The inhibition of the fatty acid amide hydrolase (FAAH), an endocannabinoid system component, emerged as a potentially new therapeutic target for a range of clinical disorders such as acute and chronic pain. Some α-ketoheterocycle derivatives demonstrated interesting analgesic and anti-inflammatory activities in vitro. Ligand-Based Drug Design techniques such as knowledge graph convolutional networks (kGCN) and hologram quantitative structure-activity relationship (HQSAR) using α-ketoheterocycle derivatives from five different datasets were generated to discover the relation between the chemical structures and the inhibition activity. Meanwhile, structure-based drug design simulations as interaction fields (MIF), molecular docking, and ligand sites studies (LSI) from FAAH were performed using Autogrid software and FTmap/FTsite servers. The results of both studies were merged to propose predictive models. The resulting kGCN model demonstrated adequate accuracy area under the curve by receiver operating characteristic (AUC-ROC 0.7922). From contribution maps of the Ligand-Based Drug Design (LBDD) models and the generated probes using MIF and LSI, it was observed that the oxazole ring, the ketone group, and the apolar chain present in the structures of the inhibitors are important, besides the evidence of the Cys269 and Val270 residues importance for the potential interaction, confirmed by carried docking studies. These fragments and structural information can be used to carry out new FAAH potential inhibitors studies and report kGCN as an accurate classification technique. FAAH, , (FAAH) component pain αketoheterocycle α ketoheterocycle antiinflammatory anti inflammatory vitro LigandBased Ligand Based (kGCN structureactivity structure activity HQSAR (HQSAR Meanwhile structurebased based MIF, (MIF) LSI (LSI FTmapFTsite FTmap FTsite servers AUCROC AUC ROC 0.7922. 07922 0.7922 . 0 7922 0.7922) LBDD (LBDD ring group important Cys Cys26 Val Val27 technique (FAAH (MIF 0792 0.792 792 Cys2 Val2 079 0.79 79 07 0.7 7 0.

ABSTRACT Knowledge of the complicated correlation between meteorological variables and crop yield is crucial for food security and agricultural sustainability. This study aimed to investigate how incident solar radiation has affected crop production in the Gadarif region of Sudan over the last 41 years. Using a predictive framework, trends in annual incident solar radiation and temporal variations during sorghum and sesame growing seasons were examined and machine learning (ML) with Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF), and K-Nearest Neighbors (K-NN) was used to predict crop yield. Significant relationships between incident solar radiation indicators and crop yields were identified via detrending approaches and correlation analyses. Results indicate a significant inverse correlation between solar radiation and sorghum yield, and a positive correlation between sesame yield and solar radiation. For both sorghum and sesame yield, K-NN was the most accurate model, demonstrating the significance of incident solar radiation and temperature in predicting crop yield. These findings highlight the potential of ML to improve agricultural forecasting models and inform adaptive agricultural practices in the region. In general, this study provides valuable insights into the dynamic relationship between incident solar radiation and crop yield, emphasizing the importance of considering meteorological factors in agricultural planning and management. sustainability 4 years framework (ML XGBoost, XGBoost , (XGBoost) BRF, BRF (BRF) KNearest K Nearest KNN NN (K-NN analyses model general management (XGBoost (BRF

RESUMO O conhecimento da complicada correlação entre as variáveis meteorológicas e o rendimento das culturas é crucial para a segurança alimentar e a sustentabilidade agrícola. Este estudo está centrado na investigação de como a radiação solar incidente afetou a produção agrícola na região de Gadarif, no Sudão, nos últimos quarenta anos. Usando uma estrutura preditiva, a pesquisa avalia tendências recentes na radiação solar incidente anual, examina variações temporais durante as estações de cultivo de sorgo e gergelim e utiliza técnicas de aprendizado de máquina para prever o rendimento das culturas. Além disso, ML, incluindo Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF) e K-Nearest Neighbours (K-NN), foram empregados para previsão de rendimento. Através de abordagens de redução de tendências e análises de correlação, foram identificadas relações significativas entre os indicadores de radiação solar incidente e o rendimento das culturas. Os resultados indicam uma correlação inversa substancial entre a radiação solar e a produção de sorgo, enquanto a produção de gergelim demonstra uma correlação positiva com a radiação solar. Tanto para o rendimento do sorgo como do gergelim, o K-NN surge como o modelo mais preciso, mostrando a importância da radiação solar incidente e da temperatura na previsão do rendimento das culturas. Estas descobertas destacam o potencial da aprendizagem de máquina para melhorar os modelos de previsão agrícola e informar as práticas agrícolas adaptativas na região. Em geral, este estudo fornece informações valiosas sobre a relação dinâmica entre a radiação solar incidente e o rendimento das culturas, enfatizando a importância de considerar fatores meteorológicos no planeamento e gestão agrícola. Gadarif Sudão anos preditiva anual disso ML XGBoost, XGBoost , (XGBoost) BRF (BRF KNearest K Nearest KNN, KNN NN (K-NN) preciso geral (XGBoost (K-NN

