Tesis

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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.

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Now showing 1 - 10 of 12
  • Tesis doctorado / doctoral thesis
    Coexistence of trust and distrust in technology: a mobile health monitoring systems comparative case study across time
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-01) Cantú Alejandro, Celeste; Güemes Castorena, David; emipsanchez; Elizondo Noriega, Armando; Smith Cornejo, Neale Ricardo; AGuayo Tpellez, Humberto; Escuela de Ingeniería y Ciencias; Campus Monterrey; Cortes Capetillo, Azael Jesus
    Emerging technologies challenge organizations by introducing information systems that transform processes and introduce a series of risks and uncertainties for stakeholders. These conditions make trust and distrust important in the adoption and implementation of technology. Most of the trust in Information Systems literature has focused on building the constructs of trust and distrust between an artifact and the final user, but there is a gap in the analysis of changes in trust and distrust beliefs and their possible coexistence across time. This case study illustrates how trust and distrust manifest across a mobile health monitoring system implementation: the attitudes, objects of trust/distrust, and the changes they exhibit across four stages. The results point at the objects of trust/distrust corresponding to the technology frames that shift during the implementation from an artifact-centric to a more encompassing technology involving other users, programmers, and themselves. The actual technology use leads to changes in perceived risks and expectations, which are fed by the interaction of all users with the system. Behaviors are shaped by coexisting trust and distrust in technology beliefs, which usually compensate for the elements of the technology they distrust. The study concludes that the coexistence of trust and distrust in technology is a constantly shifting phenomenon, leading to a complex yet stabilized use of technology.
  • Tesis doctorado / doctoral thesis
    Environmental assessment of urban rivers through a dual lens approach: machine learning based water quality analysis and metagenomic characterization of contamination effects
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Fernández del Castillo Barrón, Alberto; Gradilla Hernández, Misael Sebastián; emipsanchez; García González, Alejandro; Pacheco Moscoa, Adriana; Brown, Lee; Oscar Alejandro Aguilar Jiménez; School of Engineering and Sciences; Campus Monterrey; Senés Guerrero, Carolina
    Urban rivers are critical ecosystems increasingly threatened by pollution. Effective water quality monitoring and contamination assessment are essential for informed management decisions. The Santiago River, a key hydrologic system in Mexico, has become one of the country’s most polluted rivers, posing significant ecological risks and public health concerns for nearby communities. This study underscores the urgent need for comprehensive environmental evaluation and enhanced monitoring approaches. Chapter one introduces the motivation behind monitoring water quality in highly polluted rivers, presenting the problem statement and contextual background of the Santiago River basin. It outlines the research question and provides an overview of the proposed dual-lens approach: combining water quality analysis via machine learning algorithms with metagenomic characterization of contamination effects. Key contributions of this work to the field are also highlighted. Chapter two reviews global monitoring strategies from highly polluted rivers, focusing on nine rivers across developed and developing countries to offer a comparative perspective on water quality management needs. In Chapter three, regression and classification machine learning models are developed to predict the Santiago River Water Quality Index (SR-WQI), designed as complementary tools to strengthen the current monitoring program. Chapter four analyzes the historical water quality patterns of the Santiago River to identify the most variable and representative data for training machine learning models. This chapter also reveals that redundant data can hinder model performance by leading to overfitting. Chapter five investigates spatial variations in the microbial composition of Santiago River sediments and examines correlations with water quality. Using high-throughput sequencing, potential microbial biomarkers were identified and impacts of physicochemical parameters and heavy metals on microbial communities were assessed. Finally, chapter five highlight the main findings of this thesis and covers some limitations, perspectives for future research and final remarks.
