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 Development of Conducting Polymer and Carbon Nanostructure based Efficient Thermoelectric Materials(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Ebrahimibagha, Dariush; Mallar, Ray; emimmayorquin; Aguirre Soto, Héctor Alán; Niladri, Banerjee; Gallo Villanueva, Roberto Carlos; School of Engineering and Sciences; Campus Monterrey; Datta, ShubhabrataThermoelectric materials present a promising renewable energy technology for directly converting thermal energy into electricity and vice versa. However, their practical application is hindered by low conversion efficiencies, quantified by the dimensionless figure of merit, 𝑍𝑇 = 𝑆 2 𝜎 𝑘 𝑇 , where 𝑆,𝜎, and 𝑘 are the Seebeck coefficient, electrical onductivity, and thermal conductivity, respectively. Achieving a high 𝑍𝑇 is challenging because enhancing one parameter often degrades the others. Various nanoscale strategies have been explored, yet a comprehensive framework for improving 𝑍𝑇 remains elusive. Recently, polymer-based nanocomposites, particularly carbon nanotubes (CNTs) dispersed in polyaniline (PANI), have gained attention due to their flexibility, non-toxicity, and processability, key traits for next-generation flexible electronic devices. Despite this potential, optimizing thermoelectric performance in PANI-CNT systems is complex, as it depends on numerous factors, including CNT dimensions, functionality, and PANI's doping and morphology. This research employs machine learning (ML) and genetic algorithms (GA) to model and optimize the thermoelectric properties of PANI-CNT nanocomposites. By analyzing structural and compositional variables—such as CNT length, diameter, type, and PANI morphology—we identified strategies that enhance electrical conductivity and the Seebeck coefficient while minimizing thermal conductivity. Our ML models revealed that selecting appropriate dopants for PANI and using single-walled CNT (SWCNT) improves overall thermoelectric performance. Multi-objective GA optimization further refined these findings, demonstrating that SWCNTs help reduce thermal conductivity and that CNT length plays a dual role: shorter CNTs decrease 𝑘, while longer ones enhance both 𝑆 and 𝜎. Experimental validation was performed by fabricating PANI-CNT nanocomposite pellets, but achieving high 𝑍𝑇 remained elusive due to limitations in dataset quality and the variability introduced by diverse synthesis techniques. The synthesis method influences PANI dimensionality (e.g., 0D, 1D, 2D) and the morphology of PANI-CNT composites (core-shell vs. dispersed), complicating performance consistency. While the experiments confirmed the general trend of model predictions, they highlighted the necessity of cleaner, more comprehensive datasets for future research. Ultimately, this study lays the groundwork for designing high-efficiency thermoelectric nanocomposites and outlines the next steps in developing more accurate predictive models and synthesis methods for improved thermoelectric performance.
- Advanced modeling techniques in electric vehicles for battery sizing and Vertical Dynamic Control with CARSIM® and ADAMS(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Drivet González, Aline Raquel Lily; Cespi, Riccardo; emipsanchez; Vargas Martínez, Adriana; Lozoya Santos, Jorge de Jesús; School of Engineering and Sciences; Campus Monterrey; Tudón Martínez, Juan CarlosThis thesis addresses the rapidly accelerating shift from internal combustion engine vehicles to electric vehicles (EVs), a transition driven not only by market demands but also by the urgent need to mitigate climate change. As electrification reshapes the automotive landscape, the importance of advanced modeling techniques are essential to accelerate the adoption of EV technologies, ensuring competitiveness, and addressing environmental urgency. This research begins with a review of vehicle dynamics changes, highlighting the challenges and opportunities introduced by this swift transition to EV technology. The first contribution of this thesis is the application of modeling and simulation techniques using CARSIM®where real-world telemetry is used to optimize EV battery performance and battery sizing. This optimization focuses on maximizing efficiency while maintaining safety and reliability. The second contribution is the development of a model for EV suspension systems using ADAMS®which can be a platform to test critical dynamic behavior of EVs under various conditions. Together, these contributions advance the design and performance of electric vehicles, introducing advanced modeling tools to accelerate development processes, speeding design processes, and addressing the urgent challenges of vehicle electrification in the context of climate change. As a result of the research presented in this thesis, which includes methodologies for battery pack design and the modeling and control of active suspension systems for electric vehicles, two journal articles have been published, and four additional articles have been presented in conference proceedings, contributing significantly to the academic discourse in these areas.
