Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Methodology to improve compact extended range ev-powertrain module(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06-07) Puma Benavides, David Sebastian; Calderón Nájera, Juan de Dios; puemcuervo, emipsanchez; Galluzzi Aguilera, Renato; Bustamante Bello, Martin Rogelio; Loyola Morales, Félix; School of Engineering and Sciences; Campus Monterrey; Izquierdo Reyes, JavierThe constant growth of the vehicle fleet means that more and more emissions are being emitted into the environment, with the transportation sector contributing around 21% of CO2 in data updated to 2023. Reducing emissions and carbon footprint, leaving aside the dependence on fossil fuels, has been the premise for developing vehicles with new technologies and developing clean energy for their use. As a result, the sale of internal combustion vehicles reached its highest peak in 2017, and from there, the sale of electric and hybrid vehicles has grown yearly. However, combustion, electric and hybrid vehicles have yet to achieve optimal efficiency; therefore, generating optimizations in their powertrain is viable as research topics, as well as for the extension of the range in electric vehicles, which at the moment is a factor that makes their purchase unattractive. Therefore, this thesis aims to review and evaluate technologies that can function as range extenders for electric vehicles, considering their efficiency, low pollution levels, and compatibility for integration into electric vehicle platforms. To facilitate this evaluation, an algorithm incorporating equations representing characteristic curves of mechanical or electrical components will be developed for Extended Range Electric Vehicle EREV. This algorithm will provide valuable insights into the behavior and energy analysis of potential range extender (ICE-Alternator/Generator). Furthermore, the optimization of the entire powertrain system will be considered to ensure all components operate at peak efficiency. These objectives constitute the core of this dissertation. Through powertrain improvement, specifically by adjusting the differential gear ratio from 4.3 to 3.54, significant improvements in vehicle performance can be achieved. Energy savings during standardized driving cycles such as NEDC, WLTC-2, and WLTC-3 can reach up to 10%. Additionally, integrating an auxiliary power unit (APU) into the vehicle architecture can substantially enhance the vehicle's range. By employing an ICE-Alternator configuration with a maximum power of 12.8 kW, the vehicle's travel distance can be extended by up to 170%. Alternatively, an ICE-Generator configuration with a maximum power of 3.2 kW can increase travel distance by up to 39%. Implementing an effective control strategy that optimizes fuel consumption based on the battery's state of charge further enhances APU utilization, resulting in efficiency gains of up to 3.5%. The proposed methodology for developing extended-range electric vehicles, along with the validated algorithm through practical implementations and testing, enables comprehensive energy analyses. This approach provides a more accurate understanding of the performance of vehicle platforms incorporating range extenders.
- DC-DC high-order converters for renewable applications(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-07) Garza Arias, Enrique; Valdez Resendiz, Jesús Elias; Rosas Caro, Julio César; Escobar Valderrama, Gerardo; Castañeda Cuevas, Herman; Escuela de Ingeniería y Ciencias; Campus Monterrey; Guillén Aparicio, DanielThis dissertation develops new theory and techniques for DC-DC power converters interfacing renewable energies, specifically for converters classified as high-order based on their mathematical model, and thus generally more complex and difficult to analyze. The thesis encompasses the development of a new circuit topology, a control design focused on high-order dynamic systems, and the study of conversion systems between renewables and the utility grid. The dynamics of high-order converters present challenges for the stability and synthesis of controllers. By studying the nonlinear model of fourth-order non-minimum phase converters through zero dynamics, it has been established that some of these can attain stability under direct voltage at high regulation, in contrast to second-order converters. Controllers have been designed to take advantage of these results in order to remove the current sensing stage while still achieving high bandwidth voltage regulation. Research has also been conducted into creating novel DC converters with a lower component size than similar circuits. From this, an improved super-boost converter has been developed that requires lower energy storage on its capacitors and inductors than the traditional boost and fourth-order boost converters, for a given specification of input current and output voltage ripples. Additionally, an inverter system for a fuel cell stack has been developed using double-dual converters in cascade to increase the voltage gain and power handling of the system. Results from tests at different power and voltage levels show that the proposed system also achieves reduced switch voltage stresses and reduced output current ripples. Simulation and experimental results were obtained in order to validate the theoretical analysis. Mathematical models, steady state values and stability conditions were calculated, and prototype designs were developed to corroborate the performance of the converters, control and efficiency.
