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.
Browse
Search Results
- Evaluación de la vía de señalización involucrada en el efecto cardioprotector del cannabidiol (CBD) dependiente de PPARγ en la hipertrofia cardíaca patológica en mioblastos cardiacos.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) Morales Ochoa, Carolina Alejandra; García Rivas, Gerardo de Jesús; emimmayorquin; Chapoy Villanueva, Héctor; Contreras Torres, Flavio Fernando; El Hafidi, Mohamed; Campus Monterrey; Carvajal Aguilera, Karla GuadalupeEl corazón desempeña un papel esencial al mantener el flujo sanguíneo, suministrando nutrientes y oxígeno a los órganos periféricos. Para afrontar demandas adicionales, como el estrés y factores neurohormonales, los cardiomiocitos, que no pueden dividirse, experimentan hipertrofia (Nakamura & Sadoshima, 2018a). La hipertrofia mal adaptativa es un componente crucial en la insuficiencia cardíaca, de ahí su amplia atención como un objetivo molecular y el interés por estudiar los mecanismos moleculares involucrados. Por otro lado, se ha reportado que el cannabidiol (CBD), un compuesto de la planta Cannabis sativa, ha emergido como un potencial activador de varios receptores, incluido el receptor activado por proliferadores de peroxisomas gamma (PPARγ), que también está implicado en el desarrollo de la hipertrofia mal adaptativa. En el presente estudio, investigamos la vía de señalización que puede estar involucrada en el efecto antihipertrófico del CBD (obtenido por síntesis química y de formulación grado farmacéutico) dependiente de PPARγ. Para alcanzar este objetivo, se planteó un modelo de hipertrofia en cultivos de mioblastos cardíacos H9c2, inducida por Angiotensina II (Ang II). Se analizó el nivel expresión de marcadores de hipertrofia cardíaca y de los factores de transcripción PPARs utilizando qPCR en presencia y ausencia de CBD, de igual manera se evaluó la activación ascendente de PPARγ en la vía de señalización de la proteína quinasa activada por AMP (AMPK) en presencia y ausencia de CBD (qPCR y Western Blot). Los resultados sugieren que la administración de CBD al cultivo de mioblastos cardíacos H9c2 hipertrofiados con Ang II resultó en una reducción significativa del tamaño celular y otros parámetros asociados a la hipertrofia, ejerciendo un efecto antihipertrófico. Este efecto protector se vio comprometido en presencia de GW9662, un antagonista específico de PPARγ, confirmando que la activación de PPARγ es crucial para el efecto del CBD. Además, se descubrió una posible interacción entre PPARα y la vía de señalización AMPK ya que la expresión de PPARα aumentó significativamente con CBD, y se observó una mayor fosforilación de AMPK, sugiriendo que la activación de AMPK también está involucrada en el efecto cardioprotector del CBD. Estos resultados abren la posibilidad de usar CBD y la modulación de los receptores PPARγ como una estrategia terapéutica para prevenir o revertir la hipertrofia cardíaca. Sin embargo, es necesario un mayor entendimiento de los mecanismos de inhibición de la hipertrofia para desarrollar intervenciones terapéuticas innovadoras.
- An autonomic lift truck for a smart factory(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-10) Marín Segura, Juan Daniel; Carrillo, Luis Antonio; emimmayorquin; Torres, David Antonio; Reyes Avendaño, Jorge Antonio; Camacho León, Sergio; School of Engineering and Sciences; Campus Puebla; Hernández Zarate, Debbie CrystalRapid technological advancements have introduced the concept of Smart Factory (SF), which requires systems with high levels of flexibility, adaptability, and digitization. This the- sis explores the design and implementation of an autonomic Lift Truck to achieve these char- acteristics within a SF environment. The proposed system uses the MAPE-K (Monitoring, Analysis, Planning, Execution, and Knowledge) framework to create an autonomic system (AcS), which is a self-managing system that includes four characteristics: self-configuration, self-healing, self-optimization, and self-protection (Self-CHOP). The transformation of a Turtlebot3 into an autonomic lift truck, encompassing mechan- ical modifications, electrical system enhancements, and software integration is addressed in this research thesis. Also, the interaction of the forklift with the SF, focusing on its ability to map and adjust to layout changes (self-configuration), send alerts for human intervention during faults (self-healing), optimize energy usage based on demand (self-optimization), and prevent hardware and software failures (self-protection) are tackled out. Pilot tests demonstrate the effectiveness of the autonomic Lift Truck in bringing flexi- bility, adaptability, and digitization to the SF. Results indicate that the proposed system can dynamically adapt to changes in a factory layout, maintain operational continuity through self-healing mechanisms, optimize resource usage based on real-time demands, and protect operations against potential disruptions. Summarizing, this thesis contributes to the field of AcS by presenting one of the first implementations that integrates all the features of Self-CHOP within an Industry 4.0 context. It provides a fundamental framework for future research and development of advanced AcS into smart manufacturing environments
