0000000000873783
AUTHOR
Aiello Giuseppe
Economic benefits from food recovery at the retail stage: An application to Italian food chains
The food supply chain is affected by losses of products near to their expiry date or damaged by improper transportation or production defects. Such products are usually poorly attractive for the consumer in the target market even if they maintain their nutritional properties. On the other hand undernourished people face every day the problem of fulfilling their nutritional needs usually relying on non-profit organizations. In this field the food recovery enabling economic benefits for donors is nowadays seen as a coherent way to manage food products unsalable in the target market for various causes and thus destined to be discarded and disposed to landfill thus representing only a cost. Des…
SHIPPING 4.0: GENERAL FRAMEWORK FOR A NEW CYBERSHIPPING ERA
The fourth industrial revolution, also referred to as Industry 4.0, is a disruptive transformation process aimed which, by means of digital technologies, is substantially innovating the value creation processes in all industry contexts. With such premise, industry 4.0 is in particular expected to have a substantial impact on the maritime transport and shipping sectors, where smart ships and autonomous vessels are expected to be the building blocks of a new interconnected maritime ecosystem. The application of Industry 4.0 principles to the shipping domain (Shipping 4.0) has thus raised a scientific debate about the efficiency, sustainability, and safety of maritime transport. Recent researc…
Machine Learning approach towards real time assessment of hand-arm vibration risk
In industry 4,0, the establishment of an interconnected environment where human operators cooperate with the machines offers the opportunity for substantially improving the ergonomics and safety conditions of the workplace. This topic is discussed in the paper referring to the vibration risk, which is a well-known cause of work-related pathologies. A wearable device has been developed to collect vibration data and to segment the signals obtained in time windows. A machine learning classifier is then proposed to recognize the worker’s activity and to evaluate the exposure to vibration risks. The experimental results demonstrate the feasibility and effectiveness of the methodology proposed.