Volume 14 | Issue 2 |

 Abstract

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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The gig economy has emerged as a significant form of non-standard employment, particularly with the rise of digital platforms facilitating short-term and task-based work. While this model promises flexibility and income opportunities, it has simultaneously raised serious human rights concerns. Gig workers frequently experience precarious working conditions, including unpredictable earnings, lack of social security, limited labour protections, and excessive algorithmic control. These challenges are particularly acute for women workers, who face compounded vulnerabilities due to safety concerns, discrimination, and inadequate public infrastructure. This paper examines the impact of the gig economy on human rights in India, analyses the challenges faced by gig workers--especially women--and evaluates legal frameworks, judicial developments, and policy responses. It concludes by proposing reforms and suggestions to ensure dignity, equality, and social security for gig and platform workers.

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Gig economy, human rights, platform workers, women workers, social security

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Creative Commons Attribution 4.0 and The Open Definition

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The Relevance of the Social Values Inherent in the Vedic system of Sanskar in the present context

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The Relevance of the Social Values Inherent in the Vedic system of Sanskar in the present context

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Creative Commons Attribution 4.0 and The Open Definition

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Analytical method validation, Atomic Absorption Spectrophotometry, High-Performance Liquid Chromatography, Combined antacid suspension, Simultaneous estimation, Magnesium Hydroxide, Aluminium Hydroxide, Simethicone.

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ABSTRACT Background: Chronic schizophrenia with persistent residual symptoms poses significant therapeutic challenges, particularly in patients with long-standing illness and repeated relapses due to medication non-adherence. Individualized homoeopathic prescribing has been explored as an adjunctive approach in chronic psychiatric conditions. Case: A 57-year-old woman with a 20-year history of schizophrenia presented with persistent social withdrawal, guilt feelings, irritability, subjective cognitive complaints, and a fixed erotomaniac delusion. The illness was precipitated by significant psychosocial stressors, including financial loss and bereavement. The patient had a history of multiple hospitalizations, long-term institutional care, repeated modified electroconvulsive therapy (mECT), and ongoing antipsychotic treatment. The PANSS score of 45 at baseline. Intervention: An individualized homoeopathic remedy, Ignatia Amara 200C, was prescribed based on totality of symptoms, including ailments from grief, self-reproach, sensitivity, aversion to company, desire for sweets and music, and chronic constipation. The patient continued conventional psychiatric medications concurrently. Outcome: Over a three-month follow-up period, improvement was observed in mood, affective reactivity, and guilt intensity. Subjective forgetfulness improved, and overall functioning showed stabilization. However, the fixed erotomaniac delusion persisted. The PANSS total score at last follow-up was 33. Conclusion: This case highlights the complexity of chronic schizophrenia with residual features and suggests that individualized homoeopathic treatment may serve as a complementary approach in long-term integrative management. Controlled studies are required to further evaluate therapeutic effects.

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Keywords: Schizophrenia, Residual symptoms, Erotomania, Homoeopathy, Ignatia amara, PANSS.

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