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5 to 7 June 2026, Waterfront Hotel, London
How you transform your business as tech, consumer, habits industry dynamic change, Find out from those leading the charge.
How you transform your business as technology, consumer, habits industry dynamic
1How you transform your business as technology, consumer, habits industry dynamic
3How you transform your business as technology, consumer, habits industry dynamic
2How you transform your business as technology, consumer, habits industry dynamic
4Institut Teknologi Sepuluh Nopember, Indonesia
Norwegian University of Science and Technology, Norway
National Institute for Materials Science, Japan
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How you transform your business as technology, consumer, habits industry dynamic s change? Find out from those leading the charge.
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Abstract : As 5G commercialization and 6G research accelerate, high-frequency bands are becoming the mainstream spectrum for future communications, emphasizing the critical role of AI in network optimization and decision-making. This study proposes the Distributed MLOps Management Platform (DMMP) to integrate MLOps into the 6G network system, enabling automated model deployment, monitoring, and optimization while exploring its feasibility with O-RAN. Leveraging a subscription-based architecture, DMMP interacts with third-party AI/ML platforms to support power forecasting and network function scheduling, demonstrating high scalability and dynamic orchestration. Its core features include scalable edge model deployment and version management, enhancing AI-RAN’s real-time scheduling and stability in dynamic environments. By integrating with O-RAN, DMMP further strengthens AI-RAN capabilities, laying a robust foundation for the communications industry as it advances into the 6G era.
Abstract: The global transition from fossil fuels to sustainable energy systems presents complex technical, economic, and environmental challenges. Mathematics plays a foundational role in enabling and accelerating this transformation by providing tools for modeling, optimization, and decision-making across multiple scales. In wind energy, mathematical techniques are employed to model atmospheric flow, optimize turbine placement through computational fluid dynamics, and forecast power generation using time-series analysis and machine learning. Similarly, in battery storage systems, mathematics supports the design of battery management algorithms, the optimization of charge-discharge cycles, and the integration of storage with variable renewable sources in power grids. From differential equations governing physical dynamics to stochastic models capturing uncertainty in supply and demand, mathematics bridges theory and application in shaping reliable, efficient, and resilient energy systems. This talk explores key mathematical contributions to the energy transition, highlighting their critical impact on advancing technologies and informing policy and infrastructure development.
Abstract: First-principles calculations based on the density functional theory (DFT) have been playing important roles to clarify the atomic structures and electronic properties of various materials. However, conventional DFT methods cannot treat large and complex systems containing many thousands of atoms, since the calculation cost increases rapidly when the number of atoms in a target system becomes large. To overcome this size limitation in DFT calculations, we have developed a large-scale and linear-scaling DFT code CONQUEST1). The code has high efficiency on massively parallel computers, and it is possible to perform DFT calculations of million-atom systems. In this talk, I will show the overview of the code and introduce some of the recent applications. It will be demonstrated that CONQUEST can calculate the structures of complex nano-scale materials observed in experiments at atomic scale and can clarify the unique electronic properties of these materials. A new method based on the machine learning techniques to analyse the local atomic structures observed in large-scale DFT-MD simulations will be also discussed2). CONQUEST is now open to public3) under the MIT license. This work has been done in collaboration with D. R. Bowler (UCL, UK), A. Nakata, S. Li, A. Yatmeidhy, M. Tamura (NIMS, Japan), L. Truflandier (U. Bordeaux, France), A.K.A. Lu (U. Tokyo, Japan), Y. Futamura and T. Sakurai (U. Tsukuba, Japan).
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Name: Ronaldo König
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Email: info@example.com
Name: Ronaldo König
Phone: 009-215-5595
Email: info@example.com
1Hd- 50, 010 Avenue, NY 90001
United States
Name: Ronaldo König
Phone: 009-215-5595
Email: info@example.com
Name: Ronaldo König
Phone: 009-215-5595
Email: info@example.com
