Lectura de contexto y abordaje psicosocial desde los enfoques narrativos Bogotá, Coveñas.

Durante la última fase de aprendizaje en el diplomado de profundización acompañamiento psicosocial en escenarios de violencia se abordarán los enfoques narrativos, estos nos brindan herramientas trasformadoras de historias logrando cambiar el guion del dolor a la esperanza. Para ello se analizarán relatos de victimas reales y a partir de ellos generar estrategias de moldeamiento de la historia de forma que ayude a la persona a salir adelante. De esta forma, la narrativa y en especial la pregunta se convierte en una herramienta de eficaz pues nos lleva a desarrollar habilidades para el acercamiento a la comunidad y a la realidad del conflicto, por otra parte, permite a la víctima descubrir aspectos más allá de la situación vivida y tener conciencia frente a las potencialidades que puede desarrollar a partir de un hecho traumático.During the last phase of learning in the course of deepening psychosocial accompaniment in scenarios of violence, narrative approaches will be addressed, these provide us with transforming tools of stories managing to change the script from pain to hope. For this purpose, stories of real victims will be analyzed and from them generate strategies for shaping the story in a way that helps the person to get ahead. In this way, the narrative and especially the question becomes an effective tool as it leads us to develop skills to approach the community and the reality of the conflict, on the other hand it allows the victim to discover aspects beyond the situation lived and be aware of the potentialities that can develop from a traumatic event

Robots miniaturizados: diseño, implementación y aplicaciones

Proyecto de Investigación (Código: 5402-1360-2401 FI-298-09) Instituto Tecnológico de Costa Rica. Escuela de Ingeniería Electrónica, Escuela de Física, Escuela de Ingeniería en Computación, 2014Este informe presenta los resultados obtenidos en el proyecto “Robots miniaturizados: diseño, implementación y aplicaciones”, con número de proyecto 5402-1360-2401. Se presentan los resultados obtenidos con respecto a locomoción autónoma, comunicación inalámbrica óptica y alimentación inalámbrica, actuadores para manipulación y microcorte e interfaz con una PC para datos y comandos. El minirobot fue diseñado para operar en un área de al menos 30cm de diámetro y tiene un tamaño de 2.3 cm x 2.6 cm x 1.78 cm. Para el estudio de los actuadores del minirobot se utilizó simulación multifísica por el método de elementos finitos con el software COMSOL Multiphysics. La estructura del informe es la siguiente: en la sección de Introducción se tratan los antecedentes, la descripción del problema a resolver, así como los objetivos planteados en el proyecto de investigación. En la sección de metodología se resume el método de investigación y los flujos de diseño. En la sección de resultados se presentan los resultados obtenidos correspondientes a: comunicación inlámbrica óptica, sistema de locomoción, interfaz humano-máquina y software, alimentación inalámbrica de energia y actuadores. Posteriormente, se discuten los resultados obtenidos junto con las principales conclusiones del proyecto y las recomendaciones para su continuación y/o aprovechamiento de los resultados.Instituto Tecnológico de Costa Rica. Escuela de Ingeniería Electrónica. Escuela de Física. Escuela de Ingeniería en Computación

The Caldera. No. 23

La pandemia, sin lugar a dudas, nos ha cambiado la vida a todos; un viernes nos fuimos para nuestros hogares, en el marco de una educación presencial; al lunes siguiente, después de dos días, estábamos iniciando el camino hacia una educación remota, una educación virtual, que se ha convertido en una gran alternativa para seguir contribuyendo con la formación de nuestros niños y jóvenes caldistas y al mejoramiento de nuestra calidad de vida que halla, en la educación, nuevamente la respuesta; han sido meses de cambios drásticos, inimaginables pero, cambios positivos que nos han permitido crecer como individuos, como familia, como escuela y como sociedad.Especial pandemia. Una generación Resiliente por promoción DINASTIA…06 VII Concurso Intercolegiado departamental de Oratoria. Ulibro 2020…51 Deporte en el Caldas…64 Expresiones Caldistas…71 Celebremos la palabra…93 Nuestros Maestros…102 Galería de Imágenes…107The pandemic, without a doubt, has changed the lives of all of us; One Friday we went to our homes, as part of a face-to-face education; The following Monday, after two days, we were starting the path towards a remote education, a virtual education, which has become a great alternative to continue contributing to the training of our children and young Caldistas and to the improvement of our quality of life. that finds, in education, the answer again; They have been months of drastic changes, unimaginable but positive changes that have allowed us to grow as individuals, as a family, as a school and as a society

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

XXV Curso Monográfico de Psiquiatría Infantil y la Adolescencia: Tópicos de Psicofarmacología Infantil - 2023

El XXV Curso Monográfico de Psiquiatría Infantil y de la Adolescencia, titulado "Tópicos de Psicofarmacología Infantil," fue un evento destacado en el campo de la salud mental infantil y adolescente. Durante tres días en septiembre de 2023, expertos líderes en la materia se reunieron para explorar a fondo la psicofarmacología en este grupo de edad. El evento, dedicado a la memoria del Dr. Francisco Javier Valencia Granados, comenzó con una ceremonia de inauguración en la que participaron autoridades institucionales. Luego, se sucedieron conferencias magistrales que abordaron una amplia variedad de temas cruciales. Estos incluyeron aspectos fundamentales como la neurobioquímica farmacológica y una introducción a la psicofarmacología. El programa se adentró en cuestiones específicas, como el uso de antipsicóticos en paidopsiquiatría, el abordaje de trastornos del aprendizaje, el tratamiento del suicidio desde una perspectiva psicofarmacológica, y la gestión farmacológica del insomnio en niños. Se exploraron temas especializados, como el tratamiento de la esquizofrenia en pacientes infantiles. El segundo día se centró en trastornos emocionales en niños y adolescentes, destacando el tratamiento del trastorno depresivo, los trastornos ansiosos y el espectro autista. Se presentaron enfoques vanguardistas, como el uso de psicodélicos en adolescentes y las novedades en psicofarmacología, como el dextrometorfano y el bupropión. También se discutió el manejo de la epilepsia y la adicción a los videojuegos. El tercer día se enfocó en el tratamiento farmacológico de trastornos pediátricos específicos, como el trastorno bipolar, el déficit de atención e hiperactividad, la enuresis y encopresis, parasomnias, y el abordaje neuropsiquiátrico en pacientes pediátricos con VIH. Se exploraron también trastornos de la conducta alimentaria y la disforia de género. El evento culminó con una reflexión sobre la salud mental en niños y un emotivo tributo al Dr. Francisco Javier

Characterization of normal fault scarp using convolutional neural network: application to Mexico

Fault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)

Characterization of normal fault scarp using convolutional neural network: application to Mexico

Fault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)

Characterization of normal fault scarp using convolutional neural network: application to Mexico

International audienceFault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)

Characterization of normal fault scarp using convolutional neural network: application to Mexico

Fault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)

Characterization of normal fault scarp using convolutional neural network: application to Mexico

Fault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)