Abstract
Guerrilla marketing is an innovative approach to communicate with customers and to capture their attention essentially due to its inherent creativity, unconventional media, and low cost. Despite the interesting contributions in the literature on this topic, most of what is known about guerrilla marketing is confined to its use and impact on consumers. This study aims to fill a gap identified in the guerrilla marketing literature by conducting an exploratory research study on the propensity of performing guerrilla marketing campaigns in a Business-to-Business (B2B) context. The research objectives of this paper are (i) identifying the perceptions of B2B managers on guerrilla marketing campaigns and (ii) exploring determinants of the adoption of guerrilla marketing campaigns targeted at corporate customers.We present the results of a qualitative research study comprising 12 semi-structured interviews with managers of different business areas. A content analysis was performed using Nvivo software.Participants in this study demonstrated that B2B managers recognize and value the advantages associated with guerrilla communication, which is in many instances seen as useful and viable for the B2B sector. The propensity for adoption is dependent on internal factors such as corporate culture, managers’ and collaborators’ profiles, risk-proneness, market share, and product innovativeness, but also on the sector’s usual practices of innovation and communication. Guerrilla marketing campaigns are more appropriate for attracting new B2B customers and need to be carefully adapted to the targets' profiles and preferences.

Abstract
Data imbalance is characterized by a discrepancy in the number of examples per class of a dataset. This phenomenon is known to deteriorate the performance of classifiers, since they are less able to learn the characteristics of the less represented classes. For most imbalanced datasets, the application of sampling techniques improves the classifier's performance. For small datasets, oversampling has been shown to be the most appropriate strategy since it augments the original set of samples. Although several oversampling strategies have been proposed and tested over the years, the work has mostly focused on binary or multi-class tasks. Motivated by medical applications, where there is often an order associated with the classes (increasing likelihood of malignancy, for instance), the present work tests some existing oversampling techniques in ordinal contexts. Moreover, four new oversampling techniques are proposed. Experiments were made both on private and public datasets. Private datasets concern the assessment of response to treatment on oncologic diseases. The 15 public datasets were chosen since they are widely used in the literature. Results show that data balance techniques improve classification results on ordinal imbalanced datasets, even when these techniques are not specifically designed for ordinal problems. With our pipeline, better or equal to published results were obtained for 10 out of the 15 public datasets with improvements upon a decrease of 0.43 on MMAE.

Abstract
Case-based reasoning enables solving new problems using past experience, by reusing solutions for past problems. The simplicity of this technique has made it very popular in several domains. However, the use of this type of approach to support decisions in the power and energy domain is still rather unexplored, especially regarding the flexibility of consumption in buildings in response to recent environmental concerns and consequent governmental policies that envisage the increase of energy efficiency. In order to determine the amount of consumption reduction that should be applied in a building, this article proposes a methodology that adapts the past results of similar cases in order to achieve a decision for the new case. A clustering methodology is used to identify the most similar previous cases, and an expert system is developed to refine the final solution after the combination of the similar cases results. The proposed CBR methodology is evaluated using a set of real data from a residential building. Results prove the advantages of the proposed methodology, demonstrating its applicability to enhance house energy management systems by determining the amount of reduction that should be applied in each moment, thus allowing such systems to carry out the reduction through the different loads of the building.

Abstract
In this work, we address through model predictive control (MPC) a constrained nonlinear plant described by a continuous-time dynamical model, which naturally leads to a sampled data control system. The numerical solution of the optimal control problems involved in MPC must utilize, eventually, some form of discretization. Nevertheless, there are several advantages in maintaining a continuous-time model until later stages. One advantage is that we can devise numerical procedures which, by exploiting additional freedom in selecting the discretization points, are more efficient when continuous-time models are used. Here, we discuss an extension to MPC of an Adaptive Mesh Refinement (AMR) algorithm, which has shown to be efficient in solving nonlinear optimal control problems. We derive a sufficient condition that guarantees that an MPC scheme using an adaptive time mesh refinement algorithm preserves stability.

Abstract
This paper investigates the performance of the largest Brazilian hydropower plants. This study covers 78% of the total installed capacity from hydros in the country, and considers indicators reflecting operational and maintenance costs as well as quality of service. The assessment was conducted using a new approach for the construction of composite indicators, based on a directional distance function model. First, we assessed the hydropower plants allowing for complete flexibility in the definition of weights, enabling the identification of underperforming plants, and quantification of their potential for improvement. Next, we assessed the plants considering different perspectives regarding the importance attributed to each indicator. This allowed reflecting different points of view, focusing primarily on operation and maintenance costs or quality issues. The results identify the hydropower plants that can be considered benchmarks in different scenarios, and allow testing the robustness of plants' classification as benchmarks in the unrestricted model.

Abstract
This work presents a technique that uses the immersion of patients in an interactive 3D virtual environment in the orthoptic treatment of strabismus. The most important part of this work is the act of forcing the eyes to cooperate, increasing the level of adaptation of the nervous system to the binocular vision, allowing the diverted eye to be rehabilitated. Returning to the patient better visual comfort and quality of life. The virtual environment, because it is attractive, has the function of entertainment, possessing as its property the ability to propose challenges directed towards specific objectives. In addition to offering real-time biological feedback to the healthcare professional who is making use of this product. Another point is that this interface has ideal approaches to be used in orthoptic treatment. And all of it was developed with free software and made by a low-cost virtual reality glasses, Google Cardboard, which uses a smartphone as a display for its display.