Abstract
Despite connections and common traits between geopolitics and international business based on geography and location, literature on this matter is somewhat scarce. This chapter aims to contribute to this literature gap. Using the Russian Federation as a case study and by framing its geopolitical situation, it seeks to answer the following question: What is the importance of geopolitical factors in international location decisions? Applying a hybrid methodology which combines qualitative and quantitative analyses, the chapter concludes though Russia has an innately favorable geopolitical situation, its full potential is not being exploited, remaining latent and underutilized. Additionally, the pragmatic standing of Russia's foreign policy, the permanence of some structures recalling the USSR and the persistence of corruption, and an unsteady business environment place constraints on improving the open market and raises obstacles to FDI.

Abstract
The early classification of university students according to their potential academic performance can be a useful strategy to mitigate failure, to promote the achievement of better results and to better manage resources in higher education institutions. This paper proposes a two-stage model, supported by data mining techniques, that uses the information available at the end of the first year of students' academic career (path) to predict their overall academic performance. Unlike most literature on educational data mining, academic success is inferred from both the average grade achieved and the time taken to conclude the degree. Furthermore, this study proposes to segment students based on the dichotomy between the evidence of failure or high performance at the beginning of the degree program, and the students' performance levels predicted by the model. A data set of 2459 students, spanning the years from 2003 to 2015, from a European Engineering School of a public research University, is used to validate the proposed methodology. The empirical results demonstrate the ability of the proposed model to predict the students' performance level with an accuracy above 95%, in an early stage of the students' academic path. It is found that random forests are superior to the other classification techniques that were considered (decision trees, support vector machines, naive Bayes, bagged trees and boosted trees). Together with the prediction model, the suggested segmentation framework represents a useful tool to delineate the optimum strategies to apply, in order to promote higher performance levels and mitigate academic failure, overall increasing the quality of the academic experience provided by a higher education institution.

Abstract
Rheumatic heart disease is the serious consequence of repeated episodes of acute rheumatic fever. It is the major cause of heart valve damage resulting in morbidity and mortality. Its early detection is considered vital to control the disease's progression. The key manifestations that are visible in the early stages of this disease are changes in the thickness, shape and mobility of the mitral valve leaflets. Echocardiography based screening is sensitive enough to identify these changes in early stages of the disease. In this work, an automatic approach is proposed to measure, quantify and analyze the thickness of the anterior mitral leaflet, in an echocardiographic video. The shape of the anterior mitral leaflet is simplified via morphological skeletonization and spline modelling to get the central line of the leaflet. To analyze the overall thickness from the tip to its base, the anterior mitral leaflet is divided into four quartiles. In ach quartile the thickness is measured as the length of the line segment resulting from the intersection of the contour with the normal direction of the central point of each quartile. Finally, the thickness is analyzed by measuring the variance per quartile, divided by leaflet position (open, straight and closed). The comparison between the normal and pathological leaflets are also presented, exhibiting statistical significant differences in all quartiles, especially near the tip of the leaflet.

Abstract
In a global market characterized by strong competition and quickly changing boundary conditions, flexible and reconfigurable production systems can rapidly react to both endogenous and exogenous drivers. To this extent, it is necessary to define a new production system model, which can combine the most significant key business factors (KBFs), in order to meet the specified objectives and the relevant KPIs and to control the system. The model can be used within a cyber-physical system, to properly support the different functions and take the right decisions through simulation ICT tools. This research task is part of PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery), a European funded project, which aims at developing an innovative manufacturing system based on a new agile concept introducing the implementation of methods, methodologies and strategies for transforming existing production systems into plug-and-produce production ones based on Cyber-Physical Systems technologies. In particular, this paper aims at describing the relationships among the KBFs (Key Business Factors), namely the drivers of the production system, and the relevant KPIs. The model has been validated through an industrial use case, in order to gain important information about constraints and opportunities for improvement in other contexts. © 2017 IEEE.

Abstract
Probiotics benefits in fish farming have been usually inferred appraising the effects observed on the host and not through the direct assessment of probiotic dynamics in the host gut microbiota. To overcome this gap, quantitative PCR (qPCR) can be a powerful approach to study the bacterial dynamics in fish gut microbiota. The presented work proposes four B. licheniformis-specific DNA markers and details a qPCR method to track putative probiotics B. licheniformis on fish gut. The four B. licheniformis-specific DNA markers - BL5B (hypothetical protein BL00303), BL8A (serA2), BL13C (rfaB) and BL18A (ligD) - were selected and validated by PCR and multiplex-PCR with 20 B. licheniformis isolates and a broad range of non-target bacteria. To assess the dynamics of B. licheniformis in the digesta of farmed fish, a qPCR was validated using markers BL8A and BL18A and calibration curves obtained for both markers with digesta samples spiked with B. licheniformis cells showed a high correlation (R-2 > 0.99) over 6 log units (CFU/ reaction), and a limit of detection (LOD) as low as 247 CFUs/reaction. Furthermore, the consistent qPCR repeatability and reproducibility underline the specificity and reliability of the qPCR proposed. Ultimately, the possibility to monitor the dynamics of B. licheniformis probiotics in the gut microbiota of farmed fish might be instrumental to optimize best practices in aquaculture.

Abstract
In this paper, a function-based modulation control strategy for modular multilevel converters (MMCs) in a distributed generation (DG) system is proposed. Two novel modulation functions are introduced in this paper for the switching state functions of the lower and upper sub-modules of the interfaced MMC, which is considered as the main contribution of this control technique over other control methods. The amplitude and phase angle of the output current of the interfaced MMC can be easily applied to the proposed modulation functions to prepare a specific active and reactive power injection into the demand side. In addition, the equivalent capacitors of the lower and upper sub-modules are defined by taking into account the introduced modulation functions to guarantee an appropriate operation for the interfaced MMC in DG systems. Simulation results validate the capability of the proposed control method for MMCs under load variations.