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Indice Guiraud (Script para Python y R) Documento Técnico
No full textEl presente documento describe el procedimiento computacional utilizado para calcular el Índice de Guiraud (R y Python), una medida ampliamente empleada en lingüística y psicolingüística para evaluar la diversidad léxica en producciones verbales. El índice se define como la razón entre el número de unidades léxicas únicas (tipos) y la raíz cuadrada del número total de palabras (tokens), lo que permite obtener una medida robusta de riqueza léxica relativamente independiente de la longitud del texto. Esta característica lo convierte en un indicador especialmente adecuado para analizar respuestas breves, como las producidas en tareas de pensamiento divergente o evaluaciones abiertas.
Con el fin de estandarizar el cálculo y facilitar su aplicación en entornos educativos y de investigación, se desarrollaron dos scripts reproducibles en los lenguajes de programación Python y R. Ambos scripts realizan los mismos pasos fundamentales: normalización del texto, tokenización, identificación de tipos léxicos y cálculo automatizado del índice de Guiraud para cada sujeto o unidad de análisis. El usuario únicamente debe proporcionar un conjunto de textos en forma de diccionario (Python) o lista nombrada (R), pudiendo adaptar los ejemplos incluidos a sus propios datos.
Este procedimiento ha sido aplicado previamente en investigaciones sobre creatividad, lenguaje y desarrollo cognitivo, incluyendo el estudio “Creative thinking and lexical richness development and the impact of cooperative learning over time” (Segundo-Marcos et al., 2025), donde la diversidad léxica se utilizó como indicador complementario para comprender la relación entre riqueza lingüística y pensamiento creativo en alumnado de Educación Primaria.
La disponibilidad pública de estos scripts contribuye a la transparencia, replicabilidad y reutilización del método en futuros trabajos científicos, permitiendo que otros investigadores, docentes o profesionales puedan aplicar el índice de Guiraud de forma sencilla, precisa y estandarizada en sus propios proyectos
Regulación Emocional en la Agresión Escolar, el Bullying y el Cyberbullying en adolescentes: Una Revisión Sistemática sobre la Supresión Expresiva y la Reevaluación Cognitiva
No full textEste capítulo analiza cómo las estrategias de regulación emocional influyen en las conductas agresivas entre adolescentes de 10 a 19 años. A partir de una revisión sistemática de 22 estudios publicados entre 2020 y 2025 en Web of Science, Scopus y ProQuest, se examinan las estrategias de supresión expresiva (SE) y reevaluación cognitiva (RC) en relación con el rol que los jóvenes desempeñan en el acoso (víctimas, agresores u observadores). Los resultados muestran que la supresión expresiva suele asociarse con mayor malestar emocional, ansiedad, depresión y autolesión, actuando como un mecanismo defensivo a corto plazo pero perjudicial a largo plazo. En cambio, la reevaluación cognitiva tiende a tener un efecto protector, al reducir la ansiedad y la depresión en víctimas, aunque en ciertos casos puede facilitar la desconexión moral o justificar conductas agresivas, sobre todo en varones. Se observan diferencias según el sexo y el contexto cultural: las chicas usan más reevaluación cognitiva, mientras que los chicos recurren a estrategias más externas como la culpabilización. En conjunto, el capítulo concluye que ninguna estrategia de regulación emocional es intrínsecamente buena o mala, ya que sus efectos dependen del contexto social y del modo en que se apliquen. Se recomienda fomentar un uso adaptativo de la reevaluación cognitiva y reducir la dependencia de la supresión expresiva en programas educativos y de prevención del acoso escolar y digital
Deep learning event detector from long-term signal variation for seismic activity warning out of Schumann resonance
Full text linkDeep Learning (DL) has shown capability in many areas of impact on everyday life. The paper proposes a DL architecture tailored for event detection from examining the time evolution of a signal. With temporal characteristics extracted by a Convolutional Neural Network (CNN) encoder and fed as input to a recurrent neural network, the model targets the detection of a possibly occurring investigated event in the given time interval. The utility of DL methodologies to solve physical problems is demonstrated for an application of the complex experimentally-studied existing interaction between Schumann Resonance (SR) and seismic activity. SR signals are electromagnetic waves propagating along the Earth-ionosphere cavity. Intense lightning activity is continuously present at the same locations around the world, being sensitive to physical perturbation. Seismic activity modifies this steady lightning pattern. The new DL model is applied to answer the research question of whether the variation of the SR signal is truly a verifiable forerunner of seismic activity. Several parameter configurations are explored, either model-related or linked to criteria for selecting seismic events. Results show preliminary evidence about the relation between distance-intensity space and SR perturbation, and provide valuable corroboration about the sensitivity of the sensor to a specific azimuth between the observatory and the Earthquake (EQ) epicenter, hence argumentatively supporting the SR temporal characteristics as an early seismic warning. This is the first generalization of seismic disturbance as a derivative of the SR, based only on its signal time series variation, as a hypothesized precursor of the EQ event
Análisis del pensamiento histórico en la prueba ENEM (1998– 2023)
No full textEl estudio trata sobre la evolución de la enseñanza de la historia en el Examen Nacional de Enseñanza Media (ENEM), especialmente de los enfoques metodológicos centrados en el “pensamiento histórico” y la “conciencia histórica”. En Brasil, el ENEM, creado en 1998, desempeña un papel central en el acceso a la educación superior y ha sido progresivamente reformulado para integrar temas interdisciplinarios y evaluar habilidades cognitivas. El estudio analiza las cuestiones de carácter histórico desde 1998 hasta 2023.
