Abstract
Given a data-set of Ribonucleic acid (RNA) sequences we can infer the phylogenetics of the samples and tackle the information for scientific purposes. Based on current data and knowledge, the SARS-CoV-2 seemingly mutates much more slowly than the influenza virus that causes seasonal flu. However, very recent evolution poses some doubts about such conjecture and shadows the out-coming light of people vaccination. This paper adopts mathematical and computational tools for handling the challenge of analyzing the data-set of different clades of the severe acute respiratory syndrome virus-2 (SARS-CoV-2). On one hand, based on the mathematical paraphernalia of tools, the concept of distance associated with the Kolmogorov complexity and Shannon information theories, as well as with the Hamming scheme, are considered. On the other, advanced data processing computational techniques, such as, data compression, clustering and visualization, are borrowed for tackling the problem. The results of the synergistic approach reveal the complex time dynamics of the evolutionary process and may help to clarify future directions of the SARS-CoV-2 evolution.

Abstract

Abstract
The backroom of retail stores has structural differences when compared with other warehouses and distribution centres, which are more traditionally studied in the literature. This paper presents a mathematical optimisation approach for an unequal area facility layout problem, applied in designing the backroom layout in grocery retail. A set of rectangular facilities (backroom departments) with given area requirements has to be placed, without overlapping, on a limited floor space (backroom area), which can have a regular or an irregular shape. The objective is to find the location and format of the storage departments, such that the walking distances in the store by store employees are minimised. The proposed approach is tested in a European grocery retailer. In the computational experiments, several real store layouts are compared with the ones suggested by the proposed model. The decrease in the walking distances is, on average, 30 percent. In order to understand what the current designers' strategy is, a set of scenarios was created and compared with the real layouts. Each scenario ignores a characteristic of the problem. The goal is to understand what aspect designers are currently discarding. The findings indicate that, currently, designers neglect the different replenishment frequencies of storage departments.

Abstract
The classification of the type of trapped particles is a crucial task for an efficient integration of optical-tweezers in intelligent microfluidic devices. In the recent years, the use of the temporal scattering signal of the trapped particle paved for the use of versatile optical fiber solutions for performing such tasks, a feature previously unavailable as most methods required conventional optical tweezer setups. This work presents a comprehensive comparison of performances achieved with two distinct implementations – i)optical fiber and ii)conventional optical tweezers – for the classification of the material of particles through the analysis of the scattering signal with machine learning algorithms. The results suggest that while micron-sized particles can be accurately classified using the forward scattering information in conventional optical tweezers, when equipped with a quadrant photodetector, the optical fiber tweezers solutions can easily surpass its performance using the back-scattered information if the laser is modulated. Together with the advantages of being simpler, less expensive and more versatile, the results presented suggest that optical fiber solutions can become a valuable tool for miniaturization and integration of intelligent microfluidic devices working towards nanoscopic scales. © 2021 The Authors

Abstract
This paper presents a Cyber Physical System (CPS) laboratory based on Open Design, called Open Science Laboratory for Manufacturing (OSLab4Man), with educational and research purpose and the potential contribute and (direct and indirect) effects regarding the 17 Sustainable Development Goals (SDGs). A short introduction describing the 17 SDG, the education for sustainability role, and the relevance of the framed CPS. A literature review of CPS laboratories that are addressed to sustainability is presented. The OSLab4Man is describe in the educational purpose lens. Then, the contribute of the OSLab4Man as an education tool for sustainability regarding the 17 SDGs is reflected. Finally, the conclusions of this work and future works are highlighted.

Abstract
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce. Existing methods are either metric- or model-specific, require access to sensitive attributes at inference time, or carry high development or deployment costs. This work explores the unfairness that emerges when optimizing ML models solely for predictive performance, and how to mitigate it with a simple and easily deployed intervention: fairness-aware hyperparameter optimization (HO). We propose and evaluate fairness-aware variants of three popular HO algorithms: Fair Random Search, Fair TPE, and Fairband. We validate our approach on a real-world bank account opening fraud casestudy, as well as on three datasets from the fairness literature. Results show that, without extra training cost, it is feasible to find models with 111% mean fairness increase and just 6% decrease in performance when compared with fairness-blind HO.(1)