Abstract
There are many factors that influence the success of the management of Customer Relationship Management systems projects (CRM). This article presents a systematic review of the literature of the past fifteen years, identifying and discussing the key success factors in CRM project management. The identified success factors were structured into four categories: strategic factors; operating factors; organizational factors; technological factors. The obtained results allow a better understanding of the success factors for the implementation of CRM projects and provide a theoretical basis for further work focused on the evaluation of such projects.

Abstract
We propose a deep learning-based pipeline that, given a low-dose computed tomography of a patient chest, recommends if a patient should be submitted to further lung cancer assessment. The algorithm is composed of a nodule detection block that uses the object detection framework YOLOv2, followed by a U-Net based segmentation. The found structures of interest are then characterized in terms of diameter and texture to produce a final referral recommendation according to the National Lung Screen Trial (NLST) criteria. Our method is trained using the public LUNA16 and LIDC-IDRI datasets and tested on an independent dataset composed of 500 scans from the Kaggle DSB 2017 challenge. The proposed system achieves a patient-wise recall of 89% while providing an explanation to the referral decision and thus may serve as a second opinion tool to speed-up and improve lung cancer screening.

Abstract
This paper proposes a new methodology to automatically segment and measure the volume of pulmonary nodules in lung computed tomography (CT) scans. Estimating the malignancy likelihood of a pulmonary nodule based on lesion characteristics motivated the development of an unsupervised pulmonary nodule segmentation and volume measurement as a preliminary stage for pulmonary nodule characterization. The idea is to optimally cluster a set of feature vectors composed by intensity and shape-related features in a given feature data space extracted from a pre-detected nodule. For that purpose, a metaheuristic search based on evolutionary computation is used for clustering the corresponding feature vectors. The proposed method is simple, unsupervised and is able to segment different types of nodules in terms of location and texture without the need for any manual annotation. We validate the proposed segmentation and volume measurement on the Lung Image Database Consortium and Image Database Resource Initiative - LIDC-IDRI dataset. The first dataset is a group of 705 solid and sub-solid (assessed as part-solid and non-solid) nodules located in different regions of the lungs, and the second, more challenging, is a group of 59 sub-solid nodules. The average Dice scores of 82.35% and 71.05% for the two datasets show the good performance of the segmentation proposal. Comparisons with previous state-of-the-art techniques also show acceptable and comparable segmentation results. The volumes of the segmented nodules are measured via ellipsoid approximation. The correlation and statistical significance between the measured volumes of the segmented nodules and the ground-truth are obtained by Pearson correlation coefficient value, obtaining an R-value >= 92.16% with a significance level of 5%.

Abstract
Lung cancer is one of the most common causes of death in the world. The early detection of lung nodules allows an appropriate follow-up, timely treatment and potentially can avoid greater damage in the patient health. The texture is one of the nodule characteristics that is correlated with the malignancy. We developed convolutional neural network architectures to classify automatically the texture of nodules into the non-solid, part-solid and solid classes. The different architectures were tested to determine if the context, the number of slices considered as input and the relation between slices influence on the texture classification performance. The architecture that obtained better performance took into account different scales, different rotations and the context of the nodule, obtaining an accuracy of 0.833 ± 0.041. © Springer Nature Switzerland AG 2019.

Abstract
Endoscopic capsules are vitamin-sized devices that create 8 to 10 hour videos of the digestive tract. They are the leading diagnosing method for the small bowel, a region not easily accessible with traditional endoscopy techniques. However, these capsules do not provide localization information, even though it is crucial for the diagnosis, follow-ups and surgical interventions. Currently, the capsule localization is either estimated based on scarce gastrointestinal tract landmarks or given by additional hardware that causes discomfort to the patient and represents a cost increase. Current software methods show great potential, but still need to improve in order to overcome their limitations. In this work, a visual odometry method for capsule localization inside the small bowel is proposed.

Abstract
The characterization of water and energy consumptions is essential in order to define strategies for their rational use. The way these resources are used in households is the path for efficient and rational management, interdependent from each other. It is believed that there are significant differences between the patterns of water and energy consumption in rural and urban areas, where influencing factors should also be identified. This article aims to provide some preliminary results of a research project named ENERWAT, with the main goal to characterize the relation between water and energy consumption at the end use level for urban and rural environments. One of the goals of the aforementioned project was the design, application, and results analysis of a survey, in order to find the main differences in the water and energy consumptions at the end use level and the factors that influence it in urban and rural households. A total of 245 households participated in the research during 2016 (110 urban dwellings and 135 rural), responding to questions on their family composition, dwellings characterization, water and energy consumption habits, and conservation behaviors of these resources. The project also includes the instrumentation and monitoring of dwellings in rural and urban environments to quantify the water consumption and related energy consumption. This stage is still in progress and includes in situ measurements of nine different households (four in rural and five in urban environments) during at least one year. In this article, some of the results obtained by the survey application and the in situ measurements are presented. Despite the large number of data and the associated complexity, it can be concluded that the joint analysis of the results allows identification of a connection between water and energy consumption, as well as a household’s consumption patterns.