Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Efficient analysis and compression of urban green areas in RGB drone imagery using the OSAVI index(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06-12) Hernández Animas, Edwin; Camacho León, Sergio; emipsanchez; Mendoza Montoya,Omar; Barrios Piña, Héctor Alfonso; School of Engineering and Sciences; Campus MonterreyGreen urban area detection is essential for environmental planning; traditional field surveys are laborintensive and time-consuming, making remote sensing (drone and satellite imagery) a powerful alternative. Three main problems are detected by working with these technologies: i) While enabling detailed analysis of lawns and individual trees due to their high spatial resolution, this results in data storage demands. ii) Moreover, the Normalized Difference Vegetation Index (NDVI), the most widely used for analyzing general vegetation, is highly sensitive to soil brightness, making it less suitable for examining urban greenery where bare soil, artificial surfaces, and mixed land covers are common. iii) Additionally, existing tree inventory algorithms in urban or heterogeneous environments remain labor-intensive, as they require annotated training samples to effectively distinguish trees from surrounding features. This study presents high-resolution multispectral and RGB imagery captured by an Unmanned Aerial Vehicle (UAV), the DJI MAVIC 3M, used to measure general vegetation. A masking process based on morphological operations was applied to segment green urban areas in the RGB image, to optimize both image storage size with lossless compression (Deflate & LZW) and traditional tree inventory based on crown detection (DeepForest). The segmentation based on Optimized Soil-Adjusted Index (OSAVI) mask applied in urban areas presents multiple advantages in terms of reduction of storage size due to the increase in homogeneous regions with pixel values sharing identical color characteristics. By using the OSAVI vegetation index as the masking criterion, the dense vegetation (trees) is not affected during the process, preserving its location and color (pixel values) of the original image, excelling current tree inventory algorithms (DeepForest) based on orthoimages without the need to prepare additional training data. Using the OSAVI instead of NDVI outperformed traditional green urban area segmentation, demonstrating 25% more robustness in avoiding saturation caused by Near-Infrared (NIR) reflecting areas. The segmentation performance achieved by OSAVI and morphological operations resulted in: IoU = 0.85 | Dice = 0.91 | Precision = 0.89 | Recall = 0.94 | Accuracy = 0.96. The final storage sizes of the masked RGB images were equal to the percentage of vegetation multiplied by the storage size of the non-masked (original) compressed images, with a Pearson Correlation of 0.98, being the Deflate method superior (bpp = 9) to the LZW method (bpp = 11.40) in terms of storage efficiency. The comparison between the tree inventory on the original RGB scenarios presented a Mean Absolute Error (MAE) = 193.25, and the RGB images masked by OSAVI index MAE = 98.25, 49% better and closer to the real tree inventory, with a Friedman test p-value = 0.046 rejecting the null hypothesis that all methods (Baseline: Precision = 0.33 | Recall = 0.45 | F1 = 0.38 and Proposed: Precision = 0.54 | Recall = 0.53 | F1 = 0.53 ) perform equally.
- Investigation of structural phase transitions in ferroelectric BaTiO3 thin films from composite solutions(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Lugo Martínez, Enrique; Ulloa Castillo, Nicolás Antonio; emipsanchez; De León Covián, Lina Melva; Morales Luna, Michael; Segura Cárdenas, Emmanuel; Melo Máximo, Dulce Viridiana; School of Engineering and Sciences; Campus Monterrey; Rodríguez Aranda, Ma. del CarmenIn this study, we investigate the synthesis and structural phase evolution of barium titanate (BaTiO3) thin films produced using a polymer-assisted sol-gel method. This innovative approach integrates the advantages of polymer-based solutions with the sol-gel method. The unique viscoelastic properties of polyvinylpyrrolidone (PVP) enhance the development of homogenous BaTiO3 thin films with controlled microstructures. To optimize these properties, we formulated composite solutions by varying the concentrations of PVP in conjunction with the sol-gel precursor. The composite precursor solutions were deposited onto quartz substrates via the spin-coating technique and subsequently sintered at temperatures of 400, 500, 700, and 900°C to promote optimal BaTiO3 crystallization. Structural phase characterization was performed using X-ray diffraction (XRD), infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA). TGA data indicated significant weight loss due to moisture evaporation occurring around 200°C, followed by PVP decomposition at approximately 430°C, with a flash point thermal event occurring at around 450°C. The monitoring of structural phase transitions throughout the sintering process revealed a transformation from an amorphous phase to crystalline barium carbonate (BaCO3), ultimately leading to the development of the ferroelectric BaTiO3 phase. The BaCO3 phase was identified at 400°C and gradually decomposed at 500, 700 and 900°C, while the BaTiO3 phase emerged at 700°C and achieved full consolidation above 900°C. The morphological evolution was monitored through scanning electron microscopy (SEM), and the chemical composition was analyzed using energy dispersive spectroscopy (EDS) for elemental mapping of Ba, Ti, C and O. Notably, films with higher concentrations of the polymer exhibited an increased content of BaCO3. The structural and electronic properties of the obtained phases during sintering were evaluated through photoluminescence (PL). This revealed defect-mediated emissions that intensified and exhibited a blue shift with increased sintering temperature. The findings of this research highlight the potential of the polymer-assisted sol-gel method as an effective route for synthesizing ferroelectric thin-film materials with controlled microstructures. This study provides valuable insights into the synthesis and characterization of ferroelectric materials and establishes a novel methodology for optimizing thin film properties that can be used in piezoelectric and microelectronic applications.
