An Analytic Hierarchy Process for The Evaluation of Transport Policies to Reduce Climate Change Impacts

Transport is the sector with the fastest growth of greenhouse gases emissions, both in developed and in developing countries, leading to adverse climate change impacts. As the experts disagree on the occurrence of these impacts, by applying the analytic hierarchy process (AHP), we have faced the question on how to form transport policies when the experts have different opinions and beliefs. The opinions of experts have been investigated by a means of a survey questionnaire. The results show that tax schemes aiming at promoting environmental-friendly transport mode are the best policy. This incentives public and environmental-friendly transport modes, such as car sharing and car pooling.Analytic Hierarchy Process, Transport Policies, Climate Change

How sustainability factors influence maintenance of water distribution systems feeding manufacturing industries

This work aims to analyse the role played by relevant sustainability factors towards the implementation of maintenance interventions in the manufacturing industrial sector. In this context, we focus on industrial water distribution systems, on whose effective work depends the functioning of core plants as well as general industrial facilities. In detail, we propose aMulti-Criteria Decision-Making (MCDM) application based on the use of the Analytic Network Process (ANP) as amethodological way to prioritise maintenance interventions while considering the influence of some of themost relevant sustainability factors identified in literature. The main advantage of such an approach consists in the elaboration of a flexible maintenance procedure for companies based on a well-known and reliablemulti-criteria application. The novelty of our work refers to the development of a structured link between sustainability factors and maintenance management of industrial water distribution systems, something that is fundamental in manufacturing but also in other fields of application

PageRank vs. ANP: A Comparative Analysis for Prioritizing Maintenance Activities in Industrial Water Distribution Systems.

This paper proposes the implementation of the PageRank algorithm as an alternative to the Analytic Network Process (ANP) for prioritizing maintenance activities in water distribution systems. We demonstrate the comparable performance of the PageRank algorithm to the ANP by comparing the results obtained from a previous conference paper that utilized the ANP for decision-making in sustainability-related problems involving water distribution systems feeding manufacturing industries. The ANP is commonly used for decision-making in complex systems, but has limitations such as subjective weighting and handling large datasets. In contrast, the PageRank algorithm, originally designed for web page ranking, offers a scalable and objective approach for analyzing complex systems. To showcase the effectiveness of the PageRank algorithm, we compare the results obtained from the ANP in our previous conference paper with the PageRank algorithm. Our findings reveal that the PageRank algorithm yields identical results to the ANP, while addressing its limitations. The results of this study demonstrate the viability and effectiveness of the PageRank algorithm in achieving identical outcomes as the ANP, with potential advantages in scalability and objectivity. The proposed implementation of the PageRank algorithm as an alternative to the ANP offers a promising approach for prioritizing maintenance activities in water distribution systems, as similar considerations can be extended to any sector of activity

A Multi-Objective Approach to Optimize a Periodic Maintenance Policy

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 non-linear 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 \u3b5-constraint methods. To explain the whole procedure a case study is solved and the related considerations are given

Diversity and regulatory impact of copy number variation in the primate Macaca fascicularis.

Copy number variations (CNVs) are a significant source of genetic diversity and commonly found in mammalian genomes. We have generated a genome-wide CNV map for Cynomolgus monkeys (Macaca fascicularis). This crab-eating macaque is the closest animal model to humans that is used in biomedical research. We show that Cynomolgus monkey CNVs are in general much smaller in size than gene loci and are specific to the population of origin. Genome-wide expression data from five vitally important organs demonstrates that CNVs in close proximity to transcription start sites associate strongly with expression changes. Among these eQTL genes we find an overrepresentation of genes involved in metabolism, receptor activity, and transcription. These results provide evidence that CNVs shape tissue transcriptomes in monkey populations, potentially offering an adaptive advantage. We suggest that this genetic diversity should be taken into account when using Cynomolgus macaques as models

Relationship of serum prolactin with severity of drug use and treatment outcome in cocaine dependence.

RATIONALE: Alteration in serum prolactin (PRL) levels may reflect changes in central dopamine activity, which modulates the behavioral effects of cocaine. Therefore, serum PRL may have a potential role as a biological marker of drug severity and treatment outcome in cocaine dependence. OBJECTIVE: We investigated whether serum PRL levels differed between cocaine-dependent (CD) subjects and controls, and whether PRL levels were associated with severity of drug use and treatment outcome in CD subjects. METHODS: Basal PRL concentrations were assayed in 141 African-American (AA) CD patients attending an outpatient treatment program and 60 AA controls. Severity of drug use was assessed using the Addiction Severity Index (ASI). Measures of abstinence and retention during 12 weeks of treatment and at 6-month follow-up were employed as outcome variables. RESULTS: The basal PRL (ng/ml) in CD patients (9.28+/-4.13) was significantly higher than controls (7.33+/-2.94) (t=3.77, P\u3c0.01). At baseline, PRL was positively correlated with ASI-drug (r=0.38, P\u3c0.01), ASI-alcohol (r=0.19, P\u3c0.05), and ASI-psychological (r=0.25, P\u3c0.01) composite scores, and with the quantity of cocaine use (r=0.18, P\u3c0.05). However, PRL levels were not significantly associated with number of negative urine screens, days in treatment, number of sessions attended, dropout rate or changes in ASI scores during treatment and at follow-up. Also, basal PRL did not significantly contribute toward the variance in predicting any of the outcome measures. CONCLUSION: Although cocaine use seems to influence PRL levels, it does not appear that PRL is a predictor of treatment outcome in cocaine dependence

