Volume 14 | Issue 2 |

 Abstract

Ethno botany deals about that how indigenous people of a particular geographical area with specific culture make use of local flora in various purposes like medicines, food, fodder, shelter, oils, etc. from prehistoric time. This knowledge of using local plants in various purposes has been transferred through generations as a part of cultural heritage. Means plants play a key role in Human life and health. Ethno medicinal plants are the common jari butis that's are used by folklores/ local peoples to cure various diseases of human beings. Manikpur is one of the tahsil of Chitrakoot district, located in Southern part. This area is highly enriched with natural vegetation, where a large number of medicinal plants are growing naturally that's are used by local peoples to treat diseases. In this area mainly two tribal groups like Kol and Mawasi are inhabited besides common population. Kol are much more in population as compare to Mawasi. They inhabit in a large tracts of Central India, mainly M.P. and some areas of Southern U.P. They trace their lineage to the legendary Sabari of the Ramayana, who has been revered by generations of Hindus for her extreme devotion to Lord Rama during his exile. They mainly depend upon jari butis to treat diseases and used them either solely or in mixture form in a fixed/determined combination to treat various diseases like cold &cough, skin diseases ,diabetes, digestive problems etc. This traditional knowledge is diversified, but because of modernization this is going on decreasing day by day at high rate. So there is need to conserve and explore this ancestral knowledge (Folklores) for novel drug discovery and maintain it from generation to generation for human kind. There is also need to conserve these medicinal plants due to unplanned over exploitation.

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Kol, folklores, Chitrakoot, Jari butis, Manikpur. Agnisikha

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ABSTRACT- Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressively recognized form of infiltrative cardiomyopathy due to the extracellular deposition of aberrantly folded transthyretin protein fibrils in the myocardium. Historically, ATTR-CM was perceived as a rare disease and was frequently diagnosed post-mortem. However, with the advent of multimodality imaging and molecular therapies, ATTR-CM has emerged as a potentially treatable disease. The aim of this narrative clinical review is to offer a comprehensive clinical perspective on ATTR-CM, including its epidemiology, molecular mechanisms, clinical manifestations, diagnostic strategies, therapeutic developments, preventive measures, prognosis, and future prospects. This narrative clinical review integrates the latest knowledge from the literature, consensus statements, and major clinical trials assessing the efficacy of transthyretin stabilizers, gene-silencing therapies, and ATTR-CM has two prevalent presentations: wild-type (ATTRwt), which mainly occurs in elderly men, and hereditary (ATTRv), due to autosomal dominant mutations in the TTR gene. The pathophysiology involves tetramer instability, monomer misfolding, and amyloid fibril accumulation, culminating in restrictive cardiomyopathy and systemic involvement. Red flags such as bilateral carpal tunnel syndrome, incongruent ECG-echo changes, and idiopathic HFpEF are essential for prompt diagnosis. Non-invasive bone scintigraphy using technetium-labeled agents has largely supplanted endomyocardial biopsy for diagnosis. Prognostic therapies such as tafamidis, RNA interference agents (patisiran, vutrisiran), antisense oligonucleotides (inotersen, eplontersen), and CRISPR/Cas9 gene editing (NTLA-2001) are revolutionizing prognosis. ATTR-CM is rapidly evolving from a fatal, underdiagnosed cardiomyopathy to a chronic, treatable disease. Early diagnosis, genetic testing, and prompt initiation of disease-modifying therapy are essential. The next horizon of research will focus on not only arresting disease progression but also reversing amyloid accumulation and potentially curing the hereditary variant.

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 Keywords

1.Transthyretin amyloid cardiomyopathy 2.ATTR-CM 3.Wild-type ATTR 4.Hereditary ATTR 5.HFpEF 6.Amyloid cardiomyopathy 7.Tafamidis; Patisiran 8.Vutrisiran; Inotersen 9.Gene silencing 10.CRISPR therapy 11.Bone scintigraphy 12.Cardiac MRI 13.Multisystem amyloidosis 14.Rare cardiac diseases.

