Papers by Juan Frausto Solis
Julieta Fierro: La Voz del Universo, 2026
Resumen Este relato es un homenaje a la destacada investigadora mexicana Julieta Fierro. A lo lar... more Resumen Este relato es un homenaje a la destacada investigadora mexicana Julieta Fierro. A lo largo del texto se incluyen sucesos, diálogos y eventos que han sido adaptados o recreados, aun cuando no ocurrieran con total exactitud-o incluso no hayan acaecido-de la forma en que aquí se describen. El enfoque de este trabajo corresponde a un tipo de narrativa inspirada en un estilo panorámico-interpretativo, en la que el autor construye una visión integradora de los hechos, sustentada en fuentes, observaciones y reflexiones basadas en la ciencia. La presente aproximación se aproxima a la narrativa fuzzy-omnisciente, en honor a Lotfi Zadeh, creador de la lógica difusa (fuzzy logic). Bajo este tipo de narrativa, los hechos pueden presentarse con distintos grados de correspondencia con la realidad: sucesos que pasaron exactamente, otros que pudieron haber ocurrido de forma aproximada, e incluso algunos que posiblemente nunca existieron. El propósito de emplear la narrativa fuzzy-omnisciente es construir una representación más clara y significativa de la grandeza científica y del profundo valor humano que Julieta Fierro ha aportado mediante su extraordinaria labor como divulgadora de la ciencia en México. En el relato, está la puesta en escena de la Catedral Metropolitana Ciudad de México-antes de 2016 Distrito Federal-, cuya construcción concluyó en 1813, tras desarrollarse en varias etapas a lo largo de más de dos siglos, periodo durante el cual el templo ya funcionaba como el principal recinto religioso de la ciudad. El templo, realzó su majestuosidad con un icónico reloj instalado en 1698 y modernizado en 1807, mostrando la hora desde una altura de 67 metros, valiéndose de un notable mecanismo para la época. Pues bien, justo cuando ese reloj marcaba las 6:48 de la mañana del martes 24 de febrero de 1948, los primeros rayos de sol comenzaron a iluminar los bellos monumentos de la Ciudad de los Palacios. El aire se movía lento, arrullador como si estuviera influenciado por el descenso de una estrella que aquel día bajaría del cielo, no una hermana de las incontables estrellas del cosmos, sino otra con el designio divino de ser un Sol en la Tierra: una luz tan brillante y singular que, con el paso del tiempo, observará minuciosamente el Sistema Solar y el Universo entero, con sus millones de galaxias, agujeros negros, nebulosas y más, mucho más. La misión de esa estrella naciente será descifrar, explicar, describir y acercar al mundo ese Todo incomprensible e inalcanzable, con una sencillez y una simpatía tan sobresalientes que, en su momento, dejará una huella imborrable, aun después de su regreso a los cielos. Es así como Julieta Fierro inspira y se busca personificar en este trabajo.
Axioms
Proteins are macromolecules essential for living organisms. However, to perform their function, p... more Proteins are macromolecules essential for living organisms. However, to perform their function, proteins need to achieve their Native Structure (NS). The NS is reached fast in nature. By contrast, in silico, it is obtained by solving the Protein Folding problem (PFP) which currently has a long execution time. PFP is computationally an NP-hard problem and is considered one of the biggest current challenges. There are several methods following different strategies for solving PFP. The most successful combine computational methods and biological information: I-TASSER, Rosetta (Robetta server), AlphaFold2 (CASP14 Champion), QUARK, PEP-FOLD3, TopModel, and GRSA2-SSP. The first three named methods obtained the highest quality at CASP events, and all apply the Simulated Annealing or Monte Carlo method, Neural Network, and fragments assembly methodologies. In the present work, we propose the GRSA2-FCNN methodology, which assembles fragments applied to peptides and is based on the GRSA2 and ...
Research Article Chaotic Multiquenching Annealing Applied to the Protein Folding Problem
Copyright © 2014 Juan Frausto-Solis et al. This is an open access article distributed under the C... more Copyright © 2014 Juan Frausto-Solis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Chaotic Multiquenching Annealing algorithm (CMQA) is proposed. CMQA is a new algorithm, which is applied to protein folding problem (PFP).This algorithm is divided into three phases: (i)multiquenching phase (MQP), (ii) annealing phase (AP), and (iii) dynamical equilibrium phase (DEP). MQP enforces several stages of quick quenching processes that include chaotic functions. The chaotic functions can increase the exploration potential of solutions space of PFP. AP phase implements a simulated annealing algorithm (SA) with an exponential cooling function. MQP and AP are delimited by different ranges of temperatures; MQP is applied for a range of temperatures which goes from extremely high values to very high values; AP searches f...
