As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, specifically designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, by countries or by revenues buckets. We also compare our results to those of other providers and find our estimates to be more accurate. Thanks to the proposed explainability tools using Shapley values, our model is fully interpretable, the user being able to understand which factors split explain the GHG emissions for each particular company.

We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants. A hypernetwork based approach is used to enable the domain-decomposed PINN learn the thermal behavior of the heat exchanger in response to dynamic boundary conditions, eliminating the need to re-train. As a result, we achieve orders of magnitude reduction in inference time in comparison to existing PINNs, while maintaining the accuracy on par with the physics-based simulations. This makes the approach very attractive for predictive maintenance of the heat exchanger in digital twin environments.

Nowadays, with the rapid development of the Internet, the era of big data has come. The Internet generates huge amounts of data every day. However, extracting meaningful information from massive data is like looking for a needle in a haystack. Data mining techniques can provide various feasible methods to solve this problem. At present, many sequential rule mining (SRM) algorithms are presented to find sequential rules in databases with sequential characteristics. These rules help people extract a lot of meaningful information from massive amounts of data. How can we achieve compression of mined results and reduce data size to save storage space and transmission time? Until now, there has been little research on the compression of SRM. In this paper, combined with the Minimum Description Length (MDL) principle and under the two metrics (support and confidence), we introduce the problem of compression of SRM and also propose a solution named ComSR for MDL-based compressing of sequential rules based on the designed sequential rule coding scheme. To our knowledge, we are the first to use sequential rules to encode an entire database. A heuristic method is proposed to find a set of compact and meaningful sequential rules as much as possible. ComSR has two trade-off algorithms, ComSR_non and ComSR_ful, based on whether the database can be completely compressed. Experiments done on a real dataset with different thresholds show that a set of compact and meaningful sequential rules can be found. This shows that the proposed method works.

Humans can leverage physical interaction to teach robot arms. As the human kinesthetically guides the robot through demonstrations, the robot learns the desired task. While prior works focus on how the robot learns, it is equally important for the human teacher to understand what their robot is learning. Visual displays can communicate this information; however, we hypothesize that visual feedback alone misses out on the physical connection between the human and robot. In this paper we introduce a novel class of soft haptic displays that wrap around the robot arm, adding signals without affecting that interaction. We first design a pneumatic actuation array that remains flexible in mounting. We then develop single and multi-dimensional versions of this wrapped haptic display, and explore human perception of the rendered signals during psychophysic tests and robot learning. We ultimately find that people accurately distinguish single-dimensional feedback with a Weber fraction of 11.4%, and identify multi-dimensional feedback with 94.5% accuracy. When physically teaching robot arms, humans leverage the single- and multi-dimensional feedback to provide better demonstrations than with visual feedback: our wrapped haptic display decreases teaching time while increasing demonstration quality. This improvement depends on the location and distribution of the wrapped haptic display. You can see videos of our device and experiments here: https://youtu.be/yPcMGeqsjdM

Yes, you heard that right. Researchers at the Korea Advanced Institute of Science and Technology have developed a four-legged robot that can climb up iron and steel walls and ceilings, as described a study published in the journal Science Robotics on Wednesday. They call it MARVEL, short for Magnetically Adhesive Robot for Versatile and Expeditious Locomotion, and it only weighs about 18 pounds and isn't any larger than a tiny puppy at roughly 13 in long. MARVEL isn't the first robot that can climb walls, but unlike most others, it makes use of magnetic legs rather than wheels, grippers, suction cups, or propellers. It's also seriously dexterous, its designers say, adroitly navigating curved surfaces like that of a rusted metal storage tank, in part thanks to its innovative feet that use electro-magnets and a cutting edge, rubber-like smart material known as magnetorheological elastomers.

Yes, you heard that right. Researchers at the Korea Advanced Institute of Science and Technology have developed a four-legged robot that can climb up iron and steel walls and ceilings, as described a study published in the journal Science Robotics on Wednesday. They call it MARVEL, short for Magnetically Adhesive Robot for Versatile and Expeditious Locomotion, and it only weighs about 18 pounds and isn't any larger than a tiny puppy at roughly 13 in long. MARVEL isn't the first robot that can climb walls, but unlike most others, it makes use of magnetic legs rather than wheels, grippers, suction cups, or propellers. It's also seriously dexterous, its designers say, adroitly navigating curved surfaces like that of a rusted metal storage tank, in part thanks to its innovative feet that use electro-magnets and a cutting edge, rubber-like smart material known as magnetorheological elastomers.

