CLAVIER's central purpose is to find the most The use of composite materials, especially in aerospace applications, is on the increase because of their unique weight and strength qualities. Depending on the orientation of the graphite fibers, a part can be extremely flexible in one direction but rigid in another. In addition, a part made from composite material is both lighter and stronger than aluminum. The increased use of graphite parts, as well as the high cost of a spoiled part (as much as $50,000 for a single part), has put greater reliability and efficiency demands on a relatively new and complex manufacturing process. Composite part fabrication requires two major steps: layup and curing.

A Norwegian container ship called the Yara Birkeland will be the world's first electric, autonomous, zero-emissions ship. With a capacity of up to 150 shipping containers, the battery-powered ship will be small compared to modern standards (the biggest container ship in the world holds 19,000 containers, and an average-size ship holds 3,500), but its launch will mark the beginning of a transformation of the global shipping industry. This transformation could heavily impact global trade as well as the environment. The Yara Birkeland is being jointly developed by two Norwegian companies: agricultural firm Yara International, and agricultural firm, and Kongsberg Gruppen, which builds guidance systems for both civilian and military use. The ship will be equipped with a GPS and various types of sensors, including lidar, radar, and cameras--much like self-driving cars.

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for uncertainty modeling. A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure. The outer optimization problem follows a two-stage stochastic programming approach to optimize the expected objective across different data classes; adaptive robust optimization is nested as the inner problem to ensure the robustness of the solution while maintaining computational tractability. A decomposition-based algorithm is further developed to solve the resulting multi-level optimization problem efficiently. Case studies on process network design and planning are presented to demonstrate the applicability of the proposed framework and algorithm.

His team provides crop protection services such as fertilizers and herbicides to farmers across Illinois. After a year-long test of a variety of new technologies, Evergreen FS found artificial intelligence could identify trouble, such as fungus growth and water shortages, in corn and soybean crops weeks before the naked eye would ever realize it. The tech, which comes from startup Ceres Imaging, offers farmers an AI analysis of photos taken from planes flying several thousand feet above fields. Previously, the technology was generally limited to orchards and vineyards. After images are taken, Ceres provides maps that highlight trouble spots on farms. Free's team visited the marked areas, but couldn't detect any issues with their own eyes.

The world is emitting data at a very high pace and everyone wants to gain insights from the huge number of data coming their way. Data mining provides a way of finding these insights and R has become the go-to-tool for it among the data analysts and data scientists. If you're looking forward to working on complex data mining projects and gaining deeper insights of data, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's get on this data mining journey together!

Data mining isn't a new invention that came with the digital age. The concept has been around for over a century, but came into greater public focus in the 1930s. According to Hacker Bits, one of the first modern moments of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform computations similar to those of modern-day computers. Forbes also reported on Turing's development of the "Turing Test" in 1950 to determine if a computer has real intelligence or not. To pass his test, a computer needed to fool a human into believing it was also human.

Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple parametric distribution. However, existing network models mainly focus on binary or discrete networks and weighted networks with known parametric weight distributions. We propose a principled nonparametric weighted network model based on exponential-family random graph models and local likelihood estimation and study its model-based clustering with application to large-scale water pollution network analysis. We do not require any parametric distribution assumption on network weights. The proposed method greatly extends the methodology and applicability of statistical network models. Furthermore, it is scalable to large and complex networks in large-scale environmental studies and geoscientific research. The power of our proposed methods is demonstrated in simulation studies.

The Industrial Internet of Things has already started changing the way businesses have been handling their operations or at least has made them comprehend its potential, showing what it can do for them. But we are yet to realize the real value of IIoT, which can be gained by blending it with machine learning. Looking at the way Internet of Things is advancing and impacting various verticals, the maxim "experience makes a man perfect" seems to be true for machines as well. As humans, machines too can develop abilities to operate efficiently by taking decisions based on their experiences; this realization has led to the invention of machine learning. As machines work in varying conditions, they collect data from different experiences.

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. This includes rotationally invariant energy predictions and a smooth, differentiable potential energy surface. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.