Abstract This study aims to analyse the influence of the operation mode and demographic and anthropometric variables on the self-assessed productivity of users of mixed-mode and fully air-conditioned buildings in Florianópolis, southern Brazil. Demographic and anthropometric data were collected using electronic questionnaires made available to users. A total of 1,247 responses were recorded. The influence of the mode of operation on productivity was analysed using histograms. A predictive model was developed by means of linear and quadratic regressions to identify the environmental and demographic/anthropometric parameters influencing productivity. It was observed no influence of hybrid buildings or those with central air-conditioning systems on the perception of productivity. Although the predictive model did not show a good ability to explain users’ performance, it showed statistical correlations between productivity and operative temperature, gender and age. It was concluded that office users’ perception of productivity is affected by the configuration of environments in the humid subtropical climate of Florianópolis. selfassessed self assessed mixedmode mixed airconditioned air conditioned Florianópolis Brazil 1247 1 247 1,24 recorded histograms demographicanthropometric airconditioning conditioning performance temperature age 124 24 1,2 12 2 1,

Resumo O objetivo deste estudo é analisar a influência do modo de operação e de variáveis demográficas e antropométricas na produtividade autoavaliada de usuários de edificações de escritório com sistema de ventilação híbrido e central, em Florianópolis - SC. Os dados demográficos e antropométricos foram coletados por meio de questionários eletrônicos. A influência do modo de operação na produtividade foi analisada por meio de histogramas. Um modelo preditivo foi desenvolvido por meio de regressões lineares e quadráticas para identificar os parâmetros ambientais e demográficos/antropométricos que exercem influência na produtividade. Os resultados mostraram que não houve distinção da influência exercida por edificações híbridas ou com sistema de ar-condicionado central na percepção da produtividade. Apesar de o modelo preditivo não ter apresentado boa capacidade de explicar o desempenho dos usuários, evidenciou correlações estatísticas entre produtividade e temperatura operativa, gênero e idade. Concluiu-se que a percepção de produtividade dos usuários de escritórios é afetada pela configuração dos ambientes no clima subtropical úmido de Florianópolis. SC eletrônicos histogramas demográficosantropométricos arcondicionado ar condicionado operativa idade Concluiuse Concluiu se

Abstract Introduction. The cultivation of dragon fruit (Hylocereus costaricensis and H. monocanthus) is increasing in Costa Rica, but technical information for its management, such as nutritional requirements, fertilization timing, and growth behavior, is lacking. Objective. To quantify and model the growth and nutrient uptake of dragon fruit (H. costaricensis and H. monocanthus) under dry tropics conditions in Costa Rica. Materials and methods. The research was conducted from 2017 to 2020 in Guanacaste, Costa Rica. Plants were sampled at 76, 165, 308, 450, 607, 903, 1249, and 1706 days after planting (DAP) and 205 fruits were analyzed. Dry matter was quantified, and the uptake of N, P, Ca, Mg, K, S, Fe, Cu, Zn, Mn, and B was calculated. Two logistic models were fitted to determine nutrient uptake, and linear regressions were calibrated for fresh fruit. Results. For a planting density of 3000 plants/ha, the nutritional requirements per hectare were: 137 kg N, 32 kg P, 327 kg K, 63 kg Mg, 32 kg S, 1,60 kg Zn, and 0,21 kg B. The best-fitting logistic model (RMSE = 0.09) accurately estimated the uptake of N, P, Ca, S, Zn, and B. The linear regression model demonstrated high predictive capacity (R2 > 0.85) for the nutrient requirements of N, P, K, Mg, and S in the fruit. Conclusion. Logistic models provided high precision in deterministically modeling nutrient uptake in dragon fruit plants. The study affectively quantified and modeled the nutritional behavior of the plants under Costa Rica’s dry tropical conditions, providing essential information for optimizing fertilization programs for the crop.

