The combination of data-rich remote sensing and new machine learning approaches is creating a revolution in yield forecasting, with accurate and …

The University of Surrey has built an artificial intelligence (AI) model that identifies chemical compounds that promote healthy aging -- paving the way towards pharmaceutical innovations that extend a person's lifespan. In a paper published by Nature Communication's Scientific Reports, a team of chemists from Surrey built a machine learning model based on the information from the DrugAge database to predict whether a compound can extend the life of Caenorhabditis elegans -- a translucent worm that shares a similar metabolism to humans. The worm's shorter lifespan gave the researchers the opportunity to see the impact of the chemical compounds. "Ageing is increasingly being recognized as a set of diseases in modern medicine, and we can apply the tools of the digital world, such as AI, to help slow down or protect against aging and age-related diseases. Our study demonstrates the revolutionary ability of AI to aid the identification of compounds with anti-aging properties."

It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient ({\lambda}) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, {\lambda} data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of feature-{\lambda}.

The Internet of Things (IoT) – and what it will enable – has been a discussion point for well over a decade, but the speed, low latency and reliability of 5G promise to bring the concept to life. Network slicing will allow a wide range of product types, with distinct reliability and throughput requirements, to be run out of the same architecture, and edge computing will allow nodes to communicate directly with one another, bypassing the network's core and enhancing speed and reliability. These characteristics underpin some the most interesting projects currently making use of 5G, and have made a plethora of 5G use cases possible. Here are 18 of the best. Robots are already widely used in factories, particularly in the automotive industry.

Construction technology continues to evolve, including the creation of a variety of infrastructure, including three-dimensional (3D) prints. Now, the world's first 3D-printed steel structure, a'living laboratory' bridge, has been unveiled by a robot in Amsterdam. This pedestrian bridge with smart sensors will replace the old bridge under external restoration for the next two years. The 3D-printed footbridge, which is over four years in the making, is the result of a unique collaboration between MX3D, software company Autodesk, chief engineer Arup, steel giant ArcelorMittal, the City of Amsterdam, and the University of Twente, among others. MX3D made this design possible by turning welding robots with intelligent software into industrial 3D printers.

Labeled data is essential in modern systems that rely on Machine Learning (ML) for predictive modelling. Such systems may suffer from the cold-start problem: supervised models work well but, initially, there are no labels, which are costly or slow to obtain. This problem is even worse in imbalanced data scenarios. Online financial fraud detection is an example where labeling is: i) expensive, or ii) it suffers from long delays, if relying on victims filing complaints. The latter may not be viable if a model has to be in place immediately, so an option is to ask analysts to label events while minimizing the number of annotations to control costs. We propose an Active Learning (AL) annotation system for datasets with orders of magnitude of class imbalance, in a cold start streaming scenario. We present a computationally efficient Outlier-based Discriminative AL approach (ODAL) and design a novel 3-stage sequence of AL labeling policies where it is used as warm-up. Then, we perform empirical studies in four real world datasets, with various magnitudes of class imbalance. The results show that our method can more quickly reach a high performance model than standard AL policies. Its observed gains over random sampling can reach 80% and be competitive with policies with an unlimited annotation budget or additional historical data (with 1/10 to 1/50 of the labels).

The first ever 3D-printed steel bridge has opened in Amsterdam, the Netherlands. It was created by robotic arms using welding torches to deposit the structure of the bridge layer by layer, and is made of 4500 kilograms of stainless steel. The 12-metre-long MX3D Bridge was built by four commercially available industrial robots and took six months to print. The structure was transported to its location over the Oudezijds Achterburgwal canal in central Amsterdam last week and is now open to pedestrians and cyclists. More than a dozen sensors attached to the bridge after the printing was completed will monitor strain, movement, vibration and temperature across the structure as people pass over it and the weather changes.

Intelligent diagnosis method based on data-driven and deep learning is an attractive and meaningful field in recent years. However, in practical application scenarios, the imbalance of time-series fault is an urgent problem to be solved. This paper proposes a novel deep metric learning model, where imbalanced fault data and a quadruplet data pair design manner are considered. Based on such data pair, a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution are proposed. This quadruplet loss pays special attention to imbalanced sample pair. The reasonable combination of quadruplet loss and softmax loss function can reduce the impact of imbalance. Experiment results on two open-source datasets show that the proposed method can effectively and robustly improve the performance of imbalanced fault diagnosis.

A new technique of moon scanning could help automatically categorize the essential lunar features from telescopic images and can also remarkably enhance the efficiency of choosing sites for exploration. There is much more to choosing a landing or exploration site on the Moon than what is just observed. The lunar surface's visible area is bigger compared to Russia and is pockmarked by thousands of craters and crisscrossed by canyon-like rilles. However, scanning the larger area by eyes, searching for features that measure a few hundred meters across is strenuous and not typically perfect. This makes it hard to select an ideal area for exploration.

Organic farming, which does not involve any agricultural chemicals or synthesized fertilizers, is attracting attention in Japan, especially because it puts less strain on the environment. While the government has launched a strategy to promote organic agriculture, there are still many challenges to overcome, including ways to reduce physical burdens on farmers and costs, and expand sales channels. "We've recently been seeing an increase in the number of environmentally aware young customers," said Naoya Okada, president of Bio c' Bon Japon Co., which operates an organic food store in Tokyo's Ebisu district. Organic farming uses compost for soil cultivation, with farmers digging up weeds and not relying on agricultural chemicals. Momentum for compiling international organic agriculture standards is building, especially in Europe, as the farming method is believe to contribute to the conservation of biodiversity and the fight against global warming.