Resumen: El artículo presenta las conclusiones de la investigación doctoral en medios, comunicación y cultura, centrándose en la narrativa audiovisual de cuatro cortometrajes producidos por el colectivo de comunicación popular Alharaca por la paz y las acciones de cambio social producto de su movilización, con el fin de conocer cómo desde la comunicación comunitaria, local y participativa producida por niñas, niños y adolescentes de Caquetá, Colombia, se contribuye a la reconstrucción del tejido social en sociedades divididas por el conflicto armado. Abstract: The article presents the conclusions of the doctoral research in media, communication and culture, focusing on the audiovisual narrative of four short films produced by the popular communication collective Alharaca por la paz and the actions of social change resulting from its mobilization, in order to learn how community, local and participatory communication produced by girls, boys and adolescents from Caquetá, Colombia, contributes to the reconstruction of the social fabric in societies divided by the armed conflict.

Abstract: Artificial Intelligence-based (AI) analysis of large, curated medical datasets is promising for providing early detection, faster diagnosis, and more effective treatment using low-power Electrocardiography (ECG) monitoring devices information. However, accessing sensitive medical data from diverse sources is highly restricted since improper use, unsafe storage, or data leakage could violate a person's privacy. This work uses a Federated Learning (FL) privacy-preserving methodology to train AI models over heterogeneous sets of high-definition ECG from 12-lead sensor arrays collected from six heterogeneous sources. We evaluated the capacity of the resulting models to achieve equivalent performance compared to state-of-the-art models trained in a Centralized Learning (CL) fashion. Moreover, we assessed the performance of our solution over Independent and Identical distributed (IID) and Non-IID federated data. Our methodology involves machine learning techniques based on Deep Neural Networks and Long-Short-Term Memory models. It has a robust data preprocessing pipeline with feature engineering, selection, and data balancing techniques. Our AI models demonstrated comparable performance to models trained using CL, IID, and Non-IID approaches. They showcased advantages in reduced complexity and faster training time, making them well-suited for cloud-edge architectures. Resumen: El análisis basado en Inteligencia Artificial (IA) de grandes conjuntos de datos médicos curados promete proporcionar detección temprana, diagnóstico más rápido y tratamiento más efectivo utilizando información de dispositivos de monitoreo de Electrocardiografía (ECG) de bajo consumo. Sin embargo, el acceso a datos médicos sensibles de diversas fuentes está altamente restringido, ya que el uso indebido, el almacenamiento inseguro o la fuga de datos podrían violar la privacidad de una persona. Este trabajo utiliza una metodología de Aprendizaje Federado (FL) preservadora de la privacidad para entrenar modelos de IA sobre conjuntos heterogéneos de ECG de alta definición provenientes de matrices de sensores de 12 derivaciones recopilados de seis fuentes heterogéneas. Evaluamos la capacidad de los modelos resultantes para lograr un rendimiento equivalente en comparación con modelos de vanguardia entrenados de manera centralizada (CL). Además, evaluamos el rendimiento de nuestra solución sobre datos federados Independientes e Idénticamente Distribuidos (IID) y No IID. Nuestra metodología involucra técnicas de aprendizaje automático basadas en Redes Neuronales Profundas y modelos de Memoria a Corto y Largo Plazo. Cuenta con un sólido pipeline de preprocesamiento de datos con técnicas de ingeniería de características, selección y equilibrio de datos. Nuestros modelos de IA demostraron un rendimiento comparable a los modelos entrenados mediante CL, IID y enfoques No IID. Exhibieron ventajas en términos de complejidad reducida y tiempo de entrenamiento más rápido, haciéndolos adecuados para arquitecturas de nube-edge.

