Abstract
Absenteeism is a major problem faced particularly by companies with a large number of employees. Therefore, the existence of absenteeism prediction tools is essential for such companies depending on intensive human-resources. This paper focuses on using machine learning technologies for predicting the absences of employees from work. More precisely, a few prediction models were tuned and tested with 241 features extracted from a population of 13.805 employees. This target population was sampled from the help desk work force of a major Brazilian phone company. The features were extracted from the profile of the help desk agents and then filtered by processes of correlation and feature selection. The selected features were then used to compare absenteeism prediction given by different classification algorithm (cf. Random Forest, Multilayer Perceptron, Support Vector Machine, Naive Bayes, XGBoost and Long Short Term Memory). The parameterization of these ML models was also studied to reach the classifier best suited for the prediction problem. Such parameterizations were tuned through the use of evolutionary algorithms, from which considerable precision was reached, the best being 72% (XGBoost) and 71% (Random Forest). © 2019, Springer Nature Switzerland AG.

Abstract
Batalha Abbey is a 14th century UNESCO world heritage site that shows signs of decay. During the last years, high resolution geophysical methods have been used to contribute to the knowledge of its construction characteristics and to an informed maintenance and rehabilitation project. Here in it is presented a multimethod high-resolution geophysical investigation of its main tower. A 3D resistivity survey was carried out on the surface around the tower to investigate the ground beneath it. A GPR survey was used on the tower walls surface to investigate its interior. Three frequencies, 250MHz, 500MHz and 800MHz, were used. Finally, a seismic tomography study was done around the tower with both geophones and sources on the tower walls to provide a 3D velocity image of the tower interior. 3D resistivity results give a clear image of the walls foundations and of the ground beneath the tower. GPR 250MHz data provide a complete GPR image across the tower, although of low resolution. Higher resolution GPR results provided clearer information on the constructive elements of the tower. Finally, the seismic tomography results gave, for the first time, a complete image of the tower interior and proved it a compact construction with no voids.

Abstract

Abstract
The dynamic behavior of urban mobility patterns makes matching taxi supply with demand as one of the biggest challenges in this industry. Recently, the increasing availability of massive broadcast GPS data has encouraged the exploration of this issue under different perspectives. One possible solution is to build a data-driven real-time taxi-dispatching recommender system. However, existing systems are based on strong assumptions such as stationary demand distributions and finite training sets, which make them inadequate for modeling the dynamic nature of the network. In this paper, we propose BRIGHT: a drift-aware supervised learning framework which aims to provide accurate predictions for short-term horizon taxi demand quantities through a creative ensemble of time series analysis methods that handle distinct types of concept drift. A large experimental set-up which includes three real-world transportation networks and a synthetic test-bed with artificially inserted concept drifts, was employed to illustrate the advantages of BRIGHT when compared to S.o.A methods for this problem.

Abstract
The following paper presents a novel strategy named Maximum Search Limitations (MS) for the Evolutionary Particle Swarm Optimization (EPSO). The approach combines EPSO standard search mechanism with a set of rules and position-wise statistics, allowing candidate solutions to carry a more thorough search around the neighborhood of the best particle found in the swarm. The union of both techniques results in an EPSO variant named MS-EPSO. MS-EPSO crucial premise is to enhance the exploration phase while maintaining the exploitation potential of EPSO. Algorithm performance is measured on eight unconstrained and two constrained engineering design optimization problems. Simulations are made and its results are compared against other techniques including the classic Particle Swarm Optimization (PSO). Lastly, results suggest that MS-EPSO can be a rival to other optimization methods. © Springer Nature Switzerland AG 2019.

Abstract
Increasing power injection of distributed energy resources (DER) (including prosumers) has been changing the way the distribution system is operated and managed. Thus, conventional network usage tariffs are no longer fair enough to distribute the network costs to the various system participants. Within this scope, this work studies innovative cost allocation models that fairly distribute fixed, network usage and power losses costs to all system participants. A three-stage model is designed, in which: (i) an alternating current optimal power flow (AC OPF) for the distribution grid is performed; (ii) two different power tracing models (namely, the Abdelkader's and Bialek's tracing methods) are implemented and compared; and (iii) the distribution of costs through a MW-mile variant. The model is tested and validated in a 33-node distribution network considering high penetration of DER. © 2019 IEEE.