The aim of this research was to identify students' preconceptions concerning statistical association in contingency tables. An experimental study was carried out with 213 pre-university students, and it was based on students' responses to a written questionnaire including 2x2, 2x3 and 3x3 contingency tables. In this article, the students' judgments of association and solution strategies are compared with finding of previous psychological research on 2x2 contingency tables. We also present an original classification of students' strategies, from a mathematical point of view. Correspondence analysis is used to show the effect of item task variables on students' strategies. Finally, we include a qualitative analysis of the strategies of 51 students, which has served to characterize three misconceptions concerning statistical association.

When telecommunication infrastructure is damaged by natural disasters, creating a network that can handle voice channels can be vital for search and rescue missions. Unmanned Aerial Vehicles (UAV) equipped with WiFi access points could be rapidly deployed to provide wireless coverage to ground users. This WiFi access network can in turn be used to provide a reliable communication service to be used in search and rescue missions. We formulate a new problem for UAVs optimal deployment which considers not only WiFi coverage but also the mac sublayer (i.e., quality of service). Our goal is to dispatch the minimum number of UAVs for provisioning a WiFi network that enables reliable VoIP communications in disaster scenarios. Among valid solutions, we choose the one that minimizes energy expenditure at the user's WiFi interface card in order to extend ground user's smartphone battery life as much as possible. Solutions are found using well-known heuristics such as K-means clusterization and genetic algorithms. Via numerical results, we show that the IEEE 802.11 standard revision has a decisive impact on the number of UAVs required to cover large areas, and that the user's average energy expenditure (attributable to communications) can be reduced by limiting the maximum altitude for drones or by increasing the VoIP speech quality.

The new capabilities of autonomous cars can be used to mitigate to a large extent safety concerns and nuisance traditionally associated with double parking. In this paper double parking for autonomous cars is proposed as a new approach to temporarily increase parking capacity in locations in clear need for extra provision when best alternatives cannot be found. The basic requirements, operation, and procedures of the proposed solution are outlined. A curbside parking has been simulated implementing the suggested double parking operation and important advantages have been identified for drivers, the environment, and the city. Double parking can increase over 50% the parking capacity of a given area. Autonomous car owners would (at least) double their probabilities of finding parking compared to traditional drivers, saving cruising time and emissions. However, significant work and technological advances are still needed in order to make this feasible in the near future.

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Voice over IP (VoIP) applications can choose a plethora of different speech codecs, which differ in bandwidth, listening speech quality, and resilience to quality degradation under packet loss. However, VoIP Codecs also exhibit differences in facets such as computational complexity or traffic generated that impact on the energy consumption of smartphones due to the use of processor.This work deals with the study of energy consumption differences among VoIP codecs. We compare the execution time required to encode/decode reference conversations. Our results show that computational complexity has a significant impact on battery consumption (a factor of up to 10 was found between different codecs). Based on our results, we provide a ranking of energy efficiency. We also propose a simple algorithm for codec dynamic selection considering the factors of quality, energy and bandwidth. Our algorithm reacts to network conditions choosing the codec that provides less battery consumption constrained to user-defined targets for minimum quality and maximum codec bitrate.

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