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A significant amount of electricity in numerous regions worldwide is used for lighting roads, squares, and other public spaces. Renewable energy can contribute notably to electricity usage for public lighting. This paper focuses on the day‐ahead scheduling of a hybrid renewable energy system (HRES) exploiting solar–wind energy potential to meet the electrical energy needs of public lighting. The studied HRES provides electricity for a Wi‐Fi hotspot and a charging hotspot for the end users and has an energy storage system that ensures a reliable electricity supply without interruptions. The day‐ahead scheduling of the studied HRES is based on electricity generation forecasting by using a deep‐learning approach. Particularly, the long short‐term memory model is utilized, considering the fact that it is able to perceive long‐term dependencies among the time series. Moreover, the model's performance is investigated through the determination of diverse inputs: (a) historical data, (b) weather predictions, and (c) historical data with weather predictions. Multiple scenarios of energy consumption are assumed and applied to optimize the day‐ahead scheduling. A new recommendation method is proposed and applied for day‐ahead scheduling, utilizing the power forecasts to achieve optimum operation and energy savings. The results point out that the utilization of the proposed recommendation method controls loads when a shortage in power generation and battery capacity is forecasted for the day ahead, leading to significant energy savings and minimizing the power demand from the grid. [ABSTRACT FROM AUTHOR]

Collaboration is a key requirement in several contemporary interventional radiology procedures (IRPs). This work proposes a multicast hybrid satellite system capable of supporting advanced IRP collaboration, and evaluates its feasibility and applicability. Following a detailed IRP requirements study, we have developed a system which supports IRP collaboration through the employment of a hybrid satellite-terrestrial network, a prototype multicast version of wavelet based interactive communication system (WinVicos) application, and a partition aggregation and conditional coding (PACC) wavelet codec. A semistructured questionnaire was also used to receive evaluative feedback from collaborating participants. The departments of interventional radiology of University Hospital of Patras, Greece and of Charite Hospital of Berlin, Germany have been connected on the system. Eight interventional radiologists and a vascular surgeon participated periodically in three satellite-terrestrial "fully collaborative" IRPs (average time 90 min) of high complexity and in four terrestrial educational sessions with great success, evidenced by considerable improving the IRP outcomes (clinical and educational). In case of high complexity, where the simultaneous presence of remote interventional expert and/or surgeon is required, advanced collaboration among staff of geographically dispersed international centers is feasible via integration of existing networking and other technologies. [ABSTRACT FROM AUTHOR]