Resumen Introducción. El cultivo de pitahaya (Hylocereus costaricensis y H. monocanthus) está en aumento en Costa Rica, pero carece de información técnica para su manejo, como necesidades nutricionales, momentos de fertilización y comportamiento del crecimiento. Objetivo. Cuantificar y modelar el crecimiento y la absorción de nutrimentos del cultivo de pitahaya (H. costaricensis y H. monocanthus) en condiciones del trópico estacional seco de Costa Rica. Materiales y métodos. La investigación se realizó durante los años 2017-2020, en Guanacaste, Costa Rica. Se muestrearon plantas a los 76, 165, 308, 450, 607, 903, 1249 y 1706 días después de siembra (DDS) y 205 frutas. Se cuantificó la materia seca y se calculó la absorción de N, P, Ca, Mg, K, S, Fe, Cu, Zn, Mn y B. Se ajustaron dos modelos logísticos para determinar la absorción de nutrientes y se calibraron regresiones lineales en fruta fresca. Resultados. Para una densidad de siembra de 3000 planta/ha los requerimientos nutricionales del cultivo por hectárea fueron: 137 kg N, 32 kg P, 327 kg K, 63 kg Mg, 32 kg S, 1,60 kg Zn y 0,21 kg B. El modelo logístico de mejor ajuste (RMSE = 0,09) estimó de forma precisa la absorción de N, P, Ca, S, Zn y B. El modelo de regresión lineal tuvo alta capacidad predictora (R2 > 0,85) para los requerimientos de N, P, K, Mg y S en fruta. Conclusión. Los modelos logísticos tuvieron alta precisión para modelar de manera determinística la absorción de nutrientes en plantas de pitahaya. Además, se logró cuantificar y modelar de forma eficiente el comportamiento nutricional de las plantas en condiciones de trópico estacional seco de Costa Rica, lo que proporciona información esencial para los programas de fertilización del cultivo.

Resumen La corrosión es un problema recurrente en la industria del gas natural, debido a la presencia de gases corrosivos como el CO2 y el H2S que en presencia de agua pueden atacar el acero y producir daños y fugas que generen accidentes y contaminación, es por ello que la predicción de la tendencia del gas natural hacia la corrosión es fundamental para desarrollar estrategias de inhibición y mitigación de este proceso. El objetivo del estudio fue desarrollar una Red Neuronal Artificial (RNA) entrenada con datos de composición de muestras de gas natural para predecir la tendencia corrosiva, como método alternativo que pueda ser utilizado como una herramienta predictiva eficiente. La RNA se entrenó con una muestra de 46 gases naturales y 11 componentes por cada una, además de la presión y la temperatura como condiciones operacionales, lo que representa un total de 598 datos de entrenamiento de la RNA, además se utilizaron 8 muestras adicionales para la validación externa del modelo. La RNA que desarrolló fue dentro de los principios de la estadística Bayesiana, con una arquitectura de Perceptrón Multicapa. Se obtuvo que la RNA es capaz de clasificar correctamente el 95,65% de la tendencia corrosiva de las muestras, siendo aquellas con tendencia no corrosiva las que presentaron el menor porcentaje de predicción (75%). La predicción de las muestras externas siguió la tendencia esperada, con menor capacidad para clasificar a las no corrosivas, y las muestras con posible corrosión y corrosivas fueron predichas en 80%. Se concluye que la RNA es una herramienta eficiente para la predicción de la tendencia corrosiva del gas natural en presencia de CO2 y su efectividad depende de que se utilicen muestras con todos los componentes necesarios para entrenar la misma, además puede ser mejorada al introducir otros parámetros como la cantidad de agua presente.

Abstract Corrosion is a recurrent problem in the natural gas industry, due to the presence of corrosive gases such as CO2 and H2S that in the presence of water can attack steel and produce damages and leaks that generate accidents and contamination, that is why the prediction of the tendency of natural gas towards corrosion is essential to develop strategies to inhibit and mitigate this process. The objective of the study was to develop an Artificial Neural Network (ANN) trained with composition data of natural gas samples to predict the corrosive tendency, as an alternative method that can be used as an efficient predictive tool. The ANN was trained with a sample of 46 natural gases and 11 components for each one, in addition to pressure and temperature as operational conditions, representing a total of 598 training data of the ANN, also 8 additional samples were used for external validation of the model. The ANN that developed was within the principles of Bayesian statistics, with a Multilayer Perceptron architecture. It was obtained that the ANN is able to correctly classify 95.65% of the corrosive tendency of the samples, being those with non-corrosive tendency the ones that presented the lowest prediction percentage (75%). The prediction of the external samples followed the expected trend, with lower capacity to classify the non-corrosive ones, and the samples with possible corrosion and corrosive were predicted in 80%. It is concluded that ANN is an efficient tool for predicting the corrosive tendency of natural gas in the presence of CO2 and its effectiveness depends on using samples with all the necessary components to train it, and it can be improved by introducing other parameters such as the amount of water present.