Abstract: The use of computer vision, deep learning, and drones has revolutionized agriculture by enabling efficient crop monitoring and disease detection. Still, many challenges need to be overcome due to the vast diversity of plant species and their unique regional characteristics. Olive trees, which have been cultivated for thousands of years, present a particularly complex case for leaf-based disease diagnosis as disease symptoms can vary widely, both between different plant variations and even within individual leaves on the same plant. This complexity, coupled with the susceptibility of olive groves to various pathogens, including bacterial blight, olive knot, aculus olearius, and olive peacock spot, has hindered the development of effective disease detection algorithms. To address this challenge, we have devised a novel approach that combines deep learning techniques, leveraging convolutional neural networks, vision transformers, and cloud computing-based models. Aiming to detect and classify olive tree diseases the experimental results of our study have been highly promising, demonstrating the effectiveness of the combined transformer and cloud-based machine learning models, achieving an impressive accuracy of approximately 99.6% for multiclass classification cases including healthy, aculus olearius, and peacock spot infected leaves. These results highlight the potential of deep learning models in tackling the complex-ities of olive leaf disease detection and the need for further research in the field. Resumen: El uso de visión por computadora, aprendizaje profundo y drones ha revolucionado la agricultura al permitir un monitoreo eficiente de cultivos y la detección de enfermedades. Sin embargo, aún existen muchos desafíos debido a la vasta diversidad de especies vegetales y sus características regionales únicas. Los olivos, que han sido cultivados durante miles de años, presentan un caso especialmente complejo para el diagnóstico de enfermedades basado en las hojas, ya que los síntomas pueden variar ampliamente, tanto entre diferentes variaciones de plantas como incluso dentro de hojas individuales en la misma planta. Esta complejidad, junto con la susceptibilidad de los olivares a varios patógenos, como la bacteriosis, el nudo del olivo, el aculo olearius y la mancha de pavo real del olivo, ha dificultado el desarrollo de algoritmos efectivos de detección de enfermedades. Para abordar este desafío, hemos ideado un enfoque novedoso que combina técnicas de aprendizaje profundo, aprovechando redes neuronales convolucionales, transformadores de visión y modelos basados en computación en la nube. Con el objetivo de detectar y clasificar enfermedades de los olivos, los resultados experimentales de nuestro estudio han sido muy prometedores, demostrando la efectividad de los modelos combinados de transformadores y aprendizaje automático basado en la nube, logrando una impresionante precisión de aproximadamente el 99.6% para casos de clasificación multiclase, que incluyen hojas sanas, infectadas con aculo olearius y mancha de pavo real. Estos resultados destacan el potencial de los modelos de aprendizaje profundo para abordar las complejidades de la detección de enfermedades en las hojas de olivo y la necesidad de más investigaciones en este campo.

Abstract: Dissolved air flotation (DAF) is considered better than conventional gravity settling (CGS) for the treatment of algal-laden waters, and ballasted sedimentation (BS), a high-rate separation process, is now often used instead of CGS. Our initial literature search did not identify DAF-BS comparisons for the removal of algae and cyanobacteria from algal laden waters. The objective of this benchscale study was to compare DAF with BS and CGS for the treatment of water from a eutrophic waterway (Bay of Quinte, ON). The study was performed mid-summer when there was substantial algal growth. The optimized BS jar tests had 3% lower average turbidity removal than the DAF jar tests, however BS required 33% more coagulant, as well as 0.25 mg/L anionic polymer and microsand additions. The removal of cyanobacteria and algae (quantified using chlorophyll-a and c-phycocyanin concentrations) by DAF and BS were very similar, and they were superior to that achieved by CGS. The DOC removals and the disinfection by-products formation potential (DBPFP) of DAF and BS treated water were also similar. Based on the chlorophyll-a and c-phycocyanin removals, both BS and DAF performed better than CGS and can be considered suitable for the treatment of algal/cyanobacteria laden waters.

Abstract: This paper aims to estimate the prevalence of client-perpetrated violence against female sex workers (FSWs) in Bogotá and to understand what structural and environmental factors are associated with such victimization. The project used secondary data from interviews with 2,684 FSWs. Multivariable binary logistic regression was used to test for associations with client-perpetrated physical and sexual violence. Findings reveal that factors such as experiencing police harassment and social stigma were positively associated with client-perpetrated violence. Situational factors such as providing services in motels, hotels, on the street, and in cars were also associated with increased odds of becoming a victim of both types of violence. Resumen: Este artículo busca estimar la prevalencia de la violencia perpetrada por clientes contra trabajadoras sexuales en Bogotá y entender cuáles son los factores estructurales y ambientales asociados con esta victimización. El proyecto utiliza datos secundarios de entrevistas con 2.684 mujeres que realizan actividades sexuales pagas. Modelos de regresión logística multivariable fueron utilizados para probar asociaciones con violencia sexual y física perpetrada por clientes. Los resultados revelan que factores como haber reportado acoso policial y sentirse estigmatizadas por su comunidad tuvieron una asociación positiva con la victimización. Factores situacionales como prestar servicios en moteles, hoteles, en la calle y en carros también fueron asociados con un incremento en la probabilidad de ser víctima de ambos tipos de violencia.