  • Tesis doctorado / doctoral thesis
    A minutiae-based indexing algorithm for latent palmprints
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Khodadoust, Javad; Monroy Borja, Raúl; emipsanchez; Aparecida Paulino, Alessandra; Valdes Ramírez, Danilo; Rodríguez Ruiz, Jorge; School of Engineering and Sciences; Campus Monterrey; Medina Pérez, Miguel Ángel
    Today, many countries rely on biometric traits for individual authentication, necessitating at least one high-quality sample from each person. However, countries with large populations like China and India, as well as those with high visitor and tourist volumes like France, face challenges such as data storage and database identification. Latent palmprints, comprising about one-third of prints recovered from crime scenes in forensic applications, require inclu sion in law enforcement and forensic databases. Unlike fingerprints, palmprints are larger, and features such as minutiae are approximately ten times more abundant, accompanied by more prominent and wider creases. Consequently, accurately and efficiently identifying la tent palmprints within stored reference palmprints poses significant challenges. Using fre quency domain approaches and deep convolutional neural networks (DCNNs), we present a new palmprint segmentation method in this work that can be used for both latent and full impression prints. The method creates a binary mask. Additionally, we introduce a palmprint quality estimation technique for latent and full impression prints. This method involves parti tioning each palmprint into non-overlapping blocks and considering larger windows centered on each block to derive frequency domain values, effectively accounting for creases and en hancing overall quality mapping. Furthermore, we present a region-growing-based palmprint enhancement approach, starting from high-quality blocks identified through our quality es timation method. Similar to the quality estimation process, this method operates on blocks and windows, transforming high-quality windows into the frequency domain for processing before reverting to the spatial domain, resulting in improved neighboring block outcomes. Finally, we propose two distinct minutiae-based indexing methods and enhance an existing matching-based indexing approach. Our experiments leverage three palmprint datasets, with only one containing latent palmprints, showcasing superior accuracy compared to existing methods
  • Tesis doctorado / doctoral thesis
    Design of an acoustic virtual environment of the mexican archaeological site Edzna
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Navas Reascos, Gustavo Sebastián; Ibarra Zárate, David Isaac; emipsanchez; Recuero López, Manuel; Zalaquett Rock, Francisca Amelia; Lopez Caudana, Edgar Omar; School of Engineering and Sciences; Campus Monterrey; Alonso Valerdi, Luz María
    Archaeoacoustics is an acoustic field that has great potential in Mexico since the existence of archaeological places inherited from the native people who inhabited these territories in the past. The objective of this project was the design and implementation of a virtual acoustic environment of the archaeological place Edzna. To achieve this goal, the research was conducted as follows: (1) to select a strategically archaeological Mexican place in terms of minimal archaeological deterioration, minimal environmental noise, flexible access, and with both open and enclosed places; (2) to characterize acoustically the selected place; (3) to recreate the recorded sounds; (4) to design and implement an acoustic virtual environment based on the acoustic characterization of the selected place; and (5) to evaluate the User Experience of the acoustic virtual environment from participants in an exposition at MARCO museum in Monterrey. This investigation aimed to contribute to the dissemination and exposure of vivid archaeological sites along in the country, which could help to foster the awareness of Mexican history and heritage
  • Tesis doctorado / doctoral thesis
    Practical inventory models with the warm-up process
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-10) Nobil, Erfan; Cárdenas Barrón, Leopoldo Eduardo; emipsanchez; Loera Hernández, Imelda de Jesús; Treviño Garza,Gerardo; Smith Cornejo, Neale Ricardo; Bourguet Díaz, Rafael Ernesto; School of Engineering and Sciences; Campus Monterrey
    As the global population continues to grow, there is an increasing need to enhance the efficiency of production processes. On one hand, manufacturing processes face numerous challenges; on the other hand, various machines in the production line require an initial warm-up phase, which intersects with the fields of operations research and optimization. This dissertation explores the introduction of several concepts along with the warm-up process into the manufacturing workflow. It also addresses a range of issues associated with the warm-up in manufacturing, proposing solutions to these challenges. It tackles common problems in the production line, such as shortages, the environmental impact of carbon emissions, and the production of faulty items. The work at hand employs a diverse set of approaches, from mathematical solutions like the application of the Hessian matrix to the implementation of Karush-Kuhn-Tucker conditions. A variety of methodologies have been applied, ranging from analytical approaches to metaheuristics and innovative deep reinforcement learning techniques. The outcomes of this thesis have resulted in three published papers, with two additional works finished. The publications explore the effect of warm-up process in sustainable EPQ model, the effect of machine downtime on warm-up process, presence of shortage and faulty products with warm-up, machine downtime effect along with shortage on warm-up, and finally multi-product lot scheduling problem with warm-up process. The findings can be regarded as determination of optimal total cost for the system which provides higher revenue for corporations. In case of three published papers, this is done due to analytical approach and mathematical framework, in other words, a closed-form solution represents the whole structure. The solution methodology highlights key concepts, such as shortages and environmental regulations, by comparing results that show how the additional cost of carbon policies and the system’s ability to handle shortages contribute to lower overall costs. In cases involving rework and scrap, rework is shown to incur less cost. Finally, the multi-agent reinforcement learning effectively tackled the stochastic nature of metaheuristic algorithms in fine-tuning the control parameters. Altogether, each paper presents a specific direction within this thesis, and collectively, these provide practical insights for decision-makers in the industry.
