En este estudio se presentan los resultados obtenidos a partir de la aplicación de un instrumento de evaluación, el cual tuvo como objetivo mostrar la falta de orientación vocacional y cómo esto puede motivar la deserción universitaria. La investigación se llevó a cabo en la Escuela Preparatoria Oficial n.° 143de la zona Oriente del Valle de México. Para ello, se implementó la metodología mixta. En la parte de la investigación documental se utilizó el método explicativo, el cual consiste en interpretar una de las causantes de los fenómenos (en este caso, la deserción escolar a nivel universitario); y para el análisis de los datos se empleó la metodología cuantitativa. Asimismo, se aplicó un cuestionario con siete preguntas sobre el contexto de la orientación vocacional a 231 estudiantes de nivel medio superior con edades entre 15 y 20 años. Los resultados muestran que la falta de orientación vocacional es un factor relevante en la deserción universitaria. Además, el 36.8 % de los estudiantes afirmó que la escuela no les proporcionó algún tipo de herramienta de orientación vocacional. En consecuencia, 87 % están interesados en utilizar algún tipo de herramienta que les ayude a tener una mejor orientación vocacional. Estos resultados indican la importancia de contar con herramientas en dicha área accesibles y eficientes para los estudiantes de bachillerato, lo cual podría reducir el nivel de deserción escolar de nivel universitario.

The increasing volume of medical information available in digital format presents a significant challenge for researchers seeking to extract relevant information. Manually analyzing voluminous data is a time-consuming process that constrains researchers’ productivity. In this context, innovative and intelligent computational approaches to information search, such as large language models (LLMs), offer a promising solution. LLMs understand natural language questions and respond accurately to complex queries, even in the specialized domain of medicine. This paper presents MédicoBERT, a medical language model in Spanish developed by adapting a general domain language model (BERT) to medical terminology and vocabulary related to diseases, treatments, symptoms, and medications. The model was pre-trained with 3 M medical texts containing 1.1 B words. Furthermore, with promising results, MédicoBERT was adapted and evaluated to answer medical questions in Spanish. The question-answering (QA) task was fine-tuned using a Spanish corpus of over 34,000 medical questions and answers. A search was then conducted to identify the optimal hyperparameter configuration using heuristic methods and nonlinear regression models. The evaluation of MédicoBERT was carried out using metrics such as perplexity to measure the adaptation of the language model to the medical vocabulary in Spanish, where it obtained a value of 4.28, and the average F1 metric for the task of answering medical questions, where it obtained a value of 62.35%. The objective of MédicoBERT is to provide support for research in the field of natural language processing (NLP) in Spanish, with a particular emphasis on applications within the medical domain.

In recent years, modern technology has been increasing, and this has grown a derivate in big challenges related to the network and application infrastructures. New devices have been providing more high functionalities to users than ever before; however, these devices depend on a high functionality of network in order to ensure a correct functioning ability over applications. This is essential for mobile networking systems to evolve in order to meet the future requirements of capacity, coverage, and data rate. In addition, when a network problem happens, it could be converted into somethingmore disastrous and difficult to solve. A crucial point is the network physical change and the difficulties, such as loss continuity of services and the decision to select the future network to be connected. In this article, a new framework is proposed to forecast a future network to be connected through a mobile node in WLAN environments. The proposed framework considers a decision-making process based on five classifiers and the user’s position and acceleration data in order to anticipate the network change, reaching up to 96.75% accuracy in predicting the connection of this future network. In this way, an early change of network is obtained without packet and time loss during the network change.

Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.

Conjunctions have different interpretations: they eliminate redundancies: “María se bañó y se peinó” (Maria bathed and she combed her hair), unite different ideas: “Hoy llovió y no fui a corer” (Today it rained and I did not go to run) and use of lists: “Eran Alma, Edith y Omar” (They were Alma, Edith and Omar). Conjunctions take different semantic contexts. We understand each other because of the common sense despite expressing ourselves incorrectly from the standpoint of semantics, but for a computer it is difficult. In order to “understand” the sentences, the machine must solve semantics problems; this article exposes one of these problems. ANACONJ is an algorithm of pattern recognition of texts, which uses rules and syntactic patterns that analyze each word of a sentence in a phrase, identifying those sentences with conjunctions to build a semantic tree of the sentence where the conjunction connects words (nouns, verbs, etc.) according to their meaning. ANACONJ could be used as a teaching Spanish software tool and as an app for a service robot too.

This paper describes a semantic module called DESPRE (DESambiguador de PREposiciones, or Disambiguator of prepositions) that solves the prepositional phrase attachment for Spanish and complements the syntactic analysis (based in tags and syntactic rules) of project FreeLing. Syntactic analysis is not enough to correctly disambiguate prepositions and attach each prepositional phrase to the appropriate head. This is because, depending on the context, the same preposition conveys different meanings. DESPRE analyzes the preposition and its surroundings, assigning to it its correct sense; once the preposition sense is disambiguated, parsing rules can more easily determine which the right head is, thus building the correct dependency tree. In this manner, with the help of the preposition semantics, the sentence is disambiguated and the prepositional phrase is correctly attached. DESPRE uses an ontology with semantic frames (for each prepositional sense) to disambiguate and solve prepositional phrase attachment. We have added new rules to the FreeLing dependency parser to handle the different preposition senses. Thus, DESPRE, the modified version of FreeLing, uses syntactic and semantic analysis. Several examples show the potential effectiveness of DESPRE, which was able to correctly attach prepositional phrases that the syntactic analysis of Freeling alone could not.