Tesis
Permanent URI for this communityhttps://hdl.handle.net/11285/345119
Colección de Tesis y Trabajos de grado (informe final del proyecto de investigación, tesina, u otro trabajo académico diferente a Tesis, sujeto a la revisión y aceptación de una comisión dictaminadora) presentados por alumnos para obtener un grado académico del Tecnológico de Monterrey.
Para enviar tu trabajo académico al RITEC, puedes consultar este Infográfico con los pasos generales para que tu tesis sea depositada en el RITEC.
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- Synthesis and Characterization of FAPbI3 Perovskite and its Incorporation into a Photovoltaic Heterostructure(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-10) Miró Zárate, Jorge Luis; Elias Espinosa, Miilton Carlos; emimmayorquin; Rosas Meléndez, Samuel Antonio; Melo Máximo, Dulce Viridiana; Flores Ruíz, Francisco Javier; School of Engineering and Sciences; Campus Ciudad de México; Diliegros Godines, Carolina JananiConsidering the importance of having the α-FAPbI3 as it is the photoactive and functional phase for the use of this perovskite in a solar cell and understanding the growth process by incorporating an additive. In this work, it is presented a methodology that combine a method for deposition called sequential deposition with the incorporation of a pseudo halogen additive NH4SCN at various concentration of moles into the PbI2 solution, in order to have α-FAPbI3 perovskite deposited at open atmosphere. This research focuses on the mechanisms of growth of the FAPbI3 perovskite films over glass with the NH4SCN additive. Subsequently, the incorporation of the FAPbI3 perovskite into a heterostructure is presented. The architecture FAPbI3/ETL/ITO/Glass is presented, where the ETLs used are TiO2 and SnO2. The incorporation of FAPbI3 into a heterostructure allows us to evaluate the perovskite's properties for its photovoltaic application. Based on the outstanding electrical properties, WS2 was incorporated into the heterostructure through interface engineering, forming the heterostructure FAPbI3/WS2/ETL/ITO/Glass. Both architectures are compared in terms of their optoelectronic and morphological properties to determine the best FAPbI3-based heterostructure for improved photovoltaic application.
- Influence of human error and situational awareness in decision-making in complex tasks. Case of study: forklifts operators(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-19) Arias Portela, Claudia Yohana; Mora Vargas, Jaime; emipsanchez; Castillo Martínez, Juan Alberto; González Mendoza, Miguel; Thierry Aguilera, Ricardo; School of Engineering and Sciences; Campus Ciudad de México; Caro Gutiérrez, Martha PatriciaThis dissertation investigates situational awareness (SA) and human errors in logistics operations, using a multiphase and multifactorial approach as an innovative approach. The research responds the question of how SA errors can be assessed, along with their influence on decision-making in complex tasks, by considering a comprehensive HFE approach to various triggering factors. Characterization of the process with ethnography and process mapping, analysis of visual attention with Eye-tracking and retrospective think-aloud (RTA), an Error taxonomy and the bases of a data science approach were used to study the diverse cognitive, behavioral, and operational aspects affecting SA. Analyzing 566 events across 18 tasks, the research highlights eye-tracking's potential by offering real-time insights into operator behavior, and RTA as a method for cross-checking the causal factors underlying errors. Critical tasks, like positioning forklifts and lowering pallets, significantly impact incident occurrence, while high cognitive demand tasks such as hoisting and identifying pedestrians/obstacles, reduce SA and increase errors. Driving tasks are particularly vulnerable and are the most affected by operator risk generators (ORG), representing 42% of events with a risk of incident. The study identifies driving, hoisting and lowering loads as the tasks most influenced by system factors. Limitations include the task difficulty levels, managing physical risk, and training. Future research is suggested in autonomous industrial vehicles and advanced driver assistance systems (ADAS). This study provides valuable insights for improving safety in logistics operations by proposing a multiphase and multifactorial approach to uncover patterns of attention, perception and cognitive errors, and their impact on decision-making in the logistic field
- lmproved Diagnosis of Breast Cancer via NLP Analysis of Radiological Reports(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Sosa Silva, Patricia Angelli; Tamez Peña, José Gerardo; emimmayorquin; Martínez Ledezma, Emmanuel; Avendaño Davalos, Betzabeth; School of Engineering and Sciences; Campus Monterrey; Santos Díaz, AlejandroToe main objective of this thesis was to evaluate the use of natural language processing (NLP) techniques and machine learning models to improve the specificity of breast cancer diagnosis and reduce false-positive rates using a dataset of radiological reports from Mexican hospitals. Toe methodology involved text preprocessing, feature extraction using NLP techniques and classification using machine learning models for the radiological reports. The preprocessing consisted of lemmatization, stop-word removal, and tokenization. Various NLP techniques were then applied, including bag-of-words, TF-IDF, Word2Vec embeddings, and