Abstract The objective of the study was to analyze the relationship between academic stress and self-concept, to compare both variables according to gender, and to identify the predictive capacity of self-concept factors in the levels of academic stress in university students. The results confirmed a negative association between academic stress and self-concept and significant differences between men and women. Additionally, a multiple linear regression analysis differentiated by sex was performed. In the case of women, the model was constituted by the emotional, academic-work, social, family and physical factors of self-concept; in the case of men, only the emotional and academic-work dimensions were integrated. It is concluded that the self-concept contributes to the prediction of the behavior of academic stress in higher education students. For these reasons, it is convenient to promote activities for students in university institutions that improve their perception of themselves.

Resumen Los objetivos centrales de la indagatoria fueron analizar la asociación entre el estrés académico y el autoconcepto, comparar dichos fenómenos psicológicos entre géneros e identificar el poder predictivo de los factores del autoconcepto en los índices de estrés académico en estudiantes universitarios. Los resultados confirmaron una asociación negativa entre las variables abordadas y diferencias hombres y mujeres. Adicionalmente, se realizaron dos análisis de regresión lineal múltiple para cado uno de los géneros. En mujeres, los factores emocional, académico-laboral, social, familiar y físico del autoconcepto aportaron a la explicación del estrés académico; en el de los hombres, solo se integraron las dimensiones emocional y académico-laboral. Se concluye que el autoconcepto aporta a la predicción del comportamiento del estrés académico en estudiantes de educación superior. Por tales razones, resulta conveniente suscitar en las instituciones universitarias, actividades tendientes a la mejora de la percepción de sí mismos por parte de los jóvenes.

Abstract Introduction: Experiences of racial-ethnic discrimination are social stressors that affect the mental health of those who suffer it. Although it is a current phenomenon, few studies have examined the effects of discrimination on mental health in Peru and Latin America. Therefore, the aim of this study was to explore the mediating role of neuroticism and anger in the relationship between experiences of discrimination and dark personality traits in a Peruvian population. Method: A total of 632 participants between 18 and 63 years of age participated (M = 25.03; SD = 8.03). The Experiences of Discrimination Scale (EOD), the items measuring neuroticism from the Big Five Inventory - 10 (BFI-10), the Anger Propensity Scale (APS - G) and the Dirty Dozen Dark Triad (DDDT) were used. An explanatory model was created using structural regression. Results: The model fit was excellent (CFI = .975, TLI = .970, RMSEA = .043 y SRMR = .051), showing that discrimination experiences have a direct effect on neuroticism, which robustly predicts anger proneness and this, in turn, is more predictive of Machiavellianism, narcissism and psychopathy, respectively. Conclusion: Neuroticism and anger are serial mediators in the relationship between discrimination experiences and dark personality traits.

Resumen Introducción: Las experiencias de discriminación étnico-raciales son estresores sociales que afectan la salud mental de quienes las sufren. A pesar de que se trata de un fenómeno vigente, los trabajos que han examinado los efectos de la discriminación en la salud mental en Perú y Latinoamérica son escasos. Por ello, el objetivo principal de este estudio es explorar el rol mediador del neuroticismo y la ira en la relación entre las experiencias de discriminación y los rasgos de personalidad oscuros en población peruana. Método: Se contó con la participación de 632 personas con edades de entre 18 y 63 años (M = 25.03; DE = 8.03). Se utilizaron la Escala de Experiencias de Discriminación (EOD), los ítems que miden neuroticismo del Big Five Inventory - 10 (BFI-10), la Escala de Propensión a la Ira (APS - G) y la Dirty Dozen Dark Triad (DDDT). Mediante regresión estructural se creó un modelo explicativo. Resultados: El ajuste del modelo fue excelente (CFI = .975, TLI = .970, RMSEA = .043 y SRMR = .051) y mostró que las experiencias de discriminación tienen un efecto directo sobre el neuroticismo, el cual predice de manera robusta la propensión a la ira, y esta, a su vez, es predictora, en mayor medida, del maquiavelismo, el narcisismo y la psicopatía, respectivamente. Conclusión: El neuroticismo y la ira son mediadores seriados en la relación entre las experiencias de discriminación y los rasgos oscuros de personalidad.

