The deal, disclosed by the companies Tuesday, highlights a contrast between the two electronics makers, both of which faced multibillion-dollar losses and management turmoil several years ago. Sharp has managed to turn itself around quickly under foreign management while Toshiba, which received more support from the Japanese government during its restructuring, is still trying to streamline its unprofitable portfolio. Toshiba's laptop PCs, sold under the Dynabook name, helped make the conglomerate famous among consumers outside Japan, but the business has lost money for the past five years and was at the center of a profit-padding scandal that the company disclosed in 2015. That scandal and the bankruptcy last year of Toshiba's U.S. nuclear subsidiary, Westinghouse Electric Co., have pushed Toshiba to shed many of its money-losing consumer businesses as well as more profitable units to raise funds. It has sold its television and appliance businesses to Chinese companies and its medical-equipment business to Canon Inc.

The is the first entry in a five-part insideHPC series that takes an in-depth look at how machine learning, deep learning and AI are being used in the energy industry. Read on to find out how machine learning is driving energy exploration. The Internet of Things (IoT) is changing the way industrial machinery and related processes work together and report back with information such as position, temperature, pressure, humidity and so on. The worldwide market for the number of these types of sensors that will be deployed is estimated to be in the billions. With so much information being generated many times per second, in some instances, filtering and analytics must be performed where the sensors are located (or close by), rather than in faraway data centers.

The global energy sector is going through a paradigm shift in the way it produces and distributes power. There is a massive demand for reliable, clean, and cost-effective energy, and AI (artificial intelligence) is bound to play a vital role in meeting this future global demand. While it is clear that solar, wind, nuclear, and hydroelectric energy will gradually replace the traditional coal-fired power plants, one of the current hurdles in this path is the inconsistency and unpredictability of renewable power. A windless day or a cloudy afternoon can restrain the generation of renewable energy and lead to temporary power shortfalls. Similarly, an abnormally windy or sunny weather can help generate excess energy, which can be wasteful or costly to store.

The global energy sector is going through a paradigm shift in the way it produces and distributes power. There is a massive demand for reliable, clean, and cost-effective energy, and AI (artificial intelligence) is bound to play a vital role in meeting this future global demand. While it is clear that solar, wind, nuclear, and hydroelectric energy will gradually replace the traditional coal-fired power plants, one of the current hurdles in this path is the inconsistency and unpredictability of renewable power. A windless day or a cloudy afternoon can restrain the generation of renewable energy and lead to temporary power shortfalls. Similarly, an abnormally windy or sunny weather can help generate excess energy, which can be wasteful or costly to store.

In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared. Four of the five methods rely on machine learning (ML) algorithms to approximate important aspects of the brittle fracture problem. In addition to the ML algorithms, each method incorporates different physics-based assumptions in order to reduce the computational complexity while maintaining the physics as much as possible. This work specifically focuses on using the ML approaches to model a 2D concrete sample under low strain rate pure tensile loading conditions with 20 preexisting cracks present. A high-fidelity finite element-discrete element model is used to both produce a training dataset of 150 simulations and an additional 35 simulations for validation. Results from the ML approaches are directly compared against the results from the high-fidelity model. Strengths and weaknesses of each approach are discussed and the most important conclusion is that a combination of physics-informed and data-driven features are necessary for emulating the physics of crack propagation, interaction and coalescence. All of the models presented here have runtimes that are orders of magnitude faster than the original high-fidelity model and pave the path for developing accurate reduced order models that could be used to inform larger length-scale models with important sub-scale physics that often cannot be accounted for due to computational cost.

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables.

Spider silk transformed into fiber for tissue reconstruction; paper that conducts electricity; renewable diesel fuel; and new techniques for regenerating aging or diseased skin. These are just a handful of examples from a new database of over 1,300 new technologies currently making their way through Israeli Technology Transfer Organizations [TTOs] associated with universities, research institutes, and medical institutions. The new searchable database is designed as a layer in Start-Up Nation Finder, a free-to-use innovation discovery platform from Start-Up Nation Central (SNC), an Israeli non-profit that connects businesses, governments, and organizations around the world to Israeli innovation. The new TTO layer gives users the ability to look over the horizon at emerging technologies in drug discovery, advanced materials science, gene sequencing, robotics, and other fields, through cataloguing the patents, companies, and researchers that are registered at 16 TTOs in Israel. SNC combined data from the TTOs themselves, the Israel Technology Transfer Network, and its own data from Start-Up Nation Finder.

What do you think when you hear the words'artificial intelligence?' Robots? Do images of a world that looks less human flash across your mind? Before you panic, know this. Artificial Intelligence (AI) is much more than the idea of pending doom. Defined as"intelligence displayed by machines, in contrast with the natural intelligence displayed by humans and other animals," AI is simply another means to improve the way that we, as a society, address some of the biggest challenges we face.

New artificial intelligence technology could make buildings smart enough to become their own energy supplier – and even sell back what they don't use. That's the view of Vijay Natarajan, co-founder and marketing director of AI-powered energy analytics business Qbots, which is based at Manchester Science Park. "We enable buildings to actively participate in the energy markets, rather than just passively receive energy bills," Natarajan explained. This way of looking at energy bills is still relatively new. Most buildings purchase electricity from a supplier, which Natarajan says costs twice as much as the energy itself.