Volume 13 | Issue 5 |

 Abstract

Abstract "Animation in Tamil Cinema" provides an extensive examination of the origins and evolution of animation techniques as well as the present-day trends in Tamil film production. Initially confined to cartoons, animation penetrated mainstream Tamil cinema through experimental title sequences like Aadutha Veettu Penn (1960) before progressing throughout subsequent decades. The Tamil film industry marked significant achievements with its first innovative animation in Mouna Geethangal (1981), followed by the fusion of live-action and cartoons in Raja Chinna Roja (1989), and motion capture and 3D effects used in Jeans (1998) and Enthiran (2010). The document demonstrates the technical advancement and rising ambition of Indian studios through full-length animated films including Pandavas (2000), Inimey Naangathan (2007), and Anumanum Mayilravanum (2018). The films Kochadaiiyaan (2014) and Teddy (2021) demonstrate how motion capture technology together with 3D animation can be merged with live-action elements. The document discusses the impact of foreign animations while exploring the development of VR/AR storytelling through platforms such as the metaverse and highlights the significance of translating Hollywood animations into Tamil. To reach international standards in Tamil animation requires greater investment along with advanced technology and wider distribution capabilities. The conclusion emphasizes animation as a medium with commercial potential that extends beyond children's entertainment and can grow to become an important element in Indian cinema when given adequate support. The development of animation education and studio openings in Chennai and Hyderabad show potential yet face ongoing obstacles related to funding and audience expansion. The report recommends creating pan-Indian animated movies with celebrity narrators in multiple languages released on streaming services to address current challenges and bring Tamil animation to the global platform.

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KEYWORDS: Animation, Tamil Cinema, Motion capture, OTT platform, Metaverse

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In Ayurveda, Shirashoola (headache) is both a symptom of various diseases and a primary disorder categorized under Shirorogas. Acharya Charaka emphasized the significance of the head, declaring it the most vital organ. Among the causative factors, Vata dosha predominance is notably associated with Shirashoola, particularly aligning with the symptoms of modern tension-type headaches (TTH). TTH is the most prevalent form of primary headache, affecting nearly 90% of headache sufferers. In 2013, approximately 1.6 billion individuals (20.8% of the global population) were reported to have TTH. According to the WHO, about 50% of adults suffer from current headache disorders globally.In Ayurvedic management of Vatika Shirashoola, therapies such as Snehana (oleation), Swedana (sudation), and Nasya (nasal administration of medicated oils) are advocated. Nasya is a Panchakarma procedure aimed at treating head-related disorders. Rasnadi Taila, as mentioned in Charaka Chikitsa, and Balahatadi Taila from Sahasrayogam, are two medicated oils used for Nasya in treating Shirashoola, both containing Vata-shamaka and nourishing (Brimhana) ingredients.This comparative clinical study aimed to evaluate the efficacy of Nasya with Rasnadi Taila versus Balahatadi Taila in treating Vatika Shirashoola. A minimum of 40 patients, meeting the diagnostic criteria, were randomly divided into two groups: Group A received Rasnadi Taila and Group B received Balahatadi Taila Nasya for 7 days. Assessment was done before, during, and after treatment using symptom grading, and statistical analysis was performed using paired and unpaired t-tests. Results showed that both treatments provided statistically significant relief in symptoms. However, Group A (Rasnadi Taila) exhibited greater improvement with a 49.16% efficacy compared to Group B (Balahatadi Taila) with 42.56% efficacy. Thus, Rasnadi Taila Nasya was found to be more effective in managing Vatika Shirashoola.

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Nasya, Rasnadi taila, Balahathadi taila, Vatika Shirashoola

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The exponential growth of digital news consumption has led to an alarming rise in the dissemination of fake news, posing serious threats to societal trust, public opinion, and democratic processes. Manual fact-checking methods are often inadequate due to the sheer volume and velocity of online content. This research presents an automated Fake News Detection system utilizing Machine Learning (ML) algorithms to classify news articles as real or fake with high accuracy. The system employs advanced text feature extraction techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) and word embeddings to capture semantic patterns and linguistic nuances in news content. A supervised learning model is trained on a labeled dataset, and real-time adaptability is achieved by incorporating continuous user feedback and live data streams. The architecture is designed to be scalable and modular, with dedicated components for user registration, login, prediction, and history tracking. The proposed system not only aids in curbing the spread of misinformation but also serves as a reliable tool for journalists, researchers, and content verification platforms. Future enhancements may involve multimodal analysis incorporating visual data such as images and videos. This research contributes toward building a more informed and resilient digital information ecosystem.

