Abstract
Optimization problems that are motivated by real-world settings are often complex to solve. Bridging the gap between theory and practice in this field starts by understanding the causes of complexity of each problem and measuring its impact in order to make better decisions on approaches and methods. The Job-Shop Scheduling Problem (JSSP) is a well-known complex combinatorial problem with several industrial applications. This problem is used to analyse what makes some instances difficult to solve for a commonly used solution approach - Mathematical Integer Programming (MIP) - and to compare the power of an alternative approach: Constraint Programming (CP). The causes of complexity are analysed and compared for both approaches and a measure of MIP complexity is proposed, based on the concept of load per machine. Also, the impact of problem-specific global constraints in CP modelling is analysed, making proof of the industrial practical interest of commercially available CP models for the JSSP.

Abstract
Large corpus of scientific research papers have been available for a long time. However, most of those corpus store only the title and the abstract of the paper. For some domains this information may not be enough to achieve high performance in text mining tasks. This problem has been recently reduced by the growing availability of full text scientific research papers. A full text version provides more detailed information but, on the other hand, a large amount of data needs to be processed. A priori, it is difficult to know if the extra work of the full text analysis has a significant impact in the performance of text mining tasks, or if the effect depends on the scientific domain or the specific corpus under analysis. The goal of this paper is to show a framework for full text analysis, called LearnSec, which incorporates domain specific knowledge and information about the content of the document sections to improve the classification process with propositional and relational learning. To demonstrate the usefulness of the tool, we process a scientific corpus based on OSHUMED, generating an attribute/value dataset in Weka format and a First Order Logic dataset in Inductive Logic Programming (ILP) format. Results show a successful assessment of the framework.

Abstract
Source routing (SR) minimum cost forwarding (MCF) – SRMCF – is a reactive, energy-efficient routing protocol proposed to improve the existent MCF methods utilized in heterogeneous wireless sensor networks (WSN). This paper presents an analytical analysis with experimental support that demonstrates the effectiveness of the proposed protocol. SRMCF stems from SR concepts and MCF methods exploited in ad hoc WSNs, where all unicast communications (between sensor nodes and the base station, or vice versa) use minimum cost paths. The protocol utilized in the present work was updated and now also handles link and node failures. Theoretical analysis and simulations show that the final protocol exhibits better throughput and energy consumption than MCF. Memory requirements for the routing table in the base station are also analyzed. Experimental results in a real scenario were obtained for implementations of both protocols, MCF and SRMCF, deployed in a small network of TelosB motes. Results show that SRMCF presents a 33% higher throughput and 24% less energy consumption than MCF. Extensive © 2019 River Publishers

Abstract
A patient's health literacy has a direct impact on their health, but more than a third of the USA population has "basic" or "below basic" levels of health literacy. An individual's wellbeing is also affected by the communication with their physician, as the use of technical terminology may hinder the patient's understanding. A patient's ability to, later on, recall or retrieve helpful information could reduce these comprehension problems and this can be improved by a good management of personal health information. To help overcome some of these problems, we created HealthTalks, a mobile app that empowers the patients, easing their daily health tasks and self-care ability. It does so by recording the audio of a medical appointment, transcribing its dialogue, giving more information about medical concepts employed, and allowing information associated with medical appointments to be easily managed by the patient. Usability tests were conducted with elderly people, ranging from the icons used to the general user experience. Results were very positive, with users accomplishing most tasks successfully and often with the least amount of clicks. We also evaluated the speech recognition software used, Google Cloud Speech API, reaching an error rate of 12 percent in medical texts.

Abstract
The personal characteristics of entrepreneurs can be importantly related to entrepreneurial startup intentions and behaviors. A country-moderated hypothesis including the relationship between an individual's risk-taking propensity and entrepreneurship (behaviors or intentions of the person) was conceptually developed and empirically tested in this study. The data collection was performed through a structured questionnaire. Multinominal logistic regression was used for analyzing data obtained from 1,414 students in six countries. The crucial contribution of this research is the clarification of the character of risk-taking propensity in entrepreneurship and the indication that the risk-taking propensity-entrepreneurship relationship can be moderated contingent on power distance.

Abstract
Detection of Patient Zero is an increasing concern in a world where fast international transports makes pandemia a Public Health issue and a social fear, in cases such as Ebola or H5N1. The development of a medical social network and data visualization information system, which would work as an interface between the patient medical data and geographical and/or social connections, could be an interesting solution, as it would allow to quickly evaluate not only individuals at risk but also the prospective geographical areas for imminent contagion. In this work we propose an ideal model, and contrast it with the status quo of present medical social networks, within the context of medical data visualization. From recent publications, it is clear that our model converges with the identified aspects of prospective medical networks, though data protection is a key concern and implementation would have to seriously consider it.