Facultad de Ingeniería - CampCM

City
Ciudad de México
Country
MX
Director
Hiram Eredi Ponce Espinosa
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    Preface
    (Springer Science and Business Media Deutschland GmbH, 2026)
    Martinez-Villaseñor, Lourdes  
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    RESEARCH ECOSYSTEM 2025
    (2025)
    Llergo Bay, María Guadalupe Fernanda  
    ;
    Sergio Velázquez Rodríguez
    ;
    Antonio de los Reyes Moreno
    ;
    Mendoza, Abraham  
    ;
    García Álvarez, Santiago  
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    Evaluating user perception and satisfaction in a digital platform designed for executive function assessment: a user-centered evaluation of Bloom 2.0 in higher education
    (Frontiers Media SA, 2026-06-12)
    Atristain-Suárez, Connie  
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    Martínez-Orozco, Elizabeth
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    Escobar Castillejos, David  
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    Paipa-Galeano, Luis A.
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    García-Becerra, Andrea Milena
    Introduction Digital platforms are increasingly used in higher education to deliver content, assess learning, and measure complex cognitive processes such as executive functions. Although the DeLone and McLean information systems success framework has been widely applied to e-learning platforms, empirical evidence on user-centered evaluation of digital cognitive assessment tools in higher education remains limited. Methods The study evaluated Bloom 2.0, a web-based platform that administers six executive function tasks: Tower of Hanoi (3-disc and 4-disc), Raven’s Progressive Matrices, Stroop, Token Test, and the Wisconsin Card Sorting Test. Sixty-four undergraduate students from Universidad Panamericana (Mexico City) completed the full six-task battery in March 2026. Of these, 55 also responded to a 15-item Spanish adaptation of Doll and Torkzadeh’s End-User Computing Satisfaction (EUCS) survey and two open-ended prompts. Data were analyzed using descriptive statistics, Cronbach’s α , and inductive thematic coding of open-ended responses. Results Overall satisfaction was high ( M = 4.28 , S D = 0.91 on a 1–5 scale), with 81.0% of responses rated 4 or 5. Internal consistency was high (Cronbach’s α = 0.936 for the full scale; α = 0.749 – 0.874 across dimensions). Accuracy was the highest-rated EUCS dimension ( M = 4.39 ), whereas Content received the lowest mean rating ( M = 4.20 ). Open-ended responses yielded six themes, with instruction clarity the most frequent (58%). The remaining themes were overall positive comments, interface and visual design, cognitive fatigue during the Raven task, the closing and feedback experience, and anxiety related to visible timers or error counters. Discussion Bloom 2.0 was usable in this undergraduate sample and produced internally consistent satisfaction responses. The findings address users’ perceptions of system quality and satisfaction and do not constitute psychometric validation of the underlying cognitive tasks. No criterion or convergent validity was evaluated against a clinician-administered reference battery in this study. The main issues were related to task instructions, interpretation of feedback, and usability elements associated with task guidance. The next version should revise task instructions, improve the closing feedback screen, and recalibrate task cutoffs before the platform is compared with a clinician-administered reference battery.
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      2
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    Edge-Enhanced Knowledge Distillation System for Diabetic Retinopathy Lesions Computer-Aided Diagnosis
    (Springer Nature Switzerland, 2025)
    Lopez-Figueroa, Alberto
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    Jacome-Herrera, Sebastian
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    Moya-Albor, Ernesto  
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    Renza, Diego
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    Brieva, Jorge  
    This work addresses the challenge of deploying computationally intensive Deep Learning (DL) models for Diabetic Retinopathy (DR) lesion detection in clinical settings, particularly on resource-constrained edge devices. DR is a significant global health issue and a leading cause of preventable blindness, making early and accessible detection crucial. We propose a proof-of-concept system utilizing Knowledge Distillation (KD) to create a tiny, efficient DL model for DR lesion detection, specifically designed for embedding into retinal scanners via the NVIDIA Jetson Nano platform. Our novel approach employs a KD framework where a pre-trained Inception-v3 model acts as the ‘teacher,’ fine-tuned on fundus image data. This teacher model distills its knowledge into a compact ‘student’ model based on the MobileNet-v2 architecture, which is trained on a small, synthetically generated dataset optimized through an iterative distillation process using a custom loss function combining Kullback-Leibler divergence and Categorical Cross-Entropy. This method significantly reduces model size and computational requirements while maintaining high diagnostic accuracy, comparable to larger, state-of-the-art models. By enabling real-time, on-device analysis, this embedded AI solution enhances data privacy, ensures consistent performance, and improves the accessibility of advanced DR screening, particularly in remote or underserved healthcare environments. This makes AI-assisted DR detection feasible for widespread clinical adoption directly within scanning devices. ©The authors ©Springer.
