Sosa-Gómez, Guillermo

Main Affiliation

Preferred name

Official Name

Sosa Gómez, Guillermo

ORCID

0000-0001-7793-896X

Researcher ID

ABA-2857-2020

Scopus Author ID

57202400202

Now showing 1 - 10 of 44

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Statistical Optimization in the Fermentation Stage for Organic Ethanol: A Sustainable Approach
(MDPI AG, 2025-08-22)
Eliani Sosa-Gómez
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Irenia Gallardo Aguilar
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Ana Celia de Armas Mártínez
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<jats:p>The growing demand for organic products is having a transformative effect on the alcoholic beverage industry. This work investigates the possibility of producing organic ethanol only from sugarcane final molasses as a nutrient vector and Saccharomyces cerevisiae in the absence of inorganic nitrogen or phosphorus compounds. The Plackett–Bürman design included the pseudo-factors (X4–X6) due to the experimental design requirements. These factors represent the possible influence of uncontrolled variables, such as pH or nutrient interactions. Subsequently, a predictive quadratic model using Box–Behnken design with the real variables (sugar concentration, yeast dose, and incubation time) was developed and validated (R2=0.977) with internal validation; given the lack of replications and the sample size, this value should be interpreted with caution and not as generalizable predictive evidence. Further experiments with replications and cross-validation will be required to confirm its predictive capacity. Through statistical optimization, the maximum cell proliferation of 432×106 cells/mL was achieved under optimal conditions of 8°Brix sugar concentration, 20 g/L dry yeast, and 3 h incubation time. The optimized fermentation process produced 7.8% v/v ethanol with a theoretical fermentation efficiency of 78.52%, an alcohol-to-substrate yield of 62.15%, and a productivity of 1.86 g/L·h, representing significant improvements of 21.9%, 24.6%, 31.0%, and 10.1%, respectively, compared with non-optimized conditions. The fermentation time was reduced from 48 to 42 h while maintaining superior performance. These results demonstrate the technical feasibility of producing organic ethanol using certified organic molasses and no chemical additives. Overall, these findings should be regarded as proof of concept. All experiments were single-run without biological or technical replicates; consequently, the optimization and models are preliminary and require confirmation with replicated experiments and external validation.</jats:p>
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Generation of Affine-Shifted S-Boxes with Constant Confusion Coefficient Variance and Application in the Partitioning of the S-Box Space
(MDPI AG, 2025-06-14)
Ismel Martínez-Díaz
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Carlos Miguel Legón-Pérez
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<jats:p>Among the multiple important properties that characterize strong S-boxes for symmetric cryptography and are used in their designs, this study focuses on two: the non-linearity property, a classical security metric, and the confusion coefficient variance property, a statistical proxy for side channel resistance under the Hamming weight leakage model. Given an S-box, two sets can be created: the set of affine-shifted S-boxes, where S-boxes have the same non-linearity value, and the set of Hamming weight classes, where S-boxes have the same confusion coefficient variance value. The inherent values of these two properties ensure resistance to cryptographic attacks; however, if the value of one property increases, it will imply a decrease in the value of the other property. In view of the aforementioned fact, attaining a trade-off becomes a complex undertaking. The impetus for this research stems from the following hypothesis: if an initial S-box already exhibits a trade-off, it would be advantageous to employ a method that generates new S-boxes while preserving the balance. A thorough review of the extant literature reveals the absence of any methodology that encompasses the aforementioned elements. The present paper proposes a novel methodology for generating an affine-shifted subset of S-boxes, ensuring that the resulting subset possesses the same confusion coefficient variance value. We provide insights on the optimal search strategy to optimize non-linearity and confusion coefficient variance. The proposed methodology guarantees the preservation of constant values on the designated. It is possible to incorporate these properties into a comprehensive design scheme, in which case the remaining S-box properties are to be examined. We also demonstrate that, despite the fact that this subset contains S-boxes with the theoretical resistance to side channel attacks under the Hamming weight model, the S-boxes are in different Hamming weight classes.</jats:p>
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Correction: Assessment of the environmental effect of carbon taxation in Chile using a bayesian difference-in-differences approach
(Springer Science and Business Media LLC, 2025-12-12)
Reinier Fernández-López
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Cristian Mardones
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Jean Paul Navarrete
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Structural Equation Modeling for Analyzing Pro-Environmental Behavior in Switzerland
(MDPI AG, 2025-04-17)
Emilio Cedrún Vázquez
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<jats:p>Using data from Round 11 of the European Social Survey, covering Switzerland, this article employs Structural Equation Modeling to investigate the associations between psychosocial factors and pro-environmental behavior. Based on the Theory of Planned Behavior, which integrates subjective norms, perceived behavioral control, and attitudes to predict behavioral intentions and actual pro-environmental behaviors. The findings reveal that perceived social norms are strong predictors of pro-environmental behavioral intentions, illustrating the power of expectations from other people on the propensity to act sustainably. However, the effects of attitudes toward the environment and perceived behavioral control on behavioral intention were minimal or unobserved. It indicates that respondents may support environmental and sustainability issues, but this is often not matched by strong intentions to act. Additionally, the perceived control over pro-environmental behavior does not directly affect actual behavior. This suggests that external barriers may prevent individuals from performing sustainable behavior, even if they are motivated. The measurement invariance across gender groups was also examined in the study, which is essential to confirm the equivalence of measurements, and observed differences in latent variables could not be an artifact due to potential measurement bias. The findings suggest that subjective norms and pro-environmental behaviors are more likely to be higher among women, while men scored higher on perceived control. This indicates that women internalize social expectations stronger than men do to act, but for men, they feel able to act, but this confidence does not imply action. This research focuses on the significance of social influences in ecologically friendly action and on the discrepancies between intention and behavior. This study adds to our understanding of the determinants of pro-environmental behavior, providing relevant insights for policymakers to inform more effective interventions that close the gap between awareness and action.</jats:p>
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Sustainable development and human potential: Advanced tools for practical actions
