¿Es la autoestima una variable relevante para los programas de prevención del inicio temprano de actividad sexual?

This article presents the results of three non experimental, correlational and crossectional studies. The studies respond to the questions that Vargas-Trujillo, Gambara and Botella (submitted for publication) proposed at the end of a meta-analytic review conducted to examine the difference in self-esteem between adolescents with high and low sexual risk activity. The results showed that self-esteem is not a relevant variable for prevention programs directed towards high school students in Colombia. In contrast, non virgin adolescents and those who have initiated sexual intercourse at early age are significantly different in levels of self-efficacy and perceived social norms than their non sexual activity counterparts.Se presentan los resultados de tres estudios transversales, no experimentales, correlacionales que se realizaron para responder a las preguntas que proponen Vargas Trujillo, Gambara y Botella (sometido a publicación) al final del estudio meta-analítico que realizaron para examinar la relación de la autoestima con el inicio de actividad sexual en la adolescencia. Los hallazgos indican que en Colombia, al menos en el grupo de adolescentes vinculados al sistema educativo en Bogotá, la autoestima no es una variable relevante para los programas de prevención del inicio temprano de actividad sexual. Los análisis sugieren que la autoeficacia y el contexto normativo en el que se desarrollan los adolescentes sí establecen diferencias significativas en el estatus sexual y en la edad a la que se tienen relaciones sexuales por primera vez

Volatility Spillover and Time-Varying Conditional Correlation Between DDGS, Corn, and Soybean Meal Markets

We find distiller\u27s dried grains with solubles (DDGS) prices to be positively correlated with both corn and soybean meal prices in the long run. However, neither corn nor soybean meal prices respond to deviations from this long-run relationship. We also identify strong time-varying dynamic conditional correlations between the markets, with the correlation between DDGS and corn strengthened after the expansion of ethanol production. There also appear to exist significant volatility spillovers from both the corn and soybean meal markets to the DDGS market, with the impact from corn shocks much larger compared to soybean meal shocks

Evaluación del Efecto de la Incorporación de Cinco Especies de Abono Verde con Base en Tres Variables Físicas de un Suelo Oxisol en la Altillanura Colombiana. /

61 p. Libro ElectrónicoBúsqueda de una especie de planta de abono verde eficiente bajo las condiciones de la altillanura colombiana y apropiada a las exigencias de la empresa Bioenergy S.A engranada en el sistema del cultivo de caña, teniendo como eje primordial el mejoramiento de los suelos y la elevación del rendimiento del cultivo nombrado.Resultado para Obtener el Título de Ingeniero Agrónomo. Material Acompañado por un Disco Compacto (CD-ROM), ubicado en la Colección Multimedia, identificado con el número AGR/0824.PregradoIngeniero Agrónom

Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data

With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable and sustainable energy. One of the challenges to achieve this vision lies on the low reliability of certain renewable sources. While both particulars and public entities try to reach self-sufficiency through sustainable energy generation, it is unclear how much investment is needed to mitigate the unreliability introduced by natural factors such as varying wind speed and daylight across the year. In this sense, a tool that aids predicting the energy output of sustainable sources across the year for a particular location can aid greatly in making sustainable energy investments more efficient. In this paper, we make use of Open Data sources, Internet of Things (IoT) sensors and installations distributed across Europe to create such tool through the application of Artificial Neural Networks. We analyze how the different factors affect the prediction of energy production and how Open Data can be used to predict the expected output of sustainable sources. As a result, we facilitate users the necessary information to decide how much they wish to invest according to the desired energy output for their particular location. Compared to state-of-the-art proposals, our solution provides an abstraction layer focused on energy production, rather that radiation data, and can be trained and tailored for different locations using Open Data. Finally, our tests show that our proposal improves the accuracy of the forecasting, obtaining a lower mean squared error (MSE) of 0.040 compared to an MSE 0.055 from other proposals in the literature.This paper has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project from the Spanish Ministry of Science, Innovation, and Universities; both Jose M. Barrera (I-PI 98/18) and Alejandro Reina (I-PI 13/20) hold an Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

