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Research Paper

A Comparative Review of Nanomaterials for Neuroprotection in Neurodegenerative Diseases

Neurodegenerative illnesses like Alzheimer's, Parkinson's, and ALS cause progressive injury to the brain, causing loss of memory, movement, and cognitive functions over time. The problem with these diseases is that most drugs cannot pass through the brain's protective barrier—the blood-brain barrier (BBB). In this review, the new use of nanomaterials—extremely tiny particles measured in nanometers—is described to transport drugs across the BBB without harming brain cells. We analyzed eight of the most well-researched types of nanomaterials: fat-based, plastic-like, dendrimers, carbon-based, gold, cerium oxide, iron oxide nanoparticles, and quantum dots. Each was assessed on its ability to deliver drugs to the brain, safety, stability, and performance in laboratory tests. Fat-based and plastic-like nanoparticles outperformed all others based on biocompatibility and drug delivery ability. Gold nanoparticles were highly multifunctional and versatile for therapy and imaging. Cerium oxide proved to be a great antioxidant and could protect neurons from injury. However, some nanomaterials, like carbon nanotubes and quantum dots, were of concern due to toxicity. The review concludes that just as there is no single nanomaterial that is perfect, their benefits can be leveraged in hybrid systems to enable more powerful, targeted, and safer treatment. Nanotechnology has tremendous potential for future advances in the fight against brain disease by enabling precise and protective drug delivery to the brain.

Published by: Ayaan Bansal

Author: Ayaan Bansal

Paper ID: V11I3-1369

Paper Status: published

Published: June 28, 2025

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Research Paper

Autonomous Threat Detection and Response in Cloud Environments Using AI and Machine Learning: Focus on Real-Time AI-Driven Anomaly Detection and Self-Healing Cloud Security Architectures

Cloud threat detection and response technologies are at the forefront of maintaining cybersecurity amid increasingly dynamic and complex infrastructures. The technologies are programmed to identify, assess, and respond to likely threats in real time to provide cloud service integrity and availability. Static rule-based mechanisms and manual control mechanisms find it challenging to keep pace with evolving attack patterns, and this has necessitated the use of automated, intelligent solutions. This paper explores the employment of autonomous threat detection and response systems using artificial intelligence (AI) and machine learning (ML). The binary classification model was used to identify harmless and threat-related network traffic from a Kaggle-based DDoS dataset. The data underwent rigorous preprocessing, exploratory data analysis, and feature engineering. Five machine learning (ML) models were trained and evaluated against performance measures like accuracy, precision, F1-score, and detection time. The Decision Tree model gave better performance, with a high accuracy of 98.0% and real-time capability. Its integration into cloud infrastructures allows for self-healing, adaptive cybersecurity defenses.

Published by: Mariam Sanusi, Tolulope Onasanya, Oduwunmi Odukoya, Moyinoluwa Senjobi

Author: Mariam Sanusi

Paper ID: V11I3-1375

Paper Status: published

Published: June 28, 2025

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Case Study

La Corruption Et La Mediocrite Dans L’Administration : Deux Facteurs Cles De La Decadence Socio-Economique D’Un éTat

La présente étude scientifique s'intéresse à l’impact conjugué de deux fléaux structurels au sein de l’administration publique : la corruption et la médiocrité. Ces phénomènes, souvent analysés séparément, forment en réalité un système de réciprocité qui nourrit la déchéance progressive de l’État. À travers une approche pluridisciplinaire intégrant les sciences politiques, la sociologie et l’économie du développement, cet article met en lumière les mécanismes par lesquels ces deux facteurs se renforcent mutuellement, engendrant une gouvernance inefficace, un affaiblissement des institutions et un ralentissement notable de la croissance socio-économique. Le cas de Madagascar constitue le centre d’attention de cette étude, illustrant comment une culture administrative tolérant l’incompétence et la corruption a conduit à une perte de confiance généralisée envers les structures étatiques. En recourant à une méthodologie combinant revue documentaire, analyse statistique des données disponibles (indices de gouvernance, classements internationaux, etc.) et entretiens semi-directifs avec des experts locaux, l’étude met en exergue l’urgence d’une réforme structurelle. Les résultats révèlent que la mauvaise qualité du capital humain dans l’administration malgache découle souvent de pratiques de nomination non méritocratiques, alimentées par le clientélisme politique. L’article plaide pour une refondation de la gouvernance basée sur l’éthique, la compétence et la transparence. Il met également en évidence des comparaisons utiles avec d’autres pays africains ayant entrepris des réformes réussies en matière de gouvernance publique.

