Volume 13 | Issue 8 |

 Abstract

The Concept of Dharma in the Upanishads: A Philosophical Analysis in the Perspective of Rta, Karma and Moksha

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The Concept of Dharma in the Upanishads: A Philosophical Analysis in the Perspective of Rta, Karma and Moksha

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ABSTRACT : The fast technological revolution of the 21st century has positioned blockchain as a transformative force in marketing, redefining how manufacturers interact with consumers. This observe investigates the position of blockchain in creating transparent, secure, and decentralized advertising and marketing structures that decorate consider, reduce fraud, and streamline records control. With its capacity to authenticate transactions, confirm virtual identities, and facilitate smart contracts, blockchain is revolutionizing virtual marketing, purchaser engagement, and loyalty packages. The studies highlights blockchain's potential not simply as a technological advancement, however as a strategic device shaping ethical, green, and progressive marketing practices within the contemporary digital economic system.. The twenty first century has witnessed a transformative technological revolution, with blockchain emerging as one of the maximum disruptive innovations. at the beginning evolved to assist crypto currencies, blockchain generation has developed past finance, presenting good sized ability in advertising and marketing by using making sure transparency, safety, and decentralization. This have a look at explores how blockchain is reshaping marketing strategies, improving consumer accept as true with, and optimizing data-driven decision-making inside the virtual age. Blockchain empowers marketers via permitting at ease and verifiable client data management, preventing ad fraud, and facilitating peer-to-peer interactions without intermediaries. Through smart contracts, organizations can make sure transparent transactions and actual-time engagement with stakeholders. moreover, decentralized structures are helping brands construct real relationships with clients, redefining loyalty applications and digital identification verification. these innovations represent a paradigm shift in how brands connect, speak, and transact in an increasing number of virtual marketplaces. This studies highlights the strategic implications of blockchain integration in advertising and marketing and evaluates its impact on customer conduct, data privateness, and logo popularity. As businesses strive to adapt to converting virtual landscapes, blockchain stands proud as a catalyst for agree with and innovation.

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Data Blockchain, Digital Marketing, crypto currencies

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Ayurvedic herbs with therapeutic qualities are used to make the natural skincare product Polyherbal Ubtan Soap. For generations, people have utilized these plants to treat skin conditions and encourage the growth of healthy skin. This herbal soap provides an antibacterial treatment that helps treat skin issues like bacterial infections and tans, in contrast to commercial soaps that frequently include dangerous chemicals. Because of its skin-rejuvenating and antioxidant qualities, it is a safe, affordable, and efficient substitute for natural skincare products that promote healthier skin.

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Polyherbal Ubtan Soap, Skincare, Ayurveda, Antioxidant, Antibacterial.

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Children and adolescents of parents with mental illness (CAPMI) face significant psychological, social, and developmental challenges. Despite growing awareness, interventions often fail to adequately target those most at risk or effectively deliver support tailored to their needs. This study aims to identify which children are most vulnerable (the 'Who') and how interventions can be optimally designed (the 'How') to mitigate the adverse effects of parental mental illness. Using a mixed- methods approach, this research will integrate quantitative analysis of large-scale datasets with qualitative interviews to understand CAPMI experiences and intervention effectiveness. The findings will contribute to developing evidence-based strategies for mental health practitioners, policymakers, and educators', ensuring that support is both accessible and impactful. This research is critical to breaking the cycle of mental illness transmission across generations and fostering resilience in children at risk.

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Parental mental illness, children and adolescents with parents with mental illness (CAPMI), intergenerational mental illness, evidence-based interventions, mixed-methods study design, qualitative interviews, quantitative analysis, scalability, mental health support, resilience, psychological development.

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This paper offers a comparative analysis of the short stories of Ruskin Bond and R.K. Narayan, two major Anglophone writers from India whose work--while equally accessible--reveals markedly different literary sensibilities. Using close reading of representative stories (Bond's "The Eyes Are Not Here"/"The Eyes Have It," "The Night Train at Deoli," "The Blue Umbrella," and "Panther's Moon"; Narayan's "An Astrologer's Day," "A Horse and Two Goats," "Engine Trouble," and "Lawley Road"), the paper contrasts their themes, narrative strategies, characterization, humor and irony, treatment of place, and language. It argues that Bond's lyric humanism and nature-centered intimacy create moments of tender melancholy, while Narayan's comic realism and gentle satire construct a moral universe grounded in the rhythms of small-town life. Both develop a distinct Indian idiom in English and celebrate ordinary people, but they diverge in the textures of memory, landscape, and social critique they bring to the short form.