ClinicalBERT embeddings. These were used as input features for classical machine learning models (Logistic Regression, Random Forest, Extreme Grading Boosting, Naive Bayes, k-Nearest Neighbors, Support Vector Machine and their ensemble) as well as a deep learning LSTM model. The models were trained, calibrated, and evaluated using metrics: AUC, accuracy, precision, recall, specificity and Fl-score. The key findings showed that the ensemble model with Bag-of-words and SVM using TF-IDF vectorized reports achieved the best performance, with an AUC of 0.79, specificity of 0.27 and AUC of 0.80 and specificity of 0.26, respectively. Thess model was able to identify all true positive cases while reducing the number of unnecessary biopsies by 19.49% and 15.08%, respectively. Feature importance analysis revealed that terms like "speculated", "irregular", and "4a category" were critica! for breast cancer classification. In contrast, the deep learning LSTM model performed poorly, with an AUC of only 0.52 and specificity of O. These results demonstrate the potential of NLP and machine learning techniques to enhance the reliability of breast cancer diagnosis and management, reducing the burden of unnecessary medica! procedures on patients and the healthcare system. The theoretical implications include the importance of effective feature engineering and the limitations of deep learning models for this specific task.
- Interactive recipe suggestions for diet and allergen management: utilizing llaMA with HEI and DQI for healthier eating(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-15) Estrada Beltrán, Diego; González Mendoza, Miguel; emipsanchez; Gutiérrez Uribe, Janet Alejandra; Domínguez Uscanga, Astrid; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus MonterreyChoosing daily meals can be a complex and overwhelming task, especially when considering nutritional requirements, ingredient availability, preparation time, cooking complexity, dietary restrictions, and allergens. Inadequate nutrition is linked to a variety of health problems, including cardiovascular diseases, obesity, and psychological disorders, highlighting the need for effective dietary management solutions. Existing machine learning approaches, such as food recommender systems, recipe generators, and recipe completion models, often focus on suggesting ingredients or generating recipes based on training data and with some ingredients to start from, but they typically do not address the challenge of creating complete daily meal plans that meet personalized nutritional needs. The advent of Large Language Models (LLMs), including Meta’s LLaMA, OpenAI’s ChatGPT, and Google’s Gemini, offers a promising new avenue for enhancing personalized meal recommendations due to their accessibility and interactive capabilities. This thesis introduces a novel system that leverages LLaMA 3.1 combined with Retrieval-Augmented Generation (RAG) to provide daily meal suggestions tailored to individual users’ nutritional profiles, dietary preferences, and allergen restrictions. Our system evaluates meal recommendations against established nutritional metrics such as the Healthy Eating Index (HEI) and Diet Quality Index (DQI) to ensure they align with dietary guidelines and promote healthy eating. Through the integration of LLaMA’s advanced language understanding and RAG’s contextual retrieval capabilities, the system delivers precise, personalized, and accessible meal recommendations, offering a practical tool for improving dietary management and supporting healthier eating habits. The results demonstrate the effectiveness of this approach in addressing the complexities of meal planning, making it a valuable resource for individuals seeking to optimize their dietary choices through informed and interactive guidance.
- Impact of Industry 4.0 on Small and Medium Enterprises: Evaluation of Maturity Indices and Implementation Methodologies(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Delgado González, Jessica; Román Flores, Armando; emimmayorquin; Cuan Urquizo, Enrique; School of Engineering and Sciences; Campus Monterrey; Vázquez Hurtado, CarlosThe digital transformation driven by Industry 4.0 technologies is reshaping global economic and business paradigms. Small and medium-sized enterprises (SMEs) in Mexico, which represent 99.8% of the country's economic units and contribute over 52% to its GDP, face significant barriers such as limited financial resources, technological gaps, and cultural resistance. These constraints, highlighted in recent studies, underscore the need for tailored tools to support their digitalization efforts. This thesis develops a digital maturity model specifically adapted to Mexican SMEs, integrating practical tools such as an assessment framework and a step-by-step action plan. The study begins by analyzing the theoretical foundations of Industry 4.0 and existing digital maturity models while addressing challenges unique to SMEs. Building on this foundation, the proposed model evaluates SMEs' current digital maturity and provides actionable recommendations through a simulation applied to a representative SME. The results demonstrate the model’s utility in identifying areas for improvement, fostering innovation, and enhancing competitiveness and sustainability in a globalized market. This work contributes academically by adapting global models to local contexts and practically by offering a replicable framework to bridge the digital divide in this critical economic sector.
