This study aimed to investigate how weekly training load constrains the performance of players and teams in official futsal competitions. Data from a professional male team were collected during two seasons (46 weeks). The applied monitoring system analysed the training load (as measured by session perceived exertion, sRPE), the total recovery status (TQR), the well-being score (WBs) and the variability of neuromuscular performance during each week (CMJ-cv). In addition, the performance was assessed for all the matches. A path analysis model was performed to test the associations across variables. Results from the path analysis model revealed that it explains 31% of the teams’ performance. In general, the results show that previous team performance has no significant effects on the training week. A significant negative relationship was found between CMJ-cv and match performance (β = -.34; CI95% -.359 to -.070), as well as a significant negative relationship between players’ match performance and the team’s match performance (β = -.55; CI95% -.292 to .740). Regarding indirect effects, only a negative association between CMJ-cv and team match performance via players’ match performance (β = -.19; CI95% -.342 to -.049) was identified. The small variation of the weekly CMJ (CMJ-cv) seems to be a key variable to monitor and explain both player and team performance. Based on this model, and only looking at the physical variables, it was possible to explain 31% of the team’s performance. Longitudinal and multi-team studies should be conducted to integrate other technical, tactical and psychological variables that allow the level of understanding of players’ and teams’ performance to be improved.

The idea of monitoring several types of parameters in various environments has been motivating significant research works in Internet of Things (IoT). This paper presents the design and construction of iSensA, a system for integrating and collecting information from sensors. The solution implements a multi-sensor monitoring system and then expands the monitoring concept to an IoT solution, by employing multi-network access, Web services, database and web and mobile applications for user interaction. iSensA system is highly configurable, enabling several monitoring solutions with different types of sensors. Experiments have been performed on real application scenarios to validate and evaluate our proposition.

Birds continue to be one of the main factors of loss by producers in the region of Beira Interior. Fruits such as peaches and cherries continue to be damaged and their trees destroyed due to bird crop attacks. There are several methods to disperse birds, but all have low effects in the long-term as they demonstrate low variability and high maintenance. Drones are systems that are capable of dispersing birds due to their high mobility. Together with the use of audiovisual technologies, increase the effectiveness of the bird dispersion. However, to get the most out of each flight it is required to understand birds’ movements. Thus, a monitoring system is required. In this article, a technological solution is proposed that uses drones and aggregates the monitoring and dispersion systems so maximum effectiveness in bird dispersal is achieved.

During food transportation, products are often subject to conditions which in no way promote the conservation of their biological properties. As a result of this loss of quality, there is a decrease in appreciation under the product. But more important than the loss of economic value is food waste. Remote monitoring systems, whose fundamental requirements relate to range and autonomy, make use of communications technologies to map characteristic crop parameters to reduce unnecessary application of resources or materials. This article proposes a monitoring system to record the conservation conditions inside refrigeration plants and vehicles, transmitting them via the internet of Things (IoT). This device is composed of an ARDUINO UNO Rev3 microcontroller that acquires the temperature and relative humidity every 5 minutes by means of a DHT 11 sensor, and uses the SIM800L module that provides real time communication data via GSM. It also incorporates a 3.7 V - 2600mAh battery which gives you an approximate 60 hour power range.

This project consists in the development of a remote monitoring system for important factors in the agriculture sector: ambient humidity, ambient temperature and soil humidity, through the use of wireless sensors, these factors being available in real time for monitoring in a web application available to any user with internet access. A circuit was also implemented that aims to prevent, in a certain area of cultivation, against the invasion of animals, keeping the planting safe. The developed system was tested within three test scenarios, varying the temperature, humidity and distance factors in experiments involving a heat source, soil in a dry, wet and wet state and an obstacle approach that simulates the presence of a predator on site.

“If you can’t measure it, you can’t manage it”. This aphorism comes to our mind whenever we are concerned with the sustainable tourism planning and management of a destination. Indeed, without an efficient monitoring system engaging all stakeholders and covering all the tourism activities, it’s impossible to perform a rigorous assessment of tourism impacts. In order to achieve the twofold goals of tourism management is necessary to practice: competitiveness and sustainability. The goal of this paper is to present the Tourism Observatory of Central Portugal (TOCP) and the strategies used to surpass the most evident difficulties during the initial stage of data collecting process implementation. The relevant eight attributes to this Observatory can be described, namely by: the frequency of the barometers, the information availability, the reliability of the methods, the geographical and sectorial scopes, the usefulness of the produced information as a support to forecasting and innovation and the guarantee of a long-term perspective. Moreover, this paper describes the methods of data collecting, the functionalities of an online platform, the structure of stakeholders’ network and its coordination, and the relevant indicators that are assessed. Although the European Tourism Indicators System (ETIS) stands as the basic reference for the TOCP, some relevant adaptations were introduced, like for instance in cases when the official institutions do not have the statistical data required for the core indicators. In particular, the lack of statistical data seems more evident at local/municipal level. In consequence, the monitoring model described in this paper presents and discusses the approach adopted to deal with lack of statistical data. This approach consists of generating a rhizomatic and collaborative network of tourism stakeholders, each of them providing specific inputs to the monitoring system.

