Abstract
This paper presents a model predictive control (MPC) framework for battery energy storage systems (BESS) management considering models for battery degradation, system efficiency and V-I characteristics. The optimization framework has been tested for microgrids with different renewable generation and load mix considering several operation strategies. A comparison for one-year simulations between the proposed model and a naïve BESS model, show an increase in computation times that still allows the application of the framework for real-time control. Furthermore, a trade-off between financial revenue and reduced BESS degradation was evaluated for the yearly simulation, considering the degradation model proposed. Results show that a conservative BESS usage strategy can have a high impact on the asset's lifetime and on the expected system revenues, depending on factors such as the objective function and the degradation threshold considered.

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Comparing companies solely based on tax incentives for R&D activities can be misleading. Hence, this research aims to assess how Portuguese companies that make use of tax incentives for R&D activities on an ongoing basis behave in terms of promoting and appropriating value through innovation. From an initial population of 3,156 companies, a final database with 339 Portuguese companies that made use of fiscal credits from 2013 to 2016 was analyzed. The tax incentive program targeted was the Fiscal Incentive System supporting R&D in Enterprises (SIFIDE). Using the analysis of variance (one-way ANOVA), the main results suggest that, considering the internal resources, there are no statistically significant differences between the promotion and appropriation of value through innovation according to the size and age of companies. The data indicate that good management regarding the generation and implementation of innovations may occur independently of size and age of firms, in the Portuguese case. Albeit the averages of the indicators of value appropriation of the intangible (e.g. patents, trademarks, and new processes), and the efficiencies of assets and liabilities for the promotion of the intangible are different according to the level of technological intensity. These results point to the need to reevaluate the tax incentives for R&D activities, since its generic nature may not meet the different innovation needs arising from the distinctive characteristics of these enterprises and their technological dynamics. Implications and future research directions are provided.

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The paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

Abstract
Nowadays, Artificial Intelligence (AI) and its subfields, Machine Learning (ML) and Deep Learning (DL), are used for a variety of medical applications. It can help clinicians track the patient's illness cycle, assist with diagnosis, and offer appropriate therapy alternatives. Each approach employed may address one or more AI problems, such as segmentation, prediction, recognition, classification, and regression. However, the amount of AI-featured research on Inherited Retinal Diseases (IRDs) is currently limited. Thus, this study aims to examine artificial intelligence approaches used in managing Inherited Retinal Disorders, from diagnosis to treatment. A total of 20,906 articles were identified using the Natural Language Processing (NLP) method from the IEEE Xplore, Springer, Elsevier, MDPI, and PubMed databases, and papers submitted from 2010 to 30 October 2021 are included in this systematic review. The resultant study demonstrates the AI approaches utilized on images from different IRD patient categories and the most utilized AI architectures and models with their imaging modalities, identifying the main benefits and challenges of using such methods.