  • Tesis doctorado / doctoral thesis
    Development of chitosan films using lemon Juice and impact of bimetallic and trimetallic nanoparticles on their physical properties
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Hassan, Dilawar; Torres Huerta, Ana Laura; emipsanchez; Ehsan, Muhammad; Sánchez Rodríguez, Elvia Patricia; Talha Khalil, Ali; School of Engineering and Sciences; Campus Ciudad de México; Antonio Pérez Aurora
    The global challenge of plastic pollution has driven the search for biodegradable and sustainable materials. This thesis explores the development of chitosan (CH) films, synthesized using a green chemical approach that employs lemon juice and lemon peel extract as natural alternatives to synthetic acids. The incorporation of nanoparticles, explicitly zinc ferrite (ZnFe₂O₄ NPs) and nickel zinc ferrite (NiZnFe₂O₄NPs), further manipulate the functional properties of the films, making them suitable for diverse applications. The ZnFe₂O₄ NPs, synthesized using lemon peel extract, presented a crystalline size of 16 nm and significantly improved the mechanical (TS) and barrier properties of 1.5% CH films. The TS of the films increased from 0.641 MPa for bare CH to 0.835 MPa with 2% ZnFe₂O₄ NPs, while puncture strength improved by 2.7 times. The water vapor permeability (WVP) decreased by 28%, establishing enhanced barrier properties. Conversely, NiZnFe₂O₄ NPs (crystalline size 29 nm), enhanced 2% CH film flexibility, achieving a 36.83% elongation at break with 2% NP reinforcement. These films also exhibited enhanced resistance to moisture, making them suitable for applications that require better barrier properties. Morphological testing, including SEM and AFM, revealed that NPs incorporation altered the surface texture of the films, increasing roughness uniformly with NP concentration. FTIR spectra confirmed successful NPs’ integration, with characteristic metal-oxygen bond vibrations appearing at specific wavenumbers. Optical properties showed minimal color changes after NPs addition, with both ZnFe₂O₄ and NiZnFe₂O₄ films maintaining suitable transparency for practical applications. This thesis highlights the potential of green-synthesized CH films as eco-friendly substitutes for conventional plastics. ZnFe₂O₄ films demonstrated superior mechanical strength and barrier properties, while NiZnFe₂O₄ films provided improved flexibility and moisture resistance. The integration of green chemistry with nanotechnology establishes a sustainable pathway for the development of highperformance polymeric materials, addressing pressing environmental and industrial needs.
  • Tesis doctorado / doctoral thesis
    A novel feature extraction methodology using Inter-Trial Coherence framework for signal analysis – A case study applied towards BCI
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) López Bernal, Diego; Ponce Cruz, Pedro; emipsanchez; Ponce Espinosa, Hiram; López Caudana, Edgar Omar; Bustamante Bello, Martín Rogelio; School of Engineering and Sciences; Campus Ciudad de México; Balderas Silva, David Christopher
    Signal classification in environments with low signal-to-noise ratio (SNR) presents a significant challenge across various fields, from industrial monitoring to biomedical appli cations. This work explores a novel methodology aimed at improving classification accuracy in such conditions, using EEG-based Brain-Computer Interfaces (BCIs) for inner speech decoding as a case study. EEG-based Brain-Computer Interfaces (BCIs) have emerged as a promising technology for providing communication channels for individuals with speech disabilities, such as those affected by amyotrophic lateral sclerosis (ALS), stroke, or other neurodegenerative diseases. Inner speech classification, a subset of BCI applications, aims to interpret and translate silent, inner speech into meaningful linguistic information. De spite the potential of BCIs, current methodologies for inner speech classification lack the accuracy needed for practical applications. This work investigates the use of inter-trial coherence (ITC) as a novel feature extraction technique to enhance the accuracy of in ner speech classification in EEG-based BCIs. The study introduces a methodology that integrates ITC within a complex Morlet time-frequency representation framework. EEG recordings from ten participants imagining four distinct words (up, down, right, and left) were processed and analyzed. Five different classification algorithms were evaluated: Ran dom Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). The proposed method achieved no table classification accuracies of 75.70% with RF and 66.25% with SVM, demonstrating significant improvements over traditional feature extraction methods. These findings indi cate that ITC is a viable technique for enhancing the accuracy of inner speech classification in EEG-based BCIs. The results suggest practical implications for improving communica tion and navigation capabilities for individuals with ALS or similar conditions. This work lays the foundation for future research on phase-based feature extraction, opening new avenues for understanding the neural mechanisms underlying inner speech and advancing BCI systems’ accuracy and efficiency
