Abstract
Phrase segmentation is a fundamental preprocessing step for computational folk music similarity, specifically in identifying tune families within digital corpora. Furthermore, recent literature increasingly recognizes the need for tradition-specific frameworks that accommodate the structural idiosyncrasies of each tradition. In this context, this study presents a culturally informed adaptation of the established rule-based Local Boundary Detection Model (LBDM) algorithm to underrepresented Iberian folk repertoires. Our methodological enhancement expands the LBDM baseline, which traditionally analyzes rests, pitch intervals, and inter-onset duration functions to identify potential segmentation boundaries, by integrating a sub-structure surface repetition function coupled with an optimized peak-selection algorithm. Furthermore, we implement a genetic algorithm to maximize segmentation accuracy by weighting coefficients for each function while calibrating the meta-parameters of the peak-selection process. Empirical evaluation on the I-Folk digital corpus, comprising 802 symbolically encoded folk melodies from Portuguese and Spanish traditions, demonstrates improvements in segmentation F-measure of six and sixteen percentage points~(p.p.) relative to established baseline methodologies for Portuguese and Spanish repertoires, respectively.

Abstract
Concrete structures require precise temperature and humidity monitoring during curing to ensure optimal strength and prevent defects like cracking. A compact optical sensing system was developed using a single fiber that can be embedded directly within the concrete. The system functions as both a temperature and humidity sensor when paired with a spectral interrogation unit operating in the 1500-1600 nm range. Temperature monitoring is achieved through a Fiber Bragg Grating, while humidity sensing is facilitated by a Fabry-Perot interferometer at the fiber tip. The interferometer cavity is formed with a layer of polyvinylpyrrolidone (PVP). Initial air humidity sensor tests showed a significant change in the interference period with RH, demonstrating low hysteresis and high reproducibility. Calibration of one sensor revealed an approximately 3 nm period decrease when RH increased from 55% to 95%, with results suggesting a quadratic relationship between the interference period and RH values.

Abstract
Measuring the density of tartrazine (TZ) powder allowed to develop a protocol for fast preparation of aqueous solutions with a desired concentration. The stability time of these solutions decreases exponentially with the increase of TZ concentration: solutions with TZ concentrations below 25% remain stable for more than 24 h, while the solution with 60% TZ remains stable only for 35 min. To validate the developed protocol, muscle samples were immersed in the 40% TZ solution and, as expected, the tissue transparency increased smoothly and exponentially during the whole treatment of 30 min. The diffusion time of TZ in ex vivo skeletal muscle was quantitatively determined with high accuracy as tau TZ = 5.39 +/- 0.49 min for sample thickness of 0.5 mm. By measuring the refractive index of TZ solutions during preparation, it will be easier to prepare such solutions in a fast manner for future research on tissue optical clearing.

Abstract
In real-world scenarios, a wide variety of datasets contain inconsistencies. One example of such inconsistency is missing data (MD), which refers to the absence of information in one or more variables. Missing imputation strategies emerged as a possible solution for addressing this problem, which can replace the missing values based on mean, median, or Machine Learning (ML) techniques. The performance of such strategies depends on multiple factors. One factor that influences the missing value imputation (MVI) methods is the presence of noisy instances, described as anything that obscures the relationship between the features of an instance and its class, having an adversarial effect. However, the interaction between MD and noisy instances has received little attention in the literature. This work fills this gap by investigating missing and noisy data interplay. Our experimental setup begins with generating missingness under the Missing Not at Random (MNAR) mechanism in a multivariate scenario and performing imputation using seven state-of-the-art MVI methods. Our methodology involves applying a noise filter before performing the imputation task and evaluating the quality of the imputation directly. Additionally, we measure the classification performance with the new estimates. This approach is applied to both synthetic data and 11 real-world datasets. The effects of noise filtering before imputation are evaluated. The results show that noise preprocessing before the imputation task improves the imputation quality and the classification performance for imputed datasets.

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.