We’re passionate about helping students learn more about STEM, and through our academic program we give students the opportunity to collaborate on STEM-related projects involving innovative technologies like data analytics, robotics and AI. Since 2011, we have partnered with a number of universities to sponsor research projects, internships, robotics teams, scholarships, and other educational programs that provide students with access to hands-on learning and real-world engineering experience.
Born from the deep data analysis experience of LexisNexis® Risk Solutions, HPCC Systems® is a proven, open source solution for Big Data insights that can be implemented by businesses of all sizes. With HPCC Systems®, developers can design applications with Big Data at their core, enabling businesses to better analyze and understand data at scale. HPCC Systems® offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. Access to open source Big Data technology is vital for SDG 4 Quality education and SDG 9 Industry, innovation and infrastructure.
In this episode of the "World We Want" podcast, Márcia Balisciano interviews Dr. Ana Maria Porras, Assistant Prof of Biomedical Engineering, Crochet Artist, and Science Communicator. They discuss how biomedical engineering is helping develop better treatment strategies for tropical disorders, which have historically received very little attention.
In the pursuit of improving engine performance and mitigating emissions, researchers have explored the intriguing domain of fuel blends incorporating butanol and gasoline. This innovative study aims to unravel the intricate dynamics between butanol and gasoline when utilized as a blended fuel in internal combustion engines. The current study integrates cutting-edge techniques such as Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) for the optimization of engine performance.
The research questions explore the recent progress and technological advancements in Wireless Power Transfer (WPT), the reflection of global engagement within the WPT community through publication trends and geographical distribution, and the alignment of thematic clusters with SDG goals. Questions also investigate the contribution of AI to WPT, challenges and barriers to WPT adoption revealed by bibliometric analysis, ways WPT technology can democratize technology access in marginalized regions, and specific recommendations to ensure WPT technologies effectively accelerate progress towards achieving a broader set of SDGs.
This text ties into SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure). It focuses on the development and enhancement of inorganic multifunctional nanomaterials for energy applications, which are crucial for improving energy efficiency, storage, and conversion, thereby supporting sustainable energy solutions and innovative infrastructure development.
This chapter aligns with Goals 9, 11, and 13 by outlining nondestructive evaluation methods which extend the lifecycle of these buildings and make their materials more durable.
In this episode of the "World We Want" podcast, Márcia Balisciano interviews Marco Richter, Global Head of Customer Success, LexisNexis Intellectual Property Solutions. They discuss how businesses and policymakers can leverage intellectual property (IP) frameworks to promote environment-friendly technologies and practices.
RELX SDG Inspiration Day 2024: Leading edge discussion on AI for sustainable development: the risks and opportunities
The tenth RELX SDG Inspiration Day - a free, online event for thought leaders, corporate representatives, students, investors, government and NGOs to explore issues, gain practical insights and be inspired to take action in support of the UN Sustainable Development Goals.
AI holds tremendous potential for advancing the United Nations Sustainable Development Goals (SDGs). AI, particularly generative AI, provides new opportunities to analyse data and trends at pace a
