Computing the Types of the Relationships Between Autonomous Systems

We investigate the problem of computing the types of the relationships between Internet Autonomous Systems. We refer to the model introduced by Gao [IEEE/ACM Transactions on Networking, 9(6):733-645, 2001] and Subramanian (IEEE Infocom, 2002) that bases the discovery of such relationships on the analysis of the AS paths extracted from the BGP routing tables. We characterize the time complexity of the above problem, showing both NP-completeness results and efficient algorithms for solving specific cases. Motivated by the hardness of the general problem, we propose approximation algorithms and heuristics based on a novel paradigm and show their effectiveness against publicly available data sets. The experiments provide evidence that our algorithms perform significantly better than state-of-the-art heuristics     

A network flow based heuristic approach for optimising AGV movements

Automated Guided Vehicles (AGVs) are driverless carriers that automatically navigate along planned paths by means of several guidance and control methods. This paper proposes an approach for solving the dispatching problem in an AGV system. The problem is modelled through a network by relying on the formulation of a Minimum Cost Flow Problem. In the defined graph, the nodes represent transportation tasks and AGVs while the arcs consider, through the associated weights, several system’s aspects such as pick, drop, and travel times, battery recharging, capacity constraints, congestion and error issues. Two objectives can be achieved: (i) minimisation of the average time for carrying out transportation tasks or (ii) maximisation of the utilisation degree of AGVs. The modelling and solution approach adopted has provided a novel Vehicle–Initiated dispatching rule and parameters settings for the dynamic assignments of transportation missions to AGVs. The decision making process concurrently and dynamically considers several factors. The results show a relevant reduction in the average time for transportation order fulfilment and a decrease in its variability. The proposed approach has been exploited for optimising the AGVs performance in a pharmaceutical production system.     

Learning optimal trajectories for non-holonomic systems

Many advanced robotic systems are subject to non-holonomic constraints, e.g. wheeled mobile robots, space manipulators and multifingered robot hands. Steering these mechanisms between configurations in the presence of perturbations is a difficult problem. In fact, the divide et impera strategy (first plan a trajectory, then track it by feedback) has a fundamental drawback in this case: due to the peculiar control properties of non-holonomic systems, smooth feedback cannot provide tracking of the whole trajectory. As a result, it would be necessary to give up either accuracy in the final positioning or predictability of the actual motion. We pursue here a different approach which does not rely on a separation between planning and control. Based on the learning control paradigm, a robust steering scheme is devised for systems which can be put in chained form, a canonical structure for non-holonomic systems. By overparametrizing the control law, other performance goals can be met, typically expressed as cost functions to be minimized along the trajectory. As a case study, we consider the generation of robust optimal trajectories for a car-like mobile robot, with criteria such as total length, maximum steering angle, distance from workspace obstacles, or error with respect to an offline planned trajectory.     

The ObReco-360°: a new ecological tool to memory assessment using 360° immersive technology

Virtual Reality - One important feature of a neuropsychological test is its ecological validity, which defines how much patients’ test scores are linked to real-life functioning. However,...     

Kinematic viscosity studies of the binary ethane- 1,2-diol/ n,n-dimethylformamide solvent system at various temperatures

Kinematic viscosity data (ν) are presented for ethane-1,2-diol, for N, N-dimethylformamide and for nine binary mixtures in the - 10°C to 80°C temperature range. The experimental data were fitted by several empirical relations such as Grunberg-Nissan (1949), McAllister (1960), Heric (1966), Lobe (1973), Auslander (1964) and polynomial equations. Some considerations regarding the meaning of the obtained adjustment parameters are made. Furthermore, a new best fit equation is proposed and its validity has been compared to that of the other relations on the basis of the reproducibility of the experimental data.     

The Metabolic Role of Gut Microbiota in the Development of Nonalcoholic Fatty Liver Disease and Cardiovascular Disease

The prevalence of metabolic disorders, such as type 2 diabetes (T2D), obesity, and non-alcoholic fatty liver disease (NAFLD), which are common risk factors for cardiovascular disease (CVD), has dramatically increased worldwide over the last decades. Although dietary habit is the main etiologic factor, there is an imperfect correlation between dietary habits and the development of metabolic disease. Recently, research has focused on the role of the microbiome in the development of these disorders. Indeed, gut microbiota is implicated in many metabolic functions and an altered gut microbiota is reported in metabolic disorders. Here we provide evidence linking gut microbiota and metabolic diseases, focusing on the pathogenetic mechanisms underlying this association.     

Grain-Boundary Viscosity of Calcium-Doped Silicon Nitride

Internal-friction data of calcium-doped Si₃N₄ polycrystalline materials that otherwise contain only SiO₂ at grain boundaries were examined and compared with those collected on the same polycrystal in the undoped state or doped with anions (i.e., fluorine and chlorine). Precise microstructural characterizations previously performed on these materials enabled us to quantitatively evaluate the inherent viscosity of the intergranular SiO₂ film through the analysis of the anelastic internal-friction-peak components. The intergranular glass viscosity and its scaling with increasing calcium addition followed the same trend as bulk SiO₂ glasses with the same chemical composition. Broadening of the internal-friction peak with increased calcium content in the material has also been rationalized according to the reduction of the activation energy for the viscous flow of bulk glasses. The present analysis, which is in agreement with our previous studies on undoped and anion-doped Si₃N₄, has demonstrated that the overall viscoelastic response of the polycrystal is mainly dictated by the chemistry of the intergranular glass.     

Optimum design of linear tuned mass dampers for structures with nonlinear behaviour

This paper investigates the optimum design of tuned mass dampers (TMDs) for the seismic protection of inelastic structures. A single linear TMD is treated and is assumed to be applied to a single nonlinear degree of freedom system, described by the Bouc–Wen hysteretic model. The seismic load is modelled by a stationary filtered stochastic process to consider its intrinsic stochastic nature. The optimization problem is set by taking into consideration three different possible objective functions (OFs): the maximum of the peak structural displacement standard deviation, the average hysteretic dissipated energy of a protected building with reference to an unprotected one, and a functional damage that considers the two indexes previously described. Different numerical examples and parametric analysis are shown to confront the three optimization criteria and to determine the best tuning frequency and damping ratio of the TMDs to be used in the structure. Results confirm that the application of a TMD system reduces the amount of the hysteretic dissipated energy, which is a direct measure of damage in the structure, and so it is beneficial for the protection of buildings that develop a nonlinear behaviour under severe dynamic loadings.