The CERN Large Hadron Collider is currently being upgraded to operate at a stored beam energy of 680 MJ through the High Luminosity upgrade. The LHC performance is dependent on the functionality of beam collimation systems, essential for safe beam cleaning and machine protection. A dedicated beam experiment at the CERN High Radiation to Materials facility is created under the HRMT-23 experimental campaign. This experiment investigates the behavior of three collimation jaws having novel composite absorbers made of copper diamond, molybdenum carbide graphite, and carbon fiber carbon, experiencing accidental scenarios involving the direct beam impact on the material. Material characterization is imperative for the design, execution, and analysis of such experiments. This paper presents new data and analysis of the thermostructural characteristics of some of the absorber materials commissioned within CERN facilities. In turn, characterized elastic properties are optimized through the development and implementation of a mixed numerical-experimental optimization technique.

Nowadays mobile devices have more processing power and are capable of performing complex tasks such as processing multimedia data. Free-viewpoint TV is one such complex task, where the user can choose a custom viewpoint from where to view a scene. In this work, the depth-imagebased rendering (DIBR) technique is used to generate this virtual view by utilizing the Graphical Processing Unit (GPU) of the mobile device. The limited resources of the device were taken into consideration when designing the system. Experimental results show that the maximum performance achieved for a 1024 × 768 resolution video is 19.17 frames per second and the best Peak Signal-to-Noise Ratio (PSNR) value for this resolution is 35.09 dB.

The industrial sector is a crucial economic pillar, seeing annual increases in the production output. In the last few years, a greater emphasis has been placed on the efficient and sustainable use of resources within industry. The use of compressed air in this field is hence gaining interest. These systems have numerous benefits, such as relative low investment costs and reliability; however, they suffer from low-energy efficiency and are highly susceptible to faults. Conventional detection systems, such as ultrasonic leak detection, can be used to identify faults. However, these methods are time consuming, meaning that leakages are often left unattended, contributing to additional energy wastage. Studies published in this area often focus on the supply side rather than the demand side of pneumatic systems. This paper offers a novel review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology of fault detection methods on the demand side of compressed air systems, leading towards a comprehensive understanding of smart and sustainable pneumatic systems. Fifty-three studies were classified and reviewed under the following three areas: (a) demand parameters which help in identifying fault sources; (b) approaches taken to analyse the parametric data; and (c) the role of Artificial Intelligence (AI) in pneumatic fault monitoring systems. This review shows that fault detection on the demand side has received greater importance in the last five years and that data analysis is crucial for AI to be implemented correctly. Nevertheless, it is clear that further research in this sector is essential, in order to investigate more complex systems. It is envisaged that this study can promote the adoption of such systems, contributing to an energy-efficient and cost-effective industry.

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