- Commercial delivery policies: inventory management models with power demand pattern(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-29) Khan, Md. Al-Amin; Cárdenas Barrón, Leopoldo Eduardo; emiggomez, emipsanchez; Loera Hernández, Imelda de Jesús; Smith, Neale R.; Treviño Garza, Gerardo; Bourguet Díaz, Rafael Ernesto; School of Engineering and Sciences; Campus MonterreyIn the fast-paced, ever-changing environment of contemporary business, inventory management stands as a vital, ongoing endeavor. The discipline of decision-making in inventory management assumes a central role in this dynamic environment that is marked by continuous change and intense competition. Navigating the complexities of decision-making poses challenges, particularly in accurately assessing the multifaceted aspects of decision-making processes amid varying circumstances, including demand fluctuations, different types of discounts, and contractual agreements. At the same time, the increasing concern among consumers regarding the environmental footprint of their purchases, coupled with government-mandated regulations, complicates the decision-making process for businesses. In this milieu, sustainable inventory management practices have emerged as a pertinent research area, prompting heightened scrutiny of the impacts of emission guidelines on inventory practices, not only aimed at addressing broader societal concerns but also at ensuring the financial sustainability of businesses. This thesis adopts a specialized demand structure known as the power demand pattern (PDP) to depict fluctuations in demand over the storage period of a company, providing a robust framework for understanding customer demand dynamics across various products. The company maintains its inventory by acquiring items through quantity discounts in exchange for a quantity-sensitive prepayment as part of a contractual arrangement. This thesis introduces a novel concept by considering the installment frequency for fulfilling prepayment obligations as a decision variable for the company, incorporating a transaction fee for each installment. Furthermore, theoretical formulas are developed under different sorts of demand structures, incorporating the influences of selling price and storage time, to assess the profitability of inventory management processes under a combined link-to-order prepayment and quantity discount scheme. A significant advancement by integrating sustainability considerations into both inventory management and pricing strategies within the framework of the PDP is accomplished in this thesis. Through systematic identification and comparison of sustainable inventory management practices under varying emission guidelines, this study provides valuable insights aimed at optimizing profits within the PDP. Therefore, the insights derived from this study offer organizations practical techniques to navigate the complex regulatory environment effectively and achieve sustainable financial performance. Moreover, intensive and comprehensive in-depth sustainable inventory practices under the PDP are established specifically for growing items (GIs). This thesis investigates the impact of weight loss resulting from bleeding and non-consumable components on optimal pricing and inventory strategies for a farm, delving into previously unexplored areas within the literature on GIs. A comparative analysis is conducted on the operations of a livestock farm, operating within several environmental regulations. Consequently, the comprehensive methodology improves sustainable inventory management techniques and offers practical strategies to mitigate environmental impacts and enhance economic feasibility in livestock production.
