Abstract
Self-localization of a robot is one of the most important requirements in mobile robotics. There are several approaches to providing localization data. The Ultra Wide Band Time of Flight provides position information but lacks the angle. Odometry data can be combined by using a data fusion algorithm. This paper addresses the application of data fusion algorithms based on odometry and Ultra Wide Band Time of Flight positioning using a Kalman filter that allows performing the data fusion task which outputs the position and orientation of the robot. The proposed solution, validated in a real developed platform can be applied in service and industrial robots.

Abstract
Indoor Air Quality (IAQ) describes the air quality of a room, and it refers to the health and comfort of the occupants. Typically, people spend around 90% of their time in indoor environments where the concentration of air pollutants and, occasionally, more than 100 times higher than outdoor levels. According to the World Health Organization (WHO), indoor air pollution is responsible for the death of 3.8 million people annually. It has been indicated that IAQ in residential areas or buildings is significantly affected by three primary factors, they are outdoor air quality, human activity in buildings, and building and construction materials. In this context, this work consists of a real-time IAQ system to monitor thermal comfort and gas concentration. The system has a data acquisition stage, captured by the WSN with a set of sensors that measures the data and send it to be stored on the InfluxDB database and displayed on Grafana. A Linear Regression (LR) algorithm was used to predict the behavior of the measured parameters, scoring up to 99.7% of precision. Thereafter, prediction data is stored on InfluxDB in a new database and displayed on Grafana. In this way, it is possible to monitor the actual measurement data and prediction data in real-time. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
We study a setting in which a company not only has a fleet of capacitated vehicles and drivers available to make deliveries but may also use the services of occasional drivers (ODs) willing to make deliveries using their own vehicles in return for a small fee. Under such a business model, a.k.a crowdshipping, the company seeks to make all the deliveries at the minimum total cost, i.e., the cost associated with their vehicles plus the compensation paid to the ODs.We consider a stochastic and dynamic last-mile delivery environment in which customer delivery orders, as well as ODs available for deliveries, arrive randomly throughout the day, within fixed time windows.We present a novel deep reinforcement learning (DRL) approach to the problem that can deal with large problem instances. We formulate the action selection problem as a mixed-integer optimization program.The DRL approach is compared against other optimization under uncertainty approaches, namely, sample -average approximation (SAA) and distributionally robust optimization (DRO). The results show the effective-ness of the DRL approach by examining out-of-sample performance.

Abstract
Como formar o professor para desenvolver a docência no ensino superior e na pós-graduação, considerando as transformações digitais, os desafios e as potencialidades de uma realidade hiperconectada numa era de hiperinteligência? A partir desta problemática e, com o objetivo de compreender como formar o professor para a Docência OnLIFE, o percurso da pesquisa-desenvolvimento-formação, vai se constituindo fundamentado no método cartográfico de pesquisa-intervenção. Nesse contexto, o artigo apresenta a construção de um programa de formação docente que tem início como extensão e se aprofunda e amplia como especialização, num processo de inventividade e inovação curricular e metodológica. Problematiza as competências necessárias à docência na contemporaneidade, propondo a Rede de Competências para a Docência OnLIFE, as quais são organizadas por dimensões da formação docente, apresentadas como os 5D’s da Formação Docente OnLIFE. As competências organizadas nas dimensões, são articuladas a partir da concepção de um currículo em rede. Como resultados apresenta metodologias e práticas inventivas e conclui com a proposição de uma docência OnLIFE.   Palavras-chave cultura híbrida e multimodal, metodologias inventivas, formação de professores, educação onlife

Abstract
Abstract Lightning hit a transmission power line outside London, England on 9 August 2019. There followed a loss of power from a cascade of generator outages that exceeded contingency reserves, leading to an exceptional fall in grid frequency causing widespread transport disruptions and the disconnection of over 1 m households. Simulating such events typically involves a system of differential equations representing the overall generation and load present at the time. A standard model based on the swing equation assumes unlimited capacity in aggregated resources, and the availability of these services throughout the duration of the frequency deviation. In simulating the effect of outages on the GB Grid frequency on 2019/8/9, the effect of limiting these services to the capacity of resources engaged during the event is examined. It is shown that by taking these refinements into account the timing and extent of the frequency nadir can be successfully estimated. Insight is gained into the responses of various grid characteristics and how they interact with unplanned generation imbalances. Using this adapted model, further events on the GB grid are examined to validate the influence of these features. With the model’s effectiveness validated, novel mitigating measures to preserve the stability of a low-inertia grid can be evaluated.

Abstract
Gastric Intestinal Metaplasia (GIM) is one of the precancerous conditions in the gastric carcinogenesis cascade and its optical diagnosis during endoscopic screening is challenging even for seasoned endoscopists. Several solutions leveraging pre-trained deep neural networks (DNNs) have been recently proposed in order to assist human diagnosis. In this paper, we present a comparative study of these architectures in a new dataset containing GIM and non-GIM Narrow-band imaging still frames. We find that the surveyed DNNs perform remarkably well on average, but still measure sizeable interfold variability during cross-validation. An additional ad-hoc analysis suggests that these baseline architectures may not perform equally well at all scales when diagnosing GIM.