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

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: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model’s predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists. Objective Periventricularintraventricular Periventricular intraventricular newborns birth periventricularintraventricular periventricular sequelae However Method MIMICIII MIMIC III v1.4 v14 v v1 4 (v1.4 database 2 nomogrambased nomogram based validated Results output models s 0792 0 792 0.792 power outcomes HosmerLemeshow Hosmer Lemeshow p 0.990. 0990 0.990 . 990 0.990) 0788 788 0.788 Conclusions ability userfriendly user friendly neonatologists v1. (v1. 079 79 0.79 099 0.99 99 078 78 0.78 (v1 07 7 0.7 09 0.9 9 (v 0.

Abstract Objective: To build a model based on cardiometabolic indicators that allow the identification of overweight adolescents at higher risk of subclinical atherosclerotic disease (SAD). Methods: Cross-sectional study involving 161 adolescents with a body mass index ≥ + 1 z-Score, aged 10 to 19 years. Carotid intima-media complex thickness (IMT) was evaluated using ultrasound to assess subclinical atherosclerotic disease. Cardiometabolic indicators evaluated included nutritional status, central adiposity, blood pressure, lipidic profile, glycemic profile, as well as age and sex. Data was presented using measures of central tendency and dispersion, as well as absolute and relative frequency. The relationship between IMT measurement (outcome variable) and other variables (independent variables) was assessed using Pearson or Spearman correlation, followed by multiple regression modeling with Gamma distribution to analyze predictors of IMT. Statistical analysis was performed using SPSS and R software, considering a significance level of 5 %. Results: It was observed that 23.7 % had Carotid thickening, and the prevalence of abnormal fasting glucose was the lowest. Age and fasting glucose were identified as predictors of IMT increase, with IMT decreasing with age by approximately 1 % per year and increasing with glucose by around 0.24 % per mg/dL. Conclusion: The adolescent at higher risk is younger with higher fasting glycemia levels. Objective SAD. SAD . (SAD) Methods Crosssectional Cross sectional 16 zScore, zScore z Score, Score z-Score years intimamedia intima media (IMT status adiposity pressure profile sex dispersion frequency outcome variable independent correlation software Results 237 23 7 23. thickening lowest increase 024 0 24 0.2 mgdL mg dL mg/dL Conclusion levels (SAD 2 02 0.

Abstract Objective: Reliably prediction models for coronary artery abnormalities (CAA) in children aged > 5 years with Kawasaki disease (KD) are still lacking. This study aimed to develop a nomogram model for predicting CAA at 4 to 8 weeks of illness in children with KD older than 5 years. Methods: A total of 644 eligible children were randomly assigned to a training cohort (n = 450) and a validation cohort (n = 194). The least absolute shrinkage and selection operator (LASSO) analysis was used for optimal predictors selection, and multivariate logistic regression was used to develop a nomogram model based on the selected predictors. Area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score, and decision curve analysis (DCA) were used to assess model performance. Results: Neutrophil to lymphocyte ratio, intravenous immunoglobulin resistance, and maximum baseline z-score ≥ 2.5 were identified by LASSO as significant predictors. The model incorporating these variables showed good discrimination and calibration capacities in both training and validation cohorts. The AUC of the training cohort and validation cohort were 0.854 and 0.850, respectively. The DCA confirmed the clinical usefulness of the nomogram model. Conclusions: A novel nomogram model was established to accurately assess the risk of CAA at 4–8 weeks of onset among KD children older than 5 years, which may aid clinical decisionmaking. Objective (CAA (KD lacking Methods 64 n 450 194. 194 . 194) (LASSO AUC, , (AUC) curves HosmerLemeshow Hosmer Lemeshow test score (DCA performance Results ratio resistance zscore z 25 2 2. cohorts 0854 0 854 0.85 0850 850 0.850 respectively Conclusions 48 4– decisionmaking 6 45 19 (AUC 085 85 0.8 1 08 0.