- Extracting the embedded knowledge in class visualizations from artificial neural networks for applications in dataset and model compression and combinatorial optimization(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-04-25) Abreu Pederzini, Jose Ricardo; Terashima Marín, Hugo; emiggomez, emipsanchez; González Mendoza, Miguel; Juárez Jiménez, Julio Antonio; Rosales Pérez, Alejandro; Bendre, Nihar; School of Engineering and Sciences; Campus Monterrey; Ortiz Bayliss, José CarlosArtificial neural networks are efficient learning algorithms, considered universal approxima-tors for solving numerous real-world problems in areas like computer vision, language processing, or reinforcement learning. To approximate any given function, neural networks train a large number of parameters that can go up to the millions or even billions in some cases. The large number of parameters and hidden layers in neural networks makes them hard to interpret, which is why they are often referred to as black boxes. In the quest to make artificial neural networks interpretable in the field of computer vision, feature visualization stands outas one of the most developed and promising research directions. While feature visualizations are a useful tool to gain insights about the underlying function learned by a neural network, they are still considered simply as visual aids that require human interpretation. In this doctoral work, we propose that feature visualizations—class visualizations in particular—are analogous to mental imagery in humans and contain the knowledge that the model extracted from the training data. Therefore, when correctly generated, class visualiza-tions can be considered as a conceptual compression of the data used to train the underlying model, resembling the experience of perceiving the actual training samples just as mental imagery resembles the real experience of perceiving the actual physical event. We present results showing that class visualizations can be considered a conceptual compression of the training data used to train the underlying model and present a methodology that enables the use of class visualizations as training data. To achieve this goal, we show that class visualizations can be used as training data to develop new models from scratch, achieving, in some cases, the same accuracy as the underlying model. Additionally, we explore the nature of class visualizations through different experiments to gain insights on what exactly class visualizations represent and what knowledge is embedded in them. To do so, we com- pare class visualizations to the class average image from the training data and demonstrate how the other classes that a model is trained on affect the shape and the knowledge embedded in a class visualization. We show that class visualizations are equivalent to visualizing the weight matrices of the output neurons in shallow network architectures and demonstrate that class visualizations can be used as pretrained convolutional filters. We experimentally show the potential of class visualizations for extreme model compression purposes. Finally, we present a novel methodology to enable the use of Artificial Neural Networks along with class visualizations for the solution of combinatorial optimization problems, such as the 2D Bin Packing Problem, by training an Artificial Neural Network to score potential solutions to a 2D BPP and then using that network to generate an ’optimal’ (local optima) solution to the problem by extracting a class visualization from the network via backpropagation to the network’s input. Even though we show the use of class visualizations as a tool to solve the bin packing problem, it is important to note that class visualizations have the potential to be used in the same way to solve other types of combinatorial optimization problems. For other types of combinatorial optimization problems, we just need to design a neural network that is capable of scoring solutions to the particular combinatorial optimization problem and extract class visualizations from such a network to generate a candidate solution to the problem.
- A digital twin model with knowledge graph-driven dense captioning(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) Wajid, Mohammad Saif; Ortiz Bayliss, José Carlos; 212577; Therasima Marin, Hugo; emiggomez, emipsanchez; Ortiz Bayliss, José Carlos; Carrasco Jiménez, José Carlos; Ceballos Cancino, Héctor Gibrán; School of Engineering and Sciences; Campus Monterrey; Najafirad, PeymanThis dissertation is submitted to the Graduate Programs in the School of Engineering and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science. This document explores how a digital twin model for the TEC District is developed and how the knowledge graph can be used for dense captioning of security events happening in the TEC District digital twin model. This also describes and analyses factors responsible for security breaches in the city using the concept of neutrosophy. The thesis proposes novel techniques for advancing dense captioning with the integration of knowledge graphs and neutrosophy, harnessing the capabilities of digital twin technology. Digital twins, as virtual replicas of physical entities or systems, offer a comprehensive framework for understanding and simulating real-world scenarios. They have emerged as a powerful