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The introduction of Artificial Intelligence (AI) generative language models such as GPT (Generative Pre-trained Transformer) and conversational tools such as ChatGPT has triggered a revolution that can transform how text is generated. This has many implications, for example, as AI-generated text becomes a significant fraction of the text, would this affect the language capabilities of readers and also the training of newer AI tools? Would it affect the evolution of languages? Focusing on one specific aspect of the language: words; will the use of tools such as ChatGPT increase or reduce the vocabulary used or the lexical diversity? This has implications for words, as those not included in AI-generated content will tend to be less and less popular and may eventually be lost. In this work, we perform an initial comparison of the vocabulary and lexical diversity of ChatGPT and humans when performing the same tasks. In more detail, two datasets containing the answers to different types of questions answered by ChatGPT and humans, and a third dataset in which ChatGPT paraphrases sentences and questions are used. The analysis shows that ChatGPT-3.5 tends to use fewer distinct words and lower diversity than humans while ChatGPT-4 has a similar lexical diversity as humans and in some cases even larger. These results are very preliminary and additional datasets and ChatGPT configurations have to be evaluated to extract more general conclusions. Therefore, further research is needed to understand how the use of ChatGPT and more broadly generative AI tools will affect the vocabulary and lexical diversity in different types of text and languages.

Effective waste management is essential for maintaining a clean and sustainable urban environment. This paper addresses the waste collection challenges in Torreón, Coahuila, Mexico, by integrating data mining techniques with the vehicle routing problem (VRP). The goal is to optimize waste collection routes and schedules to minimize costs, reduce fuel consumption, and enhance operational efficiency. The study leverages historical waste collection data, demographic information, and geographic data to comprehensively understand waste generation patterns, population density, and infrastructure layout. The data mining process involves exploratory analysis, clustering, and classification techniques to identify critical variables and patterns that influence waste generation and collection. This information is used to design efficient collection routes, considering factors such as distance, capacity constraints, traffic patterns, and time windows. The VRP model is then applied to allocate waste collection vehicles optimally, ensuring that each route is serviced within the designated timeframe. The model aims to minimize the number of vehicles used, optimize capacity utilization, and reduce travel distances. Additionally, real-time data on vehicle status, traffic conditions, and waste generation rates are integrated to adjust routes and schedules to improve operational efficiency dynamically. This approach provides waste management authorities in Torreón with valuable insights and tools to enhance waste collection operations. By leveraging data mining and VRP techniques, the study aims to improve waste management practices, reduce environmental impact, and promote a cleaner, healthier environment for residents of Torreón, Coahuila, Mexico.

A dominating set of a graph is a subset of vertices such that every vertex not in the subset has at least one neighbor within the subset. The corresponding optimization problem is known to be NP-hard. It is proved to be beneficial to separate the solution process in two stages. First, one can apply a fast greedy algorithm to obtain an initial dominating set and then use an iterative procedure to purify (reduce) the size of this dominating set. In this work, we develop the purification stage and propose new purification algorithms. The purification procedures that we present here outperform, in practice, the earlier known purification procedure. We have tested our algorithms for over 1300 benchmark problem instances. Compared to the estimations due to known upper bounds, the obtained solutions are about seven times better. Remarkably, for the 500 benchmark instances for which the optimum is known, the optimal solutions are obtained for 46.33% of the tested instances, whereas the average error for the remaining instances is about 1.01.

Vocabulary tests, once a cornerstone of language modeling evaluation, have been largely overlooked in the current landscape of Large Language Models (LLMs) like Llama 2, Mistral, and GPT. While most LLM evaluation benchmarks focus on specific tasks or domain-specific knowledge, they often neglect the fundamental linguistic aspects of language understanding. In this paper, we advocate for the revival of vocabulary tests as a valuable tool for assessing LLM performance. We evaluate seven LLMs using two vocabulary test formats across two languages and uncover surprising gaps in their lexical knowledge. These findings shed light on the intricacies of LLM word representations, their learning mechanisms, and performance variations across models and languages. Moreover, the ability to automatically generate and perform vocabulary tests offers new opportunities to expand the approach and provide a more complete picture of LLMs' language skills.

