Martínez Velasco, Antonieta Teodora
Main Affiliation
Preferred name
Martínez Velasco, Antonieta Teodora
Official Name
Martínez Velasco, Antonieta Teodora
ORCID
0000-0001-6535-1440 
Researcher ID
DWK-4326-2022
Scopus Author ID
57192978286
38 results
Now showing 1 - 10 of 38
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Branding interno en una empresa de servicios en MéxicoEl 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 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Knowledge Management and Innovation in the Furniture Industry in Mexico(Springer, 2023); Knowledge is an asset for all companies. Consequently, learning and accumulating new knowledge over time constitute the essence of the innovation process. Thus, knowledge management is of the utmost importance. This research aims to analyze and propose a knowledge management model for innovation through Bayesian networks with machine learning techniques in the furniture industry. We develop a model where we identify and quantify the impact of critical factors on the generation of innovative value. The results show that the most relevant factors for optimal knowledge management in the furniture industry are suppliers, distributors, the enterprises’ business model structure, quality and risk management, strategic planning, value system model, national and international markets, e-commerce, and human capital. ©Springer ©The authors.5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Metodología para determinar los factores de riesgo asociados con enfermedades complejas: Degeneración Macular Relacionada con la Edad y Preeclampsia(2021); 7 21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Explainable artificial hydrocarbon networks classifier applied to preeclampsiaExplainability is crucial in domains where system decisions have significant implications for human trust in black-box models. Lack of understanding regarding how these decisions are made hinders the adoption of so-called clinical decision support systems. While neural networks and deep learning methods exhibit impressive performance, they remain less explainable than white-box approaches. Artificial Hydrocarbon Networks (AHN) is an effective black-box model that can be used to support critical clinical decisions if accompanied by explainability mechanisms to instill confidence among clinicians. In this paper, we present a use case involving global and local explanations for AHN models, provided with an automatic procedure so-called eXplainable Artificial Hydrocarbon Networks (XAHN). We apply XAHN to preeclampsia prognosis, enabling interpretability within an accurate black-box model. Our approach involves training a suitable AHN model using the cross-validation with ten repetitions, followed by a comparative analysis against four well-known machine learning techniques. Notably, the AHN model outperformed the others, achieving an F1-score of 74.91%. Additionally, we assess the efficacy of our XAHN explainer through a survey applied to clinicians, evaluating the goodness and satisfaction of the provided explanations. To the best of our knowledge, this work represents one of the earliest attempts to address the explainability challenge in preeclampsia prediction.© 2024 The Author(s). Published by Elsevier Inc.14Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine learning method to establish the connection between age related macular degeneration and some genetic variations(2016); ; 2 16Scopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Management of scientific and ancestral knowledge: a decision-making model in mezcal industry in Mexico(Frontiers Media SA, 2025); ; Leyva-Hernández, Sandra NellyIntroduction: Knowledge management is essential to ensure the sustainability of rural communities and small producers since it generates value for innovation, productivity, and competitiveness. The aim of this study is to identify relevant factors for adequate decision-making in managing knowledge in the Mexican mezcal industry and its impact on developing rural communities and small producers - mezcaleros. For this purpose, a decision-making model for managing scientific and ancestral knowledge is created to support links with universities, research centers, and rural communities to accelerate innovation and competitiveness in this sector. Methods: The analysis methods were carried out through decision-making, machine-learning techniques, and fuzzy logic. Results: The Bayesian Network model suggests that the preceding variables to optimize the Mezcaleros Knowledge Management are the Mezcaleros Indigenous community, the Denomination of Origin, Scientific and Ancestral Knowledge, Waste Management and Use, and Jima. Discussion: This knowledge management model aims to guide small producers to be more productive and competitive through the support of a facilitator. ©The authors ©Frontiers in Artificial Intelligence ©Frontiers Media SA.16 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Physicochemical, Microbiological, Proximate, and Consumer Characterization of Traditional Tenate Cheese in Two Mexican Regions(MDPI AG, 2026); ; Tenate cheese is a traditional Mexican pressed semi-hard cheese made from raw cow’s milk and wrapped in palm fiber. The characterization of this cheese remains scarce. This study presents an exploratory characterization of a single production