From Brussels to Belgrade: Challenges in Conducting Research and Constructing Explanations of the Collapse of Yugoslavia

While acknowledging that it is important to examine events within their appropriate context, this article is interested in the capacity of qualitative research methods to assist us so that we can get a more accurate picture of European Community involvement in the Yugoslav federation and the decisions that terminated its existence. More precisely, the article is concerned with the extent to which archival collections and interviews with state as well as nonstate actors can shape our ideas and consequent explanations of the Yugoslav state crisis. In addition, the last section elaborates on a number of challenges one may encounter while being on such a demanding research journey. As suggested by the concluding remarks, new interpretations, apart from managing to satisfy the researcher’s own ambition to complement the existing scholarship, should also serve to encourage fresh questions and answers

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

ASSESSMENT OF VARIOUS REMOTE SENSING TECHNOLOGIES IN BIOMASS AND NITROGEN CONTENT ESTIMATION USING AN AGRICULTURAL TEST FIELD

Multispectral and hyperspectral imaging is usually acquired by satellite and aircraft platforms. Recently, miniaturized hyperspectral 2D frame cameras have showed great potential to precise agriculture estimations and they are feasible to combine with lightweight platforms, such as drones. Drone platform is a flexible tool for remote sensing applications with environment and agriculture. The assessment and comparison of different platforms such as satellite, aircraft and drones with different sensors, such as hyperspectral and RGB cameras is an important task in order to understand the potential of the data provided by these equipment and to select the most appropriate according to the user applications and requirements. In this context, open and permanent test fields are very significant and helpful experimental environment, since they provide a comparative data for different platforms, sensors and users, allowing multi-temporal analyses as well. Objective of this work was to investigate the feasibility of an open permanent test field in context of precision agriculture. Satellite (Sentinel-2), aircraft and drones with hyperspectral and RGB cameras were assessed in this study to estimate biomass, using linear regression models and in-situ samples. Spectral data and 3D information were used and compared in different combinations to investigate the quality of the models. The biomass estimation accuracies using linear regression models were better than 90 % for the drone based datasets. The results showed that the use of spectral and 3D features together improved the estimation model. However, estimation of nitrogen content was less accurate with the evaluated remote sensing sensors. The open and permanent test field showed to be suitable to provide an accurate and reliable reference data for the commercial users and farmers