Abstract
The increasing complexity of recent Wi-Fi amendments is making optimal Rate Adaptation (RA) a challenge. The use of classic algorithms or heuristic models to address RA is becoming unfeasible due to the large combination of configuration parameters along with the variability of the wireless channel. We propose a simple Deep Reinforcement Learning approach for the automatic RA in Wi-Fi networks, named Data-driven Algorithm for Rate Adaptation (DARA). DARA is standard-compliant. It dynamically adjusts the Wi-Fi Modulation and Coding Scheme (MCS) solely based on the observation of the Signal-to-Noise Ratio (SNR) of the received frames at the transmitter. Our simulation results show that DARA achieves higher throughput when compared with Minstrel High Throughput (HT)

Abstract
Digitalization is undoubtedly a major challenge for companies in the coming years. Applying a Design Science methodology this paper aims to describe the process for the development of a solution for obtaining an overview of the Digital Maturity in the manufacturing industry of the region of Tamega e Sousa (an industrial region located in the north of Portugal). The evaluation process consisted of a sample of 53 companies that allowed to get a first picture of the region. Summing up, it is possible to say that a digital strategy is in the companies' plans with a focus on processes digitalization. In general, an overall digital strategy for the companies is in line with the marketing and human resources, in a middle position, with a few companies taking the lead, the majority following, and some others still now awakening to this reality.

Abstract
The rapid developments in Internet-of-Things (IoT), cloud computing, and big data technologies have increased the popularity of machine learning (ML) techniques. As a result, of all ML techniques, deep learning (DL) is at the forefront of innovation, outperforming all other techniques in many application domains. DL has made breakthroughs in speech recognition, image processing, forecasting, natural language processing, fault detection, power disturbance classification, energy trading, and much more. DL is a complex ML approach composed of multiple processing layers, which allows pattern and structure recognition on huge datasets. This chapter takes an in-depth look at the most recent and promising DL works in the literature for intelligent power and energy systems (PES). Several types of problems are explored, including regression, classification, and decision-making problems. The presented works show an increasing trend of new DL techniques that outperform traditional approaches, either through novel architectures or hybrid systems. © 2023 The Institute of Electrical and Electronics Engineers, Inc.

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Abstract
In the areas of machine-learning/big data, feature selection is normally regarded as a very important problem to be solved, as it directly impacts both data analysis and model creation. The problem of optimizing the selected features of a given dataset is not always trivial, however, throughout the years various ways to counter this optimization problem have been presented. This work presents how feature-selection fits in the larger context of multi-objective problems as well as a review of how both multi-objective evolutionary algorithms and metaheuristics are being used in order to solve feature selection problems.