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Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution’s accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG.

The quest for youthful, healthy skin and full, vibrant hair has long been a driving force in the dermocosmetics field. However, traditional approaches often struggle to address the underlying causes of aging, damage, and hair loss. Regenerative cosmetics powered by skin tissue engineering offer a transformative alternative. This review explores the emerging field of using engineered skin tissues for cosmetic purposes, focusing specifically on their potential for anti-aging, repair, and hair restoration applications. We discuss how these technologies aim to rejuvenate aging skin by promoting collagen production, reducing wrinkles, and improving overall skin function. Additionally, the use of engineered skin for wound healing and scar reduction is examined, highlighting their potential to improve the appearance and functionality of damaged skin. Finally, we advance the exciting prospects of utilizing skin tissue engineering techniques to regenerate hair follicles, potentially offering solutions for hair loss and promoting denser hair growth.

El Campus Virtual de Salud Pública es la plataforma educativa de la Organización Panamericana de la Salud, concebida como herramienta para la cooperación técnica. Los objetivos de este artículo son caracterizar la oferta de formación del Campus Virtual de Salud Pública identificando las propuestas de cursos virtuales; caracterizar a los participantes de los cursos; describir los procesos de actualización tecnológica y sus avances en términos de accesibilidad; e identificar su relación con las principales líneas de cooperación de la Organización Panamericana de la Salud. El Campus Virtual de Salud Pública ha desarrollado 210 cursos con tutoría y 226 de autoaprendizaje desde 2007, relacionados con las políticas de la Organización. Se verificó un uso heterogéneo del campus en las distintas temáticas de la cooperación. La cantidad de cursos autoadministrados realizados durante la pandemia superó la cifra total acumulada en años previos. Los participantes provienen principalmente de Latinoamérica. Un 67,5% son mujeres, con edades entre 26 y 45 años. Un 57,1% posee educación universitaria, principalmente en enfermería o medicina. La mitad trabaja en hospitales y un 35,8% en el primer nivel. Más de un 90% de los participantes evaluaron favorablemente los temas abordados, recursos de conocimiento y características del aula virtual. Como dificultades, señalan escasa disponibilidad de tiempo y bajo acceso a internet. Como ventajas, destacan autonomía de horarios y acceso a diversas fuentes de información. Las herramientas de evaluación disponibles no son suficientes para reconocer el impacto de los programas educativos. Se plantean los desafíos de profundizar en la accesibilidad y calidad de las propuestas educativas, fortalecer la relación con los temas de cooperación y mejorar la evaluación de los cursos y el conocimiento sobre los usuarios.

Abstract Developing electronic devices capable of reproducing synaptic functionality is essential in the context of implementing fast, low‐energy consumption neuromorphic computing systems. Hybrid ionic/electronic three‐terminal synaptic transistors are promising as efficient artificial synapses since they can process information and learn simultaneously. In this work, an electrolyte‐gated synaptic transistor is reported based on an ultra‐thin epitaxial La0.7Sr0.3MnO3 (LSMO) film, a half‐metallic system close to a metal‐insulator transition. The dynamic control of oxygen composition of the manganite ultra‐thin film with voltage pulses applied through the gate terminal allows reversible modulation of its electronic properties in a non‐volatile manner. The conductance modulation can be finely tuned with the amplitude, duration, and number of gating pulses, providing different alternatives to gradually update the synaptic weights. The transistor implements essential synaptic features such as excitatory postsynaptic potential, paired‐pulse facilitation, long‐term potentiation/depression of synaptic weights, and spike‐time‐dependent plasticity. These results constitute an important step toward the development of neuromorphic computing devices leveraging the tunable electronic properties of correlated oxides, and pave the way toward enhancing future device functionalities by exploiting the magnetic (spin) degree of freedom of the half metallic transistor channel.

During the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer’s decision-making process through further automatic applications.

The objective of this article is to study the evolution of Colombian liberalization and integration into world trade from 1996 to 2018. We achieved our objective by measuring Colombia’s importance in the world trade network through the employment of several types of network centrality metrics and the examination of their dynamics against a set of regional peers. We were able to conclude that Colombia’s centrality in the world trade network did not improve, whereas that of some of her regional peers did manifestly. Results highlighted the perils of analyzing a country’s trade dynamics in isolation and emphasized the usefulness of examining the world trade network.

