Quantum key distribution (QKD) is a viable technique for safeguarding image transmission against numerous attacks. To address the limitations of existing methods in securing electronic medical records, we have explored an encryption technique using a set of real-time medical images along with existing standard images using QKD protocols including BB84 (Bennett-Brassard 1984), CASCADE (Cascading Error Correction Algorithm for Data Exchange), and differential-phase-shift (DPS) under the fiber-based (OFC) and free-space (FSO) channels. It utilizes the quantum bit-plane representation of the Real Ket (QBRK) model and a logistic chaotic system with XOR operation. The performance of the developed framework is tested via visual illustration and 11 different quantitative assessments. The average entropy of the scrambled image for the medical source image is 7.9896, which is quite near to the optimum value of 8. The UACI (Unified Averaged Changed Intensity) and the NPCR (Number of Pixels Change Rate) metrics values lie between 33.7879 to 33.9711 and 99.6014 to 99.8000 for the FSO channel. The BB84 QKD protocol achieves a QBER (Quantum Bit Error Rate) of 0.0028 for an FSO channel with DCR (Dark Count Rate), which improves to 0.0012 when DCR is ignored for a 10 km link range. In addition, we analyzed and compared the effect of OFC on system output as the quantum channel. From several experiments, it is observed that the BB84 provides better results against CASCADE and DPS. The proposed work is validated using the Optisystem 15.1 software under FSO and for OFC.

Over the last decade, there has been notable academic interest in the multidisciplinary studies of science education with the emergence of STEAM. Accordingly, this study aimed to conduct an experimental study to explore the effectiveness of a STEAM-based space-themed learning module in science education taught to primary school students. The sample of the study was 6th- and 7th-grade students from the Central Anatolia Region of Turkiye. The experimental group consisted of 180 students, whereas the control group consisted of 94 students. The STEAM-based learning module was applied to the students in the experimental group, and the traditional lecture-based approach was applied to students in the control group. The traditional education and intervention lasted eight weeks. The chi-square test and one-way ANOVA were used to analyze the differences and associations between the variables. The results indicated that gender and academic achievement level were the two key factors determining the effectiveness of the STEAM-based learning module. The comparison of the pre-test and post-test scores indicated that the scores of the female students who participated in the module increased the most. This suggested that girls benefited more from the STEAM-based learning module than boys. Further, the lecture-based approach led to a decrease in some high-achieving students. The findings have implications for educational policymakers, curriculum developers, and syllabus designers.

Simulation-based education is a critical component of Intelligent Transportation Systems (ITS) education. This study discusses the experimental success of using Virtual Reality (VR) technology in simulation-based ITS education, which was found to improve the quality of education while increasing immersion and motivation. The study documents the application of VR technology to a microscopic simulation model within the ITS curriculum. Furthermore, the study also proposes using a standardized methodology to capture and evaluate students’ subjective experiences. The findings of the study are discussed, along with potential ways to improve the planned VR technology implementation in the ITS laboratory.

Authorship recognition is a technique used to identify the author of an unclaimed document, or in case when more than one author claims a document. Authorship recognition has great potential for applications in Computer forensics. The intended goal of this research is to identify a Chat bot by analyzing conversation log files. This is a novel area of investigation, as artificially intelligent authors have not been profiled based on their linguistic behavior. The collected data comes from chat logs between different Chat Bots and between Chat Bots and Human users. The initial experiments utilizing collected data demonstrate the feasibility of our approach.