Abstract The objective of the study was to evaluate the relationship between the preference index (IP) and synchrony (IS) of forage species from Baja California Sur. Samples of the 23 forage species of trees and shrubs were obtained to perform in situ digestibility analyzes using four Creole-Nubian goats. Collections were carried out in winter and spring of 2004, and summer and autumn of 2005. The extent of degradation per hour and total of 24 h was calculated from the amount of OM and PC degraded per hour; these data were used to calculate the IS. The seasonal PI data set was obtained from previously published information. A logistic regression model was used to explain the probability of occurrence of the IP with the IS. The regression model to explain the IP from the IS was significant and predictive for six species, being positive for Adelia virgata, Amaratus palmeri, Haematoxilon brasiletto, Lippia palmeri and Prosopis sp., and negative for Bursera microphyla. For other tree species and legumes in general, the model was significant, but not predictive, therefore, it is necessary to include other factors to fit the model.

Resumen El objetivo del estudio fue evaluar la relación existente entre el índice de preferencia (IP) y sincronía (IS) de especies forrajeras de Baja California Sur. Se obtuvieron muestras de las 23 especies forrajeras de árboles y arbustos para realizar análisis de digestibilidad in situ utilizando para ello cuatro cabras con encaste de criollas-Nubio. Se llevaron a cabo colecciones en invierno y primavera del 2004 y verano y otoño de 2005. La extensión de degradación por hora y total de 24 h se calculó a partir de la cantidad de MO y PC degradados por hora; estos datos se utilizaron para calcular el IS. El conjunto de datos del IP estacional se obtuvo de información publicada previamente. Se utilizó un modelo de regresión logística para explicar la probabilidad de ocurrencia del IP con el IS. El modelo de regresión para explicar el IP a partir del IS fue significativo y predictivo para seis especies, siendo positiva para Adelia virgata, Amaratus palmeri, Haematoxilon brasiletto, Lippia palmeri y Prosopis sp., y negativa para Bursera microphyla. Para otras especies arbóreas y las leguminosas en general, el modelo fue significativo, pero no predictivo, por lo tanto, es necesaria incluir otros factores para ajustar el modelo.

Abstract Introduction: The Perceived Stress Scale-4 (PSS-4) is an ultra-brief self-report measure to assess psychological stress. This study evaluated the psychometric properties of the PSS-4 in Colombia. Method: A total of 1,911 adult participants with a university education completed the PSS-4. The characteristics of the items and the subscales were explored. The dimensionality was assessed using principal component analysis and confirmatory factor analysis (CFA), incorporating an examination of invariance (configural, metric, scalar, and strict) across socio-demographic characteristics. Construct validity (convergent and discriminant), reliability indices, know-groups, and predictive validity were also computed. Results: CFA showed that the one-factor model (psychological stress) with method effects (correlated error terms on the negative-phrased items) was the most appropriate according to fit indices and parsimony considerations, being invariant across gender and age, but not across income level and work status. Internal consistency was adequate for the PSS-4. Significant positive correlations of the PSS-4 were identified with depression and anxiety, as well as significant negative correlations were identified with post-traumatic growth and resilience. The PSS-4 showed adequate capacity to predict potential depressive and anxiety symptoms, as well as protective factors such as resilience and post-traumatic growth. Higher scores on the PSS-4 were observed among young people, as well as among people with lower incomes and those who are unemployed. Conclusions: These findings suggest that the PSS-4 can be a reliable and valid tool for assessing psychological stress in Colombians with a university education.