Chemoresistance is one of the most important challenges in cancer therapy. The presence of cancer stem cells within the tumor may contribute to chemotherapy resistance since these cells express high levels of extrusion pumps and xenobiotic metabolizing enzymes that inactivate the therapeutic drug. Despite the recent advances in cancer cell metabolism adaptations, little is known about the metabolic adaptations of the cancer stem cells resistant to chemotherapy. In this study, we have undertaken an untargeted metabolomic analysis by liquid chromatography–high-resolution spectrometry combined with cytotoxicity assay, western blot, quantitative real-time polymerase chain reaction (qPCR), and fatty acid oxidation in a prostate cancer cell line resistant to the antiandrogen 2-hydroxiflutamide with features of cancer stem cells, compared to its parental androgen-sensitive cell line. Metabolic fingerprinting revealed 106 out of the 850 metabolites in ESI+ and 67 out of 446 in ESI- with significant differences between the sensitive and the resistant cell lines. Pathway analysis performed with the unequivocally identified metabolites, revealed changes in pathways involved in energy metabolism as well as posttranscriptional regulation. Validation by enzyme expression analysis indicated that the chemotherapy-resistant prostate cancer stem cells were metabolically dormant with decreased fatty acid oxidation, methionine metabolism and ADP-ribosylation. Our results shed light on the pathways underlying the entry of cancer cells into dormancy that might contribute to the mechanisms of drug resistance.

Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems.

This paper outlines the development of an onboard computer prototype for data registration, storage, transformation, and analysis. The system is intended for health and use monitoring systems in military tactical vehicles according to the North Atlantic Treaty Organization Standard Agreement for designing vehicle systems using an open architecture. The processor includes a data processing pipeline with three main modules. The first module captures the data received from sensor sources and vehicle network buses, performs a data fusion, and saves the data in a local database or sends them to a remote system for further analysis and fleet management. The second module provides filtering, translation, and interpretation for fault detection; this module will be completed in the future with a condition analysis module. The third module is a communication module for web serving data and data distribution systems according to the standards for interoperability. This development will allow us to analyze the driving performance for efficiency, which helps us to know the vehicle’s condition; the development will also help us deliver information for better tactical decisions in mission systems. This development has been implemented using open software, allowing us to measure the amount of data registered and filter only the relevant data for mission systems, which avoids communication bottlenecks. The on-board pre-analysis will help to conduct condition-based maintenance approaches and fault forecasting using the on-board uploaded fault models, which are trained off-board using the collected data.

Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification techniques, which are easier to implement in distributed environments. However, the solution to some problems is based on the usage of mathematical equations or stochastic models, which are more difficult to implement in distributed environments. Usually, these types of problems need to centralize the required information, and then a modelling technique is applied. In some environments, this centralization may cause an overloading of the communication channels due to massive data transmission and may also cause privacy issues when sending sensitive data. To mitigate this problem, this paper describes a general-purpose distributed analytic platform based on edge computing for distributed networks. Through the distributed analytical engine (DAE), the calculation process of the expressions (that requires data from diverse sources) is decomposed and distributed between the existing nodes, and this allows sending partial results without exchanging the original information. In this way, the master node ultimately obtains the result of the expressions. The proposed solution is examined using three different computational intelligence algorithms, i.e., genetic algorithm, genetic algorithm with evolution control, and particle swarm optimization, to decompose the expression to be calculated and to distribute the calculation tasks between the existing nodes. This engine has been successfully applied in a case study focused on the calculation of key performance indicators of a smart grid, achieving a reduction in the number of communication messages by more than 91% compared to the traditional approach.

El 24 de agosto de 1724, la flota de azogues, compuesta por los navíos Nuestra Señora de Guadalupe y San Joseph (alias Conde de Tolosa), sufrió un terrible temporal o un huracán cuando navegaba junto a la bahía de Samaná (República Dominicana) que hizo naufragar a los dos navíos en las costas de la actual población de Miches. Ambos transportaban desde Cádiz un cargamento de azogue, clavazón y hierro a granel, aceite, aceitunas, vino, aguardiente, bulas, papeles del Estado y pequeños cargamentos y objetos particulares, entre ellos, una magnífica colección de vidrios decorados a la rueda que es el objeto de estudio de este artículo. En este trabajo nos centramos en la colección transportada por el navío Nuestra Señora de Guadalupe, sobre cuyos restos pudimos llevar a cabo, entre 1994 y 1995, una intervención para dibujar y documentar el navío bajo el agua. El magnífico estado de conservación de los vidrios, además de su cantidad (364 piezas completas y cientos de fragmentos solo en el Nuestra Señora de Guadalupe) y su variedad decorativa, hacen de esta colección de vidrio europeo una de las más importantes y mejor fechadas de principios del siglo XVIII.

