Volume 11 | Issue 4 |

 Abstract

TESTING OF POLITICAL THOUGHT OF ARASTU

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TESTING OF POLITICAL THOUGHT OF ARASTU

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Tea garden along with its factory is known as tea industry. Thousands of acres of unfertile land of Dooars of North Bengal came under the ambit of the tea plantation from long past. To satisfy the growing demand of green tea leaf, the concept of Bought leaf tea factory is introduced in India. Consequently, the plain fertile area of Mekhliganj, Cooch Behar covered very rapidly by the mushroom like growth of small tea garden. This study showed that by nature, 85% the soil of this very area is, deep to very deep fine loamy & coarse loamy and suitable for crop plant like Paddy, Wheat, Jute, Tobacco and other winter vegetables etc. Tea plant needs huge amount of water, in every year a large amount of water come out of the soil. Water table of soil gradually declined. An average crop of 2000 kg made-tea/hector will remove around 100 kg nitrogen, 20 kg phosphate and 40 kg potash per hector per year from soil in addition to nutrients locked up in the bush frame. So, the growers of small holdings used to provide large amount of fertilizer mainly of chemical nature such as urea and potash to the soil to satisfy the fertilizer requirement of the tea plants. Nature of soil degrades in course of time. Unscientific use of herbicides like Glyphosate, Paraquat became the cause of complete eradication of ferns and other angiospermic herb. This paper, focus that the profit- making attitude of cultivating tea plant in plain area ultimately leads to spoil the soil nature, lowered the ground water table and loss of biodiversity of this very region.

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Tea, Bought-leaf factory, Herbicides

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 Abstract

Varn Sankar Jaatiyon ke Sansarg se Utpann Antyaj ek Sanakshipt Adhyayan

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Varn Sankar Jaatiyon ke Sansarg se Utpann Antyaj ek Sanakshipt Adhyayan

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Cloud data pipeline architectures are at the forefront of modern data engineering, enabling organizations to process, transform, and analyse vast amounts of data efficiently. As businesses increasingly adopt cloud solutions, selecting the right data pipeline architecture becomes critical to achieving optimal performance, scalability, and cost-effectiveness. This paper presents a comparative analysis of two prominent cloud data pipeline architectures: Snowflake and Azure Data Factory. Both platforms offer robust solutions for managing and orchestrating data pipelines, but they differ in their underlying technologies, capabilities, and use case suitability. Snowflake, a cloud-native data platform, has gained significant traction due to its unique architecture that decouples storage and compute, enabling elastic scaling and seamless data sharing. It supports multi-cloud deployments, offering flexibility to organizations with diverse cloud strategies. Snowflake's ability to handle structured and semi-structured data, combined with its advanced query optimization features, makes it a compelling choice for enterprises focused on high-performance analytics and data warehousing. On the other hand, Azure Data Factory (ADF) is a comprehensive data integration service within the Azure ecosystem. ADF is designed to facilitate the creation, scheduling, and management of complex data pipelines, supporting both batch and real-time data processing. As part of the Azure ecosystem, ADF seamlessly integrates with other Azure services, providing a unified platform for organizations already invested in Microsoft's cloud offerings. ADF's rich set of connectors and pre-built activities allows for easy integration with various data sources and destinations, making it a versatile tool for diverse data engineering tasks. This comparative analysis explores the strengths and weaknesses of Snowflake and Azure Data Factory across several key dimensions, including scalability, performance, ease of use, integration capabilities, cost efficiency, and security. The study delves into real-world use cases and performance benchmarks to highlight scenarios where each platform excels or faces limitations. Additionally, it examines the impact of each platform's architecture on data processing efficiency, considering factors such as data ingestion speed, transformation capabilities, and support for complex data workflows. The findings reveal that while Snowflake excels in scenarios requiring high-performance analytics and cross-cloud flexibility, Azure Data Factory offers a more integrated and cost-effective solution for organizations deeply embedded in the Azure ecosystem. The choice between Snowflake and Azure Data Factory ultimately depends on the specific needs and priorities of the organization, including factors such as existing cloud infrastructure, budget constraints, and the complexity of data workflows.

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Cloud Data Pipeline o Snowflake o Azure Data Factory o Data Engineering o Data Integration o Scalability o Performance o Elastic Scaling o Data Warehousing o Real-time Data Processing o Multi-cloud Deployment o Query Optimization o Data Ingestion o Cost Efficiency o Data Management

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In the era of digital transformation, optimizing enterprise resource planning (ERP) systems is pivotal for large enterprises aiming to enhance operational efficiency and competitiveness. This research paper presents a comparative analysis of strategies for optimizing SAP S/4HANA in large enterprises. SAP S/4HANA, SAP's next-generation business suite, offers significant advantages, including real-time analytics, streamlined processes, and enhanced user experiences. However, its implementation and optimization pose challenges that vary across industries and organizational structures. This study investigates these challenges and evaluates optimization strategies to provide a comprehensive understanding of the factors influencing successful SAP S/4HANA deployment. Through a systematic review of existing literature and case studies, this research identifies critical factors for optimizing SAP S/4HANA, including data migration, process re-engineering, and customization. It highlights the importance of a clear implementation roadmap, stakeholder engagement, and continuous performance monitoring. The paper compares various approaches adopted by large enterprises, such as phased versus big-bang implementation, on-premises versus cloud deployment, and the integration of artificial intelligence and machine learning for predictive analytics and decision-making. The comparative analysis reveals that a phased implementation approach, coupled with cloud deployment, tends to offer greater flexibility and scalability, allowing enterprises to adapt to evolving business needs. Additionally, the integration of AI and machine learning within SAP S/4HANA enhances predictive capabilities, enabling proactive decision-making and improved resource allocation. Customization, while beneficial for addressing specific business requirements, is identified as a double-edged sword that can lead to increased complexity and maintenance overhead if not managed judiciously. Furthermore, the study underscores the significance of change management and training programs in ensuring user adoption and minimizing disruption during the transition to SAP S/4HANA. It also emphasizes the role of continuous performance monitoring and optimization post-implementation to sustain the benefits of the new system.