Resumen Introducción: La Escala de Estrés Percibido-4 (EEP-4) es una medida de autoinforme ultrabreve para evaluar el estrés psicológico. Este estudio evaluó las propiedades psicométricas del EEP-4 en Colombia. Método: Un total de 1911 participantes adultos con educación universitaria completaron el EEP-4. Se exploraron las características de los ítems y las subescalas. La dimensionalidad se evaluó mediante análisis de componentes principales y análisis factorial confirmatorio (AFC), incorporando una examinación de invarianza (configural, métrica, escalar y estricta) según las características sociodemográficas. También se calculó la validez de constructo (convergente y discriminante), los índices de fiabilidad, los grupos conocidos y la validez predictiva. Resultados: El AFC mostró que el modelo de un factor (estrés psicológico) con efectos de método (términos de error correlacionados en los ítems con frases negativas) era el más apropiado según los índices de ajuste y las consideraciones de parsimonia, siendo invariante según el género y la edad, pero no según el nivel de ingresos y la situación laboral. La consistencia interna fue adecuada para el EEP-4. Se identificaron correlaciones positivas significativas del EEP-4 con la depresión y la ansiedad, así como correlaciones negativas significativas con el crecimiento postraumático y la resiliencia. El EEP-4 mostró una capacidad adecuada para predecir posibles síntomas de depresión y ansiedad, así como factores protectores como la resiliencia y el crecimiento postraumático. Se observaron puntuaciones más altas en la EEP-4 entre los jóvenes, así como entre las personas con ingresos más bajos y en situación de desempleo. Conclusiones: Estos hallazgos sugieren que la EEP-4 podría ser una herramienta fiable y válida para evaluar el estrés psicológico en colombianos con educación universitaria.

Resumen: El comercio electrónico del sector de la moda ha aumentado en los últimos años en Latinoamérica, especialmente a raíz de la pandemia, sin embargo, todavía existen problemas en la aceptación de este canal de compra. La presente investigación se centra en algunos factores que podrían influir en la intención de compra y tiene como objetivos comprobar cómo en ella influyen la confianza, los riesgos percibidos, los costos y la conveniencia. Para esto, se suministró un cuestionario a 223 estudiantes de cuatro universidades del Ecuador. Para el análisis de los datos obtenidos se desarrolló un modelo PLS-SEM, en el cual la confianza en el vendedor actúa como variable mediadora en la relación entre los otros tres factores y la intención de compra. Los resultados muestran que dicho modelo tiene un aceptable poder predictivo y explicativo. Asimismo, se comprueba que los riesgos, la conveniencia y la confianza afectan significativamente a la intención de compra, pero no los costos. Además, la confianza mediaría la relación entre las tres variables y la intención de compra en línea. Se concluye que el modelo propuesto puede servir de base para estudios similares y, a su vez, se considera que los resultados obtenidos pueden ser utilizados por las empresas en el sector de la moda que quieren vender a través de plataformas de e-Commerce para el desarrollo de estrategias de marketing.

Abstract: E-commerce in the fashion sector has increased in recent years in Latin America, especially in the wake of the pandemic, however, there are still issues in the acceptance of this shopping channel. The present research focuses on some factors that could influence purchase intention and aims to test how it is influenced by trust, perceived risks, costs and convenience. For this purpose, a questionnaire was given to 223 students from four universities in Ecuador. A PLS-SEM model was developed for the analysis of the data, in which trust in the seller has a mediating role in the relationship between the other three factors and purchase intention. Results show that this model has acceptable predictive and explanatory power. Likewise, it validates that risks, convenience and trust significantly affect intention, but not costs. In addition, trust would mediate the relationship between the other variables and online shopping intention. It is concluded that the proposed model can serve as a basis for similar studies, and, at the same time, it is considered that the results obtained can be used by business in the fashion sector that pursue to sell through e-commerce platforms for marketing strategies development.

This research presents the implementation and evaluation of machine learning models to predict solar irradiance (W/m²). The objective is to provide valuable insights for making informed decisions regarding solar system design and financing. A thorough exploratory data analysis was conducted on the Southern African Universities Radiometric Network (SAURAN) data collected at the University of Pretoria's station to gain insights into the patterns of solar irradiance over the past 10 years. Python's functions and libraries are utilized extensively for conducting exploratory data analysis, model implementation, model testing, forecasting, and data visualization. Random Forest (RF), k-Nearest Neighbors (KNN), Feedforward Neural Network (FFNN), Support Vector Regression (SVR), and extreme Gradient Boosting models (XGBoost) are implemented and evaluated. The KNN model was found to be superior achieving a relative Root Mean Squared Error (RMSE), relative Mean Absolute Error (MAE), and R-Squared (R²) of 5.77%, 4.51% and 0.89 respectively on testing data. The variable importance analysis revealed that temperature (°C) exerted the greatest influence on predicting solar irradiance, accounting for 44% of the predictive power. The KNN model is suitable to inform solar systems design and financing decisions. Directions for future studies are identified and suggestions for areas of exploration are provided to contribute to the advancement of solar irradiance predictions.

