Abstract
One of the challenges in a kidney exchange program (KEP) is to choose policies that ensure an effective and fair management of all participating patients. In order to understand the implications of different policies of patient allocation and pool management, decision makers should be supported by a simulation tool capable of tackling realistic exchange pools and modeling their dynamic behavior. In this paper, we propose a KEP simulator that takes into consideration the wide typology of actors found in practice (incompatible pairs, altruistic donors, and compatible pairs) and handles different matching policies. Additionally, it includes the possibility of evaluating the impact of positive crossmatch of a selected transplant, and of dropouts, in a dynamic environment. Results are compared to those obtained with a complete information model, with knowledge of future events, which provides an upper bound to the objective values. Final results show that shorter time intervals between matches lead to higher number of effective transplants and to shorter waiting times for patients. Furthermore, the inclusion of compatible pairs is essential to match pairs of specific patient-donor blood type. In particular, O-blood type patients benefit greatly from this inclusion.

Abstract
This paper presents an orthogonal implementation for power system state estimators based on the Maximum Correntropy Criterion (MCC). The proposed approach leads to a numerically robust estimator which exhibits self -healing properties, in the sense that gross errors in analog measurements are automatically rejected. As a consequence, robust estimates are produced without the need of running the state estimator again after bad data identification and removal. Numerical robustness is achieved by means of a specialized orthogonal algorithm based on fast Givens Rotations, which is able to handle the dynamic measurement weighting mechanism implied by the Parzen window concept associated to MCC. Results for a 3 -bus test system are presented to properly illustrate the Correntropy principles, and several case studies conducted on the IEEE 30 -bus and 57 -bus benchmark systems are used to validate the proposed methodology.

Abstract
Software’s structure profoundly affects its development and maintenance costs. Poor software’s structure may lead to well-known design flaws, such as large modules or long methods. A possible ap- proach to reduce a module’s complexity is the Extract Method refactor- ing technique. This technique allows the decomposition of a large and complex method into smaller and simpler ones, while reducing the orig- inal method’s size and improving its readability and comprehension. The OutSystems platform is a low-code platform that allows the de- velopment of web and mobile applications that rely on a set of visual Domain-Specific Languages (DSLs). Even low-code languages when im- properly used can lead to software that has maintenance issues like long methods. Thus, the purpose of this paper is to present the research and devel- opment done to provide the OutSystems platform with a tool that au- tomatically suggests Extract Method refactoring opportunities. The re- search combines program slicing techniques with code complexity metrics to calculate the best refactoring opportunities that preserve programs’ functionality. The proposed approach was tested on typical OutSystems apps and was shown to be able to reduce the overall applications’ complexity.

Abstract

Abstract
The main purpose of this work was to assess the (i) short-term effect of the main nitrification and denitrification variables on the nitrogen's biological removal via nitrite from highstrength leachates, and (ii) the effect of the presence/absence of nitrites/nitrates in a downstream photo-oxidation process. The biological reaction rates were evaluated as a function of several parameters: (i) temperature, dissolved oxygen (DO) concentration and pH, on the nitrification; and (ii) pH, temperature and the addition of phosphate ions, on the denitrification. At the beginning of most nitrification assays, it was verified that the ammonia stripping occurred simultaneously to the nitrification, reaching up to 31% removal of total dissolved nitrogen. The maximum nitrification rate obtained was 37 +/- 2 mg NH4+-N/(h.g VSS) (25 degrees C, 1.02.0 mg O-2/L, pH not controlled), consuming 5.3 +/- 0.4 mg CaCO3/mg NH4+-N. The highest denitrification rate achieved was 27 +/- 1 mg NO2--N/(h.g VSS) (pH between 7.5 and 8.0, 30 degrees C, adding 30 mg PO43-/L), with a C/N consumption ratio of 1.6 +/- 0.1 mg CH3OH/mg. NO2--N and an overall alkalinity production of 3.2 +/- 0.1 mg CaCO3/mg NO2--N. The denitrification process showed to be sensitive to all studied parameters, while the nitrification reaction did not suffered significant change when DO content was changed. The two most abundant bacterial groups in the nitrification and denitrification processes, as indicated by the 454-pyrosequencing analysis of the 16S rRNA gene, were affiliated to Saprospiraceae/Nitrosomonadaceae and Hyphomicrobiaceae/Saprospiraceae, respectively. The abundance of Nitrosomonadaceae and Hyphomicrobiaceae (in particular, Hyphomicrobium) in the nitrification and denitrification process, respectively, is in agreement with the nitrifying and denitrifying activity of these bacterial members. The photo-Fenton reaction rate was assessed considering the presence of nitrites and nitrates and the absence of both in a leachate after biological oxidation and coagulation/sedimentation steps. The results showed that for a pre-treated leachate without nitrogen, the DOC degradation rate decreased 28%, while for a bio-treated leachate containing nitrites, the H2O2 consumption was 2.4 times higher.

Abstract
This paper proposes a long-term framework for generation expansion and transmission expansion planning taking into account the renewable energy integration. To solve the problem, a hybrid technique is used. The mechanism of this technique is based on decomposing the original problem into master and slave subproblems where the master subproblem is solved using a heuristic optimization algorithm and slave subproblems are solved using general algebraic modeling system, which is a well-known software with powerful mathematical solvers. The proposed heuristic algorithm is a combination of the intelligent particle swarm optimization and chaotic searching technique. Finally, the proposed model is simulated using 3 case studies including 6-bus Garver test system, IEEE 24-bus, and modified IEEE 118-bus test systems to validate the effectiveness of the long-term planning framework while the simulation results are compared to those obtained from classic genetic algorithm (GA-Classic) and classic particle swarm optimization (PSO-Classic) to verify the efficiency of the technique used in this paper. Copyright © 2017 John Wiley & Sons, Ltd.