Abstract
Municipal solid waste (MSW) management has become a critical issue today, posing substantial economic, environmental, and social challenges. Identifying and analyzing dominant themes in this field is essential for advancing research and policies towards sustainable MSW management practices. This study aims to explore the key issues related to MSW management that have been addressed by both the scientific community and policymakers through funded projects. By doing so, the study seeks to guide the scientific community as a knowledge producer and the EU as a key funder. Two Latent Dirichlet Allocation (LDA) models were applied to analyze the themes from two corpora: one representing scientific literature and another focusing on EU-funded projects. Additionally, this analysis was complemented by a quantitative estimation of the similarity between the two corpora, providing a measure of alignment between the scientific community and policymakers. The results generally indicate that the two spheres are aligned and highlight the diversity of topics explored by the scientific community. Nevertheless, it is concluded that there are opportunities for further research on specific topics, such as leaching and the extraction of heavy metals. Additionally, the popularity of topics identified in European Union-funded projects has fluctuated considerably over time, focusing primarily on waste management rather than its prevention. In light of these findings, waste prevention emerges as a promising avenue for future EU-funded research initiatives.

Abstract
As in school, one teacher to cover all subjects is insufficient to distill equally robust information to a student. Hence, each subject is taught by a highly specialised teacher. Following a similar philosophy, we propose a multiple specialized teacher framework to distill knowledge to a student network. In our approach, directed at face recognition use cases, we train four teachers on one specific ethnicity, leading to four highly specialized and biased teachers. Our strategy learns a project of these four teachers into a common space and distill that information to a student network. Our results highlighted increased performance and reduced bias for all our experiments. In addition, we further show that having biased/specialized teachers is crucial by showing that our approach achieves better results than when knowledge is distilled from four teachers trained on balanced datasets. Our approach represents a step forward to the understanding of the importance of ethnicity-specific features.

Abstract
Networks appear across various domains, and identifying central nodes in temporal networks is more challenging than in static networks. Temporal betweenness centrality is the widely used method to assess the importance of the nodes. This method is based on shortest temporal path calculations. However, computing this centrality metrics value is computationally intensive, especially for large-scale networks. Various approximation algorithms exist, but they often lack efficiency or accuracy. We introduce TGNN-Bet, a temporal graph neural network model, to approximate temporal betweenness centrality. In TGNN-Bet, each node gathers features from multi-hop neighbors, enabling the model to simulate paths and capture the reachability of nodes. The model's effectiveness is validated using the Spearman correlation (?) performance metric and comparing system runtimes with the existing temporal betweenness centrality method. Experimental results on six real-world temporal networks demonstrate that TGNN-Bet strongly correlates with existing temporal betweenness centrality methods. The proposed TGNN-Bet model achieves an average computation time reduction of 94.216% compared to conventional temporal betweenness centrality methods. © 2025 IEEE.

Abstract
Building rehabilitation is a reality; all rehabilitation work phases must be efficient and sustainable and promote healthy living places. Current procedures for assessing construction conditions are time-consuming, labour-intensive, and costly. They can threaten engineers' health and safety, especially when inspecting hard-to-reach and high-altitude sites. At the initial stage, a survey of the condition of the building is conducted, which later implies the preparation of a report on the existing pathologies, intervention solutions and associated costs. This procedure involves an inspection of the site (through photographs and videos). In addition, biological growths can threaten the health of those who frequent these places. The World Health Organization states that the most important effects are the increased prevalence of respiratory symptoms, allergies, asthma, and immune system disorders. This work aims to raise awareness of this fact and contribute to the identification of an automatic form of biological growth-type defects in images of buildings. To make this possible, we need a dataset of imaging building components with and without biological growths. Subsequently, deep learning methods are applied to allow the automatic identification of this type of defect in the images, and the results are analysed. A pre-trained VGG16 model was used. The dataset was annotated and divided into groups for training, validation, and testing. The model achieved an overall accuracy of 90%. This work demonstrates the potential of using Deep Learning (DL) in the maintenance and rehabilitation of urban infrastructures, highlighting the efficiency and sustainability of these processes and the importance of adjustments to ensure the stability of AI models. © 2025 The Author(s).

Abstract
Hyperbolic Metamaterials (HMM) are a class of photonic metamaterials exhibiting hyperbolic dispersion due to strong anisotropy. This work presents a numerical analysis and experimental characterization of a hyperbolic multilayer structure supporting surface plasmon polaritons for refractometric sensing applications. The device consists of a multilayer HMM composed of alternate Au and TiO2 layers, and the interaction of different plasmonic modes at each interface of the HMM is reported to enhance light- matter coupling, leading to an increased refractometric sensitivity. The hyperbolic dispersion and its effects on sensor performance are numerically investigated using the Effective Medium Theory (EMT) and validated through the Transfer Matrix Method (TMM). A fair match was obtained between EMT and TMM simulated spectra, validating the EMT approach for simulation of the optical properties of multilayer HMMs. Despite not predicting figures of merit (FOM) accurately, both the TMM and EMT approaches closely replicated the obtained experimental refractometric sensitivity.

Abstract
We studied the characteristics, regions of existence, and stability of different types of solitons for a distributed model of a mode-locked laser whose dispersion is purely quartic and normal. Among the different types of solitons, we identified three main branches that are named according to their different amplitude: low, medium, and high amplitude solitons. It was found that the first solitons are always unstable while the latter two exist and are stable in relatively large regions of the parameter space. Moreover, the stability regions of medium and high amplitude solitons overlap over a certain range of parameters, manifesting effects of bistability. The energy of high amplitude solitons increases quadratically with their width, whereas the energy of medium amplitude solitons may decrease or increase with the width depending on the parameter region. Furthermore, we have investigated the long term evolution of the continuous-wave solutions under modulational instability, showing that medium amplitude solitons can arise in this scenario. Additionally, we assessed the effects of second- and third-order dispersion on medium and high amplitude solitons and found that both remain stable in the presence of these terms.