RESUMEN: Para diagnosticar el Síndrome Apnea Obstructiva del Sueño (SAOS) se utiliza la polisomnografía, el cual es un costoso y extenso estudio que exige que el paciente duerma en un laboratorio. El SAOS ha sido asociado con características de la morfología facial y mediante un modelo predictivo de la Inteligencia Artificial (IA), se podría realizar un diagnóstico preliminar. El objetivo de este estudio fue analizar por medio de una revisión de alcance, las opciones tecnológicas basadas en IA aplicadas al diagnóstico del SAOS, y los parámetros evaluados en dichos análisis en las estructuras craneofaciales. Se realizó una búsqueda sistemática de la literatura hasta febrero del 2024 y mediante criterios de inclusión y exclusión se determino los estudios a analizar. Los títulos y resúmenes fueron seleccionados de forma independiente por dos investigadores. Se seleccionaron 14 estudios, incluyeron un total de 13.293 sujetos analizados. El rango edad de la muestra oscilo entre 18 y 90 años. 9.912 (74.56 %) sujetos eran de sexo masculino y 3.381 (25,43 %) eran de sexo femenino. Los estudios incluidos presentaron diagnóstico de SAOS mediante polisomnografía, siete estudios presentaron un grupo control de sujetos con ausencia de SAOS y otro grupo con presencia de SAOS. Mientras que los demás estudios, presentaron grupos de SAOS en relación con su severidad. Todos los estudios tuvieron una precisión media del 80 % en la predicción de SAOS utilizando variables como la edad, el género, mediciones y/o mediciones imagenológicas. no existen exámenes previos al diagnóstico por polisomnografía que permitan orientar al usuario en la probable presencia de SAOS. En este sentido, existen factores de riesgo para desarrollar SAOS vinculados a la forma facial, la obesidad, la edad y otras condiciones, que sumados a los avances con IA para diagnóstico y orientación en SAOS podrían ser utilizados para la detección precoz del mismo.

SUMMARY: To diagnose obstructive sleep apnea syndrome (OSAS), polysomnography is used, an expensive and extensive study requiring the patient to sleep in a laboratory. OSAS has been associated with features of facial morphology, and a preliminary diagnosis could be made using an artificial intelligence (AI) predictive model. This study aimed to analyze, using a scoping review, the AI-based technological options applied to diagnosing OSAS and the parameters evaluated in such analyses on craniofacial structures. A systematic search of the literature was carried out up to February 2024, and, using inclusion and exclusion criteria, the studies to be analyzed were determined. Titles and abstracts were independently selected by two researchers. Fourteen studies were selected, including a total of 13,293 subjects analyzed. The age of the sample ranged from 18 to 90 years. 9,912 (74.56 %) subjects were male, and 3,381 (25.43 %) were female. The included studies presented a diagnosis of OSAS by polysomnography; seven presented a control group of subjects without OSAS and another group with OSAS. The remaining studies presented OSAS groups in relation to their severity. All studies had a mean accuracy of 80 % in predicting OSAS using variables such as age, gender, measurements, and/or imaging measurements. There are no tests before diagnosis by polysomnography to guide the user in the likely presence of OSAS. In this sense, there are risk factors for developing OSA linked to facial shape, obesity, age, and other conditions, which, together with the advances in AI for diagnosis and guidance in OSAS, could be used for early detection.

ABSTRACT The objective of the research was to analyze whether motivation affects the productivity of the cashiers at Plaza Vea El Cortijo - Barranco 2022. Regarding the methodology, a non-experimental design was used, with a quantitative approach, with an explanatory-causal scope. The census-type sample, made up of 50 ATMs from Plaza Vea El Cortijo. With respect to data collection, the survey was used as a technique and the questionnaire as an instrument, which had 19 items. Regarding the data treatment in reference to the inferential environment, linear regression was applied to calculate if the model is adequate and has predictive capacity. For the general hypothesis, the R2 found was (0.688), indicating that the incidence is moderate, concluding that motivation significantly affects productivity, highlighting that communication, recognition and working conditions play a crucial role in the motivation of employees. the cashiers of the investigated entity and that in turn this condition has a significant impact on the productivity goals established by the store.