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Bhabani Bhattacharya, one of the foremost Indian English novelists, is widely recognized for his humanistic vision and commitment to social reform. Music for Mohini presents a sensitive portrayal of the tension between entrenched patriarchal traditions and the emerging consciousness of women in post-independence Indian society. The novel foregrounds the conflict between rigid cultural norms that seek to confine women within domestic silence and the growing aspiration for selfhood and autonomy. This paper examines Mohini as a crucial site of negotiation where submission to tradition coexists with subtle forms of resistance. Rather than portraying overt rebellion, Bhattacharya delineates women's agency as an inward, ethical process shaped by emotional strength, moral reasoning, and self-realization. Employing a feminist and socio-cultural framework, the study undertakes a close textual analysis to explore how patriarchal power operates within familial and social institutions and how Mohini gradually asserts her identity within these constraints. The paper argues that Music for Mohini advocates a model of transformation that is gradual, non-violent, and humanistic, emphasizing reform through understanding rather than confrontation. The novel remains relevant to contemporary debates on gender, patriarchy, and women's empowerment, particularly in societies negotiating the complex interface between tradition and modernity.

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Women's Agency, Patriarchy, Tradition, Transformation, Feminism, humanistic values

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Effective grievance handling is a critical requirement in educational institutions to ensure transparency, accountability and student satisfaction. Conventional complaint management and inefficient, often resulting in delayed resolutions and poor communication between students and authorities. To overcome these limitations, this project proposes a Student Complaint Management System, a web -based application designed to automate and streamline the grievance redressal process within college environment. The system allows students to securely authenticate using department-wise login credentials and submit complaints relate to academic, transportation, or infrastructural issues through a centralized platform. Complaints are categorized and forwarded to the concerned authorities to ensure organized handling and timely resolution. A real-time complaint tracking feature enables students to monitor the status and progress of their complaints via an intuitive dashboard. Automated notifications keep students informed about complaint acknowledgment, updated, and final resolution, thereby improving transparency and communication. From the administrative perspective, the system maintains a centralized complaint database that supports efficient monitoring, management, and resolution of grievances. It also provides reporting and analytical features to identify recurring issues and areas requiring institutional improvement. By facilitating direct interaction between student and higher authorities, the proposed system enhances accountability, responsiveness, and efficiency. Overall, the Student Complaint Management System promotes a student centric approach and contributes to continuous improvement in institutional grievance management.

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Student Registration, Account Activation, Complaint Submission, Complaint Tracking, Application Submission, Notification system, Dashboard Monitoring, Report Generation and User Profile Management.

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Bharat Me Mahila Shashaktikaran Per Ek Nazar

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Bharat Me Mahila Shashaktikaran Per Ek Nazar

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Abstract Digitalization has significantly transformed consumer behaviour across India, including rural regions. This study examines the evolving consumer trust toward online shopping platforms in rural Odisha in the post-digitalization era. With increased internet penetration, smartphone usage, and digital payment adoption, rural consumers are gradually shifting from traditional retail to online platforms such as Amazon India and Flipkart. However, trust remains a critical factor influencing adoption. The study investigates key determinants of trust including payment security, product authenticity, delivery reliability, digital literacy, and social influence. Using survey-based primary data collected from rural consumers in selected districts of Odisha, the research analyses behavioural changes and trust patterns. The findings indicate that while trust has improved due to positive user experiences and digital awareness, concerns regarding fraud, return policies, and digital skills still persist. The study concludes that strengthening digital education, ensuring secure payment systems, and improving last-mile delivery services are essential for enhancing consumer trust in rural Odisha's e-commerce ecosystem.

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Keywords: Transformed, Digitalization, Authenticity, Awareness, Strengthening

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Artificial Intelligence (AI) has transformed financial fraud detection by enabling more accurate, scalable, and adaptive systems across sectors such as banking, insurance, and healthcare. This systematic review examines the effectiveness of AI-based approaches in detecting financial fraud and explores the challenges and limitations associated with their implementation. Peer-reviewed studies from major academic databases were systematically analyzed, focusing on machine learning and deep learning techniques used to evaluate the performance of AI-driven fraud detection systems. The results demonstrate that AI significantly enhances real-time fraud detection and improves adaptability to evolving fraud patterns when compared to traditional rule-based methods. Despite these advantages, several challenges hinder widespread adoption, including ethical concerns, algorithmic bias, data privacy risks, system vulnerabilities, and scalability constraints--particularly for smaller organizations. Overall, AI-based fraud detection represents a transformative solution for combating financial fraud; however, realizing its full potential requires improved data quality, the development of explainable AI models, strengthened cybersecurity measures, and updated regulatory frameworks. Collaboration among policymakers and stakeholders is essential to ensure the ethical and effective deployment of AI in fraud detection.