This paper proposes a novel Adaptive Clustering-based Reduced-Order Modeling (ACROM) framework to significantly improve and extend the recent family of clustering-based reduced-order models (CROMs). This adaptive framework enables the clustering-based domain decomposition to evolve dynamically throughout the problem solution, ensuring optimum refinement in regions where the relevant fields present steeper gradients. It offers a new route to fast and accurate material modeling of history-dependent nonlinear problems involving highly localized plasticity and damage phenomena. The overall approach is composed of three main building blocks: target clusters selection criterion, adaptive cluster analysis, and computation of cluster interaction tensors. In addition, an adaptive clustering solution rewinding procedure and a dynamic adaptivity split factor strategy are suggested to further enhance the adaptive process. The coined Adaptive Self-Consistent Clustering Analysis (ASCA) is shown to perform better than its static counterpart when capturing the multi-scale elasto-plastic behavior of a particle-matrix composite and predicting the associated fracture and toughness. Given the encouraging results shown in this paper, the ACROM framework sets the stage and opens new avenues to explore adaptivity in the context of CROMs.

We describe an approach for empirical modeling of steel phase kinetics based on symbolic regression and genetic programming. The algorithm takes processed data gathered from dilatometer measurements and produces a system of differential equations that models the phase kinetics. Our initial results demonstrate that the proposed approach allows to identify compact differential equations that fit the data. The model predicts ferrite, pearlite and bainite formation for a single steel type. Martensite is not yet included in the model. Future work shall incorporate martensite and generalize to multiple steel types with different chemical compositions.

The application of machine learning to predicting the properties of small and large discrete (single) molecules and complex materials (polymeric, extended or mixtures of molecules) has been increasing exponentially over the past few decades. Unlike physics-based and rule-based computational systems, machine learning algorithms can learn complex relationships between physicochemical and process parameters and their useful properties for an extremely diverse range of molecular entities. Both the breadth of machine learning methods and the range of physical, chemical, materials, biological, medical and many other application areas have increased markedly in the past decade. This Account summarises three decades of research into improved cheminformatics and machine learning methods and their application to drug design, regenerative medicine, biomaterials, porous and 2D materials, catalysts, biomarkers, surface science, physicochemical and phase properties, nanomaterials, electrical and optical properties, corrosion and battery research. Science has always been fascinated by change, uncovering new aspects of Nature and finding useful ways to exploit them to meet global challenges. The rate of change is accelerating, with average time between innovations decreasing exponentially (Figure 1). Computational molecular design prior to 1990 was focused on the use of computationally expensive physics-based methods like molecular modelling, molecular mechanics, molecular dynamics and quantum chemistry. The quantitative structure–activity relationship (QSAR) methods, developed by Hansch and Fujita in the 1960s, were based on the observation that changes in the constitution of small organic molecules generated a corresponding change in their biological activities. Regression methods were used to find relationships between structure, encoded by mathematical entities called descriptors or features, and biological properties of small organic molecules, also numerically encoded. QSAR use was limited to modelling of small data sets of molecules with similar scaffolds, with the primary aim of understanding the molecular basis for drug (or agrochemical) action. As they were not mechanism- or physics-based, their empirical nature created doubt as to their efficacy, the question of when correlation means causation (still an important issue), and lack of data were major barriers to their wider adoption. After that time, technological developments involving automation, computational power, algorithms, synthesis and informatics have maintained this exponential acceleration.

Numerical analysis methods, such as Finite Element Analysis (FEA), are typically used to conduct stress analysis of various structures and systems for which it is impractical or hard to determine an analytical solution. Researchers commonly use FEA methods to evaluate the design, safety and maintenance of different structures in various fields, including aerospace, automotive, architecture and civil structural systems. The current workflow for FEA applications includes: (i) modeling the geometry and its components, (ii) specifying material properties, boundary conditions, meshing, and loading, (iii) dynamic analysis, which may be time-consuming based on the complexity of the model. The time requirement constraint and the complexity of the current FEA workflow make it impractical for real-time or near real-time applications, such as in the aftermath of a disaster or during extreme disruptive events that require immediate corrections to avoid catastrophic failures. Based on the steps of FEA described above, performing a complete stress analysis with conventional FEA has a high computational cost.