Facultad de Ingeniería - CampCM

City

Ciudad de México

Country

Director

Hiram Eredi Ponce Espinosa

Parent Organisation

Campus Ciudad de México

OrgUnit's Publications

Now showing 1 - 10 of 978

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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.
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García-Giménez, José Luis
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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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Sistema de tele operación de robots móviles omnidireccionales con emulación háptica de los sentidos de la vista y el oído
(2010)
Aguilera Sánchez, José Antonio
;
Varona, Jorge
Maestría en Ciencias - Tesis presentada en UP Aguascalientes
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Multiobjective model to optimize charging station location for the decarbonization process in Mexico
(2025)
Ruiz Barajas, Francisco
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Olivares-Benitez, Elias
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Adrian Ramirez‐Nafarrate
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Rosa G. González‐Ramírez
<jats:title>Abstract</jats:title><jats:p>Electric vehicles (EVs) offer significant potential for advancing sustainable environmental goals. However, their widespread adoption has been concentrated in urban areas, raising challenges for interurban travel. In many countries, charging station networks are primarily located within cities, highlighting a key opportunity for expansion to support longer distance journeys. This article addresses the facility location problem for EV charging stations to enable interurban travel. We propose a multiobjective optimization model based on the flow refueling location model with three objectives: maximizing CO<jats:sub>2</jats:sub> emissions reduction, minimizing total costs, and reducing user charging time. The model is solved using an epsilon constraint approach, and Mexico's charging station network is used as a case study. Through computational experiments, various scenarios are evaluated, and a comparative analysis is performed between electric and internal combustion vehicles. Results show that deploying 20 strategically located charging stations could mitigate 3.1 million tons of CO<jats:sub>2</jats:sub>, requiring an investment of nearly USD 3.9 million.</jats:p>
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Optimización de proyectos bajo modelos de gestión
(2013)
Barajas Huerta, Jaaziel Daniel
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Cybernetic Insights on GenAI and Learning in Higher Education
(IEEE, 2026)
Salinas-Navarro, David Ernesto
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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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The effects of supply variability on the performance of assembly systems
(2022)
Romero-Silva, Rodrigo
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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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Plan estratégico de desarrollo de "Quesería Gutiérrez"
(2021-10)
Martínez Gutiérrez, Moisés
En México, el consumo de queso es elevado, esto se debe a la gran variedad de quesos que tenemos actualmente en el país tales como el panela, añejo, Oaxaca, Cotija, asadero, chihuahua, etc. Su uso está en la mayoría de los platillos mexicanos típicos, como quesadillas, sopes, tostadas o enchiladas. Tan sólo el consumo de queso nacional es de más de 400 mil toneladas, de manera que los mexicanos consumimos actualmente entre 2.1 y hasta 6 kilos de queso al año. Sus estilos y sabores son resultado de diferentes tipos de leche, variaciones en el tiempo de curación y diversos tratamientos en su proceso. Se estima que en el mundo hay más de 2,000 tipos de quesos.
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Regulación de interacciones celulares a través de estrategias de control retroalimentadas
(2008)
Ortiz Vargas, Moisés
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Puebla Núñez, Héctor
3
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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.
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Facultad de Ingeniería - CampCM

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