A constructive heuristic for the Undirected Rural Postman Problem

This paper describes a constructive heuristic for the well-known Undirected Rural Postman Problem. At each iteration, the procedure inserts a connected component of the required edges and performs a local postoptimization. Computational results on a set of benchmark instances with up to 350 vertices show that the proposed procedure is competitive with the classical Frederickson procedure. Its use is recommended when a high-quality solution is needed in a short amount of time (e.g., in laser plotter applications).     

Bayesian reliability assessment of repairable systems during multi-stage development programs

New repairable systems are generally subjected to development programs in order to improve system reliability before starting mass production. This paper proposes a Bayesian approach to analyze failure data from repairable systems undergoing a Test-Find-Test program. The system failure process in each testing stage is modeled using a Power-Law Process (PLP). Information on the effect of design modifications introduced into the system before starting a new testing stage is used, together with the posterior density of the PLP parameters at the current stage, to formalize the prior density at the beginning of the new stage. Contrary to the usual assumption, in this paper the PLP parameters are assumed to be dependent random variables. The system reliability is measured in terms of the number of failures that will occur in a batch of new units in a given time interval, for example the warranty period. A numerical example is presented to illustrate the proposed procedure.     

Modeling Discrete Bivariate Data with Applications to Failure and Count Data

In this study, we propose a new class of flexible bivariate distributions for discrete random variables. The proposed class of distribution is based on the notion of conditional failure rate for a discrete‐type random variable. We derive general formulae for the joint distributions belonging to the proposed class that, unlike other discrete bivariate models already proposed in the literature such as the well‐known and most popular Holgate's bivariate Poisson distribution, can model both positive and negative dependence. We discuss general statistical properties of the proposed class as well. Specific families of bivariate distributions can be generated from the general class proposed in this paper just by specifying the ‘baseline distributions’. Furthermore, specific discrete bivariate distributions belonging to the proposed class are applied to analyze three real data sets, and the results are compared with those obtained from conventional models. Copyright © 2017 John Wiley & Sons, Ltd.     

Modeling of the intestinal peptide transporter hPepT1 and analysis of its transport capacities by docking and pharmacophore mapping

An early pharmacokinetic screen for peptidomimetic drugs should have the ability to predict molecules with high affinity for intestinal transporters, as peptide-like derivatives are seldom absorbed passively. Hence, the first objective of this study was to generate a reliable model for the structure of the hPepT1 protein, which is the main intestinal transporter involved in the absorption of both dietary peptides and peptidomimetics. The modeling was based on the resolved structure of the homologous bacterial lactose permease LacY using a fragmental strategy. The interaction capacities of the hPepT1 model were explored by docking a set of 50 known ligands. Despite the known predilection of hPepT1 for hydrophobic ligands, docking results unveiled the key role of the polar interactions stabilized by charged termini, especially concerning the ammonium head group. The docking results were further verified by developing a pharmacophore model that confirmed the key features required for optimal hPepT1 affinity. The consistency of the docking results and the agreement with the pharmacophore model afford an encouraging validation for the proposed model and suggest that it can be exploited to design peptide-like molecules with an improved affinity for such a transporter.     

Glass and wine: a good example of the deep relationship between drinkware and beverage

As the choice of the drinkware could deeply affect consumer perception during beverage consumption, an experimental research was carried out by expert testers to investigate the evolution of chemical–physical and sensory profiles of a typical rosé wine maintained in different types of glasses. The aim of this research was to select some parameters to well correlate beverage sensory properties with glass characteristics which can be used for drinkware design. The different sensorial profiles, obtained as a function of the geometric characteristics of glasses, were compared with one another. In order to find any significant parameter useful to represent the time evolution of the wine inside each vessel, the wine poured in all glasses was also characterized at different times from a chemical and physical point of view. As the design parameters are now guided more by aesthetic reasons rather than by functional ones, this new ‘integrated approach’, deriving from the merging of chemical, physical and sensorial data, can be used to design the optimum vessel for the enjoyment of the consumer during assessing sessions. This innovative procedure could be easily adapted for every beverage such as fruit juices, sparkling wines or beer.     

Reliability Target Assessment Based on Integrated Factors Method (IFM): A Real Case Study of a Sintering Plant

The success of a company depends on customer’s satisfaction: quality, price, and service. These three goals depend in particular on R.A.M.S. characteristics: reliability, availability, maintainability, and safety. In the last few years, in order to guarantee high standards of reliability and maintainability, new methodologies and techniques have been developed to estimate the R.A.M.S. targets. In particular, the reliability target represents both the starting and the ending point of R.A.M.S. analysis. The design of the reliability target of a system is a crucial aspect of reliability analysis, as it affects the performance of the system and components. This paper aims to develop a new approach called “IFM Target,” to define the reliability target for complex systems through the integrated factors method, to combine the advantages of usually used approaches, and to overcome some criticalities highlighted in a careful literature analysis. The proposed method has been applied on a sintering system. The results show the effectiveness of the proposed approach.     

Allocating production batches to subcontractors by fuzzy goal programming

Allocating production batches to subcontractors arises frequently in industry. When subcontractors operate different equipment, batch assignment is a complex decision that must take into account both throughput and quality of finished goods. In this paper, a Mixed Integer Linear Goal Programming Model, where productivity uncertainty is taken into account through Fuzzy Set Theory, is developed. Our study was motivated by a real-world application arising in an Italian textile company. Computational results show that this method outperforms the hand-made solutions put to use by the management so far.     

Detection of Geometric Temporal Changes in Point Clouds

Detecting geometric changes between two 3D captures of the same location performed at different moments is a critical operation for all systems requiring a precise segmentation between change and no‐change regions. Such application scenarios include 3D surface reconstruction, environment monitoring, natural events management and forensic science. Unfortunately, typical 3D scanning setups cannot provide any one‐to‐one mapping between measured samples in static regions: in particular, both extrinsic and intrinsic sensor parameters may vary over time while sensor noise and outliers additionally corrupt the data. In this paper, we adopt a multi‐scale approach to robustly tackle these issues. Starting from two point clouds, we first remove outliers using a probabilistic operator. Then, we detect the actual change using the implicit surface defined by the point clouds under a Growing Least Square reconstruction that, compared to the classical proximity measure, offers a more robust change/no‐change characterization near the temporal intersection of the scans and in the areas exhibiting different sampling density and direction. The resulting classification is enhanced with a spatial reasoning step to solve critical geometric configurations that are common in man‐made environments. We validate our approach on a synthetic test case and on a collection of real data sets acquired using commodity hardware. Finally, we show how 3D reconstruction benefits from the resulting precise change/no‐change segmentation.