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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- Design and control for stabilization and tracking of a tilted-motors hexacopter UAV with cable-suspended payload(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-01-31) Arizaga León, Jorge Manuel; Castañeda Cuevas, Herman; emipsanchez; Alvaro Mendoza, Carlos Enrique; Abaunza González, Hernán; Guerrero Sánchez, María Eusebia; Escuela de Ingeniería y Ciencias; Campus Monterrey; Castillo García, PedroIn recent years, technological progress has allowed to develop unmanned aerial vehicles (UAVs) capable of performing challenging trajectories and complex maneuvers related to the transport, handling, or deployment of payloads, as seen in firefighting tasks, package delivery, and in emergencies and natural disaster assistance situations. Multi-rotor type UAVs are attractive for these applications given their weight-carrying capability, however, there are design and control constraints that limit their performance. Typically, this class of vehicles has their rotors arranged in parallel, generating thrust along a fixed axis, which categorizes them as underactuated systems. This represents a limitation when flying in environments subject to disturbances caused by variations in environmental conditions, and even by disturbances associated with the movement of the payload. Moreover, conventional approaches for representing UAV's rotation using Euler angles and rotation matrices have important disadvantages such as gimbal lock, and high computational cost. Regarding the payload transport method, the simplest one is the hanging cable, since it does not require complex mechanisms. However, a suitable control scheme is essential to counteract the oscillations produced by the pendular movement and to guarantee the stability of the payload and the aircraft, even in adverse environmental conditions. Therefore, this work proposes the use of a six-tilted-rotor UAV, which provides full actuation and allows to exert horizontal forces and displace the aircraft without roll or pitch variations. A hanging cable with a suspended payload is considered. To avoid gimbal lock and to obtain a linear representation of the system orientation, it is described by unit quaternions and the axis-angle approach. In addition, given its properties of finite-time convergence, proper control effort management, and robustness against bounded disturbances, the control scheme is based on an adaptive sliding mode class. To reduce payload oscillations, an extended high gain observer is proposed for the position dynamics, which allows us to estimate the state, as well as the external disturbances and those associated with the oscillations, allowing us to improve the controller and counteract the pendulum effects without extra sensors. The proposed methodology is demostrated by a closed-loop stability of the observer-based control scheme, theoretically guaranteed by the Lyapunov method. Then, an emulation environment is developed in MATLAB/Simulink with Simscape Multibody, allowing it to interact graphically and in real-time with the system model. Finally, multiple experiments are deployed with a real hexacopter in different conditions. The results exhibited the capabilities of the vehicle, and of the control scheme, to fly in trajectories subject to perturbations while reducing the oscillations of the payload.
- Fabrication of micro- and milli-fluidic components with photopolymer additive manufacturing—analysis of the effects of material properties and printing parameters(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025) Torres Alvarez, Dagoberto; Aguirre Soto, Héctor Alán; emipsanchez; Alcalá Rodriguez, Mónica María; López Guajardo, Enrique Alfonso; José Luis López Salinas; Gallo Villanueva, Roberto Carlos; Madadelahi, Masoud; School of Engineering and Sciencies; Campus Monterrey; Morones Ramírez, José RubénTo date, several methods are available for the development of microfluidic platforms. Polydimethylsiloxane (PDMS), polymethyl methacrylate (PMMA), and glass are three of the most employed materials. Despite their widespread use, there are still limitations in the fabrication of microfluidic devices when complex three-dimensional structures are desired. Additive manufacturing (AM) has recently arisen as an alternative in the creation of microfluidic platforms due to its reproducibility, automatization, and fast prototyping. However, the lack of a well-established protocol in the fabrication of hollow embedded structures and the difficulty in the obtention of channels with a diameter below 500 μm are two of the major drawbacks of this technology. From all the AM technologies, vat photopolymerization (VPP) is preferred for microfluidic applications, due to a xy resolution as small as 20 μm when a LCD screen is used as the light source. In this study, the variables that affect the printing of negative embedded structures were studied in three different VPP printers. Our results showed that printing orientation, channel length, exposure time, and resin viscosity greatly influence the printer's capability to successfully print channels below 500 μm. When all these values are optimized, a commercial resin can reach a channel diameter as low as 420 μm at room temperature and 340 μm at 32°C. Additionally, a relation between polymerization kinetics and the presence of anisotropy was identified, suggesting that fast polymerization limits the crosslinking between layers, decreasing the mechanical properties, such as young’s modulus and maximum stress, when a force is applied perpendicular to the layers. Consequently, a model to predict the lowest achievable channel characteristic length (LPCL) was developed after evaluating six different commercial resins. After a classic Pi Buckingham analysis, it was found that LPCL is a function of the resin’s viscosity, density, surface tension, and contact angle between unpolymerized and polymerized resin. Finally, all the previous knowledge was used in the fabrication of a droplet generator, concluding that reducing the channel diameter of the dispersed aqueous phase is essential to reach droplets with a size comparable to those obtained by PDMS-based microfluidics. We expect that all this knowledge could be utilized as a tool in the development of new resin and printer technology that can compete with conventional fabrication methods.
