IoT, as an emerging technology along with GIS, can result in advanced and user-friendly features in Smart Cities. In order to investigate the capabilities offered by these technologies, this paper provides an overview of GIS and IoT integration focusing on applications and architecture. Specifically, this paper starts with investigating the role of GIS and IoT separately and jointly in different domains. Then, a review of GIS and IoT integration studies is provided to examine how GIS could be used in IoT architecture. The results showed that the capabilities of GIS in dealing with geospatial data and attributes along with offering visualization and analyzing tools make it possible to develop an integrated system benefiting from real-time data collection and real-time monitoring provided by IoT. The presented details would assist researchers in future studies on utilizing GIS and IoT at the same time.

The importance of early diagnosis of Alzheimer's Disease (AD) is by no means negligible because no cure has been recognized for it rather than some therapies only lowering the pace of progression. The research gap reveals information on the lack of an automatic non-invasive approach toward the diagnosis of AD, in particular with the help of Virtual Reality (VR) and Artificial Intelligence. Another perspective highlights that current VR studies fail to incorporate a comprehensive range of cognitive tests and consider design notes for elderlies, leading to unreliable results. This paper tried to design a VR environment suitable for older adults in which three cognitive assessments namely: ADAS-Cog, Montreal Cognitive Assessment (MoCA), and Mini Mental State Exam (MMSE), are implemented. Moreover, a 3DCNN-ML model was trained based on the corresponding cognitive tests and Magnetic Resonance Imaging (MRI) with different modalities using the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset and incorporated into the application to predict if the patient suffers from AD. The model has undergone three experiments with different modalities (Cognitive Scores (CS), MRI images, and CS-MRI). As for the CS-MRI experiment, the trained model achieved 97%, 95%, 95%, 96%, and 94% in terms of precision, recall, F1-score, AUC, and accuracy respectively. The considered design notes were also assessed using a new proposed questionnaire based on existing ones in terms of user experience, user interface, mechanics, in-env assistance, and VR induced symptoms and effects. The designed VR system provided an acceptable level of user experience, with participants reporting an enjoyable and immersive experience. While there were areas for improvement, including graphics and sound quality, as well as comfort issues with prolonged HMD use, the user interface and mechanics of the system were generally well-received. The reported results state that our method's comprehensive analysis of 3D brain volumes and incorporation of cognitive scores enabled earlier detection of AD progression, potentially allowing for timely interventions and improved patient outcomes. The proposed integrated system provided us with promising insights for improvements in the diagnosis of AD using technologies.

Education has always been modified by employing different technologies to enhance the knowledge acquisition and performance of students. Virtual Reality (VR) along with the Game Industry is among those evolving technologies for educational applications. This study aimed to design, implement, and evaluate an immersive VR-based Educational Game (IVREG) for learning topology relations. Topology relations are one of the fundamental topics which exist in Geospatial Information Science (GIS); due to the great capabilities offered by GIS, learning these basic topis is of great importance. A total of thirty-seven male middle-school students participated in this study. A total of four questionnaires were designed to evaluate the suitability of the proposed learning environment and its components at schools, particularly for learning geospatial topics. In conclusion, students found the IVREG useful and effective in classrooms. Additionally, the results showed that the components, namely the integrated pedagogical approach, gamification, and VR technology, were all suitable for being used at schools. On a final note, however, the study indicated that the immersion aspect of such learning environments should be enhanced in future studies.

Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While SST is highly affected by different oceanic, atmospheric, and climatic parameters, few papers have investigated time-series SST prediction based on multiple features. This paper utilized multi features of air pressure, water temperature, wind direction, and wind speed for time-series hourly SST prediction using deep neural networks of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM. Models were trained and validated by different epochs, and feature importance was evaluated by the leave-one-feature-out method. Air pressure and water temperature were significantly more important than wind direction and wind speed. Accordingly, feature selection is an essential step for time-series SST prediction. Findings also revealed that all models performed well with low prediction errors, and increasing the epochs did not necessarily improve the modeling. While all models were similarly practical, CNN was considered the most suitable as its training speed was several times faster than the other two models. With all this, the low variance of time-series data helped models make accurate predictions, and the proposed method may have higher errors while working with more variant features.

Augmented Reality (AR) is a technology that enhances a person’s sensory perception by overlaying virtual objects in the user’s immediate surroundings. Furthermore, with the development of technologies, devices such as smartphones and head-mounted displays are being launched and are expanding the AR technology application sectors from research labs to a wide range of domains. On the other hand, Geospatial Information System (GIS) is capable of dealing with geospatial information, so it can be beneficial in most AR systems mainly because those systems are connected to location and information related to locations. The ultimate integrated solution could be beneficial for Sustainable Development Goals (SDGs). This paper investigates the combination of AR and GIS. Specifically, it studies the advantages of integration to address the challenges available in systems employing merely one of the technologies. The presented findings would assist researchers in future studies on utilizing GIS and AR simultaneously by giving an overview of the current applications and challenges.

The rapid advancement of technology is reshaping education, and the metaverse stands at the forefront of this transformation. Beyond its roots in gaming and entertainment, the metaverse is poised to revolutionize how we learn by creating immersive, interactive, and personalized educational environments. However, despite its potential, a comprehensive framework for integrating the metaverse into education remains absent. This study conducts a systematic literature review to analyze and categorize existing research on education in the metaverse. The review follows a PRISMA-based methodology, utilizing Scopus, Web of Science, and PubMed databases for data collection. Studies were analyzed to identify trends, goals, and types of metaverse technologies explored. Building on this review, this paper proposes a Meta-education framework that addresses the interconnected roles of learners, instructors, and institutions in metaverse-driven education systems. The framework highlights how the metaverse enhances learner engagement, supports innovative teaching strategies, and enables institutional scalability. This study concludes that while the metaverse holds great potential to revolutionize education, significant challenges remain, including content development, instructor readiness, and integration of AI technologies. The proposed framework provides practical guidelines for future research and offers implications for educators, policymakers, and researchers aiming to create inclusive, sustainable, and transformative learning environments.