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Ruskin Bond; R.K. Narayan; Indian short story; Malgudi; Himalaya; realism; nostalgia; satire; nature writing; postcolonial literature

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The kinetics of L-threonine oxidation in an acidic medium using tetramethylguanidinium chlorochromate (TMGCC) as an oxidant was systematically investigated. The study explores the impact of various reaction parameters, including substrate concentration, oxidant concentration, acidity, and temperature on the rate of oxidation. A first-order dependence on both L-threonine and TMGCC was observed, while the reaction showed a fractional-order dependence on hydrogen ion concentration. Activation parameters such as the enthalpy, entropy, and the energy of activation were calculated from temperature-dependent data using the Arrhenius equation. A plausible reaction mechanism has been proposed based on experimental results, supported by the formation of intermediate complexes. This study provides comprehensive insights into the oxidation process of L-threonine, which could contribute to a deeper understanding of amino acid oxidation reactions in biological systems.

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L-Threonine, Kinetics, Oxidation, Mechanism

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Feminism, as both a socio-political movement and philosophical framework, has undergone profound transformation since its emergence in the late 19th century. What began as a concentrated demand for women's suffrage has expanded into a broad, multidimensional struggle for gender justice and inclusive human rights. This paper traces the trajectory of feminist thought through its four major waves--each uniquely shaped by historical, cultural, and political conditions. From the early battles for political agency to the digital activism of the fourth wave, feminism has grown more inclusive and intersectional, incorporating diverse voices and identities. This study aims to offer a critical yet accessible overview of each wave's core issues, achievements, and limitations, ultimately emphasizing the need for continued evolution as feminism navigates contemporary challenges such as systemic inequality, digital misogyny, and the politics of identity.

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Feminism, gender equality, first wave, second wave, third wave, fourth wave, intersectionality, digital feminism, feminist movements, feminist theory, gender justice, identity politics.

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Deep neural networks (DNNs) have revolutionized social media by personalizing content feeds, yet their pervasive influence raises significant concerns regarding user behavior and mental well-being. This paper examines how these advanced recommendation systems, including those leveraging Reinforcement Learning (RL) and Large Language Models (LLMs), create feedback loops that can narrow users' informational exposure and foster addictive behaviors, contributing to phenomena like "Brain-rot." Through a comprehensive literature review and the development of a semantically enriched recommendation model, we analyze the intricate mechanisms of DNNs, their psychological and cognitive impacts, and propose design strategies for more balanced algorithms. Findings indicate that while DNNs enhance engagement, their optimization for metrics like watch time can inadvertently lead to increased anxiety, reduced attention spans, and diminished critical thinking. The study highlights the urgent need for ethical algorithm design, advocating for the integration of diversity, transparency, and privacy to create systems that not only engage but also empower and protect users. This research underscores that the future of AI-driven personalization must prioritize human values and well-being over mere engagement maximization.

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Deep Neural Networks(DNNs), Recommendation System, Reinforcement Learning(RL), Large Language Model, Algorithmic Personalization, Brain-rot, Ethical AI, Social Media Addiction.

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In some situations, it is needed to give some time to items in inventory before allowing it for sell and it spends time in the inventory besides the starting time of depleting it from the inventory up to the zero level of inventory. This service time is cost of holding on all received items in the inventory. If we can reduce the service time by increasing service persons or facilities and the total cost goes near to cost of classical EOQ model.

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Inventory system, Demand rate, Service rate, Replenishment rate, Lead time, Queuing system Service time, re-order point.

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The rapid expansion of connected and autonomous vehicles necessitates real-time anomaly detection methods that uphold both data privacy and computational efficiency. Conventional centralized learning systems often require the transfer of raw sensor streams to external cloud infrastructures, introducing latency challenges and potential data exposure. To mitigate these issues, this paper presents a software-defined, federated learning architecture tailored for vehicular edge networks. Our framework leverages a lightweight Convolution Neural Network (CNN) design, optimized for temporal sensor data such as accelerometer and gyroscope readings, enabling each vehicle to conduct localized model training. Instead of sharing raw inputs, nodes transmit securely encrypted model updates, which are aggregated at a central coordinator using a privacy- aware averaging scheme. To further protect individual data characteristics, differential privacy is implemented by injecting calibrated noise into gradient updates, ensuring obfuscation of user- specific behaviour. Experiments conducted on a synthetic vehicular dataset demonstrate a detection accuracy exceeding 96%, alongside a notable 40% reduction in communication overhead. The model also maintains efficient performance under resource-constrained conditions, confirming its suitability for real-time deployment in smart vehicular ecosystems. These results support the framework's potential as a decentralized, privacy-conscious solution for anomaly detection in next-generation transportation systems.

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Federated Learning, Vehicular Edge Networks, Anomaly Detection, Differential Privacy

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