- Earthquake response in mega cities: A mathematical model for the selection of relief shelters in the Valley of Mexico and a qualitative assessment for Istanbul, Turkey(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) Huerta De Rubín, José Gerardo; Mora Vargas, Jaime; emimmayorquin; Akhavan-Tabatabaei, Raha; Santos Borbolla, Cipriano Arturo; González Mendoza, Miguel; Escuela de Ingeniería y Ciencias; Campus Ciudad de México; Serrato García, Marco AntonioNatural disasters represent challenging situations globally, with earthquakes being some of the most devastating type of disasters. The difficulties to cope with them arise in contexts like mega cities, large urban agglomerations where the population dynamics make seismic disaster more struggling. In this sense, the use of humanitarian logistics techniques is an effective way to provide solutions in these contexts. In this thesis, a mathematical model is developed and used to support the selection of relief shelters from a set of potential locations. The model aims to minimize both the distances between the affected areas and the chosen shelter locations, as well as the costs of adapting the selected locations into relief shelters. The model was applied to different instances, from a small controlled one to understand and validate the way the model works, to scenarios with dimensions similar to (and even larger than) mega cities. The application of the model considered both real information gathered from a mega city in a seismic region (the Metropolitan Area of the Valley of Mexico; formed by Mexico City and several municipalities in the surrounding States of Mexico and Hidalgo), and simulated data based on the real one to face the difficulties to obtain all the required real data. Once the results were obtained for every model, a graphical tool was used to visualize them. That tool is ©QGIS, an open source Geographic Information System that allows to create maps using the resulting data from the application of the models. This visualization of results permits to identify if the results seem practical and reasonable, and represent a powerful tool to sustain the decision making after an earthquake. Finally, this work also contains a qualitative assessment of the circumstances of a different mega city with seismic danger: Istanbul, focused on a brief summary of the Istanbul Seismic Risk Mitigation and Emergency Preparedness Project (ISMEP); a document related to the way of coping with earthquakes in a different mega city located in a very seismic region.
- Harnessing machine learning for short-to-long range weather forecasting: a Monterrey case study(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05) Machado Guillén, Gustavo de Jesús; Cruz Duarte, Jorge Mario; mtyahinojosa, emimmayorquin; Filus, Katarzyna; Falcón, Jesús Guillermo; Ibarra, Gerardo; Departamento de Ciencias Computacionales; Campus Monterrey; Conant, Santiago EnriqueWeather forecasting is crucial in adapting and integrating renewable energy sources, particularly in regions with complex climatic conditions like Monterrey. This study aims to provide reliable weather prediction methodologies by evaluating the performance of various traditional Machine Learning models, including Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Recurrent Neural Networks (RNN) such as SimpleRNN, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Cascade LSTM, Bidirectional RNNs, and a novel Convolutional LSTM/LSTM architecture that handles spatial and temporal data. The research employs a dataset of historical weather data from Automatic Weather Stations and Advanced Baseline Imager Level 2 GOES-16 products, including key weather features like air temperature, solar radiation, wind speed, relative humidity, and precipitation. The models were trained and evaluated across different predictive ranges by combining distinct sampling and model output sizes. This study’s findings underscore the effectiveness of the Cascade LSTM models, achieving a Mean Absolute Error of 1.6 °C for 72-hour air temperature predictions and 85.79 W/m2 for solar radiation forecasts. The ConvLSTM/LSTM model also significantly improves short-term predictions, particularly for solar radiation and humidity. The main contribution of this work is a comprehensive methodology that can be generalized to other regions and datasets, supporting the nationwide implementation of localized machine-learning forecasting models. This methodology includes steps for data collection, preprocessing, creation of lagged features, and model implementation, as well as applying distinct approaches to forecasting by using autoregressive and fixed window models. This framework enables the development of accurate, region-specific forecasting models, facilitating better weather prediction and planning nationwide.