Revealing the Mechanistic Details for the Selective Deoxygenation of Carboxylic Acids over Dynamic MoO3 Catalysts Abstract: The selective activation of renewable carboxylic acids could enable the formation of a variety of highly valuable renewable products, including surfactants, valuable dienes, and renewable hydrogen carriers. A kinetic study is performed to enhance understanding of the selective deoxygenation of carboxylic acid on promoted MoO3 at mild temperatures. Although several studies indicate that deoxygenation of oxygenated biomass-derived compounds on MoO3 occurs via a reverse Mars–van Krevelen mechanism, this study suggests that the deoxygenation of pentanoic acid (PA) on an oxygen vacancy can also be explained by a Langmuir–Hinshelwood mechanism. A detailed analysis of the experimental data indicates that the incorporation of Pt on MoO3 shifts the reaction order with respect to hydrogen from 1 to 0.5 at a low partial pressure of PA. We reveal that the rate-determining step (RDS) shifts upon the incorporation of Pt from H2 dissociation to H addition to adsorbed acid molecules. We further illustrate how the RDS can shift as a function of PA coverage. The inhibition effect of PA and its possible causes are discussed for both MoO3 and 0.05 wt % Pt/MoO3 catalysts. Here, we decouple promoter effects from the creation of highly active sites located at the Pt/MoO3 interface. The nature of the active site involved upon Pt incorporation is also studied by separating Pt from MoO3 at a controlled distance using carbon nanotubes as hydrogen bridges, confirming that the kinetically relevant role of Pt is to serve as a promoter of the MoO3. Resumen: La activación selectiva de ácidos carboxílicos renovables podría permitir la formación de una variedad de productos renovables altamente valiosos, incluyendo tensioactivos, dienos valiosos y portadores de hidrógeno renovable. Se lleva a cabo un estudio cinético para mejorar la comprensión de la desoxigenación selectiva de ácido carboxílico sobre MoO3 promovido a temperaturas moderadas. Aunque varios estudios indican que la desoxigenación de compuestos derivados de biomasa oxigenada sobre MoO3 ocurre a través de un mecanismo Mars-van Krevelen inverso, este estudio sugiere que la desoxigenación del ácido pentanoico (PA) en una vacante de oxígeno también puede explicarse mediante un mecanismo de Langmuir-Hinshelwood. Un análisis detallado de los datos experimentales indica que la incorporación de Pt en MoO3 cambia el orden de reacción con respecto al hidrógeno de 1 a 0.5 a baja presión parcial de PA. Revelamos que el paso determinante de la velocidad (PDV) cambia con la incorporación de Pt desde la disociación de H2 hasta la adición de H a las moléculas de ácido adsorbidas. Ilustramos además cómo el PDV puede cambiar en función de la cobertura de PA. Se discute el efecto de inhibición de PA y sus posibles causas tanto para MoO3 como para los catalizadores 0.05% en peso de Pt/MoO3. Aquí, se desvinculan los efectos del promotor de la creación de sitios altamente activos ubicados en la interfaz Pt/MoO3. También se estudia la naturaleza del sitio activo involucrado en la incorporación de Pt al separar Pt de MoO3 a una distancia controlada usando nanotubos de carbono como puentes de hidrógeno, confirmando que el papel cinéticamente relevante de Pt es servir como promotor del MoO3.

Background: Data are rare about the incidence of severe Fontan-associated liver disease (FALD) and its association with mortality. We sought to: (1) estimate the probability of developing severe FALD in patients who undergo the Fontan procedure (Fontan patients), compared with severe liver complications in patients with a ventricular septal defect; (2) assess the severe FALD-mortality association; and (3) identify risk factors for developing severe FALD. Methods and Results: Using the Quebec Congenital Heart Disease database, a total of 512 Fontan patients and 10232 patients with a ventricular septal defect were identified. Kaplan-Meier curves demonstrated significantly higher cumulative risk of severe FALD in Fontan patients (11.95% and 52.24% at 10 and 35years, respectively), than the risk of severe liver complica- tions in patients with a ventricular septal defect (0.50% and 2.75%, respectively). At 5years, the cumulative risk of death was 12.60% in patients with severe FALD versus 3.70% in Fontan patients without FALD (log-rank P=0.0171). Cox proportional hazard models identified significant associations between the development of severe FALD and congestive heart failure and supraventricular tachycardia, with hazard ratios (HRs) of 2.36 (95% CI, 1.38–4.02) and 2.45 (95% CI, 1.37–4.39), respectively. More recent Fontan completion was related to reduced risks of severe FALD, with an HR of 0.95 (95% CI, 0.93–0.97) for each more recent year. Conclusions: This large-scale population-based study documents that severe FALD in Fontan patients was associated with a >3-fold increase in mortality. The risk of FALD is time-dependent and can reach >50% by 35years after the Fontan operation. Conditions promoting poor Fontan hemodynamics were associated with severe FALD development.

