Volume 14 | Issue 3

 Abstract

A book titled Naduppattu, written by Mahakavi Bharathiyar, was first published by Parali S. Nellaiappar in 1917. Although contemporary researchers are aware of this, no one has seen the publication till the date. The first edition (1917) of the book Naduppattu, which has not been seen by anyone until now, has now been made available. This article is described to that edition, which was rare to find. The first edition of Naduppattu (1917) is the primary data for this paper.

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Rare document discovered: First edition of Bharathiyar's Nattupattu

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Flood hazard management across Tamil Nadu demands broader spatial coverage, faster evidence fusion, plus dependable public messaging. This paper presents a statewide flood risk monitoring and early alert framework that scans reservoir status across Tamil Nadu, evaluates weather signals for any district, reviews multi-year satellite image archives from 2023-2026, and delivers decision support through multimodal agent reasoning. The implementation follows a Python pipeline that couples Twilio messaging, SerpApi web retrieval, Gemini agents, LangChain orchestration, and PIL-based image handling with dashboard-level analytics. A district request triggers weather acquisition, statewide reservoir scraping, district-level reservoir filtering, multi-year image retrieval, risk summarization, and message generation. High-risk cases trigger alert SMS delivery; low-risk cases trigger routine forecast messages that carry general weather status rather than alarm language. Figure-guided discussion highlights the transition from a narrow, partially manual flow toward a broader reservoir-linked architecture with archive-backed visual review. The resulting framework offers scalable monitoring, timely communication, and stronger operational value for local administrators, emergency teams, and residents across flood-prone districts of Tamil Nadu.

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Tamil Nadu; flood risk monitoring; reservoir scanning; satellite archive analytics; multimodal agents; Twilio alerts

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The proliferation of misinformation across social media platforms has necessitated the development of advanced automated detection systems capable of handling multimodal content. While early fake news detection approaches relied on handcrafted linguistic features and unimodal deep learning models, recent research has shifted toward multimodal transformer-based architectures that enable joint reasoning over textual and visual information. This survey provides a comprehensive review of fake news detection methods, with a particular emphasis on multimodal transformer frameworks developed recently. We present a structured taxonomy that categorizes detection approaches into traditional machine learning, deep neural text models, CNN-based multimodal systems, unimodal transformers, and state-of-the-art multimodal transformer architectures. The survey further examines the transition from binary classification to fine-grained misinformation detection, highlighting the importance of cross-modal alignment and semantic consistency modelling. Comparative analysis demonstrates that multimodal transformers consistently outperform earlier paradigms due to their cross-modal attention mechanisms and shared representation learning capabilities. In addition, we identify key research challenges, including dataset bias, annotation subjectivity, adversarial robustness, computational scalability, and explainability. Emerging directions such as multimodal large language models, causal reasoning frameworks, graph-enhanced architectures, and lightweight deployment strategies are discussed to outline future research opportunities. This survey synthesizes recent advances and provides a unified analytical framework and research roadmap for advancing multimodal transformer-based fake news detection toward robust, interpretable, and real-world deployment-ready systems.

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Multimodal Fake News Detection, Multimodal Transformers, Fine-Grained Classification, Vision-Language Models, Misinformation Analysis.

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ABSTRACT Background Artificial Intelligence (AI) has emerged as a transformative technology in modern healthcare, including dentistry. AI systems have demonstrated potential in assisting with diagnosis, radiographic interpretation, treatment planning, and prediction of treatment outcomes. However, the level of awareness, perception, and clinical exposure to AI among dental professionals remains variable. Aim The present study aimed to evaluate the knowledge, attitude, and practice related to Artificial Intelligence in dentistry among dental students and practitioners. Methods A cross-sectional questionnaire-based study was conducted among 112 dental students and practitioners. A structured and pre-validated questionnaire consisting of four sections--demographic data, knowledge, attitude, and practice regarding AI in dentistry--was distributed. Data were analyzed using descriptive statistical methods and presented as frequencies and percentages. Results Among the participants, 75% were female, while 18.75% were male. Undergraduate students constituted the largest proportion (46.5%), followed by interns (40%). A majority (85%) were aware that AI could be integrated with CBCT and OPG for automated radiographic interpretation. About 70% believed AI could assist in early detection of dental problems. Positive attitudes toward AI-based treatment simulations and outcome prediction were reported by more than half of the respondents. Regarding practice, 68.8% reported using AI-based tools in dental learning or clinical work, while 80.3% used AI tools for research activities. Conclusion The study findings indicate that dental students and practitioners possess moderate to good knowledge and a generally positive attitude toward Artificial Intelligence in dentistry. However, structured training and formal education regarding AI applications remain limited. Incorporating AI education into dental curricula may facilitate effective adoption of AI technologies in dental practice.