Comparative Study of ARIMA Methods for Forecasting Time Series of the Mexican Stock Exchange
Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, 2018
Predicting volatility in stock market price indices is a major economic problem. The idea of fore... more Predicting volatility in stock market price indices is a major economic problem. The idea of forecasting time series is that the patterns associated with past values in a data series can be used to project future values. The study of volatility can be applied to solving these economic problems, because volatility allows measuring the risk of asset portfolios, since it shows the behavior of the variation of asset prices. In order to be able to predict effectively the future behavior of a time series, it is necessary to know the attributes of the series with the correct prediction method and thus to be able to define training patterns. The accurate selection of the attributes evaluated in a time series defines the impact on prediction accuracy. In this work the study of kurtosis and the comparison between different ARIMA methods for the solution of time series of the Mexican Stock Exchange and the Makridakis contests are shown.
Advances in Bioinformatics, 2016
A new hybrid Multiphase Simulated Annealing Algorithm using Boltzmann and Bose-Einstein distribut... more A new hybrid Multiphase Simulated Annealing Algorithm using Boltzmann and Bose-Einstein distributions (MPSABBE) is proposed. MPSABBE was designed for solving the Protein Folding Problem (PFP) instances. This new approach has four phases: (i) Multiquenching Phase (MQP), (ii) Boltzmann Annealing Phase (BAP), (iii) Bose-Einstein Annealing Phase (BEAP), and (iv) Dynamical Equilibrium Phase (DEP). BAP and BEAP are simulated annealing searching procedures based on Boltzmann and Bose-Einstein distributions, respectively. DEP is also a simulated annealing search procedure, which is applied at the final temperature of the fourth phase, which can be seen as a second Bose-Einstein phase. MQP is a search process that ranges from extremely high to high temperatures, applying a very fast cooling process, and is not very restrictive to accept new solutions. However, BAP and BEAP range from high to low and from low to very low temperatures, respectively. They are more restrictive for accepting new ...
International Journal of Combinatorial Optimizaion Method
This paper presents a forecasting method that is called SVR-ESAR for the estimation of confirmed ... more This paper presents a forecasting method that is called SVR-ESAR for the estimation of confirmed (infected) cases of COVID-19. The name comes from Support Vector Regression (SVR) with Exponential Smoothing (ES) and ARIMA, and is applied to forecast confirmed COVID-19 cases in four scenarios: a) the Whole World, b) China c) the US, and d) Mexico. This method is straightforward for using an iterative adjustment phase in SVR. phase. Besides, we applied ES and ARIMA algorithms to predict the residuals of the prediction performed in the last phase. SVR-ESAR obtained the best or equivalent results to other methods for all these scenarios. However, for the data of Mexico SVR-ESAR obtained modest quality results. The results of SVR-ESAR were compared with those published in the literature for the US and China, and we found that the proposed method achieved the best predictions.
Mathematicas & Comput. Applications, 2025
Climate change presents significant challenges due to the increasing frequency and intensity of e... more Climate change presents significant challenges due to the increasing frequency and intensity of extreme weather events. Mexico, with its diverse climate and geographic position, is particularly vulnerable, underscoring the need for robust strategies to predict atmospheric variables. This work presents TAE Predict (Time series Analysis and Ensemble-based Prediction with relevant feature selection) based on relevant feature selection and ensemble models of machine learning. Dimensionality in multivariate time series is reduced through Principal Component Analysis, ensuring interpretability and efficiency. Additionally, data remediation techniques improve data set quality. The ensemble combines Long Short-Term Memory neural networks, Random Forest regression, and Support Vector Machines, optimizing their contributions using heuristic algorithms such as Particle Swarm Optimization. Experimental results from meteorological time series in key Mexican cities demonstrate that the proposed strategy outperforms individual models in accuracy and robustness. This methodology provides a replicable framework for climate variable forecasting, delivering analytical tools that support decision-making in critical sectors, such as agriculture and water resource management. The findings highlight the potential of integrating modern techniques to address complex, high-dimensional problems. By combining advanced prediction models and feature selection strategies, this study advances the reliability of climate forecasts and contributes to the development of effective adaptation and mitigation measures in response to climate change challenges.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Review Article Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction
Copyright © 2014 Alberto Gonzalez-Sanchez et al. This is an open access article distributed under... more Copyright © 2014 Alberto Gonzalez-Sanchez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-drivenmodeling technique comparisons have beenmade, looking for the bestmodel to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for eachmodel. Multiple linear regression, stepwise linear regression,M5 ...
