Climate Change Presentation

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Climate change

Climate Change

“Change is the Law of Nature”.Climate Change is a reality. It has changed in Past, is changing in Present and will change in Future. The variation and shifts in weather conditions over space and time of different scales and magnitude resulting into changes of Climatic Type is defined as Climate Change. The factor that affect climate are,Continental drift,Variation in the earth’s orbit,Plate tectonics,Volcanic activity,Ocean currents,Greenhouse Gases,Atmospheric Aerosols etc. It effect climate in different ways such as Higher Temperatures,Changing Landscapes,Wildlife/Ecosystem at Risk,Ocean acidification /Rising Seas level,Increased Risk of Drought, Fire and Floods,Intensified Storms and Increased Storm Damages,Illness and Disease,Economic Losses,Agriculture Productivity/Food Security etc.

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Climate is a long term pattern of weather. and we humans are continuously disturbing our climate just because of some pathetic reasons. There are many Human are natural factors responsible for climate change. This will help you in identifying the factors responsible for Climate Change. Please try to eradicate factors of climate change. Otherwise it will make this environment deadly for us and for our coming generations.

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Rice is the staple food for more than half of the global population. In India, it is grown on an area of about 43.97 m ha with total production and productivity of about 104.32 mt and 2.37 t/ha respectively (Anonymous 2013). In Punjab, it occupied an area of 2.82 m ha with production and productivity of 10.54 mt and 3.74 t/ha respectively and in Haryana, it was grown on an area of 1.24 m ha with production and productivity of 3.76 mt and 3.02 t/ha respectively (Anonymous 2013). The most common practice for establishing rice in rice wheat system of indo-gangatic plains region is puddling before transplanting. Alternative to traditional method direct seeding may be adopted because it does not require that heavy amount of labour, water and capital input initially and also crop mature earlier (7-10 days) than transplanted crop allowing timely sowing of succeeding wheat crop. Recent research suggests that new methods of rice establishment, viz zero till rice, bed planting and SRI has potential to reduce cost and increase sustainability of irrigated rice culture while maintaining yield. Irrigation plays a pivotal role in increasing productivity of rice. The efficiency and productivity of irrigation water is quite low owing to percolation losses and high water requirement. There is an urgent need to save water and increase its efficiency in rice production. Various agronomic practice like proper land levelling, proper transplanting time, selection of suitable variety and increasing interval between successive irrigation can play a lead role in water saving and to obtain sustainable yield of the crop. The sustainability of rice production in north-west India is threatened by scarcity of water. So there is need to increase water use efficiency in rice production. Gangwar and Singh (2010) resulted that among different crop establishment methods, highest yield and yield attributing characters of rice was obtained with drum seeding wet bed method. Gill et al (2006) revealed that dry matter accumulation, leaf area index, effective tillers and grain yield were significantly more in direct seeding than transplanted rice. Water productivity in direct seeded rice was higher as compared to transplanted rice clearly showing the more water use efficiency in DSR. Jagtap et al (2013) concluded that the crop established by transplanting recorded significantly higher growth as well as yield attributes resulting in to significantly more grain and straw yield. Grain yield found to be highest in Japanese manual transplanted rice followed by dry drilling (30 kg/ha), dry drilling (15 kg/ha) and drum seeding (Dixit et al 2010). Singh et al (2005) found that mechanical transplanting of rice resulted in highest grain and straw yield which was at par with manual transplanting but significantly higher than both direct seeding methods.

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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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