Every day the dust settles on thousands of square kilometres of solar panels around the world, cutting the amount of electricity they produce. A robot designed by an Israeli start-up can autonomously clean rooftop solar panels that other cleaning robots can't access, increasing the panels' electricity generation by as much as 15 per cent. Autonomous robots are widely used to clean large-scale solar arrays on the ground.
A consortium of Swiss research institutes has begun working on UrbanTwin to make an AI-driven, ecologically-sensitive model of the energy, water and waste systems of the town of Aigle to help boost sustainability. Aigle has been chosen due to its size and because it has an extensive range of water sources and includes very detailed energy monitoring infrastructure previously developed by the Energy Center of EPFL. The UrbanTwin team aims to develop and validate a holistic tool to support decision-makers in achieving environmental goals, such as the Energy Strategy 2050 and the vision of climate-adaptive "sponge cities". The tool will be based on a detailed model of critical urban infrastructure, such as energy, water, buildings, and mobility, accurately simulating the evolution of these interlinked infrastructures under various climate scenarios and assessing the effectiveness of climate-change-related actions. "Urban areas are responsible for 75% of greenhouse gas emissions while rising temperatures significantly impact their liveability. They represent a natural integrator of several systems, including energy, water, buildings, and transport. So, they represent the ideal setting for implementing a coordinated, multi-sectoral response to climate changes leveraging digitalization as a systemic approach."
Using a box plot, one can know the spread and skewness of data. It is a standardized way of displaying the five-number summary of the data: The minimum The maximum The median The first quartile or 25th percentile and The third quartile or 75th percentile A box plot usually includes two parts. It includes a […]
Chesapeake Conservancy's data science team developed an artificial intelligence deep learning model for mapping wetlands, which resulted in 94% accuracy. This method for wetland mapping could deliver important outcomes for protecting and conserving wetlands. "We're happy to support this exciting project as it explores new methods for wetlands delineation using satellite imagery," said EPRI Principal Technical Leader Dr. Nalini Rao. "It has the potential to save natural resource managers time in the field by using a GIS tool right from their desks. Plus, it can help companies and the public manage impacts to wetlands as infrastructure builds are planned to meet decarbonization targets."
Suzuki Shuzoten, a sake brewery in Namie, Fukushima Prefecture, a town devastated by the 2011 Great East Japan Earthquake and tsunami, has created sake that matches different fish species caught off the coast of the prefecture by using artificial intelligence. The pairing of sake and certain fish species with different tastes can be enhanced by analyzing them with special sensors, the brewery claims. While concerns are growing over possible stigmatization of the region's seafood products after the government decided to release treated radioactive water from the nearby Fukushima No. 1 nuclear power plant into the sea, Suzuki Shuzoten President Daisuke Suzuki, 49, said, "I want to support the fisheries industry by further promoting the attractiveness of marine products caught in the area." This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.
Computing pervades all aspects of society in ways once imagined by only a few. Within science and engineering, computing has often been called the third paradigm, complementing theory and experiment, with big data and artificial intelligence (AI) often called the fourth paradigm.14 Spanning both data analysis and disciplinary and multidisciplinary modeling, scientific computing systems have grown ever larger and more complex, and today's exascale scientific computing systems rival global scientific facilities in cost and complexity. However, all is not well in the land of scientific computing. In the initial decades of digital computing, government investments and the insights from designing and deploying supercomputers often shaped the next generation of mainstream and consumer computing products. Today, that economic and technological influence has increasingly shifted to smartphone and cloud service companies. Moreover, the end of Dennard scaling,3 slowdowns in Moore's Law, and the rising costs for continuing semiconductor advances have made building ever-faster supercomputers more economically challenging and intellectually difficult. As Figure 1 suggests, we believe current approaches to designing and constructing leading-edge high-performance computing (HPC) systems must change in deep and fundamental ways, embracing end-to-end co-design; custom hardware configurations and packaging; large-scale prototyping; and collaboration between the dominant computing companies, smartphone and cloud computing vendors, and traditional computing vendors.
The almost unfathomable scale of the energy transition required for rapid decarbonization: in the energy sector alone, reaching net-zero greenhouse gas emissions will require infrastructure investments costing between $92 trillion and $173 trillion of by 2050, according to estimates by BloombergNEF. AI has a massive role to play here, as "even small gains in flexibility, efficiency or capacity in clean energy and low-carbon industry can therefore lead to trillions in value and savings."
CGG (www.cgg.com) is a global technology and HPC leader that provides data, products, services and solutions in Earth science, data science, sensing and monitoring. Our unique portfolio supports our clients in efficiently and responsibly solving complex digital, energy transition, natural resource, environmental, and infrastructure challenges for a more sustainable future. We are looking for experienced and talented individuals to join our team! As a Machine Learning engineer, you'll play a vital role in the continual development of our geoscience analytic techniques. You will work closely with researchers, the software development group and the scientists in our geoscience teams to implement machine learning and deep learning solutions.