Foundation: Sleep, as a physiological process, can be addressed pedagogically and clinically; it constitutes a passive factor in sports performance, being systematically integrated as part of the sports training management process.Objective: To analyze the effects of an educational program for youth volleyball players on sleep hygiene and muscle recovery.Methods: A quasi-experimental study with a sample of 50 male volleyball players (n = 25 per group), using validated instruments (Pittsburgh Sleep Quality Index and Total Quality Recovery Scale) after eight weeks of intervention, which included educational sessions, sleep hygiene routines, and post-training muscle recovery strategies. Results: Intragroup analysis showed that the experimental group presented significant improvements, reducing central tendency measures from 6.4/6.0 to 4.8/5.0 (Z = -3.21; p = 0.001) in the PSQI, while the control group showed no changes (p = 0.263). Intergroup analysis revealed no significant differences in the pretest, confirming initial homogeneity. However, in the posttest, notable differences were identified in favor of the experimental group (U = 187.0; Z = -2.89; p = 0.004). Regarding the TQR, the experimental group improved significantly, from moderate to good recovery (12.6/13.0-' 16.1/16.0; Z = -3.45; p < 0.001), while the control group showed a slight non-significant increase (p = 0.311). Additionally, intergroup analysis confirmed significant posttest differences in favor of the experimental group (U = 162.0; Z = -3.18; p = 0.001). Conclusions: It is confirmed that a structured educational intervention program for sleep hygiene and muscle recovery, can be an effective strategy to improve sleep quality and perceived recovery in youth volleyball players.
Universities face growing pressure to deliver social and economic value through teaching, research and knowledge transfer. From a triple-helix perspective (university-industry-government), this article presents an empirical diagnosis aimed at proposing a smart-university model in a public Latin American institution and developing a transferable governance-and-data framework. Drawing on 25 in-depth interviews with internal and external stakeholders and a grounded theory approach, we identify cross-cutting constraints that limit U-I cooperation and data-informed decision-making: bureaucratic rigidity, fragmented information systems, limited-service capacity, and weak university-community-industry linkages. Priorities converge on data governance (centralization, interoperability and automation), purposeful ICT integration, institutional well-being and inclusion, and alignment with public policy. We propose a preliminary framework that links governance and decision-making with data infrastructures and sustainability, together with a cooperation-oriented governance playbook and a set of transferable indicators to guide implementation and evaluation. Taken together, the findings show that regulatory and resource constraints limit transformation in public HEIs; accordingly, this work suggests operational, institution-level recommendations to shorten timelines, standardize processes and improve coordination with firms, thereby strengthening university-industry cooperation within triple-helix ecosystems.
Phoradendron nervosum (hemiparasitic plant) exhibits remarkable metabolic plasticity, with significant variations in bioactive compound content, metabolite profiles, and antioxidant capacity depending on the host species (Laurus nobilis L., Hibiscus rosa-sinensis L., Populus alba L.) and organ type (leaves vs. fruits). A multi-method approach combining spectrophotometry, complementary antioxidant assays (ABTS, DPPH, FRAP), and complete metabolomic profiling by LC–MS was used to elucidate the biochemical interactions between P. nervosum and its hosts. The results obtained showed a differential distribution of total phenolic and flavonoid compounds, with leaves characterized by higher flavonoid concentrations (e.g., up to 0.286 mg QE/g DW). At the same time, fruits exhibited host-mediated modulations, especially in the species L. nobilis (e.g., the highest TPC at 2.723 mg GAE/g DW). Antioxidant activity was consistently higher in leaves (e.g., up to 41.26 µmol Trolox/g DW in DPPH assay), correlating with their enriched presence of phenolic acids and glycosylated flavonoids. The host species played a determining role in antioxidant capacity, as H. rosa-sinensis-associated plants exhibited the highest levels (e.g., 14.53 µmol Trolox/g DW in ABTS, 24.06 µmol Fe2+/g DW in FRAP). Correlation analysis suggested complex biochemical trade-offs influencing metabolic allocations. A diversity of bioactive compounds was identified by LC–MS, highlighting the synergistic interaction between metabolites in defining the physiological response of P. nervosum. Our results highlight its adaptive versatility and ecological significance, but also potential pharmacological applications, given that related mistletoe species from Europe are already used in antitumor treatments.
Given the potential destructiveness of volcanic eruptions, the study of volcanic microearthquakes is a key tool for improving our understanding of the relationship between a volcano and its environment. In the last decade, Convolutional Neural Networks (CNN) have shown great potential for the automatic classification of microearthquakes. However, one major limitation is their "black box" nature, it is often unclear which features drive their decisions. In this work, we propose the use of Explainability Models in conjunction with CNNs and two novel Time-Frequency representations: Adaptive Superlets (ASLT) and Spectrogram-based Periodogram with Window Switching (SPWS). The aim of this paper is to extract the Time-Frequency features leveraged by CNNs for microearthquake classification. This could be used to increase the reliability of CNN-based recognition systems and to identify possible new Time-Frequency characteristics that identify microearthquakes. This proposal verifies the frequency bands for Long Period (LP), Volcano Tectonic (VT), Tectonic (TC), and Tremor (TR) microearthquakes. Moreover, this could be useful for identifying frequency components that can be used to distinguish between LP and VT events and to determine the starting point of overlapping events within the same detection window.
This research deals with the problem of extending the conventional finite automata theory to the study of the equality theorem. For this purpose, an algebraic approach centered on the concepts of semi-rings, recognizable K-subsets and K-Sigma-automata is proposed. The decidability of any pair of recognizable K-subsets is proved in this context. This means that the semiring K must be known well enough to permit such decisions.
