Angelica Gonzalez

Synaesthesia is a condition in which one type of stimulation evokes the sensation of another, as when the hearing of a sound produces photisms, i.e. mental percepts of colours. R is a 20 year old colour blind subject who, in addition to the relatively common grapheme-colour synaesthesia, presents a rarely reported cross modal perception in which a variety of visual stimuli elicit aura-like percepts of colour. In R, photisms seem to be closely related to the affective valence of stimuli and typically bring out a consistent pattern of emotional responses. The present case study suggests that colours might be an intrinsic category of the human brain. We developed an empirical methodology that allowed us to study the subject's otherwise inaccessible phenomenological experience. First, we found that R shows a Stroop effect elicited by photisms despite the fact that he does not show a regular Stroop with real colours. Secondly, by manipulating the colour context we confirmed that colours can alter R's emotional evaluation of the stimuli. Furthermore, we demonstrated that R's auras may actually lead to a partially inverted emotional spectrum where certain stimuli bring out emotional reactions opposite to the normal ones. These findings can only be accounted for by considering R's subjective colour experience or qualia. Therefore the present paper defends the view that qualia are a useful scientific concept that can be approached and studied by experimental methods

In recent years, a new factor for work stress has been studied along with stress as an offense to self-theory. Illegitimate tasks refer to assignments that are unnecessary or are not related to the employee’s role. Because of this, the Bern Illegitimate Tasks Scale was developed, which measures illegitimate tasks in terms of unreasonable tasks and unnecessary tasks. There are no studies in Latin America on illegitimate tasks, so the purpose of this research is to translate and validate the Bern Illegitimate Tasks Scale. The study was performed with a sample of nursing staff from a hospital in Guayaquil, Ecuador. Written informed consent was obtained from each of the participants. The reliability of the questionnaire was evaluated and its structural validity was verified by exploratory factor analysis and confirmatory factor analysis. The internal consistency of the whole scale, measured by Cronbach’s alpha, was 0.857. Moreover, the unnecessary and unreasonable subscales measure were 0.846 and 0.841, respectively. The exploratory factor analysis supported a two-factor model that explained 73.96% of the variance. Additionally, the confirmatory factor analysis showed good indexes of fit. The Spanish version of the Bern Illegitimate Tasks Scale presents good psychometric properties and can be applied to nurses in the Ecuadorian population.

Databases play a fundamental role in today’s world, being used by most companies, especially those that offer services through the Internet. Today there is a wide variety of database models, each adapted for use according to the specific requirements of each application. Traditionally, the relational models with centralized architectures have been used mostly due to their simplicity and general-purpose query language, which made relational systems suitable for almost any application. However, with the growth of the Internet in recent decades, both in the number of users and in the amount of information, those centralized models began to suffer availability and scalability issues. To address those issues, the use of decentralized architectures and alternative database models began to arise, eventually replacing relational databases and centralized architectures when the requirements on availability and scalability are high. Those database models alternative to the traditional relational model are grouped under the name of NoSQL. In this article, we present a NoSQL database developed as an end of degree work, with a flexible data model based on documents and a fully decentralized architecture based on the Gossip protocol for node discovery and a distributed hash table, in particular the rendezvous hashing algorithm, used to distribute and replicate the data across all the nodes. The main goals of the system are to achieve high availability and high scalability. High availability is achieved thanks to the replication of the data, while high scalability is achieved by its decentralized architecture, which allows multiple entry points from the requests, and the data distribution, effectively increasing the database capacity by increasing the number of nodes.

Clustering is a branch of multivariate analysis that is used to create groups of data. Most of the existing clustering techniques require defining additional information, including the actual number of clusters, before they can be carried out. This article presents a novel neural network that is capable of creating groups by using a combination of hierarchical clustering and self-organizing maps, without requiring the number of existing clusters to be specified beforehand. The self-organized cluster automatic detection neural network is described in detail, focusing on the density, the average distance, the division algorithm, the update algorithm and the training phase. Three case studies have been carried out in this research in order to evaluate the performance of the neural network, and the results obtained are presented within this article