(Elsevier BV, 2025-10)
Magdalena Alejandra Gaete-Sepúlveda
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Olga Dymarskaya
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Irina Seliverstova
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The relevance of lead prioritization: a B2B lead scoring model based on machine learning
(Frontiers Media SA, 2025-03-07)
González-flores Laura
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Jessica Rubiano-Moreno
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<jats:p>In business-to-business (B2B) companies, marketing and sales teams face significant challenges in identifying, qualifying, and prioritizing a large number of leads. Lead prioritization is a critical task for B2B organizations because it allows them to allocate resources more effectively, focus their sales force on the most viable and valuable opportunities, optimize their time spent qualifying leads, and maximize their B2B digital marketing strategies. This article addresses the topic by presenting a case study of a B2B software company's development of a lead scoring model based on data analytics and machine learning under the consumer theory approach. The model was developed using real lead data generated between January 2020 and April 2024, extracted from the company's CRM, which were analyzed and evaluated by fifteen classification algorithms, where the results in terms of accuracy and ROC AUC showed a superior performance of the Gradient Boosting Classifier over the other classifiers. At the same time, the feature importance analysis allowed the identification of features such as “source” and “lead status,” which increased the accuracy of the conversion prediction. The developed model significantly improved the company's ability to identify high quality leads compared to the traditional methods used. This research confirms and complements existing theories related to understanding the application of consumer behavior theory and the application of machine learning in the development of B2B lead scoring models. This study also contributes to bridging the gap between marketers and data scientists in jointly understanding lead scoring as a critical activity because of its impact on overall marketing strategy performance and sales revenue performance in B2B organizations.</jats:p>
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A Black-Box Analysis of the Capacity of ChatGPT to Generate Datasets of Human-like Comments
(MDPI AG, 2025-04-27)
Alejandro Rosete
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<jats:p>This paper examines the ability of ChatGPT to generate synthetic comment datasets that mimic those produced by humans. To this end, a collection of datasets containing human comments, freely available in the Kaggle repository, was compared to comments generated via ChatGPT. The latter were based on prompts designed to provide the necessary context for approximating human results. It was hypothesized that the responses obtained from ChatGPT would demonstrate a high degree of similarity with the human-generated datasets with regard to vocabulary usage. Two categories of prompts were analyzed, depending on whether they specified the desired length of the generated comments. The evaluation of the results primarily focused on the vocabulary used in each comment dataset, employing several analytical measures. This analysis yielded noteworthy observations, which reflect the current capabilities of ChatGPT in this particular task domain. It was observed that ChatGPT typically employs a reduced number of words compared to human respondents and tends to provide repetitive answers. Furthermore, the responses of ChatGPT have been observed to vary considerably when the length is specified. It is noteworthy that ChatGPT employs a smaller vocabulary, which does not always align with human language. Furthermore, the proportion of non-stop words in ChatGPT’s output is higher than that found in human communication. Finally, the vocabulary of ChatGPT is more closely aligned with human language than the similarity between the two configurations of ChatGPT. This alignment is particularly evident in the use of stop words. While it does not fully achieve the intended purpose, the generated vocabulary serves as a reasonable approximation, enabling specific applications such as the creation of word clouds.</jats:p>
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Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy
(2021)
Lianet Contreras Rodríguez
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Evaristo José Madarro-Capó
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Carlos Miguel Legón-Pérez
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<jats:p>Entropy makes it possible to measure the uncertainty about an information source from the distribution of its output symbols. It is known that the maximum Shannon’s entropy of a discrete source of information is reached when its symbols follow a Uniform distribution. In cryptography, these sources have great applications since they allow for the highest security standards to be reached. In this work, the most effective estimator is selected to estimate entropy in short samples of bytes and bits with maximum entropy. For this, 18 estimators were compared. Results concerning the comparisons published in the literature between these estimators are discussed. The most suitable estimator is determined experimentally, based on its bias, the mean square error short samples of bytes and bits.</jats:p>
Scopus© Citations 10 36 1
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Optimization models and techniques applied to transportation problems
(2024-04-01)
Calabrese, Bernardo
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Suarez, Alejandro Rosete
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Luis Angel Suarez Gonzalez
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Perez, Ana Camila Pérez
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Humberto Díaz-pando
52
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Conscious Exploration of Alpha-Cuts in the Parametric Solution of the School Bus Routing Problem with Fuzzy Walking Distance
(2022)
Eduardo Sánchez-Ansola
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Ana C. Pérez-Pérez
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Alejandro Rosete
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Isis Torres-Pérez
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<jats:p>Combinatorial optimization problems allow for modeling multiple situations in which proper allocation of resources is needed. For some real-world problems, the use of fuzzy elements in the models allows for incorporating certain levels of uncertainty to better approximate such real-world situations. One way to solve combinatorial optimization problems with fuzzy elements is the parametric approach, where it is necessary to define how to explore different relaxation levels using alpha-cuts. Researchers tend to select such alpha-cuts uniformly. The current investigation proposes a novel strategy for selecting alpha-cuts in the School Bus Routing Problem with fuzzy students’ maximum walking distance. This proposal bases its foundations on the number of student-bus stop pairs available according to the different levels of relaxations allowed. Results demonstrate how the proposed strategy gives attractive solutions with more diverse trade-offs, contrasted with other methods in the literature. Furthermore, it decreases the computational cost for those instances where the maximum relaxation does not provide new pairs of students-bus stops.</jats:p>
Scopus© Citations 3 5 1

Sosa-Gómez, Guillermo

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