Abstract
This paper aims to identity the significant impact factors (IFs) of the residential electricity consumption level (RECL) and to better understand the influence mechanism of IFs on RECL. The analysis of influence mechanism is commonly through regression model where feature selection must first be performed to pick out non-redundant IFs that is highly correlated with RECL. In contrast to the existing studies, this study recognizes the problem that majority feature selection methods (e.g., step regression) are limited to the identification of linear relationships and proposes a novel wrapper feature selection (WFS) method to address this issue. The WFS is based on genetic algorithm (GA) and multinomial logistic regression (MLR). GA is a searching algorithm used to generate different feature subsets (FSs) that consist of several IFs. MLR is a modeling algorithm used to score these FSs. Further, maximal information coefficient (MIC) is utilized to verify the validity of WFS for selecting IFs. Finally, MLR based explanatory model is established to excavate the relationship between selected IFs and RECL. The results of Ireland dataset based case study show that WFS can identify the significant and non-redundant IFs that are linearly or nonlinearly related to RECL. The details about how selected IFs affect RECL are also provided via the explanatory model. Such research can provide useful guidance for a wide range of stakeholders including local governments, electric power companies, and individual households.

Abstract
Internationalization involves an active behavior to compete in international markets. Several theories, as well as several entry modes, have been developed to explain why and how firms compete internationally. Nevertheless, the internationalization process is difficult to implement as it involves not only historical reasons, as well as traditional strategies that sometimes are usually not questioned by the firm. This chapter aims to depict a case study in which a firm is trying to deploy an opportunity-driven internationalization, shifting its traditional modus operandi. The firm is analyzed based on several theories, namely the Uppsala model, the network-based theory, the born globals and the born again globals, and it is possible to conclude that despite its more than 80 years of existence, neither of the four theories can be properly used to explain the firm's international behavior. Moreover, to embrace international challenges, the firm needs to reposition its traditional business behavior.

Abstract
This paper investigates the coordination problem of overcurrent relays (OCRs) in presence of wind power generation and electrical energy storage (EES) systems. As the injected short-circuit current of inverter-based devices connected to the electrical grid is a function of the power electronic withstand capacity, the short-circuit level would be limited for these types of devices. Furthermore, since the short-circuit current is a function of the pre-fault current, it is highly needed to take different conditions into account to accurately evaluate the injected current by such devices. This would mainly matter for the EES system operating in either charging or discharging modes, as well. This paper evaluates different operation strategies considering the variations of the load demand and the presence of large-scale wind farms as well as an EES system, while validating the suggested method for coordinating the directional OCRs (DOCRs). © 2018 IEEE.

Abstract

Abstract
The burst of social networks and the possibility of being continuously connected has provided a fast way for information diffusion. More specifically, real-time posting allowed news and events to be reported quicker through social networks than traditional news media. However, the massive data that is daily available makes newsworthy information a needle in a haystack. Therefore, our goal is to build models that can detect journalistic relevance automatically in social networks. In order to do it, it is essential to establish a ground truth with a large number of entries that can provide a suitable basis for the learning algorithms due to the difficulty inherent to the ambiguity and wide scope associated with the concept of relevance. In this paper, we propose and compare two different methodologies to annotate posts regarding their relevance: automatic and human annotation. Preliminary results show that supervised models trained with the automatic annotation methodology tend to perform better than using human annotation in a test dataset labeled by experts.

Abstract
The ambition to create start-ups with exponential valuations is the goal of many entrepreneurs. How may one create the so-called unicorns (start-ups with a valuation in excess of one billion US dollars)? Our study involved interviews and a focus group and a theoretical model was created. Business plans are surprisingly seen to be less important than is normally perceived, as is a higher education degree. The research also points to variables such as one’s DNA, experience, implementation capacity, intrinsic motivation, vision and timing, and the ability to seek feedback and learn from mistakes and not just from success. © 2018, Springer International Publishing AG, part of Springer Nature.