tool in various industries, including manufacturing, healthcare, and urban planning. These models rely on detailed simulations of cities, including video data, to analyze and describe various security events using dense captioning. However, the accuracy and relevance of these simulations depend heavily on the quality of the captions generated for the video content. Captioning of videos based on temporal information presents a challenging task, involving the limitation of distracting information over time and space, which is crucial but poses difficulties. Additionally, ensuring robustness to false positives during captioning and addressing storage issues are significant challenges obtained from the literature. Also gathering information from knowledge graphs and providing context is another key task because of the presence of indeterminacy in data. This poses a challenge for defining aggression subjectively and automatically describing events while optimizing classifiers for faster caption generation and selecting optimal parameters. A widely used technique for dense video captioning is a knowledge graph that provides a structured representation of knowledge, organizing and connecting information extracted from videos. By incorporating knowledge graphs into the digital twin model, the relevance and context of the captions are significantly enhanced. However, knowledge graphs may fail to capture indeterminate factors that can dramatically impact situation analysis. Indeterminate factors, such as unpredictable human behavior or environmental conditions, are crucial in determining event sequences in digital twin models. In this dissertation, we aim to create a digital twin model for the TEC District for effective dense captioning of events with the knowledge graph model district area. In the proposed model, knowledge graphs play a crucial role in enhancing the context and relevance of captions by organizing and connecting information extracted from videos. It provides a structured representation of knowledge, enabling a more comprehensive understanding of video content. We have also utilized neutrosophy to address indeterminate and uncertain events, thereby enhancing the efficiency of dense captioning. This work is carried out in three phases; the first phase identifies various traits of character taken from datasets and literature, leading to different events among the masses using Neutrosophic Cognitive Maps (NCMs). This is done to identify the significance of various determinate and indeterminate factors while analyzing the security events. This task was earlier performed using Fuzzy Cognitive Maps (FCMs) in some research domains other than dense video captioning where indeterminate or uncertain factors were not considered. Therefore, we provide a brief comparison between NCMs and FCMs and show how effective NCMs are when considering the uncertainty of concepts while carrying out tests for describing events. In the second phase, a knowledge graph model for dense captioning is developed. As captioning is based on a knowledge graph, the time consumption for generating the video captions was considerably reduced. Also, we used the Bidirectional Long Short-Term Memory (BiLSTM) classifier to analyze the flow of the information provided by the captions, and the efficiency is further enhanced by using the Recurrent Neural Network (RNN). The enabling of the Squacc optimization algorithm in both RNN and BiLSTM effectively optimized the classifier’s parameters and helped to obtain an efficient output. The performance metrics BLEU, ROUGE, CIDEr, METEOR, and SPICE demonstrated the superiority of the research. Later in the third phase, we developed a digital twin for the TEC District, Monterrey, Nuevo Leon, Mexico. We carried out this work by defining and developing five layers in our digital twin model: the ground layer, BIM layer, Mobility infrastructure, district 3D model, and finally, the digital twin. Here, we used some common software applications for the development of TEC District Digital Twin, such as Esri ArcGIS for data management (Map data, GeoJson, and 2D data), City Engine for assigning rule files of buildings, vegetation, water, road network and manipulation of 2D, 3D data, and QGIS for shape files. 3D modeling software Blender, and Nvidia Omniverse for the final digital twin was used. Using the potential of these tools and techniques, Digital Twin is proposed for the buildings, road network, and vegetation of the TEC District (Tecnologico De Monterrey District) region. Here, we integrated our dense captioning model with the TEC Distrcit digital twin to obtain captions of security events using knowledge graphs. The general idea of this investigation is to provide a better understanding of digital twins and dense video captioning. By leveraging the capabilities of these technologies, organizations can generate more accurate and insightful analyses of digital twin models, enabling a wide range of applications in various fields. These technologies will also aid surveillance and security in urban planning, offering significant benefits for organizations looking to optimize their operations and enhance their decision-making processes. All the models described in this investigation can be applied to a wider range of instances to achieve acceptable results with respect to time and quality.
