Marco Lorenzini

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A back-propagation neural network to predict the carcinogenicity of aromatic nitrogen compounds was developed. The inputs were molecular descriptors of different types: electrostatic, topological, quantum-chemical, physicochemical, etc. For the output the index TD50 as introduced by Gold and colleagues was used, giving a continuous numerical parameter(More)
Until recently, problem solvers have typically used single-technique-based tools to build the solution. Also in the field of predictive toxicology, a few systems have been developed in that way, with positive preliminary results. One approach to deal with real complex systems is to use two or more techniques in order to combine their different strenghts and(More)
One approach to deal with real complex systems is to use two or more techniques in order to combine their different strengths and overcome each other’s weakness to generate hybrid solutions. In this project we pointed out the needs of an improved system in toxicology prediction. An architecture able to satisfy these needs has been developed. The main tools(More)
PV (photovoltaic) solar panels generally produce electricity in the 6% to 12% efficiency range, the rest is being dissipated in thermal losses. To recover this amount, hybrid photovoltaic thermal systems (PV/T) have been devised. These are devices that simultaneously convert solar energy into electricity and heat. It is thus interesting to study the PV/T(More)
Micro heat exchangers (MHXs) may achieve very high heat transfer coefficients thanks to their small dimensions and high Area-to-Volume ratio even in laminar flow. The main drawback of these devices is the high frictional losses – especially for liquid flows – that make viscous dissipation no longer negligible. In order to enhance heat transfer, modification(More)
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