The diffusion of the Internet of Things allows nowadays to sense human mobility in great detail, fostering human mobility studies and their applications in various contexts, from traffic management to public security and computational epidemiology. A mobility task that is becoming prominent is crowd flow prediction, i.e., forecasting aggregated incoming and outgoing flows in the locations of a geographic region. Although several deep learning approaches have been proposed to solve this problem, their usage is limited to specific types of spatial tessellations and cannot provide sufficient explanations of their predictions. We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks. Compared with state-of-the-art solutions, CrowdNet can be used with regions of irregular shapes and provide meaningful explanations of the predicted crowd flows. We conduct experiments on public data varying the spatio-temporal granularity of crowd flows to show the superiority of our model with respect to existing methods, and we investigate CrowdNet’s reliability to missing or noisy input data. Our model is a step forward in the design of reliable deep learning models to predict and explain human displacements in urban environments.

This study addresses the need for a rapid and accurate process monitoring by developing an innovative approach for wastewater quality assessment to enhance the Industry 4.0’s vision. By integrating Ultraviolet-Visible (UV-Vis) spectroscopy with Machine Learning (ML), we focus on accurately determine key indicators such as Chemical Oxygen Demand, Total Suspended Solids (TSS), chlorides, and conductivity. Our findings demonstrate the efficacy of ML models in accurately predicting water quality from UV-Vis spectral data, underscoring their potential for real-time monitoring and analysis in industrial settings. The study also revealed the potential for both single and multitarget predictions. Additionally, the feature importance analysis provided valuable insights into the spectral regions most relevant for predicting each water quality indicator. This approach aligns with the goals of Industry 4.0, offering a smart, efficient solution for environmental monitoring and sustainable resource management.

The Volume 10 published for IJEPR Journal.