Multi-objective optimization of environmentally conscious chemical supply chains under demand uncertainty

In this work, we analyze the effect of demand uncertainty on the multi-objective optimization of chemical supply chains (SC) considering simultaneously their economic and environmental performance. To this end, we present a stochastic multi-scenario mixed-integer linear program (MILP) with the unique feature of incorporating explicitly the demand uncertainty using scenarios with given probability of occurrence. The environmental performance is quantified following life cycle assessment (LCA) principles, which are represented in the model formulation through standard algebraic equations. The capabilities of our approach are illustrated through a case study. We show that the stochastic solution improves the economic performance of the SC in comparison with the deterministic one at any level of the environmental impact.

On Modeling an Innovative Monitoring Network for Protecting and Managing Cultural Heritage from Risk Events

In this paper the model of an Innovative Monitoring Network involving properly connected nodes to develop an Information and Communication Technology (ICT) solution for preventive maintenance of historical centres from early warnings is proposed. It is well known that the protection of historical centres generally goes from a large-scale monitoring to a local one and it could be supported by a unique ICT solution. More in detail, the models of a virtually organized monitoring system could enable the implementation of automated analyses by presenting various alert levels. An adequate ICT solution tool would allow to define a monitoring network for a shared processing of data and results. Thus, a possible retrofit solution could be planned for pilot cases shared among the nodes of the network on the basis of a suitable procedure utilizing a retrofit catalogue. The final objective would consist in providing a model of an innovative tool to identify hazards, damages and possible retrofit solutions for historical centres, assuring an easy early warning support for stakeholders. The action could proactively target the needs and requirements of users, such as decision makers responsible for damage mitigation and safeguarding of cultural heritage assets.

Distortion of self-image: risk factor for obesity in children and teenagers

Introduction: Self-image is important in the behaviour and lifestyle of children and adolescents. Analysing the self-image they have and the factors that might influence their distortion, can be used to prevent problems of obesity and anorexia. The main objective of present publication was to analyse the risk factors that may contribute to self-image distortion. Material and Methods: A descriptive survey study was conducted among 659 children and adolescents in two social classes (low and medium-high), measuring height and weight, calculating BMI percentile for age and gender. Body image and self-perception were registered. Results: The percentage of overweight-obesity is higher in scholars (41.8% boys, 28.7% girls) than in adolescents (30.1% and 22.2% respectively), with no difference between socioeconomic classes. The multinomial logistic regression analysis gives a risk of believing thinner higher (p=0.000) among boys OR=2.9(95%CI:1.43-3.37), school (p=0.000) OR=2.42(95%CI:1.56-3.76) and much lower (p=0.000) between normally nourished OR=0.08(95%CI:0.05-0.13), with no differences according to socioeconomic status. The risk of believing fatter is lower (p=0.000) between boys OR=0.28(95%CI:0.14-0.57), school(p=0.072) OR=0.54(95%CI:0.27-1.6), and much higher among underweight (p=0.000) OR=9x108(95% CI:4x108-19x108). Conclusions: Are risk factors of believing thinner: males, being in a group of schoolchildren and overweight-obesity. Conversely, are risk factors of believing fatter: females, teen and above all, be thin.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.