Mobile wallets replicate physical wallets on a mobile device, in which users can store different payment instruments to make mobile payments. As the mobile wallet is adopted, a mobile payment system emerges. I study the mobile payment system of Movii—the first fintech firm in Colombia operating under a financial non-banking license for electronic deposits and payments. Based on a unique dataset of daily bilateral transfers between Movii's mobile wallet users, I build, visualize, and analyze Movii's network. Besides the anticipated increase in the number of users and the value of transfers, the visual and quantitative complexity of the network of transfers increases over time. This increase in complexity is likely to be linked to the adoption of Movii's mobile wallet. The behavior of users in the network of transfers shows that they find new ways to use mobile payments beyond person-to-person transfers, including person-to-business and business-to-business.

Reseña del libro, Charles Taylor, Imaginarios Sociales Modernos, Paidós, Barcelona, 2006, 226 pp.

We implement a modified version of DebtRank to recursively measure the contagion effects caused by the default of a selected financial institution. In our case contagion is a liquidity issue, measured as the decrease in financial institutions’ short-term liquidity position across the Colombian interbank network. We find that contagion negative effects are concentrated in a few financial institutions. However, as most of their impact is conditional on the occurrence of unlikely major widespread illiquidity events, and due to the subsidiary contribution of the interbank market to the local money market, their overall systemic importance is still to be confirmed.

Introduccion: El uso de Citología Convencional de Papanicoloau (CC) frente a Base Líquida (CBL), el profesional que realiza la extracción, primera lectura y diagnóstico de la muestra; el uso de la clasificación Bethesda, la detección de ADN del VPH y el seguimiento de hallazgos ASCUS son eslabones en el tamizaje del Cáncer del Cuello Uterino (CCU) poco estudiadas en nuestro medio. Objetivo: Determinar las características del tamizaje para cáncer CCU en 08 establecimientos de salud (EESS) de Lima Metropolitana sedes del Internado Médico de la FAMURP en el 2017. Métodos: Descriptivo, transversal. Muestreo por conveniencia. Se entrevistó a los Jefes de los Servicios de Patología. Resultados: Participaron 8 EESS de Nivel III del MINSA, EsSalud y FFAA. Todos realizaron la CC, ninguno CBL. Las muestras son extraídas por el Ginecólogo o la Obstetriz, y en 1 caso por enfermería, la primera lectura es realizada por el Tecnólogo Médico y en un caso por el Anátomo Patólogo; el diagnóstico, por el Anátomo Patólogo. Se usa el Sistema de Bethesda para el Reporte Final. No se usan pruebas de detección para ADN del VPH. En 8, se hace seguimiento para hallazgos ASCUS. Conclusión: La CBL, la lectura automatizada, y la identificación del ADN de VPH no están implementados. Se recomienda realizar estudios costo-efectivo para proponer su implementación futura, y realizar estudios respecto de las fases del proceso del Tamizaje.

En esta investigación se estudia la red de coautoría de artículos publicados entre el 2010 y el 2019 en revistas indexadas en Scopus, del área de economía, econometría y finanzas, en las que alguno de los autores está afiliado a una institución en Colombia. Conseguimos visualizar y describir la estructura conectiva de la red de coautoría. Pese a que la red es poco interconectada, existen unos cuantos autores altamente vinculados que la mantienen unida y con una corta distancia entre los investigadores que la conforman, en lo que se conoce como una red de mundo pequeño. Luego de descartar a autores ocasionales, se cuantificó la importancia de estos por su contribución a la red de coautoría. Es de resaltar la importancia de los autores afiliados al Banco de la República (el banco central de Colombia), quienes contribuyen de gran manera a la estructura conectiva de la red. Este es un primer paso hacia el estudio de la red de publicaciones indexadas del área de economía en el caso colombiano.