Use of a i*extension for Machine Learning: a real case study

Capturing requirements in machine learning projects is a challenging task. It requires domain knowledge as well as experience in the machine learning field. The i* framework is a popular high abstraction-layer requirements capturing tool. However, the use of i* directly in the machine learning field (ML) is unfeasible due to it cannot capture all the restrictions and relationships of ML elements. In previous works we have extended i* to better capture machine learning requirements. In this paper, we apply the i* for machine learning extension to a real machine learning case study, in the context of a project focused on the diagnosis and treatment of Attention-Deficit/Hyperactivity Disorder (ADHD). The results show that the use of the i* for machine learning extension provides insights about the correct path to follow, aiding in the definition and selection of machine learning solutions that better fulfill the project requirements. Moreover, it facilitates faster development of the machine learning solution in a more structured way, avoiding errors and making the application of i* an effective tool for managing machine learning requirements

An extension of iStar for Machine Learning requirements by following the PRISE methodology

The rise of Artificial Intelligence (AI) and Deep Learning has led to Machine Learning (ML) becoming a common practice in academia and enterprise. However, a successful ML project requires deep domain knowledge as well as expertise in a plethora of algorithms and data processing techniques. This leads to a stronger dependency and need for communication between developers and stakeholders where numerous requirements come into play. More specifically, in addition to functional requirements such as the output of the model (e.g. classification, clustering or regression), ML projects need to pay special attention to a number of non-functional and quality aspects particular to ML. These include explainability, noise robustness or equity among others. Failure to identify and consider these aspects will lead to inadequate algorithm selection and the failure of the project. In this sense, capturing ML requirements becomes critical. Unfortunately, there is currently an absence of ML requirements modeling approaches. Therefore, in this paper we present the first i* extension for capturing ML requirements and apply it to two real-world projects. Our study covers two main objectives for ML requirements: (i) allows domain experts to specify objectives and quality aspects to be met by the ML solution, and (ii) facilitates the selection and justification of the most adequate ML approaches. Our case studies show that our work enables better ML algorithm selection, preprocessing implementation tailored to each algorithm, and aids in identifying missing data. In addition, they also demonstrate the flexibility of our study to adapt to different domains.This work has been co-funded by the AETHER-UA project (PID2020-112540RB-C43), a smart data holistic approach for context-aware data analytics: smarter machine learning for business modeling and analytics, funded by the Spanish Ministry of Science and Innovation. And the BALLADEER (PROMETEO/2021/088) project, a Big Data analytical platform for the diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD) featuring extended reality, funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana). A. Reina-Reina (I-PI 13/20) hold Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

Evaluation of the ethical leadership of leaders and intermediate managers in educational organizations

Ante la inexistencia de modelos de liderazgo ético validados científicamente, se desarrolló en la Universidad Autónoma de Madrid una investigación basada en el diseño de un modelo de liderazgo ético (Unda, 2013). Este modelo, denominado MOMUCLE, fue validado por cuatro reconocidos expertos en el área del liderazgo y la ética, facilitando en gran medida la conceptualización y evaluación del ejercicio de liderazgo ético de profesionales con responsabilidad en la gestión y toma de decisiones. Existen diferentes modelos y autores que han aportado a la investigación instrumentos para poder evaluar diversos estilos de liderazgo (Avolio et al., 1999; Goleman et al., 2002; Gardner, 2011; Moriano, J.A., 2011), si bien es cierto que se echan en falta estudios que pongan en relación la ética con el liderazgo (Marco, 2000), especialmente, en el ámbito educativo. En la actualidad, el grupo de investigación REGELO de la Universidad Internacional de la Rioja, ha diseñado un instrumento de evaluación denominado CLE-OR (Cuestionario de Liderazgo Ético y Eficaz para dirigentes y mandos intermedios en Organizaciones). Este cuestionario ha pasado por una fase de validación previa con 50 empleados de una universidad privada de España obteniendo unos resultados de fiabilidad del cuestionario muy elevados (Alpha de Cronbach de 0,966). Durante el 2018 y 2019 se prevé realizar con este instrumento de evaluación un estudio sobre liderazgo ético de directores y mandos intermedios de centros educativos de titularidad pública, concertada y privada dentro del ámbito estatalThe Universidad Autónoma de Madrid conducted research on the design of a model of ethical leadership as there were not any models scientifically validated (Unda, 2013). The so-called model MOMUCLE, which was validated by four prestigious experts in the area of leadership and ethics, provided the conceptualization and evaluation of the exercise of ethical leadership by professionals in charge of management and decision making. We can find models and scholars that have contributed with tools to evaluate diverse leadership styles (Avolio et al., Bass, 1998; Bass y Avolio, 1994; Gar dner, 2011; Goleman et al., 2002; Moriano, 2011), however there is a lack of studies relating ethics to leadership (Marco, 2000). REGELO, research group funded by Universidad Internacional de La Rioja, has designed CLE-OR, which stands for Ethical and Effective Leadership Questionnaire for Leaders and Middle Management. This questionnaire has been validated by 50 employees of a private university in Spain obtaining a high degree of reliability (Alpha de Cronbach: 0,966). During 2018 and 2019 this evaluation tool will be used in a study on ethical leadership of leaders and middle management within Public, Charter and Private education center