This study aimed to investigate how weekly training load constrains the performance of players and teams in official futsal competitions. Data from a professional male team were collected during two seasons (46 weeks). The applied monitoring system analysed the training load (as measured by session perceived exertion, sRPE), the total recovery status (TQR), the well-being score (WBs) and the variability of neuromuscular performance during each week (CMJ-cv). In addition, the performance was assessed for all the matches. A path analysis model was performed to test the associations across variables. Results from the path analysis model revealed that it explains 31% of the teams’ performance. In general, the results show that previous team performance has no significant effects on the training week. A significant negative relationship was found between CMJ-cv and match performance (β = -.34; CI95% -.359 to -.070), as well as a significant negative relationship between players’ match performance and the team’s match performance (β = -.55; CI95% -.292 to .740). Regarding indirect effects, only a negative association between CMJ-cv and team match performance via players’ match performance (β = -.19; CI95% -.342 to -.049) was identified. The small variation of the weekly CMJ (CMJ-cv) seems to be a key variable to monitor and explain both player and team performance. Based on this model, and only looking at the physical variables, it was possible to explain 31% of the team’s performance. Longitudinal and multi-team studies should be conducted to integrate other technical, tactical and psychological variables that allow the level of understanding of players’ and teams’ performance to be improved.

When destination management organizations (DMO) are about to create (or adopt) a monitoring system for a continuous assessment of tourism performance in terms of sustainability and competitiveness, they face some crucial questions, such as: (1) what are the most relevant indicators in terms of its utility for tourism management?; (2) how many indicators have to be included to ensure a satisfactory coverage of all relevant aspects of tourism activity?; (3) what are the appropriate measures for each indicator and how get the necessary information?; (4) what are the pertinent sources of information? These and other related questions are considered as a preliminary step when introducing the monitoring system in tourism destinations. Aiming to get knowledge on this matter and to anchorage the empirical model of the Tourism Observatory of Center of Portugal (TOCP), a team of affiliated researchers of NID carried out a qualitative survey with a group of 40 tourism experts asking them a set of questions concerning the level of utility and trustiness of the statistical data are provided by tourism authorities to the stakeholders of tourism industry. Data analysis allowed us to identify the most relevant gaps regarding the quality of tourism information that has been provided to tourism decision makers, as well as to give some useful cues to improve the set of relevant indicators to be considered in the TOCP monitoring system.

Indoor air quality (IAQ) is a term describing the air quality of a room, it refers to the health and comfort of the occupants. Normally, people spend around 90% of their time in indoor environments where the concentration of air pollutants, such CO, CO2, VOCs, SO2, O3 and NOx, may be two to five times — and occasionally, more than 100 times — higher than outdoor levels. According to the World Health Organization (WHO), the indoor air pollution is responsible for the deaths of 3.8 million people annually. It has been indicated that IAQ in residential areas or buildings is significantly affected by three primary factors: (i) Outdoor air quality, (ii) human activity in buildings, and (iii) building and construction materials, equipment, and furniture. In this contest, this work consist in a real time IAQ system to monitoring and control thermal comfort and gas concentration. The system has a data acquisition stage, where the data is measured by a set of sensors and then stored on InfluxDB database and displayed in Grafana. To track the behavior of the measured parameters, two machine learning algorithms are developed, a mathematical model linear regression, and an artificial intelligence model neural network. In a test made to see how precise were the prediction of the two models, linear regression model performed better then neural network, presenting cases of up to 99.7% and 98.1% of score prediction, respectively. After that, a test with smoke was done to validate the models where the results shows that both learning models can detect adverse cases. Finally, prediction data are storage on InfluxDB and displayed on Grafana to monitoring in real-time measured data and prediction data.

Renewable energy systems are quickly becoming one of the most efficient way to generate electricity. Solar energy is one of the most appealing renewable energy sources for electrification. Harnessing solar energy requires a photovoltaic system that converts light energy from the sun into direct electricity. Therefore, to evaluate its performance, real-time monitoring system is needed. Some of photovoltaic system are installed in inaccessible locations and thus unable to be monitored from a dedicated location, so the monitoring must be remotely using web based interfaces. This project proposes to develop a prototype based on IoT technology for monitoring remotely and evaluating the performance of a solar photovoltaic system. This will facilitate preventive maintenance, fault detection, historical analysis, in addition to real time monitoring. For the project development, an ESP32 device is used to measure a set of sensors and transmit them through the MQTT protocol via WiFi technology to a MQTT Broker that manages and publishes data to a flow editor named Node-RED to be collected and sent to a database named InfluxDB, where data are stored and secured, and finally a visualization platform named Grafana, displays the measurements to be analyzed over time.