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This article adopts the assumption that different wireless access technologies (e.g., UMTS, HIPERLAN2, and DVB-T) can be cooperating components of a composite radio infrastructure. The coordinated management of services and network in this heterogeneous context is a complex task. This article presents a service and network resource management platform for wireless systems that operate in a composite radio context. Aspects addressed are the requirements, high-level platform design, functionality of the components that conduct monitoring, resource brokerage, service management, and network resource optimization. Indicative results are presented and concluding remarks provided.

This paper investigates the different possibilities of architectural integration of active solar systems on the facades and roofs of existing buildings, as well as in the urban open spaces of urban centres of countries such as Greece and Cyprus. It includes the presentation of the various possibilities of architectural integration of active solar systems and an analysis of the benefits and the difficulties caused by the particularities of the urban fabric and of the buildings that define it.

Calibration of highly dynamic multi-physics manufacturing processes such as electrohydrodynamics-based additive manufacturing (AM) technologies (E-jet printing) is still performed by labor-intensive trial-and-error practices. Such practices have hindered the broad adoption of these technologies, demanding a new paradigm of self-calibrating E-jet printing machines. Here we develop an end-to-end physics-informed Bayesian learning framework (GPJet) which can learn the jet process dynamics with minimum experimental cost. GPJet consists of three modules: the machine vision module, the physics-based modeling module, and the machine learning (ML) module. GPJet was tested on a virtual E-jet printing machine with in-process jet monitoring capabilities. Our results show that the Machine Vision module can extract high-fidelity jet features in real-time from video data using an automated parallelized computer vision workflow. The Machine Vision module, combined with the Physics-based modeling module, can also act as closed-loop sensory feedback to the Machine Learning module of high- and low-fidelity data. This work extends the application of intelligent AM machines to more complex working conditions while reducing cost and increasing computational efficiency.