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Artificial Intelligence; Fraud Detection; Machine Learning; Data Privacy; Algorithmic Bias; Financial Sector;Cybersecurity

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Artificial intelligence (AI) is steadily reshaping financial services by making operations more automated and strongly driven by data. Tools such as machine learning, natural language processing (NLP), predictive analysis, and robotic process automation (RPA) allow banks and other financial organisations to perform tasks faster, reduce errors, control risks, and improve the way they interact with customers. These technologies are commonly applied in areas such as identifying fraudulent transactions, assessing creditworthiness, and providing investment-related guidance. AI also supports regulatory activities through Reg Tech applications and helps widen access to financial services through Robo-advisory platforms, thereby encouraging innovation and financial inclusion. However, along with these advantages, several challenges remain, particularly concerns about data protection, ethical use of intelligent systems, and limited transparency in algorithm-based decisions. This study focuses on understanding the effect of AI on operational performance, customer experience, and governance practices, while also discussing the opportunities, limitations, and future scope of AI in achieving sustainable digital transformation in the financial sector. Keywords: Artificial Intelligence, Machine Learning, Predictive Analytics, Natural Language Processing, Robotic Process Automation, FinTech, Reg Tech, Risk Control, Financial Inclusion, Ethical Use of AI, Data Protection, Digital Change. Artificial intelligence (AI) is steadily reshaping financial services by making operations more automated and strongly driven by data. Tools such as machine learning, natural language processing (NLP), predictive analysis, and robotic process automation (RPA) allow banks and other financial organisations to perform tasks faster, reduce errors, control risks, and improve the way they interact with customers. These technologies are commonly applied in areas such as identifying fraudulent transactions, assessing creditworthiness, and providing investment-related guidance. AI also supports regulatory activities through Reg Tech applications and helps widen access to financial services through Robo-advisory platforms, thereby encouraging innovation and financial inclusion. However, along with these advantages, several challenges remain, particularly concerns about data protection, ethical use of intelligent systems, and limited transparency in algorithm-based decisions. This study focuses on understanding the effect of AI on operational performance, customer experience, and governance practices, while also discussing the opportunities, limitations, and future scope of AI in achieving sustainable digital transformation in the financial sector.

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Artificial Intelligence, Machine Learning, Predictive Analytics, Natural Language Processing, Robotic Process Automation, FinTech, Reg Tech, Risk Control, Financial Inclusion, Ethical Use of AI, Data Protection, Digital Change.

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Solid state kinetics, thermogravimetric analysis, Schiff base, iron complex, Freeman-Carroll method, Doyle-Zsako method

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Nanomaterials have attracted significant attention in modern chemistry due to their unique size-dependent physical, chemical, optical, and catalytic properties. Materials in the nanoscale range (1-100 nm) exhibit enhanced surface area, improved reactivity, and superior functional performance compared to their bulk counterparts. The present study focuses on the synthesis, characterization, and applications of nanomaterials with special emphasis on eco-friendly green synthesis methods. Conventional techniques such as sol-gel, hydrothermal, and chemical reduction methods are compared with plant-mediated green synthesis for safer and sustainable nanoparticle production. Characterization of the prepared nanomaterials was carried out using X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Ultraviolet-Visible (UV-Vis) spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, and Brunauer-Emmett-Teller (BET) surface area analysis. The results confirm that green synthesized nanoparticles show uniform morphology, good stability, and reduced toxicity. Applications in catalysis, drug delivery, environmental remediation, and energy devices demonstrate their practical significance. This study highlights that green nanotechnology provides an efficient, low-cost, and environmentally responsible pathway for future chemical research and industrial applications

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: Nanomaterials, Synthesis, Characterization, Green Chemistry, Applications, Nanotechnology

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