This paper presents a validation and acceptance analysis of the Higia Way software to aid the learning process of pregnant and postpartum women. This analysis consists of validation by applying the Technology Acceptance Model (TAM). We identify that the interviewees’ perception about the ease of use, the relevance of the content, and usefulness are some of the main factors that favor technology acceptance. We perform the analysis through questionnaires and interviews, and the results allow future investigations and improvements in this field of study.

Aquaculture is an important activity for the animal protein global supply. In Brazil, this area is showing significant growth in recent years. Among the activities carried out during the production process, the counting of animals in the initial stages can be highlighted. A large number of Brazilian aquaculture farms are small, turning difficult to acquire novel solutions for the automatic count of post-larvae. To mitigate this issue, this paper intends to develop a solution based on the performance evaluation of a set of counting models for use in embedded structures. In addition, this application can be scalable for counting different species and sizes. Besides, the dataset named Vivarium and its specifications is presented as proof of concept and used in the model evaluation. Results show that the prediction model based on convolutional neural networks is capable of verifying the compliance of images, achieving an accuracy of 99%.

The rapid appropriation of digital platforms has been transforming the experience of tourism practice in a broad and profound way, its temporality and materiality. When it comes to the experience of interacting in the place you visit, there are numerous kinds of platforms. During software development it is necessary to develop solutions that meet the needs of users.This paper reports the initial design phase of an e-tourism platform to support the relationships between different tourists while in the territory. We adopted an Ethnographic Design method to engage with users in the emerging touristic experiences. We engage distinct techniques personas, scenarios and prototyping to promote a profound understanding about the experience. The joint use of the techniques leaded the formulations of a set of requirements identified throughout the execution of each step of the method. These requirements were prototyped and will serve for the application developed to meet the needs of the identified target audience.

Despite the efforts of the Ministry of Health, the Brazilian public health service is still precarious, mainly due to the difficulty in accessing the patient's health history during the medical consultation. This paper proposes an intelligent health scenario that captures, enriches and provides clinical, regulatory, drug information and similar cases in real time in order to improve the quality of Primary Health Care. This scenario uses inferring mechanisms that allow the extraction and add value to relevant clinical information, helping physicians make better diagnoses and prescriptions. Its construction results from the integration of Wearable and Linked Data technologies in Electronic Health Records context.

This paper presents an ontology called OntoHedu for modelling the archaeological heritage domain. The ontology aims to subsidise learning within the school and non-school field for heritage education. Methodology 101 was used to develop the ontology. Its specification was conceptualised from the knowledge extracted from the literature review and the case study at an archaeological site. A web application called WebHedu has been developed and integrated with OntoHedu to disseminate, share, search, and retrieve information through its functionalities, serving as an interface to the subjects who use it.

With significant advances in mobile technologies in recent years, educational institutions are providing new opportunities for teaching and learning and, as an example, the popularization of serious games as an educational tool. Based on this context, a data survey was conducted with medical professors from a federal university in northeastern Brazil, to investigate new technological needs aimed to teaching and learning. After analyzing the data, it was decided to design and develop a serious smartphone game focused on training medical students in clinical cases. The development process was divided into two phases. The first one involves the design and development of the gaming activities, and the second one includes activities of data collection and analysis for the validation process. This paper presents results obtained from the requirements gathering with the professors up to the implementation of the serious game.

People with Autism Spectrum Disorder (ASD) have characteristics described as the disability to relate to others, communication disorders, and obsessive concern about what is unchanging. In this sense, this paper aims to present an application for mobile devices, in a serious game format, whose purpose is to aid the teaching-learning process of autistic children, also seeking to reduce their isolation tendencies. Also, it shows both ontologies, and the Multiagent System integrated into the application. The validation occurred with autism specialists through questionnaires. The results indicate that the proposed application presents great potential to reach its objectives.

In recent years, medical students from Brazil have obtained poor performance in the CREMESP (Regional Medical Council of São Paulo State) exam. Although the most recent assessment shows improvements, they still perform poorly in specialties such as clinical cases, diabetes, and myocardial infarction. In this context, several strategies emerge to improve the teaching and learning of medical students, one of which is serious games. However, there is a problem with trying to train medical students in clinical cases through a game because there are so many diseases, and processing a database containing those diseases is laborious. This paper describes an intelligent module of a serious game for training medical students. This module has a website for data dissemination, management and expansion. The information is made available for serious game through a web service. We used databases of a clinical case, heart disease and diabetes. These databases are rated by the machine learning ensemble classifiers. The smart module was validated by F-measure using percentage division and cross-validation. With the results found, it was provision that the intelligent module can manage the of serious game data, because it has a whole structure for this purpose.