ABSTRACT By modulating the structure, diversity, and trophic outputs of microbial communities, phages play crucial roles in many biomes. In oligotrophic polar deserts, the effects of katabatic winds, constrained nutrients, and low water availability are known to limit microbial activity. Although phages may substantially govern trophic interactions in cold deserts, relatively little is known regarding the precise ecological mechanisms. Here, we provide the first evidence of widespread antiphage innate immunity in Antarctic environments using metagenomic sequence data from hypolith communities as model systems. In particular, immunity systems such as DISARM and BREX are shown to be dominant systems in these communities. Additionally, we show a direct correlation between the CRISPR-Cas adaptive immunity and the metavirome of hypolith communities, suggesting the existence of dynamic host-phage interactions. In addition to providing the first exploration of immune systems in cold deserts, our results suggest that phages actively challenge niche communities in Antarctic polar deserts. We provide evidence suggesting that the regulatory role played by phages in this system is an important determinant of bacterial host interactions in this environment. IMPORTANCE In Antarctic environments, the combination of both abiotic and biotic stressors results in simple trophic levels dominated by microbiomes. Although the past two decades have revealed substantial insights regarding the diversity and structure of microbiomes, we lack mechanistic insights regarding community interactions and how phages may affect these. By providing the first evidence of widespread antiphage innate immunity, we shed light on phage-host dynamics in Antarctic niche communities. Our analyses reveal several antiphage defense systems, including DISARM and BREX, which appear to dominate in cold desert niche communities. In contrast, our analyses revealed that genes which encode antiphage adaptive immunity were underrepresented in these communities, suggesting lower infection frequencies in cold edaphic environments. We propose that by actively challenging niche communities, phages play crucial roles in the diversification of Antarctic communities.

La homogeneidad, entendida como la falta de diversidad, es una fuente de fragilidad en sistemas complejos. Del mismo modo, la homogeneidad del sistema financiero ha sido documentada como un factor determinante del riesgo sistémico. En este documento se evalúa la homogeneidad en el caso colombiano, para lo cual se mide qué tan similares son los bancos según la estructura de sus estados financieros generales, así como de sus portafolios de cartera, de inversiones y de pasivos. La similitud entre bancos y una metodología de agrupamiento por aglomeración arrojan la estructura jerárquica del sistema bancario, la cual muestra cómo los bancos se relacionan entre ellos de acuerdo con su estructura financiera. El sector bancario colombiano muestra homogeneidad, en especial entre los bancos de mayor tamaño. Así mismo, es evidente que el tamaño es un factor importante en la estructura jerárquica de este sector. Los resultados son robustos a partir de un procedimiento de selección de variables basado en análisis de componentes principales, el cual reduce la dimensionalidad y redundancia de la base de datos. Los resultados permiten estudiar qué tan homogéneo es el sistema bancario, así como identificar aquellas instituciones bancarias que tienen una estructura financiera común (particular) y, por lo tanto, permiten estudiar de mejor manera el riesgo sistémico.

El síndrome de Sjögren primario es una enfermedad autoinmune sistémica que afecta principalmente glándulas exocrinas y confiere un mayor riesgo de presentar linfomas comparado con la población general e incluso con otras enfermedades autoinmunes. Es la comorbilidad más severa que ocurre en esta entidad, la cual aumenta su mortalidad. Se han estudiado características clínicas, serológicas e histológicas para predecir el desarrollo de estos desórdenes linfoproliferativos. El síntoma más frecuente en este grupo es el agrandamiento unilateral parotídeo y son principalmente linfomas no Hodgkin, siendo el tipo más común el linfoma marginal extranodal, derivado de tejido linfoide asociado a mucosas o tipo MALT. Se cree que su desarrollo se debe a estimulación antigénica de células B reactivas y policlonales que sufren expansión y transición a células B monoclonales, junto con activación de vías de señalización que perpetúan la proliferación celular; actualmente se estudia la terapia blanco dirigida contra la activación de estas células. La mayoría de estos linfomas tienen curso indolente y son localizados, por lo que pueden solo observarse, en caso de ser agresivos o diseminados se prefiere el uso de poliquimioterapia más rituximab.

Anomaly-detection methods are aimed at identifying observations that deviate manifestly from what is expected. Such methods are usually run on low-dimensional data, such as time series data. However, the increasing importance of high-dimensional payments and exposure data for financial oversight requires methods for detecting anomalous networks. To detect an anomalous network, dimensionality reduction allows measuring of the extent to which the network's main connective features (i.e. the structure) deviate from those regarded as typical. The key to dimensionality-reduction methods is the ability to reconstruct data with an error; this reconstruction error serves as a yardstick for deviation from what is typical. Principal component analysis (PCA) is used as a dimensionality-reduction method, and a clustering algorithm is used to classify reconstruction errors as normal or anomalous. Based on data from Colombia's large-value payments system and a set of synthetic anomalous networks created through simulations of intraday payments, detecting anomalous payments networks is feasible and promising for financial-oversight purposes.