HIV/AIDS remains one of the world's most significant public health and economic challenges, with approximately 36 million people currently living with the disease. Considerable progress has been made to reduce the impact of HIV/AIDS in the past years through successful multiple HIV/AIDS prevention and treatment interventions. However, barriers such as lack of engagement, limited availability of early HIV-infection detection tools, high rates of HIV/sexually transmitted infections (STIs), barriers to access antiretroviral therapy, lack of innovative resource optimisation and distribution strategies, and poor prevention services for vulnerable populations still exist and substantially affect the attainment of the UNAIDS 95-95-95 targets. A rapid review was conducted from 24 October 2022 to 5 November 2022. Literature searches were conducted in different prominent and reputable electronic database repositories including PubMed, Google Scholar, Science Direct, Scopus, Web of Science, IEEE Xplore, and Springer. The study used various search keywords to search for relevant publications. From a list of collected publications, researchers used inclusion and exclusion criteria to screen and select relevant papers for inclusion in this review. This study unpacks emerging opportunities that can be explored by applying machine learning techniques to further knowledge and understanding about HIV service design, prediction, implementation, and evaluation. Therefore, there is a need to explore innovative and more effective analytic strategies including machine learning approaches to understand and improve HIV service design, planning, implementation, and evaluation to strengthen HIV/AIDS prevention, treatment, and awareness strategies.

RESUMEN Los pronósticos de las precipitaciones tienen gran importancia para prevenir desastres, pérdidas económicas en cultivos y varios aspectos importantes para la hidroeconomía del país, de ahí la necesidad de desarrollar modelos de predicción de precipitaciones. En el Centro Meteorológico Provincial de Villa Clara (CMPVC) se emplean modelos matemáticos con un nivel de precisión alto, pero requieren mucho tiempo de procesamiento. De ahí la necesidad de buscar maneras más actuales y precisas para realizar pronósticos de lluvia acertados, pero de manera más rápida. El progreso de la inteligencia artificial y en especial, las redes neuronales artificiales han permitido desarrollar modelos para dar solución al problema de predicción de precipitaciones; mediante las métricas proporcionadas por la Organización Meteorológica Mundial (OMM) comprendidas entre los años 1975 hasta el 2016. En este trabajo se realiza un análisis de los diferentes modelos de regresión con múltiples salidas para predecir las precipitaciones en los meses mayo, junio, julio y agosto. Los modelos de regresión que más se ajustaron a esta problemática fueron la regresión lineal, el árbol de regresión, el k-vecinos más cercanos, la regresión directa, la regresión encadenada y el perceptrón multicapa. Luego de realizar las corridas de los modelos resultó el perceptrón multicapa, el modelo de regresión con mejores resultados, con un alto grado de eficacia para el pronóstico de las precipitaciones.

ABSTRACT Rainfall forecasts are of great importance to prevent disasters, economic losses in crops and several important aspects for the country's hydroeconomy, hence the need to develop rainfall prediction models. At the Villa Clara Provincial Meteorological Center (CMPVC), mathematical models with a high level of accuracy are used, but they require a lot of processing time. Hence, the need to look for more current and accurate, ways to make accurate rainfall forecasts but in a faster way. The progress of artificial intelligence and especially artificial neural networks have allowed the development of models to solve the problem of rainfall prediction; using the metrics provided by the World Meteorological Organization (WMO) from 1975 to 2016. In this work, an analysis of the different regression models with multiple outputs to predict rainfall is carry out in the months of May, June, July and August. The regression models that best fit this problem were linear regression, regression tree, k-nearest neighbors, direct regression, chained regression and multilayer perceptron. After running the models, the multilayer perceptron was the regression model with the best results, with a high degree of efficiency for rainfall forecasting.

Energy consumption from the residential sector forms a large portion of the electricity grid demand. The growing accessibility of residential load profile data presents an opportunity for improved residential load forecasting and the implementation of demand-side management (DSM) strategies. Machine learning is a tool well-suited for predicting stochastic processes, such as residential power usage due to human behavior. Long short-term memory (LSTM) recurrent neural networks are especially suited for predicting time-series data such as electrical load profiles. This paper investigates the impact of LSTM hyperparameters to the predictive performance of models, which include the tradeoffs associated with training data size, horizon ratios, model fidelity, prediction horizon and computational intensity. This paper provides a framework to evaluate the choice of LSTM hyperparameters for understanding trade-offs in a practical application of load profile predictions for the context of Grid-interactive Efficient Buildings (GEBs).

