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Abstract The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format.

Modern livestock farming systems face the challenge of meeting the growing demand for dairy and meat products while ensuring the well-being of animals. Body Condition Scoring serves as a vital process for assessing the body reserves in animals, impacting their health, welfare, and productivity. However, traditional body condition score (BCS) evaluation methods via observation and palpation of specific anatomical regions are labor-intensive and subjective, hindering their widespread adoption. To address this issue, Precision Livestock Farming (PLF) techniques, particularly those involving Internet of Things (IoT) devices and artificial intelligence (AI), have emerged as promising solutions. In this work, we explore the use of AI, specifically Convolutional Neural Networks (CNNs), to automate the assessment of BCS in goats utilizing imagery data. Our model was trained on 5000 images illustrating the dorsal view of the backside of goats achieving an overall accuracy of 97.94 % which was the highest compared to other popular deep learning architectures from literature (e.g. VGG16, ResNet34, ResNet50, DenseNet, GoogleNet). The proposed custom CNN model for goat-specific BCS estimation overcomes the limitations of manual sketching, providing automatic region identification for BCS assessment. Moreover, it is a lightweight model specifically designed for seamless integration with IoT devices, allowing for efficient on-board processing via cameras. The model's pose-independent nature and adaptability to environmental constraints make it a valuable tool for efficient and sustainable goat farming. This research advances the application of AI as a precision livestock farming tool, contributing to the reinforcement of the animal welfare and productivity, and supporting evidence-based decision-making processes to increase farms' resilience.

Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and institutions, enhancing model robustness and generalizability without compromising patient privacy. In this paper, we propose DPS-GAT, a novel approach integrating graph attention networks (GATs) with differentially private client selection and resource allocation strategies in FL. Our methodology addresses the challenges of data heterogeneity and limited communication resources inherent in medical applications. By employing graph neural networks (GNNs), we effectively capture the relational structures among clients, optimizing the selection process and ensuring efficient resource distribution. Differential privacy mechanisms are incorporated, to safeguard sensitive information throughout the training process. Our extensive experiments, based on the Regensburg pediatric appendicitis open dataset, demonstrated the superiority of our approach, in terms of model accuracy, privacy preservation, and resource efficiency, compared to traditional FL methods. The ability of DPS-GAT to maintain a high and stable number of client selections across various rounds and differential privacy budgets has significant practical implications, indicating that FL systems can achieve strong privacy guarantees without compromising client engagement and model performance. This balance is essential for real-world applications where both privacy and performance are paramount. This study suggests a promising direction for more secure and efficient FL medical applications, which could improve patient care through enhanced predictive models and collaborative data utilization.

In this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is applied on a hypothetical coastal aquifer system in order to calculate the optimal pumping rates which are used as a benchmark for the lower-fidelity approaches. The examined lower-fidelity models could be classified in two categories: (1) physics-based models, which include several widely used variations of the sharp-interface approximation and (2) machine learning assisted models, which aim to improve the efficiency of the SI approach. The Random Forest method was utilized to create a spatially adaptive correction factor for the original sharp-interface model, which improves its accuracy without compromising its efficiency as a lower-fidelity model. Both the original sharp-interface and Machine Learning assisted model are then tested in a single-fidelity optimization method. The optimal pumping rated which were calculated using the Machine Learning based SI model sufficiently approximate the solution from the variable density model. The Machine Learning assisted approximation seems to be a promising surrogate for the high-fidelity, variable density model and could be utilized in multi-fidelity groundwater management frameworks.

Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostly novel even for the more general task of quantifying predictive uncertainty in spatial prediction settings. On 15 years of monthly data from over the contiguous United States, we compared quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machine (LightGBM), and quantile regression neural networks (QRNN). Their ability to issue predictive precipitation quantiles at nine quantile levels (0.025, 0.050, 0.100, 0.250, 0.500, 0.750, 0.900, 0.950, 0.975), approximating the full probability distribution, was evaluated using quantile scoring functions and the quantile scoring rule. Predictors at a site were nearby values from two satellite precipitation retrievals, namely Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Integrated Multi-satellitE Retrievals (IMERG), and the site’s elevation. The dependent variable was the monthly mean gauge precipitation. With respect to QR, LightGBM showed improved performance in terms of the quantile scoring rule by 11.10%, also surpassing QRF (7.96%), GRF (7.44%), GBM (4.64%) and QRNN (1.73%). Notably, LightGBM outperformed all random forest variants, the current standard in spatial prediction with machine learning. To conclude, we propose a suite of machine learning algorithms for estimating uncertainty in spatial data prediction, supported with a formal evaluation framework based on scoring functions and scoring rules.