Energy and Food Commodity Prices Linkage: An Examination with Mixed-Frequency Data

Abstract Is the relationship between energy and agricultural commodities an important factor in the increasing price variability of food commodities? Findings from the literature appear to be mixed and highly influenced by the data frequency used in those analysis. A recurrent task in time series applied work is to match up data at different frequencies, while macroeconomic variables are often found at monthly or quarterly observations, financial variables are sampled daily or even at higher frequencies. In order to match up time series at different frequencies a common procedure is to aggregate the higher frequency to fit in the low frequency, this has the potential of losing valuable information, and generating misspecification. We study whether the use of mixed frequency estimations with data for the 2006-2011 period helps to improve the out of sample performance of a model that explains grain prices as a function of energy prices, macroeconomic variables such as exchange rate, interest rate, and inflation. Preliminary results suggest that an improvement is feasible, however it is tenuous beyond two months horizons

Relación de las actitudes personales y de la norma social con la actividad sexual de los adolescentes

Con una muestra de 326 estudiantes de secundaria (178 hombres, 144 mujeres, 4 participantes no indicaron su sexo) entre 13 y 18 años, de niveles socio-económicos bajo, medio y alto, se examinó la relación de la actitud personal y de la norma social percibida (percepción de la actitud de los amigos y de la norma de pares) con varios indicadores de la actividad sexual de los participantes. Se usó un cuestionario de autorreporte para obtener información sobre las variables del estudio. El análisis de la información mostró que sólo el 31% de los jóvenes participantes había tenido relaciones sexuales penetrativas. Los análisis de correlación y regresión apoyan los planteamientos de la Teoría de la Acción Razonada acerca de la importancia de la actitud personal y de la percepción que tienen los jóvenes de la norma social en la explicación de la actividad sexual durante la adolescencia. Las variables predictoras del estudio explican entre el 10% y el 60% de la variabilidad de cada uno de los indicadores de la actividad sexual. Se verificó estadísticamente que la influencia de la percepción de la actitud sexual de los amigos sobre la actividad sexual penetrativa se da a través de la actitud sexual personal. Estos resultados confirman el papel de las cogniciones en los comportamientos de salud y señalan la necesidad de considerarlas en los programas de promoción de la salud sexual y reproductiva.Using a sample of 326 high-school students (178 males, 144 females, 4 participants who did not indicate their sex) between 13 and 18 years old, from lower, middle and upper socio-economic levels, the relationship between personal attitude and the perceived social norm (perception of friends' attitude and of peer norm) was examined with several indicators of the sexual activity of participants. A self-report questionnaire was used in order to get information about the variables of the study. The analysis of information showed that only one 31 percent of the young participants had had penetrative sexual relationships. Correlation and regression analyses support the statements of Reasoned Action Theory on the importance of personal attitude and the youth's perception of the social norm in explaining sexual activity during adolescence. The study's predictive variables explain between a 10 percent and a 60 percent variability of each one of the sexual activity indicators. It was statistically verified that the influence of perception of sexual attitudes of friends on penetrative sexual activity takes place through the personal sexual attitude. These results confirm the role of cognition in healthy behaviour and point to the need to bear it in mind in programs of sexual and reproductive health promotion campaigns