After carrying out a successful pilot at its Bagdad copper operation, Freeport McMoRan says it is rolling out a program across its North America and South America mines involving the use of data science, machine learning and integrated functional teams. The program, aimed at addressing bottlenecks, providing cost benefits and driving improved overall performance, was announced in its December quarter results this week. It said: "During 2019, FCX (Freeport) advanced initiatives in its North America and South America mining operations to enhance productivity, expand margins and reduce the capital intensity of the business through the utilisation of new technology applications in combination with a more interactive operating structure." It said the Bagdad mine (Arizona, USA) pilot program, initiated in late 2018, was "highly successful" in utilising these innovative technologies and it would build on this for the implementation across its other mines in North and South America. According to a report in the Financial Times, the system at Bagdad found that the mine was producing seven distinct types of ore and that the processing method, which involves flotation, could be adjusted to recover more copper by adjusting the PH level.
Cities around the world are getting smarter. Already, street lights in places like San Diego are turning off, and conserving energy, when vehicles and pedestrians aren't around. Soon, connected garbage cans will tell waste haulers when they need to be emptied, optimizing collection routes. Smart buildings will notify maintenance staff of impending repair needs. And parking spots will find you, instead of the other way around.
The AIOps Catalyst team's work has resulted in a new collaborative workstream focused around the topic within TM Forum. Artificial intelligence (AI) offers huge opportunities for communications service providers (CSPs) to do things better, faster and cheaper. In fact, they have no choice but to introduce AI into operations and business processes due to growing complexity and the sheer volume of data and transactions. However, as well as delivering huge benefits, the introduction of AI also creates new challenges relating to the management of services and processes. A TM Forum Catalyst team is taking a two-pronged approach, tackling both these areas simultaneously to ensure CSPs – and their customers – reap the rewards of AI.
The front end of recycling is familiar to the point of invisibility: Blue bins, clear bags, and barely comprehensible signs designating which material goes where. Once the right plastic or paper is put in the right place, most people forget all about it. For the actual recycled material, though, that's not the end of the journey but rather the beginning. Most of it gets trucked to a special recycling facility, where it is unceremoniously dumped on a concrete floor. Front-end loaders scoop bottles, papers, and myriad other materials onto conveyors, which zoom off in various directions, often climbing to different levels like staircases.
NIRSpectroscopy NIRS Sensors NearInfrared Analyzers DigitalTransformation QualityControl foodtech machinelearning AI datascience LINK SAFE COST IN MAINTAINING NIR-SPECTROSCOPY METHODS NIRSpectroscopy NIRS Spectroscopy DigitalTransformation Analysis Lab Laboratory Application Quantitative Analysis Methods Measurements Analytical Parameters Spectrometer Quality Accuracy LINK Do you develop NIR / NIRS calibrations by yourself? Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near Infrared "Study of chemical compound spatial distribution in biodegradable active films using NIR hyperspectral imaging and multivariate curve resolution" LINK "Advances in Near Infrared Spectroscopy and Related Computational Methods" MDPI Books – Pages: 496 OpenAccess NIRSpectroscopy NIRS NIR LINK " Ampliación de una librería espectral de mezclas unifeed analizadas en un instrumento NIRS de laboratorio" LINK "Applied Sciences, Vol. 9, Pages 5058: Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy" LINK " Visible-near Infrared (VIS-NIR) Spectroscopy as a Rapid Measurement Tool to Assess the Effect of Tillage on Oil Contaminated Sites" LINK "Non-invasive measurements of'Yunhe'pears by vis-NIRS technology coupled with deviation fusion modeling approach" LINK "Standard Analytical Methods, Sensory Evaluation, NIRS and Electronic Tongue for Sensing Taste Attributes of Different Melon Varieties." LINK "Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis" LINK "Prediction Model of the Key Components for Lodging Resistance in Rapeseed Stalk Using Near-Infrared Reflectance Spectroscopy (NIRS)" LINK "NIR spectroscopic determination of urine components in spot urine: preliminary investigation towards optical point-of-care test." LINK "O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression."
New technologies like artificial intelligence and machine learning are changing the way work gets done all over the world. We believe that 2020 is the year that companies will embrace these powerful technologies and apply them to revolutionize their business processes. Here's how process-minded leaders can capture the opportunity. In the past few years, Process Mining grew faster than any other technology in the BPM and process excellence space -- even faster than RPA, according to theInternational Data Corporation, IDC. In 2020, the rapid growth will continue.
Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games. However, the understanding of their performance in industrial applications is still limited. We investigate MCTS and Depth-First Proof-Number (DFPN) Search, a PNS variant, in the domain of Retrosynthetic Analysis (RA). We find that DFPN's strengths, that justify its success in games, have limited value in RA, and that an enhanced MCTS variant by Segler et al. significantly outperforms DFPN. We address this disadvantage of DFPN in RA with a novel approach to combine DFPN with Heuristic Edge Initialization.
We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds upon a new analysis of the accelerated proximal point algorithm. Our approach consists of minimizing a convex objective by approximately solving a sequence of well-chosen auxiliary problems, leading to faster convergence. This strategy applies to a large class of algorithms, including gradient descent, block coordinate descent, SAG, SAGA, SDCA, SVRG, Finito/MISO, and their proximal variants. For all of these methods, we provide acceleration and explicit support for non-strongly convex objectives. In addition to theoretical speed-up, we also show that acceleration is useful in practice, especially for ill-conditioned problems where we measure significant improvements.
UK artificial intelligence company Exscientia has added another big pharma company to its partner roster, with Bayer seeking to use its platform to find new cardiovascular and cancer drugs. Bayer is pledging up to €240 million ($266 million) in upfront fees, ongoing research funding and clinical milestone payments under the terms of the three-year deal. The collaboration will use AI to accelerate discovery of small molecule drug candidates against targets in oncology and cardiovascular disease, with Bayer claiming rights to the compounds and Dundee-based Exscientia eligible for royalties on sales if they reach the market. Cancer and heart disease are at the forefront of Bayer's R&D focus along with women's health, haematology and ophthalmology. For eight-year-old Exscientia, Bayer joins a growing list of drugmakers who see its AI platform as a way to accelerate drug discovery and improve drug development productivity, potentially trimming years off the current 12 to 15 year cycle from early research to marketed product.
Sign in to report inappropriate content. Dorabot's Robot for recycling, can identify, pick, and sort recyclable items such as plastic bottles, glass bottles, paper, cartons, and aluminum cans. The robot has deep learning-based computer vision and dynamic planning to select items in a moving conveyor belt. It also includes customized and erosion resistant grippers to pick irregularly shaped items, which results in a cost-effective integrated solution. Follow us on LinkedIn: https://bit.ly/2I6znIJ