Abstract
Water is a fundamental human resource and its scarcity is reflected in social, economic and environmental problems. Water used in human activities must be treated before reusing or returning to nature. This treatment takes place in wastewater treatment plants (WWTPs), which need to perform their functions with high quality, low cost, and reduced environmental impact. This paper aims to identify failures in real-time, using streaming data to provide the necessary preventive actions to minimize damage to WWTPs, heavy fines and, ultimately, environmental hazards. Convolutional and Long short-term memory (LSTM) autoencoders (AEs) were used to identify failures in the functioning of the dissolved oxygen sensor used in WWTPs. Five faults were considered (drift, bias, precision degradation, spike and stuck) in three different scenarios with variations in the appearance order, intensity and duration of the faults. The best performance, considering different model configurations, was achieved by Convolutional-AE.

Abstract
Fire has major impacts on forest ecosystems, with heightened relevance in a Mediterranean country such as Portugal, which within Europe features the highest number of wildfires and the second larger burnt area. After each significant wildfire, the Portuguese Institute for Nature Conservation and Forests (ICNF) assesses the main environmental impacts and proposes emergency stabilisation measures following specific regulations. This study seeks to improve such assessments by using a data triangulation approach to characterise the impacts of wildfires on ecosystem services in the country. First, a systematic literature review is performed to identify the scientific studies that address the issue. Next, a document analysis of all the emergency stabilisation reports and technical reports available on ICNF's website is performed. Finally, a survey of experts' perceptions on the topic completes the analysis. The Economics of Ecosystems and Biodiversity definitions of ecosystem services were employed to compare the different findings. The results indicate that the experts perceive wildfires to significantly impact all ecosystem services, even though the literature has so far only focused on 12 of them, and ICNF has so far only focused on 7 in its reports. The potential underlying motives are discussed. In particular, some important impacts identified in the literature, as is the case of Climate regulation, a topic of the highest priority in the European environmental agenda, have not so far been a topic of focus in ICNF's reports, which suggests relevant opportunities for enhancing its reporting process in the future.

Abstract
In the last few years, the industrial, scientific, and technological fields have been subject to a revolutionary process of digitalization and automation called Industry 4.0. Its implementation has been successful mainly in the economic field of sustainability, while the environmental field has been gaining more attention from researchers recently. However, the social scope of Industry 4.0 is still somewhat neglected by researchers and organizations. This research aimed to study Industry 4.0 and sustainability themes using data science, by incorporating open data and open-source tools to achieve sustainable Industry 4.0. To that end, a quantitative analysis based on open data was developed using open-source software in order to study Industry 4.0 and sustainability trends. The main results show that manufacturing is a relevant value-added activity in the worldwide economy; that, foreseeing the importance of Industry 4.0, countries in America, Asia, Europe, and Oceania are incorporating technological principles of Industry 4.0 in their cities, creating so-called smart cities; and that the industries that invest most in technology are computers and electronics, pharmaceuticals, transport equipment, and IT (information technology) services. Furthermore, the G7 countries have a prevalent positive trend for the migration of technological and social skills toward sustainability, as it relates to the social pillar, and to Industry 4.0. Finally, on the global scale, a positive correlation between data openness and happiness was found.

Abstract
Type systems are responsible for assigning types to terms in programs. That way, they enforce the actions that can be taken and can, consequently, detect type errors during compilation. However, while they are able to flag the existence of an error, they often fail to pinpoint its cause or provide a helpful error message. Thus, without adequate support, debugging this kind of errors can take a considerable amount of effort. Recently, neural network models have been developed that are able to understand programming languages and perform several downstream tasks. We argue that type error debugging can be enhanced by taking advantage of this deeper understanding of the language's structure. In this paper, we present a technique that leverages GPT-3's capabilities to automatically fix type errors in OCaml programs. We perform multiple source code analysis tasks to produce useful prompts that are then provided to GPT-3 to generate potential patches. Our publicly available tool, Mentat, supports multiple modes and was validated on an existing public dataset with thousands of OCaml programs. We automatically validate successful repairs by using Quickcheck to verify which generated patches produce the same output as the user-intended fixed version, achieving a 39% repair rate. In a comparative study, Mentat outperformed two other techniques in automatically fixing ill-typed OCaml programs.

Abstract
In this paper, a different Fiber Loop Mirror (FLM) configuration with two circulators is presented. This configuration is demonstrated and characterized for sensing applications. This new design concept was used for strain and torsion discrimination. For strain measurement, the interference fringe displacement has a sensitivity of (0.576 +/- 0.009) pm.mu epsilon(-1). When the FFT (Fast Fourier Transformer) is calculated and the frequency shift and signal amplitude are monitored, the sensitivities are (-2.1 +/- 0.3) x 10(-4) nm(-1) mu epsilon(-1) and (4.9 +/- 0.3) x 10(-7) mu epsilon(-1), respectively. For the characterization in torsion, an FFT peaks variation of (-2.177 +/- 0.002) x 10(-12) nm(-1)/degrees and an amplitude variation of (1.02 +/- 0.06) x 10(-3)/degrees are achieved. This configuration allows the use of a wide range of fiber lengths and with different refractive indices for controlling the free spectral range (FSR) and achieving refractive index differences, i.e., birefringence, higher than 10(-2), which is essential for the development of high sensitivity physical parameter sensors, such as operating on the Vernier effect. Furthermore, this FLM configuration allows the system to be balanced, which is not possible with traditional FLMs.

Abstract
Special care must be taken when considering robots designed to operate collaboratively, such as a swarm, to prevent these agents from being damaged due to unwanted collisions. This work proposes integrating techniques used to move robots, using the Robot Operating System (ROS) and Python's Scikit-Fuzzy module. Thus, this work developed a fuzzy-controlled collaborative obstacle avoidance system for a type of robot whose dynamics are based on motors' vibration. Thus, these robots were designed to participate in a swarm, and the collision must be avoided. In the search for navigation stability, optimal values were sought for the engines' pulse width modulation (PWM).