Surprisingly, distance education is quite an old concept. Its origins date back to the first correspondence-based course, which took place via the postal service in Boston, USA, in the 18th century. Rapid technological developments, especially in video and audio streaming, have increased the availability of such courses and moved learning into the virtual world. Due to the ongoing COVID-19 pandemic, we are witnessing an accelerated revolution in the learning process, as nearly all forms of education have been shifted online. Will this have a destructive effect on the human psyche? Is humanity sufficiently aware and ready for such a dramatic change? Will we return to physical in-classroom studies, or is remote distance education set to become the new norm? In particular, in medicine, computer science, fine arts, or architectural design, such a rapid change in the way students learn can be quite challenging. In this paper, we provide an overview of the history of distance learning, taking into account teachers’ and students’ points of view in both secondary and higher education.

Recently, during the COVID-19 pandemic, distance education became mainstream. Many students were not prepared for this situation—they lacked equipment or were not even connected to the Internet. Schools and government institutions had to react quickly to allow students to learn remotely. They had to provide students with equipment (e.g., computers, tablets, and goggles) but also provide them with access to the Internet and other necessary tools. On the other hand, teachers were trying to adopt new technologies in the teaching process to enable more interactivity, mitigate feelings of isolation and disconnection, and enhance student engagement. New technologies, including Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Extended Reality (XR, so-called Metaverse), Big Data, Blockchain, and Free Space Optics (FSO) changed learning, teaching, and assessing. Despite that, some tools were implemented fast, and the COVID-19 pandemic was the trigger for this process; most of these technologies will be used further, even in classroom teaching in both schools and universities. This paper presents a concise review of the emerging technologies applied in distance education. The main emphasis was placed on their influence on the efficiency of the learning process and their psychological impact on users. It turned out that both students and teachers were satisfied with remote learning, while in the case of undergraduate children and high-school students, parents very often expressed their dissatisfaction. The limitation of the availability of remote learning is related to access to stable Internet and computer equipment, which turned out to be a rarity. In the current social context, the obtained results provided valuable insights into factors affecting the acceptance and emerging technologies applied in distance education. Finally, this paper suggests a research direction for the development of effective remote learning techniques.

Education is one of the essential foundations of the sustainable development of societies, in particular, the objectives SDG4 of the UN General Assembly. Extended Reality (XR, so-called Metaverse) enables multisensory interaction with virtual environments, computer-created objects, and avatars. Also, the dynamic development of Head Mounted Displays (HDMs) allows for an increasingly deeper experience of the virtual world, especially through the development of depth perception, including the rendering of several modalities like vision, touch, and hearing. This creates a unique opportunity to revolutionize the higher education system by adding a new dimension of cognition and making it accessible to more people, especially those living in hard-to-reach areas. It is also a perfect complement to the process of educating students during a pandemic, such as the recent COVID-19 pandemic. In this paper, based on the literature and our experience, we provided an overview of the possibility of the Metaverse application in higher education taking into account the advantages and limitations of the systems. It turned out that XR-based solutions can be successfully applied in medical education, chemistry courses as well as in Science, Technology, Engineering, and Mathematics (STEM) education. Moreover, the XR-based systems are useful for learning spatial skills such as navigation, spatial reasoning, and perception. In the case of remote learning, XR enables easier adaptation to the educational formula. Also, during lockdowns, an XR-based application can be considered a tool to promote socialization in the event. Thus, it enables to implementation of open and inclusive learning and teaching space, namely Edu-Metaverse. In the current social context, the obtained results provided valuable insights into factors affecting the users during the application of Metaverse in education processes, including remote learning. Finally, this paper suggests a research direction for the development of effective Metaverse-based educational solutions.

Textile and cosmetic industries generate large amounts of dye effluents requiring treatment before discharge. This wastewater contains high levels of reactive dyes, low to none-biodegradable materials and chemical residues. Technically, dye wastewater is characterised by high chemical and biological oxygen demand. Biological, physical and pressure-driven membrane processes have been extensively used in textile wastewater treatment plants. However, these technologies are characterised by process complexity and are often costly. Also, process efficiency is not achieved in cost-effective biochemical and physical treatment processes. Membrane distillation (MD) emerged as a promising technology harnessing challenges faced by pressure-driven membrane processes. To ensure high cost-effectiveness, the MD can be operated by solar energy or low-grade waste heat. Herein, the MD purification of dye wastewater is comprehensively and yet concisely discussed. This involved research advancement in MD processes towards removal of dyes from industrial effluents. Also, challenges faced by this process with a specific focus on fouling are reviewed. Current literature mainly tested MD setups in the laboratory scale suggesting a deep need of further optimization of membrane and module designs in near future, especially for textile wastewater treatment. There is a need to deliver customized high-porosity hydrophobic membrane design with the appropriate thickness and module configuration to reduce concentration and temperature polarization. Also, energy loss should be minimized while increasing dye rejection and permeate flux. Although laboratory experiments remain pivotal in optimizing the MD process for treating dye wastewater, their time-intensive nature poses a challenge. Given the multitude of parameters involved in MD process optimization, artificial intelligence (AI) methodologies present a promising avenue for assistance. Thus, AI-driven algorithms have the potential to enhance overall process efficiency, cutting down on time, fine-tuning parameters, and driving cost reductions. However, achieving an optimal balance between efficiency enhancements and financial outlays is a complex process. Finally, this paper suggests a research direction for the development of effective synthetic and natural dye removal from industrially discharged wastewater.

Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.