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Fake News Detection, Machine Learning, TF-IDF, Word Embeddings, Real-Time Classification, Natural Language Processing, Misinformation, News Verification, Text Classification, User Feedback Integration

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The Co (II) complex synthesized from the Schiff base of 5-amino-1,2,3,4-thiatriazole and ortho-methoxy benzaldehyde exhibits significant structural stability and functional versatility. The study revealed that mass measurements taken at various temperatures, analysed using Doyle's method as modified by Zsako, facilitated the determination of kinetic parameters for the presumed reaction order. The most precise activation energy (E) was identified based on the smallest standard deviation (?), offering insights into how well the decomposition process aligned with the assumed kinetic function F(?). Findings showed that a reaction order of b = 0.36, an activation energy of 12.16 kcal/mol, and an average ((B_0 ) ?) = 7.37011 resulted in the minimum ?min = 0.14296. By comparing different kinetic models, the research emphasized the effectiveness of statistical methods in accurately matching experimental results with theoretical predictions. The apparent entropy of activation (?S) was found to be -23.45 e.u.. The results highlight the effectiveness of Doyle's modified method and its consistency with Freeman and Carroll's approach, ensuring a reliable determination of kinetic parameters and confirming the relevance of the proposed kinetic models. Its promising antimicrobial and antioxidant activities also position it as a potential candidate for pharmaceutical and biomedical use. In addition, the cobalt complex shows considerable potential in materials science, particularly in the development of electrochemical sensors, rechargeable batteries, and fuel cell components.

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Cobalt-complex; Schiff base; Entropy of activation; Activation energy; TGA;

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This project describes the design and construction of a solar-powered power inverter system for converting solar power into usable electricity for domestic and industrial use a 12v solar panel provides dc power which is stored in a battery and then converted to 220v ac 50hz by a solar inverter the system automatically switches between solar and main supply through a relay-based switching mechanism to provide an uninterrupted supply the inverter offers a high-efficiency ac sine wave output with lesser losses and is capable of handling multiple loads at the same time it's an environmentally friendly system that decreases reliance on fossil fuels helps in reducing carbon emission and provides a clean affordable option for rural tribal and urban areas with poor grid connectivity.

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solar based power inverter solar powered inverter clean energy photovoltaic array dc ac conversion pure sine wave inverter auto switching relay control energy storage facility green power low total harmonic distortion and stable power

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Autism Spectrum Disorder (ASD) is a neurodevelopmental condition marked by difficulties in social interaction, communication, and behavior. Current diagnosis relies on clinician-led assessments, which can be subjective and time-consuming. This report proposes an AI based framework using Convolutional Neural Networks (CNNs) to analyze multimodal data-- audio and video--to aid in ASD screening. Audio inputs are processed using Mel-Frequency Cepstral Coefficients (MFCCs) to capture vocal traits, while video data is analyzed for facial expressions, eye contact, and posture. The extracted features are fused using early or late multimodal fusion techniques to enhance classification accuracy, reaching up to ~95%. The model provides confidence scores to assist clinicians in diagnosis and prioritization.

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Autism Spectrum Disorder, Convolutional Neural Networks, Mel Frequency Cepstral Coefficients

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Mild steel pipelines are extensively used in various industrial sectors for transporting water, oil, gas, and other fluids. When buried underground, these pipelines are constantly exposed to corrosive environments due to the presence of moisture, oxygen, salts, and microorganisms in the soil. Corrosion not only weakens the structural integrity of the pipeline but also leads to potential leakage, safety hazards, and high maintenance costs. Therefore, effective corrosion protection methods are essential to ensure the long-term performance and safety of underground pipeline systems.

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Surface Preparation, Primer Application, Heat Shrinkable Sleeves Application, Inspection and Corrosion Prevention.

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Transport infrastructure is a key driver of socio-economic development, especially in rural areas where connectivity challenges persist. This study examines the impact of rural road connectivity on socio-economic benefits and regional economic development in Bangladesh. Using mixed methods including field surveys, interviews and secondary data analysis, the study identifies key areas for improvement such as agricultural productivity, employment generation and access to essential services. The findings reveal significant positive relationships between improved rural road infrastructure and socio-economic outcomes, providing insights for policymakers aiming to increase regional equity and economic integration.

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Rural roads, transportation infrastructure, socio-economic development, regional economic development, Bangladesh, connectivity

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The growing demand for plant-based medicines has driven interest in traditional species such as Acacia nilotica and Acacia senegal, which are known for their pharmacologically active bark. This research investigates the extraction and biological evaluation of key compounds--namely flavonoids, alkaloids, tannins, and gums--from the bark of these Acacia species. Using three extraction methods--maceration, Soxhlet extraction, and supercritical CO? extraction--we compared the efficiency of each in isolating target compounds. The extracted materials were then analyzed for antimicrobial properties against common bacterial strains and assessed for their prebiotic potential. Results revealed that Soxhlet extraction provided the highest flavonoid yield and strongest antibacterial activity, while supercritical CO? extraction was superior in isolating gum compounds beneficial for digestive health. These findings offer significant implications for natural, affordable, and environmentally sustainable alternatives to synthetic drugs.

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Acacia bark, Bioactive compounds, Flavonoids, Soxhlet extraction, Supercritical CO?, Antimicrobial, Prebiotic, Natural medicine

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