In this paper the Pareto optimization of the Heterogeneous Computing Scheduling Multi-Objective P... more In this paper the Pareto optimization of the Heterogeneous Computing Scheduling Multi-Objective Problem (HCSMOP) is approached. The goal is to minimize two objectives which are in conflict: the overall completion time (makespan) and the energy consumed. In the revised literature, there are no reported exact algorithms which solve the HCSMOP. In this work, we propose a Branch and Bound algorithm to solve the problem and it is used to find the optimal Pareto front of a set of instances of the literature. This set is the first available benchmark to assess the performance of multiobjective algorithms with quality metrics that requires known the optimal front of the instances.
Entropy, 2020
Entropy is a key concept in the characterization of uncertainty for any given signal, and its ext... more Entropy is a key concept in the characterization of uncertainty for any given signal, and its extensions such as Spectral Entropy and Permutation Entropy. They have been used to measure the complexity of time series. However, these measures are subject to the discretization employed to study the states of the system, and identifying the relationship between complexity measures and the expected performance of the four selected forecasting methods that participate in the M4 Competition. This relationship allows the decision, in advance, of which algorithm is adequate. Therefore, in this paper, we found the relationships between entropy-based complexity framework and the forecasting error of four selected methods (Smyl, Theta, ARIMA, and ETS). Moreover, we present a framework extension based on the Emergence, Self-Organization, and Complexity paradigm. The experimentation with both synthetic and M4 Competition time series show that the feature space induced by complexities, visually co...
Tuned Simulated Annealing Based on Boltzmann and Bose-Einstein Distribution Applied to Maxsat Problem
Asian Journal of Scientific Research
In this paper, a hybrid Simulated Annealing algorithm using Boltzmann and Bose-Einstein Distribut... more In this paper, a hybrid Simulated Annealing algorithm using Boltzmann and Bose-Einstein Distributions (SABBE) is proposed. SABBE was designed for solving satisfiability (SAT) instances, and it has three phases: i) BP (Boltzmann Phase), ii) BEP (Bose-Einstein Phase), and iii) DEP (Dynamical Equilibrium Phase). BP and BEP are simulated annealing searching procedures based on Boltzmann and Bose-Einstein distributions respectively. BP ranges from high to low temperature values, while BEP goes from low to very low temperatures. Another simulated annealing search procedure, DEP, is applied at the final temperature of the second phase. However, DEP uses a particular heuristic for detection of stochastic equilibrium by employing a least squares method during its execution. Finally, SABBE parameters are tuned with an analytical method, which considers the maximal and minimal deterioration of SAT instances.
Markov decision processes for infinite horizon problems solved with the cosine simplex method
Optimization, 2012
This article presents a new method of linear programming (LP) for solving Markov decision process... more This article presents a new method of linear programming (LP) for solving Markov decision processes (MDPs) based on the simplex method (SM). SM has shown to be the most efficient method in many practical problems; unfortunately, classical SM has an exponential complexity. Therefore, new SMs have emerged for obtaining optimal solutions in the most efficient way. The cosine simplex method (CSM) is one of them. CSM is based on the Karush Kuhn Tucker conditions, and is able to efficiently solve general LP problems. This work presents a new method named the Markov Cosine Simplex Method (MCSM) for solving MDP problems, which is an extension of CSM. In this article, the efficiency of MCSM is compared to the traditional revised simplex method (RSM); experimental results show that MCSM is far more efficient than RSM.
