Viabilidad de integrar energía fotovoltaica en una empresa constructora uruguaya e impacto en su desempeño ambiental.
Tesis
Materias > Ingeniería
Universidad Europea del Atlántico > Docencia > Trabajos finales de Máster
Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster
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Uruguay ha implementado a partir de 2005 medidas orientadas a diversificar su matriz energética, incorporando fuentes de energías renovables y ampliando las posibilidades para la microgeneración. Como parte de esas medidas se establecieron beneficios e incentivos fiscales. En los últimos dos años ha habido un cambio en los énfasis de estos incentivos respecto a la orientación de los recursos. En este contexto este trabajo busca. evaluar la viabilidad de integrar energía fotovoltaica en una empresa constructora y el impacto en su desempeño ambiental. Como base se han tomado los trabajos de Fundación Bariloche y Programa de Estudios e Investigaciones en Energía. (2008) ¨Estudios de base para el diseño de estrategias y políticas energéticas: relevamiento de consumos de energía sectoriales en términos de energía útil a nivel nacional¨ de la Dirección Nacional de Energía y Tecnología Nuclear, de Maestre (2016) ´Análisis y cuantificación del coste de la energía de los equipos de obra durante la ejecución de las edificaciones. Propuesta de reducción mediante la utilización de energías renovables.¨ y Grupo Empresarial Oikos (2013) ¨Estudio de caracterización y prospectiva para la industria de la construcción¨. Los datos del caso de estudio se relevaron a partir de entrevistas a distintos referentes dentro de la empresa y se procesaron mediante planillas electrónicas elaboradas con este fin. Como conclusiones generales, se plantea que la incorporación de autogeneracion FV a los procesos para la empresa caso de estudio, es en el contexto actual una inversión que presenta debilidades desde el punto de vista económico y no implica en todos los casos una mejora en el desempeño ambiental. Sin embargo, el camino que se presenta es hacia ese modelo productivo por lo que deben comenzar a tomarse medidas para preparse en ese sentido.
metadata
Pérez Mastandrea, Guillermina
mail
guipemas@gmail.com
(2022)
Viabilidad de integrar energía fotovoltaica en una empresa constructora uruguaya e impacto en su desempeño ambiental.
Masters thesis, SIN ESPECIFICAR.
Resumen
Uruguay ha implementado a partir de 2005 medidas orientadas a diversificar su matriz energética, incorporando fuentes de energías renovables y ampliando las posibilidades para la microgeneración. Como parte de esas medidas se establecieron beneficios e incentivos fiscales. En los últimos dos años ha habido un cambio en los énfasis de estos incentivos respecto a la orientación de los recursos. En este contexto este trabajo busca. evaluar la viabilidad de integrar energía fotovoltaica en una empresa constructora y el impacto en su desempeño ambiental. Como base se han tomado los trabajos de Fundación Bariloche y Programa de Estudios e Investigaciones en Energía. (2008) ¨Estudios de base para el diseño de estrategias y políticas energéticas: relevamiento de consumos de energía sectoriales en términos de energía útil a nivel nacional¨ de la Dirección Nacional de Energía y Tecnología Nuclear, de Maestre (2016) ´Análisis y cuantificación del coste de la energía de los equipos de obra durante la ejecución de las edificaciones. Propuesta de reducción mediante la utilización de energías renovables.¨ y Grupo Empresarial Oikos (2013) ¨Estudio de caracterización y prospectiva para la industria de la construcción¨. Los datos del caso de estudio se relevaron a partir de entrevistas a distintos referentes dentro de la empresa y se procesaron mediante planillas electrónicas elaboradas con este fin. Como conclusiones generales, se plantea que la incorporación de autogeneracion FV a los procesos para la empresa caso de estudio, es en el contexto actual una inversión que presenta debilidades desde el punto de vista económico y no implica en todos los casos una mejora en el desempeño ambiental. Sin embargo, el camino que se presenta es hacia ese modelo productivo por lo que deben comenzar a tomarse medidas para preparse en ese sentido.