- Assessment of phthalic acid esters (PAEs) and pharmaceutical compounds (carbamazepine, diclofenac, ibuprofen, and naproxen) in waters of the Atoyac River basin, Puebla-Tlaxcala, Mexico.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06) Vázquez Tapia, Ivón; MORA POLANCO, ABRAHAN RAFAEL; 3016740; Mora Polanco, Abrahan Rafael; emipsanchez; Dueñas Moreno, Jaime; School of Engineering and Sciences; Campus Monterrey; Mahlknecht, JürgenThe Atoyac River basin has a critical state of contamination, generated, to a large extent, by the discharge of wastewater without prior treatment from municipal and industrial sources. In the states of Puebla and Tlaxcala there is a high percentage of land use for agricultural development, in which water from the Atoyac River is commonly used for crop irrigation. The characterization of occurrence of contaminating compounds allows to understand the possible risk of exposure that farmers, as well as other organisms, may have. Therefore, the determination of occurrence of 9 phthalates and 4 pharmaceutical compounds (DIC, IBF, NP, and CBZ) was carried out in the system comprised by the Zahuapan River, the Atoyac River and the Valsequillo reservoir. The measured concentration levels exceed the permissible limits in environmental regulations such as the US-EPA and they are comparable to concentrations measured in other highly polluted rivers around the world. Distribution and correlation analysis show that the areas of greatest contamination are associated with the presence of industrial parks, which use these compounds in their manufacturing processes. In addition, the toxicological study reports a medium and high risk to aquatic species due to exposure to these compounds.
- Evaluación del uso de microalgas para la biofijación de CO2 de centrales de ciclo combinado(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-05) Mercado Alemán, Andrea; Parra Saldívar, Roberto; mtyzramirez; Sosa Hernández, Juan Eduardo; Coronado Apodaca, Karina Guadalupe; Salazar Silva, Jesús Fidencio; Escuela de Ingeniería y Ciencias; Campus Monterrey; Martínez Ruiz, ManuelEn México, el reducir las emisiones de las Centrales de Ciclo Combinado (CCC) es un tema de vital importancia en cuestiones de cambio climático, ya que estas centrales son las principales en satisfacer la demanda eléctrica del país, por lo que constituyen un factor clave para reducir las emisiones del sector energético. Sin embargo, debido a la composición y características que presentan los gases de combustión de estas centrales, los actuales sistemas de captura de carbono no son compatibles para mitigar sus emisiones, por lo que nuevas formas para capturar dióxido de carbono deben ser investigadas. El uso de microalgas para la biofijación de CO2 de los gases de combustión de Centrales de Ciclo Combinado se presenta como una alternativa viable, ya que estos microorganismos cuentan con cualidades capaces de afrontar los problemas asociados a las emisiones de estas centrales, y tienen la capacidad de biofijar cantidades considerables de CO2 mediante su proceso de fotosíntesis. Por lo anterior, el objetivo de este trabajo es determinar si efectivamente el uso de microalgas representa una opción factible para reducir las emisiones del sector eléctrico mexicano, por lo que esta tesis primero presenta una investigación sobre los estudios más recientes respecto al uso de microalgas para la captura de dióxido de carbono. Posteriormente, se realizó un análisis con los datos encontrados en la literatura para de esta manera, estimar el potencial de recuperación de CO2 que se podría obtener en las centrales en cuestión. Por último, y para corroborar el posible potencial de recuperación de CO2, se realizó un trabajo experimental, en el cual que se cultivó Chlorella vulgaris bajo condiciones ambientales y diferentes parámetros de cultivo utilizando una mezcla de gases de simulación de CCC como su fuente de carbono.