2LR An Algorithm for Solving Satisfiability Instances
Axioms
Computer vision methodologies using machine learning techniques usually consist of the following ... more Computer vision methodologies using machine learning techniques usually consist of the following phases: pre-processing, segmentation, feature extraction, selection of relevant variables, classification, and evaluation. In this work, a methodology for object recognition is proposed. The methodology is called PSEV-BF (pre-segmentation and enhanced variables for bird features). PSEV-BF includes two new phases compared to the traditional computer vision methodologies, namely: pre-segmentation and enhancement of variables. Pre-segmentation is performed using the third version of YOLO (you only look once), a convolutional neural network (CNN) architecture designed for object detection. Additionally, a simulated annealing (SA) algorithm is proposed for the selection and enhancement of relevant variables. To test PSEV-BF, the repository commons object in Context (COCO) was used with images exhibiting uncontrolled environments. Finally, the APIoU metric (average precision intersection over ...
Simulated Annealing - Advances, Applications and Hybridizations, 2012
Axioms, 2023
Computer vision methodologies using machine learning techniques usually consist of the following ... more Computer vision methodologies using machine learning techniques usually consist of the following phases: pre-processing, segmentation, feature extraction, selection of relevant variables, classification, and evaluation. In this work, a methodology for object recognition is proposed.
The methodology is called PSEV-BF (pre-segmentation and enhanced variables for bird features). PSEV-BF includes two new phases compared to the traditional computer vision methodologies, namely: pre-segmentation and enhancement of variables. Pre-segmentation is performed using the third version of YOLO (you only look once), a convolutional neural network (CNN) architecture designed for object detection. Additionally, a simulated annealing (SA) algorithm is proposed for the selection and enhancement of relevant variables. To test PSEV-BF, the repository commons object in Context (COCO) was used with images exhibiting uncontrolled environments. Finally, the APIoU metric (average precision intersection over union) is used as an evaluation benchmark to compare our methodology with standard configurations. The results show that PSEV-BF has the highest performance in all tests
Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007), 2007
We propose a new partition rule for DPLL-based SAT Solvers. Most of the complete SAT solvers usua... more We propose a new partition rule for DPLL-based SAT Solvers. Most of the complete SAT solvers usually are based on Davis, Logemann and Loveland (DPLL) rules. One most DPLL rule actually used in the modern algorithms is the Classical Partition Rule (CPR), that divides the problem into sub-problems (resolvents) and thereby it finds a solution through a decision tree. In this paper a new partition rule named Multiple Partition Rule (MPR) is presented. MPR generates a new decision tree according to clauses instead CPR which generates a decision tree according to variables. MPR can be used for developing new SAT's algorithms and to improve existence ones that use CPR. Experimental results comparing MPR versus CPR show that using MPR makes more efficient solutions than CPR.
Mathematical and Computational Applications, 2021
The Job Shop Scheduling Problem (JSSP) has enormous industrial applicability. This problem refers... more The Job Shop Scheduling Problem (JSSP) has enormous industrial applicability. This problem refers to a set of jobs that should be processed in a specific order using a set of machines. For the single-objective optimization JSSP problem, Simulated Annealing is among the best algorithms. However, in Multi-Objective JSSP (MOJSSP), these algorithms have barely been analyzed, and the Threshold Accepting Algorithm has not been published for this problem. It is worth mentioning that the researchers in this area have not reported studies with more than three objectives, and the number of metrics they used to measure their performance is less than two or three. In this paper, we present two MOJSSP metaheuristics based on Simulated Annealing: Chaotic Multi-Objective Simulated Annealing (CMOSA) and Chaotic Multi-Objective Threshold Accepting (CMOTA). We developed these algorithms to minimize three objective functions and compared them using the HV metric with the recently published algorithms,... 
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Papers by Juan Frausto Solis
The methodology is called PSEV-BF (pre-segmentation and enhanced variables for bird features). PSEV-BF includes two new phases compared to the traditional computer vision methodologies, namely: pre-segmentation and enhancement of variables. Pre-segmentation is performed using the third version of YOLO (you only look once), a convolutional neural network (CNN) architecture designed for object detection. Additionally, a simulated annealing (SA) algorithm is proposed for the selection and enhancement of relevant variables. To test PSEV-BF, the repository commons object in Context (COCO) was used with images exhibiting uncontrolled environments. Finally, the APIoU metric (average precision intersection over union) is used as an evaluation benchmark to compare our methodology with standard configurations. The results show that PSEV-BF has the highest performance in all tests