Publications

The security of communication and computer systems is an increasingly important issue, nowadays pervading all areas of human activity (e.g., credit cards, website encryption, medical data, etc.). Furthermore, the development of high-speed and light-weight implementations of the encryption algorithms is fundamental to improve and widespread their application in low-cost, low-power and portable systems. In this scientific article, a high-speed implementation of the AES-128 algorithm is reported, developed for a short-range and high-frequency communication system, called Wireless Connector; a Xilinx ZCU102 Field Programmable Gate Array (FPGA) platform represents the core of this communication system since manages all the base-band operations, including the encryption/decryption of the data packets. Specifically, a pipelined implementation of the Advanced Encryption Standard (AES) algorithm has been developed, allowing simultaneous processing of distinct rounds on multiple successive plaintext packets for each clock period and thus obtaining higher data throughput. The proposed encryption system supports 220 MHz maximum operating frequency, ensuring encryption and decryption times both equal to only 10 clock periods. Thanks to the pipelined approach and optimized solutions for the Substitute Bytes operation, the proposed implementation can process and provide the encrypted packets each clock period, thus obtaining a maximum data throughput higher than 28 Gbit/s. Also, the simulation results demonstrate that the proposed architecture is very efficient in using hardware resources, requiring only 1631 Configurable Logic Blocks (CLBs) for the encryption block and 3464 CLBs for the decryption one.

Science, technology, engineering and mathematics (STEM) needs to be promoted and there has been a lot of talk about it. An attempt has been made to share science and it is intended that more young people participate in the space field. However, what happens after they get into college? What is the big step young people who decide to study STEM take to become recognized scientists or engineers? What motivates them? What is their perspective on science?\vspace{5mm} %5mm vertical space Of 125 million Mexicans, there are 12,576,736 who are between 18 and 23 years old, however only 4,705,400 study at the higher level, and only 3 \% of that number study a scientific career. I am a Physics student at the UNAM Faculty of Sciences and I conducted a survey of 500 Mexican students from various careers such as: Mathematics, Applied Mathematics, Aerospace Engineering, Physics, Biomedical Physics, Earth Sciences... and I found very interesting results .\vspace{5mm} %5mm vertical space Asking students interested in the space sector, what prevents them from achieving their objectives? I found dozens of testimonies from mexican students who dreamed of being astronauts, but they see it as an unattainable dream. 50\% feel unmotivated when they think about their future job and 80\% want to leave Latin America, to pursue their dreams in countries like Germany, United States and Russia.\vspace{5mm} %5mm vertical space Although it was a survey in Mexico, we can expect less enthusiastic data in other countries less spatial development. The few young people who want to dedicate themselves to the space sector are not sure of their field of work. What data will we obtain from a survey of this type in Latin America? In developed countries, do half of STEM students also feel unmotivated?\vspace{5mm} %5mm vertical space In addition to motivating young people to enter the wonders of the cosmos, we must encourage them not to give up and follow their dreams. We propose mentoring programs, free resources, job offers, scholarships and support to avoid school dropouts. This message is not only for the Academy in Mexico, it is also directed to the entire world, where the education and motivation of young people is the future of humanity.

Heart diseases are the most important causes of death in the world and over the years, the study of cardiac movement has been carried out mainly in two dimensions, however, it is important to consider that the deformations due to the movement of the heart occur in a three-dimensional space. The 3D + t analysis allows to describe most of the motions of the heart, for example, the twisting motion that takes place on every beat cycle that allows us identifying abnormalities of the heart walls. Therefore, it is necessary to develop algorithms that help specialists understand the cardiac movement. In this work, we developed a new approach to determine the cardiac movement in three dimensions using a differential optical flow approach in which we use the steered Hermite transform (SHT) which allows us to decompose cardiac volumes taking advantage of it as a model of the human vision system (HVS). Our proposal was tested in complete cardiac computed tomography (CT) volumes ( 3D + t), as well as its respective left ventricular segmentation. The robustness to noise was tested with good results. The evaluation of the results was carried out through errors in forwarding reconstruction, from the volume at time t to time t + 1 using the optical flow obtained (interpolation errors). The parameters were tuned extensively. In the case of the 2D algorithm, the interpolation errors and normalized interpolation errors are very close and below the values reported in ground truth flows. In the case of the 3D algorithm, the results were compared with another similar method in 3D and the interpolation errors remained below 0.1. These results of interpolation errors for complete cardiac volumes and the left ventricle are shown graphically for clarity. Finally, a series of graphs are observed where the characteristic of contraction and dilation of the left ventricle is evident through the representation of the 3D optical flow. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Heart diseases are one of the most important causes of death in the Western world. It is, then, important to implement algorithms to aid the specialist in analyzing the heart motion. We propose a new strategy to estimate the cardiac motion through a 3D optical flow differential technique that uses the Steered Hermite transform (SHT). SHT is a tool that performs a decomposition of the images in a base that model the visual patterns used by the human vision system (HSV) for processing the information. The 3D + t analysis allows to describe most of motions of the heart, for example, the twisting motion that takes place on every beat cycle and to identify abnormalities of the heart walls. Our proposal was tested on two phantoms and on two sequences of cardiac CT images corresponding to two different patients. We evaluate our method using a reconstruction schema, for this, the resulting 3D optical flow was applied over the volume at time t to obtain a estimated volume at time t + 1. We compared our 3D optical flow approach to the classical Horn and Shunk's 3D algorithm for different levels of noise. © 2017 SPIE.

