Syngenta Group is a $28B leading science-based agtech company, operating in more than 100 countries, with more than 50'000 employees. We are proud to stand at the forefront of the tech revolution in agriculture. Using the latest digital innovations, data, and cutting-edge technologies we want to transform the way that crops are managed and enable farmers and agronomists to enhance efficiency and sustainable food production. Our business success reflects the quality and skill of our people. We recognize that human diversity is as important to our business as biodiversity.

The monograph summarizes and analyzes the current state of development of computer and mathematical simulation and modeling, the automation of management processes, the use of information technologies in education, the design of information systems and software complexes, the development of computer telecommunication networks and technologies most areas that are united by the term Industry 4.0

The atom & bond features are ranked based on an average score of their influences to the reactivity ratio prediction of trained samples. Each dot represents the impact of the corresponding pair of monomers and copolymer in the training set. Red and blue color indicates the high and low impact on the reactivity ratios, respectively. A positive SHAP value indicates a positive influence on the prediction, and a negative SHAP value indicates a negative influence on the prediction.

$\mathrm{\gamma}$-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. In this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing $\mathrm{\gamma}$-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application.

These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main objective of these notes is to introduce a student who is familiar with concepts in linear algebra and partial differential equations to select topics in deep learning. These lecture notes exploit the strong connections between deep learning algorithms and the more conventional techniques of computational physics to achieve two goals. First, they use concepts from computational physics to develop an understanding of deep learning algorithms. Not surprisingly, many concepts in deep learning can be connected to similar concepts in computational physics, and one can utilize this connection to better understand these algorithms. Second, several novel deep learning algorithms can be used to solve challenging problems in computational physics. Thus, they offer someone who is interested in modeling a physical phenomena with a complementary set of tools.

Market Statistics: The report provides market sizing and forecast across 7 major currencies - USD, EUR, JPY, GBP, AUD, CAD, and CHF. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2020 are considered as historical years, 2021 as the base year, 2022 as the estimated year, and years from 2023 to 2027 are considered as the forecast period. Market Segmentation & Coverage: This research report categorizes the Artificial Intelligence in Oil & Gas to forecast the revenues and analyze the trends in each of the following sub-markets: Based on Function, the market was studied across Field Services, Material Movement, Predictive Maintenance & Machine Inspection, Production Planning, Quality Control, and Reclamation. Based on Component, the market was studied across Hardware, Services, and Software.

Advances in artificially intelligent robots have enabled pioneering scientists to endow them with human-like capabilities. A new study by Cornell University researchers published in Science Advances introduces an autonomous, self-healing soft robot that is not only able to identify injuries but also heal itself. The scientists plan to integrate these soft robots with artificial intelligence (AI) machine learning in the future. "We introduce an autonomous self-healing optical sensing mechanism, networks of self-healing light guides for dynamic sensing (SHeaLDS), which exploit the damage-resilient properties intrinsic to light propagation, in combination with an intrinsic self-healing material, to achieve an optomechanical sensor that both autonomously self-heals and provides reliable dynamic sensing performance," wrote Rob Shepherd, associate professor of mechanical and aerospace engineering at Cornell, in collaboration with Young Seong Kim and Hedan Bai. The soft robot consists of a flexible material called poly elastomer (polyurethane urea).

NAS has been explosively studied to automate the discovery of top-performer neural networks, but suffers from heavy resource consumption and often incurs search bias due to truncated training or approximations. Recent NAS works [1], [2], [3] start to explore indicators that can predict a network's performance without training. However, they either leveraged limited properties of deep networks, or the benefits of their training-free indicators are not applied to more extensive search methods. By rigorous correlation analysis, we present a unified framework to understand and accelerate NAS, by disentangling "TEG" characteristics of searched networks - Trainability, Expressivity, Generalization - all assessed in a training-free manner. The TEG indicators could be scaled up and integrated with various NAS search methods, including both supernet and single-path NAS approaches. Extensive studies validate the effective and efficient guidance from our TEG-NAS framework, leading to both improved search accuracy and over 56% reduction in search time cost. Moreover, we visualize search trajectories on three landscapes of "TEG" characteristics, observing that a good local minimum is easier to find on NAS-Bench-201 given its simple topology, whereas balancing "TEG" characteristics is much harder on the DARTS space due to its complex landscape geometry.

From a pig heart being successfully transplanted into a human, to being able to redirect an asteroid on a collision course with Earth, there have been all manner of weird and wonderful scientific discoveries in 2022. They include the human genome finally been mapped after two decades, the unearthing of Africa's oldest known dinosaur, and the release of the first ever image of a supermassive black hole at the heart of our Milky Way galaxy. There was also the alarming discovery that microplastics are everywhere – including in us – and the hugely-anticipated first images from the world's most powerful space telescope James Webb, which will peer back to the dawn of the universe. Here, MailOnline looks at 10 of the most interesting advances this year. The year began with a bang scientifically when just a week into it a dying man became the first patient in the world to get a heart transplant from a genetically-modified pig.

Using Structural Health Monitoring (SHM) systems with extensive sensing arrangements on every civil structure can be costly and impractical. Various concepts have been introduced to alleviate such difficulties, such as Population-based SHM (PBSHM). Nevertheless, the studies presented in the literature do not adequately address the challenge of accessing the information on different structural states (conditions) of dissimilar civil structures. The study herein introduces a novel framework named Structural State Translation (SST), which aims to estimate the response data of different civil structures based on the information obtained from a dissimilar structure. SST can be defined as Translating a state of one civil structure to another state after discovering and learning the domain-invariant representation in the source domains of a dissimilar civil structure. SST employs a Domain-Generalized Cycle-Generative (DGCG) model to learn the domain-invariant representation in the acceleration datasets obtained from a numeric bridge structure that is in two different structural conditions. In other words, the model is tested on three dissimilar numeric bridge models to translate their structural conditions. The evaluation results of SST via Mean Magnitude-Squared Coherence (MMSC) and modal identifiers showed that the translated bridge states (synthetic states) are significantly similar to the real ones. As such, the minimum and maximum average MMSC values of real and translated bridge states are 91.2% and 97.1%, the minimum and the maximum difference in natural frequencies are 5.71% and 0%, and the minimum and maximum Modal Assurance Criterion (MAC) values are 0.998 and 0.870. This study is critical for data scarcity and PBSHM, as it demonstrates that it is possible to obtain data from structures while the structure is actually in a different condition or state.