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Keywords Artificial Intelligence, Dentistry, Knowledge, Attitude, Practice, Dental Education

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Abstract: Marriage is an important social institution in Tripuri society. This paper examines the continuity and change in marriage rituals and the marital system. Some traditional practices are still followed, such as Wathop worship performed by the Ochai, the use of the Bedi with Chanduwa cloth, and the wearing of traditional attire like Rignai and Risa during the ceremony. In the past, marriages were mainly arranged by elders through a mediator known as Raibai. Old marriage customs like child marriage, apprenticeship of a bridegroom, marriage by capture, marriage by exchange, and marriage by purchase were very common. Now people do not follow most of these marriage customs. As time went on, factors like education became more widespread, and people moving to cities began to change how society thought about marriage. The influence of Bengali culture also played a role in shaping these perspectives. More and more people have started to convert to Christianity. Now love marriages have become more common and socially accepted. Some traditional rituals have been simplified, and Christian marriage ceremonies are now followed by a section of the community. Even with these changes, many old customs and indigenous practices are still maintained.

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Keywords: Tripuri Marriage, Socio-Cultural, Continuity, Change, Social Institution.

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The concept of nationalism in India developed significantly during the period of the anti-colonial struggle for independence. The Indian nationalist movement was essentially a movement aimed at achieving freedom from colonial rule. Any discussion of the Indian nationalist movement would remain incomplete without acknowledging the contributions of women. In the history of the national movement, women fulfilled their responsibilities both within the household and in the public sphere, demonstrating remarkable courage and determination. In a patriarchal society, it was not easy for women to stand alongside men and participate in the struggle as equal fighters. During that time, women's lives were largely confined to domestic responsibilities and child-rearing. However, despite these social constraints, many women overcame such barriers and actively joined the freedom struggle, participating in movements against British rule. Therefore, the participation of women in the freedom movement was in no way less significant than that of men. Prominent figures such as Mahatma Gandhi and Bankim Chandra Chattopadhyay played an important role in encouraging women's participation in the freedom struggle. They emphasized the idea of the "ideal woman," who would act as the bearer and preserver of civilization. Bankim Chandra Chattopadhyay, in his conceptualization of nationalism, compared the nation to the image of a mother, thereby symbolizing the country as a maternal figure. Women's participation can be observed in various nationalist movements in India, including the Partition of Bengal and the Swadeshi Movement. Women also played an important role in the revolutionary or extremist nationalist movements, and their active involvement was evident in several Gandhian movements as well. Later, in 1946, many women participated in peasant struggles such as the Tebhaga Movement and the Telangana Rebellion. Women's participation in these various nationalist movements added a new dimension to the struggle. Many women displayed extraordinary courage in India's national movement, and the sacrifices of these individuals played a crucial role in achieving independence. Among them were figures such as Rani Lakshmibai, Sarojini Naidu, Matangini Hazra, and Pritilata Waddedar. In addition to these well-known personalities, thousands of other women participated in the freedom struggle, and their contributions remain undeniable. Alongside their involvement in nationalist activities both inside and outside the home, many of these women also engaged in various social welfare and women's welfare initiatives. However, in most cases, their contributions remained largely unrecognized and hidden from mainstream historical narratives. By analyzing reform movements, literary nationalism, Gandhian mobilization, and agrarian struggles, the article argues that women's participation in nationalist movements significantly contributed to political transformation while simultaneously exposing the limitations of nationalist ideology in addressing gender equality.

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Nationalism, Women's Movement, Swadeshi Movement, Gandhian Movement, Tebhaga Movement, Telangana Movement.

