Abstract
ABSTR A C T Since energy hubs meet the needs of customers for different energies, their construction rate has increased in recent years. The annual growth of load demand on the one hand and the declining efficiency of hub converters on the other hand have posed many challenges for hub designers. Therefore, this study develops a multi-objective model for the design of hub considering converters' variable efficiency, degradation of equipment and annual growth of the load and energy prices. The proposed hub is equipped by a power-to-gas (P2G) technology and its consumers participate in an integrated demand response (IDR) program. The problem is formulated in mixed-integer non-linear programming (MINLP) format and is solved via DICOPT in GAMS environment. The simu-lation results substantiate that dynamic framework has led to the much more accurate determination of equipment capacity. Besides, the results indicate that the P2G technology reduces CO2 emissions by 9.89% through consuming CO2 emitted from the CHP and boiler. The results also illustrate that P2G increases the ef-ficiency of gas-fired converters by injecting hydrogen into them, thus reducing losses by 9.2%.

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Abstract
Vocational high school is a secondary education whose practice portion is larger than its theoretical portion. This allows students to do more hands-on practice in the laboratory, as skill competency is very important in vocational education. Through practice, students have the skills to become competent and skilled technicians in the future. When students practice in a hands-on laboratory, errors may occur that can injure students, equipment, and components. In addition, short circuits can also endanger student safety. Therefore, to improve practical skills in the laboratory, teachers must find innovative ways to incorporate these methods into the learning process. One of the things that can be done to improve students’ practical skills is to use simulation software before doing direct practice in the laboratory. In-depth interviews were conducted with three electrical engineering teachers to verify the perspective of the proposed model. The results suggest that the proposed design is likely to improve problem-solving skills when an error occurs during the simulation, and it will improve practical skills when using hands-on laboratories so that students learn more about hands-on lab practice in vocational high school. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Unmanned Aerial Systems (UAS) are becoming more attractive in diverse applications due to their efficiency in performing tasks with a reduced time execution, covering a larger area, and lowering human risks at harmful tasks. In the context of Oil & Gas (O&G), the scenario is even more attractive for the application of UAS for inspection activities due to the large extension of these facilities and the operational risks involved in the processes. Many authors proposed solutions to detect gas leaks regarding the onshore unburied pipeline structures. However, only a few addressed the navigation and tracking problem for the autonomous navigation of UAS over these structures. Most proposed solutions rely on traditional computer vision strategies for tracking. As a drawback, depending on lighting conditions, the obtained path line may be inaccurate, making a strategy to force the UAS to continue on the path necessary. Therefore, this research describes the potential of an autonomous UAS based on image processing technique and Convolutional Neural Network (CNN) strategy to navigate appropriately in complex unburied pipeline networks contributing to the monitoring procedure of the Oil & Gas Industry structures. A CNN is used to detect the pipe, while image processing techniques such as Canny edge detection and Hough Transform are used to detect the pipe line reference, which is used by a line following algorithm to guide the UAS along the pipe. The framework is assessed by a PX4 flight controller Software-in-The-Loop (SITL) simulations performed with the Robot Operating System (ROS) along with the Gazebo platform to simulate the proposed operational environment and verify the approach’s functionality as a proof of concept. Real tests were also conducted. The results showed that the solution is robust and feasible to deploy in this proposed task, achieving 72% of mean average precision on detecting different types of pipes and 0.0111 m of mean squared error on the path following with a drone 2 m away from a tube.

Abstract
In recent years the Internet of Things, in addition to use cases in 'smart cities', has also increasingly been used in precision agriculture. As in the rest of the world, it has been a growing reality in Portugal. In an agricultural environment, where energy resources can be scarce and dispersed, the implementation of a LoRa network with autonomous sensor nodes must consider the limitations imposed by the energy consumed by the sensor node, when powered by a battery and a solar panel. For this, experimental tests must be carried out so that there is enough data for the implementation and optimization of the devices. This article presents a work focused on the study of the autonomy and energy efficiency of the sensor device, using algorithms capable of managing energy consumption as a function of the luminosity of the place. Preliminary results attest to the relevance of this approach, keeping the sensor node in operation without interruptions.

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