Abstract
Artificial intelligence techniques have been applied to diverse business and governmental areas, in order to take advantage of the huge amount of information that is generated within specific organizations or institutions. Business intelligence can be seen as the process of converting such information into actionable knowledge, which is the basis for data-driven decision making. With this in mind, this work is framed in a project that seeks to improve the activity of the Portuguese Food and Economic Safety Authority, regarding prevention in the areas of food safety and economic enforcement. More specifically, this paper focuses on the generation and optimization of flexible inspection routes. An optimal inspection route seeks to maximize the number of targeted Economic Operators, or the utility gained from the set of Economic Operators that are actually inspected. For that, each Economic Operator is assigned an inspection utility value. The problem was then modelled as a Multi-Depot Periodic Vehicle Routing Problem with Time Windows, and solved using both exact and meta-heuristic methods. The comparison of the meta-heuristic algorithms showed a versatile Hill Climbing implementation in different test cases that explored the effect of the Economic Operators dispersion and density.

Abstract
The beginning of an innovation process, also known as "front-end of innovation" (FEI), counts as an essential contributor to the successful development of new products and for their market appeal. Nevertheless, while helpful procedures and techniques for developing new products are well-known and widely applied, FEI is still an understudied area, and models for managing it are not yet commonly used in technology-oriented companies. FEI, also known as "fuzzy front end", can even be "fuzzier" in not-for-profit research centers. That is because the focus of these centers is advancing of scientific knowledge, rather than commercializing the results of those activities. This study summarizes the insights from a literature review on the topic of "project portfolio management" (PPM) in relation to innovation and, more specifically, with FEI and its components of ideation, innovation management, innovation strategy, foresight, and incremental or radical innovation. The authors selected and reviewed content from 170 papers published in SCOPUS prior to February 2019. The discussion uses a theoretical framework called "Front-End of Innovation Integrative Ontology (FEI2O)" to assist in framing the discussion. © 2020, SPb RAACI.

Abstract
Internet-of-Things systems are comprised of highly heterogeneous architectures, where different protocols, application stacks, integration services, and orchestration engines co-exist. As they permeate our everyday lives, more of them become safety-critical, increasing the need for making them testable and fault-tolerant, with minimal human intervention. In this paper, we present a set of self-healing extensions for Node-RED, a popular visual programming solution for IoT systems. These extensions add runtime verification mechanisms and self-healing capabilities via new reusable nodes, some of them leveraging meta-programming techniques. With them, we were able to implement self-modification of flows, empowering the system with self-monitoring and self-testing capabilities, that search for malfunctions, and take subsequent actions towards the maintenance of health and recovery. We tested these mechanisms on a set of scenarios using a live physical setup that we called SmartLab. Our results indicate that this approach can improve a system’s reliability and dependability, both by being able to detect failing conditions, as well as reacting to them by self-modifying flows, or triggering countermeasures. © Springer Nature Switzerland AG 2020.

Abstract
In this letter, we demonstrate a mapper that enables all waveforms of OFDM with Index Modulation (OFDM-IM) while preserving polynomial time and space computational complexities. Enabling all OFDM-IM waveforms maximizes the spectral efficiency (SE) gain over the classic OFDM but, as far as we know, the computational overhead of the resulting mapper remains conjectured as prohibitive across the OFDM-IM literature. We show that the largest number of binomial coefficient calculations performed by the original OFDM-IM mapper is polynomial on the number of subcarriers, even under the setup that maximizes the SE gain over OFDM. Also, such coefficients match the entries of the so-called Pascal's triangle (PT). Thus, by assisting the OFDM-IM mapper with a PT table, we show that the maximum SE gain over OFDM can be achieved under polynomial (rather than exponential) time and space complexities.

Abstract
Although considerable research has been devoted to the study of the effect of entrepreneurship on economic growth, fewer studies have analyzed the effect of the types (opportunity vs necessity) of entrepreneurship on economic growth. Moreover, the latter set of studies overlooked the relevance of human capital as a mediating factor in the relation between (types of) entrepreneurship and economic growth. The aim of the present study is to fill in this gap by assessing the extent to which the direct and indirect effect of (the types of) entrepreneurship, via human capital, matters for countries' economic growth. In methodological terms, we resort to fixed effects panel data estimations, involving a large set of (OECD and non-OECD) countries, over a relatively long time span (1990-2016). The results suggest total entrepreneurship has a positive effect on economic growth. Distinguishing between types of entrepreneurship, there is clear evidence that OE fosters economic growth, whereas necessity entrepreneurship inhibits it. Interestingly, human capital tends to mitigate the negative effect of necessity entrepreneurship on economic growth. In the case of opportunity entrepreneurship, the direct positive effect observed is reduced in contexts characterized by high levels of human capital, which might reflect increased opportunity costs.

Abstract
This paper presents a scholastic model for energy management of smart microgrids with demand response (DR) considering demand and supply uncertainties. In this model, responsive loads are modeled by using price elasticity of demand concept and with the aim of minimizing the consumption cost of customers. In the proposed program, the operator tries to schedule energy resources optimally by considering islanding events of microgrids, that may occur due to upstream network disturbances. Therefore, in addition to uncertainties caused by load forecasting, renewable power generation and electricity prices, uncertainties caused by the prediction error of microgrid islanding events are also considered. The proposed approach is implemented using existing commercial software on a typical microgrid and the effects of DR programs on system operation via sensitive analyses. The results show that, when customers participate in DR programs, the amount of mandatory load shedding at islanding duration of microgrid reduces, and the expected profit of operator increases.