772 research outputs found

    Supplemental Material - Risk Factors, Clinical Presentation, Therapeutic Trends, and Outcomes in Arterial Thrombosis Complicating Unvaccinated COVID-19 Patients: A Systematic Review

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    Supplemental Material for Risk Factors, Clinical Presentation, Therapeutic Trends, and Outcomes in Arterial Thrombosis Complicating Unvaccinated COVID-19 Patients: A Systematic Review by Alejandra Castro-Varela, Daniela M Martinez-Magallanes, Maria Fernanda Reyes-Chavez, Jose Manuel Gonzalez-Rayas, Jose Gildardo Paredes-Vazquez, Eduardo Vazquez-Garza, Mauricio Castillo-Perez, Yoezer Z Flores-Sayavedra, Arturo Martinez, Ray Erick Ramos Cazares, Jaime Guajardo, Hector Lopez-de la Garza, Jose Alfredo Salinas-Casanova, Hector Betancourt, Abigail Montserrat Molina-Rodriguez, Jathniel Panneflek, Mario Alejandro Fabiani, and Carlos Jerjes-Sanchez in Angiology</p

    [Differential effects of instructions and consequences on human conditional discrimination performance]

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    Two studies evaluated the differential behavioral effects of instructions and feedback in matching-to-sample procedures. In Experiment 1, 20 college students received true or false instructions and trial-by-trial or delayed feedback in three phases. In a fourth, final phase the instructions remained the same, but feedback changed from trial-by-trial to delayed, or from delayed to trial-by-trial. In Experiment 2, half of another 20 participants received true instructions during three phases, followed by false instruction in a fourth phase; the other half of the participants received false instructions during three phases, followed by true instructions in the fourth phase. Feedback sequences were as in Experiment 1. The results of both experiments revealed historical effects of instructions and feedback. Most participants demonstrated strong instructional control, overriding the control by contingencies. These results suggest that the present procedure offers optimal possibilities to make the differential effects of instructions and feedback on human behavior clearly identifiable when conditional discrimination tasks are used

    Herramientas de inteligencia artificial en la gestión de proyectos

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    Este trabajo investiga la incorporación de la Inteligencia Artificial (IA) en la gestión de proyectos, con un enfoque en la optimización de la adquisición y análisis de datos en proyectos contemporáneos. La investigación identifica enfoques de IA vigentes y su aplicabilidad en distintas etapas del ciclo de vida del proyecto, subrayando su impacto en las tareas correspondientes. Los objetivos abarcan el análisis de herramientas de IA y su relevancia en diversas fases del proyecto. La metodología empleada es cualitativa-documental, se centra en la investigación de herramientas de IA y su análisis, organizando los hallazgos de manera estructurada. La gestión de proyectos, crítica en diversas industrias, se beneficia de la IA al mejorar la automatización y la toma de decisiones. Tecnologías como el Aprendizaje Automático, Redes Neuronales Artificiales, Procesamiento del Lenguaje Natural y la Visión por Computadora se han aplicado exitosamente en campos como la medicina, la robótica y la gestión de proyectos. El análisis ético y la seguridad en la implementación de la IA en la gestión de proyectos son destacados, abordando cuestiones de privacidad de datos, transparencia algorítmica, equidad en decisiones y ciberseguridad para salvaguardar sistemas y datos sensibles. Se enfatiza la importancia de la transparencia y la responsabilidad en las decisiones basadas en IA. Los resultados de este estudio subrayan el impacto positivo de la IA en la gestión de proyectos, demostrando mejoras sustanciales en la adquisición y análisis de datos. El futuro se vislumbra con una adopción ética y responsable de la IA, lo que permitirá una gestión de proyectos más eficiente y segura.This research investigates the integration of Artificial Intelligence (AI) into project management, with a focus on optimizing data acquisition and analysis in contemporary projects. The study identifies current AI approaches and their applicability at various stages of the project life cycle, emphasizing their impact on related tasks. The objectives encompass the analysis of AI tools and their relevance in different project phases. The methodology employed is qualitative-documentary, centering on the research of AI tools and their analysis, structured for organization. Project management, critical in various industries, benefits from AI by enhancing automation and decision-making. Technologies such as Machine Learning, Artificial Neural Networks, Natural Language Processing, and Computer Vision have been successfully applied in fields like medicine, robotics, and project management. Ethical analysis and security in AI implementation in project management are highlighted, addressing issues of data privacy, algorithm transparency, equity in decision-making, and cybersecurity for safeguarding sensitive systems and data. Emphasis is placed on the importance of transparency and responsibility in AI-driven decisions. The results of this study underline the positive impact of AI in project management, demonstrating substantial improvements in data acquisition and analysis. The future holds promise with an ethical and responsible adoption of AI, paving the way for more efficient and secure project management
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