Abstract
The recent pandemic along with the rapid increase in the number of patients that require continuous remote monitoring imposes several challenges to support the high quality of services (QoS) in remote health applications. Remote-health (r-health) systems typically demand intense data collection from different locations within a strict time constraint to support sustainable health services. On the contrary, the end-users with mobile devices have limited batteries that need to run for a long time, while continuously acquiring and transmitting health-related information. Thus, this paper proposes an adaptive deep reinforcement learning (DRL) framework for network selection over heteroge-neous r-health systems to enable continuous remote monitoring for patients with chronic diseases. The proposed framework allows for selecting the optimal network(s) that maximizes the accumulative reward of the patients while considering the patients' state. Moreover, it adopts an adaptive compression scheme at the patient level to further optimize the energy consumption, cost, and latency. Our results depict that the proposed framework outperforms the state-of-the-art techniques in terms of battery lifetime and reward maximization.

Abstract
Digital platforms have, in the past decades, undergone a revolution, evolving from its technical roots so much that nowadays value is mostly generated, not by the technologies that power platforms, but by the ecosystem of applications, developers and users it is able to generate and support. In this paper, we seek to understand the importance industrial platform owners place on the community building and platform growth components of the platform development process by reviewing 50 Horizon 2020 financed projects that stand on the development of platforms. This evidence is leveraged for the case of a validation strategy definition for a platform ecosystem aiming at sharing production capacity. Key findings point to platform developing practices focused on the development of technical components to the detriment of the ecosystem generation element. We also shed light on how different business models and funding schemes impacted the steering of these platforms.

Abstract

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.