Artificial intelligence, 3D printing, IOT and blockchain are just some of many emerging technologies that are disrupting financial and logistics systems around the world. Spurred on by an ever-expanding global market, enterprises everywhere are exploring new opportunities through these technologies to improve their internal systems and generate billions of dollars in value for customers and shareholders. The world is experiencing a renaissance period of technological innovation, while still only scratching the surface of its potential. This is because while AI, 3D printing, IOT and blockchain disrupt their way through traditional systems, these technologies are only just beginning to understand each other and converge to spur new hybrid solutions. Industries like aerospace, pharmaceuticals and energy are amongst the first to receive the benefits of this technological convergence, with hybrid solutions that are reinventing the way items are designed, produced, tracked and distributed.

It's accepted science that carbon dioxide emissions contribute to climate change. CO2 molecules trap heat in the atmosphere, and they stick around for decades -- 40 percent will remain for 100 years, and 20 percent for 1,000 years. If wood, coal, natural gas, oil, and gasoline consumption remain on their current trajectory, the global temperature will rise between 2.5 to 10 degrees Fahrenheit over the next century, according to the Intergovernmental Panel on Climate Change (IPCC). So what can be done? If you ask Apple alum and serial entrepreneur Matt Provo, plenty.

Over an 11-month period--May 2016 to April 2017--51% of all noise complaints in the focus area were related to after-hours construction activity (6 P.M.–7 A.M.), three times the amount in the next category. Note combining all construction-related complaints adds up to 70% of this sample, highlighting how disruptive to the lives of ordinary citizens this particular category of noise can be. Figure 4c includes SPL values (blue line) at a five-minute resolution for the after-hours period during or immediately preceding a subset of the complaints. Dotted green lines correspond to background levels, computed as the moving average of SPL measurements within a two-hour window. Dotted black lines correspond to SPL values 10dB above the background, the threshold defined by the city's noise code to indicate potential violations.

YOKOHAMA - Toshiba Corp. unveiled a remote-controlled robot with tongs on Monday that it hopes will be able to probe the inside of one of the three damaged reactors at Japan's tsunami-hit Fukushima nuclear plant and grip chunks of highly radioactive melted fuel. The device is designed to slide down an extendable 11-meter (36-foot) long pipe and touch melted fuel inside reactor 2's primary containment vessel. The reactor was built by Toshiba and GE. An earlier probe carrying a camera captured images of pieces of melted fuel in the reactor last year, and robotic probes in the two other reactors have detected traces of damaged fuel, but the exact location, contents and other details remain largely unknown. Toshiba's energy systems unit said experiments with the new probe planned in February are key to determining the proper equipment and technologies needed to remove the fuel debris, the most challenging part of the decommissioning process expected to take decades.

Miles of contaminated pipe must be measured, foot by foot, as part of the decommissioning effort at deactivated gaseous diffusion enrichment facilities. The current method requires cutting away asbestos-lined thermal enclosures and performing repeated, elevated operations to manually measure pipe from the outside. The RadPiper robot, part of the Pipe Crawling Activity Measurement System (PCAMS) developed by Carnegie Mellon University and commissioned for use at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, automatically measures U-235 in pipes from the inside. This improves certainty, increases safety, and greatly reduces measurement time. The heart of the RadPiper robot is a sodium iodide scintillation detector in an innovative disc-collimated assembly. By measuring from inside pipes, the robot significantly increases its count rate relative to external through-pipe measurements. The robot also provides imagery, models interior pipe geometry, and precisely measures distance in order to localize radiation measurements. Data collected by this system provides insight into pipe interiors that is simply not possible from exterior measurements, all while keeping operators safer. This paper describes the technical details of the PCAMS RadPiper robot. Key features for this robot include precision distance measurement, in-pipe obstacle detection, ability to transform for two pipe sizes, and robustness in autonomous operation. Test results demonstrating the robot's functionality are presented, including deployment tolerance tests, safeguarding tests, and localization tests. Integrated robot tests are also shown.

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy.

Fault prediction is offered at two different levels based on a data-driven approach: (a) generic fault/status prediction and (b) specific fault class prediction, implemented by means of two different machine learning based modules built on an unsupervised clustering algorithm and a Pattern Recognition Neural Network, respectively. Model has been assessed on a park of six photovoltaic (PV) plants up to 10 MW and on more than one hundred inverter modules of three different technology brands. The results indicate that the proposed method is effective in (a) predicting incipient generic faults up to 7 days in advance with sensitivity up to 95% and (b) anticipating damage of specific fault classes with times ranging from few hours up to 7 days. The model is easily deployable for online monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA and fault data, fault taxonomy and inverter electrical datasheet. Keywords: Data Mining, Fault Prediction, Inverter Module, Key Performance Indicator, Lost Production 1 INTRODUCTION The provision of a Preventive Maintenance strategy is emerging nowadays as an essential field to keep high technical and economic performances of solar PV plants over time [1]. Analytical monitoring systems have been installed therefore worldwide to timely detect possible malfunctions through the assessment of PV system performances [2-3].

Geometrical and appearance quality requirements set the limits of the current industrial performance in injection molding. To guarantee the product's quality, it is necessary to adjust the process settings in a closed loop. Those adjustments cannot rely on the final quality because a part takes days to be geometrically stable. Thus, the final part geometry must be predicted from measurements on hot parts. In this paper, we use recent success of Generative Adversarial Networks (GAN) with the pix2pix network architecture to predict the final part geometry, using only hot parts thermographic images, measured right after production. Our dataset is really small, and the GAN learns to translate thermography to geometry. We firstly study prediction performances using different image similarity comparison algorithms. Moreover, we introduce the innovative use of Discrete Modal Decomposition (DMD) to analyze network predictions. The DMD is a geometrical parameterization technique using a modal space projection to geometrically describe surfaces. We study GAN performances to retrieve geometrical parameterization of surfaces.

Toshiba unveiled a remote-controlled robot with tongs on Monday that it hopes will be able to probe the inside of one of the three damaged reactors at Japan's tsunami-hit Fukushima nuclear plant and grip chunks of highly radioactive melted fuel. The device is designed to slide down an extendable 11-meter (36-foot) long pipe and touch melted fuel inside the Unit 2 reactor's primary containment vessel. The reactor was built by Toshiba and GE. An earlier probe carrying a camera captured images of pieces of melted fuel in the reactor last year, and robotic probes in the two other reactors have detected traces of damaged fuel, but the exact location, contents and other details remain largely unknown. Toshiba unveiled the device carrying tongs that comes out of a long telescopic pipe for an internal probe in one of three damaged reactor chambers at Japan's tsunami-hit Fukushima nuclear plant - this time to touch chunks of melted fuel Toshiba's energy systems unit said experiments with the new probe planned in February are key to determining the proper equipment and technologies needed to remove the fuel debris, the most challenging part of the decommissioning process expected to take decades.

Toshiba Corp. has unveiled a remote-controlled robot with tongs that it hopes will be able to probe the inside of one of the three damaged reactors at Japan's tsunami-hit Fukushima nuclear plant and manipulate chunks of melted fuel.