Failure Factors in MSMEs in Mexico Evidenced by COVID-2019 Before the pandemic, the economic development of Micro, Small and Medium-sized Enterprises (MSME) in Latin America showed gradual signs of recovery after the problems that occurred in years 2009 and 2016. As of 2017, Mexico maintained a growing inflation of 5% on average, the price of energy increased 20%, an external debt growth of 12% and a GDP lesser than 1.6% per year (Banco Mundial, 2020), (OCDE, 2019), (INEGI, 2018). By the end of 2019, SARS-CoV-2 (COVID - 2019) evidenced failure factors that exacerbated the permanence of MSMEs, since closed more than 30.6% of them (Naciones Unidas, 2021). In this research, these factors are discussed, by compiling information on economic behavior, and quantitative studies of the commercial, manufacturing and services sectors. To identify these factors, 246 Mexican MSMEs that closed between September 2019 and December 2020 were selected using stratified random sampling. The results showed failure factors associated with the capabilities of the administrator and internal aspects of the company, among which the lack of vision to identify new businesses and little knowledge of Information Technology stand out.

The Traveling Salesman Problem (TSP) aims to find the shortest tour for a salesman who starts and ends in the same city and visits the remaining n−1 cities exactly once. There are a number of common generalizations of the problem including the Multiple Traveling Salesman Problem (MTSP), where instead of one salesman, there are k salesmen and the same amount of individual tours are to be constructed. We consider the Euclidean version of the problem where the distances between the cities are calculated in two-dimensional Euclidean space. Both general the TSP and its Euclidean version are strongly NP-hard. Hence, approximation algorithms with a good practical behavior are of primary interest. We describe a general method for the solution of the Euclidean versions of the TSP (including MTSP) that yields approximation algorithms with a favorable practical behavior for large real-life instances. Our method creates special types of convex hulls, which serve as a basis for the constructions of our initial and intermediate partial solutions. Here, we overview three algorithms; one of them is for the bounded version of the MTSP. The proposed novel algorithm for the Euclidean TSP provides close-to-optimal solutions for some real-life instances.

This paper develops a hybrid algorithm that combines evolutionary programming to improve the optimization of a virtual social networking in the Facebook's social network game named “My Tribe”. This prototype takes a collection of individuals and interprets the expectedly of collective studies in different activities. Using a specific model to create and verify the method, led by means of a collection of structure heuristics acquired from the prospect of reaching the ideal tribe, this prototype can operate in two ways: by generating a tribe without restrictions or by a creation of a tribe accordant to one of the three preset architecture of the tribe. Seven construction heuristics are taken into account, through different combinations of two batches of the creation of the initial population, the first generated from an established combination, one popularly used in anthropology and the other using the psychosocial analysis of habitat patterns. The goal is to make sure the relative importance of the initial population size tribe and the construction of a Heuristic based on the general acceptability of the resulting tribe, which is validated with the Cultural Algorithm.

We propose an approximation algorithm for scheduling jobs with release and delivery times on a single machine with the objective to minimize the makespan. The algorithm is based on an implicit enumeration of the set of complete solutions in a search tree. By analyzing specific structural properties of the solutions created in each branch of the solution tree, a certain approximation factor of each solution from that branch is calculated. Our algorithm guarantees approximation factor 1+1/κ for a generated solution if there are κ jobs with a specified property in that solution (typically, the longer the length of the path from the root to the node representing that solution in the solution tree, the larger the κ value). We have carried out an extensive computational study to verify the practical performance of our algorithm and the effectiveness of the approximation factor provided by us. While our problem instances are generated randomly, we have discarded a considerable number of the instances, ones which were already solved optimally by the earlier known dominance rules. For the vast majority of the tested problem instances, within a short running time of our algorithm parameter κ becomes sufficiently large so that the approximation factor which the algorithm guarantees becomes better than that provided by the earlier known approximation algorithms.

Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy.

Esta investigación está orientada a clasificar textos usando Redes Neuronales Artificiales (RNA) específicamente el Perceptron Multicapa (PMC) con Técnicas básicas de palabras embebidas. La clasificación consiste en determinar ya sea que el texto tenga un contexto criminal o no por medio de reconocimiento de patrones. El PMC fue entrenado bajo entrenamiento supervisado y en un rango corto de vocabulario y registros de entrenamiento, cada uno de los cuales tiene una longitud máxima de 300 palabras para hacer procesos de clasificación. Analizar estos tipos de textos podría ayudar a las fuerzas de seguridad del gobierno, a los militares, etc. para fácilmente detectar gente que podría dañar a la población y predecir posibles ataques y prevenirlos. El software desarrollado necesita más técnicas de palabras embebidas, un vocabulario más grande y más registros de entrenamiento para ser más eficiente. El conjunto de datos consiste de dos clases principales que están organizadas como textos de tipo criminal y regular.

