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AbstractThe paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

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Antarctic deserts, such as the McMurdo Dry Valleys (MDV), represent extremely cold and dry environments. Consequently, MDV are suitable for studying the environment limits on the cycling of key elements that are necessary for life, like nitrogen. The spatial distribution and biogeochemical drivers of nitrogen-cycling pathways remain elusive in the Antarctic deserts because most studies focus on specific nitrogen-cycling genes and/or organisms. In this study, we analyzed metagenome and relevant environmental data of 32 MDV soils to generate a complete picture of the nitrogen-cycling potential in MDV microbial communities and advance our knowledge of the complexity and distribution of nitrogen biogeochemistry in these harsh environments. We found evidence of nitrogen-cycling genes potentially capable of fully oxidizing and reducing molecular nitrogen, despite the inhospitable conditions of MDV. Strong positive correlations were identified between genes involved in nitrogen cycling. Clear relationships between nitrogen-cycling pathways and environmental parameters also indicate abiotic and biotic variables, like pH, water availability, and biological complexity that collectively impose limits on the distribution of nitrogen-cycling genes. Accordingly, the spatial distribution of nitrogen-cycling genes was more concentrated near the lakes and glaciers. Association rules revealed non-linear correlations between complex combinations of environmental variables and nitrogen-cycling genes. Association rules for the presence of denitrification genes presented a distinct combination of environmental variables from the remaining nitrogen-cycling genes. This study contributes to an integrative picture of the nitrogen-cycling potential in MDV.

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Climate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os- Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases. Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).

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