Abstract
Web 2.0 has allowed collaboration, interaction and sharing of information online, such as online review platforms. Consequently, these short and straightforward opinions have increasingly proved to be essential sources of information not only for consumers but also for companies, as they represent the consumer's sincere evaluation, free from any kind of bias. In this sense, there should be an interest in the analysis and monitoring of online reviews by companies, as the result of these actions may provide guidelines to readjust their strategy, support decision-making and ensure the satisfaction of their consumers. To generate useful information to assist decision-making and strategies' implementation by retailers in the Municipality of Porto, online reviews from the GoogleMyBusiness platform were organised, classified, and analysed. 9945 online reviews were extracted, directed to 246 retail adaptations of the Municipality of Porto, from 2017 to 2020, which were later classified by the polarity of sentiment (positive, negative, neutral, or mixed). Sentiment analysis was conducted, combined with statistical tests and frequency distribution tables to discover relevant information for retailers. With sentiment analysis, retailers can understand their consumers and their behaviour to adapt their strategies and make the right decisions to ensure their customers' satisfaction. With the results obtained, this study proves that it is possible to extract useful information from online reviews and reveals that it is still an area of little interest for retailers in the Municipality of Porto.

Abstract
The fusion of data from different sensors often requires that an accurate geometric transformation between the sensors is known. The procedure by which these transformations are estimated is known as sensor calibration. The vast majority of calibration approaches focus on specific pairwise combinations of sensor modalities, unsuitable to calibrate robotic systems containing multiple sensors of varied modalities. This paper presents a novel calibration methodology which is applicable to multi-sensor, multi-modal robotic systems. The approach formulates the calibration as an extended optimization problem, in which the poses of the calibration patterns are also estimated. It makes use of a topological representation of the coordinate frames in the system, in order to recalculate the poses of the sensors throughout the optimization. Sensor poses are retrieved from the combination of geometric transformations which are atomic, in the sense that they are indivisible. As such, we refer to this approach as ATOM - Atomic Transformations Optimization Method. This makes the approach applicable to different calibration problems, such as sensor to sensor, sensor in motion, or sensor to coordinate frame. Additionally, the proposed approach provides advanced functionalities, integrated into ROS, designed to support the several stages of a complete calibration procedure. Results covering several robotic platforms and a large spectrum of calibration problems show that the methodology is in fact general, and achieves calibrations which are as accurate as the ones provided by state of the art methods designed to operate only for specific combinations of pairwise modalities.

Abstract
Currently, there has been a great development of the wind energy market, which is accompanied by an increase in the number of wind farms at sea, the offshore wind farms. Therefore, it is crucial to ensure that efficiency in energy production is maximum and that the levelized cost of energy (LCOE) is minimal. In this paper, a mixed-integer linear programming model (MILP) is proposed to find the best wind farm layout taking into account the wake effect in order to maximize energy production. The design of an offshore wind farm located at the North Sea is considered as a case study, contemplating three situations regarding the number of wind turbines to be installed and to determine the best positioning of them in order to maximize energy production, taking into account the wake effect and the lowest LCOE. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

Abstract
Natural gas will play a key role in the transition to a lower-carbon economy, constituting a natural alternative to coal and acting as a backup resource to the intermittent nature of renewable generation. These energy carriers can be structurally linked together by Power-to-X technologies because of their interaction to increase energy efficiency. For this purpose, this paper proposes an innovative model to optimally manage the electricity and natural gas grids in a cost-efficient manner. In this model, an energy hub has water, electricity, and gas oil as inputs, supplying electric and thermal loads. Besides, the energy hub uses the Power-to-gas (P2G) technology to produce natural gas, selling it to a gas network to reduce the congestion in gas pipelines and the energy hub owner's costs. A demand response program has been also applied in this model to shift the loads from on-peak times to off-peak ones. Various technologies such as energy storage and distributed generation have been used in the modeling to reach the goals targeted by operators. Furthermore, a scenario generation method has been applied to model the uncertainty of wind turbine output. The proposed problem has been finally formulated as mixed-integer linear programming that has been solved under GAMS software by using CPLEX solver to reach the global optimality. The results obtained from simulations demonstrate that the proposed model can significantly reduce the operation cost, while properly alleviating gas network congestion.

Abstract
Proximity sensing approaches with a wide array of sensors available for use in precision viticulture contexts can nowadays be considered both well-know and mature technologies. Still, several in-field practices performed throughout different crops rely on direct visual observation supported on gained experience to assess aspects of plants' phenological development, as well as indicators relating to the onset of common plagues and diseases. Aiming to mimic in-field direct observation, this paper presents VineInspector: a low-cost, self-contained and easy-to-install system, which is able to measure microclimatic parameters, and also to acquire images using multiple cameras. It is built upon a stake structure, rendering it suitable for deployment across a vineyard. The approach through which distinguishable attributes are detected, classified and tallied in the periodically acquired images, makes use of artificial intelligence approaches. Furthermore, it is made available through an IoT cloud-based support system. VineInspector was field-tested under real operating conditions to assess not only the robustness and the operating functionality of the hardware solution, but also the AI approaches' accuracy. Two applications were developed to evaluate Vinelnspector's consistency while a viticulturist' assistant in everyday practices. One was intended to determine the size of the very first grapevines' shoots, one of the required parameters of the well known 3-10 rule to predict primary downy mildew infection. The other was developed to tally grapevine moth males captured in sex traps. Results show that VineInspector is a logical step in smart proximity monitoring by mimicking direct visual observation from experienced viticulturists. While the latter traditionally are responsible for a set of everyday practices in the field, these are time and resource consuming. VineInspector was proven to be effective in two of these practices, performing them automatically. Therefore, it enables both the continuous monitoring and assessment of a vineyard's phenological development in a more efficient manner, making way to more assertive and timely practices against pests and diseases.

Abstract
In view of the actual climate change scenario felt across the globe, resource management is crucial, especially with regard to water. In this sense, continuous monitoring of plant water status is essential to optimise not only crop management but also water resources. Currently, monitoring of vine water status is done through expensive and time-consuming methods that do not allow continuous monitoring, which is especially inconvenient in places with difficult access. The aim of the developed work was to install three groups of sensors (Environmental, Plant and Soil) in a vineyard and connect them through LoRaWAN protocol for data transmission. The results demonstrate that the implemented system is capable of continuous data communication without data loss. The reduced cost and superior range of LoRaWAN compared to WiFi or Bluetooth is especially important for applications in remote areas where cellular networks have little coverage. Altogether, this methodology provides a remote, continuous and more effective method to monitor plant water status and is capable of supporting producers in more efficient management of their farms and water resources.