The explosion of the Internet has deeply affected the labour market. Identifying most rewarded and demanded items in job offers is key for recruiters and candidates. This work analyses 4, 000 job offers from a Spanish IT recruitment portal. We conclude that (1) experience is more rewarded than education, (2) we identify five profile clusters based on required skills and (3) we develop an accurate salary-range classifier by using tree-based ensembles.

El análisis de los datos (DA – Data Analytics por sus siglas en Inglés-) es un factor primordial para la toma de decisiones y con ello establecer las estrategias adecuadas que respondan a la mejora de la situación actual. En esta investigación se analiza el impacto que tiene el nivel de desempeño de los profesores de una universidad pública en relación a los cursos de formación, capacitación y/o actualización docente, para ello se tomaron dos muestras de 600 registros cada una, de las evaluaciones de los profesores, una de los que han tomado cursos de capacitación y otra de los que no tomaron cursos de capacitación durante el año 2016, se aplicaron técnicas de DA para su tratamiento y se compararon las evaluaciones mediante la prueba de t de Student para identificar diferencias significativas entre medias. Los resultados indican que los profesores que tomaron cursos tienen una evaluación menor que los que no tomaron cursos y sí existen diferencias significativas entre estas evaluaciones.

ElAlgoritmo Competitivo Imperialista (ACI) usa un sistema básico de conocimiento fuente para determinar las mejores situaciones bajo incertidumbre usando un modelo de países, cada uno relacionado al conocimiento observado en varios aspectos del comportamiento social. Este conocimiento está combinado para direccionar las decisiones de los agentes individuales para resolver problemas de optimización o en la solución de la distribución de recursos en diferentes comunidades. En la presente investigación, nosotros simulamos un modelo reactivo bajo incertidumbre para integrar estas diversas fuentes de conocimiento para dirigir la población del agente. Las diferentes fases de solución del problema emergen combinando el uso de estas fuentes de conocimiento y estas fases dan lugar a la aparición de roles individuales dentro de la población en términos de líderes y seguidores para cada país (grupo de agentes). Estos roles dan lugar a la salida de un grupo organizado o grupos organizados en nivel de población y grupos de conocimientos o conocimiento agrupado en el espacio de creencias sociales. Esta aplicación optimiza una función revalorizada en el diseño de problemas de modelado social, permitiendo ilustrar un mejor modelo reactivo bajo incertidumbre.

Condition monitoring of Internal Combustion Engines (ICE) benefits cost-effective operations in the modern industrial sector. Because of this, vibration signals are commonly monitored as part of a non-invasive approach to ICE analysis. However, vibration-based ICE monitoring poses a challenge due to the properties of this kind of signals. They are highly dynamic and non-stationary, let alone the diverse sources involved in the combustion process. In this paper, we propose a feature relevance estimation strategy for vibration-based ICE analysis. Our approach is divided into three main stages: signal decomposition using an Ensemble Empirical Mode Decomposition algorithm, multi-domain parameter estimation from time and frequency representations, and a supervised feature selection based on the Relief-F technique. Accordingly, we decomposed the vibration signals by using self-adaptive analysis to represent nonlinear and non-stationary time series. Afterwards, time and frequency-based parameters were calculated to code complex and/or non-stationary dynamics. Subsequently, we computed a relevance vector index to measure the contribution of each multi-domain feature to the discrimination of different fuel blend estimation/diagnosis categories for ICE. In particular, we worked with an ICE dataset collected from fuel blends under normal and fault scenarios at different engine speeds to test our approach. Our classification results presented nearly 98% of accuracy after using a k-Nearest Neighbors machine. They reveal the way our approach identifies a relevant subset of features for ICE condition monitoring. One of the benefits is the reduced number of parameters.

The purpose of this research is to understand from a Multivariable optimization perspective, the optimi- zation related with a group of Food truck vehicles in a large city, we try to analyze a specific number of issues and determine the optimal route involve speed, food storage, perish goods and travel resources as oil and gas to cooking for determining the cost benefit have partnered with a daily travel plan to sell specialized food, which has as principal basis the orography and distances restrictions, although this problem has been studied on several occasions by the literature, failed to establish a solution by supporting ubiquitous computing for interacting with the various values associated with the achieve- ment of the group of vehicles and their cost-benefit of each issue related with the same company and comparing their individual trips associated with the financial improve of the company. There are several factors that can influence in the achievement of a Food truck Company. For our research we propose use Algorithm Bat on, which has proven to be efficient for the convergence of several issues (artificial bats), when they have such restrictions and obstacles should use this energy to avoid a lose resource as food, which in our case is represented as the use of food optimally for the duration of a long travels, with the uncertainty of not knowing when having a resupply or is limited to fuel per day.

