Text categorization, which consists of automatically assigning documents to a set of categories, usually involves the management of a huge number of features. Most of them are irrelevant and others introduce noise which could mislead the classifiers. Thus, feature reduction is often performed in order to increase the efficiency and effectiveness of the classification. In this paper, we propose to select relevant features by means of a family of linear filtering measures which are simpler than the usual measures applied for this purpose. We carry out experiments over two different corpora and find that the proposed measures perform better than the existing ones.

Biological rhythms are driven by endogenous biological clocks; in mammals, the master clock is located in the suprachiasmatic nucleus (SCN) of the hypothalamus. This master pacemaker can synchronize other peripheral oscillators in several tissues such as some involved in endocrine or reproductive functions. The presence of an endogenous placental clock has received little attention. In fact, there are no studies in human full-term placentas. To test the existence of an endogenous pacemaker in this tissue we have studied the expression of circadian locomoter output cycles kaput (Clock), brain and muscle arnt-like (Bmal)1, period (Per)2, and cryptochrome (Cry)1 mRNAs at 00, 04, 08, 12, 16, and 20 hours by qPCR. The four clock genes studied are expressed in full-term human placenta. The results obtained allow us to suggest that a peripheral oscillator exists in human placenta. Data were analyzed using Fourier series where only the Clock and Bmal1 expression shows a circadian rhythm.

In the realm of K-12 Education, the growing significance of Computational Thinking has sparked extensive inquiry into its nature and instructional methodologies. Despite a wealth of literature on the subject, ongoing debates persist regarding its fundamental components. Across global educational landscapes, Computational Thinking is being incorporated into curricula with varying degrees of emphasis, ranging from robotics to computer coding to broader algorithmic problem-solving approaches. This article aims to reassess established definitions of Computational Thinking and propose a nuanced understanding that highlights the necessity and importance of data in contemporary problem-solving contexts. Additionally, we present a comprehensive framework aimed at cultivating Computational Thinking skills in students, encompassing technical competencies alongside cognitive and metacognitive processes. Subsequently, we offer (1) a refined definition emphasizing the importance of data, (2) a comprehensive framework outlining essential components to foster Computational Thinking skills in students, and (3) a discussion of these concepts within the context of K-12 education.

University dropout is a growing problem with considerable academic, social and economic consequences. Conclusions and limitations of previous studies highlight the difficulty of analyzing the phenomenon from a broad perspective and with bigger data sets. This paper proposes a new, machine-learning based method, able to examine the problem using a holistic approach. Advantages of this method include the lack of strong distribution hypothesis, the capacity for handling bigger data sets and the interpretability of the results. Results are consistent with previous research, showing the influence of personal and contextual variables and the importance of academic performance in the first year, but other factors are also highlighted with this model, such as the importance of dedication (part or full time), and the vulnerability of the students with respect to their age. Additionally, a comprehensive graphic output is included to make it easier to interpret the discovered rules.