Published by: Dr. Georges Solofoson, Zoelison Andriandratoarivo, Georges Emma Rakotomalala, Julieph Ranaivo

Author: Dr. Georges Solofoson

Paper ID: V11I3-1380

Paper Status: published

Published: June 28, 2025

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Research Paper

Quantum-Inspired Deep Learning for High-Dimensional Data Processing in Finance

This paper explores the application of quantum-inspired deep learning techniques for processing high-dimensional financial data, specifically focusing on predicting stock price movement using Apple Inc. (AAPL) stock data. We investigate the effectiveness of a quantum-inspired model in comparison to a standard Long Short-Term Memory (LSTM) network. The study incorporates various technical indicators as features and evaluates model performance using standard classification metrics and visualizations. The challenges of high-dimensional data in finance are discussed, and the potential benefits of quantum-inspired approaches in this domain are explored.

Published by: R. Vasuki, Manupati Ramana Kumar, Kokkiligadda Nischal varma, Kamireddy Yaswanth Reddy

Author: R. Vasuki

Paper ID: V11I3-1184

Paper Status: published

Published: June 28, 2025

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Research Paper

Impacts Economiques D’Exploitations Non-Certifiees D’Ail/Oignon Sur Des Agriculteurs De La Region Sofia

For several decades, farmers in the Sofia region of Madagascar have cultivated garlic (Allium sativum) and onion (Allium cepa) using traditional, uncertified organic methods. This study investigates the economic impact of such non-certified agricultural practices on rural household livelihoods. The research was conducted in four communes: Ambatosia and Ambodiampana (Bealanana District), as well as Bekoratsaka and Mampikony II (Mampikony District), all known for their high concentration of non-certified organic farmers. Data was collected through semi-structured interviews with 100 producers and analyzed using correlation and linear regression methods. Findings reveal a positive relationship between the cultivation of uncertified organic garlic/onion and increases in annual household agricultural income. Thanks to low input costs, ancestral techniques, and stable local demand, these farmers often exceed the national poverty line. Approximately 42 to 44% of the surveyed households live above this threshold, despite lacking official organic certification. However, disparities remain based on market access, technical skills, and yield consistency. While uncertified organic farming offers a promising path for rural income improvement and poverty alleviation, it remains fragile in the absence of structured value chains, supportive public policies, and stable market integration. This research highlights the socio-economic viability of alternative agricultural systems, while underlining their limits in terms of long-term resilience and financial security.

Published by: Mme Razafindrakoto Andriamanalina Notsimbinina, Dr. Solofoson Georges, Dr. Maminindriana Razafindrakoto Andriamanalina Miorintsoa

Author: Mme Razafindrakoto Andriamanalina Notsimbinina

Paper ID: V11I3-1388

Paper Status: published

Published: June 27, 2025

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Research Paper

From Tradition to Modernity: The Changing Landscape of Dance

This research paper explores how dance has changed over time and what those changes say about our cultures, identities, and ways of expressing ourselves. It looks at how traditional dance has always been a meaningful way to pass down stories, values, and history through generations. These dances are more than just movement—they connect people to their roots and reflect what communities believe in and celebrate. At the same time, the paper shows how dance has grown and evolved, especially with the rise of the internet and social media. Platforms like TikTok and YouTube have made it easy for dancers from all over the world to learn from each other, share their styles, and mix different traditions. This has led to exciting new forms of dance that blend the old and the new in creative ways. By looking at both traditional and modern dance, the paper highlights how movement can be a powerful way to express identity, adapt to change, and bring people together. It shows that even though styles and trends may change, dance will always be an important part of human life, helping us tell our stories, connect with others, and celebrate both where we come from and where we’re headed.

Published by: Prisha Mundhra

Author: Prisha Mundhra

Paper ID: V11I3-1383

Paper Status: published

Published: June 27, 2025

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