Applications of Hybrid Solar Streetlamps: Electrical Performance Measurements and Development of Algorithms for Their Optimal Management

This study examines the electrical performance and management of hybrid solar street lighting systems with the objective of optimizing their operation for sustainable urban development. Hybrid solar streetlights, which integrate photovoltaic panels with additional power sources, offer resilience and reliability that are crucial for urban settings. A hybrid solar streetlamp was installed in a city in central Italy and monitored for over a year to analyze its electrical behavior and the illuminances obtainable under different boundary conditions and operational programs. The measured data permit the development of an optimization algorithm in a Python program for the optimal management of the solar streetlamp and the forecasting of the battery charging/discharging cycles, as well as the electricity taken from the grid. The simulation scenarios permit the development of a novel management algorithm that is capable of optimizing the battery usage with a minimal draw on the grid in order to achieve a state of near self-sufficiency for the solar streetlamp. The results demonstrate that tilted solar panels enhance energy production, while optimized LED power profiles and system management enhance efficiency. The study highlights the importance of maintaining the state of charge (SOC) of the battery above 20% to extend its lifetime and reduce replacement needs. Economic analysis indicates significant potential energy savings, emphasizing the necessity of system optimization for economic viability and environmental sustainability in urban lighting. Despite initial investment costs and challenges, adopting hybrid solar lighting in urban environments presents substantial benefits, paving the way for a more sustainable and energy-efficient urban future.

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A Metaverse: Taxonomy, Components, Applications, and Open Challenges

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is based on the social value of Generation Z that online and offline selves are not different. With the technological development of deep learning-based high-precision recognition models and natural generation models, Metaverse is being strengthened with various factors, from mobile-based always-on access to connectivity with reality using virtual currency. The integration of enhanced social activities and neural-net methods requires a new definition of Metaverse suitable for the present, different from the previous Metaverse. This paper divides the concepts and essential techniques necessary for realizing the Metaverse into three components (i.e., hardware, software, and contents) and three approaches (i.e., user interaction, implementation, and application) rather than marketing or hardware approach to conduct a comprehensive analysis. Furthermore, we describe essential methods based on three components and techniques to Metaverse’s representative Ready Player One, Roblox, and Facebook research in the domain of films, games, and studies. Finally, we summarize the limitations and directions for implementing the immersive Metaverse as social influences, constraints, and open challenges.

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Autonomous Detection and Deterrence of Pigeons on Buildings by Drones

Pigeons may transmit diseases to humans and cause damages to buildings, monuments, and other infrastructure. Therefore, several control strategies have been developed, but they have been found to be either ineffective or harmful to animals and often depend on human operation. This study proposes a system capable of autonomously detecting and deterring pigeons on building roofs using a drone. The presence and position of pigeons were detected in real time by a neural network using images taken by a video camera located on the roof. Moreover, a drone was utilized to deter the animals. Field experiments were conducted in a real-world urban setting to assess the proposed system by comparing the number of animals and their stay durations for over five days against the 21-day-trial experiment without the drone. During the five days of experiments, the drone was automatically deployed 55 times and was significantly effective in reducing the number of birds and their stay durations without causing any harm to them. In conclusion, this study has proven the effectiveness of this system in deterring birds, and this approach can be seen as a fully autonomous alternative to the already existing methods.

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