| Tipo de Documento: | Tesis (Masters) |
|---|---|
| Palabras Clave: | Energías renovables, Energía fotovoltaica, Desempeño ambiental, Viabilidad económica, Industria Construcción. |
| Clasificación temática: | Materias > Ingeniería |
| Divisiones: | Universidad Europea del Atlántico > Docencia > Trabajos finales de Máster Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster |
| Depositado: | 30 Oct 2023 23:30 |
| Ultima Modificación: | 30 Oct 2023 23:30 |
| URI: | https://repositorio.unib.org/id/eprint/1317 |
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Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults
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Hand-drawn mathematical geometric shapes are geometric figures, such as circles, triangles, squares, and polygons, sketched manually using pen and paper or digital tools. These shapes are fundamental in mathematics education and geometric problem-solving, serving as intuitive visual aids for understanding complex concepts and theories. Recognizing hand-drawn shapes accurately enables more efficient digitization of handwritten notes, enhances educational tools, and improves user interaction with mathematical software. This research proposes an innovative machine learning algorithm for the automatic classification of mathematical geometric shapes to identify and interpret these shapes from handwritten input, facilitating seamless integration with digital systems. We utilized a benchmark dataset of mathematical shapes based on a total of 20,000 images with eight classes circle, kite, parallelogram, square, rectangle, rhombus, trapezoid, and triangle. We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. Experimental results illustrate that using the CnN-RFc method, the Light Gradient Boosting Machine (LGBM) algorithm surpasses state-of-the-art approaches with high accuracy scores of 98% for hand-drawn shape classification. Applications of the proposed mathematical geometric shape classification algorithm span various domains, including education, where it enhances interactive learning platforms and provides instant feedback to students.
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The main aim of this study was to analyse the influence of e-learning training on the acquisition of competences in basketball coaches in Cantabria. The current landscape of basketball coach training shows an increasing demand for innovative training models and emerging pedagogies, including e-learning-based methodologies. The study sample consisted of fifty students from these courses, all above 16 years of age (36 males, 14 females). Among them, 16% resided outside the autonomous community of Cantabria, 10% resided more than 50 km from the city of Santander, 36% between 10 and 50 km, 14% less than 10 km, and 24% resided within Santander city. Data were collected through a Google Forms survey distributed by the Cantabrian Basketball Federation to training course students. Participation was voluntary and anonymous. The survey, consisting of 56 questions, was validated by two sports and health doctors and two senior basketball coaches. The collected data were processed and analysed using Microsoft® Excel version 16.74, and the results were expressed in percentages. The analysis revealed that 24.60% of the students trained through the e-learning methodology considered themselves fully qualified as basketball coaches, contrasting with 10.98% of those trained via traditional face-to-face methodology. The results of the study provide insights into important characteristics that can be adjusted and improved within the investigated educational process. Moreover, the study concludes that e-learning training effectively qualifies basketball coaches in Cantabria.
Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Javier Jorge mail , Kamil Giglio mail ,
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Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.
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Background/Objectives. Traditional dietary patterns are being abandoned in Mediterranean countries, especially among younger generations. This study aimed to investigate the potential lifestyle determinants that can increase adherence to the Mediterranean diet in children and adolescents. Methods. This study is a cross-sectional analysis of data from five Mediterranean countries (Italy, Spain, Portugal, Egypt, and Lebanon) within the context of the EU-funded project DELICIOUS (UnDErstanding consumer food choices & promotion of healthy and sustainable Mediterranean Diet and LIfestyle in Children and adolescents through behavIOUral change actionS). This study comprised information on 2011 children and adolescents aged 6–17 years old collected during 2023. The main background characteristics of both children and parents, including age, sex, education, and family situation, were collected. Children’s eating (i.e., breakfast, place of eating, etc.) and lifestyle habits (i.e., physical activity level, sleep, and screen time) were also investigated. The level of adherence to the Mediterranean diet was assessed using the KIDMED index. Logistic regression analyses were performed to test for likelihood of higher adherence to the Mediterranean diet. Results. Major determinants of higher adherence to the Mediterranean diet were younger age, higher physical activity level, adequate sleep duration, and, among dietary habits, having breakfast and eating with family members and at school. Parents’ younger age and higher education were also determinants of higher adherence. Multivariate adjusted analyses showed that an overall healthier lifestyle and parents’ education were the factors independently associated with higher adherence to the Mediterranean diet. Conclusions. Higher adherence to the Mediterranean diet in children and adolescents living in the Mediterranean area is part of an overall healthy lifestyle possibly depending on parents’ cultural background.
Alice Rosi mail , Francesca Scazzina mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Ludwig Álvarez-Córdova mail , Osama Abdelkarim mail , Achraf Ammar mail , Mohamed Aly mail , Evelyn Frias-Toral mail , Juancho Pons mail , Laura Vázquez-Araújo mail , Carmen Lilí Rodríguez Velasco mail carmen.rodriguez@uneatlantico.es, Julién Brito Ballester mail julien.brito@uneatlantico.es, Lorenzo Monasta mail , Ana Mata mail , Adrián Chacón mail , Pablo Busó mail , Giuseppe Grosso mail ,
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