Improving energy efficiency in industrial foundry processes is a critical challenge, as these operations are highly energy-intensive and marked by complex interdependencies among process variables. Correlation-based analyses often fail to distinguish true causal drivers from spurious associations, limiting their usefulness for decision-making. This paper applies a time-series causal inference framework to identify the operational factors that directly affect energy efficiency in induction furnace melting. Using production data from a Danish foundry, the study integrates time-series clustering to segment melting cycles into distinct operational modes with the PCMCI+ algorithm, a state-of-the-art causal discovery method, to uncover cause-effect relationships within each mode. Across clusters, robust causal relations among energy consumption, furnace temperature, and material weight define the core drivers of efficiency, while voltage consistently influences cooling water temperature with a delayed response. Cluster-specific differences further distinguish operational regimes: efficient clusters are characterized by stable causal structures, whereas inefficient ones exhibit reinforcing feedback loops and atypical dependencies. The contributions of this study are twofold. First, it introduces an integrated clustering-causal inference pipeline as a methodological innovation for analyzing energy-intensive processes. Second, it provides actionable insights that enable foundry operators to optimize performance, reduce energy consumption, and lower emissions.

Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealing with discrete and continuous variables simultaneously, model misspecification, and lastly fast massive parallelisation. To address these challenges, we introduce a versatile and modular framework for batch Bayesian optimisation via probabilistic lifting with kernel quadrature, called SOBER, which we present as a Python library based on GPyTorch/BoTorch. Our framework offers the following unique benefits: (1) Versatility in downstream tasks under a unified approach.

The beverage industry is a typical food processing industry and accounts for significant energy consumption, e.g., 1 % of The grid stability and security of supply are challenged Danish energy consumption [10]. The beverage industry can due to the increasing penetration of renewable energy sources be further divided based on the beverage type, with beer in the electricity grid [1]. Furthermore, conventional balancing production being the category with the highest energy of the electricity grid through supply-side management is consumption accounting for 40 % of the beverages industry's becoming costly, and the capacity required to ensure the combined energy consumption [10]. For instance, Denmark security of supply would be inefficient [2]. Demand-side has the highest number of breweries per capita [11] among the management has seen increasing potential to mitigate the European nations. As of April 2022, there were 275 breweries impact of fluctuations in the electricity grid and aid in in Denmark. A survey based on the Danish Brewery stabilization by adjusting consumer demand subject to Associations members shows that approximately 50 % of electricity market conditions [3]. Danish beverage facilities might be permanently close or go Demand side management can be divided based on the bankrupt due to COVID-19 and the increasing energy prices load-shape objective, e.g., peak clipping, valley filling, and [12].

Data covering the entire population of Denmark was used to train an AI to predict people's life outcomes An artificial intelligence trained on personal data covering the entire population of Denmark can predict people's chances of dying more accurately than any existing model, even those used in the insurance industry. The researchers behind the technology say it could also have a positive impact in early prediction of social and health problems – but must be kept out of the hands of big business. Sune Lehmann Jørgensen at the Technical University of Denmark and his colleagues used a rich dataset from Denmark that covers education, visits to doctors and hospitals, any resulting diagnoses, income and occupation for 6 million people from 2008 to 2020. They converted this dataset into words that could be used to train a large language model, the same technology that powers AI apps such as ChatGPT. These models work by looking at a series of words and determining which word is statistically most likely to come next, based on vast amounts of examples. In a similar way, the researchers' Life2vec model can look at a series of life events that form a person's history and determine what is most likely to happen next.

Data augmentation is often used to incorporate inductive biases into models. Traditionally, these are hand-crafted and tuned with cross validation. The Bayesian paradigm for model selection provides a path towards end-to-end learning of invariances using only the training data, by optimising the marginal likelihood. We work towards bringing this approach to neural networks by using an architecture with a Gaussian process in the last layer, a model for which the marginal likelihood can be computed. Experimentally, we improve performance by learning appropriate invariances in standard benchmarks, the low data regime and in a medical imaging task. Optimisation challenges for invariant Deep Kernel Gaussian processes are identified, and a systematic analysis is presented to arrive at a robust training scheme. We introduce a new lower bound to the marginal likelihood, which allows us to perform inference for a larger class of likelihood functions than before, thereby overcoming some of the training challenges that existed with previous approaches.

As the new coronavirus burns its way across the world, scientists are rushing to find ways to identify those who have been infected -- including those who have recovered from COVID-19. Those people, the thinking goes, may be immune to the deadly virus and could theoretically help restart the economy without fear of reinfection. One key piece of this puzzle is rolling out what are known as serological tests that look for specific antibodies in a person's blood. So far, they have been used to estimate how much of the population has been exposed in different areas, such as New York City and Los Angeles. But what are these tests, and can they really help to identify who is immune to SARS-CoV-2? From how they work to what they tell us, here's everything you need to know about coronavirus antibody testing.

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

Scientists in Denmark are developing an agricultural robot for identifying and eliminating weeds. While this might seem like a relatively easy task, it actually requires a lot of machine intelligence to pick out the weeds among the crops. The robot is still in the early stages of development, but the researchers hope that it will ultimately lead to a reduction in the amount of herbicides used by farmers and therefore cut costs. Called Hortibot, the semi-autonomous robot is a navigational platform designed to have different agricultural tools fitted to it to either mechanically remove weeds or precision-spray them with herbicide. "The original purpose was to build a robot that was simple to use and could be operated by an unskilled worker," says Rasmus Jørgensen, an agricultural scientist at the Institute of Agricultural Engineering at Aarhus University, in Horsens, Denmark.