The construction industry contributes to 39% of global carbon emissions while aviation contributes to only 2% which means we need to look for alternative building materials if we are to make a big impact on the climate crisis soon. We've seen buildings being made using mushrooms, bricks made from recycled plastic and sand waste, organic concrete, and now are seeing another innovative solution – a floating 3D printed house! Prvok is the name of this project and it will be the first 3D printed house in the Czech Republic built by Michal Trpak, a sculptor, and Stavebni Sporitelna Ceske Sporitelny who is a notable member of the Erste building society. The house is designed to float and only takes 48 hours to build! Not only is that seven times faster than traditional houses, but it also reduces construction costs by 50%.

Mining is no stranger to digitalisation. The widely held perception of the resources industry is one of workers in mines and not one of machines running almost everything. But technological advances have already resulted in adoption of mechanisation, automation and data-driven production optimisation. Companies such as BHP, Anglo American and Rio Tinto have embraced digitalisation to gain a competitive advantage, mitigate risk and improve performance. They use advanced data analytics, virtual reality and artificial intelligence to reduce costs and increase efficiency in their processes, leading to enhanced ore recovery and less waste, to name a couple of benefits.

Various structured output prediction problems (e.g., sequential tagging) involve constraints over the output space. By identifying these constraints, we can filter out infeasible solutions and build an accountable model. To this end, we present a general integer linear programming (ILP) framework for mining constraints from data. We model the inference of structured output prediction as an ILP problem. Then, given the coefficients of the objective function and the corresponding solution, we mine the underlying constraints by estimating the outer and inner polytopes of the feasible set. We verify the proposed constraint mining algorithm in various synthetic and real-world applications and demonstrate that the proposed approach successfully identifies the feasible set at scale. In particular, we show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules. We also demonstrate results on hierarchical multi-label classification and conduct a theoretical analysis on how close the mined constraints are from the ground truth.

Yara, one of the world's leading fertilizer companies and a provider of environmental solutions, have created an industry-wide business platform to connect and empower independent farmers. It will use IoT sensors and AI and TWC to provide hyperlocal weather forecasting, crop damage prediction and real-time recommendations. Already downloaded by over 1,300,000 farmers, this platform is transforming existing supplier relationships and expanding its value. Yara built a digital farming platform that connects and empowers independent farmers, expanding its business model as a first-of-a-kind, competitive differentiator in the industry. Yara, one of the world's leading fertilizer companies and a provider of environmental solutions, have created an industry-wide business platform to connect and empower independent farmers.

Air Quality Index is based on the measurement of particulate matter, Ozone, Nitrogen Dioxide, Sulfur Dioxide, and Carbon Monoxide emissions. Most of the stations on the map are monitoring both PM2.5 and PM10 data, but there are few exceptions where only PM10 is available, Here we are using the Bangalore weather data, and some of the features might even make the predictions worse. An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another, here it would help us how we can make neurons on-air live air data, try to find the best mean squared error. Input Layer - This is the first layer in the neural network.

Model fitting often aims to fit a single model, assuming that the imposed form of the model is correct. However, there may be multiple possible underlying explanatory patterns in a set of predictors that could explain a response. Model selection without regarding model uncertainty can fail to bring these patterns to light. We present multi-model penalized regression (MMPR) to acknowledge model uncertainty in the context of penalized regression. In the penalty form introduced here, we explore how different settings can promote either shrinkage or sparsity of coefficients in separate models. A choice of penalty form that enforces variable selection is applied to predict stacking force energy (SFE) from steel alloy composition. The aim is to identify multiple models with different subsets of covariates that explain a single type of response.

Soft, edible robots that mimic real organisms could be used to deliver drugs to animals. That is just one potential application of a new material made from biodegradable gel. "The question is, could we develop a material that is, at the same time, very reliable while you use it, but once triggered can completely degrade?" says Martin Kaltenbrunner at Johannes Kepler University Linz in Austria. Kaltenbrunner and his colleagues created a gel out of ingredients that are safe to eat, including gelatine – which can be fully degraded by the body – citric acid to stop bacterial growth and glycerol for softness and to prevent dehydration. The biogel is designed to be eaten by bacteria commonly found in waste water, meaning it will break down naturally if it ends up in landfill, for instance, but remain stable otherwise.

Where data is available, it is desirable in geostatistical modelling to make use of additional covariates, for example terrain data, in order to improve prediction accuracy in the modelling task. While elevation itself may be important, additional explanatory power for any given problem can be sought (but not necessarily found) by filtering digital elevation models to extract higher-order derivatives such as slope angles, curvatures, and roughness. In essence, it would be beneficial to extract as much task-relevant information as possible from the elevation grid. However, given the complexities of the natural world, chance dictates that the use of 'off-the-shelf' filters is unlikely to derive covariates that provide strong explanatory power to the target variable at hand, and any attempt to manually design informative covariates is likely to be a trial-and-error process -- not optimal. In this paper we present a solution to this problem in the form of a deep learning approach to automatically deriving optimal task-specific terrain texture covariates from a standard SRTM 90m gridded digital elevation model (DEM). For our target variables we use point-sampled geochemical data from the British Geological Survey: concentrations of potassium, calcium and arsenic in stream sediments. We find that our deep learning approach produces covariates for geostatistical modelling that have surprisingly strong explanatory power on their own, with R-squared values around 0.6 for all three elements (with arsenic on the log scale). These results are achieved without the neural network being provided with easting, northing, or absolute elevation as inputs, and purely reflect the capacity of our deep neural network to extract task-specific information from terrain texture. We hope that these results will inspire further investigation into the capabilities of deep learning within geostatistical applications.

For many systems in science and engineering, the governing differential equation is either not known or known in an approximate sense. Analyses and design of such systems are governed by data collected from the field and/or laboratory experiments. This challenging scenario is further worsened when data-collection is expensive and time-consuming. To address this issue, this paper presents a novel multi-fidelity physics informed deep neural network (MF-PIDNN). The framework proposed is particularly suitable when the physics of the problem is known in an approximate sense (low-fidelity physics) and only a few high-fidelity data are available. MF-PIDNN blends physics informed and data-driven deep learning techniques by using the concept of transfer learning. The approximate governing equation is first used to train a low-fidelity physics informed deep neural network. This is followed by transfer learning where the low-fidelity model is updated by using the available high-fidelity data. MF-PIDNN is able to encode useful information on the physics of the problem from the {\it approximate} governing differential equation and hence, provides accurate prediction even in zones with no data. Additionally, no low-fidelity data is required for training this model. Applicability and utility of MF-PIDNN are illustrated in solving four benchmark reliability analysis problems. Case studies to illustrate interesting features of the proposed approach are also presented.

In the materials design domain, much of the data from materials calculations are stored in different heterogeneous databases. Materials databases usually have different data models. Therefore, the users have to face the challenges to find the data from adequate sources and integrate data from multiple sources. Ontologies and ontology-based techniques can address such problems as the formal representation of domain knowledge can make data more available and interoperable among different systems. In this paper, we introduce the Materials Design Ontology (MDO), which defines concepts and relations to cover knowledge in the field of materials design. MDO is designed using domain knowledge in materials science (especially in solid-state physics), and is guided by the data from several databases in the materials design field. We show the application of the MDO to materials data retrieved from well-known materials databases.