Heart rate, pr, and qt intervals in normal children: A 24‐hour holter monitoring study

A dynamic electrocardiographic Holter monitoring study was performed in 32 healthy children (20 males and 12 females, age range 6-11 years old), without heart disease, according to clinical and noninvasive instrumental examination. We evaluated atrioventricular conduction time (PR), heart rate (HR), and QT interval patterns defining the range of normality of these electrocardiographic parameters. The PR interval ranged from 154 +/- 10 ms (mean +/- SD) for HR less than or equal to 60 to 102 +/- 12 ms for HR greater than or equal to 120 (range 85-180). The absolute mean HR was 87 +/- 10 beats/min (range 72-104), the minimum observed HR being 61 +/- 10 (range 51-79), the maximum 160 +/- 20 beats/min (range 129-186). Daytime mean HR gave a mean value of 93 +/- 10 (range 71-148), while during night hours it was 74 +/- 11 (range 54-98). The minimum QT interval averaged 261 +/- 10 ms for HR greater than 120 and the maximum 389 +/- 9 ms for HR less than or equal to 60; the corresponding mean value of QTc (i.e., QT corrected for HR) ranged from 388 +/- 8 for HR less than or equal to 60 beats/min to 403 +/- 14 ms for HR greater than 120 beats/min. The results of the present study provide data of normal children which can be readily compared against those of subjects in whom cardiac abnormalities are suspect or patient.(ABSTRACT TRUNCATED AT 250 WORDS

What About my Privacy, Habibi? Understanding Privacy Concerns and Perceptions of Users From Different Socioeconomic Groups in the Arab World

This paper contributes an in-depth understanding of privacy concerns and perceptions of Arab users. We report on the first comparison of privacy perceptions among (1) users from high socioeconomic groups in Arab countries (HSA), (2) users from medium to low socioeconomic groups in Arab countries (LSA), and (3) as a baseline, users from high socioeconomic groups in Germany (HSG). Our work is motivated by the fact that most research in privacy focused on Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. This excludes a segment of the population whose cultural norms and socioeconomic status influence privacy perception and needs. We report on multiple novel findings and unexpected similarities and differences across the user groups. For example, shoulder surfing is more common across LSA and HSG, and defamation is a major threat in LSA. We discuss the implications of our findings on the design of privacy protection measures for investigated groups

Voices Raised, Issue 06

Included in this issue: Immaculate Mary; Grants augment women’s research; Mentoring grows; Women’s Studies take root in the neighborhood; Solution-oriented VP to retire; Muslim students strive to educate, support; Don’t let stress ruin your holidays; Dining services dishes up more than you’d expect; Marianist Images Across Campus; Confronting Disrespect: We Owe it to Each Other.https://ecommons.udayton.edu/wc_newsletter/1005/thumbnail.jp

Applicazioni di analisi statistica di dati testuali

Pubblicazione di lavori selezionati della giornata di studio su "Applicazioni di analisi statistica di dati testuali", che testimoniano il passaggio della statistica linguistica alla statistica testuale.Il volume presenta una panoramica dei campi di applicazione del settore e una indagine sulla comunità degli studiosi di questo ambito. Una vasta bibliografia ne documenta i tipi di lavori

Network Selection: A Method for Ranked Lists Selection

We consider the problem of finding the set of rankings that best represents a given group of orderings on the same collection of elements (preference lists). This problem arises from social choice and voting theory, in which each voter gives a preference on a set of alternatives, and a system outputs a single preference order based on the observed voters’ preferences. In this paper, we observe that, if the given set of preference lists is not homogeneous, a unique true underling ranking might not exist. Moreover only the lists that share the highest amount of information should be aggregated, and thus multiple rankings might provide a more feasible solution to the problem. In this light, we propose Network Selection, an algorithm that, given a heterogeneous group of rankings, first discovers the different communities of homogeneous rankings and then combines only the rank orderings belonging to the same community into a single final ordering. Our novel approach is inspired by graph theory; indeed our set of lists can be loosely read as the nodes of a network. As a consequence, only the lists populating the same community in the network would then be aggregated. In order to highlight the strength of our proposal, we show an application both on simulated and on two real datasets, namely a financial and a biological dataset. Experimental results on simulated data show that Network Selection can significantly outperform existing related methods. The other way around, the empirical evidence achieved on real financial data reveals that Network Selection is also able to select the most relevant variables in data mining predictive models, providing a clear superiority in terms of predictive power of the models built. Furthermore, we show the potentiality of our proposal in the bioinformatics field, providing an application to a biological microarray dataset