Abstract
In spite of the significant advance achieved in the development of optimal power flow (OPF) programs, most of the solution methods reported in the literature have considerable difficulties in dealing with different-nature objective functions simultaneously. By leveraging recent progress on the semidefinite programming (SDP) relaxations of OPF, in the present article, attention is focused on modeling a new SDP-based multiobjective OPF (MO-OPF) problem. The proposed OPF model incorporates the classical epsilon-constraint approach through a parameterization strategy to handle the multiple objective functions and produce Pareto front. This article emphasizes the extension of the SDP-based model for MO-OPF problems to generate globally nondominated Pareto optimal solutions with uniform distribution. Numerical results on IEEE 30-, 57-, 118-bus, and Indian utility 62-bus test systems with all security and operating constraints show that the proposed convex model can produce the nondominated solutions with no duality gap in polynomial time, generate efficient Pareto set, and outperform the well-known heuristic methods generally used for the solution of MO-OPF. For instance, in comparison with the obtained results of NSGA-II for the 57-bus test system, the best compromise solution obtained by SDP has 1.55% and 7.42% less fuel cost and transmission losses, respectively.

Abstract
ABSTRACT Objective To make a critical and comparative analysis of curricula of Brazilian and Portuguese higher education institutions in terms of clock hours and semester distribution of food and nutrition education in undergraduate nutrition programs, also assessing the main differences among courses classified into thematic axes and professional practice areas. Methods The curricula of fifteen Brazilian and eleven Portuguese nutrition programs were collected and classified into thematic axes and professional practice areas with the method of Document Analysis. Next, we performed statistical analysis regarding the total and proportional clock hours of instruction and their semester distribution to assess the differences between the two countries. The variables of interest were the hours of Food and Nutrition Education and their semester distribution. Results The Food and Nutrition Education axis was the second smallest one, with statistically significant differences among the axes (2.2% of curricula; p<0.001). Brazilian higher education institutions showed greater total clock hours of Food and Nutrition Education (p=0.018), Human and Social Sciences (p=0.003), Public Health (p<0.001), as well as a wider dispersion and lower relative weighted mean for the semester offering of courses (p=0.004) of Food and Nutrition Education courses. Portuguese higher education institutions showed greater total and proportional clock hours of instruction for Exact Sciences (p<0.005; p=0.001, respectively) and more proportional hours of Biologic and Health Sciences (p<0.001). Conclusion Our study found a reduced presence of the area of Food and Nutrition Education in the undergraduate training of nutritionists in both countries.

Abstract
Customer segmentation is increasingly needed in a context where customer interests are vital for companies to survive. This study proposes the use of the weighted RFM (Recency, Frequency, Monetary) supported by data mining techniques and the Analytic Hierarchy Process (AHP), to classify the customers according to their lifetime value (CLV). The customer segments obtained can be used to boost marketing strategies, as these segments enable to differentiate the customers. Each segment of customers is described by a set of rules based on the customers’ purchasing patterns. The methodology developed is validated by using a real case study, i.e. a food industry company, whose core business is the production of biscuits. © IEOM Society International.

Abstract
Self-healing (SH) functions have been studied through pilots on E-REDES Medium Voltage (MV) network with positive results. The natural next step would be to apply the SH concept to Low Voltage (LV) networks. However, LV and MV networks have distinct characteristics (criticality, capillarity, complexity, energy distributed by km of network, technology, etc.). The economic criteria that justify SH on MV network are not applicable to LV networks. This article presents and discusses several challenges related to implement SH to LV networks and other aspects to be considered. The SH concept is discussed when applied to LV network. Also, the advantages that operational management can achieve with this concept available on daily operations. Other big challenge is the technology evolution that must occur on sensors and, most of all, actuators, to accommodate automatisms and to be remotely monitored and controlled. Also, a telecommunication solution needs to be established to support the real-time interaction between all the components. Last, but not least, the economic aspect. How and when can an extra cost be justifiable on a network that didn't felt the necessity to be automated for so many years. Should we start to consider it now? Two use-cases are proposed. © 2021 The Institution of Engineering and Technology.

Abstract
Co-branding in the hospitality luxury sector is still understudied in the literature. This study aims at tackling this gap through the analysis of a case of a co-branding strategy between a vinous-concept luxury hotel in Portugal and premium wine brands of domestic producers. Fourteen in-depth interviews with managers of the luxury hotel and wine brand partners supported the exploratory research. This chapter represents a case of qualitative data application to an underestimated topic in the literature from the managers' point of view. The study offers evidence on the benefits for both parties, reasons for adopting co-branding, and partners' selection attributes. The improvement of brand image emerges as one of the main advantages of co-branding with a luxury hotel. Based on the literature review and the interviews with managers, the study proposes a set of hypotheses to be tested in future research. This chapter provides interesting cues for academics and practitioners. © 2021, IGI Global.

Abstract
In the last years, organizations and companies in general have found the true potential value of collecting and using data for supporting decision-making. As a consequence, data are being collected at an unprecedented rate. This poses several challenges, including, for example, regarding the storage and processing of these data. Machine Learning (ML) is also not an exception, in the sense that algorithms must now deal with novel challenges, such as learn from streaming data or deal with concept drift. ML engineers also have a harder task when it comes to selecting the most appropriate model, given the wealth of algorithms and possible configurations that exist nowadays. At the same time, training time is a stronger restriction as the computational complexity of the training model increases. In this paper we propose a framework for dealing with these challenges, based on meta-learning. Specifically, we tackle two well-defined problems: automatic algorithm selection and continuous algorithm updates that do not require the retraining of the whole algorithm to adapt to new data. Results show that the proposed framework can contribute to ameliorate the identified issues.