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Machine learning can be effectively used to generate models capable of representing the dynamic of production processes of small and medium-sized enterprises. These models enable the estimation of key performance indicators, and are often used for optimizing production processes. However, in most industrial applications, modeling and optimization of production processes are currently carried out as separate tasks, manually in a very costly and inefficient way. Automated machine learning tools and frameworks facilitate the path for deriving models, reducing modeling time and cost. However, optimization by exploiting production models is still in infancy. This work presents a methodology for integrating a fully automated procedure that embraces automated machine learning pipelines and a multi-objective optimization algorithm for improving the production processes, with special focus on small and medium-sized enterprises. This procedure is supported on embedding the generated models as objective functions of a reference point based non-dominated sorting genetic algorithm, resulting in preference-based Pareto-optimal parametrizations of the corresponding production processes. The methodology was implemented and validated using data from a manufacturing production process of a small manufacturing enterprise, generating highly accurate machine learning-based models for the analyzed indicators. Additionally, by applying the optimization step of the proposed methodology it was possible to increase the productivity of the manufacturing process by 3.19 % and reduce its defect rate by 2.15 %, outperforming the results obtained with traditional trial and error method focused on productivity alone.

El artículo tiene como modalidad la investigación documental, relacionado con la educación emocional en la escuela realizado a lo largo del año 2023. La metodología es heurística y, posteriormente, hermenéutica Se expone una recopilación crítica que parte de la pregunta de investigación ¿Por qué y de qué manera ha llegado la educación emocional a la escuela y cómo ésta se proyecta en víctimas del conflicto armado en Colombia? La sistematización de las investigaciones seleccionadas permitió desplegar un análisis deductivo, orientado en organizar, contrastar, e interpretar los datos hallados, y, en consecuencia, determinar cinco tendencias: la educación emocional en maestros. La educación emocional en el estudiantado; teorías y políticas públicas sobre la educación emocional; propuestas pedagógicas y sugerencias didácticas; emocionalidad y conflicto armado. Como conclusión, en los últimos años se naturaliza la idea que la educación emocional es una innovación que responde a nuevas necesidades sociales, pedagógicas y de bienestar en las escuelas.

The irruption of advanced technologies and the limited knowledge of software architectures are making it difficult for many small and medium-sized manufacturing companies to keep up with what is being called the fourth industrial revolution (Industry 4.0, Industry of the Future). Container orchestration platforms provide layers of simplification for key requirements such as interoperability, security, and privacy, and provide mechanisms that allow companies and technology providers to focus on their specific functionalities and goals, instead of investing considerable time and effort in the underlying platform on which the solution will operate. This article focuses on these platforms and the issues when developing them, and proposes a risk- and goal-oriented hybrid meta-framework for security and privacy analysis. The meta-framework uses well-known security and privacy standards and frameworks as a reference and can be used to understand assets and requirements and, in particular, to select and configure countermeasures. For practical evaluation of the meta-framework, it was applied to a real case. This case shows how the needs of the KITT4SME project platform were analyzed to support, among others, four manufacturing pilot cases and to define the key security and privacy features that should be introduced when implementing a software platform for easy uptake by small and medium enterprises.

En este artí­culo se evalua el desempeño por apuntamiento de una serie de estrategias de control para seguidores solares de alta concentracion. Dado el alto grado de precisión que se necesita en el apuntamiento real, por debajo del semi ángulo de aceptancia global del sistema de captacion de energí­a, en el artí­culo se revisan las incertidumbres mas caracterí­sticas del montaje de un seguidor solar, y se establece un marco teorico que permite representar imperfecciones de montaje en forma de relaciones de rotación de marcos de referencias asociados a los elementos del seguidor. Se expone un proceso de calibracion del sistema en dos etapas, que permite estimar incertidumbres parametricas de las rotaciones. Finalmente, se analizan cinco estrategias de control y los resultados experimentales obtenidos con ellas al implementarlas sobre un seguidor solar HCPV industrial de altas prestaciones. Los resultados muestran que con el sistema bien calibrado, todas las estrategias proporcionan un desempeno similar. Sin embargo, solo la basada en realimentacion en potencia es suficientemente robusta como para proporcionar buen desempeño cuando el sistema de descalibra.

The enterprises to be competitive are constantly looking for continuous increase of productivity, quality and level of services. With the development of Industry 4.0 concept, manufacturers are more confident about new advantages of automation and systems integration. Lean management is well developed and empirically proven effective managerial approach. Combining Lean and Industry 4.0 practices seems to be necessary evolutionary step for further raise the level of operational excellence (exploitation of finance, workload, materials, machines/devices). There is an increasing number of Industry 4.0 solutions used to reduce waste (as known from Lean Management). Therefore, the main objective of this article aims at presenting the results of a literature review on the concept of ‘Lean Industry 4.0ʹ. Dynamic methodology called “Systematic Literature Network Analysis (SLNA)” was used. It combines the Systematic Literature Review approach with the quantitative analysis of bibliographic networks to detect emerging topics and the dynamic evolution of the topics. The paper is a comprehensive systematization and rationalization of knowledge about the integration of LM and I4.0 concepts, identifies the most important research trends and defines directions for future research. The article contains a framework that presents the current state of knowledge in the area of Lean Industry 4.0.