A Feasible Framework for Maintenance Digitalization

The entire industry is changing as a result of new developments in digital technology, and maintenance management is a crucial procedure that may take advantage of the opportunities brought about by industrial digitalization. To support digital innovation in maintenance management, this study intends to meet the cutting-edge necessity of addressing a transformation strategy in industrial contexts. Setting up a customized pathway with adequate methodologies, digitalization tools, and collaboration between the several stakeholders involved in the maintenance environment is the first step in this process. The results of a previous conference contribution, which revealed important digitalization variables in maintenance management, served as the foundation for the research approach herein suggested. We lead a thorough assessment of the literature to categorize the potential benefits and challenges in maintenance digitalization to be assessed in conjunction with the important digitalization aspects previously stated. As a starting point for maintenance management transformation, we offer a feasible framework for maintenance digitalization that businesses operating in a variety of industries can use. As industrial processes and machines have become more sophisticated and complex and as there is a growing desire for more secure, dependable, and safe systems, we see that this transition needs to be tailored to the specific application context

Management of uncertain pairwise comparisons in AHP through probabilistic concepts

[EN] Fast and judicious decision-making is paramount for the success of many activities and processes. However, various degrees of difficulty may affect the achievement of effective and optimal solutions. Decisions should ideally meet the best trade-off among as many of the involved factors as possible, especially in the case of complex problems. Substantial cognitive and technical skills are indispensable, while not always sufficient, to carry out optimal evaluations. One of the most common causes of wrong decisions derives from uncertainty and vagueness in making forecasts or attributing judgments. The literature shows numerous efforts towards the optimization and modeling of uncertain contexts by means of probabilistic approaches. This paper proposes the use of probability theory to estimate uncertain expert judgments within the framework of the analytic hierarchy process and, more specifically, within a linearization scheme developed by the authors. After describing the necessary probabilistic concepts of interest, the main results are developed. These results can be summarized as using various kinds of random variables with uncertainty embodied in undecided pairwise comparisons. A case study focused on the maintenance management of an industrial water distribution system exemplifies the approach.Benítez López, J.; Carpitella, S.; Certa, A.; Izquierdo Sebastián, J. (2019). Management of uncertain pairwise comparisons in AHP through probabilistic concepts. Applied Soft Computing. 78:274-285. https://doi.org/10.1016/j.asoc.2019.02.020S2742857

Food safety risk analysis from the producers' perspective: prioritisation of production process stages by HACCP and TOPSIS

[EN] From the manufacturers perspective, the Hazard Analysis and Critical Control Point (HACCP) system nowadays represents the mainly way to implement the food safety risk management in food industries. Nevertheless, the identification and prioritization of hazards as the outcome of the first principle of HACCP is not sufficient to identify production process stages that more significantly and critically contribute to the consumer¿s risks. With this recognition, the present paper proposes a Quantitative Risk Assessment (QRA) approach based on HACCP and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to individuate production process phases on which implementing corrective actions to improve the consumers¿ safety. The designed methodological approach is implemented on the smoked salmon manufacturing process of a real Sicilian industry.Certa, A.; Enea, M.; Galante, G.; Izquierdo Sebastián, J.; La Fata, CM. (2018). Food safety risk analysis from the producers' perspective: prioritisation of production process stages by HACCP and TOPSIS. International Journal of Management and Decision Making. 17(4):396-414. https://doi.org/10.1504/IJMDM.2018.095720S39641417

Consistent Clustering of Elements in Large Pairwise Comparison Matrices

[EN] In multi-attribute decision making the number of decision elements under consideration may be huge, especially for complex, real-world problems. Typically these elements are clustered and then the clusters organized hierarchically to reduce the number of elements to be simultaneously handled. These decomposition methodologies are intended to bring the problem within the cognitive ability of decision makers. However, such methodologies have disadvantages, and it may happen that such a priori clustering is not clear, and/or the problem has previously been addressed without any grouping action. This is the situation for the case study we address, in which a panel of experts gives opinions about the operation of 15 previously established district metered areas in a real water distribution system. Large pairwise comparison matrices may also be found when building comparisons of elements using large bodies of information. In this paper, we address a consistent compression of an AHP comparison matrix that collapses the judgments corresponding to a given number of compared elements. As a result, an a posteriori clustering of various elements becomes possible. In our case study, such a clustering offers several added benefits, including the identification of hidden or unknown criteria to cluster the considered elements of the problem. (C) 2018 Elsevier B.V. All rights reserved.Benítez López, J.; Carpitella, S.; Certa, A.; Ilaya-Ayza, AE.; Izquierdo Sebastián, J. (2018). Consistent Clustering of Elements in Large Pairwise Comparison Matrices. Journal of Computational and Applied Mathematics. 343:98-112. https://doi.org/10.1016/j.cam.2018.04.041S9811234