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Digital transformation o Enterprise resource planning o SAP S/4HANA o Large enterprises o Optimization strategies o Real-time analytics o Process re-engineering o Customization o Implementation roadmap o Stakeholder engagement o Performance monitoring o Phased implementation o Cloud deployment o Artificial intelligence o Predictive analytics

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Water stands as one of the most crucial and indispensable resources for all life on Earth, including humans. However, the rampant spread of pollution and contamination poses a severe threat to its quality and availability. A significant contributor to this crisis is the unchecked disposal of untreated industrial waste into nearby bodies of water like lakes, ponds, and rivers. It's imperative that educational institutions prioritize the establishment of efficient sewage treatment facilities to manage the wastewater generated on their premises. Before constructing such a facility, a comprehensive analysis of the wastewater's characteristics is essential. Parameters such as pH level, total solids, suspended particles, hardness, alkalinity, fluoride, iron, ammonia, nitrate, and phosphate require thorough examination. The primary goal of a sewage treatment plant is to receive and process wastewater from various sources like hostels, colleges, and canteens, eliminating any harmful substances that could endanger the surrounding environment. The ultimate aim of sewage treatment is twofold: to produce a treated effluent that meets environmental safety standards and to generate treated sludge suitable for disposal or potential reuse, typically as fertilizer in agriculture. This process involves the removal of diverse pollutants including solids, organic matter, nutrients, salts, metals, and pathogens. Effective wastewater management is paramount for safeguarding both environmental integrity and public health. Various methods are employed for sewage/wastewater treatment, all aimed at reducing water and organic content. The overarching objective of wastewater management is to ensure environmental protection while addressing public health and socioeconomic considerations. This project focuses on the meticulous design of a comprehensive sewage treatment system, encompassing key components such as storage tanks, screen chambers, primary sedimentation tanks, the activated sludge process (ASP), and secondary sedimentation tanks.

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sewage, BOD, Solids, ASP, public health. Sewage

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Forests play a vital role of our planet's ecosystem and have played a very crucial role in the development of human civilization. They provide a wide range of natural resources. Forest has cultural, spiritual and recreational significance also. From the ancient times, forest is the main part of the human life. The medieval historical sources also provide ample information about the Indian forest and the covered area. Forest as symbols of power for the different states and authorities, so that rules and regulation for promoting agriculture to enhance economic resources are majorly defined by the historical sources. The present paper is based on majorly on the primary sources of the Mughal empire. In the Mughal period forests played a significant role in the economy, cultural and ecology.

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Terai forest, sarkar, suba, Doab

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Bangalore, also known as Bengaluru, is facing a severe water crisis exacerbated by rapid urbanization, population growth, and climate change. The city's water demand far exceeds its supply, leading to critical shortages and over-extraction of groundwater. This abstract delves into the causes of Bangalore's water crisis, including inefficient water management, pollution of water bodies, and encroachment of lakes. It also explores potential solutions such as rainwater harvesting, wastewater recycling, lake rejuvenation, and policy reforms aimed at sustainable water usage. Addressing these issues requires a multifaceted approach involving government intervention, community participation, and technological innovation to ensure a sustainable water future for Bangalore.

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Water Crisis, Bangalore, Urbanization, Population Growth, Groundwater Depletion, Water Management, Pollution, Climate Change, Lake Encroachment, Rainwater Harvesting, Wastewater Recycling, Lake Rejuvenation, Policy Reforms, Community Participation, Technological Innovations, Water Conservation, Sustainable Water Usage, Public Awareness, Water Supply, Water Demand, Introduction, Bangalore, often referred to as the Silicon Valley of India, has experienced exponentia

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In this paper, we concentrate on the compressible viscoelastic streams in three-layered entire space. We use the energy method to find a one-of-a-kind global solution assuming minimal initial data. The higher-order spatial derivatives of the solution's time decay rates are also obtained. if the initial data belong to additionally.

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Research on viscoelastic compressible flows

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Dividend policy, a crucial corporate finance decision, determines the portion of company income distributed to shareholders. Despite extensive research since Lintner's work in 1956, whether dividends enhance or diminish firm value remains unresolved. Dividend strategy, rewarding shareholders while reducing internal capital, is intertwined with investment and financing decisions, impacting shareholder value maximization. Utilizing databases like Scopus and Web of Science, a systematic literature review on dividend policy examined 768 articles over 47 years. The analysis highlights a surge in publications since 2005, indicative of increasing research interest. Affiliation statistics show the US as the highest contributor, followed by the UK, with most top contributing organizations based in the US. This study underscores the ongoing academic curiosity and the importance of dividend policy in corporate finance, shedding light on areas for future research and emphasizing the role of databases like Web of Science in facilitating such investigations.

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Dividend Policy, decision making, shareholder's wealth

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