Critical Flow Method: A New Reliability Allocation Approach for a Thermonuclear System

In the present work, starting from well‐known methodologies, a new reliability allocation method [critical flow method (CFM)] has been proposed. We focused on the most important conventional methods and discussed their limitations in order to motivate the current research. The results show the main common problem of the most conventional reliability allocation methods: they are developed for complex systems with series configurations but not for series–parallel ones. The consequence is an increase of the required units' reliability (series configuration) in order to guarantee the reliability system target. Actually, the design and manufacturing of a subsystem with an extremely low failure rate would consume a considerable amount of economic resources. The proposed method can solve the shortcomings of the conventional methods with a new reliability approach useful to series–parallel configurations in order to obtain an important cost saving. The CFM has been applied to a liquid nitrogen cooling installation in a thermonuclear system, with many series–parallel configurations in order to guarantee the whole safety system. The proposed technique can be applied to working complex systems, and, in general, in the design phase of new installations. By comparing the CFM application results with real parameters, the new technique has been validated. The computational results clearly demonstrate the advantages of the proposed method. In particular, by applying the method to series–parallel configurations, it allocates failure rates higher than conventional methods, with a component cost reduction. Copyright © 2015 John Wiley & Sons, Ltd.     

Heat and mass transfer modeling during continuous flow processing of fluid food by direct steam injection

Fluid food products, in the agri-food industry, are commonly subject to thermal treatments to ensure their safety and quality characteristics. Therefore, these treatments must be accurately selected and monitored to avoid over-processing, as consumer safety and product acceptability must be preserved.     

Modelling preventive measures and their effect on generation times in emerging epidemics

We present a stochastic epidemic model to study the effect of various preventive measures, such as uniform reduction of contacts and transmission, vaccination, isolation, screening and contact tracing, on a disease outbreak in a homogeneously mixing community. The model is based on an infectivity process, which we define through stochastic contact and infectiousness processes, so that each individual has an independent infectivity profile. In particular, we monitor variations of the reproduction number and of the distribution of generation times. We show that some interventions, i.e. uniform reduction and vaccination, affect the former while leaving the latter unchanged, whereas other interventions, i.e. isolation, screening and contact tracing, affect both quantities. We provide a theoretical analysis of the variation of these quantities, and we show that, in practice, the variation of the generation time distribution can be significant and that it can cause biases in the estimation of reproduction numbers. The framework, because of its general nature, captures the properties of many infectious diseases, but particular emphasis is on COVID-19, for which numerical results are provided.     

An approximate A algorithm and its application to the SCS problem

In this paper we deal with algorithm A^* and its application to the problem of finding the shortest common supersequence of a set of sequences. A^* is a powerful search algorithm which may be used to carry out concurrently the construction of a network and the solution of a shortest path problem on it. We prove a general approximation property of A^* which, by building a smaller network, allows us to find a solution with a given approximation ratio. This is particularly useful when dealing with large instances of some problem. We apply this approach to the solution of the shortest common supersequence problem and show its effectiveness.     

A self-training automatic infant-cry detector

Neural Computing and Applications - Infant cry is one of the first distinctive and informative life signals observed after birth. Neonatologists and automatic assistive systems can analyse infant...     

A transformed gamma process for bounded degradation phenomena

Most of the stochastic models adopted to describe the evolution over time of degradation phenomena of technological units assume that their degradation level can increase indeterminately. However, these degradation phenomena are typically subjected to obvious bounds, if only because technological units have finite size. In fact, very often, this inconsistency does not significantly affect the effectiveness of unbounded degradation models, since degrading units are usually assumed to fail when their degradation level exceeds a failure threshold that is much smaller than the obvious bounds. Nevertheless, in some cases, due to the very nature of the underlying degradation mechanism, less obvious bounds could exist, which are not necessarily far from the failure thresholds. The question that arises is whether the use of a bounded degradation model, in this latter type of experimental situations, could be beneficial. For this purpose, since a bounded degradation process should necessarily have dependent increments, in this paper we investigate the potential of a new bounded transformed gamma (TG) process to adequately describe bounded degradation phenomena and predict their future evolution. Differently from other existing gamma process based bounded degradation models, here the upper bound is treated as an unknown parameter that has to be estimated from the available degradation data. A numerical example is presented where the parameters of the proposed model are estimated from simulated data. Then the model is applied to a set of wear measures of cylinder liners that equip a diesel engine for marine propulsion, which have also stimulated this study. Model parameters are estimated by using the maximum likelihood (ML) method. The fitting ability of the proposed new bounded process is compared to that of an unbounded gamma process, which was previously adopted to analyze the same liner wear data. Obtained results are critically discussed in the paper.