The use of technology, especially mobile phones, has significantly increased over the past few decades and has become an essential element in our daily lives. Mobile and desktop applications based on Augmented Reality (AR) proved to be a revolutionary step in different areas, especially in the educational sector. AR provides an improved and extended version of reality with the superimposition of a virtual object in the real-world environment. These objects can be interacted with and visualized in many ways to provide full AR experiences. By interacting with augmented virtual objects, students can visualize and completely understand various difficult concepts during their studies rather than traditional learning, allowing collaborative learning and hence improving learnability, motivation, and focus. This survey paper discusses the concept of AR and its types, the need for AR applications in education, analysis of various state-of-the-art AR applications in terms of platform, augmented virtual content, interactions, usability, usefulness, performance, effectiveness, and ease-of-use under a single taxonomy. Although applications of AR in education for learning, teaching, and simulating have proven quite effective in delivering the concepts via interactive study, there are also several limitations in terms of complexity and availability of hardware, platforms, internet connections, portability, computational, and rendering speed. This study offers insights into potential trends and directions that AR technology might take in education, furnishing readers with a glimpse of forthcoming developments. This comprehensive perspective equips educators, researchers, and practitioners with valuable insights into the pedagogical implications, technological aspects, and practical considerations related to AR.

Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.