La investigación utiliza métodos cuantitativos y cualitativos para analizar 434 preguntas. Los resultados mostraron que la mayoría de las cuestiones requieren habilidades cognitivas básicas, con un 94,4% de las preguntas que exigen solamente lectura y memorización, mientras que únicamente el 5,4% requieren un análisis más profundo. Las preguntas abordan principalmente temas de historia política y social, con énfasis en el siglo XX y cuestiones contemporáneas, como democracia, derechos y esclavitud. El uso de textos es dominante en las preguntas, mientras que los gráficos y las imágenes son menos frecuentes. De la misma manera, el examen ENEM destaca por incluir temas socialmente relevantes y un enfoque inclusivo. Tras los análisis, se concluye que el ENEM ha evolucionado, pero aún prioriza conceptos históricos básicos y preguntas expositivas. El estudio señala que Brasil está más avanzado que otros países en la evaluación del pensamiento histórico, pero aún hay espacio para mejoras, especialmente en el análisis de las preguntas y en
el alineamiento con las prácticas pedagógicas, facilitando así el aprendizaje y la evaluación del pensamiento histórico
Stability assessment of preservative-free losartan potassium eye drops compounded for ophthalmic use
Full text linkObjectives To evaluate the physicochemical and microbiological stability of preservative- free losartan potassium (LP) eye drops (0.8 mg/mL) prepared using balanced salt solution (BSS), normal saline (NS) or glucose saline (GS) and stored in sterile polypropylene eye drop bottles under different temperature conditions for 30 days.
Methods Three independent batches of each formulation (BSS, NS and GS) were aseptically prepared in a laminar flow cabinet and stored under three conditions: room temperature (25±4°C), refrigeration (4±3°C) and freezing (–20±5°C). Three samples from each batch were analysed for each condition and time point. LP concentrations were measured on days 0, 7, 15, 22 and 30 using high- performance liquid
chromatography with photodiode- array detection (HPLC-DAD) and ultraviolet (UV) – visible spectrophotometry. Visual inspection was performed to identify macroscopic changes and physicochemical parameters — including pH, osmolality and refractive index — were evaluated. Microbiological quality was assessed on days 0, 10, 20 and 30.
Results BSS and NS formulations maintained 90–110% of their initial LP concentration under all storage conditions. The GS formulation remained stable when refrigerated or frozen but showed a marked decline at 25°C, with turbidity and concentrations falling below 50% by day 20. pH, osmolality and refractive index remained within acceptable physiological ranges for ophthalmic solutions. All samples tested negative for microbial growth.