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Background: Parkinson's disease (PD) is a progressive neurological condition associated with impaired balance, altered gait and decreased functional mobility. These problems increase the likelihood of falls and can negatively affect quality of life. Exercise-based physiotherapy programs emphasizing balance and strengthening are commonly used in rehabilitation practice; however, comparative evidence between different structured balance rehabilitation protocols remains limited. Aim: To compare the effectiveness of two structured balance rehabilitation protocols on dynamic balance and functional mobility in individuals with Parkinson's disease. Methods: Sixty individuals diagnosed with Parkinson's disease (Hoehn and Yahr stage I-II) aged 50 to 70 years participated in the study. Participants were randomly allocated into two groups (n=30 each). Group A received an Otago-based balance and strengthening program, while Group B received task-oriented functional balance training with progressive resistance exercises. Both groups underwent intervention for 40 minutes per session, three sessions per week for six weeks. Outcome measures included the Timed Up and Go Test (TUG) and Dynamic Gait Index (DGI). Statistical analysis was performed using paired and unpaired t-tests with significance set at p < 0.05. Results: Both intervention groups showed statistically significant improvements in TUG and DGI scores after six weeks of training. However, the Otago-based balance training group demonstrated greater improvements compared to the functional balance training group. Conclusion: Structured balance rehabilitation programs are effective in improving functional mobility and gait in individuals with Parkinson's disease. The Otago-based balance training program demonstrated superior outcomes and may be considered a preferred rehabilitation strategy.

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Parkinson's disease, balance training, functional mobility, gait, rehabilitation, exercise therapy.

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The Prajamandal Movement arose as a powerful weapon of popular resistance against the exploitative and authoritarian structure of feudatory rule in princely states of British India. In Nayagarh State of Odisha, the movement denoted a significant phase in the political awakening of the people against the arbitrary authority of the feudatory ruler and the repressive administrative system. This paper surveys the origin, growth, and nature of the Prajamandal Movement in Nayagarh, also highlighting the role of peasants, local leaders, and nationalist influences in mobilizing mass participation. It examines the socio-economic grievances arising by people against excessive taxation, forced labour, denial of civil liberties, and lack of representative governance that fuelled the movement. This paper to highlight the significant role of Thuabari village in the Prajamandal Movement of Nayagarh which still neglected in History of Orissa. The Prajamandal Movement thus stands as a crucial chapter in the people's struggle for political rights, social justice, and responsible governance in feudal Odisha.

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feudatory, leaders, Durbar, Tax, labour, movement, protest, struggle. Bethi, Rasad, Magana.

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Retail and e-commerce systems that recommend products and services are widely used but remain underutilized in bio-industrial workflows where specific details, timing, and contextual arrangement are important. This paper presents a hybrid recommendation framework, combining (i) a Transition-Based Model for sequential SKU prediction, (ii)FP-Growth for co-purchased item discovery, and Alternating Least Squares (ALS) for personalized, collaborative recommendations. The framework was evaluated across six anonymized sub workflows (A-F), demonstrating high precision, recall, and recommendation diversity. Outputs are optimized for enterprise-scale deployment using DeltaLake over AWSS3. Our Transition Based Model introduces domain- aware circular neighborhood logic--making it uniquely suitable for scientific workflows. The hybrid framework outperforms traditional approaches not only in accuracy but also in explainability and robustness, making it suitable for critical laboratory and industrial operations.

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Recommender Systems, PySpark, Transition Model, FP-Growth, ALS, SKU Prediction, Hybrid Systems, Delta Lake, Workflow Intelligence

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In today's fast-paced digital era, traditional paper-based notice boards are becoming obsolete due to their reliance on manual updates, higher maintenance costs, and limited reach. To address these inefficiencies, the Voice-Controlled Wireless Notice Board offers an advanced electronic communication system that replaces outdated methods with a real-time, hands-free solution. The system's core objective is to develop a digital display board that can be updated instantly via spoken commands, typically integrating an Android-based smartphone application as the transmitter and a microcontroller-driven display unit (such as an Arduino Uno or 8051 series) as the receiver. The user speaks a message into a mobile app, which uses speech recognition technology to convert the audio signal into a text string that is then transmitted wirelessly via Bluetooth (HC-05/HC-06) for local range or Wi-Fi (NodeMCU ESP8266) for long-range IoT-based updates. Once the microcontroller processes this incoming data, the message is rendered on an LCD or a large-scale P10 LED Matrix with professional scrolling effects. Index Terms - Electronic Notice Board, Voice Recognition, Android Application, Wireless Communication, Bluetooth (HC-05), Microcontroller (Arduino/ESP8266), LED Matrix (P10), Speech-to-Text (STT), Internet of Things (IoT), Real-time Data Transmission