batch of traditional Tenate cheese obtained from one artisanal producer, providing preliminary information on its physicochemical, microbiological, and proximate analyses, combined with consumer evaluation. The latter was analyzed using hierarchical cluster analysis (HCA) as an exploratory segmentation tool. Tenate cheese was characterized as a semi-hard cheese with active lactic fermentation, a lactic aroma, acidic and umami flavors, and a firm, granular texture. Microbiological analyses showed the absence of coliforms, enterobacteria, and Staphylococcus aureus among the microorganisms evaluated, whereas yeast counts exceeded the regulatory limit. As major foodborne pathogens were not included in the microbiological assessment, the overall microbiological safety of the product could not be confirmed. A total of 318 consumers from Aguascalientes (AGS, n = 149) and the Guadalajara Metropolitan Area (GMA, n = 169) evaluated the product using hedonic and Just-About-Right scales. Consumers from AGS reported significantly higher liking scores than those from GMA. Penalty analysis identified insufficient softness as the main attribute associated with lower liking in AGS, whereas low flavor intensity and weak aftertaste reduced acceptance in GMA. Hierarchical cluster analysis (HCA) identified three consumer segments in each location, revealing distinct preference patterns linked to regional expectations. The main contributions of this study are threefold. First, it contributes to the limited scientific knowledge available on Tenate cheese by providing a comprehensive characterization of the analyzed sample. Second, it shows that consumer acceptance differed between two regional markets, comparing two university-affiliated consumer groups, highlighting the value of consumer segmentation for product positioning. Third, it proposes and applies an integrated framework combining physicochemical, microbiological, nutritional, sensory, and consumer segmentation analyses that can be applied to the study of other artisanal cheeses. © The authors © Applied Sciences © MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Decision-making model in ancestral knowledge management: The case of the Raicilla in Mexico(2024); ; 42Scopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, University–Industry Collaboration: A Sustainable Technology Transfer ModelFaced with the pandemic caused by COVID-19, universities worldwide are giving a powerful response to support their communities. One way to provide support is via the collaboration between universities and industries, allowing the co-creation of knowledge that leads to innovation. Historically, universities, as knowledge-intensive organizations (KIOs), have produced knowledge through research. At present, its important contribution to countries’ economy is widely recognized through the development of new knowledge and technical know-how. Universities are a source of innovation for firms, which ultimately translates into social welfare improvements. The objective of this research is to analyze the university–firm linkage. The methodological strategy is carried out using Bayesian networks through a model where the main elements of university–industry linking, which impact competitiveness and innovation, are identified and quantified. The technology transfer model shows that the most crucial processes are Technology Strategy, Value Proposal, Knowledge Management, Control and Monitoring, Innovation Management, Needs Detection, Knowledge Creation, New Products and Services, and Absorption Capacity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1 18Scopus© Citations 29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Most Relevant Factors in the Gender Gap in European Countries(Academic Conferences International Ltd, 2025); ; Gender equality is essential for the sustainable development of all countries. It brings economic growth, improved education and health for the entire population, poverty reduction, and social and political stability as democracy is strengthened and more peaceful communities are generated. However, its study is complex and includes various dimensions. This research aims to analyze the most relevant factors of the gender gap in European countries. The methodological strategy is based on machine learning techniques applied to the Gender Equality Index, which includes the EU27 countries and was developed by EIGE. These machine-learning techniques are methods computers use to learn from data and make predictions without being explicitly programmed. This index has 31 relevant indicators that are grouped into 14 subdimensions, which are, in turn, divided into six dimensions. The relevant dimensions in the study of gender equality are I. work (5 indicators), II. Money (4 indicators), III. Knowledge (3 indicators), IV. Time (4 indicators), V. power (8 indicators), and VI. Health (7 indicators). The results show a women's gap. Three of the most relevant dimensions from this research inhibit gender equity: I. Power in its three economic, political, and social dimensions; II. Knowledge in its two dimensions of attainment, participation, and segregation, and III. Time in its dimension of social activities. Women's most significant factors for the gender gap are power, knowledge, and time. ©The authors ©International Conference on Gender Research.32 100