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La primera condena a los libros De Rege et Regis institutione por parte de la Sorbona, con fecha de 4 de junio de 1610, se produjo en un contexto de frenética producción propagandística e intelectual que tuvo como objetivo desprestigiar a la Compañía de Jesús. A raíz de la presunta involucración de algunos de sus miembros en hechos como los intentos fallidos de asesinato de Enrique IV de Francia o, posteriormente, el intento que sí tuvo éxito, numerosos panfletos y otros textos usaron las palabras de Mariana como causa de acusación principal. También se echó manos de otros autores como Ribadeneira o Belarmino. La intervención de Pierre Coton con su Lettre déclaratoire no apaciguó a sus enemigos, sino que avivó el ya encendido debate. La respuesta más destacada fue el Anticoton, que obtuvo respuesta por parte de un padre jesuita no identificado en la Response apologetique à l’Anticoton. Tal respuesta obligó a la Sorbona a reunirse de nuevo para decretar una segunda condena a Mariana y una primera a este texto. Este trabajo transcribe la censura publicada por la Sorbona el 1 de febrero de 1611.

Las empresas que buscan ser diferenciadas y aumentar su rentabilidad, incluyendo las de consultoría, cuyo principal activo es el conocimiento, suelen implementar procesos de innovación. Sin embargo, existe poca comprensión sobre cómo se desarrolla la innovación en estas organizaciones. Este artículo presenta las prácticas de innovación en empresas de consultoría especializadas en seguridad y salud en Bogotá. Tras comparar varios métodos para caracterizar la innovación, se seleccionó uno que contempla el conocimiento y su gestión para desarrollar un estudio descriptivo. El estudio identificó tres aspectos clave del desarrollo de la innovación: la creación de espacios para el intercambio informal de conocimientos, la utilización de tecnologías para reunir los conocimientos generados en cada servicio y la creación del registro de excelencia para controlar las actividades y los resultados de innovación. Estos resultados permiten a estas organizaciones asignar recursos a este tipo de acciones para mejorar la implementación de la innovación.

Background: Cell migration is essential for the immune system and is frequently analyzed in adult non-pregnant animals but poorly explored in pregnant animals. However, a physiologic increased size in the spleen and periaortic lymph nodes had been reported in pregnant mice. Methods: Using a mouse model, we transferred PKH26-stained thymocytes and splenocytes from pregnant or non-pregnant animals to receptor mice in the presence or absence of pregnancy. Percentage of PKH-26 cells and Mean Fluorescence Intensity were calculated. Non-parametric ANOVA analysis was performed. Results: We detected that the percentage of PKH26+ thymocytes in the spleen, lymph nodes, and peripheral blood is higher in females than in males (p = 0.039). Our results showed a similar frequency of thymocytes and splenocytes from pregnant and non-pregnant mice located in receptor lymphoid organs (p > 0.05). Also, the location of marked cells was similar during the perinatal period (p > 0.05). Conclusions: The mobility of thymocytes and splenocytes in pregnant and non-pregnant mice is similar. Therefore, we suggest that the larger size of the spleen and periaortic lymph nodes noted previously in pregnant mice could result from the retention of leukocytes in the secondary lymphoid organs.

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO2 levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO2 and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.

This article provides a detailed analysis of non-invasive techniques for the prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state of the art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to improve fault diagnosis, in addition to highlighting the crucial role of non-invasive techniques such as the digital twin in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop the integration of AI algorithms with digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.

Purpose: The aim of the research was to analyze the impact of the regional innovation system on the efficiency and productivity enterprises dedicated to protected agriculture in Hidalgo, Mexico. Methodology: The regional innovation system was analyzed by identifying the innovation networks. Subsequently, a typology of enterprises was generated according to the networks in which they participate and an analysis of variance and comparison tests of Scheffé means between clusters. Findings: In the regional innovation system, the networks were made up of government institutions, teaching and research institutions, and suppliers of inputs and services, where the latter have the highest degrees of articulation. Also, positive effects were identified in the efficiency indicators of the enterprises related to the networks made up of government institutions, teaching and research institutions, and the extension services. Practical implications: The identification of these interactions is essential for the design of public policies for the promotion of innovation. For example, for the design of a smart extension program that articulates all the actors and that all benefit from their interactions. Theoretical implications: The study reveals the positive influence of the regional innovation system on enterprises. In addition, it rekindles the discussion on the relevance of the State in the operation of innovation systems, the role of universities in the generation of knowledge and the extension service in the diffusion of new knowledge, information and practices. Originality/value: The study generates important knowledge about the regional innovation system and about the design of an innovation management strategy.