Abstract Land value patterns show very distinct spatial associations with accessibility to urban centralities and physical factors in a territory. However, predictions based on models of this structure can be highly uncertain, as the underlying data also may show clustering (thus allowing for better predictions in more densely sampled areas). An assessment of this uncertainty for land value extrapolations in the San José Metropolitan Region of Costa Rica is presented, via conditional Gaussian simulation, and the determinants of this uncertainty were explored, to find spatial strengths and weaknesses in the modeling efforts. The E-Type prediction from the conditional Gaussian simulation was found to marginally improve on ordinary kriging methods and it also provided explicit uncertainty patterns, which are the inverse of the land value prediction. The estimated uncertainty was found to decrease with characteristics that identify suitability for urban land use (and thus higher land values).

Resumen Los patrones de valor del suelo muestran asociaciones espaciales claras con accesibilidad a centralidades urbanas y a factores físicos de un territorio. Sin embargo, las predicciones basadas en esta estructura pueden ser altamente inciertas, dado que los datos mismos también exhiben aglomeración (y, por tanto, permiten mejores predicciones en las zonas más densamente muestreadas). Se presenta una evaluación de esta incertidumbre para extrapolaciones de valor del suelo en la Gran Área Metropolitana de Costa Rica mediante simulaciones gaussianas condicionales y una exploración de los determinantes de esta incertidumbre, como forma de reconocer fortalezas y debilidades de esta predicción. La predicción E-Type simulada resultó marginalmente mejor que extrapolaciones mediante kriging ordinario y produjo una cuantificación espacialmente explícita de la incertidumbre. El patrón de incertidumbre resultó ser un espejo de los valores del suelo. Se encontró que la incertidumbre se reduce con características asociadas a mayor aptitud del suelo para usos urbanos y, por tanto, de mayor precio.

ABSTRACT Objective: To describe the chronology, evolution and impact of global research into chronic postoperative pain. Materials and methods: A bibliometric study was conducted using the Scopus database. A structured search was designed and validated, thereby allowing the collection of metadata, which were analyzed through the Bibliometrix package of the R programming language. The study involved the description of the general characteristics, evolution and calculation of impact metrics of global research into chronic postoperative pain. Results: The study included 1,496 documents, which spanned from 1983 to 2023. Original articles accounted for 70.7 % (n =1,059) of the total output, followed by reviews (n = 357; 23.8 %). There was an international collaboration rate of 15.6 %, and there has been sustained growth in output since 1983, with a sharp increase in the last 13 years, 2022 being the most prolific one (n =191 published documents). It was identified that Canada and Denmark lead the impact of global research and have the most productive authors and institutions. However, the United States is the most prolific country because it leads significant collaboration, mainly with European and Latin American countries. Neuropathic pain, risk factor assessment and pain management were identified as some of the most frequent topics. Over the past approximately 10 years, there has been persistent interest in research on quality of life, prediction, prevention, and risk factor assessment. Recently, there has been interest in studying pain in video-assisted thoracoscopic surgery and developing predictive models. Conclusions: The study revealed sustained growth in global research on chronic postoperative pain over the past 40 years. Such growth has been mainly led by Canadian and Danish institutions, despite the United States being the most prolific country. Moreover, there has been a significant transition in the studied topics, moving from the use of drugs and identification of risk factors to the study of predictive models, data systematization, and video-assisted surgery.

RESUMEN Objetivo: Describir la cronología, evolución e impacto de la investigación global sobre el dolor crónico posoperatorio. Materiales y métodos: Estudio bibliométrico que utilizó la base de datos Scopus. Se diseñó y validó una búsqueda estructurada que permitió la recolección de metadatos, los cuales se analizaron a través del paquete Bibliometrix del lenguaje de programación R. Se realizó la descripción de las características generales, la evolución y el cálculo de métricas de impacto de la investigación global sobre el dolor crónico postoperatorio. Resultados: Se incluyeron 1496 documentos, con una ventana de tiempo entre 1983 y 2023. El 70,7 % (n = 1059) de la producción total fueron artículos originales, seguido de revisiones (n = 357; 23,8 %). Existió una colaboración internacional del 15,6 %, y, desde 1983, ha existido un crecimiento sostenido de la producción, con un incremento marcado en los últimos 13 años, donde el 2022 fue el año más fecundo (n = 191 documentos publicados). Se identificó que Canadá y Dinamarca lideran el impacto de la investigación global, y cuentan con los autores e instituciones más prolíficas. Sin embargo, Estados Unidos es el país más productivo, ya que lidera una importante colaboración, esencialmente junto a países europeos y latinos. Se encontró que el dolor neuropático, la valoración de factores de riesgo y el manejo del dolor son algunos de los tópicos más frecuentes. En los últimos 10 años, aproximadamente, ha existido un interés persistente en la investigación sobre calidad de vida, predicción, prevención y valoración de factores de riesgo. Últimamente, ha existido especial interés en el estudio del dolor en cirugía toracoscópica videoasistida y construcción de modelos de predicción. Conclusiones: Se reveló un crecimiento sostenido en la investigación global sobre dolor crónico postoperatorio, en los últimos 40 años. Dicho crecimiento ha estado liderado esencialmente por instituciones canadienses y danesas, a pesar de que se ha considerado a Estados Unidos como el país más prolífico. Asimismo, ha existido una transición importante en los tópicos estudiados, se ha pasado del uso de fármacos y determinación de factores de riesgo al estudio de modelos de predicción, sistematización de datos y cirugía videoasistida.