- A generalist reinforcement learning agent for compressing multiple convolutional neural networks(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) González Sahagún, Gabriel; Conant ablos, Santiago Enrique; emipsanchez; Ortíz Bayliss, José Carlos; Cruz Duarte, Jorge Mario; Gutiérrez Rodríguez, Andrés Eduardo; School of Engineering and Sciences; Campus MonterreyDeep Learning has achieved state-of-the-art accuracy in multiple fields. A common practice in computer vision is to reuse a pre-trained model for a completely different dataset of the same type of task, a process known as transfer learning, which reduces training time by reusing the filters of the convolutional layers. However, while transfer learning can reduce training time, the model might overestimate the number of parameters needed for the new dataset. As models now achieve near-human performance or better, there is a growing need to reduce their size to facilitate deployment on devices with limited computational resources. Various compression techniques have been proposed to address this issue, but their effectiveness varies depending on hyperparameters. To navigate these options, researchers have worked on automating model compression. Some have proposed using reinforcement learning to teach a deep learning model how to compress another deep learning model. This study compares multiple approaches for automating the compression of convolutional neural networks and proposes a method for training a reinforcement learning agent that works across multiple datasets without the need for transfer learning. The agents were tested using leaveone- out cross-validation, learning to compress a set of LeNet-5 models and testing on another LeNet-5 model with different parameters. The metrics used to evaluate these solutions were accuracy loss and the number of parameters of the compressed model. The agents suggested compression schemes that were on or near the Pareto front for these metrics. Furthermore, the models were compressed by more than 80% with minimal accuracy loss in most cases. The significance of these results is that by escalating this methodology for larger models and datasets, an AI assistant for model compression similar to ChatGPT can be developed, potentially revolutionizing model compression practices and enabling advanced deployments in resource-constrained environments.
- Evaluation of the impact on energy and thermal comfort of PCM-enhanced on roof buildings in semi-arid climates(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-08) Godoy Rangel, Caribay; Gijón Rivera, Miguel Ángel; emipsanchez; López Salinas, José Luis; Chávez Chena, Yvonne; Bates Prieto, Rafael; School of Engineering and Sciences; Campus Monterrey; Rivera Solorio, Carlos IvánIn recent decades, buildings have emerged as a significant contributor to urban energy consumption. In order to mitigate the environmental impact of this phenomenon, the utilization of thermal storage through phase change materials (PCM) in the building envelope has become an innovative strategy. Nevertheless, despite the assumption that the incorporation of PCM will improve the thermal efficiency of buildings, this is not a guaranteed outcome in all instances. In climates that exhibit pronounced seasonal extremes, the capacity of PCM for thermal storage is constrained. This experimental and numerical study, focused on semi-arid climates, aimed to enhance the performance of PCM in combination with natural ventilation thus the thermal performance of building roofs. In addition, the study extends the evaluation by combining PCM with insulating materials and reflective paint. It is anticipated that the integration of PCM with another passive strategy will enhance its thermal performance, particularly in extreme climates where the full potential of this material is often constrained. The results of the study focus on the analysis of the thermal behavior of the PCM, thermal comfort, and annual energy demand, with the environmental and cost implications that this represents. The experimental study, conducted in real climatic conditions, focused on the combination of PCM with natural ventilation. The numerical study generated 108 new scenarios, which were evaluated during a typical meteorological year. The results demonstrated that the incorporation of natural ventilation optimizes the thermal behavior of the PCM, enabling an increase in the time spent in a solid state by up to 41%. This enhances the efficiency of the complete cycle, ensuring its completion within a single day. Moreover, the utilization of PCM resulted in a reduction of the maximum peak indoor air temperature during the summer months by between 1.9% and 7.2%, while an increase in the minimum valley during the winter months was observed, ranging from 4.4% to 10.4%. The duration of time spent within the comfort zone was increased, up to a maximum of one hour during the summer months. Similarly, the introduction of natural ventilation through ducts into an air chamber with PCM has been demonstrated to reduce annual energy gains by up to 92.9%. Ultimately, the combination of PCM with reflective paint and XPS insulation has the potential to result in annual savings of up to 12.95 tCO₂e, with an estimated investment return period of 2.8 years.