  • Tesis doctorado / doctoral thesis
    A stable real-time implementation model predictive control for fast nonlinear systems
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Rodríguez Guevara, Daniel Orlando; Favela Contreras, Antonio Ramón Xicoténcatl; emipsanchez; Lozoya Gámez, Rafael Camilo; Sotelo Molina, Carlos Gustavo; Sotelo Molina, David Alejandro; School of Engineering and Sciences; Campus Monterrey; Beltrán Carbajal, Francisco
    This dissertation presents two novel approaches for real-time implementation of robust Model Predictive Control (MPC) for fast complex nonlinear systems. These approaches use a linearization step of the model of the system by two different strategies depending on the nature of the nonlinear system. Linear Parameter Varying (LPV) modeling and Differential Flatness representation are the strategies chosen to develop the Model Predictive Controller. LPV modeling consists of the embedding of the nonlinear terms of the system into a series of scheduling parameters. Therefore, the Model Predictive Control is designed using a linear model being a function of the scheduling parameter to predict the behavior of the states of the system along the prediction horizon. The future values of the scheduling parameters are estimated using a recursive least squares algorithm. Both stability and robustness conditions are ensured using Linear Matrix Inequalities (LMI) constraints included in the optimization problem of the MPC. Finally, terminal ellipsoidal sets are introduced in the cost function to improve the performance of the controller. On the other hand, Differential Flatness representation is used to build a linear MPC to exploit the flatness property of some nonlinear systems. In this approach, the nonlinear model is solved as a function of the flat outputs of the system and its derivatives. Thus, a linear optimization problem is solved to predict the future behavior of the flat output and its derivatives as a function of an auxiliary control variable. Afterward, a feedforward controller is designed to define the optimal control action to be inputted into the system as a function of the auxiliary control variable. Finally, the performance of both control strategies is tested with several simulations of complex nonlinear systems using the Matlab-Simulink environment
  • Tesis doctorado / doctoral thesis
    Analysis of optimization models under different approaches to deal with uncertainty regarding pre-disaster planning in food bank supply chains
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-11) Rivera Morales, Adrian Fernando; Smith Cornejo, Neale Ricardo; emipsanchez; Cárdenas Barrón, Leopoldo Eduardo; Bourguet Díaz, Rafael Ernesto; Güemes Castorena, David; School of Engineering and Sciences; Rectoría Tec de Monterrey; Ruiz, Angel
    A critical decision in humanitarian logistics pre-disaster planning is the sufficient pre-establishment of relief supplies to provide efficient and quick operations in the aftermath of the event. This thesis identifies some of the challenges faced by food banks from an operations management perspective. To support managers making such decisions, we propose four mathematical formulations that seek to optimize food prepositioning (before the event) and further distribution (after the event) in order to minimize unmet demand (MUD). These formulations will first be analyzed under the assumption of uncertainty in demand, finally comparing results considering uncertainty in supply. The two first formulations adopt the cardinality-constrained (CC) approach to handle uncertainty. These formulations differ in their objective functions, the first formulation’s objective seeks to MUD, whilst the second incorporates equity in the way that demand is satisfied. The two remaining formulations are scenario-based (SB), and as in the previous two formulations, seek to MUD with and without equity considerations, respectively. The formulations are applied to a case study where a food bank faces the arrival of a hurricane in Mexico. For the formulations with uncertainty in demand, we compare the differences between the solutions produced by the proposed formulations and the solutions that would have been produced without uncertainty (perfect information) to have a better understanding of their performance and their behavior. A discussion of the strengths and weaknesses of each model is provided to help managers choose the model that best suits their needs. For the formulations with uncertainty in supply, a series of experiments was done to compare results and further conclusions. Our results demonstrate that the CC approach has an acceptable behavior for every situation, while the SB approach can have exceptional outcomes when the predicted scenarios are good, but worst solutions otherwise, making this approach dependent of the capability of predicting good scenarios.
