Facultad de Ingeniería - CampCM

City

Ciudad de México

Country

Director

Hiram Eredi Ponce Espinosa

Parent Organisation

Campus Ciudad de México

OrgUnit's Researchers publications

Now showing 1 - 10 of 514

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Preface
(Springer Science and Business Media Deutschland GmbH, 2026)
Martinez-Villaseñor, Lourdes
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Mapping the Antibiofilm Peptide Space with Similarity Networks and Curated Negative Sets
(American Chemical Society (ACS), 2025)
Castillo-Mendieta, Kevin
;
Marrero Ponce, Yovani
;
Márquez, Edgar A.
;
García-Giménez, José Luis
;
Antunes, Agostinho
Biofilm-forming microorganisms pose a growing threat in clinical and industrial settings due to their tolerance to conventional antimicrobials. Antibiofilm peptides (ABFPs) offer a promising alternative, yet their discovery is challenging due to sequence diversity and complex mechanisms of action. We present the most comprehensive curated ABFP dataset to date and systematically compare it against two negative sets: quorum sensing peptides (QSPs) and random peptides (RPs). Classical statistical analyses combined with alignment-free similarity networks─Chemical Space Networks (CSNs) and Half-Space Proximal Networks (HSPNs)─identified compositional and physicochemical features that distinguish ABFPs from negative sets. Integration of quantitative biofilm inhibition (MBIC) and eradication (MBEC) data enhanced the resolution of meaningful patterns in the antibiofilm chemical space. Network analyses revealed conserved ABFP-enriched clusters with bioactivity data, distinctive motifs absent in negative sets, and central peptides with high topological importance that may serve as scaffolds for developing potent antimicrobials. QSPs and RPs, as biologically distinct comparators, served as robust filters refining ABFP signatures and reducing false positives. Projection onto the HSPN confirmed that ABFPs occupy a unique and well-defined region of sequence space. This integrative framework─combining curated datasets, compositional profiling, and network topology─provides a practical platform to accelerate ABFP discovery by identifying key features that can be incorporated into rule-based filters for prioritizing promising candidates. © 2025 The Authors. Published by American Chemical Society.
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Cybernetic Insights on GenAI and Learning in Higher Education
(IEEE, 2026)
Salinas-Navarro, David Ernesto
;
Vilalta-Perdomo, Eliseo
The rapid diffusion of Generative Artificial Intelligence (GenAI) is reshaping learning conditions, not by introducing new activities but by reorganising existing feedback structures among students, teachers, and learning environments. This study adopts a cybernetic perspective to explore how Gen AI influences the recursive processes of enquiry, collaboration, and reflection. Drawing on the concepts of feedback, adaptation, and regulation, this paper frames GenAI as an active participant in learning systems, redistributing agency, and reshaping the rhythm of interaction. A methodological guide is proposed for constructing case studies across disciplines, combining documentary artefacts and participant accounts to map the cybernetic dynamics in practice. A case study of a Quality Management module illustrates how GenAI reconfigures individual reflection, group collaboration, and assessment practices. This study contributes to cybernetic learning theory by showing how GenAI affects agency and feedback structures, and by providing a framework for designing ethical GenAI learning environments. This study acknowledges the limitations that future studies must address to examine student learning outcomes. The paper concludes by highlighting the research question: How does Gen AI reshape learning as a recursive cybernetic process? Scholars are invited to explore this in their own contexts in future studies. © The authors © IEEE.
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Research ecosystem 2025
(Universidad Panamericana, 2025)
Llergo Bay, María Guadalupe Fernanda
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Velázquez Rodríguez, Sergio
;
Reyes Moreno, Antonio de los
;
Mendoza, Abraham
;
García Álvarez, Santiago
Dear campus members, It is with great satisfaction that we present our Research Ecosystem 2025, a publication that reflects the sustained growth and strategic maturation of research at Universidad Panamericana. This edition builds on an increasingly comprehensive vision of university research, one in which technology transfer and innovation, institutional funding for research, and international rankings positioning affirm themselves as complementary pillars of our scientific activity. Our scientific production maintains its commitment to quality, with more than 50% of our indexed publications appearing in high-impact Q1 and Q2 journals. This year’s report also introduces, for the first time, a dedicated section on copyright and industrial models alongside patents, reflecting the growing diversity and maturity of our intellectual property portfolio, now spanning areas as varied as aeronautics, semiconductor technology, healthcare devices, and sustainable materials. © Universidad Panamericana.
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Data-Driven Innovation for Intelligent Technology : Perspectives and Applications in ICT
(Springer Cham, 2024)
Ponce, Hiram
;
Brieva, Jorge
;
Lozada Flores, Octavio
;
Martinez-Villaseñor, Lourdes
;
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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The effects of supply variability on the performance of assembly systems
(2022)
Romero-Silva, Rodrigo
;
Hurtado-Hernandez, Margarita
Assembly processes play a big role in the current business context as global supply chains depend on many subcomponents to produce a single finished product. Previous studies have shown contrasting results regarding the effect that supply variability (the variability of feeding stations) has on the performance of assembly systems, as opposed to the variability of the station matching and assembling the components. This paper aims to close this gap by studying the behaviour of simple assembly systems with differing degrees of variability allocation among the stations through an experimental simulation study. Results suggest that a reduction in feeding station variability results in higher throughput, even in systems where the variability of one of the feeding stations increases while the other decreases. Furthermore, in scenarios with high total variance, the highest throughput is reached by transferring both variance and work from one of the feeding stations to any other station, whereas in low variance systems symmetrical work transfer to the feeding stations results in the highest throughput, as previously shown. Finally, reducing feeding station variability decreased the time spent in the assembly station (waiting time for component matching plus time for the assembly operation) only in experiments with high total variance. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Scopus© Citations 3 1 11
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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
;
Moya-Albor, Ernesto
;
Renza, Diego
;
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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Branding interno en una empresa de servicios en México
(2020)
Terán-Bustamante, Antonia
;
Martínez Velasco, Antonieta Teodora
El objetivo de la presente investigación es analizar los procesos de branding interno de una empresa de servicios del conocimiento en México, de manera que estos permitan fortalecer sus ventajas competitivas a través del liderazgo de su capital humano. Lo anterior se logra a través de dilucidar si la capacitación y motivación hacia la marca influyen positivamente en el desempeño de esta. La estrategia metodológica es de tipo cuantitativo a través de herramientas estadísticas y de machine learning. La evidencia muestra que la alineación por parte de los empleados con los valores de la marca muestra un impacto positivo y significativo con su motivación a favor de la marca y el desempeño de esta. Los factores más significativos dentro de la dimensión de compromiso es el binomio capacitación y liderazgo de los jefes.
1 14
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Preface
(2025-01-01)
Martinez-Villaseñor, Lourdes
;
Gilberto Ochoa-ruiz
17
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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
;
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.
15Scopus© Citations 4

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