Due to digitalization and AI technology, the whole world is moving towards the modernization of all digital applications. So here in this paper we have upgraded the existing online election system by adding RSA-KEM, AES and blockchain technology. These new technologies with respect to higher throughput, and achieved major outcomes with respect to security principles as confidentiality is preserved, integrity is achieved through Meta mask and availability is maintained. The study focuses on designing a safe, attack proof and translucent methodology which will store user votes in a more prominent secured way. We used Ethereum blockchain with ganache for local development to achieve immutability and transparency. The voting process is initialized by written smart contracts based on solidity. Solidity is used for registration of voters and counting of votes. A two factor authentication and email verification through OTP is used to guarantee voter identification. Interplanetary file systems (IPFS) are used to store lists of voters and votes cast by voters. By using angular built frontend, the registration of voters, choices of ballots and authentication is made so simple. java spring boot is used to develop backend which was used by Web3j. This backend was used to offer blockchain interactions, authentication, and encryption. This proposed system, is useful for governmental organizations, multinational corporations, Educational institutions and commercial associations for conducting their elections smoothly.
Dementia encompasses a spectrum of progressive neurodegenerative conditions that gradually erode memory, cognitive abilities, and behavioral functioning, severely diminishing quality of life. Traditional diagnostic practices often rely on clinical assessments and neuroimaging, which may be costly or lack early-stage sensitivity. In contrast, electroencephalography (EEG) has emerged as a non-invasive, cost-effective tool capable of capturing subtle neural abnormalities linked to cognitive decline. With the rise of artificial intelligence (AI), particularly deep learning, novel approaches have surfaced for analyzing complex biomedical data. Among them, Convolutional Neural Networks (CNNs)—renowned for their strength in image and signal processing—offer significant promise in interpreting EEG signals for dementia detection. By learning spatial and temporal patterns directly from EEG recordings, CNNs can identify characteristic changes in brain activity associated with various stages of dementia. This paper delves into the dual facets of this approach: the cause, examining why EEG signals are suitable for dementia diagnosis and how CNNs process them; and the effect, exploring the practical outcomes, clinical relevance, and challenges of adopting this methodology. We first investigate the neurophysiological alterations induced by dementia and their manifestations in EEG patterns. Early and precise identification of these signatures is crucial for timely intervention and improved long-term care. Through comprehensive literature analysis and empirical findings, we highlight how CNN-based models consistently outperform traditional machine learning techniques and even expert evaluations in some scenarios. The adoption of such systems in clinical and research environments has shown promising outcomes—ranging from increased diagnostic accuracy and early detection capabilities to economic scalability and improved patient management. Nonetheless, this paradigm is not without limitations. Issues such as inter-subject variability in EEG data, black-box behavior of deep learning models, and ethical implications surrounding data privacy and algorithmic fairness must be addressed. To that end, we propose forward- looking strategies, including multi-modal diagnostic frameworks, federated learning for privacy- preserving model training, and the development of explainable AI (XAI) to foster transparency and trust. Ultimately, integrating EEG with CNN-based analysis holds transformative potential in redefining how dementia is detected and managed—offering a pathway toward accessible, scalable, and non-invasive neurological diagnostics.
This research investigates the collaborative framework involved in developing an immersive Virtual Reality (VR) module integrated into a Learning Management System (LMS) for immersive teaching in automotive engineering for higher education. Findings from team reflections, process documentation, and stakeholders’ interviews reveal several key enablers of effective collaboration: clearly defined roles, shared ownership of pedagogical design, iterative feedback cycles, and the use of immersive walkthroughs to align interdisciplinary perspectives. Challenges such as communication barriers, technical-pedagogical misalignments, and differing expectations were addressed through structured co-design sessions and transparent workflows. These insights informed the development of a four- phase collaborative framework: Shared Vision Formation, Cross-Disciplinary Design Mapping, Agile Iterative Development, and Joint Evaluation & Feedback Loops. This research contributes to SDG 11 (Sustainable Cities and Communities) agenda by offering a replicable methodology for conducting multidisciplinary educational research. It also supports SDG 4 (Quality Education) and SDG 17 (Partnerships for the Goals) by promoting inclusive, co-developed, and technology-enhanced learning environments. The framework presented can guide institutions seeking to integrate immersive technologies while strengthening cross-functional collaboration in digital education innovation.
Keywords: virtual reality, learning management system, immersive learning, multidisciplinary collaboration, co- design, agile principles, higher education
The purpose of this presentation is to analyze the role of Soviet Uzbek writers in shaping the discursive foundations of nation-building and the construction of collective memory in their republic. Employing a constructivist framework, the author investigates the historical trajectory of modern Uzbekistan—from the emergence of the first national movement in Transoxiana at the turn of the 19th and 20th centuries to the consolidation of a distinct Uzbek political nation in the second half of the 20th century. Central to this analysis is the Bolshevik nationalities policy, which functioned as an institutional mechanism of nation-building across all Soviet republics. Within this ideological and political framework, the Uzbek Soviet Socialist Republic cultivated its own intellectual elite, whose contribution was crucial to the codification of national culture. Writers, in particular, assumed a formative role: through their literary works, they mediated historical consciousness for mass audiences, providing narratives of pivotal events and valorizing prominent figures of Central Asia’s pre-revolutionary past. Among this literary generation, Aybek, Pirimkul Qodirov, and Adyl Yakubov were especially influential. Their historical novels not only reimagined the past but also canonized figures such as Mirzo Ulugh Beg, Alisher Navoi, and Babur, thereby integrating them into the symbolic repertoire of Uzbek nationhood. These figures continue to be venerated as national heroes, underscoring the enduring impact of literature on the nation-building process in Uzbekistan.
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