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National Science Foundation USA and Department of Science and Technology India

Collaborative research opportunity between the U.S. National Science Foundation (NSF) and the Department of Science and Technology (DST) of the Government of India aims to develop new knowledge in computing and communications fields. Investigators from both countries collaborate to write a single pr

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Understanding Daylight Saving Time Changes and Their Impact

Daylight Saving Time (DST) involves adjusting clocks to shift an hour of sunlight from morning to evening, impacting sunrise and sunset times. This change aims to maximize daylight during working hours and conserve energy. Explore how the transition from Standard Time to DST affects sunrise and suns

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Unraveling the Complexities of Beyond Steiner Tree Problem with DST-N and DGA: An Exploration

Dive into the intricacies of the Beyond Steiner Tree Problem and its solutions involving DST-N and DGA. Explore lower and upper bounds, add-only constraints, and various algorithms through a series of detailed images and descriptions. Delve deep into the world of optimization and beyond with Dean Or

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Efficient Trustworthy AI: Harnessing Data for Success

Information and Brokerage Event Horizon Europe 2023, co-funded by DST, Government of India, focuses on the topic "Efficient Trustworthy AI - Making the Best of Data." Participants are invited to create flash presentations on project proposals or entity profiles related to AI, data, and robotics part

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Reflection and Evaluation of CATCH DST Tool Training on Climate Change Adaptation

Reflection and evaluation are key components of the CATCH DST Tool Training on climate change adaptation conducted by Helge Bormann at Jade Hochschule. The evaluation includes assessing the program content, training format, trainers' knowledge and skills, facilities, and gathering general comments.

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Fostering Scientific Analysis: Fun4All - A Powerful Data Processing Tool

Fun4All is a mature data processing framework that started in 2003 to reconstruct and analyze PHENIX data, later adopted by sPHENIX. It handles large volumes of raw data, processing about 1PB DST data per week for user analysis. The design principle emphasizes simplicity and readability for users, a

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