Abstract
Customer relationship management is critical for organizations. Public institutions, in particular municipalities, are no exception to this. Since the process of implementing a CRM system is not risk-free, it is important to know the factors that influence its success. From studies conducted, it was possible to verify that there is a gap in the literature regarding the influential factors of the successful adoption of CRM systems in public institutions (CzRM). Also, through interviews conducted in some municipalities and CRM suppliers, it was possible to identify the relevant factors for the adoption of CRM systems. The purpose of this article is to present the influence factors of the success of the implementation of CzRM systems. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Abstract
Recently, indoor swimming pool activities have increased to promote health-enhancing physical activities, which require establishing suitable protocols for disinfection and water quality control. Normally, the assessment of the microbial quality of the water in the pools only considers the presence of different bacteria. However, other less frequent but more resistant pathogens, such as free-living amoebas (FLA), are not contemplated in both existing recommendation and research activities. FLA represent a relevant human health risk, not only due to their pathogenicity but also due to the ability to act as vehicles of other pathogens, such as bacteria. Therefore, this work aimed to study the physicochemical characteristics and the occurrence of potentially pathogenic FLA and bacteria in water samples from 20 public indoor swimming facilities in Northern Portugal. Our results showed that some swimming pools presented levels of pH, free chlorine, and conductivity out of the recommended limits. Pathogenic FLA species were detected in two of the facilities under study, where we also report the presence of both, FLA and pathogenic bacteria. Our findings evidence the need to assess the occurrence of FLA and their existence in the same environmental niche as pathogenic bacteria in swimming pool facilities worldwide and to establish recommendations to safeguard the health of the users.

Abstract
This research aims to identify the corruption of the civil servants in the Federal District, Brazilian Public Administration. For this purpose, a predictive model was created integrating data from eight different systems and applying logistic regression to real datasets that, by their nature, present a low percentage of examples of interest in identifying patterns for machine learning, a situation defined as a class imbalance. In this study, the imbalance of classeswas considered extreme at a ratio of 1:707 or, in percentage terms, 0.14% of the interest class to the population. Two possible approaches were used, balancing with resampling techniques using synthetic minority oversampling techniqueSMOTEand applying algorithms with specific parameterization to obtain the desired standards of the minority classwithout generating bias from the dominant class. The best modeling resultwas obtained by applying it to the second approach, generating an area value on the ROC curve of around 0.69. Based on sixty-eight features, the respective coefficients that correspond to the risk factors for corruption were found. A subset of twenty features is discussed in order to find practical utility after the discovery process.

Abstract
Developing safe autonomous robotic applications for outdoor agricultural environments is a research field that still presents many challenges. Simultaneous Localization and Mapping can be crucial to endow the robot to localize itself with accuracy and, consequently, perform tasks such as crop monitoring and harvesting autonomously. In these environments, the robotic localization and mapping systems usually benefit from the high density of visual features. When using filter-based solutions to localize the robot, such an environment usually uses a high number of particles to perform accurately. These two facts can lead to computationally expensive localization algorithms that are intended to perform in real-time. This work proposes a refinement step to a standard high-dimensional filter based localization solution through the novelty of downsampling the filter using an online clustering algorithm and applying a scan-match procedure to each cluster. Thus, this approach allows scan matchers without high computational cost, even in high dimensional filters. Experiments using real data in an agricultural environment show that this approach improves the Particle Filter performance estimating the robot pose. Additionally, results show that this approach can build a precise 3D reconstruction of agricultural environments using visual scans, i.e., 3D scans with RGB information.

Abstract
The maritime industry has been following the paradigm shift toward the automation of typically intelligent procedures, with research regarding autonomous surface vehicles (ASVs) having seen an upward trend in recent years. However, this type of vehicle cannot be employed on a full scale until a few challenges are solved. For example, the docking process of an ASV is still a demanding task that currently requires human intervention. This research work proposes a volumetric convolutional neural network (vCNN) for the detection of docking structures from 3-D data, developed according to a balance between precision and speed. Another contribution of this article is a set of synthetically generated data regarding the context of docking structures. The dataset is composed of LiDAR point clouds, stereo images, GPS, and Inertial Measurement Unit (IMU) information. Several robustness tests carried out with different levels of Gaussian noise demonstrated an average accuracy of 93.34% and a deviation of 5.46% for the worst case. Furthermore, the system was fine-tuned and evaluated in a real commercial harbor, achieving an accuracy of over 96%. The developed classifier is able to detect different types of structures and works faster than other state-of-the-art methods that establish their performance in real environments.

Abstract