Conclusions LP eye drops prepared with BSS or NS were stable in closed sterile eye drop bottles for at least 30 days under all tested conditions. Frozen and refrigerated conditions are preferred for storage. Because the formulation does not contain preservatives, its use is recommended for no longer than 7 days after opening. GS- based formulations are not recommended for ophthalmic use. The conducted stability study provides hospital pharmacies with data supporting the safe preparation, storage and use of non- commercial
ophthalmic formulations
Reinforcement learning for heliostat aiming: Improving the performance of Solar Tower plants
Full text linkSolar Tower (ST) systems use heliostats to concentrate solar radiation onto a tower-mounted receiver. Optimizing the aiming strategy for these heliostats over the receiver remains a critical challenge due to the dynamic nature of solar radiation and the need to maximize energy capture while ensuring operational safety. This paper introduces a novel, model-free deep Reinforcement Learning (RL) approach to optimize heliostat aiming strategies, utilizing the Soft Actor–Critic (SAC) algorithm. This advanced RL method enhances the traditional Actor–Critic framework with two neural networks. The proposal dynamically adjusts the aiming points across the receiver surface in real time, trying to improve the overall performance of the ST plant. The strategy was simulated and evaluated over a full operational year and compared with traditional methods. The results show an increase of more than 8.8% in yearly absorbed power, a significant improvement that directly enhances performance and contributes to better economic outcomes for the technology. This technique also eliminates the need for constant human intervention and is applicable to both existing and future plants
Application of targeted and suspect screening workflows for cyclic peptide cyanotoxin profiling in spirulina- and klamath-based food supplements
Full text linkDomination in cylindrical graphs
Full text linkThe domination number γ(Cm□Pn) of the Cartesian product Cm□Pn of a cycle and a path has been computed when m ≡ 0, 2 (mod 5). In the remaining cases m ≡ 1, 3, 4 (mod 5), exact formulae for γ(Cm□Pn) have been determined when either m ≤ 30 or n ≤ 22. For the rest of the cases, only lower and upper bounds for γ(Cm□Pn) are known. In this paper, we study γ(Cm□Pn) when m ≡ 1, 3, 4 (mod 5). In particular, we compute γ(Cm□Pn) if m ≡ 1 (mod 5), m ≥ 30 and n ≥ 22, and we provide tighter lower and upper bounds for γ(Cm□Pn) if m ≡ 3, 4 (mod 5)
Sex differences in compulsive alcohol drinking phenotypes: implications for decision-making and social behavior in a preclinical model
Full text linkRationale
Compulsivity is increasingly recognized as a transdiagnostic trait that amplifies vulnerability to alcohol use disorders. However, its specific role in shaping social behavior and decision-making remains underexplored.
Objective
This study aimed to identify a vulnerable phenotype characterized by compulsive alcohol drinking and evaluate its behavioral alterations within the social behavior and cognitive processes domains of the Research Domain Criteria (RDoC), considering sex as a modulatory factor.
Methods
Male and female Wistar rats were exposed to Schedule-Induced Polydipsia (SIP), first with water and then with alcohol. Distinct groups were formed based on intake patterns following a cluster-based analysis. We then assessed social subordination with the social dominance tube test (SDTT), sociability and social novelty with the three-chambered Crawley’s test (3CT), and decision-making with the rodent Gambling Task (rGT).
Results
We identified four distinct behavioral profiles: Low Compulsive, Compulsive Alcohol, Compulsive Water, and High Compulsive. This segmentation revealed sex-specific distributions: males were overrepresented in high alcohol consumption clusters, while females were more prevalent in low-consumption profiles, indicating sex-related susceptibility. The High Compulsive phenotype diverged from the Compulsive Alcohol group, showing lower hierarchical status and a less risky decision-making strategy, whereas no significant differences were found in overall social interaction between groups. However, general alcohol consumption diminished general sociability and abolished sex differences, suggesting a disruption of innate social motivation.
Conclusions
These findings support that the combination of compulsivity and alcohol intake increases behavioral vulnerability, specifically in domains of social competence and decision-making
Key insights into microplastic pollution in agricultural soils: A comprehensive review of worldwide trends, sources, distribution, characteristics and analytical approaches
No full textMicroplastics in agricultural ecosystems have gained recent attention due to their widespread prevalence and the reported impacts. The current study reviews close to 90 relevant publications. Seventy studies were selected using ResNetBot, a tool designed to identify key works based on their significance to other studies matching the search criteria. The selection focused on global occurrence of microplastics within agricultural landscapes, emphasizing their distribution, origins, concentrations, morphology, color, polymer types, dimensions, vertical distribution, and the implications of land use. Furthermore, this study included predominant methodologies for
extracting, identifying, and characterizing microplastics. Plastic mulch films represent the primary source of input, accompanied by sewage sludge, biosolids, irrigation water (notably wastewater), atmospheric deposition, and polymer-coated fertilizers, among others. Concentrations exhibit considerable variability, ranging from a few particles/kg in low-input regions to exceeding 80,000 particles/kg in areas subjected to prolonged mulching practices. Polyethylene and polypropylene are the most frequently encountered polymers, while fragments, fibers, and films are predominant forms, with films strongly linked to mulching and fibers typically associated with
biosolid applications. Transparent/white and black particles are predominant, reflecting the materials commonly utilized in film applications. Microplastic concentrations generally decrease with soil depth; however, smaller particles penetrate deeper soil layers. Intensive agricultural practices demonstrate significantly elevated microplastic loads in comparison to less intensive or natural ecosystems. Density separation methods (commonly utilizing NaCl) followed by digestion processes (often employing H2O2) are standard practices for isolating microplastics while spectroscopic methodologies (including FTIR/μ-FTIR and Raman spectroscopy) are essential for verifying polymer composition and ensuring precise identification