In this paper, a dynamic line-rating experiment is presented in which four machine-learning algorithms (generalized linear models, multivariate adaptive regression splines, random forests and quantile random forests) are used in conjunction with numerical weather predictions to model and predict the ampacity up to 27 h ahead in two conductor lines located in Northern Ireland. The results are evaluated against reference models and show a significant improvement in performance for point and probabilistic forecasts. The usefulness of probabilistic forecasts in this field is shown through the computation of a safety-margin forecast which can be used to avoid risk situations. With respect to the state of the art, the main contributions of this paper are an in depth look at explanatory variables and their relation to ampacity, the use of machine learning with numerical weather predictions to model ampacity, the development of a probabilistic forecast from standard point forecasts, and a favorable comparison to standard reference models. These results are directly applicable to protect and monitor transmission and distribution infrastructures, especially if renewable energy sources and/or distributed power generation systems are present.

In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Its complete automatization would save a high quantity of resources and provide valuable improvements especially for allergy-related information systems, but also for other application fields as paleoclimate reconstruction, quality control of honey based products, collection of evidences in criminal investigations or fabric dating and tracking. This paper presents three state-of-the-art deep learning classification methods applied to the recently published POLEN23E image dataset. The three methods make use of convolutional neural networks: the first one is strictly based on the idea of transfer learning, the second one is based on feature extraction and the third one represents a hybrid approach, combining transfer learning and feature extraction. The results from the three methods are indeed very good, reaching over 97% correct classification rates in images not previously seen by the models, where other authors reported around 70.

In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and pharmaceutical, rely on the accuracy of this manual classification process, which is reported to be around 67%. In this paper, we propose a new method to automatically classify pollen grains using deep learning techniques that improve the correct classification rates in images not previously seen by the models. Our proposal manages to properly classify up to 98% of the examples from a dataset with 46 different classes of pollen grains, produced by the Classifynder classification system. This is an unprecedented result which surpasses all previous attempts both in accuracy and number and difficulty of taxa under consideration, which include types previously considered as indistinguishable.