Contemporary mormon literature: Phyllis Barber's writing

612 p.

Keep an "eye" on the text: The Use of Self-mention in the Academic Writing of Experts

One of the most controversial inquiries in academic writing is whether it is admissible to use first person pronouns in a scientific paper or not. Many professors discourage their students from using them, rather favoring a more passive tone, and thus causing novices to avoid inserting themselves into their texts in an expert-like manner. Abundant research, however, has recently attested that negotiation of identity is plausible in academic prose, and there is no need for a paper to be void of an authorial identity. Because in the course of the English Studies Degree we have received opposing prompts in the use of I, the aim of this dissertation is to throw some light upon this vexed issue. To this end, I compiled a corpus of 16 Research Articles (RAs) that comprises two sub-corpora, one featuring Linguistics RAs and the other one Literature RAs, and each, in turn, consists of articles written by American and British authors. I then searched for real occurrences of I, me, my, mine, we, us, our and ours, and studied their frequency, rhetorical functions and distribution along each paper. The results obtained certainly show that academic writing is no longer the faceless prose that it used to be, for I is highly used in both disciplines and varieties of English. Concerning functions, the most typically used roles were the use of I to take credit for the writer’s research process, and also those involving plural forms. With respect to the spatial disposition, all sections welcomed first person pronouns, but the Method and the Results/Discussion sections seem to stimulate their appearance. On the basis of these findings, I suggest that an L2 writing pedagogy that is mindful not only of the language proficiency, but also of the students’ own identity may have a beneficial effect on the composition of their texts.

MW, Medical Watches

My project is a business plan about the set up of a company and the development of a new and innovative product aimed for the elders. I decide do this project when I discover that one of the more important needs that have the elders is to remember the medicines that they have to take. I thought that a good way could be through a smart watch. My watch have an only function, is a cheap device, easy to use, easy to understand and easy to set up, because the elders usually do not know to use complex electronics devices. There are other similar smart watches and other devices but do not have the necessary characteristics to be a good reminder for elders. My watch is centred to improve the life of the elders, but my product could also be useful for ill people who have to take many medicines during the day. After realizing this business plan, I have proved that my company is viable in the environment and profitable in the market.

Towards Application of One-Class Classification Methods to Medical Data

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques-Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.