Many studies on distance perception in a virtual environment exist. Most of them were conducted using head-mounted displays (HMD) and less with large screen displays such as CAVE systems. In this paper, we propose to measure the accuracy of perceived distances in a virtual space ranging from 0 to 15 m in a CAVE system compared to an HMD. Eight subjects with different vision performances took part in an experiment. Results show that the HMD provides the best results for distances above 8 m while the CAVE provides the best results for close distances.

This paper describes some results of authors' research in machine vision, as a key technology, essential in connecting the manufacturing processes with the digital twin of the manufacturing architecture. The focus is on designing a system that uses machine vision in combination with deep learning algorithms. The result is a human-like vision system for realtime analysis and interpretation of all video streams of cameras to continuously detect and count the various products on the production line. All detected features and counting information are available in real-time by augmenting on a video screen and streamed to the cloud where they can be easily processed and stored.

As formed by humans, which are living creatures full of contradictions, our society is characterized as well by lots of paradoxes. One could say that it has never been so wealthy and educated, while others would declare themselves as being grateful for a simple glass of water or a slice of bread, as the world wide abundance of goods and opportunities is counterpointed by deep scarcity, sometimes not too far from the sources of waste. Therefore, quite large amounts of edible food that could have been consumed end up in landfills, thus contributing to environmental pollution and social disparities. Despite many studies conducted in order to better understand the causes of this phenomenon, and although at the EU and UN level some actions were taken in order to reduce consumer food waste, the topic still remains open and it lacks a clear and impactful approach. In this light, we made use of the results of previous studies, and we built the causal model, FEED, based on system dynamics, with the aim to explore the impact of the evolution of educational attainment on the aggregate of household food waste. We then translated the model into the tenet of the dynamic simulation software, TRUE. There was no reinforcing loop displayed by FEED causal loop version, fact aligned with the evolution of our goal-variable when the simulation of the model was run, a result that make us to suspect the possibility of reducing food waste in the foreseeable future.

Augmented Reality (AR) is a technology widely used in smart manufacturing, which makes data visible in real time, while the user is interacting with the real world. This paper describes a new web based AR application developed by the authors for smart manufacturing architectures, providing portability to any device, free from any existing AR platforms based on reading and interpreting markers placed on different components of industrial machines. The application provides useful real-time information to the operators: technical manuals, operating diagrams, maintenance history, components availability in the warehouse or in supplier's stock with the possibility of direct order, training videos for maintenance and work safety. Also, being connected to the smart manufacturing software and performing easier data analysis, the application is especially useful for time crucial predictive maintenance.

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