Online advertising has become the most important area of publicity. From a post-2020 perspective, we identify three trends in online advertising comprising: the rapid evolution of online advertising mainly over mobile networks, how to cope with big companies leading digital marketing, and the exploration of new methods to handle the dynamics of the e-commerce ecosystem. We proposed a new methodology for online advertising in small ad networks using supervised machine learning and metaheuristic methods. Our research will be beneficial for addressing the above-mentioned trends in online advertising focusing on small ad networks. It contributes to the establishment of an information system technology and practice within the scope of the development of marketing business strategies in e-commerce. Currently, we are exploring how to improve the flexibility of our approach to make it easier to adapt to new ad campaigns, analyzing and comparing different computational methods, and how to increase the performance of presenting custom ads to users when dealing with small data sets. Online advertising in small ad networks will be very useful in the following years. Hence, there are still many challenges to be dealt with in order to implement it in the business strategies of the new digital marketing. © 2020 Elsevier B.V.

In this work, a vision-based system for the electronic band-gap prediction of organic molecules is proposed using a multichannel 2D convolutional neural network (CNN) and a 3D CNN, applied to the recognition and classification of 2D projected images from 3D molecular structure models. The generated images are input into the CNN for an estimation of the energy gap, associated with the molecular structure. The public data set used in this research was the Organic Materials Database (OMDB-GAP1). A data transformation from the descriptive information contained in the data set to three 2D orthogonal images of molecules was done. The training set is composed of 30,000 images, whereas the testing set was composed of 7500 images, from 12,500 different molecules. The multichannel 2D CNN architecture was optimized via Bayesian optimization. Experimental results showed that the proposed CNN model obtained an acceptable mean absolute error of 0.6780 eV and root mean-squared error of 0.7673 eV, in contrast to two machine learning methods reported in the literature used for band-gap prediction based on conventional density function theory (DFT) methods. These results demonstrate the feasibility of CNN models to materials science routines using orthogonal images projections of molecules. © 2021 Elsevier B.V.

What would have happened if a relatively looser fisheries policy had been implemented in the European Union (EU)? Using Bayesian methods a Dynamic Stochastic General Equilibrium (DSGE) model is estimated to assess the impact of the European Common Fisheries Policy (CFP) on the economic performance of a Galician (north-west of Spain) fleet highly dependant on the EU Atlantic southern stock of hake. Our counterfactual analysis shows that if a less effective CFP had been implemented during the period 1986–2012, fishing opportunities would have increased, leading to an increase in labour hours of 4.87%. However, this increase in fishing activity would have worsened the profitability of the fleet, dropping wages and rental price of capital by 6.79% and 0.88%, respectively. Welfare would also be negatively affected since, in addition to the increase in hours worked, consumption would have reduced by 0.59%.

<p>This paper is aimed at studying a MS-GARCH model applied to Bitcoin. The Bayesian estimation of the model shows that Bitcoin’s volatility can be modelled using two states of volatility, high and low. The modelled volatility is not stable over time. Twenty eight periods of high volatility were found, the largest period of volatility occurred during 2013. The findings help explain what happened during these high volatility periods.</p><p> </p><p><strong> </strong></p>

Designing efficient vaccination programs that consider the needs of the population is very relevant to prevent reoccurrence of the COVID-19 pandemic. The government needs to provide vaccination points to give out vaccine doses to the population. In this paper, the authors analyze the location of vaccination points whilst addressing the inhabitants’ preferences. Two objectives that prevent crowding of inhabitants are considered. The government aims for the minimum distance between located vaccination points is maximized, and for the number of inhabitants that attend the different vaccination points to be equitable. One of the key aspects of this problem is the assumption that inhabitants freely choose the located vaccination point to go. That decision affects the objectives of the government, since crowding at vaccination points may appear due to the inhabitants’ decisions. This problem is modeled as a bi-objective, bi-level program, in which the upper level is associated to the government and the lower level to the inhabitants. To approximate the Pareto front of this problem, a cross-entropy metaheuristic is proposed. The algorithm incorporates criteria to handle two objective functions in a simultaneous manner, and optimally solve the lower-level problem for each government decision. The proposed algorithm is tested over an adapted set of benchmark instances and pertinent analysis of the results is included. An important managerial insight is that locating far vaccination points does not lead us to a more equitable allocation of inhabitants.

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.

