Abstract
Current developments in heat pumps, supported by innovative business models, are driving several industry sectors to take a proactive role in future district heating and cooling networks in cities. For instance, supermarkets and data centers have been assessing the reuse of waste heat as an extra source for the district heating network, which would offset the additional investment in heat pumps. This innovative business model requires complete deregulation of the district heating market to allow industrial heat producers to provide waste heat as an additional source in the district heating network. This work proposes the application of innovative market designs for district heating networks, inspired by new practices seen in the electricity sector. More precisely, pool and Peer-to-Peer (P2P) market designs are addressed, comparing centralized and decentralized market proposals. An illustrative case of a Nordic district heating network is used to assess the performance of each market design, as well as the potential revenue that different heat producers can obtain by participating in the market. An important conclusion of this work is that the proposed market designs are in line with the new trends, encouraging the inclusion of new excess heat recovery players in district heating networks.

Abstract
This work proposes an architecture to treat georeferenced data from the OpenStreetMap to plan routes. The methodology considers the following steps: collecting data, incorporating data into a data manager, importing data into a data model, executing routing algorithms, and visualizing routes. Our proposal incorporates the following features characterizing each street segment: safety & security, comfort, accessibility, air quality, time, and distance. Routes can be calculated considering any specified weighting system of these features. The outcome of the application of this architecture allows to calculate and visualize routes from georeferenced data, which can support researchers in the study of multi-criteria routes. Furthermore, this framework enhances the OSM data model adding a multi-criteria dimension for route planning.

Abstract
Neonatal sepsis is characterized by the system’s extreme response to an infection and persists as one of the biggest life-threatening diseases. The gold standard treatment is administrating an antibiotic, which, unfortunately, is often made too late. The diagnosis should be easier, faster, and achieved through non-invasive methods. Recently, entropy, a non-linear feature, has been applied to different physiological signals to detect diseases having very promising results. In this study, several entropy measures were applied to the breathing cycle duration (TTot) of the respiratory signals for 20 neonates. In total, 18 distinct methods of entropy were initially applied to 30-minute segments. Using Spearman’s correlation, it was detected strong correlation similarities between some of the measures. On the other hand, bubble, attention, phase, and spectral entropies were negatively correlated with all the other measures. To detect the presence of Sepsis, the slope of the multiscale entropy index was analyzed. Also, a changing point in the slope was probed, when possible, and then was applied linear regression to two subsets of data, before and after the changing point. Effectively, the Wilcoxon Sign Rank Test showed that the results for the total slope of the Sample, Corrected Conditional, Distribution, Permutation, Fuzzy, Gridded Distribution, Incremental, and Entropy of Entropy were statistically significant to infer that entropy decreases with time. Nonetheless, further work should confirm these results with a larger dataset that includes healthy and pathological neonates. © 2022 IEEE.

Abstract
This research is a part of the Erasmus+ internationally funded InDo research project, involving a consortium led by an Italian higher education institution. An objective of the project is to include desk and field research on the topics of Understanding by Design and Differentiated Instruction, which led to this article having been produced, for knowledge transfer purposes. To carry out this research study, the Boolean operators "Understanding by design"; "Differentiated instruction"; "Higher education" and, "Cross-disciplinary material" applied to the title, abstract, and keywords in the Scopus database were used. Using all operators simultaneously returned no results. Only the Boolean operators "Differentiated instruction" AND "Higher education" and the operator "Cross-disciplinary material" alone brought results. 24 articles were found on Differentiated instruction and Higher education. This group of articles was previously analyzed in a Bibliometric way, using the "Bibliometrix" package from the free software R Studio. Regarding the searches for the term "Cross-disciplinary material", the Scopus database returned only 1 result, which was combined with the 24 articles previously identified, totaling a total of 25 articles related to the two themes. Given the difficulty of access - articles, books, and book chapters with restricted access - 6 investigations were excluded, leaving 19 potentially relevant articles, which were read in their entirety. From the analysis of the 19 articles selected for full reading, 1 investigation was excluded for not fitting the parameters of this research, resulting in a total of 18 articles that were analyzed using a meta-synthesis. After the analysis performed, it can be seen that the main theory used has been differentiated instruction coupled with issues such as standardized assessments for the identification of learning styles, personalized feedback instruments, online applications, the perception of self-efficacy, as well as concern for the development of analytical models for differentiated instruction. Less expressively, other theories that emerge from the analysis performed, are the flipped classroom, Universal Learning Design, a diagnostic assessment and interdisciplinary education.

Abstract
In the areas ofmachine learning / big data, when collecting data, sometimes too many features may be stored. Some of them may be redundant or irrelevant for the problem to be solved, adding noise to the dataset. Feature selection allows to create a subset from the original feature set, according to certain criteria. By creating a smaller subset of relevant features, it is possible to improve the learning accuracy while reducing the amount of data. This means means better results obtained in a shorter learning time. However, feature selection is normally regarded as a very important problem to be solved, as it directly impacts both data analysis and model creation. The problem of optimizing the selected features of a given dataset is not always trivial but, throughout the years, different ways to counter this optimization problem have been presented. This work presents how feature selection fits in the larger context of multi-objective problems as well as a review of how both multi-objective evolutionary algorithms and metaheuristics are being used in order to solve feature selection problems © MIR Labs, www.mirlabs.net/ijcisim/index.html

Abstract
The evaluation of the diffusion properties of optical clearing agents in biological tissues, which are necessary to characterize the transparency mechanisms, has been traditionally made using ex vivo tissues. With the objective of performing such evaluation in vivo, this study was made to evaluate and compare those properties for propylene glycol in skeletal muscle, as obtained with the collimated transmittance and diffuse reflectance kinetics. The diffusion time and the diffusion coefficient of propylene glycol in the muscle that were calculated both from transmittance and reflectance kinetics presented a deviation of 0.8%, a result that opens the possibility to use such a method in vivo. © 2022 IEEE.