- Mechanical characterization and design of square honeycombs with the aid of additive manufacturing and AI(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-07) Herrera Ramos, Gustavo; Cuan Urquizo, Enrique; emimmayorquin; Román Flores, Armando; Mora Córdova, Ángel; Escuela de Ingeniería y Ciencias; Campus Monterrey; Batres Prieto, RafaelMetamaterials offer a viable mean to attain targeted mechanical characteristics tailored to particular loading conditions. Aperiodic metamaterials provide higher tailorability of mechanical behavior by providing a customizable deformation mode, properties, and mechanical response. Artificial intelligence has enhanced metamaterial design by discerning correlations between parameters and mechanical characteristics. This work studies two types of gradation on square honeycombs: wall thickness and wall angle. The studied gradation characteristics were wall inclination, pattern distribution, and direction. Fifteen designs were proposed, each combining different gradation characteristics. The designs were additively manufactured with PLA on an FFF 3D printer and experimentally tested under compression. The effects of the gradation characteristics on the mechanical response, mechanical properties, and deformation mode were analyzed. The results confirmed the influence of gradation on the mechanical behavior of the structures. The gradation characteristics influence specific properties or responses, such as a 30% energy absorption difference between graded honeycombs with aligned and not aligned walls. The metamodel evolutionary optimizer (MEVO) algorithm was used to assist in the design of a tailored square honeycomb with an angle gradation to minimize the displacement of a designated point in the structure. The algorithm was tested on multiple nonconventional loading scenarios to prove its versatility.
- Assesment of a modern convNet model in the detection of breast cancer in the mexican population(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05) Monsivais Molina, Mario Alexis; Tamez Peña, Jose Gerardo; emimmayorquin; School of Engineering and Sciences; Campus MonterreyThis thesis presents an evaluation of a modern Convolutional Neural Network (ConvNet) model for detecting breast cancer in mammograms from the Mexican population. The study focused on implementing and testing a state-of-the-art ConvNet model, known as ConvNeXt, to assess its performance and reliability in diagnosing breast cancer. By employing the Tec-Salud dataset, which includes mammograms annotated by expert radiologists, and comparing it against the RSNA dataset, the research aimed to verify the model’s efficacy across different demographic and technological settings. The methodology involved preprocessing the images to standardize the data, followed by extensive training and validation of the ConvNeXt model. Performance metrics such as accuracy, sensitivity, specificity, and the area under the ROC curve were calculated to gauge the model's diagnostic power. Additionally, the study explored the impact of data augmentation and image normalization on model performance, emphasizing the challenges of applying AI in medical diagnostics across diverse populations. The findings revealed that while the ConvNeXt model demonstrated high accuracy and reliability, challenges such as overfitting and data bias persisted, highlighting the importance of continuous model training and validation. The study contributes significantly to the ongoing efforts in integrating AI into breast cancer diagnostics, offering insights into the potential of modern deep learning models to enhance early detection and treatment strategies in a demographically diverse patient population.
- Numerical study of heat transfer of a double layered PCM building roof in a semi-arid climate(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-12-05) Contreras Aguilar, José Alberto; Gijón Rivera, Miguel Ángel; emimmayorquin; Rivera Solorio, Carlos Iván; Godoy Rangel, Caribay; School of Engineering and Sciences; Campus MonterreyThe thermal performance of a concrete roof with a double phase change material (PCM) layer on the interior concrete surface under the semi-arid weather of Monterrey´s city is presented. The roof was numerically analyzed based on a computational fluid dynamic (CFD) simulation with three types of models: The base concrete case (RC), the concrete roof with one PCM layer (RC-PCMi), and the concrete roof adding a double PCM layer (RC-PCMi-PCMj). The numerical CFD simulations were conducted during the warmest, coldest and typical day of the year. The software used for the simulation was Ansys Fluent. The results indicate that the RC-PCM29 has the lowest thermal values during the warmest day reducing the indoor energy by 96.51 W/m2 representing a 7.4% reduction. In the other hand the RC-PCM25 obtained the best performance for the coldest day, reducing indoor energy by 300.29 Wh/m2 or 32.7% reduction. Furthermore, the RC-PCM29-PCM25 double PCM configuration resulted in the best thermal performance, reducing the annual energy to the indoor by 3,613.89 kWh or 26.2%. Also, considering a roof area of 36 m2 of a building located in Monterrey city, the payback period will be up to 3.45 years with an internal rate of return of 29.0%, resulting a cost-effective use of the double PCM layer. Besides the annual carbon emission savings are 1 572 04 𝑘𝑔 𝐶𝑂 2 𝑒𝑞. Therefore, it is recommended that the RC-PCM29-PCM25 will improve the thermal behavior of buildings located in Monterrey.