- Botnet detection on twitter: a novel similarity-based clustering mechanism(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Samper Escalante, Luis Daniel; Monroy Borja, Raúl; emipsanchez; Castro Espinoza, Félix Agustín; González Mendoza, Miguel; School of Engineering and Sciences; Rectoría Tec de Monterrey; Loyola González, OctavioBotnet detection on Twitter represents a critical yet under-explored research problem,as botnets programmed with malicious intent threaten the platform’s security and credibility. Although Twitter has implemented mitigation strategies, such as imposing restrictions andbans, these measures remain insufficient due to botnets’ rapid creation and expansion. Existing solutions proposed by researchers for manual and automated botnet detection typically rely on individual metrics commonly used for detecting bots. However, these approaches lack the necessary group-oriented analysis and metrics critical for effectively identifying botnets of varying sizes and objectives. To address this issue, we have developed an innovative botnet detection mechanism based on similarity, which significantly enhances the detection rate of botnets on Twitter. Each bot, regardless of its complexity, leaves detectable traces of automation in its creation, behavior, or interactions with other accounts. By characterizing these traces, we can establish relationships between bots, enabling effective botnet detection. Our mechanism constructs a regression model to quantify the similarity between bots, leveraging features from user data, tweet patterns, and social interactions on the platform. Then, it uses this similarity measure to build a distance matrix, enabling the formation of groups with shared attributes, connections, and objectives through clustering methods. Our botnet detection mechanism achieved extraordinary success, evidenced by high scores on external Clustering Validation Indices (CVIs) and the Area under the ROC Curve (AUC) compared to existing solutions from the literature. Furthermore, the mechanism proved effective when confronted with unknown botnets with varied objectives. Our experimental findings suggest that this work is well-positioned to strengthen future botnet detection mechanisms, having shown the value of incorporating social interaction features. This integration offers a strategic advantage in the ongoing arms race against botmasters and their malicious objectives. Additionally, our mechanism consistently outperforms other approaches across various metrics, configurations, and algorithms, underscoring its effectiveness and adaptability in different detection scenarios.
- Environmental monitoring to estimate indoor occupancy levels based on Semi-supervised machine learning and data fusion for building management(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Mena Martínez, Alma Rosa; Ceballos Cancino, Hector Gibran; emipsanchez; Alvarado Uribe, Joanna; Cantu, Francisco J.; García, Juan Pablo; School of Engineering and Sciences; Campus Monterrey; Schmitt, JanOccupancy information is essential for space management, energy efficiency, and in times of the COVID-19 pandemic, for crowd control. Obtaining labeled data is challenging due to hardware limitations, privacy considerations, and the required underlying costs. Furthermore, venues over 200 m2 require data fusion techniques. Therefore, this thesis mainly focuses on exploring the potential of Semi-Supervised Learning (SSL), which only needs a few labeled data and a large amount of unlabeled data, to estimate the occupancy levels in enclosed spaces. This study presents an empirical comparison between Supervised ML and SSL models as well as data fusion techniques in real-life university classrooms and offices (uncontrolled conditions) at the University of the West of England, Bristol, UK, and Tecnologico de Monterrey, Mexico. The data was collected for three weeks at each scenario using an in-house developed Internet of Things (IoT) device that measures air temperature, relative humidity, and atmospheric pressure. The ground truth records were gathered through manual logging of occupancy levels. Datasets’ sizes averaged 2350 entries with only 280 labeled instances per dataset. Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) were used to define a performance baseline for supervised ML. Self-Training (ST) and Label Propagation (LP) were tested for SSL. In addition, several feature fusion methods were explored, including Chi-squared, ANOVA F-test, Spearman and Kendall’s Tau correlation, Mutual Information, Averages, Recursive Feature Elimination, and Principal Component Analysis. The models were evaluated using Accuracy, Precision, Recall, F1-score, Confusion Matrix, and High - Quality Supervised Baseline. ST achieved superior performance compared to baseline models (SVM, RF, MLP) with a highest average accuracy of 90.96% compared to SVM (86.66%). Furthermore, the data fusion results indicated that the Chi-squared approach for feature fusion outperformed others with an F1-score average of 95% and an accuracy average of 99%. These results demonstrate the effectiveness of SSL for indirect occupancy estimation while reducing the need for extensive data collection and labeling.
- 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 ÁngelToday, 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
- 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 MaríaArchaeoacoustics 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
- 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 MonterreyAs 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.
- 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 AuroraThe 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.
