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Manufacturing tiny components into gigantic structures seems unimaginable without welding connections, whether it is for materials, parts, structures, or equipment. In the recent competitive market scenario, manufacturing industries are desperately looking for a viable alternative to fusion-based conventional material joining technologies, to improve upon cost-effectiveness along with performance. The present investigation is to evaluate the performance of structural steel thick plate joints prepared by advanced solid-state friction stir welding (FSW) over conventional metal active gas welding (MAG). The FSW experiments were carried out with different tool designs and configurations. The outcomes were evaluated in terms of microstructural integrity and mechanical joint resistance. Impressive results were obtained with FSW joints, characterized by an almost defect-free microstructure with a leaner heat-affected zone and higher joint resistance as compared to conventional MAG-welded joints.

Background Leishmaniasis is one of the most important vector-borne diseases and it represents a serious world health problem affecting millions of people. High levels of Leishmania infections, affecting both humans and animals, are recognized among Italian regions. Among these, Sicily has one of the highest prevalence of Leishmania infection. Methodology/Principal Findings Seventy-eight Leishmania strains isolated from human and animal samples across Sicily, were analyzed for the polymorphic k26-gene and genotypes were assigned according to the size of the PCR products. A multilocus microsatellite typing (MLMT) approach based on the analysis of 11 independent loci was used to investigate populations structure and genetic diversity of the isolated strains. Six L. infantum reference strains were included in the analysis for comparison. Bayesian clustering analysis of microsatellite data showed that all the isolated strains clustered in two genetically distinct populations, corresponding to human and canine isolates respectively. A further subdivision was observed between the two main groups, giving a good correlation between human strains and their geographic origin, conversely canine population showed a great genetic variability diffused in the territory. Conclusions/Significance Among the 78 Leishmania isolates, K26 analysis detected 71 samples (91%) as MON-1 zymodeme, confirming it as the predominant strain in Mediterranean area and 7 human samples (9%) as non-MON-1. MLMT gives important insights into the epidemiology of leishmaniases and allows characterization of different strains to a higher resolution than possible with zymodeme typing. Two main populations presented a strong correlation respect to the different hosts, exhibiting a co-circulation of two distinct populations of L. infantum. The population found in infected humans exhibited a correlation with geographic origin. These clusters could represent a geographically restricted population of strains with the same or related genotypes. This study can contribute to an understanding of Leishmania epidemiology, including the spread of reservoirs and sand fly vectors in the different foci of infection, characterizing parasites within the different hosts., Author summary High levels of Leishmania spp. infections affecting both humans and animals are recognized among Italian regions; in particular, Sicily is an endemic area for Leishmania infantum. In this study 78 Sicilian L. infantum strains isolated from humans and dogs were assessed to investigate their biodiversity by genetic tools. Results were compared with 6 L. infantum reference strains included in the analysis. The evaluation of K26 genetic markers identified 91% of samples as belonging to the MON-1 zymodeme, confirming it as the predominant strain in the Mediterranean area and 9% of the samples–all isolated from humans–as non-MON-1. Multilocus microsatellite typing has proven to be a powerful tool to discriminate strains showing all the isolated strains clustered in two genetically distinct populations, corresponding to human and canine isolates, respectively. A further subdivision was observed between the two main groups, giving in the human population a correlation between microsatellite profile and geographical origin. Our results demonstrate that genetic tools are able to discriminate Leishmania strains and to give useful insights into the epidemiology of leishmaniasis, raising questions on the role of dogs as main reservoirs for human leishmaniasis in the Sicily region.

Nowadays, both continuous and discontinuous operating systems require higher and higher reliability levels in order to avoid the occurrence of dangerous or even disastrous consequences. Accordingly, the definition of appropriate maintenance policies and the identification of components to be maintained during the planned system’s downtimes are fundamental to ensure the reliability maximization. Therefore, the present paper proposes a mathematical programming formulation of the selective maintenance problem with the aim to maximize the system’s reliability under an uncertain environment. Specifically, the aleatory model related to the components’ failure process is well known, whereas some model parameters are affected by epistemic uncertainty. Uncertain parameters are hence gathered from experts in an interval form, and the Dempster-Shafer Theory (DST) of evidence is proposed as a structured methodology to properly deal with the interval-valued experts’ opinions. An exact and efficient algorithm is finally used to solve the optimization model.

In this paper a new approach was proposed so as to comparatively evaluate the quality of service alternatives. In particular, a fuzzy extension of the ServPerf service conceptual model was considered to estimate quality scores of fundamental service criteria, whereas the non-compensative multi-criteria decision-making ELECTRE III method was employed to point out the quality ranking of service alternatives on the basis of which the comparative service quality analysis was performed. In order to show the effectiveness of the proposed approach, an empirical study concerning service quality evaluation of the three international airports in Sicily (Italy) was conducted with detailed proposals for passenger service improvement. The results showed that only few key service aspects played a focal role in quality airport service. Moreover, the effects on the evaluation of service quality, arising from customers' uncertainties, were computed, thus demonstrating the effectiveness of the proposed approach.