Environmental education has an important role in higher education institutions as it leads to understanding the importance of sustainability, thus creating a positive impact on students. This impact extends beyond the classroom, inspiring them to actively engage in environmentally responsible behaviors and become conscientious stewards of our planet. The aim of the research was to analyze the environmental perception and ecological attitude of university students at the University of Medellín, that is, this study consisted of examining whether environmental education programs positively influenced the perception of the green campus and the ecological attitude. A structural model was proposed to empirically contrast this phenomenon at a university accredited as a green campus. The results explored that the universities that carry out environmental programs and that offer educational activities on this topic generate a responsible attitude towards sustainability and ecology in their students. This has short, medium and long-term impacts for the university.

Global corporations frequently grapple with a dilemma between fulfilling business needs and adhering to travel policies to mitigate excessive fare expenditures. This research examines the multifaceted nature of business travel, delving into its key characteristics and the inherent complexities faced by management in formulating effective policies. An optimal travel policy must both be practical to implement and contribute to budget optimization. The specific requirements of each company necessitate tailored policies; for instance, a manufacturing company with scheduled trips demands a distinct policy, unlike a consulting firm with unplanned travel. This study proposes a modified regression decision tree machine learning algorithm to incorporate the unique features of corporate travel policies. Our algorithm is designed to self-adjust based on the specific data of each individual company. The authors implement the proposed approach using travel data from a real-world company and conduct simulations in various scenarios, comparing the results with the industry standard. This research offers a machine-learning-based approach to determining the optimal advance booking policy for corporate travel.

La actividad hospitalaria dentro de los servicios de salud juega un papel preponderante, asume una misión de gran valor social y se destaca por su elevada importancia y complejidad. El hospital es también un centro de capacitación y formación de personal médico y de apoyo, como de investigación científica y bio-social. De todas las organizaciones públicas o privadas, el hospital es de las más complejas para administrar, por factores como las continuas especializaciones médicas, la incorporación de nuevos conocimientos interdisciplinarios, equipos más novedosos y el aumento del número de enfermedades de tratamiento costoso y sofisticado. Además de generar un alto grado de desechos al medio ambiente, por lo que es de vital importancia la preparación de sus trabajadores en esta temática. En este sentido se encamina este artículo, que tiene el objetivo de establecer una relación entre las dimensiones del hospital y la dimensión ambiental en la formación de los recursos humanos, de manera que aumente la efectividad de la protección ambiental en estas instituciones. Para ello fueron de utilidad métodos como el análisis-síntesis, inducción-deducción, observación, encuesta. Como principal resultado se advierte que la interrelación que se logre en dichas dimensiones facilitará el trabajo por procesos y la efectividad de las acciones para proteger el medio ambiente y aumentar las competencias de los trabajadores.

Objetivo: identificar los elementos o factores que influyen en la relación entre el greenwashing y el green brand equity. Diseño/metodología: para llevar a cabo este estudio se optó por una metodología de tipo exploratorio con un enfoque cualitativo, destacando la búsqueda conceptual que delineó a los autores clave y los factores asociados a través del análisis documental. Resultados: la indagación reveló que los factores vinculados en esta relación incluyen el green trust, green brand image, brand credibility, green satisfaction, green brand association, green concern, green confusion, green brand attitude, word of mouth, brand avoidance, green perceived risk, green brand perceived value, green loyalty y green brand legitimacy, los cuales son estudiados en la literatura y, dependiendo el contexto, tienen diferentes roles en esta relación. Conclusiones: se concluye que esta relación constituye un área de investigación subexplorada; sin embargo, se presenta como una oportunidad para profundizar en la comprensión de los efectos generados por el greenwashing en diversas marcas a través de los factores mencionados anteriormente. Originalidad: este estudio amplía el análisis de los impactos del greenwashing en el green brand equity al identificar los factores que inciden o pueden mediar en esta relación, señalando también las posibles direcciones futuras de investigación, brindando los factores que se pueden estudiar en diferentes contextos.