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Power-Watershed: a graph-based optimization framework for image and data processing

MBZUAI ·

Laurent Najman presented the Power Watershed (PW) optimization framework for image and data processing. The PW framework enhances graph-based data processing algorithms like random walker and ratio-cut clustering, leading to faster solutions. It can be adapted for graph-based cost minimization methods and integrated with deep learning networks. Why it matters: This framework could enable more efficient and scalable image and data processing algorithms relevant to computer vision and related fields in the Middle East.

Saliva-powered microbial fuel cell provides power generation source

KAUST ·

KAUST researchers have developed a saliva-powered microbial fuel cell (MFC) that generates electricity using electrogenic bacteria to consume waste and release electrons. The micro-MFC uses graphene as an anode and an air cathode, achieving high current densities (1190 A m-3). The MFC produced 40 times more power than through the use of a carbon cloth anode. Why it matters: This technology offers a novel way to power lab-on-chip or portable diagnostic devices, particularly in remote or dangerous areas, and may offer alternatives to energy-intensive water purification technologies.

President Chameau speaks on innovating for Saudi Arabia’s future at 2015 Saudi Water and Power Forum

KAUST ·

KAUST President Jean-Lou Chameau gave a keynote at the 2015 Saudi Water and Power Forum in Riyadh. The forum focused on sustainable development through innovation in the water and power sectors. Chameau highlighted KAUST's integrated research approach focusing on water, energy, food, and the environment. Why it matters: This participation underscores KAUST's commitment to addressing critical resource challenges in Saudi Arabia through research, talent development, and global collaboration.

What drives us and what powers us

KAUST ·

Nate Hagens from the University of Minnesota spoke at KAUST's Winter Enrichment Program (WEP) 2018 about the intersection of energy, human behavior, and economics. Hagens argued that society functions as an energy-dissipating "superorganism," with human preferences correlated with increasing energy needs. He emphasized that energy, not money, is the real capital, but global society is running out of it. Why it matters: The talk highlights the importance of viewing society through an ecological lens, particularly in the context of the GCC region's reliance on energy resources.

KAUST launches ACWA Power Center of Excellence for Desalination and Solar Power

KAUST ·

KAUST and ACWA Power have launched a Center of Excellence for Desalination and Solar Power following a memorandum of understanding signed on September 9. The collaboration aims to advance Saudi Arabia's position in water desalination and solar power technology. The center will focus on research in water quality monitoring and system performance modeling, leveraging KAUST's research centers. Why it matters: The partnership seeks to drive innovation and cost efficiencies in producing desalinated water and generating solar power, aligning with Saudi Arabia's sustainability goals.

Laying the foundation for future cities

KAUST ·

Khaled Alrashed, president and CEO of Saudi Electricity Company for Projects Development, discussed the challenges of future smart cities at a KAUST event. He emphasized the importance of smart grids, AI, and large-scale optimization for improving urban living. The Saudi Electricity Company is partnering with KAUST, including using the Shaheen supercomputer, to develop these technologies and predict grid load. Why it matters: This collaboration highlights Saudi Arabia's ambition to become a leader in smart city technology and renewable energy, leveraging local expertise and resources.

Scalable Community Detection in Massive Networks Using Aggregated Relational Data

MBZUAI ·

A new mini-batch strategy using aggregated relational data is proposed to fit the mixed membership stochastic blockmodel (MMSB) to large networks. The method uses nodal information and stochastic gradients of bipartite graphs for scalable inference. The approach was applied to a citation network with over two million nodes and 25 million edges, capturing explainable structure. Why it matters: This research enables more efficient community detection in massive networks, which is crucial for analyzing complex relationships in various domains, but this article has no clear connection to the Middle East.

Self-powered dental braces

KAUST ·

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