- Functional electrostimulation system for rehabilitation of the human hand using electromyography signal classification by artificial neural networks(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-12) Orona Trujillo, Laura; Alfaro Ponce, Mariel; emimmayorquin; Montesinos Silva, Luis Arturo; Alanis Espinosa, Myriam; Ramírez Nava, Gerardo Julián; Escuela de Ingeniería y Ciencias; Campus Monterrey; Chairez Oira, Jorge IsaacThe human hands serve as a vital interface through which individuals perceive and interact with the world, making them the earliest means of communication and artistic expression. Upper limb mobility impairments primarily result from accidents or strokes, frequently afflicting individuals in their productive years. Such impairments not only hinder physical functions but also exact a profound psychological toll as individuals deal with the loss of autonomy. The rehabilitation process, although indispensable, often appears monotonous and useless, leading to frustration and disengagement for both patients and caregivers. In response to this challenge, integrating technological tools into rehabilitation therapies has become more relevant to enhance the efficiency and safety of rehabilitation. One promising approach is the utilization of functional electrostimulation, which stimulates the human hand during the therapy to execute the desired movements. Due to this aid, the rehabilitation becomes less demanding and more efficient. This work compares different literature, where all the reviewed papers state that functional electrostimulation is efficient in improving muscle strength, upper limb function, and reducing pain and spasticity. Nevertheless, there remains a crucial gap in the field, defining the appropriate voltage-current amplitude for the stimulation signal. Existing studies have explored the morphology and frequency of the signal, leaving the signal amplitude and even the therapy time at the user’s discretion. To achieve this, the morphology of the electromyographic signals coming from the upper limb was studied in order to extract the most important characteristics and, thus, through a Long-Short Term Memory (LSTM) with an accuracy of 91.87% , identify which movement they corresponded to. The trajectory movements of a ealthy person used as a reference, were then compared with that of the patient requiring stimulation in order to obtain the differential error between the two of them. Based on the error vector found, we used a second LSTM with a regression layer to calculate the exact voltage amplitude the patient would need to vercome the missing voltage differential so that he or she could replicate the movement as similar as possible to the reference one. By addressing this critical aspect, the research aims to introduce an innovation of the current methods for upper limb rehabilitation, offering a more efficient approach to generating functional electroestimulation signals that could be used in human extremities rehabilitation.
- Modelo de aprendizaje profundo para la detección de errores en el contenido gráfico de paneles de instrumentos automotrices(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-10) Olagues Torres, Héctor Gabriel; Sánchez Ante, Gildardo; emimmayorquin; Escuela de Ingeniería y Ciencias; Campus GuadalajaraLas pruebas para detectar errores en la interfaz gráfica (para fines prácticos se le llamará pantalla) de paneles de instrumentos automotrices no son de fácil ejecución, ya que se requiere bastante tiempo para analizar las imágenes de video que se recopilan durante la verificación y validación del contenido gráfico, lo cual se debe a que son cientos o hasta miles las posibles combinaciones de imágenes que se pueden desplegar, reduciendo la disponibilidad de tiempo de un Ingeniero de Pruebas Funcionales. Aunque existen dispositivos electrónicos que capturan los fotogramas de una señal de video en forma digital (frame grabbers), el tiempo destinado al análisis es igualmente necesario. Por lo tanto, en este estudio se propone el desarrollo de un procedimiento con un modelo de Aprendizaje Profundo basado en una Red Neuronal Convolucional, que facilite la detección de errores en la pantalla de paneles de instrumentos automotrices del fabricante Stellantis desarrollados en el área de negocio UX de Continental en Guadalajara. Mediante el uso de una metodología cuantitativa con diseño cuasiexperimental, y realizando una recopilación de imágenes con su debido preprocesamiento para la construcción del conjunto de datos de entrada, se pretende entrenar una Red Neuronal Convolucional con regresión logística de cajas envolventes que ayude en la identificación de errores de contenido gráfico en la pantalla, cuyo rendimiento será evaluado a través de métricas de desempeño y herramientas estadísticas básicas, así como con pruebas de predicción en videos de contenido gráfico. Los hallazgos demuestran que se puede aumentar la disponibilidad de tiempo de los Ingenieros de Pruebas Funcionales a través del despliegue de un procedimiento con un modelo de Aprendizaje Profundo basado en una Red Neuronal Convolucional que apoye en la detección de errores en la interfaz gráfica de paneles de instrumentos automotrices del fabricante Stellantis.
