Mathematical modelling of tilt-rotor aircraft configurations. A comprehensive model for flight control system development and real-time piloted simulation.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Dilution Robustness for Mean Field Ferromagnets

In this work we compare two different random dilution of a mean field ferromagnet: the first model is built on a Bernoulli-diluted network while the second lives on a Poisson-diluted network. While it is known that the two models have in the thermodynamic limit the same free energy we investigate on the structural constraints that the two models must fulfill. We rigorously derive for each model the set of identities for the multi-overlaps distribution using different methods for the two dilutions: constraints in the former model are obtained by studying the consequences of the self-averaging of the internal energy density, while in the latter are obtained by a stochastic-stability technique. Finally we prove that the identities emerging in the two models are the same, showing "robustness" of the ferromagnetic properties of diluted networks with respect to the details of dilution.Comment: To appear on Journal of Statistical Mechanic

Strategic environmental assessment implementation of transport and mobility plans. The case of italian regions and provinces

Transport and mobility plans imply strategies and actions that affect the environment. The European Union has introduced in 2001 the strategic environmental assessment (SEA) to take into account and mitigate adverse environmental effects in planning and decision-making. SEA limited implementation has attracted the interest of many scholars that have sought methods able to assess the quality of SEA processes by identifying vices and virtues in practice. In this paper, we measure the quality of eight SEAs for transport and mobility plans of regional and provincial administrations of Italy. Results show that the overall quality level of SEA reports is only barely sufficient, Abruzzo is among the virtuous and Piedmont among the critical administrations. We also stress that the determination of impact significance has received the worse quality score. We finally compare our results to other Italian and British homologous cases finding interesting and generally confirmative evidence

Parallel learning by multitasking neural networks

Parallel learning, namely the simultaneous learning of multiple patterns, constitutes a modern challenge for neural networks. While this cannot be accomplished by standard Hebbian associative neural networks, in this paper we show how the multitasking Hebbian network (a variation on the theme of the Hopfield model, working on sparse datasets) is naturally able to perform this complex task. We focus on systems processing in parallel a finite (up to logarithmic growth in the size of the network) number of patterns, mirroring the low-storage setting of standard associative neural networks. When patterns to be reconstructed are mildly diluted, the network handles them hierarchically, distributing the amplitudes of their signals as power laws w.r.t. the pattern information content (hierarchical regime), while, for strong dilution, the signals pertaining to all the patterns are simultaneously raised with the same strength (parallel regime). Further, we prove that the training protocol (either supervised or unsupervised) neither alters the multitasking performances nor changes the thresholds for learning. We also highlight (analytically and by Monte Carlo simulations) that a standard cost function (i.e. the Hamiltonian) used in statistical mechanics exhibits the same minima as a standard loss function (i.e. the sum of squared errors) used in machine learning

Parallel Learning by Multitasking Neural Networks

A modern challenge of Artificial Intelligence is learning multiple patterns at once (i.e.parallel learning). While this can not be accomplished by standard Hebbian associative neural networks, in this paper we show how the Multitasking Hebbian Network (a variation on theme of the Hopfield model working on sparse data-sets) is naturally able to perform this complex task. We focus on systems processing in parallel a finite (up to logarithmic growth in the size of the network) amount of patterns, mirroring the low-storage level of standard associative neural networks at work with pattern recognition. For mild dilution in the patterns, the network handles them hierarchically, distributing the amplitudes of their signals as power-laws w.r.t. their information content (hierarchical regime), while, for strong dilution, all the signals pertaining to all the patterns are raised with the same strength (parallel regime). Further, confined to the low-storage setting (i.e., far from the spin glass limit), the presence of a teacher neither alters the multitasking performances nor changes the thresholds for learning: the latter are the same whatever the training protocol is supervised or unsupervised. Results obtained through statistical mechanics, signal-to-noise technique and Monte Carlo simulations are overall in perfect agreement and carry interesting insights on multiple learning at once: for instance, whenever the cost-function of the model is minimized in parallel on several patterns (in its description via Statistical Mechanics), the same happens to the standard sum-squared error Loss function (typically used in Machine Learning)

Perylene-diimide molecules with cyano functionalization for electron-transporting transistors

Core-cyanated perylene diimide (PDI_CY) derivatives are molecular compounds exhibiting an uncommon combination of appealing properties, including remarkable oxidative stability, high electron affinities, and excellent self-assembling properties. Such features made these compounds the subject of study for several research groups aimed at developing electron-transporting (n-type) devices with superior charge transport performances. After about fifteen years since the first report, field-effect transistors based on PDI_CY thin films are still intensely investigated by the scientific community for the attainment of n-type devices that are able to balance the performances of the best p-type ones. In this review, we summarize the main results achieved by our group in the fabrication and characterization of transistors based on PDI8-CN2 and PDIF-CN2 molecules, undoubtedly the most renowned compounds of the PDI_CY family. Our attention was mainly focused on the electrical properties, both at the micro and nanoscale, of PDI8-CN2 and PDIF-CN2 films deposited using different evaporation techniques. Specific topics, such as the contact resistance phenomenon, the bias stress effect, and the operation in liquid environment, have been also analyzed

Redundancy Optimization Strategy for Hands-On Robotic Surgery

During hands-on cooperative surgery, the use of a redundant robot allows to address encumbrance issues in the Operating Room (OR), which can occur due to the presence of large medical instrumentation, such as the surgical microscope. This work presents a new Null Space Optimization (NSO) strategy to constraint the position of the manipulator’s elbow within predefined range of motions, according to the spatial requirements of the specific procedure, also taking into account the physical joint limits of the robotic assistant. The proposed strategy was applied to the 7 degrees of freedom (dof) lightweight robot LWR4+. The performance of the NSO was compared to two state-of-the-art null space optimization strategies, i.e. damped posture and fixed optimal posture, over a pool of three non-expert users in both strict (20deg) and negligible (100deg) angular encumbrance limitations. The NSO strategy was proved versatile in providing wide elbow mobility together with safe distance from relevant continuity null space boundaries, guaranteeing smooth guidance trajectories. Future works would be performed in order to evaluate the potential feasibility of NSO in a real surgical scenario