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This work presents the proposal of an automatic method for monitoring fault codes of industrial processes, as well as its real implementation in a production line made up of 5 different processes. For the implementation, some industry 4.0 techniques were taken as a basis, such as Big Data, Industrial Networks, Manufacturing Execution Systems and Cloud Computing, integrating them into a system capable of collecting information in real time and focused on a automatic failure monitoring of the process in which it is implemented. A method is presented that consists of 12 specific steps, defined and applicable to a system of microcontrollers that guide processes where it is necessary to track the fault codes of the same process, and which are connected by some means of communication to share information. In the implementation of the method in a real case, you can see how to adapt it to manufacturing processes with great accuracy. This implementation was carried out in a serial production line made up of 5 processes dependent on each other where each process has its own controller and its own fault codes. The results of the implementation made visible the causes of the large number of process stoppages and the data returned by the system helped make important decisions to allocate resources efficiently in order to reduce the time lost due to machinery failures. It was also possible to locate the processes with the most problems in the last period, which made it possible to identify an area of ​​opportunity to increase the productivity of the monitored process.

Improving irrigation efficiency can contribute greatly to reducing production costs of vegetables, making the industry more competitive and sustainable. This publication is intended to help growers to conserve water through the use of soil moisture sensors. Written by Rafael Muñoz-Carpena, Michael D. Dukes, and Bernard Cardenas, and published by the UF/IFAS Department of Agricultural and Biological Engineering, revised August 2024.

Surface Reconstruction from raw Pointclouds generates polygonal meshes characterised by typical local issues that need to be addressed by a global reorganisation of mesh connectivity during the Remeshing process. While in the applications for which remeshing algorithms have historically been developed, the problem can be addressed without affecting the mesh polycount, for the authoring of 3D assets intended for Real-Time Rendering applications, maintaining a low polycount is mandatory. However, there is a trade-off between the number of polygons and accuracy. Hence, in cases where accuracy is a primary concern, such as with Digital Cultural Heritage Objects, the topology of the mesh that ensures this reduction becomes crucial since, to an even polycount, the dimensional discrepancy between the original and different simplified meshes can vary significantly.Therefore, it is vital to evaluate the characteristic quality obtainable from different remeshing algorithms with simplification, but despite the importance of the matter, there is a lack of metrics to perform such comparative benchmarks. This study aims to address this gap by defining and testing a Shape Complexity Index on real-world case studies, which is applicable uniformly and consistently to geometric shapes of generic complexity. Such index allows for pre-determine a target polycount and subsequently normalising signed distance computations among different test simplified meshes, posing the basis for both quantitative and qualitative analysis of accuracy achievable by any automatic remeshing tool. Additionally, the proposed workflow is employable as an instrument for planning, monitoring, and reporting a digitisation campaign of objects belonging to the Cultural Heritage.DOI: https://doi.org/10.20365/disegnarecon.32.2024.10

The use of automatic item generation (AIG) methods offers potential for assessing clinical reasoning (CR) skills in medical education, a critical skill combining intuitive and analytical thinking. In preclinical education, these skills are commonly evaluated through written exams and case-based multiple-choice questions (MCQs), which are widely used due to the high number of students, ease of standardization, and quick evaluation. This research generated CR-focused questions for medical exams using two primary AIG methods: template-based and non-template-based (using AI tools like ChatGPT for a flexible approach). A total of 18 questions were produced on ordering radiologic investigations for abdominal emergencies, alongside faculty-developed questions used in medical exams for comparison. Experienced radiologists evaluated the questions based on clarity, clinical relevance, and effectiveness in measuring CR skills. Results showed that ChatGPT-generated questions measured CR skills with an 84.52% success rate, faculty-developed questions with 82.14%, and template-based questions with 78.57%, indicating that both AIG methods are effective in CR assessment, with ChatGPT performing slightly better. Both AIG methods received high ratings for clarity and clinical suitability, showing promise in producing effective CR-assessing questions comparable to, and in some cases surpassing, faculty-developed questions. While template-based AIG is effective, it requires more time and effort, suggesting that both methods may offer time-saving potential in exam preparation for educators.