- Real-time armed individual detection in video surveillance usingdeep learning and heuristic approaches(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Amado Garfias, Alonso Javier; Conant Pablos, Santiago Enrique; emipsanchez; Ortiz bayliss, José Carlos; Tarashima Marín Hugo; Gutiérrez Rodríguez, Andrés Eduardo; School of Engineering and Sciences; Campus MonterreyThis researchaimstoenhancetheautomaticidentificationofarmedindividualsinvideo surveillanceinreal-time.Theproposedmethodologyinvolvesthedevelopmentofalgorithms specifically designedforthedetectionofindividualscarryinghandguns,whichincludepistols and revolvers.Toachievethis,theYOLOv4modelhasbeenselectedtodetectindividuals, handguns, andfaces.Subsequently,real-timeinformationisextractedfromtheYOLOmodel, including boundingboxcoordinates,distances,andintersectionareasbetweenhandgunsand individualswithineachvideoframe.Thisinformationfeedsourheuristicsanddifferentma- chine learning(ML)proposed,facilitatingtherecognitionofarmedindividuals.Severalchal- lenges mustbeaddressed,suchasocclusion,concealedguns,andproximityofindividualsto one another.Itencouragesthedevelopmentandcomparisonofdifferenttypesofsolutions. Theyaremadeupofthreeheuristics,seven-armedpeopledetectors(APD),and44APDto use ineachvideoframe(APD4F). The heuristicsaretheDeterministicMethodofCenters(DMC),theDeterministicMethod of Distances(DMD),andtheDeterministicMethodofIntersections(DMI).Furthermore, the APDmodelsareRandomForestClassifier(RFC-APD),MultilayerPerceptron(MLP- APD), k-Nearest-Neighbors(KNN-APD),SupportVectorMachine(SVM-APD),Logistic Regression(LR-APD),NaiveBayes(NB-APD),andGradientBoostingClassifier(GBC- APD). Thereby,IproposetocreateselectorsfordecidingwhichAPDtouseineachvideo frame (APD4F)toimprovethedetectionresults.Besides,weimplementedtwotypesof APD4Fs, onebasedonaRandomForestClassifier(RFC-APD4F)andanotherinaMultilayer Perceptron (MLP-APD4F).Wedeveloped44APD4FscombiningsubsetsofsixAPDs.The most ofAPD4FoutperformedoftheindependentuseofallAPDs.Amultilayerperceptron- based APD4F,whichcombinesanMLP-APD,aNB-APD,andaLR-APD,presentedthebest performance, achievinganaccuracyof95.84%,arecallof99.28%andanF1scoreof96.07%. This researchalsoproposesasolutiontooptimizetheproblemofdetectingarmedpeople when theweaponisnotvisible.Therefore,weapplyrecurrentneuralnetworks,suchasLong Short TermMemory(LSTM),topredictthecoordinatesoftheguns.Inthisway,itispossible to haveapredictionofarmedpeopleatalltimes.ThemeasurementbetweentheYOLO handgun detectionboundingboxesandtheLSTMpredictionresultedinanIoUof65.23%. When thefirearmdetectionbytheobjectdetectorisinterrupted,theweapon’spositionis generated bytheLSTMmodelsthat,togetherwiththeAPDs,identifythearmedpeople. When theLSTMsdeliveredtheirpredictionstotheAPDs,theNB-APDdemonstratedthe best performance,achievinganaccuracyof80.93%.TheLSTMsallowedtheanalysisof 5,288 recordsofthetestvideothatcouldnotbeanalyzedbeforeduetothelackofknowledge of thegun’sposition.
- Estudio clínico fase I/II-A para evaluar la tolerabilidad, seguridad y eficacia de una formulación de polifenoles contenidos en el extracto de arándano azul en conjunto con omega 3 en paciente con ojo seco y moderado(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-29) Ng Alemán, Dalia Denisse; Altamirano Vallejo, Juan Carlos; emimmayorquin; Navarro Partida, Jose; Gonzalez de la Rosa, Alejandro; Anaya Prado, Roberto; Escuela de Medicina y Ciencias de la Salud; Campus Monterrey; Santos Garcia, ArturoLa distribución mundial de la enfermedad de ojo seco (EOS), su prevalencia y potencial daño al epitelio corneal la han convertido en un problema de salud pública; a razón de nuestra exposición a la luz de monitores esta problemática ha aumentado, y la investigación sobre terapias eficaces se ha convertido en una necesidad. Debido al potencial terapéutico sobre el estrés oxidativo del medio ocular, así como para la regulación de la cascada inflamatoria los polifenoles son la propuesta en la investigación enfocada en prevenir o controlar el padecimiento. Desarrollamos un ensayo clínico aleatorizado donde los participantes fueron asignados a uno de cuatro grupos para estudiar el efecto de la formulación oral con polifenoles contra placebo, en ausencia o conjunto con lubricante; realizamos evaluaciones clínicas al inicio y al primer y tercer meses de seguimiento en los que verificamos la seguridad y tolerancia del tratamiento e implementamos pruebas específicas para medir su eficacia. Observamos mejora con significancia estadística en todos los tiempos de evaluación en la prueba de OSDI para ambos grupos de tratamiento (grupo formulación y grupo formulación y lubricante), de igual manera en el tercer mes para la puntuación del tiempo de ruptura del desgarro de la película no invasiva (NIF-BUT) y el tiempo de ruptura promedio no invasivo (NIAvg-BUT), así como en la prueba de Schirmer y tinción de verde lisamina y cálculo de osmolaridad lagrimal. Los grupos placebo no mostraron impacto en la mejora. Concluimos que la formulación oral con polifenoles es eficaz como tratamiento primario, mejora los síntomas de estrés visual y el estado de la película lacrimógena, y en tratamiento conjunto con lubricante es superior a la combinación del placebo y las lágrimas artificiales.