We present a general supervised machine-learning methodology to represent the payment behavior of financial institutions starting from a database of transactions in the Colombian large-value payment system. The methodology learns a feedforward artificial neural network parameterization to represent the payment patterns through 113 features corresponding to financial institutions’ contribution to payments, funding habits, payment timing, payment concentration, centrality in the payment network, and systemic effects due to failure to pay. We then use the representation to compare the coherence of out-of-sample payment patterns of the same institution to its characteristic patterns. The performance is remarkable, with an out-of-sample classification error around three percent. The performance is robust to reductions in the number of features by unsupervised feature selection. In addition, we confirm that network centrality and systemic effect features definitively contribute to enhancing the performance of the methodology. For financial authorities, this is a major step towards the automated detection of individual financial institutions’ anomalous behaviors in payment systems.

Shape memory materials have been playing an important role in a wide range of bioengineering applications. At the same time, recent developments of graphene-based nanostructures, such as nanoribbons, have demonstrated that, due to the unique properties of graphene, they can manifest superior electronic, thermal, mechanical, and optical characteristics ideally suited for their potential usage for the next generation of diagnostic devices, drug delivery systems, and other biomedical applications. One of the most intriguing parts of these new developments lies in the fact that certain types of such graphene nanoribbons can exhibit shape memory effects. In this paper, we apply machine learning tools to build an interatomic potential from DFT calculations for highly ordered graphene oxide nanoribbons, a material that had demonstrated shape memory effects with a recovery strain up to 14.5% for 2D layers. The graphene oxide layer can shrink to a metastable phase with lower constant lattice through the application of an electric field, and returns to the initial phase through an external mechanical force. The deformation leads to an electronic rearrangement and induces magnetization around the oxygen atoms. DFT calculations show no magnetization for sufficiently narrow nanoribbons, while the machine learning model can predict the suppression of the metastable phase for the same narrower nanoribbons. We can improve the prediction accuracy by analyzing only the evolution of the metastable phase, where no magnetization is found according to DFT calculations. The model developed here allows also us to study the evolution of the phases for wider nanoribbons, that would be computationally inaccessible through a pure DFT approach. Moreover, we extend our analysis to realistic systems that include vacancies and boron or nitrogen impurities at the oxygen atomic positions. Finally, we provide a brief overview of the current and potential applications of the materials exhibiting shape memory effects in bioengineering and biomedical fields, focusing on data-driven approaches with machine learning interatomic potentials.

In this paper, an intelligent data analysis method for modeling and optimizing energy efficiency in smart buildings through Data Analytics (DA) is proposed. The objective of this proposal is to provide a Decision Support System (DSS) able to support experts in quantifying and optimizing energy efficiency in smart buildings, as well as reveal insights that support the detection of anomalous behaviors in early stages. Firstly, historical data and Energy Efficiency Indicators (EEIs) of the building are analyzed to extract the knowledge from behavioral patterns of historical data of the building. Then, using this knowledge, a classification method to compare days with different features, seasons and other characteristics is proposed. The resulting clusters are further analyzed, inferring key features to predict and quantify energy efficiency on days with similar features but with potentially different behaviors. Finally, the results reveal some insights able to highlight inefficiencies and correlate anomalous behaviors with EE in the smart building. The approach proposed in this work was tested on the BlueNet building and also integrated with Eugene, a commercial EE tool for optimizing energy consumption in smart buildings.

The proposal of this paper is to introduce a low-level blockchain marketplace, which is a blockchain where participants could share its power generation and demand. To achieve this implementation in a secure way for each actor in the network, we proposed to deploy it over efficient and generic low-performance devices. Thus, they are installed as IoT devices, registering measurements each fifteen minutes, and also acting as blockchain nodes for the marketplace. Nevertheless, it is necessary that blockchain is lightweight, so it is implemented as a specific consensus protocol that allows each node to have enough time and computer requirements to act both as an IoT device and a blockchain node. This marketplace will be ruled by Smart Contracts deployed inside the blockchain. With them, it is possible to make registers for power generation and demand. This low-level marketplace could be connected to other services to execute matching algorithms from the data stored in the blockchain. Finally, a real test-bed implementation of the marketplace was tested, to confirm that it is technically feasible.

El interés en la catalogación del patrimonio histórico ha supuesto un creciente desarrollo de las técnicas de escaneado en tres dimensiones en los últimos años. En el presente trabajo se introduce la técnica de luz estructurada como un método de digitalización que, a pesar de sus limitaciones, es una potente herramienta para el levantamiento de detalles arquitectónicos. Los resultados obtenidos con la luz estructurada en el levantamiento arquitectónico del Hospital Real de la Universidad de Granada han sido satisfactorios como sustitutivo o complemento al escáner láser y la fotogrametría digital.