- Compliant mechanisms and joints: control and tailored stiffness via architectured metamaterials(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-05) Arredondo Soto, Mauricio; Gómez Espinosa, Alfonso; dnbsrp; Román Flores, Armando; Urbina Coronado, Pedro Daniel; Jiménez Martínez, Moisés; School of Engineering and Sciences; Campus Monterrey; Cuan Urquizo, EnriqueIn this work the compliance matrix method is used to develop an analytical methodology for the kinetostatic analysis of Flexure-Based Compliant Parallel Mechanisms (FBCPM) under arbitrary force and displacements conditions. Furthermore, the characteristics of metamaterials, specifically the zero Poisson's ratio lattice structures, are used to design a novel type of prismatic compliant joints namely Zero Poisson's ratio Prismatic Compliant Joints (ZP-PCJ) with advantageous features such as high flexibility in a desired direction while achieving favorable levels of stiffness in the non-desired directions, and accurate analytical models that allow their implementation in FBCPM. In the first chapter, the relevant concepts are presented, in addition, the main problem, hypothesis and objectives are stated. The second chapter presents the literature review on the state of the art in the topics of the kinematic analysis of compliant mechanisms and the use of metamaterials for compliance purposes. Chapter three deals with the theoretical background corresponding to the Compliance Matrix Method (CMM) and the kinematic analysis of FBCPM using this method, ending with a summary of the CMM that synthesizes and unifies all the variants commonly found in the literature. In chapter four the proposed analytical method for the kinematic analysis of FBCPM is presented and successfully validated by three cases: i) using a 2D-FBCPM comparing with FEA-simulation results, ii) using a 3D-FBCPM comparing with FEA-simulation results, and iii) using a Compliant Spherical Parallel Mechanism (CSPM) comparing with both FEA-simulation and experimental results where input displacements were also used. Chapter five deals with the concept of meta-flexures, introducing the new type of prismatic compliant joints called ZP-PCJ based on the advantageous characteristics of zero Poisson's lattice structures. The compliance matrices of these ZP-PCJs are obtained analytically using Castigliano's second theorem and compliance simplification, and successfully validated with both FEA-simulations and experimental tests. In addition, the proposed ZP-PCJs are implemented in a 2D-FBCPM whose kinetostatic analysis is performed with the method presented in the previous chapter, demonstrating via FEA-simulations, the validity and accuracy of their analytical models. Finally, conclusions and future work are described in chapter six.
- Evaluation of aging and repaired reinforced concrete structures(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-04-15) Parmiani, Maria Giulia; BARRIOS PIÑA, HECTOR ALFONSO ; 2177978; Barrios Piña, Hector Alfonso; puemcuervo, emipsanchez; Faraone, Gloria; Díaz Martínez, Gerardo; School of Engineering and Sciences; Campus Monterrey; Orta Cortés, Luis JoséThis dissertation critically examines the issues of bridges subjected to spalling because of exposure to aggressive environmental conditions, truck impacts and use of deicing salts in cold weather countries. The aging of reinforced concrete structures is one of the biggest concerns in civil engineering today since billions of dollars are spent annually on deck repairs and replacements. The first section of the thesis investigates the reliability of bridge beams with exposed reinforcement to determine when reinforced concrete continuous bridges with T-cross sections and exposure of the reinforcement should be repaired with patches and in which case the use of temporary supports is necessary to keep the bridge in service. The random nature of both resistance and load parameters is considered for an increased understanding of the reliability when reinforcement exposure occurs. Tests on beams with T-cross sections and different length of exposed reinforcement are carried out to assess the strength and stiffness deterioration. A probabilistic analysis is then performed on a continuous bridge, designed in accordance with the Canadian Code, considering different scenarios of exposure. The probability of failure and the corresponding reliability index were calculated for each scenario with Monte Carlo simulations. Over the range of steel reinforcement exposures, the reliability index changed from 3.14 to 2.81 and the probability of failure changed from 1.6X10^-3 to 4.4X10^-3. The second section of the dissertation