RESUMEN El objetivo de la investigación consideró analizar si la motivación incide en la productividad de los cajeros de Plaza Vea El Cortijo - Barranco 2022. En cuanto a la metodología, se empleó el diseño no experimental, de enfoque cuantitativo, con alcance explicativo- causal. La muestra tipo censal, conformada por 50 cajeros de Plaza Vea El Cortijo. Con respecto a la recopilación de datos se dispuso como técnica la encuesta y el cuestionario como instrumento, el cual contó de 19 ítems. En cuanto al tratamiento de datos en referencia al entorno inferencial se aplicó la regresión lineal para calcular si el modelo es adecuado y tiene capacidad predictiva. Para la hipótesis general el R2 hallada fue de (0, 688) indicando que la incidencia es moderada, concluyendo que la motivación incide significativamente en la productividad, destacando que la comunicación, el reconocimiento y las condiciones laborales juega un rol crucial en la motivación de los cajeros de la entidad investigada y que a su vez esta condición repercute significativamente en la productividad en metas, establecida por la tienda.

Abstract: The primary objectives of this research study are to provide a detailed description of machine learning (ML) technology when applied to assessing innovation and to design a model that allows predicting an institution’s degree of innovation. Machine learning technology lacks assumptions or preconceptions and is capable of processing a large amount of data and variables. After data processing, the ML model is built using variables associated with educational context, training is performed, and a web is built to predict the degree of innovation of an educational institution. This model provides an innovation accuracy prediction of 66% and allows assessing the influence of the variables analyzed when predicting the use of active methodologies at a given institution. In conclusion, this approach can open new data analysis techniques supported by ML that complement traditional statistically based approaches.

Resumen: Los principales objetivos de esta investigación son proporcionar una descripción detallada de la tecnología de aprendizaje automático (ML, por sus siglas en inglés) aplicada a la medición de la innovación y diseñar un modelo que permita predecir el grado de innovación en una determinada institución. La tecnología ML carece de suposiciones o preconcepciones y es capaz de procesar una gran cantidad de datos y variables. Tras procesar los datos, se construye el modelo ML empleando variables asociadas al contexto educativo, se realiza el entrenamiento y se construye una web para predecir el grado de innovación de una institución educativa. El modelo proporciona una precisión del 66% en la predicción de la innovación y permite discutir la influencia de las variables analizadas a la hora de predecir el uso de metodologías activas en una institución. En conclusión, este enfoque puede abrir nuevas técnicas de análisis de datos apoyadas en ML que complementen los enfoques tradicionales basados en la estadística.

Abstract Objective: Ovarian torsion (OT) represents a severe gynecological emergency in female pediatric patients, necessitating immediate surgical intervention to prevent ovarian ischemia and preserve fertility. Prompt diagnosis is, therefore, paramount. This retrospective study set out to assess the utility of combined clinical, ultrasound, and laboratory features in diagnosing OT. Methods: The authors included 326 female pediatric patients aged under 14 years who underwent surgical confirmation of OT over a five-year period. Logistic regression analysis was employed to pinpoint factors linked with OT, and the authors compared clinical presentation, laboratory results, and ultrasound characteristics between patients with OT (OT group) and without OT (N-OT group). The authors conducted receiver operating characteristic (ROC) curve analysis to gauge the predictive capacity of the combined features. Results: Among 326, OTwas confirmed in 24.23 % (79 cases) of the patients. The OT group had a higher incidence of prenatal ovarian masses than the N-OT (22 cases versus 7 cases) (p < 0.0001). Similarly, the authors observed significant differences in the presence of lower abdominal pain, suspected torsion on transabdominal ultrasound, and a high neutrophil-lymphocyte ratio (NLR > 3) between the OTand non-OT groups (p < 0.05). Furthermore, when these parameters were combined, the resulting area under the curve (AUC) was 0.868, demonstrating their potential utility in OT diagnosis. Conclusion: This study demonstrates a prediction model integrating clinical, laboratory, and ultrasound findings that can support the preoperative diagnosis of ovarian torsion, thereby enhancing diagnostic precision and improving patient management. Future prospective studies should concentrate on developing clinical predictive models for OTin pediatric patients. Objective fertility is therefore paramount Methods 32 1 fiveyear five year period presentation results NOT N group. . ROC (ROC Results 2423 24 23 24.2 79 (7 22 (2 p 0.0001. 00001 0.0001 0 0001 0.0001) Similarly pain neutrophillymphocyte neutrophil lymphocyte NLR 3 nonOT non 0.05. 005 0.05 05 0.05) Furthermore AUC (AUC 0868 868 0.868 Conclusion management 242 2 24. ( 0000 0.000 000 00 0.0 086 86 0.86 0.00 0. 08 8 0.8