Higher Himalayan catchments are often poorly monitored for hydrological activities involving flood flow prediction for the safety of riverside communities and the successful operation of hydropower projects. This study aimed to estimate the comparative performance of artificial neural network (ANN) based flow prediction models using 10 years of daily river flow data of Kaligandaki catchment at Kotagaun, Nepal, which is a snow-fed catchment in the Himalayan region. The flow prediction models were trained and tested at a hydrological station using the previous 3 days' river flow data to predict the 1-day ahead flow data. Eight different training functions were employed in an ANN model for comprehensive statistical assessment of accuracy and precision of each training function. The most significant and validated result obtained in this study is the comprehensive comparison of various training functions' performance, and identification )f the most efficient training function for the study case. Among the training functions investigated, the Levenberg-Marquardt backpropagation function exhibits the best performance for the model having Nash-Sutcliffe efficiency, root mean square error and mean absolute error values of 0.866, 209.578 and 75.422, respectively. This study provides a fundamental basis for accurate flow prediction of topographically challenged catchments where hydrological monitoring and data collection may be limited. In particular, r his model will help to improve early warning system, hydrological planning, and the safety of riverside communities in the Himalayan region.

Abstract Objective: The objective of the study was to explore red cell distribution width (RDW) as a surrogate marker of inflammation, alone and in conjunction with muscle wasting to predict malnutrition-related adverse outcomes. Methods: This was a single-center observational study including adult hospitalized patients. Demographic variables, malnutrition criteria, and RDW were captured within 24 hours of hospital admission. Correlation tests and regression models were performed between these variables (RDW and muscle wasting) and adverse outcomes (in-hospital mortality and unplanned transfer to critical care areas (CCA). Results: Five hundred and forty-five patients were included in the final analysis. Muscle wasting showed an independent association with adverse outcomes in every regression model tested. RDW alone showed fair predictive performance for both outcomes’ significance and the adjusted model with muscle wasting showed association only for unplanned transfer to CCA. Conclusion: RDW did not improve the prediction of adverse outcomes compared to muscle wasting assessed by physical examination and simple indexes for acute and chronic inflammation. Malnourished patients presented higher RDW values showing a possible metabolic profile (higher inflammation and lower muscle). It is still unknown whether nutrition support can influence RDW value over time as a response marker or if RDW can predict who may benefit the most from nutritional support.

Resumen Objetivo: Explorar el ancho de distribución eritrocitaria (ADE) como un marcador subrogado de inflamación, individualmente y en conjunto con el desgaste muscular, para predecir resultados adversos asociados a la desnutrición. Método: Estudio unicéntrico, observacional, incluyendo pacientes adultos hospitalizados. Se capturaron variables demográficas, criterios de desnutrición y el ADE en las primeras 24 horas de ingreso. Se realizaron pruebas de correlación y modelos de regresión entre dichas variables (ADE y desgaste) y resultados adversos (mortalidad hospitalaria y traslado no planeado a áreas críticas). Resultados: Se incluyeron 545 pacientes. El desgaste muscular mostró asociación independiente con los resultados adversos en cada modelo. El ADE individualmente mostró un desempeño aceptable para la predicción de ambos resultados, y en modelos ajustados con desgaste muscular mostró asociación únicamente con traslado no planeado a áreas críticas. Conclusiones: El ADE no mejoró la predicción de resultados adversos comparado con el desgaste muscular por exploración física e índices simples de inflamación. Los pacientes con desnutrición presentaron mayores valores de ADE, mostrando un posible perfil metabólico (mayor inflamación y menos músculo). Aún se desconoce si el soporte nutricional puede influenciar el ADE como un marcador de respuesta o si puede predecir una respuesta favorable al soporte nutricional.