Abstract: The hydrogen isotope composition of leaf wax biomarkers (?2Hwax) is a valuable tool for reconstructing continental paleohydrology, since it serves as a proxy for the hydrogen isotope composition of precipitation (?2Hpre). To yield robust palaeohydrological reconstructions using ?2Hwax in marine archives, it is necessary to examine the impacts of regional climate on ?2Hwax and assess the similarity between marine sedimentary ?2Hwax and the source of continental ?2Hwax. Here, we examined an aridity gradient from hyperarid to humid along the Chilean coast. We sampled sediments at the outlets of rivers draining into the Pacific as well as soils within catchments and marine surface sediments adjacent to the outlets of the studied rivers and analyzed the relationship between climatic variables and ?2Hwax values. We found that apparent fractionation between leaf waxes and source water is relatively constant in humid and semiarid regions (average: ?121 ‰). However, it becomes less negative in hyperarid regions (average: ?86 ‰) as a result of evapotranspirative processes affecting soil and leaf water 2H enrichment. We also observed that along strong aridity gradients, the 2H enrichment of ?2Hwax follows a non-linear relationship with water content and water flux variables, driven by strong soil evaporation and plant transpiration. Furthermore, our results indicate that ?2Hwax values in marine surface sediments largely reflect ?2Hwax values from the continent, confirming the robustness of marine ?2Hwax records for paleohydrological reconstructions along the Chilean margin. These findings also highlight the importance of considering the effects of hyperaridity in the interpretation of ?2Hwax values and pave the way for more quantitative paleohydrological reconstructions using ?2Hwax.

Abstract: This study investigates the combustion characteristics of blends of bituminous coal (BC) with two by products of the coffee industry, spent coffee ground (SCG) and coffee husk (CH), at 10, 30, and 50 wt% using a thermogravimetric analyzer (TGA). For comparison purposes, blends of BC and wood pellets (WP) at the same mass ratios were also investigated. The thermal behavior and fuel interactions in the blends during combustion were characterized by the ignition index (Di), combustion index (S), and potential synergistic effects. The interactions between the materials in the blends led to an enhancement of up to 93.5% and 128.5% in Di, and 175.2% and 96.35% in S of the bituminous coal when blended at 50 wt% with SCG and CH, respectively. Furthermore, clear synergistic effects were observed with the combustion of BC-SCG blends, whereas the combustion of BC-CH blends did not show any appreciable synergistic effects.

Abstract: The co-combustion of coal and waste biomass is an advantageous option for combined waste biomass disposal and energy production. However, co-firing coal with waste biomass has to overcome various ash-related operational problems, for example, ash agglomeration and bed defluidisation in fluidised bed boilers. Using spent coffee grounds (SCG) for energy generation via co-combustion is much more sustainable and environmentally friendly than SCG disposal in landfills. The research done so far on the co-combustion of coal and SCG is quite scarce and almost non-existing. Further research is needed to understand how the properties of SCG affect the co-combustion of coal and SCG in existing coal-fired boilers. This study investigates the combustion of a bituminous coal blended with SCG in a pilot-scale (30kWth) bubbling fluidised bed (BFB) combustor focusing on CO and NOx emissions, combustion performance, and agglomeration tendency. The BFB combustion tests were conducted at 900 ?C and atmospheric pressure using silica sand as the bed material. For comparison purposes, combustion tests of the same coal in pure and blended with wheat straw pellets at the same blending ratio were also performed. Further, ash fusibility studies were performed to elucidate the interactions between the coal ash and SCG ash, and the effect of ash compositions on the fusibility temperatures. Samples of the used bed material collected from the combustor and cyclone ash were characterised by SEM-EDS and XRF. The BFB combustion test results revealed that SCG could reduce the efficiency loss of coal combustion under co-combustion conditions. Despite the higher K2O content in SCG compared to wheat straw, a reduced agglomeration tendency was observed with the BFB combustion of the coal-SCG blends. The results from the characterisation of the used bed material, cyclone ash, and ash fusibility studies confirmed this finding, which was attributed to the formation of high melting temperatures Mg- and Ca-bearing compounds.