Abstract
Recent years have witnessed an increased interest, by competition agencies, in assessing the competitive effects of partial acquisitions. We propose a generalization of the two most traditional indicators used to screen unilateral anti-competitive effects - the HerfindahlHirschman Index and the Gross Up- ward Price Pressure Index - to partial horizontal acquisition settings. The proposed generalized indicators are endogenously derived under a probabilistic voting model in which the manager of each firm is elected in a shareholder assembly between two potential candidates who seek to obtain utility from an exogenous rent associated with corporate office. The model (i) can cope with settings involving all types of owners and rights: owners that can be internal to the industry (rival firms) and external to the industry; and rights that can capture financial and corporate control interests, can be direct and indirect, can be partial or full, (ii) yields an endogenous measure of the owners ultimate corporate control rights, and (iii) can also be used - in case the potential acquisition is inferred to likely enhance market power - to devise divestiture structural remedies. We also provide an empirical application of the two proposed generalized indicators to several acquisitions in the wet shaving industry, with the objective of providing practitioners with a step-by-step illustration of how to compute them in antitrust cases.

Abstract

Abstract
This work addresses the well-known capacitated lot sizing problem (CLSP) which is proven to be an NP-hard optimization problem. Simple period-by-period heuristics are popular solution approaches due to the extremely low computational effort and their suitability for rolling planning horizons. The aim of this work is to apply genetic programming (GP) to automatically generate specialized heuristics specific to the instance class. Experiments show that we are able to obtain better solutions when using GP evolved lot sizing rules compared to state-of-the-art constructive heuristics.

Abstract
In a higher education institution, professors and researchers may have to perform several distinct roles and activities such as teaching, researching, management, and community outreach. The performance evaluation of these professionals must consider a wide range of information, which is used in a complex evaluation process. This work reports the development and introduction of an information system to digitally manage the complete performance evaluation process in a higher education institution (HEI), including the collection, process, and report of information. The ultimate objective of the project was to electronically support the evaluation process, which was attained by collecting and combining data from several sources and later processed. This approach rendered a viable evaluation process that otherwise would be painful to implement. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
The use of Nanostructured SPR sensors on Plastic Optical Fibers opens new challenges, because in an SPR sensor made by a continuous metal layer, the sensor's response is basically related to the metal properties at optical frequencies and to the waveguide characteristics. On the other hand, when a Nanostructured SPR sensor is used, the behavior is also related to the geometric parameters of the Nanostructures. Working on them it is potentially possible to tune the sensor's behavior. In this work the Authors present a numerical investigation in order to evaluate the behavior of two different SPR Nanostructured platforms, made by "long" gold Nanorods, and comparing them to an SPR sensor with a continuous gold layer. The difference between these two Nanostructured platforms is the orientation of the Nanorods, with respect to the light's propagation direction. The numerical results seem to indicate an increase of the sensitivity, when an SPR Sensor with long Nanorods is used, with respect to the sensor made by a continuous gold film, with some benefits.

Abstract
Linear parameter varying models (LPV) have proven to be effective to describe non-linearities and time-varying behaviors. In this work, a new non-parametric estimation algorithm for state-space LPV models based on support vector machines is presented. This technique allows the functional dependence between the model coefficients and the scheduling signal to be "learned" from the input and output data. The proposed algorithm is formulated in the context of instrumental (IV) estimators, in order to obtain consistent estimates for general noise conditions. The method is based on a canonical state space representation and admits a predictor form that has shown to be suitable for system identification, as it leads to a convenient regression form. In addition, this predictor has an inherent filtering feature. In the context of vector support machines, such filtering mechanism leads to two-dimensional data processing, which can be used to decrease the variance of estimates due to noisy data. The performance of the proposed approach is evaluated from simulated data subject to different noise scenarios. The technique was able to reduce the error due to the variance of the estimator in most of the analyzed scenarios.