- Model development for forecasting engineering resources assigned to harness design projects(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-07-14) Cordero Rivera, Jose de la Cruz; Tamayo Enríquez, Francisco Alberto; emimmayorquin; Tecnológico de Monterrey; Campus MonterreyA wiring harness is a combination of conductors and connectors to bring signals between devices within an environment to enable a system to perform. Current CAD modeling process helps to predict physical parameters to reduce time during product verification and validation before cost reduction is performed. Design resources forecast and time estimation has been an area to improve since future projects time frames are hard to predict, currently resources are assigned according to designers needs and variate through project development. Previous attempts to develop regression model had been made considering variables hard to estimate before the design is completed. Multiple linear regression is going to be used to determine design time for single wiring harness model and thus, compute the amount of engineering resources assigned to a project. Effective team structure and communication are required to meet out of design dates, avoiding downtimes and allowing a constant workload during the project timeframe. This project was focused on developing a regression model to forecast the amount of engineering resources required to complete an electrical wiring harness routing project with an unfluctuating workload in a defined period.
- Implementación de metodología Lean SixSigma en desarrollo minero en Minera Juanicipio(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023) Bravo Sanchéz, Alexis Andrés; Vásquez Hernández, Jesús; emimmayorquin; Terrazas Terrazas, Abraham; Maestro en Gestión de la Ingeniería; Campus MonterreyEl informe titulado “Implementación de metodología Lean SixSigma en desarrollo minero en Minera Juanicipio” se centra en la Minera Juanicipio, una mina ubicada en el Distrito Minero de Fresnillo, Zacatecas, México. El análisis de registros históricos desde 2013 hasta septiembre de 2021 revela un déficit acumulado de 11,393 metros en la preparación de la mina, con un 42% de atraso en rampas de profundización. El objetivo fue aumentar la tasa de avance de la rampa de profundización, disminuyendo las fallas pasando de 60 a 120 metros en la zona de contratista SDN1 en rampa 2 en Minera Juanicipio en el segundo semestre 2023. Se estructura utilizando la metodología DMAIC (Definir, Medir, Analizar, Mejorar, Controlar). Este enfoque busca optimizar los procesos de desarrollo minero para mejorar la eficiencia y productividad. La implementación de estas estrategias pretende superar los desafíos existentes en la preparación y desarrollo de la mina, contribuyendo al crecimiento sostenible y al éxito a largo plazo de la operación minera. La metodología DMAIC ha permitido identificar y analizar problemas críticos, implementar soluciones efectivas. Esto ha resultado en una mejora notable en el proceso de barrenación, lo que ha llevado a un aumento de la eficiencia operativa. Mejorando un 13% en el avance por disparo y disminución de la dispersión del 50%. Estas mejoras han permitido incorporar cerca de 48 toneladas adicional por cada disparo realizado, es decir, gracias a la mejora del avance por disparo a permitido mantener y aumentar las reservas desarrolladas, permitiéndonos tener 2,274,000 toneladas desarrolladas actualmente.
- Synthesis and application of hydrogels with microalgae-bacteria for nutrient and PFDA removal from wastewater(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-11-28) Morán Valencia, Marien; Cervantes Avilés, Pabel Antonio; puemcuervo, emipsanchez; Cárdenas Chávez, Diana Linda; Cuevas Rodríguez, Germán; Mata Gómez, Marco Arnulfo; Huerta Aguilar, Carlos Alberto; School of Engineering and Sciences; Campus Ciudad de MéxicoThe aim of this study was to evaluate the performance of hydrogels with a consortium of microalgae bacteria and activated carbon (AC) as function of nutrient removal in wastewater containing PFDA. Hydrogels were synthetized from polyvinyl alcohol (PVA), sodium alginate (SA), and included biomass (microalgae-nitrifying bacteria), AC or both exposed to different aqueous conditions, namely raw wastewater, synthetic wastewater (SWW) with and without PFDA and PFDA solution. The performance of hydrogels was evaluated based on the change in ammonium (NH4) and nitrate (NO3) concentrations, chemical oxygen demand (COD), nitrification rate and other parameters during 72 h. Ammonium removal was possible by all hydrogels. The nitrification process was carried out by all hydrogels. Activated carbon was found to be effective as a nutrient adsorption medium in the presence of perfluorodecanoic acid (PFDA). Regarding COD, this increased in all hydrogels could be due to the leaching the components of hydrogel. The best performance was observed for the hydrogel with 5 % of biomass without AC with a nitrification rate of 0.43 mg N/g TSS·h. Hydrogel with AC (HC) was the most effective for removing PFDA (38.5%) and the hydrogel that presented that highest resistance to PFDA during nutrient removal. HBC was the most efficient hydrogel for removing nutrients in presence of PFDA. Results indicated that the presence of PFDA did affect the processes of nutrient elimination in hydrogels with biomass due to the toxicity of the emerging pollutant.