Agriculture accounts for a large percentage of nitrous oxide ($N_{2}O$) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint. These emissions are of a direct nature, released straight into the atmosphere through nitrification and denitrification, or of an indirect nature, mainly through nitrate leaching, runoff, and $N_{2}O$ volatilization processes. $N_{2}O$ emissions are largely ascribed to the agricultural sector, which represents a threat to sustainability and food production, subsequent to the radical contribution to climate change. In this connection, it is crucial to unveil the relationship between synthetic N fertilizer global use and $N_{2}O$ emissions. To this end, we worked on a dataset drawn from a recent study, which estimates direct and indirect $N_{2}O$ emissions according to each country, by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Machine learning tools are considered great explainable techniques when dealing with air quality problems. Hence, our work focuses on expectile regression (ER) based-approaches to predict $N_{2}O$ emissions based on N fertilizer use. In contrast to classical linear regression (LR), this method allows for heteroscedasticity and omits a parametric specification of the underlying distribution. ER provides a complete picture of the target variable’s distribution, especially when the tails are of interest, or in dealing with heavy-tailed distributions. In this work, we applied expectile regression and the kernel expectile regression estimator (KERE) to predict direct and indirect $N_{2}O$ emissions. The results outline both the flexibility and competitiveness of ER-based techniques in regard to the state-of-the-art regression approaches.

Clustering plays a pivotal role in exploratory data analysis, especially in scenarios where prior knowledge about a subset of samples is available, prompting a rapid development of Semi-supervised clustering approaches. In this work, we introduce a novel method for seamlessly integrating prior knowledge into the widely recognized Normalized Cut Clustering (NCC) algorithm, thereby offering a natural extension within the realm of semi-supervised clustering. Similarly to NCC, the solution of the proposed method is spectral-based in the form of an inhomogeneous eigenvalue problem. Also, we show that the proposed method can be seen as a generalization of NCC, enhancing its applicability in scenarios where prior information is available. In addition, we design an adequate and comprehensive experimental setup. This setup not only tests the competitiveness of our approach but also showcases its superiority in terms of performance metrics.

Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating modern deep learning techniques, STPPs can model these complexities more effectively than traditional approaches. Consequently, the fusion of neural methods with STPPs has become an active and rapidly evolving research area. In this review, we categorize existing approaches, unify key design choices, and explain the challenges of working with this data modality. We further highlight emerging trends and diverse application domains. Finally, we identify open challenges and gaps in the literature.