Inertial Motions of a Rigid Body with a cavity filled with a viscous liquid

We study inertial motions of the coupled system, S, constituted by a rigid body containing a cavity that is completely filled with a viscous liquid. We show that for data of arbitrary size (initial kinetic energy and total angular momentum) every weak solution (a la Leray-Hopf) converges, as time goes to infinity, to a uniform rotation, thus proving a famous "conjecture" of Zhukovskii. Moreover we show that, in a wide range of initial data, this rotation must occur along the central axis of inertia of S that has the largest moment of inertia. Furthermore, necessary and sufficient conditions for the rigorous nonlinear stability of permanent rotations are provided, which improve and/or generalize results previously given by other authors under different types of approximation of the original equations and/or suitable symmetry assumptions on the shape of the cavity. Finally, we present a number of results obtained by a targeted numerical simulation that, on the one hand, complement the analytical findings, whereas, on the other hand, point out new features that the analysis is yet not able to catch, and, as such, lay the foundation for interesting and challenging future investigation.Comment: Some of the main results proved in this paper were previously announced in Comptes Rendus Mecanique, Vol. 341, 760--765 (2013

Model checking usage policies

We study usage automata, a formal model for specifying policies on the usage of resources. Usage automata extend finite state automata with some additional features, parameters and guards, that improve their expressivity. We show that usage automata are expressive enough to model policies of real-world applications. We discuss their expressive power, and we prove that the problem of telling whether a computation complies with a usage policy is decidable. The main contribution of this paper is a model checking technique for usage automata. The model is that of usages, i.e. basic processes that describe the possible patterns of resource access and creation. In spite of the model having infinite states, because of recursion and resource creation, we devise a polynomial-time model checking technique for deciding when a usage complies with a usage policy

A Computationally Light Pruning Strategy for Single Layer Neural Networks based on Threshold Function

Embedded machine learning relies on inference functions that can fit resource-constrained, low-power computing devices. The literature proves that single layer neural networks using threshold functions can provide a suitable trade off between classification accuracy and computational cost. In this regard, the number of neurons directly impacts both on computational complexity and on resources allocation. Thus, the present research aims at designing an efficient pruning technique that can take into account the peculiarities of the threshold function. The paper shows that feature selection criteria based on filter models can effectively be applied to neuron selection. In particular, valuable outcomes can be obtained by designing ad-hoc objective functions for the selection process. An extensive experimental campaign confirms that the proposed objective function compares favourably with state-of-the-art pruning techniques

Debits and Credits in Petri Nets and Linear Logic

Exchanging resources often involves situations where a participant gives a resource without obtaining immediately the expected reward. For instance, one can buy an item without paying it in advance, but contracting a debt which must be eventually honoured. Resources, credits and debits can be represented, either implicitly or explicitly, in several formal models, among which Petri nets and linear logic. In this paper we study the relations between two of these models, namely intuitionistic linear logic with mix and Debit Petri nets. In particular, we establish a natural correspondence between provability in the logic, and marking reachability in nets

A survey on deep learning in image polarity detection: Balancing generalization performances and computational costs

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost

Vicious circles in contracts and in logic

Contracts are formal promises on the future interactions of participants, which describe the causal dependencies among their actions. An inherent feature of contracts is that such dependencies may be circular: for instance, a buyer promises to pay for an item if the seller promises to ship it, and vice versa. We establish a bridge between two formal models for contracts, one based on games over event structures, and the other one on Propositional Contract Logic. In particular, we show that winning strategies in the game-theoretic model correspond to proofs in the logi

Gas turbine prime movers fuelled by LNG as a future alternative for sustainable power in marine propulsion: current emission policy assessment and exhaust quality evaluation

In recent years, climate change has dramatically shown its effects in terms of fluctuations of temperatures because of increased global warming due to greenhouse gas emissions. Pollution control is strongly linked to atmospheric acidification and contaminants in combustion exhausts. In such a contest, marine propulsion is actually a key player that is accounting for a substantial contribution to pollution. Kyoto protocol’s obligations established decarbonisation as a compulsory commitment and contaminant constraints are defined by current emission policy and regulations both on a global and on a regional basis. In this paper, a study is carried out in order to develop a framework for current emission policy and assessment of exhausts due to constraints imposed on fuel choice. Gas fuelled marine propulsion, implemented through state-of-the-art GT areoderivative prime movers, powered by LNG, is analysed from the environmental point of view. The pollutant emissions from various size GT models are evaluated through both GT datasheets and commercial (as well as self-coded) software, in order to assess the validity of LNG as an alternative fuel option for future sustainable marine applications

Feasibility of mini combined cycles for naval applications

The objective of energy production with low environmental impact will have, in the near future, high potential of development also for naval applications. The containment of pollutant emissions can be achieved by the combined use of an innovative mini gas-steam combined cycle with thermal energy cogeneration to feed the ship thermal utilities, in place of the current Diesel engine application, and liquefied natural gas as fuel (LNG). The present work is focused on the definition of the architecture of the plant, by selecting optimal distribution of pressure and temperature and repartition of power between Gas Turbine (GT), Steam Turbine (ST) and thermal utilities, as well as on the choice and sizing of the individual components. The main purpose is the definition of a compact, high efficiency, system. The proposed basic mini-cycle ranges from 2 MW to 10 MW electric power. Thanks to the combined heat and power cogeneration plant adopted, for an overall electrical efficiency of about 30%, a total return (thermal + electricity) of about 75% can be achieved. An example of plant providing large power, in a partially modular arrangement is also proposed