- The impact of loading-unloading zones for freight vehicles on the last-mile logistics for nanostores in emerging markets(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Mora Quiñones, Camilo Andrés; Cárdenas Barrón, Leopoldo Eduardo; emimmayorquin; Fransoo, Jan C.; Smith Cornejo, Neale Ricardo; Loera Hernández, Imelda de Jesús; School of Engineering and Sciences; Campus Monterrey; Veláaquez Martínez, Josue CuauhtémocEvery year, more than 26 billion deliveries are made globally to serve nanostores, the largest grocery retail channel in the world. At each stop, company representatives face a persistent challenge: finding a place to park. While the problem seems simple, it is remarkably complex and far from easy to solve. In emerging markets, where cities have grown rapidly and often without proper planning, fragmented markets and inadequate infrastructure exacerbate the issue. Multiple stakeholders compete for limited curb space, and the lack of dedicated parking disrupts last-mile efficiency, forcing drivers to either cruise for parking or resort to illegal parking. These behaviors lead to increased vehicle emissions, noise pollution, and additional costs. This dissertation provides key insights into last-mile logistics for nanostores in emerging markets, contributing to academic literature and offering practical implications to address the parking problem. The first study addresses the parking challenges faced by freight vehicles serving nanostores, identifying key factors affecting dwell time efficiency and suggesting operational improvements. In the next study, the focus shifts to the implementation of Loading-Unloading Zones (LUZs) as a targeted intervention, analyzing their impact on reducing air and noise pollution in urban areas. The last study extends this analysis by exploring the effects of LUZs on traffic flow, evidencing how their introduction can improve vehicle speed and reduce congestion in densely populated city streets. Together, these studies provide a detailed exploration of the operational, environmental, and infrastructural challenges of last-mile logistics, while offering concrete strategies to improve urban logistics in emerging markets. This dissertation contributes by expanding the body of knowledge and offering actionable managerial insights with the potential to drive meaningful impact. These include enhancing air quality, reducing noise pollution, lowering carbon emissions, improving traffic flow, and achieving substantial cost savings for companies distributing goods to nanostores in emerging markets.
- A data-driven modeling approach for energy storage systems(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Silva Vera, Edgar Daniel; Valdez Resendíz, Jesús Elías; Rosas Caro, Julio César; emipsanchez; Escobar Valderrama, Gerardo; Guillén Aparicio, Daniel; Soriano Rangel, Carlos Abraham; School of Engineering and Sciences; Campus MonterreyThis disertation presents a versatile data-driven modeling methodology designed for various energy systems, including battery-based power systems, DC-DC power electronic converters, Lithium-Ion batteries, and Proton-Exchange Membrane Fuel Cells (PEMFC). The proposed approach captures the non linear dynamics of each system by leveraging fundamental measurements and operational data, thus eliminating the need for explicit theoretical models and significantly simplifying the modeling process. Specifically, the methodology allows for the identification of essential parameters by constructing state-space representations that describe both fast and slow system dynamics, which are crucial for accurately modeling transient behaviors and implementing adaptive control strategies. The models were validated across different applications, showing their ability to replicate real system behaviors with high precision. For instance, in the case of DC-DC converters, the models demonstrated an average error deviation of approximately 2% for current signals and 4% for voltage signals, confirming their capacity to track the actual converter dynamics. Similarly, the Lithium-Ion battery models enabled accurate estimation of state of charge (SoC) and opencircuit voltage using a modified recursive least-squares algorithm, achieving close alignment with real discharge curves. In the PEMFC stack modeling, the methodology utilized real-physic model operational data to refine model accuracy, yielding improved predictive capabilities over traditional approaches. These results underscore the efficacy and robustness of the data-driven approach in enhancing the design, control, and optimization of diverse energy systems. By providing a framework that can be readily adapted to different components and configurations, this methodology supports advancements in sustainable energy technologies, enabling the interconnection of multiple energy storage and conversion systems with minimal computational cost and measurement requirements.
