Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence.

Research in the field of Artificial Intelligence applied to emotions in the educational context has experienced significant growth in recent years. However, despite the field's profound implications for the educational community, the social impact of this scientific production on digital social media remains unclear. To address this question, the present research has been proposed, aiming to analyze the social impact of scientific production on the use of Artificial Intelligence for emotions in the educational context. For this purpose, a sample of 243 scientific publications indexed in Scopus and Web of Science has been selected, from which a second sample of 6,094 social impact records has been extracted from Altmetric, Crossref, and PlumX databases. A dual analysis has been conducted using specially designed software: on one hand, the scientific sample has been analyzed from a bibliometric perspective, and on the other hand, the social impact records have been studied. Comparative analysis based on the two dimensions, scientific and social, has focused on the evolution of scientific production with its corresponding social impact, sources, impact, and content analysis. The results indicate that scientific publications have had a high social impact (with an average of 25.08 social impact records per publication), with a significant increase in research interest starting from 2019, likely driven by the emotional implications of measures taken to curb the COVID-19 pandemic. Furthermore, a lack of alignment has been identified between articles with the highest scientific impact and those with the highest social impact, as well as a lack of alignment in the most commonly used terms from both scientific and social perspectives, a significant variability in the lag in months for scientific research to make an impact on social media, and the fact that the social impact of the research did not emerge from the interest of Twitter users unaffiliated with the research, but rather from the authors, publishers, or scientific institutions. The proposed comparative methodology can be applied to any field of study, making it a useful tool given that current trends in accreditation agencies propose the analysis of the repercussion of scientific research in social media.

Sensor platform for assessment of water usage patterns in informal settlements.

Rapid urbanization has intensified pressures on global water systems, particularly impacting informal settlements. Understanding water usage patterns within these settlements is of importance for better addressing water scarcity issues. Current methods for gaining information about water within these settings tend to lack spatio-temporal granularity and miss complex patterns of behavior related to water usage. As a consequence, there is a shortage of the reliable quantitative measurements needed to improve water management processes and modeling. Here we introduce a low-cost sensing platform for water assessment in informal settlements. Households within these types of settlements, lacking water utility connections and piping, often use storage tanks and buckets to distribute, store, and consume water; hence, the platform consists of four distinct sensor modules that can be placed on these types of water infrastructure. Evaluated in controlled settings, the sensors prove to be reliable for measuring water quantity, quality, and usage. Field testing within an informal community in Mexico reveals that the system can comprehensively track multiple tank storage levels, assess water quality, and capture bucket usage patterns without disrupting a household's common activities or infrastructure. Our validation shows the technique's potential to improve water management in informal communities, while opening opportunities for enhancement of water-related research and policy making through combinations of top-down and bottom-up interventions.

Efficacy and Safety of Valsartan or Chlorthalidone vs. Combined Valsartan and Chlorthalidone in Patients With Mild to Moderate Hypertension: The VACLOR Study.

OBJECTIVE: To evaluate the efficacy and safety of valsartan (V) or chlorthalidone (C) monotherapy in comparison with a fixed combination of valsartan and chlorthalidone (V + C). METHODS: This 12-week multicenter randomized three-arm open-label study randomly allocated 72 patients to V or C as monotherapy or a combination of V + C. The aim was to measure changes in office systolic blood pressure (SBP) and diastolic blood pressure (DBP) and in 24-hour ambulatory blood pressure monitoring (ABPM) from baseline to week 12, in addition to medication tolerability. RESULTS: The proportion of patients achieving target BP in office at week 12 was not statistically different for the three groups. However, comparisons of daytime and nighttime 24-hour ABPM values from baseline to week 12 revealed significant differences in nighttime mean SBP for the three groups, due to a significantly greater reduction in the values in patients assigned to the V + C group (-14.7 vs. -8.7 vs. -10.7, P = .042, V+C; V; C, respectively). Although patients assigned to the V + C group also had greater nighttime reduction in mean DBP values compared with those in the other groups, this difference was not statistically significant. The incidence of adverse events did not differ significantly. CONCLUSION: In patients with hypertension treated with V, C, and both medications combined, the fixed combination of V + C provided a significantly greater reduction of late night to early morning BP values when interventions were assessed with 24-hour ABPM. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT.01850160, https://clinicaltrials.gov/ct2/show/NCT01850160.