In Brazil, medical students are evaluated through several examinations, for example, Regional Medical Council of São Paulo State and National Serial Evaluation of Medical Students, which show a deficiency in teaching/learning of medical students in the country. National Institute of Educational Studies and Research also evaluates and performs the classification of undergraduate courses in the country through concepts, using the National Student Proficiency Exam. Thus, this work aims to analyze the performance of medical higher education courses in Brazil, according to the National Student Proficiency Exam through a comparative statistical analysis and data mining. The results indicate that the federal and state public institutions, and the courses located in the South, Midwest and Northeast regions, add more knowledge than the other medical courses in Brazil. Finally, possible solutions are presented according to the literature, highlighting the use of active learning methods for medical students.

Technological advances have played na important role in transforming various forms of services, including distance learning courses, which have grown significantly over the years. In this paper, a set of data about the distance courses offered by a university in Rio Grande do Norte was used to identify the efficiency of the interaction tools forum and chat, verifying which is the most used in the virtual environment and their impact on students' grades. We present some analyzes from descriptive statistics, such as mean, median and standard deviation.

Several tools are developed with the purpose of solving problems and exposing results similar to human reasoning. For this, various artificial intelligence techniques are being implemented to improve these applications. For the poorly structured and high volume data, the ontology presents itself as a technique capable of structuring this data and exposing representative results. In this way, this work describes the use of an ontology as the data classification technique and pattern recognition. The objective is to develop an ontological structure capable of analyzing and classifying the movements and sound signals of the chewing and swallowing process in solids or liquids. To validate the ontology, the tests were performed in real environments. The results obtained, based on the realized experiments, point to the viability of the use of ontologies for the problem in question.

This paper proposes a content recommendation mechanism as part of a model for implementing ubiquitous learning for supporting people with chronic diseases who are treated at home, so that they can learn more about treatments for their disease. In the proposed approach, the learning takes place based on day-to-day activities and real situations. In this case, the model supports the development of tools that can learn about the user's context, based on data obtained via sensors installed on users or in their home, as well as data supplied directly by the user interface of their mobile devices, and data provided by the healthcare team, and, after that, recommend contents about their diseases.

Real-time database management systems (RT-DBMS) have the necessary characteristics for providing efficient support to develop applications in which both, data and transactions have temporal constraints. However, in the last decade, new applications were identified and are characterized by a large geographic distribution, high heterogeneity, lack of global control, partial failures and lack of safety. Besides, they need to manage large data volumes with real-time constraints. Scheduling algorithms should consider transactions with soft deadlines and the concurrency control protocols should allow conflicting transactions to execute in parallel. The last ones should be based in their requirements, which are specified through both quality of services functions and performance metrics. In this work, a method to model and develop applications that execute in open and unpredictable environments is proposed. Based on this model, it is possible to perform analysis and simulations of systems, to guide the decision making process and to identify solutions for improving it. For validating the model, a case study considering the application domain of sensors network is discussed.

In order to provide the news demands that a real-time database needs to work properly, some improvements have been proposed in aspects well done like concurrency control, scheduling and query language. In the perspective of obtain better results, despite the accomplished efforts, these problems continuous been studied. In this paper we introduce a new query language for real-time database (QL-RTDB), that's an extension of the structured query language (SQL-99). Besides the new functionalities of the extended language presented, we also show its implementation with the applied results obtained.

When dealing with Wireless Sensor Networks (WSN) applications that demand a high level cooperation of the sensor nodes, time synchronization is necessary to provide the correctness required for their results. In this work, we show that common issues between routing and time synchronization processes make their integration possible and, thus, can solve the problems of the resources consumption and time spent to acquire the synchronization. Two proposals for integrating them are presented.

The competitive advantage of organizations is more and more related to the competence of the workers and to the capacity the organization shows in improving the organizational learning. Computer-supported learning (CSL) contributes to improve the organizational learning by supporting the knowledge sharing process. Sharing knowledge, however, is not sufficient to increase the intellectual capital of an organization. It is necessary to share the kind of knowledge that can be useful to add value to the organization business. This kind of knowledge can be identified and mapped by applying knowledge management (KM) processes. However, although positive effects of a comprehensive approach that addresses both CSL (formal learning) and KM (informal learning) could seem obvious, and despite the fact that organizations are starting to perceive the interrelations among CSL and KM, these integrations ideas are rarely implemented in practice and, in many cases, are not technologically operational. This paper fills in this gap by discussing how CSL can be useful for satisfying the requirements of KM.