Supply chain management is the basis for the execution of operations, being considered as the core of the business function in the 21st century. On the other hand, at present, factors such as the reduction of natural resources, the search for competitive advantages, government laws, and global agreements have generated a greater interest in the sustainable development, which, in order to achieve it, industries need to rethink and plan their supply chain considering a path of sustainability. So sustainable supply chain management emerges as a means to integrate stakeholders' concern for profit and cost reduction with environmental and social requirements, attracting significant interest among managers, researchers, and practitioners. The main objective of this study is to provide a synthesis of the key elements of the quantitative model offerings that use sustainability indicators in the design and management of forward supply chains. To achieve this objective, we developed a systematic literature review that includes 80 articles published during the last decade in peer-reviewed journals in English language. In addition, a 4W's analysis (when, who, what, and where) is applied and three structural dimensions are defined and grouped by categories: supply chain management, modeling, and sustainability. As part of the results, we evidenced a continuous growth in the scientific production of this type of articles, with a predominance of deterministic mathematical programming models with an environmental economic perspective. Finally, we identified research gaps, highlighting the lack of integral inclusion of a life cycle analysis in the design of supply chain networks. © 2022 Elsevier Inc. All rights reserved.

Background: Distribution systems often face the dual challenge of delivering products to customers and retrieving damaged items for repair, especially when the service center is separate from the depot. An optimized solution to this logistics problem produces benefits in terms of costs, greenhouse gas emissions, and disposal reduction. Methods: This research proposes a Capacitated Vehicle Routing Problem with Service Center (CVRPwSC) model to determine optimal routes involving customers, the depot, and the service center. AMPL-Gurobi was used to solve the model on adapted instances and new instances developed for the CVRPwSC. Additionally, a Variable Neighborhood Search (VNS) algorithm was implemented and compared with AMPL-Gurobi. Results: The model was applied to a real-world case study, achieving a 40% reduction in fuel costs, a reduction from 5 to 3 routes, and a sustainable logistics operations model with potential reductions of greenhouse gas emissions and item disposals. Conclusions: The main contribution of the proposal is a minimum-cost routing model integrating item returns for repair with customer deliveries, while the limitation is the exclusion of scenarios where return items exceed vehicle capacity. Finally, future research will enhance the CVRPwSC model by incorporating additional constraints and decision variables to address such scenarios.

An approach for the robust feedback control of a class of biomedical systems in chained form is presented. The control approach is based on modeling error compensation techniques and a recursive cascade scheme. Numerical simulations on three biomedical models of VIH-1, cancer and glucose systems are provided to illustrate our findings

This paper describes a case study about a multi-agent system for cooperative tasks, i.e. a mixing color task given three different sources of color. A reinforcement learning approach was performed by the agents, however, this type of learning exploits exponentially when the number of states in the environment is very large. In that sense, the paper proposes to use the MaxQ-Q hierarchical reinforcement learning algorithm to obtain a suitable policy for agents in order to minimize the time process to achieve the goal, and to reduce the state space. In addition, since the multi-agent system runs in a software application, a multi-threading paradigm was proposed to use. Experimental results show that this multi-agent system can reduce the time process and still maintain independence of agents. © 2015 IEEE.

Many organizations base their demand forecasts and replenishment polices only on judgmental or qualitative approaches. This paper presents an application where quantitative demand forecasting methods and classic inventory models are used to achieve a significant inventory cost reduction and improved customer service levels at a company located in Guadalajara, Mexico. The company currently uses a naive method to forecast demand. By proposing the use of Winters method, the forecast accuracy was improved by 41.12%. Additionally, as a result of an ABC analysis for the product under analysis, a particular component was chosen (it accounts for the 70.24% of the total sales and 60.06% of the total volume) and two inventory policies studied for that particular component. The first inventory policy considers the traditional EOQ model, whereas the second one uses a continuous-review (Q,R) policy. The best policy achieves a 43.69% total cost reduction, relative to the current inventory policy. This policy translates into several operational benefits for the company, e.g., improved customer demand planning, simplified production and procurement planning, lower level of uncertainty and a better service level.

The current digital age, mainly characterized by an economy based upon information technology, demands a data integrity service, even more so because organizations and companies are migrating their services over the cloud. This is not a simple task; it is cumbersome since traditional schemes in databases could be subject to modifications. However, it can be solved using blockchain technology. This paper provides a data integrity verifiability architecture for cloud systems based on blockchain. The architecture provides a mechanism to store events (as logs) within a blockchain platform from any cloud system. Users can then consult data integrity through a microservice, acting as an intermediate server that carries out a set of verification steps within the blockchain, which confirms the integrity of a previously stored log. Our architecture takes advantage of the blockchain strength concerning integrity, providing a traceability track of the stored logs. A prototype system and a case study were implemented based on the proposed architecture. Our experimental results show that the proposed decentralized architecture can be adapted to cloud existing systems that were born without blockchain technology and require a modular and scalable audit characteristic.

The aim of this study is to introduce a new clustering method for ipsatives variables. This  method can be used for nominals or ordinals variables for which responses must be mutually exclusive, and it is independent of data distribution. The proposed method is applied to outline motivational profiles for individuals based on a declared preferences set.  A case study is used to analyze the performance of the proposed algorithm by comparing proposed method results versus the PAM method. Results show that proposed method generate a better segmentation and differentiated groups. An extensive study was conducted to validate the performance clustering method against a set of random groups by clustering measures.