Climate change is killing our planet. The excess production of carbon dioxide and other greenhouse gasses are filling the atmosphere and warming the Earth faster than natural processes can effectively negate them. Since 1951, the surface temperature has risen by 0.8 degrees C, with no sign of slowing. So now it's time for humans to step in and rectify the problem they created -- by using technology to suck excess CO2 straight from the air. Direct Air Capture (DAC), is one of a number of (still largely theoretical) methods of collecting and sequestering atmospheric carbon currently being looked at.

That the modern world is a complex place will not have escaped your notice. We are all dimly, unsettlingly aware that our lives are enmeshed in systems we can't fully comprehend. The last meal you ate probably contained produce grown in another country that was harvested, processed, packaged, shipped, then sold to you. The phone in your hand is the end-product of an even more convoluted chain; one that relies on human labor from mines in Africa, assembly lines in China, and standing desks in San Francisco. Explaining how these systems connect and the effect they have on the world is not an easy task.

Elasticity images map biomechanical properties of soft tissues to aid in the detection and diagnosis of pathological states. In particular, quasi-static ultrasonic (US) elastography techniques use force-displacement measurements acquired during an US scan to parameterize the spatio-temporal stress-strain behavior. Current methods use a model-based inverse approach to estimate the parameters associated with a chosen constitutive model. However, model-based methods rely on simplifying assumptions of tissue biomechanical properties, often limiting elastography to imaging one or two linear-elastic parameters. We previously described a data-driven method for building neural network constitutive models (NNCMs) that learn stress-strain relationships from force-displacement data. Using measurements acquired on gelatin phantoms, we demonstrated the ability of NNCMs to characterize linear-elastic mechanical properties without an initial model assumption and thus circumvent the mathematical constraints typically encountered in classic model-based approaches to the inverse problem. While successful, we were required to use a priori knowledge of the internal object shape to define the spatial distribution of regions exhibiting different material properties. Here, we introduce Cartesian neural network constitutive models (CaNNCMs) that are capable of using data to model both linear-elastic mechanical properties and their distribution in space. We demonstrate the ability of CaNNCMs to capture arbitrary material property distributions using stress-strain data from simulated phantoms. Furthermore, we show that a trained CaNNCM can be used to reconstruct a Young's modulus image. CaNNCMs are an important step toward data-driven modeling and imaging the complex mechanical properties of soft tissues.

This paper is concerned with a molecular optimization framework using variational autoencoders (VAEs). In this paradigm, VAE allows us to convert a molecular graph into/from its latent continuous vector, and therefore, the molecular optimization problem can be solved by continuous optimization techniques. One of the longstanding issues in this area is that it is difficult to always generate valid molecules. The very recent work called the junction tree variational autoencoder (JT-VAE) successfully solved this issue by generating a molecule fragment-by-fragment. While it achieves the state-of-the-art performance, it requires several neural networks to be trained, which predict which atoms are used to connect fragments and stereochemistry of each bond. In this paper, we present a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to address the issue. Our idea is to develop a novel graph grammar for molecular graphs called molecular hypergraph grammar (MHG), which can specify the connections between fragments and the stereochemistry on behalf of neural networks. This capability allows us to address the issue using only a single VAE. We empirically demonstrate the effectiveness of MHG-VAE over existing methods.

The digital revolution has brought with it a new way of thinking about manufacturing and operations. Emerging challenges associated with logistics and energy costs are influencing global production and associated distribution decisions. Significant advances in technology, including big data and analytics, AI, Internet of Things, robotics and additive manufacturing, are shifting the capabilities and value proposition of global manufacturing. In response, manufacturing and operations require a digital renovation: the value chain must be redesigned and retooled and the workforce retrained. Total delivered cost must be analyzed to determine the best places to locate sources of supply, manufacturing and assembly operations around the world.

The next generation farmhand in Japan's aging rural heartland may be a drone. For several months, developers and farmers in northeast Japan have been testing a new drone that can hover above paddy fields and perform backbreaking tasks in a fraction of the time it takes for elderly farmers. "This is unprecedented high technology," said Isamu Sakakibara, a 69-year-old rice farmer in the Tome area, a region that has supplied rice to Tokyo since the 17th century. Developers of the new agricultural drone say it offers high-tech relief for rural communities facing a shortage of labor as young people leave for the cities. "As we face a shortage of next-generation farmers, it's our mission to come up with new ideas to raise productivity and farmers' income through the introduction of cutting-edge technologies such as drones," said Mr. Sakakibara, who is also the head of JA Miyagi Tome, the local agricultural cooperative. The drone can apply pesticides and fertilizer to a rice field in about 15 minutes – a job that takes more than an hour by hand and requires farmers to lug around heavy tanks.

Japanese farmers are testing a new drone that can hover above paddy fields and perform backbreaking tasks in a fraction of the time it takes a labourer. The drone applies pesticides and fertilizer to a rice field in 15 minutes - a job that takes more than an hour by hand and requires farmers to lug around heavy tanks. Developers of the new agricultural drone say it offers high-tech relief for rural communities facing a shortage of labour as young people leave for the cities. They plan to release the $36,000 (£28,000) Nile-T18, which farmers can control through an iPad app, next year. Japanese farmers are testing a new drone that can hover above paddy fields and perform backbreaking tasks in a fraction of the time it takes a labourer.

With experience spanning academia, the private sector, government and even a start-up, Kalev Ruberg came to the mining sector rather late in his career. When he arrived 12 years ago, he found a "very fractured landscape" from a technology systems point of view. "Independent development may result in a one-off success, but being entrepreneurial and working in silos is the biggest challenge we all have," said Ruberg. "We need to govern the way innovation proceeds into production. The innovation success we've had at Teck has come from a very disciplined platform approach because it has staying power."

A McKinsey & Co. study released earlier this year predicted that the engineering and construction sector will be slow to embrace artificial intelligence (AI), but despite this slow adoption, presenters during a webinar hosted by Engineering News-Record said it can help E&C companies expedite early processes, create the best plans for projects and identify if a project is starting to go awry. Rob Otani, chief technology officer at structural engineering consulting firm Thornton Tomasetti Inc., explained that AI is already all around us -- for example, look at Gmail's suggested replies, which are inputted automatically without the users' involvement as part of a pattern-learning algorithm meant to save the user time. The key is finding where to leverage AI in professional capacities, Otani said. When designing structures, for example, AI can automate mundane and repetitive tasks, thereby allowing engineers to spend more time creatively solving problems, he continued. AI's ability to process and analyze millions of data points also means it can understand patterns and even detect ones a human can't, he added. Thornton Tomasetti, which has been studying machine learning for three years, developed a software application called Asterisk that it claims performs structural design of a building in seconds.

Unrecognized hazards increase the likelihood of workplace fatalities and injuries substantially. However, recent research has demonstrated that a large proportion of hazards remain unrecognized in dynamic construction environments. Recent studies have suggested a strong correlation between viewing patterns of workers and their hazard recognition performance. Hence, it is important to study and analyze the viewing patterns of workers to gain a better understanding of their hazard recognition performance. The objective of this exploratory research is to explore hazard recognition as a visual search process to identifying various visual search factors that affect the process of hazard recognition. Further, the study also proposes a framework to develop a vision based tool capable of recording and analyzing viewing patterns of construction workers and generate feedback for personalized training and proactive safety management.