Este documento tiene como objetivo construir, visualizar y examinar la estructura de las redes de transferencias procesadas por ACH Colombia, la infraestructura financiera que compensa y liquida la mayor cantidad y valor de las transferencias electrónicas al por menor ordenadas por personas naturales y jurídicas en Colombia. La evidencia indica que las redes de transferencias procesadas por ACH Colombia son atípicas frente a aquellas comúnmente documentadas en la literatura sobre redes financieras: son densas, homogéneas en la distribución de las conexiones y con altos niveles de reciprocidad y transitividad. La función de las instituciones financieras como procesadoras de órdenes de pago determina esta tipología; sin embargo, existe una distribución heterogénea de los valores de las transferencias, la cual se relaciona con el tamaño por activos y por número de clientes de las instituciones financieras participantes. Los resultados son útiles para entender de mejor manera el funcionamiento de las ACH y de otros sistemas de pago de bajo valor.

Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy.

A comprehensive Foodomics study was carried out in this work to investigate the changes induced at gene and metabolite expression levels on HT29 colon cancer cell lines upon treatment with a bioactives-enriched extract from goldenberry calyx. As a result of the proposed multi-omics approach, the tested extract induced transcriptional activation of pro-apoptotic genes, and altered the expression of several genes related to the oxidative stress response in treated cancer cells. Metabolomics data confirm altered cellular redox homeostasis, providing additional evidence to transcriptomic results. Foodomics data integration also revealed alteration on relevant metabolic processes, suggesting inactivation of aminoacyl tRNA charging pathway, dysfunction on carnitine shuttle and beta-oxidation of fatty acids, and pyrimidine ribonucleotide interconversion impairment. These observations are in line with functional analysis and anti-proliferative activity results, where the viability of HT-29 colon cancer cells was notably reduced after 48 h treatment without affecting the viability of normal human colon fibroblast cells.

El lupus eritematoso sistémico es una enfermedad multisistémica, prototipo de las enfermedades autoinmunes, en la que las manifestaciones hematológicas son comunes y algunas incluso forman parte de sus criterios clasificatorios. La presencia de citopenias en estos pacientes es frecuente, siendo la anemia la principal. También puede observarse leucopenia y trombocitopenia. Todas tienen múltiples etiologías, aunque comúnmente resultan de destrucción mediada inmunológicamente. En algunos casos estas manifestaciones pueden deberse a otras etiologías, como causas infecciosas o farmacológicas, por lo que es adecuado descartarlas. El manejo de cada una de estas manifestaciones depende de su respectiva etiología, en algunas la vigilancia expectante puede ser suficiente yen otras se requiere la instauración de terapia específica.

The emotional response to a stimulus is typically measured in three variables called valence, arousal and dominance. Based on such dimensions, Bradley and Lang (1999) published the Affective Norms for English Words (ANEW), a corpus of affective ratings for 1,034 non-contextualized words. Expanded and adapted to many languages, ANEW provides a corpus to evaluate and to predict human responses to different stimuli, and it has been used in a number of studies involving analysis of emotions. However, ANEW seems not to appropriately predict affective responses to concepts when these are contextualized in certain situational backgrounds, in which words can have different connotations from those in non-contextualized scenarios. These contextualized affective norms have not been sufficiently contrasted yet because the literature does not provide a corpus of the ANEW list in specific contexts. On this basis, this paper reports on the creation of a new corpus of affective norms for the original 1,034 ANEW words in a particular context (a fictional scene of suspense). An extensive quantitative data analysis comparing both corpora was carried out, confirming that the affective ratings are highly influenced by the context. The corpus can be downloaded as Supplementary Material.

Hot desert surface soils are characterized by extremely low water activities for large parts of any annual cycle. It is widely assumed that microbial processes in such soils are very limited. Here we present the first metatranscriptomic survey of microbial community function in a low water activity hyperarid desert soil. Sequencing of total mRNA revealed a diverse and active community, dominated by Actinobacteria. Metatranscriptomic analysis of samples taken at different times over 3 days indicated that functional diel variations were limited at the whole community level, and mostly affected the eukaryotic subpopulation which was induced during the cooler night hours. High levels of transcription of chemoautotrophic carbon fixation genes contrasted with limited expression of photosynthetic genes, indicating that chemoautotrophy is an important alternative to photosynthesis for carbon cycling in desiccated desert soils. Analysis of the transcriptional levels of key N-cycling genes provided strong evidence that soil nitrate was the dominant nitrogen input source. Transcriptional network analyses and taxon-resolved functional profiling suggested that nutrient acquisition processes, and not diurnal environmental variation, were the main drivers of community activity in hyperarid Namib Desert soil. While we also observed significant levels of expression of common stress response genes, these genes were not dominant hubs in the co-occurrence network.