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VOICE CONTROLLED WIRELESS NOTICE BOARD

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Artificial intelligence is increasingly becoming an integral part of children's everyday play through smart toys that can interact, learn, and adapt to their users. These innovations offer unprecedented educational and recreational opportunities, fostering creativity, problem-solving, and personalized learning experiences. At the same time, AI toys collect a range of sensitive data, including children's voices, behavioural patterns, and personal information, which, if mismanaged, can expose minors to privacy risks and potential exploitation. Despite the rapid adoption of such technologies, current legal and regulatory frameworks are fragmented and often lag behind technological advancements, leaving critical gaps in the protection of children's digital rights. This study investigates the legal vacuum surrounding AI-enabled toys and examines the ethical and child-rights considerations that arise in this context. By critically analysing existing privacy laws and their limitations, the research proposes a conceptual framework for the responsible design, deployment, and oversight of AI play devices. The study underscores the unique vulnerabilities of children in digital environments, highlighting the need for regulatory interventions that balance innovation with protection. Ultimately, this paper aims to provide a theoretical foundation to guide policymakers, designers, and stakeholders in establishing privacy safeguards, ethical standards, and child-centric governance models for AI technologies targeting minors.

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Artificial Intelligence (AI) Smart Toys Children's Digital Rights Privacy Risks Data Collection in AI Toys Child Protection Ethical Considerations Privacy Laws

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The Middle class family working woman are being financially independent, hence, she want to investment for family wellness and future welfare. Investment of working women in different sectors is play important role in their income, which affect G.D.P. of the nation. Mostly working women in Muzaffarpur can also make financial planning to achievement of financial growth, which is self decision of working women, no other person influences. It is important to observed that participation of the working women in investment is great planning, because their economic under plot is changing. The main aim of our study is the detailed description of investment in different sectors of working women's in Muzaffarpur districts, which affects by various factors. Our study illustrates that the Muzaffarpur working womens investment is greatly influenced by information factor, social factor, educational factor, psychological factors, firms image etc. Out study supports educational programs, financial programs, policy formulation etc, which makes the women investors, more involvement in various sectors investment.

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Working women in Muzaffarpur, Behaviour of investment, self investment decision, social factors, psychological factors, educational factors, information factors etc.

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Maladaptive daydreaming (MD) is a psychological phenomenon characterized by excessive, immersive, and often compulsive daydreaming that significantly interferes with an individual's daily functioning, productivity, emotional regulation, and social interactions. Individuals suffering from MD may spend hours engaged in vivid fantasy worlds, which can lead to difficulties in academic, occupational, and interpersonal domains. Traditional therapeutic interventions, such as cognitive behavioral therapy (CBT) and mindfulness-based strategies, often require substantial human involvement and lack scalability, real-time monitoring, and personalized adaptation to individual user behavior. This paper introduces Daydream Detox, an innovative AI-driven therapeutic platform designed to detect, predict, and manage maladaptive daydreaming using advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). The system architecture integrates a Flutter-based mobile application for user interaction, a MERN (MongoDB, Express.js, React.js, Node.js) web dashboard for administrative and analytical oversight, a Python-based AI analytics module for emotion and behavior analysis, and a MongoDB database for secure data storage. Daydream Detox collects multidimensional user data, including activity patterns, text-based journals, mood logs, and user interaction metrics. This data is processed using NLP and ML algorithms to detect early signs of maladaptive daydreaming and generate predictive models of user behavior. The platform delivers personalized therapy plans, focus-enhancing routines, and context-aware recommendations tailored to individual needs. Furthermore, a real-time AI chatbot provides interactive interventions, motivational support, and reminders, thereby enhancing user engagement and adherence to therapy routines. Experimental evaluation involving multiple participants demonstrates that Daydream Detox significantly improves focus, reduces the frequency and duration of maladaptive daydreaming episodes, enhances emotional self-awareness, and promotes consistent adherence to personalized routines. The platform's AI-driven approach highlights the potential of digital therapeutics in providing scalable, adaptive, and real-time mental health support. This study emphasizes the importance of integrating AI, ML, and DL into digital wellbeing platforms and showcases how such technologies can address modern cognitive and emotional challenges effectively. Future work aims to expand the system with multimodal data inputs, such as speech and physiological signals, to further enhance predictive accuracy and therapeutic effectiveness.

