Traffic jams are one of the major transportation problems. The United States spends USD billions to mitigate the problem, and not always with good outcomes. This problem increases and has effects on sustainable transport, such as life quality, pollution, perishables, and costs. Large cities reduce traffic jams through congestion charges. This paper aims to reduce urban traffic congestion by estimating the charge through a multivariable model. It studies the main jammed areas in Santiago, Chile. The data came from published surveys. The model evaluation included Fisher multiple regression (F) and the determination coefficient (R²). These validations showed that the model is statistically significant. They also showed that the parameter estimation was good. Finally, this model contributes to improving the Sustainable Development Goals, such as SDG 3, SDG 11, and SDG 13, which may be successfully applied to Santiago City, as well as to any city worldwide.

Fourth graders were asked to read a text and either to fill in a compare-and-contrast graphic organizer, answer a set of structured questions, take notes, or simply read the text. Both the graphic organizer and questioning groups outperformed the read-only group on a comprehension test (d = 1.24 and 1.22, respectively) and a memory test (d = 0.54 and 0.53, respectively). No significant differences were found between the notetaking and read-only groups on the comprehension test (d = 0.30) or the memory test (d = 0.20). Results showed more integrative eye movements between paragraphs for the graphic organizer (d = 1.53) and the questioning groups (d = 1.90) than the read-only group, but not for the notetaking group (d = −0.06). On all three measures, the graphic organizer group and the questioning group exhibited a generative learning strategy, whereas the notetaking and read-only groups exhibited a linear learning strategy.

This article is about an architectural hardware-software model creation—home-based smart health model—to boost healthcare to a higher position within society. As an emerging field, smart health modeling is still insufficient. Current smart health services are hospital centered, data are scattered and application dependent, and health service provision presents attention delays. Analyses of Internet of things, Internet of medical things, and smart health applications potentials are the bases for the proposed home-based smart health model. The model aims to facilitate the smart health development and strengthening. To evaluate whether the model does what it must do, first, check lists on how the model complies with current and future devices and applications, smart health impacts and smart health potentialities, and second, a case study analyses the model conformity through an Internet of things sophisticated cloud app.