US and EU officials opened their two-day, high-level meetings in Pittsburgh on Wednesday, an effort to repair relations damaged under the administration of former president Donald Trump and boost cooperation on technology issues. The inaugural meeting of the Trade and Technology Council (TTC) comes as industries worldwide grapple with shortages of crucial semiconductors and is being held in Pittsburgh, a Pennsylvania city that was once the heart of the American steel industry and has since evolved into a tech hub. The ministers met at Mill 19, a massive World War II-era munitions factory and later steel mill on the shores of the Monongahela River that has been reborn as an advanced robotics facility for researchers from Carnegie Mellon University. The shadow of steel hangs over the meetings in other ways as well, especially as the two sides have yet to resolve a conflict over Trump-era tariffs on steel and aluminum. The former president cited US national security concerns in June 2018 when he imposed punitive tariffs of 25 percent on steel imports and 10 percent on aluminum, which have been a thorn in the side of trans-Atlantic relations since.

At the Goodwood festival of speed near Chichester, the crowds gathered at the hill-climb circuit to watch the world's fastest cars roar past, as they do every year. But not far from the high-octane action, there was a new, and quieter, attraction: a display of the latest electric vehicles, from the £28,000 Mini Electric to the £2m Lotus Evija hypercar. Even here, at one of the biggest events in Britain's petrolhead calendar, it's clear the days of the internal combustion engine are numbered. As countries strive to meet stringent carbon-emission targets, and vehicle-makers phase out combustion engines, 145m electric vehicles are predicted to be on the roads within a decade, up from 11m last year. The car batteries they require, along with storage batteries for solar and wind power, have sent demand for metals soaring, taking mining firms to the bottom of the sea in the hunt for those metals.

Classification and adulterant detection in" LINK "Agronomy: Phenotyping and Validation of Root Morphological Traits in Barley (Hordeum vulgare L.)" LINK "Comparison of wavelength selected methods for improving of prediction performance of PLS model to determine aflatoxin B1 (AFB1) in wheat samples during storage" LINK "A Brief History of Whiskey Adulteration and the Role of Spectroscopy Combined with Chemometrics in the Detection of Modern Whiskey Fraud" LINK Equipment for Spectroscopy "Feasibility study of detecting palm oil adulteration with recycled cooking oil using handheld Near-infrared spectrometer" LINK Environment NIR-Spectroscopy Application "Spatial Prediction of Calcium Carbonate and Clay Content in Soils using Airborne Hyperspectral Data" LINK "Monitoring the soil copper pollution degree based on the reflectance spectrum of an arid desert plant" LINK "Micromachines: Visualization of Local Concentration and Viscosity Distribution during Glycerol-Water Mixing in a Y-Shape ...

Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure-;which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge. In the Nature Communications study, NYU researchers and collaborators in the U.S. and Taiwan tackled this challenge using machine learning, a type of artificial intelligence used to detect patterns in data.

Abstract-- Future Moon bases will likely be constructed using resources mined from the surface of the Moon. The difficulty of maintaining a human workforce on the Moon and communications lag with Earth means that mining will need to be conducted using collaborative robots with a high degree of autonomy. In this paper, we explore the utility of robotic vision towards addressing several major challenges in autonomous mining in the lunar environment: lack of satellite positioning systems, navigation in hazardous terrain, and delicate robot interactions. The competition provided a simulated lunar environment that exhibits the complexities alluded to above. This argues for a high degree of intelligence on each agent and a robust multi-robot The need to transport resources from Earth is a serious coordination system to ensure long-term operation. In-Situ Resource some of the key challenges towards autonomous robots Utilisation (ISRU), where resources are extracted on for collaborative space mining: lack of satellite positioning other astronomical objects and exploited to support longer systems, navigation in hazardous terrain, and the need for and deeper space missions, has been proposed as a way to delicate robot interactions.

A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world -- especially in problematic regions unable to cope with these conditions. However, the movement to a low-carbon energy supply can not happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required, however, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. A well choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods.

Armed with a newly minted undergraduate degree in geology, Jasper Baur is in the mining business. Not those mines where we extract metals or minerals; the kind that kill and maim thousands of people every year. As a freshman at upstate New York's Binghamton University in 2016, Baur started working with two geophysics professors, Alex Nikulin and Timothy de Smet, to look into employing instrument-equipped drones to speed the slow, hazardous task of finding land mines. Baur stuck with the research all the way through college; now a grad student in volcanology at Columbia University's Lamont-Doherty Earth Observatory, he is still pursuing it. "It seemed like a really relevant and impactful use of science," he said.

"The pace at which themes are transforming businesses is blistering, but the adoption of many technologies -- like smartphones or renewable energy -- have surpassed experts' forecasts by decades, because we often think linearly but progress occurs exponentially," say the strategists. They say a paradigm shift in the explosion of data, faster processing power and the rise of artificial intelligence will bring about the "fastest rollout of disruptive tech in history." And in the big stock universe, an increasing few are showing investors the money. "Over the past 30 years, just 1.5% of companies generated all the net wealth on the global stock market, meaning that actually only a handful of disrupters ("superstar firms") really influence long-term financial markets," says Israel and the team. Here are the 14 technologies: 6G, brain computer interfacing (BCI), emotional artificial intelligence, synthetic biology, immortality, bionic humans, eVTOL (electrical vertical takeoff and landing vehicles), wireless electricity, holograms, metaverse, next-gen batteries, oceantech (ocean energy, precision fishing, etc.), green mining and CCS (negative-emissions technology that captures and stores carbon dioxide before it can be released).

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

Approximate methods, such as empirical force fields (FFs) [1-3], are an integral part of modern computational chemistry and materials science. While the application of first-principles methods, such as density functional theory (DFT), to even moderately sized molecular and material systems is computationally very expensive, approximate methods allow for simulations of large systems over long time scales. During the last decades, machine-learned potentials (MLPs) [4-33] have risen in popularity due to their ability to be as accurate as the respective first principles reference methods, the transferability to arbitrary-sized systems, and the capability of describing bond breaking and bond formation as opposed to empirical FFs [34]. Interpolating abilities of neural networks (NNs) [35] promoted their broad application in computational chemistry and materials science. NNs were initially applied to represent potential energy surfaces (PESs) of small atomistic systems [36, 37] and were later extended to high-dimensional systems [21].