David Hume’s historical thought was shaped before he even began writing the History of Great Britain in 1752. This article shows how Hume developed his historical thought in an attempt to combine two historical structures: the natural-jurisprudential conjectural history of the Treatise of Human Nature and the early eighteenth-century historical narratives of modern Europe that featured in his Essays. The Treatise’s conjectural history used the developmental categories “rude” and “civilised” to explain the origins of justice, government and the moral sentiment. (...) The narratives of modern Europe, in contrast, revolved around the historical categories “ancient” and “modern.” Hume’s historical thought was shaped by the attempt to merge those two structures into a single, coherent structure. The critical question concerned the relation between the ancient and the modern: was modern Europe merely a “revival” of classical antiquity? Or did it have new, “post-ancient” dimensions? The article shows how Hume gradually distanced classical antiquity from modern Europe, thereby creating space for exclusively modern concepts such as “civilised monarchies” and the narrative of modern civilisation that structured his History of England (1754–1762). The paper concludes by suggesting that this structure defined Enlightenment philosophical history, not just Hume’s version of it. (shrink)

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From the current (...) literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (shrink)