Para conocer el tamaño de parcela que permita medir con eficiencia la incidencia de la Podredumbre blanca del capítulo-PBC de girasol (Helianthus annuus L.) se evaluó, en Balcarce, un par de híbridos en ensayos de uniformidad durante 2016 y 2017. Todos los capítulos de cada ensayo se inocularon con Sclerotinia sclerotiorum (Lib.) de Bary. Durante el primer año y para cada híbrido, se delinearon 165 parcelas mínimas, de un surco de 2m x 0,70m (=1,4m2). Mientras que en el segundo, la cantidad fue de 150. En cada una de esas parcelas básicas se registró la incidencia de PBC. Luego se generaron ocho tamaños más, combinando las parcelas mínimas adyacentes en distintos arreglos. El tamaño óptimo se señaló detectando el punto de máxima curvatura en gráficos que relacionaron el coeficiente de variación y los nueve tamaños de parcelas considerados. El análisis de varianza combinado sólo detectó efectos significativos de híbridos (H), años (A) y HxA sobre la incidencia de PBC. Los resultados obtenidos no permitieron señalar un único tamaño de parcela, dado que el óptimo dependió del nivel de resistencia del híbrido y del año de experimento. Así durante 2017, año más favorable a la enfermedad, el tamaño óptimo de parcela para los dos híbridos, resultó inferior que el señalado en 2016. Bajo las condiciones en que se desarrolló el experimento, el uso de parcelas de mayor superficie al señalado, no provocaría un incremento significativo en la fiabilidad de medición de la INC, por unidad de recursos utilizados.

In this paper, we describe the needs and specific requirements of the aerospace industry in the field of metal machining; specifically, the concept of an edge-computing-based production supervision system for the aerospace industry using a tool and cutting process condition monitoring system. The new concept was developed based on experience gained during the implementation of research projects in Poland’s Aviation Valley at aerospace plants such as Pratt & Whitney and Lockheed Martin. Commercial tool condition monitoring (TCM) and production monitoring systems do not effectively meet the requirements and specificity of the aerospace industry. The main objective of the system is real-time diagnostics and sharing of data, knowledge, and system configurations among technologists, line bosses, machine tool operators, and quality control. The concept presented in this paper is a special tool condition monitoring system comprising a three-stage (natural wear, accelerated wear, and catastrophic tool failure) set of diagnostic algorithms designed for short-run machining and aimed at protecting the workpiece from damage by a damaged or worn tool.

Objetivo: presentar el manejo realizado a una mujer con evisceración intestinal transvaginal como consecuencia de un trauma genital y perineal; describir la etiología, el diagnóstico y el tratamiento de esta condición ginecológica mediante una revisión de la literatura. Materiales y métodos: se presenta el caso de una mujer de 63 años de edad, con prolapso genital, quien sufre caída desde su propia altura que ocasiona trauma contundente y por aplastamiento a nivel genital y perineal; posteriormente se presenta disrupción de la pared vaginal y salida de asas intestinales, por lo que consultó a un hospital público de referencia ubicado en Bogotá, Colombia. Se realiza una búsqueda de la literatura en las bases de datos Medline vía PubMed, Science Direct y MD Consult, por medio de las palabras clave "vaginal" "evisceration" y "trauma". La búsqueda se limitó a artículos escritos en inglés y español. Resultados: se encontraron 40 artículos. Se seleccionaron 16 estudios: un artículo descriptivo de serie de casos y el resto corresponden a reportes de caso. La mayoría de reportes de casos descritos corresponden a pacientes en etapa posmenopáusica con antecedente de cirugía pélvica ginecológica, aunque existen reportes en mujeres en edad fértil cuyo mecanismo causal es diferente. El diagnóstico se basa en la historia clínica y hallazgos al examen físico. Es considerada una urgencia quirúrgica. La vía del abordaje quirúrgico depende de los hallazgos al examen físico y de las condiciones médicas asociadas. Conclusión: la evisceración transvaginal es una entidad poco frecuente, se considera una urgencia quirúrgica por las posibles complicaciones asociadas a la contaminación peritoneal o necrosis.

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

En el presente trabajo se muestran los aspectos básicos de la aritmética de campos finitos binarios GF(2m), extendidos usando el concepto de torres de campos GF(22m), en este caso con extensión 2 o cuadrática. El uso de torres de campos agiliza el cómputo de operaciones en los campos finitos, lo cual es aplicado en el cálculo de emparejamientos bilineales, parte fundamental de la criptografía basada en identidad; se presentan los conceptos básicos de aritmética en GF(2m) y la construcción de las operaciones suma y multiplicación en campos binarios extendidos. De igual manera, se presentan los resultados de la implementación en un dispositivo FPGA XV5LX110T de Xilinx Inc., desarrollada usando lenguaje VHDL y la herramienta ISE Design Suite System Edition 14.4.

On-chip LiDAR sensors for vehicle collision avoidance are a rapidly expanding area of research and development. The assessment of reliable obstacle detection using data collected by LiDAR sensors has become a key issue that the scientific community is actively exploring. The design of a self-tuning methodology and its implementation are presented in this paper, to maximize the reliability of LiDAR sensors network for obstacle detection in the ‘Internet of Things’ (IoT) mobility scenarios. The Webots Automobile 3D simulation tool for emulating sensor interaction in complex driving environments is selected in order to achieve that objective. Furthermore, a model-based framework is defined that employs a point-cloud clustering technique, and an error-based prediction model library that is composed of a multilayer perceptron neural network, and k-nearest neighbors and linear regression models. Finally, a reinforcement learning technique, specifically a Q-learning method, is implemented to determine the number of LiDAR sensors that are required to increase sensor reliability for obstacle localization tasks. In addition, a IoT driving assistance user scenario, connecting a five LiDAR sensor network is designed and implemented to validate the accuracy of the computational intelligence-based framework. The results demonstrated that the self-tuning method is an appropriate strategy to increase the reliability of the sensor network while minimizing detection thresholds.

In general, when forward-looking on-chip LiDAR is considered, the role of these sensors in vehicle collision avoidance is very important. [...]

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.