examines reinforced concrete box girder bridges rehabilitated with concrete overlays. The restraining effect of the substrate on the shrinkage of the new layer of concrete leads to the development of stresses, which may cause cracking and unbonding of the overlay. This study carries out a two-dimension finite element analysis of a reinforced concrete box girder bridge to evaluate humidity and free shrinkage strain profiles at different times, starting from the day of casting until 50 years later. The rate of shrinkage change is taken as a function of the humidity change. The humidity gradient between the overlay and the substrate generates differential volume changes between substrate and overlay: the substrate deformations are negligible while the overlay is subjected to high shrinkage; 78% of the ultimate shrinkage strain is reached after 3 years indicating a high susceptibility to cracking. Investigating the behaviour of aging and repaired bridges is a continuing concern in civil engineering, because the service life of damaged bridges is reduced and maintenance costs are increased, generating issues in the management of a country infrastructure network. The first section of this dissertation provides the first probabilistic assessment of a continuous reinforced concrete bridge with exposed reinforcement: considering a target value for the reliability index equal to 3 for an existing bridge with exposed reinforcement, results of the probabilistic analysis suggest that rehabilitation works should be prioritized for exposure lengths larger than or equal to 43%. The second section of the thesis explores with a 2D FEA the behaviour of a reinforced concrete box girder bridge rehabilitated with a concrete overlay: the high susceptibility to cracking that emerges from the results suggests that the adopted thickness of 250 mm and the selected concrete compressive strength of 30 MPa used for the overlay are not suitable for the bridge of the case study. Moreover, humidity and free shrinkage strain equilibrium is almost reached after 10 years, therefore an analysis along this time range seems to be appropriate for practical applications or standard designs in the study of the probability of cracking of a bridge repaired with a concrete overlay. The adoption of this limited time range will reduce computational work for the prediction of stresses, probability of cracking or displacements due to restrained shrinkage of new concrete overlay.
- Automatic multi-target clinical classification and biomarker discovery in cancer(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05-10) Ayton, Sarah Gabrielle; JOSE GERARDO TAMEZ PENA; 3059469; Treviño Alvarado, Víctor; puemcuervo, emipsanchez; Tamez Peña, José Gerardo; Martínez Ledesma, Juan Emmanuel; Pavlicova, Martina; Maley, Carlo C.; Fuentes Aguilar, Rita Q; Robles Espinoza, C. Daniela; School of Engineering and Sciences; Campus MonterreyPrecision medicine relies on accurate and interpretable biomarker and subtype discovery. Many multi-omics subtyping algorithms have been developed to manage subtype identification across platforms but have yet to be evaluated with respect to identification of clinically prognostic subtypes. Further, many comprehensive characterization studies of cancer, which have identified multi-omics subtypes or molecular subtype signatures, have done so through the use of manually-derived expert-designed trees. Despite interpretability, current decision tree approaches are unable to explainably reproduce subtyping findings, owing to the complex nature of molecular and clinical factors driving the disease. Current machine learning (ML) approaches do not achieve interpretability (explainability) across disease endpoints, and models constructed manually by trained experts can be subjective. We develop a multi-objective decision tree (MuTATE) framework which performs automated, explainable, and multi-outcome segmentation to construct interpretable trees, simultaneously identifying biomarkers and subtypes of clinical relevance across disease endpoints. Molecular, clinical, and survey data may be input to identify prognostic biomarkers with either preventive or therapeutic implications. We provide a proof-of-concept for multi-objective, quantitative, explainable trees, enabling interpretable, automated molecular insights for precision medicine. This comprehensive approach can improve therapeutic decisions and has applications across complex diseases, and the availability of our method as an R package enables improved access to comprehensive and quantitaive disease modeling to identify those who may benefit from different treatment plans.
