The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared two approaches differing only by the first layer machinery, which, in one case, is instantiated as a fully-connected layer and, in the other case, results in a ConvNet equipped with kernels covering the whole input (wide-kernel ConvNet). A patchwise approach, tessellating training reference data in square patches, was adopted. Besides assessing the effectiveness of ConvNets with patched multispectral data, we analyzed how the information needed for classification spreads to patterns over convex sets of pixels. Our results show that: (a) with an F1-score of around 97% (5 × 5 patch size), ConvNets provides an excellent tool for patch-based pattern recognition with multispectral input data without requiring special feature extraction; (b) the information spreads over the limit of a single pixel: the performance of the network increases until 5 × 5 patch sizes are used and then ConvNet performance starts decreasing.

Adaptive traffic signal control (ATSC) in urban traffic networks poses a challenging task due to the complicated dynamics arising in traffic systems. In recent years, several approaches based on multi-agent deep reinforcement learning (MARL) have been studied experimentally. These approaches propose distributed techniques in which each signalized intersection is seen as an agent in a stochastic game whose purpose is to optimize the flow of vehicles in its vicinity. In this setting, the systems evolves toward an equilibrium among the agents that shows beneficial for the whole traffic network. A recently developed multi-agent variant of the well-established advantage actor-critic (A2C) algorithm, called MA2C (multi-agent A2C) exploits the promising idea of some communication among the agents. In this view, the agents share their strategies with other neighbor agents, thereby stabilizing the learning process even when the agents grow in number and variety. We experimented MA2C in two traffic networks located in Bologna (Italy) and found that its action translates into a significant decrease of the amount of pollutants released into the environment.

In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik (IS), and Tromso (NO). Our framework leverages historical ground measurements and 24 h predictions from nine models by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM₁₀ predictions for the following 24 h. Furthermore, we compare the performance of various memory cells based on artificial neural networks (ANN), including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), echo state networks (ESNs), and windowed multilayer perceptrons (MLPs). Regardless of the type of memory cell chosen, our results consistently show that the proposed framework outperforms the CAMS models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Furthermore, we examine the impact of outliers on the overall performance of the model.

This paper presents a Variational Mode Decomposition based (VMD) approach for multi-step prediction of univariate electrical load on large passenger ships. Specifically, VMD is adopted as a pre-processing step to enhance the performance of Long Short-Term Memory (LSTM)-based forecasting models. It is demonstrated that decomposing the signal into intrinsic mode functions via VMD before forecasting significantly outperforms (up to a 66% error reduction) the conventional approach of feeding the entire signal into the forecasting model. The resulting findings suggest that the integration of VMD into the forecasting pipeline could yield a useful tool for a more accurate and efficient power management on large passenger ships.

In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik (IS), and Tromso (NO). Our framework leverages historical ground measures and 24-hour predictions from 9 models provided by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM predictions for the following 24 hours. Furthermore, we compare the performance of various memory cells based on artificial neural networks (ANN), including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), echo state networks (ESNs), and windowed multi-layer perceptrons (MLPs), which are commonly employed in time series forecasting tasks. Irrespective of the chosen memory cell type, our results demonstrate that the proposed framework consistently outperforms the CAM models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Additionally, we investigate the impact of outliers on the overall model performance.

This paper introduces Multi-Channel Neighborhood-Constrained Variational Mode Decomposition (NCVMD), a novel extension of Variational Mode Decomposition (VMD). When working with multi-channel signals, aligning mode frequencies serves as a criterion for grouping modes with similar frequencies and enabling mode-level processing. This requirement must be achieved while maintaining an acceptable error in the decomposition, and has been investigated by an approach called Multivariate Variational Mode Decomposition (MVMD), which forces a perfect alignment of the channels' modes' central frequencies at the expense of the decomposition error. Our approach differs in two key ways: first, by making a hierarchical distinction in a multi-channel signal between a primary (main) channel and others that provide auxiliary information; and second, by allowing tunable alignment between channel decomposition. Both these aspects reduce the decomposition error in the main channel at the expense of the auxiliary channels, at a customizable degree. These distinctive features make the method particularly well-suited for applications such as weighted image fusion, signal denoising, and time series forecasting; in these domains, there are clear advantages to both establishing a hierarchical processing framework for the signal's channels and operating at mode level, where intrinsic oscillatory patterns can be extracted and processed independently. We demonstrate the effectiveness of NCVMD through experiments on both synthetic and real-world datasets. The results indicate that NCVMD preserves a pure VMD-based decomposition for the main channel, maintains acceptable decomposition errors for the auxiliary channels, and achieves a high degree of mode frequency alignment across channels.