La resección oncológica completa es el único procedimiento que permite la sobrevida a largo plazo en cáncer de páncreas. La afectación de la arteria hepática, tronco celíaco o arteria mesentérica superior constituyen una contraindicación quirúrgica porque se asocia a mal pronóstico y por las dificultades técnicas que implica conseguir la resección oncológica completa. Solo un grupo seleccionado de pacientes con buena respuesta a la quimioterapia y pasibles de resección R0 se benefician de la cirugía de resección del tronco celíaco. A partir de un caso clínico de un adenocarcinoma de páncreas con infiltración de la arteria hepática común y tronco celíaco que tras una buena respuesta a la neoadyuvancia y embolización de la arteria hepática común fue sometido a una esplenopancreatectomía córporo-caudal con resección del tronco celíaco, se realiza una revisión de la literatura sobre el tema y sus aspectos técnicos relevantes. El análisis realizado permite sugerir que en casos debidamente seleccionados la pancreatectomía córporo-caudal con resección del tronco celíaco en bloque es un procedimiento factible, seguro, y con buenos resultados quirúrgicos y oncológicos. En condiciones de respuesta a la quimioterapia neoadyuvante y experiencia del equipo quirúrgico pareciera que esta cirugía podría mejorar el pronóstico y calidad de vida de estos enfermos.

Buscamos identificar los préstamos de fondos interbancarios (i.e., no colateralizados) a partir de información transaccional del sistema de pagos de alto valor por medio del método de Furfine. Con base en dichos préstamos, y sin recurrir a información reportada por las instituciones financieras, calculamos las tasas y los saldos interbancarios. El resultado del contraste de los préstamos identificados con aquellos reportados sugiere que el algoritmo se ajusta adecuadamente y que es robusto a cambios en su configuración. La tasa promedio ponderada implícita en la información transaccional concuerda con las tasas interbancarias de referencia del mercado local. Con los préstamos identificados también construimos la red de acreencias interbancarias. Los tres principales productos (i.e., los préstamos interbancarios, las tasas y la red de acreencias) sonvaliososparaexaminary monitorear elmercadomonetario, paracontrastarla informaciónreportadapor lasinstituciones financieras y como insumos en modelos de contagio financiero y riesgo sistémico.

Nowadays, Distribution System Operators are increasing the digitalization of their smart grids, making it possible to measure and manage their state at any time. However, with the massive eruption of change-distributed generation (e.g., renewable resources, electric vehicles), the grid operation have become more complex, requiring specific technologies to balance it. In this sense, the demand-side management is one of its techniques; the demand response is a promising approach for providing Flexibility Services (FSs) and complying with the regulatory directives of the energy market. As a solution, this paper proposes the use of the OpenADR (Open Automated Demand Response) standard protocol in combination with a Decentralized Permissioned Market Place (DPMP) based on Blockchain. On one hand, OpenADR hierarchical architecture based on distributed nodes provides communication between stakeholders, adding monitoring and management services. Further, this architecture is compatible with an aggregator schema that guarantees the compliance with the strictest regulatory framework (i.e., European market). On the other hand, DPMP is included at different levels of this architecture, providing a global solution to Flexibility Service Providers (FSP) that can be adapted depending on the regulation of a specific country. As a proof of concept, this paper shows the result of a real experimental case, which implements a Capacity Bidding Program where the OpenADR protocol is used as a communication method to control and monitor energy consumption. In parallel, the proposed DPMP based on Blockchain makes it possible to manage the incentives of FSs, enabling the integration of local and global markets.

Sometimes it is necessary to know the transformation to apply to a mapping shape in order to locate its true place. Such an operation can be computed if a corresponding reference object exists and we can identify corresponding points in both shapes. Nevertheless our approach does not need to match any corresponding point beforehand. The method proposed defines a polygon in the frequency domain—two periodic functions are derived from a polygonal or polygon. According to the theory of elliptic Fourier descriptors those two periodic functions can be expressed by Fourier expansions. The transformation can be computed using the coefficients of the harmonics from the corresponding shapes without taking into account where each polygon vertex is placed in the spatial domain. The transformation parameters will be derived by a least squares approach. The geomatics and geosciences applications of this method go from photogrammetry, geographic information system, computer vision, to cadaster and real estates.

