Abstract
The great incidence of cardiovascular (CV) diseases in the world spurs the search for new solutions to enable an early detection of pathological processes and provides more precise diagnosis based in multi-parameters assessment. The pulse wave velocity (PWV) is considered one of the most important clinical parameters for evaluate the CV risk, vascular adaptation, and therapeutic efficacy. Several studies were dedicated to find the relationship between PWV measurement and pathological status in different diseases, and proved the relevance of this parameter. The commercial devices dedicate to PWV estimation make a regional assessment (measured between two vessels), however a local measurement is more precise evaluation of artery condition, taking into account the differences in the structure of arteries. Moreover, the current devices present some limitations due to the contact nature. Emerging trends in CV monitoring are moving away from more invasive technologies to non-invasive and non-contact solutions. The great challenge is to explore the new instrumental solutions that allow the PWV assessment with fewer approximations for an accurately evaluation and relatively inexpensive techniques in order to be used in the clinical routine.

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Abstract
The high penetration of wind and photovoltaic power in electricity markets will represent a major challenge in the forthcoming years. The main problem of both technologies is the high uncertainty in their production and their dependence on environmental conditions. The coordination between wind and photovoltaic power aims to lower imbalances, reducing their associated penalties. This paper describes two strategies: i) separate wind and photovoltaic strategy and ii) single wind-photovoltaic strategy. The strategies proposed are solved through stochastic mixed integer linear programming. The expected profits are maximized and they are obtained by selling the energy in the day-ahead market. The imbalances are penalized in the balancing market as well. The model is tested for a week, 168 hours, and the data used come from the Spanish electricity market. The results of the case study are discussed, comparing both strategies. Following the discussion, the most important conclusions are presented.

Abstract
Nowadays, organizations are often faced with the development of complex and innovative projects. This type of projects often involves performing tasks which are subject to failure. Thus, in many such projects several possible alternative actions are considered and performed simultaneously. Each alternative is characterized by cost, duration, and probability of technical success. The cost of each alternative is paid at the beginning of the alternative and the project payoff is obtained whenever an alternative has been completed successfully. For this problem one wishes to find the optimal schedule, i.e., the starting time of each alternative, such that the expected net present value is maximized. This problem has been recently proposed in Ranjbar (Int Trans Oper Res 20(2):251-266, 2013), where a branch-and-bound approach is reported. Since the problem is NP-Hard, here we propose to solve the problem using genetic algorithms.

Abstract
The continuous change in the manufacturing world is demanding more flexible, responsive and accurate planning tools, which are able to assist the decision-makers to take tactical and strategic decisions on short notice with a high level of confidence. For this purpose, these tools should dynamically explore different operative scenarios in the planning procedure and produce information about key performance indicators. This paper describes the development of an agent-based strategic planner, combining the flexibility of multi-agent systems principles with the optimization capability of a Mixed Integral Programming technique. The tool is integrated in an ecosystem of heterogeneous decision-making systems through an Enterprise Service Bus that also provides access to legacy data. © 2015 IEEE.