Abstract
The way plastics are currently produced, used and disposed does not capture the economic benefits of amore 'circular' approach and is dramatically harming the environment. It is relevant to determine which European countries can be considered more or less efficient in the end-of-life of plastic products processes, what the sources of the inefficiencies are, and howthose less efficient countries could improve their performance towards a more circular economy. Although some countries have developed a variety of quantitative indicators, there is scarcity of adequate metrics for performance measurements. This paper estimates the efficiency of 26 European countries in the context of Circular Economy, for the period 2006-2016, considering the generation of waste, recovery and recycling of plastic, with a methodology based on theMultidirectional Efficiency Analysis. Apart from identifying the most efficient countries in the studied period, results show that efficiency increases for most countries with time, and that many countries reach the full efficiency by the end of the study period, and especially by 2016. Input analysis shows that increasing capital seems to be a main driver towards efficiency, since the other inputs are used with a similar efficiency by most countries. Output analysis suggest that the difference among countries efficiency is not in their reduction of total waste or emissions, but rather in the improvement of their economic growth in a circular way, that is, improving GDP but also the recovering and recycling activities. These results could be useful to design policies towards a more efficient and circular use of plastics.

Abstract
As we write this editorial, people around the world are apprehensive about their future; some are at home; some are thinking about the loved ones they cannot visit; some, unfortunately, are dying. We watch the graphs and listen to the daily news of new coronavirus cases, but be it just one or one thousand, for the those close of the ones affected, the impact is catastrophic. (...)

Abstract
Literature on analysis of inter-organizational networks mentions the benefits that collaboration networks can provide to firms, in terms of managerial decision-making, although rarely analysed in terms of their overall performance. This paper aims to identify the existence of common factors of evolutionary patterns in the networks that determine its performance and evolution through a Multiple Factor Analysis (MFA). Subsequently, a hierarchical clustering procedure was performed on the factors that determine these networks, trying to find similarities in the evolutionary behavior. Data were collected on twelve real collaboration networks, characterized by four variables: Operational Result, Stock of Knowledge, Operational Costs and Technological Distance. The hierarchical clustering allowed the identification and distinction of the networks with the worst and best performances, as well as the variables that characterize them, allowing to recognize poorly defined strategies in the constitution of some networks. © Springer Nature Switzerland AG 2020.

Abstract
This paper investigates the risk-oriented multi-area economic dispatch (MAED) problem with high penetration of wind farms (WFs) combined with compressed air energy storage (CAES). The main objective is to help system operators to minimize the operational cost of thermal units and CAES units with an appropriate level of security through optimized WF power generation curtailment strategy and CAES charging/discharging control. In the obtained MAED model, several WFs integrated with CAES units are considered in different generation zones, and the probability to meet demand by available spinning reserve during N - 1 security contingency is characterized as a risk function. Furthermore, the contribution of CAES units in providing the system spinning reserve is taken into account in the MAED model. The proposed framework is demonstrated by a case study using the modified IEEE 40-generator system. The numerical results reveal that the proposed method brings a significant advantage to the efficient scheduling of thermal units' power generation, WF power curtailment, and CAES charging/discharging control in the power system.

Abstract
Chance-constrained program (CCP) is a popular stochastic optimization method in power system planning, and operation problems. Conditional Value-at-Risk (CVaR) provides a convex approximation for chance constraints which are nonconvex. Although CCP assumes an exact empirical distribution, and the optimum of a stochastic programming model is thought to be sensitive in the designated probability distribution, this letter discloses that CVaR reformulation of a chance constraint is intrinsically robust. A pair of indices are proposed to quantify the maximum tolerable perturbation of the probability distribution, and can be computed from a computationally-cheap dichotomy search. An example on the coordinated capacity optimization of energy storage, and transmission line for a remote wind farm validates the main claims. The above results demonstrate that stochastic optimization methods are not necessarily vulnerable to distributional uncertainty, and justify the positive effect of the conservatism brought by the CVaR reformulation.

Abstract
Accurate 3D reconstruction and realistic visualization of cultural heritage allow experts to fine-tune their theories on the lost links in the history of civilization. Although the 3D reconstruction is a significant challenge, precisely because of the state of degradation over the years, it constitutes a crucial task for experts to study and interact with long disappeared settlements and structures. Furthermore, the public, in general, will be provided with the conditions to explore them in virtual environments, thus fostering cultural, social, and scientific participation. Highly accurate reconstruction is, nevertheless, a very complex task, where all stages of image synthesis must be carefully executed from highly detailed 3D models to obtain a faithful depiction of the object of interest. Meanwhile, the textual descriptions and geospatial data collected by archaeologists on-site may be used to overcome the absence of visual information. Still, this data will not suffice, in which case procedural modeling turns out to be essential to avoid a great deal of time and labor-consuming modeling processes. Procedural modeling tools automatically generate three-dimensional models through computational processes that extend the base information according to a specific algorithm. In order to avoid reprograming the procedural modeling systems, we use mathematical methods that operate on parametrical symbolic descriptions that, flexibly, can model different types of objects. The most used mathematical methods are fractal geometry and formal grammars, particularly L-systems and shape grammars. In this chapter, we will approach the current advances in the area of procedural modeling and how these tools can be used to generate 3D models of cultural heritage. We also explore the relevant dimension of time, extending the modeling tasks to 4D. These applications do not focus on very specific landmarks, like cathedrals or palaces, which require manual effort or image-based techniques to capture the model with a high level of visual fidelity. Instead, we focus on modeling cities and their evolutions or the surroundings of these landmarks, that allow for an increased automation of the modeling process. © 2020, Springer Nature Switzerland AG.