In biggest metropolis different kind of pets are living together with their masters, but with a limited time to receive food, attention or even a home where it is possible to find shelter for the rest of its lives. Juarez City has a population over 1.2 million of habitants, many families has different pets including dogs, cats, hamsters, birds, fishes or reptiles that some finished being abandoned in a quickly time, forget in the streets or die due starvation, negligence and selfish of their masters. To understand this social behavior we propose a Multivariable optimization associated with the numerical prediction of its abandoned to establish ecological public polices and determine the social consequences to determine the ecological cost benefit related with the replacement of new issues and increase of pets without master in the streets whereby exists many restrictions, although this problem has been studied on several occasions by the literature failed to realize an adequate numerical prediction evaluating various values associated with each kind of pet and compare its individual costs in each species. There are several factors that can influence to abandon or kill a pet, for our research we propose using a novel bio-inspired algorithm named Bacterial Foraging Optimization (BFO) Algorithm which has been proven to be efficient for predicting social behaviors associated with several aspects, in our case is represented as the increase of young population or adequate ecological public polices with the uncertainty of not knowing when will be too late to change our indifference to our pets.

Muchos problemas involucran ambientes no estructurados los cuales pueden ser resueltos desde la perspectiva de los Algoritmos Bioinspirados usados en una variedad de dominios de aplicación [7,8]. En esta investigación nosotros analizamos un algoritmo híbrido que permite obtener una muestra de combinaciones desde diferentes componentes para mejorar la optimización de un transformador térmico el cual es usado como una bomba de calor. Este prototipo toma como datos de entrada un conjunto de concentraciones (Bromuro de litio – BrLi- y agua para obtener el mejor índice de refracción) y analiza la expectativa de acciones colaborativas en esta combinación. Usando un algoritmo cultural será posible generar y probar un método innovador, guiado por un conjunto de heurísticas de construcción obtenidas de la posibilidad de llegar a esta concentración ideal, debido a que la temperatura varía el resultado de este índice de refracción, el prototipo puede operar en dos modos: ya sea generando una concentración sin restricciones, o generando una concentración de acuerdo a uno de los tres mejores paradigmas predefinidos, construidos dentro del espacio de la población. Siete diferentes heurísticas de construcción son probadas sobre diferentes combinaciones de dos conjuntos de datos iniciales, uno obtenido de la literatura usada en óptica y otro usando muestras previamente obtenidas en el análisis de laboratorio. Un conjunto de parámetros numéricos son extraídos de cada prueba, y evaluados en la búsqueda de correlaciones significativas. El propósito es distinguir la importancia relativa del tamaño de la concentración inicial y la heurística de construcción, con respecto a la aceptabilidad general del transformador térmico resultante el cual ha sido validado usando Algoritmos Culturales.

We studied the use of the library Microsoft Parallel Extensions to .NET Framework 3.5 for parallel calculations. We have developed a hierarchy of nesting classes having complicated internal structure and have made the bench tests (with graphic user interface (GUI)) not only for simple static cases but also for complex dynamic types. Our tests have shown high speedup of the library. The use of other libraries allowing calculations in parallel for various cross-platform applications is discussed also.

Nosotros describimos el uso de nuestro sistema de evaluación en línea (Cliente-Servidor) equipado con capacidades de reconocimiento biométrico, aplicado para evaluar el conocimiento básico en una preparatoria. En este reporte discutimos la posibilidad de la identificación real de estudiantes remotos para resolver el importante problema de: ¿quién está ahí?. En nuestro experimento, seleccionamos aleatoriamente una muestra de estudiantes (n=54) y llevamos a cabo dos evaluaciones: una evaluación normal (en lápiz y papel), y una evaluación por computadora. En este último caso, nuestro sistema de evaluación en línea con herramientas biométricas puede autentificar a un estudiante por medio de la huella digital. Si un estudiante es autentificado, el sistema permite la evaluación en línea, mientras tanto el estudiante está siendo monitoreado por cámaras Web. Durante la evaluación en línea los estudiantes fueron afectados por el ambiente en línea, ellos obtuvieron bajas calificaciones en línea con respecto a los exámenes tradicionales. El reconocimiento de huella digital fue muy bien aceptado. Sin embargo, a 13% de los estudiantes evaluados les disgustó ser monitoreados con cámaras Web, y el 20% de ellos encontraron alguna forma de hacer trampa. Esta tecnología nos permite obtener estadísticas comparables de ambas evaluaciones, y discutir recomendaciones generales para la estrategia, tecnología y equipamiento de las evaluaciones en línea.