Abstract

Abstract
Multi-objective optimization inspired on genetic algorithms are population based search methods. The population elements, chromosomes, evolve using inheritance, mutation, selection and crossover mechanisms. The aim of these algorithms is to obtain a representative non-dominated Pareto front from a given problem. Several approaches to study the convergence and performance of algorithm variants have been proposed, particularly by accessing the final population. In this work, a novel approach by analyzing multi-objective algorithm dynamics during the algorithm execution is considered. The results indicate that Shannon entropy can be used as an algorithm indicator of diversity and convergence.

Abstract
Purpose - The purpose of this paper is to explore the concept of value co-creation in complex value networks with many actors. Electronic health records (EHRs) are innovations that warrant deep study to properly introduce such a complex system. Design/methodology/approach - The paper describes a qualitative study based on Grounded Theory to understand value co-creation from multiple actors' perspectives in a National EHR Service Project: the Portuguese Health Data Platform. Findings - Study results enabled further development of the value co-creation concept in complex environments with multiple actors. More specifically they allowed: operationalizing the value co-creation concept by identifying its factors and outcomes, understanding how value co-creation factors and outcomes are interconnected, and understanding of how value co-creation for each actor depends on his/her own actions and the actions of other actors, in a complex set of interactions and interdependencies. Practical implications - The findings have implications for service managers seeking to understand how actors participating in the network integrate resources and interact to co-create value. The study highlights the need for designing and managing services to co-create value, not only by enabling dyadic interactions between the customer and the service provider, but also by supporting and enabling value co-creation interactions among different actors in the network. Originality/value - This study responds to the need for empirical research on value co-creation in many-to-many contexts and for operationalizing the value co-creation concept.

Abstract

Abstract
Many animal species, during their life cycles, can select specific food elements that meet the special and unique metabolic needs of crucial stages such as growth, gonad maturation or larvae production and brooding. The objective of this study was to analyze the seasonal phytoplankton composition in the stomach contents of the freshwater mussel Anodonta cygnea in order to determine whether it was capable of selecting food seasonally and which were its preferences. Specimens and water samples were collected monthly from Barrinha de Mira lagoon in the northwest of Portugal during one year. From the microalgae composition found in the water and stomach content samples, Chlorophyta presented a clear predominance, followed by Cryptophyta and Bacillariophyta in water samples, and Bacillariophyta and Cyanobacteria in stomach contents. Although mussels ingested algae in a pattern very similar to its abundance in the natural habitat, in some periods specific groups were preferred even if they were present in very low concentrations in the environment. Thus, these animals are capable of selecting food by its specific characteristics and this selectivity may be associated with its physiological cycle, mainly with the reproductive cycle. Namely, the large relative abundance (ratio stomach/environment) peak of blue green algae that co-occur with gamete development covering two other smaller peaks: one of Bacillariophyta that co-occurs with gametogenesis restart and the other of Chlorophyta at the end. In addition, a significant peak of Cryptophyta co-occurs with growth and glochidia brooding periods.

Abstract
The standard continuous glucose monitoring (CGM) output provides multiple graphical and numerical summaries. A useful adjunct would be a visualization tool that facilitates immediate assessment of both long- and short-term variability. We developed an algorithm based on the mathematical method of delay maps to display CGM signals in which the glucose value at time ti is plotted against its value at time ti+1. The data points are then color-coded based on their frequency of occurrence (density). Examples of this new visualization tool, along with the accompanying time series, are presented for selected patients with type 2 diabetes and non-diabetic controls over the age of 70 years. The method reveals differences in the structure of the glucose variability between subjects with a similar range of glucose values. We also observe that patients with comparable hemoglobin A1c (HbA1c) values may have very different delay maps, consistent with marked differences in the dynamics of glucose control. These differences are not accounted by the amplitude of the fluctuations. Furthermore, the delay maps allow for rapid recognition of hypo- and hyperglycemic periods over the full duration of monitoring or any subinterval. The glucose-at-a-glance visualization tool, based on colorized delay maps, provides a way to quickly assess the complex data acquired by CGM systems. This method yields dynamical information not contained in single summary statistics, such as HbA1c values, and may also serve as the basis for developing novel metrics of glycemic control. © 2014 Diabetes Technology Society.