The scarcity of the planet's water resources is a concern of several international entities and governments. Smart solutions for water quality monitoring are gaining prominence with advances in communication technology. This work's primary goal is to develop a new online system to monitor and manage water resources, called Internet of Water Things (IoWT). The proposed system's objective would be to control and manage raw water resources. Thus, it has developed a platform based on the server-less architecture and Internet of Things Architectural Reference Model, in which it is applied in a simulation environment, considering several electronic devices to validate its performance. For this research, there is a system for capturing raw water from tubular wells. Each well has a level sensor, a temperature sensor and a rain gauge. The data is collected every minute by an electronic device and sent every hour to the IoWT system. From data analysis, the amount of memory allocated to functions minimally interferes with efficiency. The IoWT system, applied in a real case, consists of connecting a device installed in a water well to the platform, where the data is transmitted through a 3G network and then processed. Thus, the proposed approach has great potential to be considered a complementary tool in monitoring raw water and assisting in decision-making for the management of water resources.

Emotion recognition is a fundamental task that any affective computing system must perform to adapt to the user’s current mood. The analysis of electroencephalography signals has gained notoriety in studying human emotions because of its non-invasive nature. This paper presents a two-stage deep learning model to recognize emotional states by correlating facial expressions and brain signals. Most of the works related to the analysis of emotional states are based on analyzing large segments of signals, generally as long as the evoked potential lasts, which could cause many other phenomena to be involved in the recognition process. Unlike with other phenomena, such as epilepsy, there is no clearly defined marker of when an event begins or ends. The novelty of the proposed model resides in the use of facial expressions as markers to improve the recognition process. This work uses a facial emotion recognition technique (FER) to create identifiers each time an emotional response is detected and uses them to extract segments of electroencephalography (EEG) records that a priori will be considered relevant for the analysis. The proposed model was tested on the DEAP dataset.

People who have dementia (PwD) experience deteriorating executive functions, in particular their working memory, and therefore find it hard to complete multistep tasks or activities of daily living. There is no doubt that during the pandemic, PwD and their caregivers were particularly vulnerable, often isolated which affected their mental and physical health. Their ability to live independently was hampered, fomenting depression in the PwD and burnout on informal caregivers. Information technology can support dementia care improving the quality of life of PwD and easing the burden on caregivers. There is an increasing demand to support informal caregivers and improve their well-being by making dementia challenges less severe. This study uses qualitative techniques to design a model with technological strategies based on semi-structured interviews applied to seven informal caregivers from two different countries. Based on these interviews we developed design insights for implementing solutions to help informal caregivers take care of their PwD at home using conversational agents. We hope that the findings presented in this study will help researchers, and developers design solutions that can support PwD and informal caregivers.

Scrum is one of many agile frameworks and is considered the most popular and widely adopted. Although Scrum presents several advantages, process and final product quality continue to be Scrum’s main challenges. The quality assessment should be an essential activity in the software development process. Several authors have attempted to improve the quality of Scrum projects, changing some aspects of the framework, such as including new quality practices, a quality role, and quality processes. However, the quantification of quality is still a challenge. For that reason, the authors proposed a framework called Scrumlity, which was defined in a previous study. This framework proposes a change to Scrum, including a quality role and some artifacts to evaluate quality through a complete execution of a Sprint. In this study, the authors add a User Story Quality assessment to the framework. The User Story Quality Assessment includes over 250 analyzed User Stories. Results obtained after this experiment indicate the importance of executing a User Story Quality Assessment and that Scrum Team members are willing to accept adding this to the framework.