RESUMEN La modelación del tiempo y el clima a escala local en las montañas han sido relevantes para el estudio de los ecosistemas agroforestales y su evolución futura. El trabajo tuvo como objetivo integrar un conjunto de datos espaciales de alta resolución al modelo numérico de predicción e investigación meteorológica WRF, adecuado para el análisis meteorológico y del clima local en las montañas de Cuba. En el trabajo, se definió como datos espaciales prioritarios los referidos al relieve, la máscara de tierra como interface océano-suelo, el tipo de cobertura y tipo de suelo. Se seleccionaron las bases de datos espaciales de alta resolución nacionales o globales de mejor calidad y más adecuadas a las características del modelo. Las bases de datos integradas al WRF tuvieron resolución espacial entre 0,95 y 0,03 km aproximadamente en el Ecuador. El incremento y actualización de los datos espaciales robustecieron la capacidad del modelo para el análisis de los factores geográficos formadores del clima local, propiciando mejor desempeño en las montañas. Los resultados de la investigación pudieran contribuir a la vigilancia y pronóstico meteorológico y climático a escala local para las montañas de Cuba. De igual manera, beneficiarían los estudios y gestión de riesgos asociados a incendios forestales, eventos hidrometeorológicos severos, la variabilidad climática, el cambio climático, y otros peligros relacionados con el sistema climático que han afectado los ecosistemas agroforestales cubanos.

RESUMO A modelagem do tempo e do clima em escala local nas montanhas tem sido relevante para o estudo dos ecossistemas agroflorestais e sua evolução futura. O objetivo deste trabalho foi integrar um conjunto de dados espaciais de alta resolução ao modelo numérico de pesquisa e previsão do tempo WRF, adequado para a análise do tempo e do clima local nas montanhas de Cuba. No trabalho, os dados espaciais prioritários foram definidos como aqueles referentes ao relevo, à máscara de terra como uma interface oceano-solo, ao tipo de cobertura e ao tipo de solo. Foram selecionados os bancos de dados espaciais nacionais ou globais de alta resolução da mais alta qualidade e mais adequados às características do modelo. Os bancos de dados integrados ao WRF tinham resolução espacial entre 0,95 e 0,03 km aproximadamente no equador. Os dados espaciais ampliados e atualizados fortaleceram a capacidade do modelo de analisar os fatores geográficos que moldam o clima local, levando a um melhor desempenho nas montanhas. Os resultados da pesquisa podem contribuir para o monitoramento e a previsão do tempo e do clima em escala local nas montanhas de Cuba. Eles também beneficiariam estudos e gerenciamento de riscos associados a incêndios florestais, eventos hidrometeorológicos graves, variabilidade climática, mudanças climáticas e outros perigos relacionados ao sistema climático que afetaram os ecossistemas agroflorestais cubanos.

ABSTRACT Local-scale weather and climate modeling in mountains has been relevant for the study of agroforestry ecosystems and their future evolution. The study aimed to integrate a set of high-resolution spatial data to the WRF numerical meteorological prediction and research model, suitable for meteorological and local climate analysis in the mountains of Cuba. In the study, priority spatial data were defined as those referring to the relief, the land mask as the ocean-soil interface, the type of cover and type of soil. The national or global high-resolution spatial databases of the best quality and most appropriate to the characteristics of the model were selected. The databases integrated into the WRF had spatial resolution between approximately 0.95 and 0.03 km in Ecuador. The increase and updating of spatial data strengthened the model's capacity for the analysis of the geographic factors that shape the local climate, promoting better performance in the mountains. The research results could contribute to meteorological and climate monitoring and forecasting at a local scale for the mountains of Cuba. Likewise, studies and risk management associated with forest fires, severe hydrometeorological events, climate variability, climate change, and other dangers related to the climate system that have affected Cuban agroforestry ecosystems would benefit.