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Maladaptive Daydreaming, Artificial Intelligence, Machine Learning, Deep Learning, Flutter, MERN, Natural Language Processing, Emotion Recognition, Therapeutic Chatbot, Digital Wellbeing, Personalized Therapy

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Preeti Shenoy, one of the most recent and prominent Indian women writers effectively portrays the issues of women and their inner trauma. Her contribution towards women empowerment and the realistic depiction of their lives is undeniable. She boldly depicts and explicitly challenges the male domination and the pressures experienced by the women in the society. She proves herself as a responsible social reformer through her writings and strives to establish gender equality even in the so-called developed, scientifically advanced modern world. Most of the women's lives are still vulnerable and conservative when compared with few modern women in the society. Women are still not capable to express their sexual desire and their own opinions regarding their lives. But now, women have been slowly trying to stand for their self without any discrimination and inequality. The women in the past struggled in their own way and now women are still struggling to get freedom from patriarchal domination. Their issues are same but the experiences they face are totally different from one another. Shenoy's, It Happens for a Reason realistically depicts the sufferings of women. This paper focuses on the need and the importance of women emancipation and the inequalities they face both in their families and in the society.

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Inner trauma, Women empowerment, Gender equality, Gender discrimination, patriarchal domination.

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Asansol is India's 11th fastest-growing city in India and 42nd fastest-growing city in the world, known as the 'Land of Black Diamonds'. Two wings (Cole and iron industries) are the city's most influential factors for flourishing. After the extraction of the coal mine and installation of heavy industry, it made an excellent employment site and massive population attracted from neighbouring states. It got metropolitan dignity within a very few decades. Asansol belongs to a complex mosaic of economic, language, and cultural characteristics due to the migration of people from different parts of the country. This assimilation of the multi-lingual, multi-religious, multi-cultural populations from different parts of India makes it a coal mining and heavy industry-based cosmopolitan city.

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Asansol; Cosmopolitan city; Coal Mining; Industry; Cultural assimilation

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This research paper presents different perspectives on migrants' experiences of the Partition and its impact on those who migrated from Pakistan during the 1947 Partition. The study compares fictional representations of these experiences with the lived experiences of former migrants as recorded in their oral narratives. It provides an account of the challenges they faced and reflects on how these refugees created space for themselves in India, transforming it into a home and a place to earn their livelihood. A key feature of this research is the juxtaposition of the literary portrayal of Partition in Chaman Nahal's Azadi with the real-life experiences of refugees who migrated from Pakistan to India during Partition.

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Partition of India, Migration, Displacement, Refugee, Politics and Religion, Communal violence, Azadi, Hindu-Muslim relations, Pakistan, Punjab, Indian National Congress

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This article examines the revolutionary epistemological shift introduced by Immanuel Kant in his seminal work Critique of Pure Reason. Kant characterizes his philosophical approach as a "Copernican Revolution" in philosophy, drawing an analogy with the scientific transformation initiated by Nicolaus Copernicus. Prior to Kant, philosophical tradition generally assumed that human knowledge must conform to objects, implying that the mind passively receives information from the external world. Kant challenges this assumption by proposing that objects of experience must conform to the cognitive structures of the human mind. According to this view, the mind actively organizes and interprets sensory data through its inherent forms and categories, making knowledge possible. By shifting the focus from objects to the conditions of human cognition, Kant redefines the nature of epistemological inquiry. This article highlights the significance of Kant's philosophical revolution and explains how his theory reoriented modern philosophy toward an investigation of the subject's role in shaping experience and knowledge.

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: Copernican Revolution in Philosophy, Epistemology, Transcendental Idealism, Subject-Object Relationship, Human Cognition, Structure of Knowledge, Modern Philosophy.

