant serious consideration. A 7th Law of Thermodynamics would complete the suite of thermodynamic laws, unifying them into a universal solution for climate change. 90% of the warming heat going into the oceans is a reasonably recoverable reserve accessible with existing technology and existing economic circumstances. The stratified heat of the ocean’s tropical surface invites work production in accordance with the second law of thermodynamics with minimal environmental disruption. TG is the OTEC improvement that allows for producing two and a half times more energy. It is an endothermic energy reserve that obtains energy from the environment, thereby negating the production of waste heat. This likewise reduces the cost of energy and everything that relies on its consumption. The oceans have a wealth of dissolved minerals and metals that can be sourced for a renewable energy transition and for energy carriers that can deliver ocean-derived power to the land. At scale, 31,000 one-gigawatt (1-GW) TG plants are estimated to displace about 0.9 W/m² of average global surface heat into deep water, from where, at a depth of 1000 m, unconverted heat diffuses back to the surface and is available for recycling.

This review focuses on ferrites, which are gaining popularity with their unique properties like high electrical resistivity, thermal stability, and chemical stability, making them suitable for versatile applications both in industry and in biomedicine. This review is highly indicative of the importance of synthesis technique in order to control ferrite properties and, consequently, their specific applications. While synthesizing the materials with consideration of certain properties that help in certain methods of preparation using polyol route, green synthesis, sol-gel combustion, or other wise to tailor make certain properties shown by ferrites, this study also covers biomedical applications of ferrites, including magnetic resonance imaging (MRI), drug delivery systems, cancer hyperthermia therapy, and antimicrobial agents. This was able to inhibit the growth of all tested Gram-negative and positive bacteria as compared with pure ferrite nanoparticles without Co, Mn or Zn doping. In addition, ferrites possess the ability to be used in environmental remediation; such as treatment of wastewater which makes them useful for high-surface-area and adsorption capacity due heavy metals and organic pollutants. A critical analysis of functionalization strategies and possible applications are presented in this work to emphasize the capability of nanoferrites as an aid for the advancement both biomedical technology and environmental sustainability due to their versatile properties combined with a simple, cost effective synthetic methodology.

This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.

This paper discusses the concept of creating a new reality using the approaches of smart cities to develop eco-cities, in which the necessary balance between nature and progress can be maintained. The authors propose that the concept of smart cities should be used as a tool for the creation of eco-cities, and argue that the positive synergies between the two will be strongest if the smart concept acts as a tool for the creation of eco. The core elements of a smart eco-city are identified as smart sustainable use of resources, a smart sustainable healthy community, and a smart sustainable economy. The results of the article were the foundation for the development concept for Vision Bratislava 2050—the vision and strategy for the development of the capital of the Slovak Republic. The authors also discuss the challenges of transforming cities into smart eco-formats, including the need for digital resilience in the face of potential cataclysms. They suggest that this is a promising area for further research into the concept of smart eco-cities.

Naturally occurring radionuclides can be categorized into two main groups: primordial and cosmogenic, based on their origin. Primordial radionuclides stem from the Earth’s crust, occurring either individually or as part of decay chains. Conversely, cosmogenic radionuclides originate from extraterrestrial sources such as space, the sun, and nuclear reactions involving cosmic radiation and the Earth’s atmosphere. Gamma-ray spectrometry is a widely employed method in Earth sciences for detecting naturally occurring radioactive materials (NORM). Its applications vary from environmental radiation monitoring to mining exploration, with a predominant focus on quantifying the content of uranium (U), thorium (Th), and potassium (K) in rocks and soils. These elements also serve as tracers in non-radioactive processes linked to NORM paragenesis. Furthermore, the heat generated by radioactive decay within rocks plays a pivotal role in deciphering the Earth’s thermal history and interpreting data concerning continental heat flux in geophysical investigations. This paper provides a concise overview of current analytical and measuring techniques, with an emphasis on state-of-the-art mass spectrometric procedures and decay measurements. Earth scientists constantly seek information on the chemical composition of rocks, sediments, minerals, and fluids to comprehend the vast array of geological and geochemical processes. The historical precedence of geochemists in pioneering novel analytical techniques, often preceding their commercial availability, underscores the significance of such advancements. Geochemical analysis has long relied on atomic spectrometric techniques, such as X-ray fluorescence spectrometry (XRFS), renowned for its precision in analyzing solid materials, particularly major and trace elements in geological samples. XRFS proves invaluable in determining the major constituents of silicate and other rock types. This review elucidates the historical development and methodology of these techniques while showcasing their common applications in various geoscience research endeavors. Ultimately, this review aims to furnish readers with a comprehensive understanding of the fundamental concepts and potential applications of XRF, HPGes, and related technologies in geosciences. Lastly, future research directions and challenges confronting these technologies are briefly discussed.