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The use of the Internet now has a specific purpose: to find information. Unfortunately, the amount of data available on the Internet is growing exponentially, creating what can be considered a nearly infinite and ever-evolving network with no discernable structure. This rapid growth has raised the question of how to find the most relevant information. Many different techniques have been introduced to address the information overload, including search engines, Semantic Web, and recommender systems, among others. Recommender systems are computer-based techniques that are used to reduce information overload and recommend products likely to interest a user when given some information about the user's profile. This technique is mainly used in e-Commerce to suggest items that fit a customer's purchasing tendencies. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. More specifically, e-Democracy aims to increase citizens' participation in democratic processes through the use of information and communication technologies. In this chapter, an architecture of a recommender system that uses fuzzy clustering methods for e-Elections is introduced. In addition, a comparison with the smartvote system, a Web-based Voting Assistance Application (VAA) used to aid voters in finding the party or candidate that is most in line with their preferences, is presented.

Incidenceof out-of-hospital adult nontraumatic cardiac arrest presenting as a convulsion

Introduction: The majority of convulsions are due to an epileptic seizure or a convulsive syncope. The incidence of out-of-hospital cardiac arrest (OH-CA) presenting as a convulsion is unknown. Objective: This study aimed to measure the incidence of adult nontraumatic OH-CA presenting as a convulsion, a rate that has not been published so far, to the best of our knowledge. Methods: We prospectively collected all incoming calls with an out-of-hospital nontraumatic seizure as the chief complaint in patients >18 years old during a 24-month period. Among these calls, we collected cases identified as OH-CA by paramedics. Results: During the 24-month period, the emergency medical services (EMS) dispatch center received 561 calls for an out-of-hospital nontraumatic convulsion in an adult. Twelve cases were ultimately classified as CA. In this group, one bystander spontaneously reported that the patient was known for epilepsy. The incidence of OH-CA presenting as convulsions was therefore 2.1% of all calls for convulsion. Over the same period, the EMS dispatch center received 1,035 calls related to an adult nontraumatic OH-CA. Therefore, the rate of OH-CA presenting as a convulsion represented 1.2% of all adult nontraumatic OH-CA. Conclusion:L Only 12 cases out of the 531 calls for nontraumatic adult convulsions were confirmed OHCA (2.1%). Nevertheless, this unusual presentation of OH-CA must be recognized by dispatchers, even when a patient is reported by bystander as a known epileptic. Dispatchers should keep bystanders on the line or call them back before paramedics' arrival, and have them confirm the progressive return of a normal pat- tern of breathing and state of consciousness; if not, they should encourage the bystander to initiate CPR when necessary. An intervention should be implemented to improve the detection by dispatchers of OH-CA presenting as convulsion by the development of a specific interview and directed observation. For dispatchers, a past medical history of epilepsy should not be regarded as sufficient information to rule out OH-CA. It is mandatory that known epileptic patients should be monitored in the same way as nonepileptic patients.