Facultad de Ingeniería - CampCM

City
Ciudad de México
Country
MX
Director
Hiram Eredi Ponce Espinosa
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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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    Preface
    (Springer Science and Business Media Deutschland GmbH, 2026)
    Martinez-Villaseñor, Lourdes  
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    Transparency as a moderator of the relationship between web-scraping awareness and privacy-protective behavior: a quasi-experimental onboarding study
    (Frontiers Media SA, 2026)
    Betancourt-Barrita, Cecilia
    ;
    Berumen-Glinz, Lorena Alexandra  
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    Cal Y Mayor Peña, Francisco
    ;
    Escobar Castillejos, David  
    Introduction: Automated data collection practices, such as web scraping, are widely used for personalization on interactive platforms, and they raise recurring questions about privacy, transparency, and user trust. Prior work has examined these constructs separately. Less is known about whether transparency in onboarding disclosures is associated with a weaker relationship between awareness of automated data collection and privacy-protective behavior. This study examines the relationship between privacy-protective behavior and user trust in a simulated social media environment.Methods: A quantitative, between-subjects, scenario-based quasi-experimental survey was conducted with 325 participants. The study employed a simulated Instagram onboarding scenario with two transparency conditions: low and high. Privacy-related outcomes were operationalized as two binary privacy-protective behaviors (adjusting privacy settings and refusing to use a platform), summed into a behavior index that takes integer values of 0, 1, or 2. Additional measures assessed web-scraping awareness, user trust, perceived transparency, and control variables related to social media use and privacy sensitivity. Hypotheses were tested using ordinary least squares regression for the behavior index and continuous outcomes, and logistic regression for each binary behavior, including an interaction term to examine the moderating effect of the transparency condition.Results: Greater awareness of web scraping was associated with a higher likelihood of low-barrier privacy-protective behavior, particularly adjusting privacy settings. The transparency condition was associated with a weaker awareness–behavior relationship, consistent with the proposed moderation model. In contrast, the privacy-protective behavior index did not significantly predict user trust in the platform.Discussion: The findings are consistent with transparency operating as a contextual buffer of the link between awareness of automated data collection and privacy-protective behavior, rather than as a direct antecedent of trust. Because condition assignment was self-selected and baseline awareness differed across conditions, the results are interpreted as conditional associations within a simulated onboarding context rather than as causal effects. The results inform the design of onboarding disclosures in AI-driven platforms. © The authors © Frontiers in Computer Science.
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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
      3
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      2
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    Optimización de proyectos bajo modelos de gestión
    (2013)
    Barajas Huerta, Jaaziel Daniel
      4
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    Multiobjective model to optimize charging station location for the decarbonization process in Mexico
    (2025)
    Ruiz Barajas, Francisco
    ;
    Olivares-Benitez, Elias  
    ;
    Adrian Ramirez‐Nafarrate
    ;
    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>
      37
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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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    On the false assumptions of the SIR model of epidemics
    (World Scientific, 2025-02)
    Raczynski Gawin, Stanislaw Fryderyk
    The validity of the continuous susceptible-infected-recovered (SIR) is questioned. It is pointed out that the basic assumption used in the model equations is wrong. The classic SIR and susceptible-infected-recovered-susceptible (SIRS) models have the form of the state ordinary differential equation (ODE). These equations do not reflect the spatial distribution of individuals. Moreover, what is more important, the SIR model equations do not reflect the huge collective memory of the modeled system. Such issues as the probability distribution of the immunity period and other time intervals are not taken into account in the ODE SIR model. It is pointed out that the model results depend strongly on these distributions. A comparison to the agent-based model (ABM) and corresponding computer simulations is done. In the paper, the validity of SIR epidemics model is questioned, the deficiency and the basic assumptions of the SIR model are pointed out. This makes the existence of the corresponding differential equations doubtful. ©The author ©World Scientific © International Journal of Modeling, Simulation, and Scientific Computing.
      3