Abstract
This study presents a methodology for forecasting municipal water consumption to support efficient resource management. Using monthly data from 2018 to 2022 for the municipality of Vouzela, Portugal, three forecasting approaches were evaluated: SARIMA, Holt-Winters, and TimeGPT. Data preparation included logarithmic transformation and stationarity assessment using the KPSS test, ensuring appropriate conditions for statistical modelling. The SARIMA model was selected automatically based on the Akaike Information Criterion (AIC), while the Holt-Winters method was fitted with additive components and a Box-Cox transformation. In addition, TimeGPT was employed as a state-of-the-art foundation model for time series forecasting. The three methods were used to predict water consumption for the 12 months of 2023, and their performance was assessed using MAE, MSE, RMSE and MAPE. Results indicate that although all methods perform adequately, Holt-Winters and TimeGPT better capture recent consumption dynamics, providing more accurate forecasts in several periods. Overall, this study shows that combining classical statistical models with advanced forecasting techniques offers local authorities reliable and computationally accessible tools to support water supply planning and sustainability.

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Abstract Some performance-based research funding systems rely on expert-assigned journal rankings to allocate resources and guide research evaluation. In Finland, the JuFo system provides journal rankings, determined by experts who assess journals using available metadata, such as bibliometric indicators, alongside qualitative judgment. While prior work has explored machine learning approaches to approximate these rankings, the recent emergence of large language models (LLMs) offers new possibilities for automated, data-driven evaluation. In this study, we examine how well LLMs can replicate JuFo rankings when given the same structured information available to experts, including citation metrics, disciplinary assignments, and publisher metadata. We systematically compare LLM predictions to expert-assigned JuFo ranks using a confusion-matrix analysis to identify cases of alignment and deviation. Our research addresses two key questions: (1) how accurately LLMs estimate journal rankings, and (2) in which situations their predictions diverge from expert judgments and which factors explain these discrepancies. Our findings show that LLMs approximate expert-assigned rankings with high overall accuracy, with most errors occurring between adjacent levels. However, their performance varies systematically across disciplines, and they tend to under-predict top-tier journals, particularly in social sciences and humanities fields.

Abstract
Old-vine vineyards often contain dozens of grapevine varieties intermingled and irregularly distributed, making plant-level varietal identification slow and expensive when based on ampelography or molecular approaches. This paper proposes a field-oriented computer-vision pipeline for Vitis vinifera variety identification using images with a natural background from the historic Vinha Maria Teresa parcel (Quinta do Crasto, Portugal). A single-class YOLO11 detector is trained to localize the vine leaf and generate standardized crops, and a YOLO11 classifier is then fine-tuned on leaf regions of interest (ROIs) for eight selected varieties in the Douro UNESCO region. We annotated 2015 vineyard images for classification and supplemented detection training with 2648 additional leaf images; detectors (YOLO11n/s/m) were benchmarked under four augmentation regimes and evaluated on a fixed 48-image subset, including runtime on CPU and GPU. The best detector reached mAP@50-95 of 0.918 on the benchmark, while YOLO11n achieved similar to 27 FPS on CPU for fast cropping. On a 303-image test set, the best classifier (YOLO11s with mixed augmentations) achieved 94.06% Top-1 accuracy, 93.92% macro-F1, and 100% Top-5 accuracy with remaining errors concentrated among morphologically similar varieties. To assess deployment-oriented performance, classifiers trained under three input settings (manual crops, detector-generated crops, and full images) were evaluated on a held-out 48-image benchmark subset; removing the detection step reduced Top-1 accuracy from 75.00% to 68.75%, while the gap between manual and automatic crops was only 2.44 pp on successfully detected images with detection failures (14.6%) representing the primary operational bottleneck. Repeated retraining of the best manual-crop YOLO11s configuration across multiple random seeds showed stable performance with low variability in Top-1 accuracy and macro-F1. Under identical training conditions, ResNet50 and EfficientNet-B0 provided competitive baselines, but YOLO11s remained the strongest overall model on the held-out field benchmark. These results indicate that lightweight leaf detection plus crop-based classification can support scalable varietal identification in old vineyards under realistic acquisition conditions.

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Abstract
Abstract Misinformation poses one of the most urgent challenges of our society and raises the question of how to deal with it and manage its rapid spread. To address this problem, a promising approach relies on AI-based misinformation detection. This chapter of the book offers a critical analysis of the ethical implications associated with the design, deployment, and use of misinformation detectors (MDs). Designing and deploying an MD—an AI system that automatically identifies misinformation—is a complex undertaking that requires an interdisciplinary approach, as the challenges faced by MD designers and deployers encompass not only technical aspects, but also linguistic, sociological, political, and especially ethical dimensions. Our analysis is ethics-oriented and follows two main lines of inquiry: (1) Ethics by Design, which focuses on issues related to the design process of an MD, and (2) Ethics of Impact, which addresses the intended and unintended effects of MD deployment and use.