- A computer-based method to estimate the level of sensitivity of typical somatosensorial responses(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Cepeda Zapata, Luis Kevin; Alonso Valerdi, Luz Maria; emipsanchez; Angulo Sherman, Irma Nayeli; Muñoz Ubando, Luis Alberto; School of Engineering and Sciences; Campus Monterrey; Ibarra Zaratre, David IsaacUnderstanding somatosensory responses is fundamental to human interaction with the environment, yet quantitative tools for assessing typical tactile responses remain underdeveloped. This thesis introduces a novel computer-based method to evaluate somatosensory processing through electroencephalographic data, focusing on responses to different tactile stimuli. The project will be conducted in three stages: 1) registration of typical somatosensory evoked responses due to touch, air, and vibration in incremental intensities using electroencephalography, 2) validation of the prototypes to evoke tactile evoked potentials, 3) development and evaluation of a classification model to differentiate tactile stimuli and intensities. The study involved the creation of a database of Electroencephalographic recordings from 34 healthy adult volunteers exposed to air, vibration, and caress stimuli, under four diffrent intensity levels intensity levels. The neural responses were analyzed using Discrete Wavelet Transform and classified with machine learning models including K-Nearest Neighbors, Random Forest, and Multilayer Perceptron. For a generalized classification model, an accuracy of 72.6% was achieved for distinguishing stimulus type, 39.3% accuracy for intensity classification and 33.4% for both stimulus type and intensity. Individual classifiers for each subject had an increase in accuracy of 6-10%. Additionally, a deep learning model, EEGNet, was implemented, yielding similar results for stimulus type but lower performance for intensity. Analysis revealed significant inter-subject variability, with subject-specific models outperforming generalized ones, highlighting the need for individualized approaches in somatosensory assessments. This study offers a novel dataset and model framework, which enhances the understanding of neural tactile processing to advance sensory-based interfaces and diagnostic tools in neurophysiological research.
- Design and development of a biomimetic robot based on the UCA pugnax CRAB(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Arriaga Ventura, Daniel Alberto; Bustamente Bello, Martín Rogelio; emipsanchez; Navarro Tuch, Sergio Alberto; School of Engineering and Sciences; Campus Ciudad de México; López Aguilar, Ariel AlejandroThis research focused on developing a biomimetic robot modeled after the Uca Pugnax crab, utilizing a bottom-up approach that replicates the crab’s distinct locomotion and biomechanics. The primary objective was to create a robotic system capable of mimicking the crab’s movement and serving as a platform for further investigations into control systems, particularly in the implementation of bioinspired Central Pattern Generators (CPGs). By employing both Hopf and Kuramoto oscillator-based CPG systems, the robot’s locomotion was effectively demonstrated. The Kuramoto model, in particular, exhibited quicker convergence to the desired phase shifts, resulting in smoother and more reliable movement patterns. Comparative analysis of the reference trajectories generated by the CPG and the actual motor outputs revealed areas for optimizing controller performance, particularly in terms of response speed and amplitude precision. The robot’s morphology closely mirrored that of the crab, with an average proportional error of approximately 7.11%, indicating a successful bio-mimetic design. The robot’s movement dynamics also showed distinct functional push-and-pull motions, with clear advantages along different axes based on pinion position. Despite mechanical design constraints, such as the trade-off between manufacturing simplicity and accurate biomimicry, the robot’s overall performance demonstrated that biomimetic designs can effectively replicate crablike locomotion.
- Desarrollo de un material compuesto basado en metal "arcilla metálica" para aplicaciones de manufactura aditiva en la fabricación de microdispositivos(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) López Solís, Sergio Jesús; Segura Cárdenas, Emmanuel; emimmayorquin; Ulloa Castillo, Nicolas Antonio; Melo Máximo, Dulce Viridiana; Montañez Rodríguez, Abraham; Escuela de Ingeniería y Ciencias; Campus MonterreyEstá investigación explora el desarrollo y la validación de un compuesto metálico llamado “arcilla metálica” mediante fabricación aditiva por extrusión. Su objetivo es identificar los parámetros clave para la composición del material, el proceso de fabricación y las condiciones de sinterización. El estudio analiza la formulación del compuesto metálico, la adaptación de una impresora 3D para la extrusión y los hallazgos experimentales sobre materiales como Inconel 718 y acero inoxidable 316L. Se estudian y prueban varias proporciones de agua, polvo metálico y aglutinante orgánico para lograr una extruibilidad óptima, y el análisis termogravimétrico y espectroscópico ayuda a comprender las propiedades térmicas. El trabajo también evalúa las optimizaciones de la impresión 3D, incluidos los ajustes de la impresora, los tamaños de las boquillas y las resoluciones de impresión, y examina la fabricación de microcanales con un enfoque en la precisión y los procesos de sinterización para minimizar la porosidad. El estudio concluye con información sobre cómo mejorar la calidad y la reproducibilidad de la impresión, lo que contribuye a la fabricación de microdispositivos de arcilla metálica.
