American University of Armenia

The Internet of Vehicular Things (IoVT) is evolving rapidly, connecting vehicles, infrastructure, and other smart systems via the Internet. This connectivity enhances safety, efficiency, and data-driven services but also introduces significant security and privacy risks. To tackle these challenges, this paper proposes a lightweight, group-based authentication and key agreement (AKA) protocol tailored for 5G-enabled IoVT networks. The protocol achieves essential security goals, including mutual authentication, key confirmation, forward and backward secrecy, subscriber privacy, and session unlinkability. Its security has been formally validated using the AVISPA tool and BAN logic, showing strong resistance against common cryptographic attacks. Performance evaluations indicate that the proposed protocol can reduce signaling overhead by up to 47.3%, lower bandwidth consumption by up to 32.3%, and significantly decrease computational costs compared to the standard EPS-AKA protocol. Overall, these results highlight a secure, efficient, and scalable solution suitable for next-generation 5G-based IoVT systems.

The rapid adoption of generative artificial intelligence tools such as ChatGPT in educational settings has generated both enthusiasm and concern regarding their influence on student learning. While several studies report improvements in efficiency, confidence, and perceived understanding, evidence for durable conceptual learning and knowledge transfer remains mixed. This article examines these tensions through the concept of fluency illusion, a cognitive phenomenon in which information that is easy to process is mistakenly judged as being well understood. Using a narrative conceptual review approach, this study synthesizes findings from 41 publications identified through searches of Google Scholar, Scopus, Web of Science, and ERIC covering the period from 2022 to early 2026. The reviewed literature includes 28 empirical studies, nine conceptual or theoretical analyses, and four review articles addressing the use of ChatGPT in educational contexts. Across domains such as writing and language learning, STEM problem solving, feedback and tutoring, and assessment, the literature shows a recurring pattern in which fluent AI-generated responses increase learners’ confidence without consistently improving deeper conceptual understanding. Drawing on research from cognitive psychology and metacognition, this review proposes an integrative conceptual account of how fluent AI output may shape learners’ judgments of understanding and influence their engagement with learning tasks. The paper concludes by discussing implications for instructional design, assessment practices, and metacognitive scaffolding, and outlines directions for future research aimed at empirically examining the proposed framework and identifying strategies to reduce fluency-driven misjudgments while preserving the potential benefits of generative AI in education.

IntroductionThis study aimed to explore factors associated with health-related quality of life (HRQoL) among refugees aged 50 years and older from Nagorno-Karabakh residing in Armenia.MethodsWe used a structured interviewer-administered questionnaire to collect data on socio-demographic characteristics, presence of non-communicable diseases (NCDs), visual impairment (VI), eye diseases, smoking, sleep disorders, HRQoL, and healthcare utilization in a sample of 197 participants. The Short-Form Health Survey (SF-36) questionnaire was used to measure the physical health component (PHC) and mental health component (MHC) of HRQoL. A comprehensive ophthalmic examination was carried out.ResultsThe mean age of participants was 64.84 (SD = 9.28), with 65.48% being female. The mean MHC and PHC scores were 59.37 (SD = 20.04), and 59.4 (SD = 20.1) to 63.57 (SD = 26.28), respectively. Hypertension, bone/joint diseases, and heart diseases were the most frequently reported diseases in our sample. In the adjusted linear regression analysis, age, sleep disorders, respiratory diseases, bone/joint diseases, and diabetes were significantly associated with both PHC and MHC scores of SF-36. Heart disease was associated with PHC only. Having more than one NCD was negatively associated with PHC and MHC. In the domain specific adjusted analysis, bone/joint diseases and diabetes, were significantly associated with physical functioning, general health, and energy/fatigue domains of the PHC and MHC components of HRQoL.ConclusionGovernmental support and interventions targeting older refugees and those with more than one NCD and sleep disorders may be beneficial. Follow-up assessments should monitor variations in both PHC and MHC scores among refugees, as they may vary over time in response to the challenges associated with displacement.

Accurate detection of lung nodules in chest radiographs plays a critical role in the early diagnosis of pulmonary diseases, including lung cancer. In this study, we integrate deep learning techniques for nodule classification with object detection approaches. For model training and evaluation, we utilized the publicly available ChestX-ray14 dataset, which comprises approximately 112,000 chest X-ray images, including 6,371 labeled as containing lung nodules. We implemented several pretrained convolutional neural network (CNN) architectures—specifically DenseNet121, ResNet34, AlexNet, and InceptionNet—for binary classification tasks aimed at identifying the presence of lung nodules. To gain interpretability into the decision-making processes of these models, we employed Gradient-weighted Class Activation Mapping (Grad-CAM). In addition to classification, we trained an object detection model designed to identify and localize nodules by generating bounding boxes around them. We assessed both the classification and detection models by comparing their outputs and evaluating their performance, with the goal of better understanding their effectiveness in automated lung nodule detection. Overall, these findings demonstrate that transfer learning based classification models achieve high sensitivity but suffer from low precision on weakly labeled data, while object detection models provide more reliable localization when trained on expert annotated datasets. The results highlight the critical impact of annotation quality and suggest that detection based approaches may be better suited for clinically meaningful lung nodule identification. These findings also show that while ensemble classification improves sensitivity and ROC-AUC on weakly labeled data, object detection trained on expert annotations yields substantially higher precision and more clinically reliable localization.

Following the introduction of comprehensive bans on tobacco advertising, promotion, and sponsorship (TAPS), little is known about how the tobacco industry (TI) adapts its marketing strategies in low- and middle-income countries at point of sale (POS). This study examined post-ban TI marketing practices in Armenia after the 2022 comprehensive TAPS and display ban, focusing on youth exposure. We conducted a qualitative study using structured observations at 132 POS near educational facilities in Yerevan (capital city) and the Kotayk region, in-depth interviews with store owners/workers (n = 8), and focus group discussions with youth aged 15-20 (n = 28). Thematic analysis was applied with triangulation across data sources. Despite the ban, tobacco products remained widely available and visible near educational facilities. Cigarettes, e-cigarettes, and heated tobacco products were prominently displayed using power walls, illuminated fixtures, and placement near youth-appealing items. Advertising persisted through covert, legally ambiguous tactics such as illuminated displays, integrated signage, colour schemes, flavours, and loyalty-based promotions, coupled with misleading messages, often not recognized as advertising by youth or retailers. Retailer incentive schemes, such as financial rewards, gifts, and staff engagement, were used to secure product prominence and drive sales. Youth access restrictions were weakly enforced, with poor visibility of age-of-sale signage, inconsistent ID checks, and reports of informal access routes. Comprehensive TAPS and POS display bans alone are insufficient to eliminate youth exposure to tobacco marketing. Stronger enforcement, regulation of indirect and retailer-focused marketing, and sustained monitoring of POS environments are essential to protect youth from ongoing TI influence.