The following study relates the qualities and uses of the GeoAcademy Project, a program designed with the aim of geolocating scientific articles automatically, such articles would be found in Scopus, Web of Science, or similar databases. An algorithm has been developed with the intention of capturing geographical coordinates or toponyms contained within the documents in order to perform reliable geolocation. In the methodology, we describe the stages of the project that have been necessary so as to build a sample database concerning the Sierra Nevada (Spain), as well as the development of the algorithm. The technical data regarding the employment of the algorithm on the sample documents and its levels of success are included in the results, as is an explanation of the platform containing web maps which can be utilised to show the texts which have been geolocated. In conclusion we outline the obstacles faced, potential bibliometric uses and the advantages it offers as a reference resource and source of information.

In a cluster of news texts on the same event, two sentences from different documents might express different multi-document phenomena (redundancy, complementarity, and contradiction). Cross-Document Structure Theory (CST) provides labels to explicitly represent these phenomena. The automatic identification of the multi-document phenomena and their correspondent CST relations is definitely handy for Automatic Multi-Document Summarization since it helps computers understand text meaning. In this paper, we evaluated a typology of (textual) signals for the automatic detection of the CST relations of complementarity (i.e., Historical background, Follow-up and Elaboration) in a multi-document corpus of news texts in Brazilian Portuguese. Using algorithms from different machine-learning paradigms, we obtained classifiers that achieved high general accuracy (higher than 90%), indicating the potential of the signals.

Introducción: la endoscopia digestiva alta es el método estándar para diagnosticar el cáncer gástrico en etapas tempranas. Sin embargo, su precisión puede verse afectada por la variabilidad en su realización, y se estiman hasta 20% de tumores no detectados. En Colombia, la mayoría de los diagnósticos se realizan en etapas avanzadas, lo que agrava el problema. Para abordar la variabilidad, se han propuesto protocolos con el fin de asegurar la observación completa de áreas propensas a lesiones premalignas. Objetivo: construir y validar un sistema de auditoría automática para endoscopias usando técnicas de inteligencia artificial. Metodología: en este estudio, 96 pacientes de un hospital universitario se sometieron a endoscopias documentadas en video, abarcando 22 estaciones reorganizadas para minimizar solapamientos y mejorar la identificación de 13 regiones gástricas clave. Se utilizó una red convolucional avanzada para procesar las imágenes, extrayendo características visuales, lo que facilitó el entrenamiento de la inteligencia artificial en la clasificación de estas áreas. Resultados: el modelo, llamado Gastro UNAL, fue entrenado y validado con imágenes de 67 pacientes (70% de los casos) y probado con 29 pacientes distintos (30% de los casos), con lo que alcanzó una sensibilidad promedio del 85,5% y una especificidad del 98,8% en la detección de las 13 regiones gástricas. Conclusiones: la eficacia del modelo sugiere su potencial para asegurar la calidad y precisión de las endoscopias. Este enfoque podría confirmar las regiones evaluadas, alertando puntos ciegos en la exploración a los endoscopistas con menos experiencia o en entrenamiento, de tal forma que se aumente la calidad de estos procedimientos.

Introduction: Currently, the software development industry is living in its golden age due to the progress in areas related to machine learning, which is part of AI techniques. These advances have allowed tasks considered exclusively human to be solved using a computer. However, the complexity and the extensive area covered by new projects that must be developed using programming languages have slowed down project delivery times and affected the company's productivity. Objective: This research presents the methodology carried out for constructing a recurrent neural network model for the automatic generation of source code related to graphical user interfaces using Python programming language. Method: By constructing a natural language-related dataset for describing graphical interfaces programmed in Python, a deep neural network model is built to generate automatic source code. Results: The trained model achieves loss and perplexity values of 1.57 and 4.82, respectively, in the validation stage, avoiding overfitting in the model's training. Conclusions: A neural network model is trained to process the natural language related to the request to create graphical interfaces using the Python programming language to automatically generate source code that can be executed through the Python interpreter.

Recording and transcribing interviews in qualitative social research is a vital but time-consuming and resource-intensive task. To tackle this challenge, researchers have explored various alternative approaches; automatic transcription utilising speech recognition algorithms has emerged as a promising solution. The question of whether automated transcripts can match the quality of transcripts produced by humans remains unanswered. In this paper we systematically compare multiple automatic transcription tools: Amberscript, Dragon, F4x, Happy Scribe, NVivo, Sonix, Trint, Otter, and Whisper. We evaluate aspects of data protection, accuracy, time efficiency, and costs for an English and a German interview. Based on the analysis, we conclude that Whisper performs best overall and that similar local-automatic transcription tools are likely to become more relevant. For any type of transcription, we recommend reviewing the text to ensure accuracy. We hope to shed light on the effectiveness of automatic transcription services and provide a comparative frame for others interested in automatic transcription.