In recent years, the attention that the European Community has focused on the education sector has produced a new university commitment addressed to quality aspects for all education related services. In fact, a quality oriented service requires excellence in the design and planning of service activities, as well as during its delivering and also for the adopted service performance evaluation method. However, considering that service performance evaluations are deeply based on stakeholders' judgments, they can be characterized by possible uncertainties related to incompleteness for partial ignorance, imprecision for subjectivity and even vagueness. Therefore, under these conditions, unreliable results can be obtained by widely considered service analysis methods. In the present paper, a method based on a recent extension of the ServQual model and that uses in combined manner the Fuzzy Set Theory and the Analytic Hierarchy Process method is proposed to effectively handle uncertainty in service performance analyses. In particular, the Fuzzy Set Theory is considered to deal with such uncertainty, whereas the AHP method is adopted as tool to estimate the importance weights of the strategic service attributes. Subsequently, the strategic analysis of the service value tree related to the Management Engineering program at the University of Palermo (Italy) is performed by using the proposed method. The performed service analysis allows the most influencing service performance factors to be captured and commented upon. Finally, the obtained results show that the professors' perception of service quality meaningfully influences the overall service performance level.

The problem tackled by the present paper concerns the selection of the elements of a repairable and stochastically deteriorating multi-component system to replace (replacements configuration) during each scheduled and periodical system stop within a finite optimization cycle, by ensuring the simultaneous minimization of both the expected total maintenance cost and the system unavailability. To solve the considered problem, a combined approach between multi-objective optimization problem (MOOP) and multi-criteria decision making (MCDM) resolution techniques is proposed. In particular, the @e constraint method is used to single out the optimal Pareto frontier whereas the ELECTRE III multi-criteria decision support method is proposed to support the selection of the replacements configuration that represents the best compromise among the considered objectives. The proposed approach is sequentially applied at each scheduled system stop by allowing the dynamic updating of the information about the decisional context in which the decision maker has to operate. To illustrate the whole procedure a numerical case study is solved for different hypothesized scenarios related to the importance attributed by the decision maker to the system unavailability and the maintenance cost objectives.

The present paper proposes an original and innovative cooling law in the field of Simulated Annealing (SA) algorithms. Particularly, such a law is based on the evolution of different initial seeds on which the algorithm works in parallel. The efficiency control of the new proposal, executed on problems of different kind, shows that the convergence quickness by using such a new cooling law is considerably greater than that obtained by traditional laws. Furthermore, it is shown that the effectiveness of the SA algorithm arising from the proposed cooling law is independent of the problem type. This last feature reduces the number of parameters to be initially fixed, so simplifying the preliminary calibration process necessary to optimize the algorithm efficiency.

The present paper proposes a multi-objective approach to find out an optimal periodic maintenance policy for a repairable and stochastically deteriorating multi-component system over a finite time horizon. The tackled problem concerns the determination of the system elements to replace at each scheduled and periodical system inspection by ensuring the simultaneous minimization of both the expected total maintenance cost and the expected global system unavailability time. It is assumed that in the case of system elements failure they are instantaneously detected and repaired by means of minimal repair actions in order to rapidly restore the system. A nonlinear integer mathematical programming model is developed to solve the treated multi-objective problem, whereas the Pareto optimal frontier is described by the Lexicographic Goal Programming and the ε-constraint methods. To explain the whole procedure, a case study is solved and the related considerations are given.

In recent years, a variety of enhanced schemes for the c chart have been developed aimed at the effectiveness improving of the related statistical process control (SPC) procedures. However, the performance of such chart schemes can be very sensitive to values assumed for some operating and cost parameters, in particular to the considered process shift magnitude arising from out-of-control conditions. In such a circumstance, the effectiveness of such chart schemes can be subjected to substantial reductions when they are implemented in operative contexts given that, in practice, such value is unknown and it can be very difficult to accurately estimate. For this reason, in the present paper it is developed an economic optimization approach able to find out the optimal configurations of the main adaptive c chart schemes. Subsequently, economic performances of such configurations are compared in order to derive information on their relative economic robustness, considering the difference between the estimated process shift magnitude and the actual one as influential factor and costs arising from the related SPC operations as performance comparison index. In addition, also a sensitivity analysis based on a [Formula: see text] fractional factorial design scheme, to investigate on influence of several operating and cost parameters on charts economic robustness, is carried out and the related considerations are given. The obtained results show that the parameters having the most impact on economic robustness, when the out-of-control process shift is not accurately estimated, are the cost associated with the production of non-conforming parts and the failure rate of the manufacturing system.

The aim of the present paper is to develop a model for an effective maintenance policy with refer to a global service contract between a services provider company and a company for the waste management. The contract requires, with fixed performance levels of the service, the supplying of a mandatory set of maintenance services on a set of waste compactors vehicles of the outsourcer company. In particular, the service provider (SP) must perform corrective maintenance actions and the replacement of the fault parts. The tackled problem concerns the determination of an effective opportunistic maintenance policy in order to assure the required service performance levels at the minimum global maintenance cost. The problem is mathematically formulated by a constrained partition model that becomes difficult or very hard to solve by mathematical approach for large practical systems as that one here considered. For this reason, in order to solve the proposed problem of maintenance services optimisation, a genetic approach is developed.