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Histopathological Evaluation Of Thyroid Lesions Dr Nisarg Shah, 3rd-year Resident Dr Shivani Divecha, 3rd-year Resident Dr Ayushi Shah, 2nd-year Resident Dr. Shashikant Mavadiya, Professor Dr. Vaibhavi Thacker, 2nd-year Resident Department of Pathology, Dhiraj Hospital, SBKS Medical Institute & Research Centre. Corresponding Author - Dr Nisarg Shah Email ID: nisargshah96ns@gmail.com Contact number - 8511348204 BACKGROUND: Histopathological evaluation is essential for the definitive diagnosis of thyroid lesions, particularly in differentiating follicular adenoma from follicular carcinoma, which significantly influences patient management and prognosis. OBJECTIVES: To study the spectrum of thyroid lesions and evaluate their histopathological patterns and to analyze their distribution across age and sex. METHODS: A retrospective observational study was conducted in the Histopathology section of the Department of Pathology, Dhiraj Hospital, SBKS MI & RC, Vadodara, from September 2024 to October 2025. A total of 58 specimens--including tru-cut biopsies, lobectomies, hemithyroidectomies, subtotal, and total thyroidectomies--were included. Inadequate or autolysed specimens were excluded. Tissue samples were fixed in 10% neutral buffered formalin, stained with hematoxylin and eosin, and examined microscopically. Data were analyzed using Microsoft Excel. RESULTS: Patients ranged from 22-66 years, with a marked female predominance (M:F = 1:7). Non-neoplastic lesions were more common than neoplastic lesions. Among neoplasms, benign tumors predominated. Follicular adenoma was the most frequent benign lesion, while papillary thyroid carcinoma was the most common malignant tumor. CONCLUSION: This study highlights that non-neoplastic and benign neoplastic thyroid lesions are more prevalent than malignant lesions. Despite the availability of preoperative diagnostic tools, histopathology remains the gold standard for accurate diagnosis and optimal management of thyroid diseases.

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Thyroid lesions, Histopathological evaluation, Thyroidectomies, Hematoxylin and Eosin, Papillary thyroid carcinoma, Follicular adenoma

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Brain tumor diagnosis using magnetic resonance imaging (MRI) remains a challenging clinical task due to heterogeneous tumor appearance, overlapping intensity distributions, and significant interpatient variability. Accurate tumor segmentation and classification are essential for effective treatment planning and prognosis; however, manual analysis is time-consuming and subject to observer bias. This work presents an enhanced deep learning framework that integrates advanced preprocessing, an improved UNet-based segmentation network with Squeeze-and-Excitation (SE) blocks and Gaussian Error Linear Unit (GeLU) activation, and a comparative evaluation of three deep convolutional neural network architectures--ResNet50, ResNet101, and DenseNet121--for multi-class brain tumor classification. The segmentation module provides precise tumor localization, enabling the classification models to focus on clinically relevant regions and suppress background interference. The framework is evaluated on the Figshare brain tumor MRI dataset containing glioma, meningioma, and pituitary tumor classes. Experimental results demonstrate strong segmentation performance with a Dice coefficient of 91.8% and classification accuracy of up to 98.04% on unseen test data. Comparative analysis indicates that ResNet50 achieves the best balance between accuracy, generalization capability, and computational efficiency among the evaluated models. These results highlight the effectiveness of enhanced segmentation guided deep learning for reliable and real-time brain tumor diagnosis.

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Brain Tumor Classification, MRI, Deep Learning, ResNet, DenseNet, Ensemble Learning, ROC Curve, Precision-Recall Analysis

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Abstract Background: Root canal treatment (RCT) is a commonly performed endodontic procedure aimed at eliminating infection from the root canal system and preserving the natural tooth. Despite the advancement in endodontic techniques and materials, post-operative pain remains one of the most common complications experienced by patients after root canal treatment. Awareness regarding postoperative pain and its management is essential for dental students as they play an important role in providing proper patient care during clinical practice. Aim: The aim of the present study was to evaluate the awareness and knowledge regarding postoperative pain management following root canal treatment among undergraduate students. Materials and Methods: A cross-sectional questionnaire-based survey was conducted among 400 undergraduate students. A structured questionnaire consisting of questions related to post-operative pain following root canal treatment, its causes, duration, and management strategies was distributed among the participants. The responses were collected and analyzed using descriptive statistical methods. Results: The results of the present study revealed that the majority of students were aware that postoperative pain can occur after root canal treatment. Female students showed slightly higher awareness levels compared to male students. Students belonging to the age group of 21-23 years demonstrated greater awareness regarding post-operative pain and its management. Conclusion: Within the limitations of the study, it can be concluded that undergraduate students possess a moderate level of awareness regarding post-operative pain management in root canal treatment. However, additional educational programs and clinical training are required to improve their knowledge and enhance patient care

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Keywords: Root canal treatment, Post-operative pain, Pain management, Awareness, Undergraduate students, Endodontics. Introduction

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