Currently, new technologies have enabled the design of smart applications that are used as decision-making tools in the problems of daily life. The key issue in designing such an application is the increasing level of user interaction. Mixed reality (MR) is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies. Developing an MR application is complicated, and depends on the different components that have been addressed in previous literature. In addition to the extraction of such components, a comprehensive study that presents a generic framework comprising all components required to develop MR applications needs to be performed. This review studies intensive research to obtain a comprehensive framework for MR applications. The suggested framework comprises five layers: the first layer considers system components; the second and third layers focus on architectural issues for component integration; the fourth layer is the application layer that executes the architecture; and the fifth layer is the user interface layer that enables user interaction. The merits of this study are as follows: this review can act as a proper resource for MR basic concepts, and it introduces MR development steps and analytical models, a simulation toolkit, system types, and architecture types, in addition to practical issues for stakeholders such as considering MR different domains.

Education is a vital part of the development of society and it is changing over time in terms of methods, content, concepts, and models. Recently, it has been increasingly prevalent to benefit from the potentialities of Artificial Intelligence (AI) in addressing educational issues. In this research, the current state of the art of the integration of AI in K-12 education was provided. Specifically, different parts of education in which AI was employed along with the related AI categories were discussed according to different K-12 grades and courses. Additionally, technologies and environments that contributed to employing AI in education were discussed. To this end, a systematic literature review was conducted on articles and conference papers published between 2011 and 2021 in the Web of Science and Scopus databases. As the result of the initial search, 2075 documents were extracted and based on inclusive criteria and 210 documents were identified for further investigation. AI applications were categorized into Student performance, Teaching, Selection, and Behavior tasks, and Other. Machine Learning (ML) and Intelligent Tutoring System (ITS) were the most common approaches among AI categories. Furthermore, high school-related applications were more frequent and STEM courses were substantially targeted by AI. In conclusion, the remarkable impact of AI on education was concluded. The current study reveals information about the potentialities offered by AI in K-12 education which aids researchers in implementing AI-based education systems. As for future works, other databases such as ACM library and Google Scholar can be investigated as well. Furthermore, exploring the 95 papers that were excluded due to inaccessibility to their full texts can be taken into account. Finally, the papers can be also investigated in terms of pedagogical approaches or development tools.

In the future, groundwater will be the major source of water for agriculture, drinking and food production as a result of global climate change. With increasing population growth, demand for groundwater has increased. Therefore, sustainable groundwater storage management has become a major challenge. This study introduces a new ensemble data mining approach with bivariate statistical models, using FR (frequency ratio), CF (certainty factor), EBF (evidential belief function), RF (random forest) and LMT (logistic model tree) to prepare a groundwater potential map (GPM) for the Booshehr plain. In the first step, 339 wells were chosen and randomly split into two groups with groundwater yields above 11 m³/h. A total of 238 wells (70%) were used for model training, and 101 wells (30%) were used for model validation. Then, 15 effective factors, including topographic and hydrologic factors, were selected for the modeling. The accuracy of the groundwater potential maps was determined using the ROC (receiver operating characteristic) curve and the AUC (area under the curve). The results show that the AUC obtained using the CF-RF, EBF-RF, FR-RF, CF-LMT, EBF-LMT and FR-LMT methods were 0.927, 0.924, 0.917, 0.906, 0.885 and 0.83, respectively. Therefore, it can be inferred that the ensemble of bivariate statistic and data mining models can improve the effectiveness of the methods in developing a groundwater potential map.

This study aimed to prepare forest fire susceptibility mapping (FFSM) using a ubiquitous GIS and an ensemble of adaptive neuro fuzzy interface system (ANFIS) with genetic (GA) and simulated annealing (SA) algorithms (ANFIS-GA-SA) and an ensemble of radial basis function (RBF) with an imperialist competitive algorithm (ICA) (RBF-ICA) model in Chaharmahal and Bakhtiari Province, Iran. The forest fire areas were determined using MODIS satellite imagery and a field survey. The modeling and validation of the models were performed with 70% (183 locations) and 30% (79 locations) of forest fire locations (262 locations), respectively. In order to prepare the FFSM, 10 criteria were then used, namely altitude, rainfall, slope angle, temperature, slope aspect, wind effect, distance to roads, land use, distance to settlements and soil type. After the FFSM was prepared, the maps were designed and implemented for web GIS and mobile application. A receiver operating characteristic (ROC)- area under the curve (AUC) index was used to validate the prepared maps. The ROC-AUC results showed an accuracy of 0.903 for the ANFIS-GA-SA model and an accuracy of 0.878 for the RBF-ICA model. The results of the spatial autocorrelation showed that the occurrence of fire in the study area has a cluster distribution and most of the spatial dependence is related to the distance to settlement, soil and rainfall variables.

The integration of generative artificial intelligence (AI) with virtual reality (VR) is reshaping how immersive environments are designed, personalized, and experienced. Unlike traditional VR systems that rely on static and pre-scripted content, Generative VR leverages AI-driven content generation, multimodal interaction, and contextual adaptation to create dynamic virtual spaces. This paper presents a comprehensive survey of the core components, applications, and challenges associated with Generative VR. It explores its transformative impact across education, healthcare, industrial training, entertainment, and emerging fields such as environmental conservation and virtual tourism. While Generative VR enhances user engagement and realism, it also introduces critical challenges, including computational scalability, ethical concerns, AI explainability, and real-time performance constraints. The paper further identifies key research gaps and future directions, such as AI-driven multi-user collaboration, cognitive load management, and cross-domain interoperability. By addressing these challenges and advancing the integration of AI with VR, Generative VR has the potential to reshape human-computer interaction, unlocking new possibilities for creativity, accessibility, and intelligent virtual ecosystems.