- 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.
- Instant deliveries in Mexico City: a socio-economic analysis and profit maximization framework for couriers(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-27) Galindo Muro, Ana Bricia; Mora Vargas, Jaime; emipsanchez; Dablanc, Laetitia; Ugalde Monzalvo, Marisol; De Unanue Tiscareno, Adolfo Javier; School of Engineering and Sciences; Campus Ciudad de México; Cedillo Campos, Miguel GastónThis thesis introduces an engineering approach to understanding instant delivery operations within the platform economy. During the first step, through two surveys, the study highlighted couriers’ significant risks and challenges, shedding light on their precarious working conditions and financial pressures. The results emphasize the glaring disparity between the platform economy’s promise of flexibility and independence and the harsh reality experienced by most couriers. Furthermore, the study presents an assignment model to support technological advancements, which can lead to more effective decision-making, benefiting all actors involved in the urban instant delivery platform. By incorporating a fee algorithm and operational cost calculations, the quantitative model developed in this study demonstrates that a 20% increase in couriers’ income compared to traditional assignment models is advantageous for all parties. This approach seeks to raise awareness about the socioeconomic implications of emerging technologies such as Instant Deliveries and their regulation, particularly in rapidly developing urban areas. It offers valuable insights to build a more socially responsible and environmentally sustainable optimization approach in engineering.
- From classical to quantum machine learning for analyzing and predicting alumni outcomes(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Ramos Pulido, Sofía; Hernández Gress, Neil; Torres Delgado, Gabriela; Hervert Escobar, Laura; Garza Villarreal, Sara Elena; Méndez Hinojosa, Luz Marina; School of Engineering and Sciences; Campus Monterrey; Ceballos Cancino, Héctor G.This thesis is submitted to the graduate program at the School of Engineering and Sciences as part of the requirements for obtaining the degree of Doctor of Philosophy in Computer Science. This study aims to generate models using both classical and quantum machine learning (ML) methodologies to accurately predict three key outcomes for alumni: job level, career satisfaction, and first employment. The data analyzed comes from Tec de Monterrey university alumni surveys. The study’s objectives also include the identification of important and actionable features for alumni outcome predictions. Among the challenges in finding models to predict and explain alumni outcomes, we encountered issues such as handling imbalanced classification, hyperparameter tuning, model prediction interpretation, and long training times. To address the latter, we proposed a method that reduces execution time when working with large datasets, particularly in methodologies like support vector machines. This proposal effectively resolves time and memory limitations in high-dimensional classification problems without compromising performance accuracy. The results show that classical machine learning models accurately predicted alumni outcomes. For instance, gradient boosting was most accurate in predicting job level and career satisfaction, while support vector machines outperformed in employment prediction. Significant features identified included current salary and number of people supervised for job level, with higher salaries and more supervisory responsibilities correlating with higher job positions. For career satisfaction, life and income satisfaction were important indicators, as higher satisfaction levels in these areas predicted greater career satisfaction. In the case of employment, networking support resulted as the most important feature, with stronger professional connections significantly increasing the likelihood of securing employment shortly after graduation. Additionally, the research identified actionable features that can impact both educational institutions and students. For job level, soft skills, particularly communication and teamwork, were found to be crucial in advancing to higher positions. Institutions can focus on enhancing these skills through their programs, while students are encouraged to develop them actively. For career satisfaction, the effective use of skills and technological tools acquired during education was a strong predictor, indicating the importance of aligning academic training with the demands of the job market. Facilitating robust professional networks proved essential for employment, emphasizing the need for institutions to create networking opportunities and for students to build social connections proactively. Many more interesting trends and findings related to alumni outcomes are highlighted in the thesis. Regarding quantum machine learning (QML) models, this research demonstrates the v feasibility of predicting alumni outcomes. A hybrid quantum-classical approach was particularly effective in predicting the three alumni outcomes in reduced datasets without substantially affecting accuracy. For example, quantum support vector classifiers (QSVC) showed comparable performance to classical support vector classifiers (SVC) while utilizing a reduced dataset versus SVC with complete datasets. Although QML is still in its early stages, this research suggests that QML could become a viable alternative in educational data mining as the field expands.
- 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
- 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.