- Exosome-like vesicles in intercellular communication: Investigating the role of exosomal proteins in the pathophysiology of obesity and exploring the potential therapeutic use of exosomes.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06-15) Donoso Quezada, Javier Alejandro; GONZALEZ VALDEZ, JOSE GUILLERMO; 234501; González Valdez, José Guillermo; puemcuervo, emipsanchez; Ramos Parra, Perla Azucena; Genevieve Brunck, Marion Emilie; Chávez Santoscoy, Rocío Alejandra; Gómez Loredo, Alma Elizabeth; Escuela de Ingeniería y Ciencias; Campus Monterrey; Brix Pedersen, SusanneType 2 diabetes mellitus (T2DM) is a chronic condition characterized by impaired insulin sensitivity, resulting in hyperglycemia, dyslipidemia, and other metabolic changes that can damage organs and tissues over time. Obesity is the primary risk factor for the development of T2DM, as it triggers chronic inflammation in adipose tissue, leading to the secretion of adipokines that reduce insulin sensitivity in peripheral tissues. On their part, exosomes, small extracellular vesicles that cells use for intercellular communication, are critical players in fundamental biological processes such as cell growth, metabolism, and inflammation. Changes in the production or composition of exosomes can lead to health issues. In this dissertation, we explore the role of exosomes in cell-to-cell communication and their potential therapeutic use, focusing on the proteomic alterations that occur in exosomes during obesity and their potential functional consequences. The experimental work in this dissertation is divided into three parts. First, we studied the effect of hyperglycemia on cell function in adipocytic and hepatocytic cell lines. Second, we investigated the changes in exosome proteome resulting from obesity and physical training in a mouse model of diet- induced obesity, emphasizing the functional implications of these alterations. Finally, we evaluated the potential therapeutic use of exosomes to deliver bioactive compounds in vitro. This work aims to enhance our understanding of exosome biology and its relevance to health and disease, particularly metabolic disorders such as obesity and T2DM. By shedding light on the functional consequences of altered exosome proteome and exploring the potential of exosomes for therapeutic purposes, this dissertation provides important insights that may pave the way for novel therapeutic approaches for metabolic disorders.
- Un enfoque numérico para la solución de problemas dinámicos de ingeniería estructural basado en un método de elementos discretos macroscópicos.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06-21) Álvarez Reyes, Juan; ALVAREZ REYES, JUAN; 276526; Ramírez Orozco, Aldo Iván; puemcuervo, emipsanchez; Ahuett Garza, Horacio; Díaz Alvarado, Sergio Alberto; Martínez Martínez, Joel; Escuela de Ingeniería y Ciencias; Campus Monterrey; Cordero Cuevas, Raymundo AntonioEl Método de Elementos Discretos (MED) consiste en un modelo numérico que permite describir el comportamiento mecánico de objetos que se encuentran en un medio discontinuo, es decir, elementos independientes que interactúan en un sistema, formando fuerzas y movimientos rotacionales entre sí, que pueden ser ocasionadas por fuerzas externas y de gravedad. La ventaja principal de los métodos discretos consiste en la capacidad de simular fenómenos que resultaría complicado representar mediante un método continuo, debido a que se tienen elementos en donde las fuerzas no son transmitidas directamente, si no que, se generan por contacto entre cada partícula. En este documento se describe la implementación del MED mediante un enfoque numérico explícito que considera la interacción entre partículas que se encuentran en movimiento. Se utilizan figuras geométricas que van desde polígonos simples hasta figuras más complejas tridimensionales como tetraedros para la simulación de los experimentos. Se realiza una comprobación del funcionamiento de la formulación MED mediante ejemplos dinámicos sencillos con interacción entre una partícula y su superficie, hasta simulaciones más complejas con múltiples partículas interactuando entre ellas para generar un patrón de resultados a comparar. Se incluye la simulación numérica del comportamiento sísmico para una conexión autocentrante basado en el método de elementos discretos, con el objetivo de validar la respuesta histerética del modelo numérico con la prueba experimental. Los resultados representan las distorsiones horizontales de una columna, las cuales son producto de la simulación de un modelo numérico sujeto a una carga que se incrementa con el paso del tiempo con 4 coeficientes de rigidez diferentes. Se presenta la gráfica histerética como resultado de la simulación numérica del modelo, con un coeficiente de rigidez y amortiguamiento normal adecuado, sujeto a una carga cíclica que se invierte en función al tiempo. Por último, con los resultados mostrados de la prueba con conexiones autocentrantes se propone una sencilla ley de histéresis bilineal, que permita su uso simplificado para la aplicación en el modelado de edificios de concreto reforzado con conexiones similares.