RESUMEN El objetivo del presente estudio fue emplear modelos matemáticos para la predicción de los rendimientos de la madera aserrada a partir de parámetros dendrométricos de los árboles en pie, que permitan una adecuada definición de su potencial en el aserradero Combate de la Tenería. Los rendimientos de madera aserrada están influenciados por la posición de la troza en el fuste del árbol, con un decrecimiento en dependencia de la altura. Las cuatro ecuaciones construidas para determinar el rendimiento de la madera aserrada tienen aceptables coeficientes de correlación, determinación y bajos errores estándar de estimación. Sin embargo, el mejor comportamiento lo posee el modelo Ln (rend) = 1,084 + 0,464 * ln d1,30 0,1201 ln que tiene mejor ajuste, las variables independientes explican el 67 % y el menor valor del error estándar de estimación. En función del análisis de elasticidad se puede definir el diámetro a 1,30 m es la variable predictora de mayor influencia positiva sobre el incremento de los rendimientos de madera aserrada. La conicidad tiene un efecto negativo sobre el volumen y valor de la madera aserrada. Con la implementación del control estadístico del aserrado, se detectó que el proceso está fuera de control, lo cual implica la necesidad de realizar el análisis correspondiente para eliminar esta deficiencia.

RESUMO O objetivo deste estudo foi utilizar modelos matemáticos para prever o rendimento da madeira serrada com base em parâmetros dendrométricos de árvores em pé, que permitam uma definição adequada do seu potencial na serraria Combate de la Tenería. O rendimento da madeira serrada é influenciado pela posição da tora no tronco da árvore, diminuindo conforme a altura. As quatro equações construídas para determinar o rendimento da madeira serrada apresentam correlação e coeficientes de determinação aceitáveis e baixos erros padrão de estimativa. Porém, o melhor comportamento é apresentado pelo modelo Ln (rend) = 1,084 + 0,464 * ln d1,30 0,1201 ln, que apresenta o melhor ajuste, as variáveis independentes explicam 67% e o menor valor do erro padrão de estimativa. Com base na análise de elasticidade, o diâmetro pode ser definido em 1,30 m como a variável preditora com maior influência positiva no aumento do rendimento de madeira serrada. A conicidade tem um efeito negativo no volume e no valor da madeira serrada. Com a implementação do controlo estatístico da serragem, detectou-se que o processo está fora de controlo, o que implica a necessidade de realizar a análise correspondente para eliminar esta deficiência.

ABSTRACT The objective of this study was to use mathematical models for the prediction of sawnwood yields based on dendrometric parameters of standing trees, which allow an adequate definition of their potential in the Combate de la Tenería sawmill. Sawnwood yields are influenced by the position of the log in the tree trunk, with a decrease depending on the height. The four equations constructed to determine the yield of sawnwood have acceptable correlation and determination coefficients and low standard errors of estimation. However, the best behavior is shown by the model Ln (rend) = 1.084 + 0.464 * l n d 1.30 0.1201 l n which has the best fit, the independent variables explain 67% and the lowest value of the standard error of estimation. Based on the elasticity analysis, the diameter can be defined at 1.30 m, it is the predictor variable with the greatest positive influence on the increase in sawnwood yields. Taper has a negative effect on the volume and value of sawnwood. The use of the statistical control of saw detected that the process is out of control, which implies the need to carry out the corresponding analysis to eliminate this deficiency.

Abstract: The objective of this study was to evaluate the relationship between body weight (BW) and different biometric measurements in llamas (Lama glama) from the Bolivian highlands and to generate prediction models of BW. A total of 515 individual records of BW and biometric measurements were used. The measurements were taken on 202 males and 313 females aged between 0.5 and 5 years, and included: neck length (NL), withers height (WH), rump height (RH), heart girth (HG), body length (BL), abdomen circumference (AC), rib depth (RD), hip width (HW), pin bone width (PBW), thoracic width (TW), and back length (BKL). The relationships between BW and biometric measurements were developed using simple linear and multiple regression. For the evaluation, the relationship between the observed and predicted values of BW was determined by linear regression, the mean squared error of prediction (MSEP) and root MSEP (RMSEP); concordance correlation coefficient analysis was also used. The BW ranged from 22 to 122 kg. Regression equations between BW, HG and RD had an r2 of 0.94 and 0.92, respectively (RMSEP= 6.06 and 6.70 kg, respectively). The equations were highly precise (r2 >0.86) and accurate (Cb>0.98), with a reproducibility index > 0.92. The model efficiency (MEF) indicated a higher efficiency of prediction (MEF > 0.86). Using a single predictor, HG and RD accounted for more than 92% of the variation in BW. Overall, HG may be used as a single predictor to predict BW in llamas maintained under the conditions of the Bolivian highlands.