Abstract
Purpose - The intent of this work is to evaluate several generalist and health-specific search engines for retrieval of health information by consumers: to compare the retrieval effectiveness of these engines for different types of clinical queries, medical specialties and condition severity; and to compare the use of evaluation metrics for binary relevance scales and for graded ones. Design/methodology/approach - The authors conducted a study in which users evaluated the relevance of documents retrieved by four search engines for two different health information needs. Users could choose between generalist (Bing, Google, Sapo and Yahoo!) and health-specific (MedlinePlus, SapoSande and WebMD) search engines. The authors then analysed the differences between search engines and groups of information needs with six different measures: graded average precision (gap), average precision (ap), gap@5, gap@10, ap@5 and ap@10. Findings The results show that generalist web search engines surpass the precision of health-specific engines. Google has the best performance, mainly in the top ten results. It was found that information needs associated with severe conditions are associated with higher precision, as are overview and psychiatry questions. Originality/value - The study is one of the first to use a recently proposed measure to evaluate the effectiveness of retrieval systems with graded relevance scales. It includes tasks from several medical specialties, types of clinical questions and different levels of severity which, to the best of the authors' knowledge, has not been clone before. Moreover, users have considerable involvement in the experiment. The results help in understanding how search engines differ in their responses to health information needs, what types of online health information are more common on the web and how to improve this type of search.

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Abstract
We propose to use Vehicular ad hoc networks (VANET) as the infrastructure for an urban cyber-physical system for gathering up-to-date data about a city, like traffic conditions or environmental parameters. In this context, it is critical to design a data collection protocol that enables retrieving the data from the vehicles in almost real-time in an efficient way for urban scenarios. We propose Back off-based Per-hop Forwarding (BPF), a broadcast-based receiver-oriented protocol that uses the destination location information to select the forwarding order among the nodes receiving the packet. BFP does not require nodes to exchange periodic messages with their neighbors communicating their locations to keep a low management message overhead. It uses geographic information about the final destination node in the header of each data packet to route it in a hop-by-hop basis. It takes advantage of redundant forwarding to increase packet delivery to a destination, what is more critical in an urban scenario than in a highway, where the road topology does not represent a challenge for forwarding. We evaluate the performance of the BPF protocol using ns-3 and a Manhattan grid topology and compare it with well-known broadcast suppression techniques. Our results show that BPF achieves significantly higher packet delivery rates at a reduced redundancy cost. © 2011 IEEE.

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Abstract

Abstract
Metaheuristics Algorithms are widely recognized as one of most practical approaches for Combinatorial Optimization Problems. This paper presents a comparison between two metaheuristics to solve a problem of Phase Balancing in Low Voltage Electric Distribution Systems. Among the most representative mono-objective metaheuristics, was selected Simulated Annealing, to compare with a different metaheuristic approach: Evolutionary Particle Swarm Optimization. In this work, both of them are extended to fuzzy domain to modeling a multi-objective optimization, by mean of a fuzzy fitness function. A simulation on a real system is presented, and advantages of Swarm approach are evidenced.