  • Tesis doctorado / doctoral thesis
    Designing sustainable agri-food supply chain networks
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Gholiyanjouybari, Fatemeh; Hajiaghaeikeshteli, Mostafa; emipsanchez; Mejia Argueta, Christopher; Bonakdari, Hossein; School of Engineering and Sciences; Campus Monterrey; Smith Cornejo, Neale Ricardo; Rodríguez Calvo, Ericka Zulema
    Agri-food products are critical to sustaining human life, as they provide essential nutrients for maintaining bodily functions. Agri-food production must match potential demand to ensure efficient supply to industries and markets. In terms of national and international concerns, this is one of the most important, even a top priority. In recent years, the agri-food industry has prioritized the development of efficient supply chain systems based on current trends and principles, such as sustainability and circular economy. The principles of sustainability can be employed to effectively utilize or reintroduce agri-food waste back into the network. This PhD dissertation deals with designing new agri-food supply chain networks for the first time in the literature. It not only considers the most important products to study, but also focuses on the recent trends and challenging issues like circular economy, water consumption, CO2 emission, and sustainability. We considered Saffron, Coconut, Soybean, and Pistachio, respectively, in four chapters of this thesis. In this work, we formulate some novel mixed-integer linear programming models to design agri-food supply chain networks in different agriculture industries, considering the above new challenges. The multi-objective networks struggle to manage the total net profit while monitoring CO2 emissions and the satisfaction of customers within the network. Given the NP-hard nature of the networks, the solution approach embraces a set of conventional, new, modified, and hybrid metaheuristics to surmount its complexity effectively. The effectiveness of the proposed mathematical models is certified by case studies and general problems evolved from real-world practices. In Saffron's work, we consider marketing practices and develop a stochastic multiobjective programming model to improve sustainability in three main areas. A convex robust optimization approach addresses farm production capacity uncertainty and saffron demand uncertainty. The LP-metric method is used to validate the mathematical model for the saffron business. We adopt a modified Keshtel algorithm to deal with the problem of NP-hardness. Two strategies are used to evaluate the performance of the proposed solution methods: a statistical comparison and a supportive tool that is based on multicriteria decision-making (MCDM). According to the MCDM method, MOKASEO outperformed other algorithms in small, medium, and large-sized problems compared to the other algorithms tested. The secodn supply chain network that we consider to design its colsed-loop network is for the coconut industry. We propose a new mixed-integer linear programming model to design an agri-food supply chain network under sustainable terms. With the goal of resolving a multi-objective closed-loop supply chain, both forward and reverse movements of products are taken into account. During the planning process, the model monitors environmental pollution within the network as well as job opportunities. Given the NP-hardness of the model, we use six multi-objective optimizers and three hybrid algorithms, among which the multi-objective artificial rabbit optimizer is first developed and applied in this study. Therefore, fifteen practical tests are conducted to determine whether the model is compatible with real conditions. The Friedman statistical test and interval plots demonstrate that optimizers are capable of solving problems of all sizes. In both statistical tests and the hybrid Multi-Criteria Decision Making (MCDM) framework, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) outperformed solving practical tests. In the third work, we study circular economy in a closed-loop supply chain. To do this, we consider one of the most famous and valuable agricultural products, soybean. We formulate a novel mixed-integer linear programming model to design a closed-loop agri-food supply chain network under sustainability and circular economy terms. The multi-objective network strives to reduce CO2 emissions while monitoring customer satisfaction and overall net profit. Since the network is NP-hard, a combination of conventional and hybrid metaheuristics is used to overcome its complexity. Four multi-objective optimization algorithms and three hybrid algorithms are utilized to investigate the model's suitability for real-world conditions. A combination of interval plots and hybrid multi-criteria decision-making techniques demonstrates that optimizers can handle any size problem. For large and mediumsized problems, however, MOHHSA is more effective than MOGWO. Finally, in the fourth paper, we develop a new mixed linear mathematical model for the pistachio supply chain network to minimize the total fixed and variable costs of the closed-loop supply chain. This model is addressed with efficient and well-known meta-heuristic algorithms. A hybrid meta-heuristic algorithm is also developed to enhance the intensification and diversification phases. Finally, we compare and evaluate the quality of both meta-heuristic algorithms and hybrid algorithms.
En caso de no señalar algo distinto de manera particular, los materiales son compartidos bajo los siguientes términos: Atribución-No comercial-No derivadas CC BY-NC-ND http://creativecommons.org/licenses/by-nc-nd/4.0
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