The U.K. Government is seemingly backtracking on plans that would have allowed text and data mining "for any purpose," plans designed to position the U.K. as a "global AI superpower." The news emerges following months of blowback from creative industries concerned about what impact the rules might have on protected works. Text and data mining, for the uninitiated, is an essential component of just about every AI application, allowing researchers and developers to leverage disparate datasets to train their algorithms. But gaining access to a sufficient amount of data is not a straight-forward endeavor, given that data is often owned by organizations or individuals that might not want third-parties to have access to their data. Or, they may only make said data available under a commercial license, making it prohibitively expensive to harness.

Project proposal, submitted under the MINE.THE.GAP 2nd open call, passed the evaluation phase and was selected for funding. The digital solution proposed will provide a 3D interactive imaging for smart exploration and exploitation of an industrial minerals deposit according to its final uses (e.g., insulation, construction, agriculture, filtration etc.). The main objective of this PoC is a digital Demo interface that will provide a view of a future Prototype and allow use to engage Bêta-Testers of the future Prototype. This innovation will allow users to define their own scenarios regarding final applications for the deposit and retrieve a 3D bloc model of the corresponding market value. The solution will advocate a better use of mineral resources and will democratize best practices.

The classic win-win has a key flaw in that it cannot offer the parties the right amounts of winning because each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure the win-win situation in this paper. The proposed method employs Fuzzy logic to create a mathematical model that aids negotiators in quantifying their winning percentages. The model is put to the test on real-life negotiations scenarios such as the Iranian uranium enrichment negotiations, the Iraqi-Jordanian oil deal, and the iron ore negotiation (2005-2009). The presented model has shown to be a useful tool in practice and can be easily generalized to be utilized in other domains as well.

Airborne magnetic data are commonly used to produce preliminary geological maps. Machine learning has the potential to partly fulfill this task rapidly and objectively, as geological mapping is comparable to a semantic segmentation problem. Because this method requires a high-quality dataset, we developed a data augmentation workflow that uses a 3D geological and magnetic susceptibility model as input. The workflow uses soft-constrained Multi-Point Statistics, to create many synthetic 3D geological models, and Sequential Gaussian Simulation algorithms, to populate the models with the appropriate magnetic distribution. Then, forward modeling is used to compute the airborne magnetic responses of the synthetic models, which are associated with their counterpart surficial lithologies. A Gated Shape Convolutional Neural Network algorithm was trained on a generated synthetic dataset to perform geological mapping of airborne magnetic data and detect lithological contacts. The algorithm also provides attention maps highlighting the structures at different scales, and clustering was applied to its high-level features to do a semi-supervised segmentation of the area. The validation conducted on a portion of the synthetic dataset and data from adjacent areas shows that the methodology is suitable to segment the surficial geology using airborne magnetic data. Especially, the clustering shows a good segmentation of the magnetic anomalies into a pertinent geological map. Moreover, the first attention map isolates the structures at low scales and shows a pertinent representation of the original data. Thus, our method can be used to produce preliminary geological maps of good quality and new representations of any area where a geological and petrophysical 3D model exists, or in areas sharing the same geological context, using airborne magnetic data only.

Iron ore feed load control is one of the most critical settings in a mineral grinding process, directly impacting the quality of final products. The setting of the feed load is mainly determined by the characteristics of the ore pellets. However, the characterisation of ore is challenging to acquire in many production environments, leading to poor feed load settings and inefficient production processes. This paper presents our work using deep learning models for direct ore feed load estimation from ore pellet images. To address the challenges caused by the large size of a full ore pellets image and the shortage of accurately annotated data, we treat the whole modelling process as a weakly supervised learning problem. A two-stage model training algorithm and two neural network architectures are proposed. The experiment results show competitive model performance, and the trained models can be used for real-time feed load estimation for grind process optimisation.

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.

Artificial intelligence (AI) is scientific intelligence that is mostly used by machines. It involves the use of large data sets of instruction that a computer follows to perform a particular task. The more detailed these instructions are, the more accurate the result. In this article, we will look into how you can use Artificial Intelligence to cut down your carbon emission. To solve a problem with AI, there's a need to approach the problem by thinking about a step-by-step solution.

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}.

MONTREAL and VANCOUVER, British Columbia, July 08, 2021 (GLOBE NEWSWIRE) -- Rithmik Solutions, whose mission is building the world's most advanced and reliable analytics for mobile mining equipment, today announced the closing of a US$1.2M investment led by Chrysalix Venture Capital and joined by Fonds Ecofuel. The funding will accelerate the commercialization of the company's flagship product, Rithmik Asset Health Analyzer (AHA), which has been in development for the past three years and is currently undergoing real-time onsite trials in Alberta, Quebec and Zambia. Rithmik AHA applies a multi-tiered machine learning approach to increase mobile equipment uptime while reducing maintenance costs and lowering greenhouse gas emissions. Mining companies typically spend anywhere from 20%-50% of their annual operating budgets on equipment maintenance, and lost production from unplanned downtime has an even bigger financial impact. "We were impressed by the Rithmik team's deep technical experience in the space of mobile mining equipment data, across equipment types and OEM brands, and that experience has strongly resonated with their early customers," said Alicia Lenis, Vice President at Chrysalix Venture Capital, an industrial innovation fund.

LKAB, Minalyze AB and Sentian say they have joined forces in a consortium to develop the latest technology for scanning drill core. In March 2020, LKAB started a test with the Minalyzer CS drill core scanner where the goal was to improve the workflow for core logging – ie how the results of exploration drilling are analysed. The test led to a permanent installation in Kiruna (Sweden) and expansion to Malmberget where data from the Minalyzer CS is used to help geological logging of the drill core. The consortium of LKAB, Minalyze and Sentian are now set to take the use of data to the next level when boreholes in LKAB's deposits are to be investigated. The new artificial intelligence application being developed by the trio will make the analysis much faster, with the time to evaluate a drill core reduced from weeks to minutes, with increased accuracy.