      24  5
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    Non-Contact Respiratory Rate Estimation in Newborns During Quiet Sleep Using Video Magnification Techniques and a 3D Convolutional Neural Network
    (IEEE, 2024)
    Escobedo Gordillo, Andrés Emiliano
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    Rivas-Scott, Orlando Yael
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    Brieva, Jorge  
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    Moya-Albor, Ernesto  
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    Cabon, Sandie
    In this paper, we present a new non-contact strategy to estimate the respiratory rate (RR) in a neonatal intensive care unit (NICU) based on the Eulerian motion video magnification technique and a 3D Convolutional Neural Network (3D CNN). The magnification procedure was carried out using the Hermite decomposition. The RR is estimated using a 3D CNN and a region of interest (ROI) detected manually. We have tested the method on 8 infants in NICU during quiet sleep. A contact respiratory signal is acquired synchronously to the videos to compute the RR as reference for training the CNN. To compare the performance of the method, we compute the Mean Absolute Error, the Root Mean Squared Error and metrics from the Bland and Altman analysis to investigate the agreement of the method with respect to the respiratory signal reference. The proposed solution shows an agreement with respect to the reference of 95% and root mean squared error of 2.88. ©The authors ©IEEE.
      21
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    Multitarget Design of Steroidal Inhibitors Against Hormone-Dependent Breast Cancer: An Integrated In Silico Approach
    (MDPI AG, 2025)
    Rodríguez-Macías, Juan
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    Saurith-Coronell, Oscar
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    Vargas-Echeverria, Carlos
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    Insuasty Delgado, Daniel
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    Márquez Brazón, Edgar A.
    Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R2 = 0.86; Q2_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC50 > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. ©The authors ©MDPI.
      15
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    Data-Driven Innovation for Intelligent Technology : Perspectives and Applications in ICT
    (Springer Cham, 2024)
    Ponce, Hiram  
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    Brieva, Jorge  
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    Lozada Flores, Octavio  
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    Martinez-Villaseñor, Lourdes  
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    Moya-Albor, Ernesto  
    This book focuses on new perspectives and applications of data-driven innovation technologies, applied artificial intelligence, applied machine learning and deep learning, data science, and topics related to transforming data into value. It includes theory and use cases to help readers understand the basics of data-driven innovation and to highlight the applicability of the technologies. It emphasizes how the data lifecycle is applied in current technologies in different business domains and industries, such as advanced materials, healthcare and medicine, resource optimization, control and automation, among others. This book is useful for anyone interested in data-driven innovation for smart technologies, as well as those curious in implementing cutting-edge technologies to solve impactful artificial intelligence, data science, and related information technology and communication problems. ©Springer. ©The authors. ©The editors
      28
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    A visuo-haptic simulator for understanding magnetic forces for engineering majors
    (ICERI, 2024)
    Neri-Vitela, Luis
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    Robledo-Rella, Víctor
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    Noguez, Julieta
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    Escobar Castillejos, David  
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    Gonzalez-Nucamendi, Andres
    According to the embodied cognition theory, adding extra sensory channels for user interaction with an online learning scenario can improve understanding and long-term retention of physical phenomena. In this regard, the implementation of visuo-haptic simulators (VHSs), which integrate touch into a visual simulator, may enhance users' learning experiences by allowing them to manipulate objects and feel forces realistically. Haptic technology and VHSs have been used for training in various fields like surgery, navigation, and industrial applications. In education, VHSs have also been applied to explain physics concepts from elementary to undergraduate levels. Our research group has consistently applied a methodology to develop VHSs for undergraduate engineering students over several years, called VIS-HAPT. Using this methodology, several VHSs have been developed to explain physics concepts, such as electric forces between different charge distributions, friction forces exerted by a surface on a block, and the buoyant force on an object immersed in a liquid. In this project, we introduce a new VHS designed to clarify the nature of magnetic forces, a challenging concept for our students. This VHS features a fixed long straight conducting wire and a rectangular conducting loop in the same plane, with one side of the loop parallel to the wire. Users can adjust parameters like the magnitude and direction of currents in the wire and loop, the distance between them, and the loop's dimensions, and perceive the corresponding effects on the magnetic force exerted by the wire on the loop. Through the haptic feedback provided by the VHS students can feel the strength of this force, perceive its direction, and simultaneously visualize its value on the screen, enhancing their learning experience. The VHS was tested with 117 junior undergraduate engineering students at Tecnologico de Monterrey, Mexico City Campus, during the February-June 2024 term. To study the impact of the VHS on students’ learning, the student sample was randomly divided into an experimental group (N = 81), who used the VHS, and a control group (N = 36) who did not. Guided by detailed instructions, experimental students conducted several practices to explore the magnetic force by adjusting the physical parameters of the VHS. In a parallel way, control students received more traditional lecture-based instruction. A feedback perception questionnaire about the VHS experience was administered to the experimental students. The results show that most experimental students expressed a very positive opinion on their interaction with the VHS and found it user-friendly, motivating, realistic, and helpful in understanding magnetic forces. Additionally, by administering identical pre-tests and post-tests instruments to both experimental and control groups, learning gains were calculated for both student groups. The preliminary results are promising, in the sense that the average learning gains for the experimental students was larger than the one for the control students. The statistical significance of this result is discussed. Overall, the results of this work suggest that the use of the magnetic force VHS, complemented with appropriate learning strategies, can improve students' understanding and retention of electromagnetic concepts better than only applying traditional instruction methods. ©ICERI2024 Proceedings ©The authors.
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