La predicción inmediata de la actividad económica (es decir, hacer estimaciones del período actual), es conveniente porque la mayoría de las medidas tradicionales de actividad económica se realizan con rezagos sustanciales. Nuestro objetivo es predecir el ISE, un indicador de actividad económica de corto plazo en Colombia. Las entradas son los rezagos del ISE y un conjunto de datos de pagos realizados entre individuos, empresas y el gobierno central, mediante transferencias electrónicas y cheques. Bajo un enfoque de modelado predictivo, empleamos un modelo de red neuronal exógena autoregresiva no lineal. Los resultados sugieren que nuestra elección de insumos y el método predictivo, nos permiten difundir la actividad económica con una precisión razonable. Además, validamos que los datos de pagos electrónicos reducen significativamente el error de predicción en un modelo de red neuronal autoregresiva de referencia. La predicción inmediata de la actividad económica con datos de los instrumentos de pago electrónicos, no solo contribuye a la toma de decisiones de los agentes y al modelado económico, sino que también soporta nuevas vías de investigación sobre cómo utilizar dichos datos en modelos actuales.

Suspense is a key narrative issue in terms of emotional gratifications. Reactions in response to this type of entertainment are positively related to enjoyment, having a significant impact on the audience's immersion and suspension of disbelief. Related to computational modeling of this feature, some automatic storytelling systems include limited implementations of suspense management system in their core. In this way, the interest of this subject in the area of creativity has resorted to different definitions from fields as narratology and the film industry, as much as several proposals of its constituent features. Among their characteristics, uncertainty is one of the most discussed in terms of impact and need: while many authors affirm that uncertainty is essential to evoke suspense, others limit or reject its influence. Furthermore, the paradox of suspense reflects the problem of including uncertainty as a component required in suspense creation systems. Due to this need to contrast the effects of the uncertainty in order to compute a general model for automatic storytelling systems, we conducted an experiment measuring suspense experienced by a group of subjects that read a story. While a group of them were told the ending of the story in advance, the members of the other group experimented the same story in chronological order. Both the subjects' reported suspense and their physiological responses are gathered and analyzed. Results provide evidence to conclude that uncertainty affects the emotional response of readers, but independently and in a different form than suspense does. It will help to propose a model in which uncertainty is processed separately as management of the amount of knowledge about the outcome available to the spectator, which acts as a control signal to modulate the input features, but not directly in suspense computing.

Se estudian los elementos que componen el proceso de democratización de la sociedad peruana, de modo que ésta pueda ser sostenible en el tiempo; analizándose las percepciones ciudadanas (muestra de 8,935 entrevistas) presentes en la Encuesta Nacional de Hogares 2016, respecto de la vida democrática y social. Los resultados indican una profunda desconfianza (82%) en las principales entidades vinculadas a la vida democrática del país como JNE, ONPE, Poder Judicial, Congreso, Partidos y Prensa. Sin embargo, la democracia no es percibida como un problema de gran interés en el país (5%), donde la inseguridad, corrupción y pobreza siguen siendo los problemas principales. La gestión del gobierno tampoco es saludable, 41% creen que es buena y se liga principalmente al gobierno central. En suma, la democracia peruana tiene como principal baluarte la defensa de los derechos humanos, así como su rol como proceso de elecciones, pero es muy débil en materia de representatividad y logro del bienestar social. En general solo un 51% piensa que la democracia en nuestro país funciona bien.

Spatial skills are essential tools for engineers and architects during their training and career. However, consecutive curricula, in particular those in the European Higher Education Area, are assigning increasingly less credits to Graphic Expression courses, resulting in insufficient attention to the development of those skills. Considering this situation, over the past few decades many Graphic Expression teachers have conducted studies that analyse several methods for training spatial skills. This paper describes some of the main studies and their results, in which a major conclusion is that freehand sketching exercises are the most effective to develop spatial skills, more than modelling with CAD software or using innovative tools and specific applications.

The Sulfolobales have provided good model organisms for studying CRISPR-Cas systems of the crenarchaeal kingdom of the archaea. These organisms are infected by a wide range of exceptional archaea-specific viruses and conjugative plasmids, and their CRISPR-Cas systems generally exhibit extensive structural and functional diversity. They carry large and multiple CRISPR loci and often multiple copies of diverse Type I and Type III interference modules as well as more homogeneous adaptation modules. These acidothermophilic organisms have recently provided seminal insights into both the adaptation process, the diverse modes of interference, and their modes of regulation. The functions of the adaptation and interference modules tend to be loosely coupled and the stringency of the crRNA-DNA sequence matching during DNA interference is relatively low, in contrast to some more streamlined CRISPR-Cas systems of bacteria. Despite this, there is evidence for a complex and differential regulation of expression of the diverse functional modules in response to viral infection. Recent work also supports critical roles for non-core Cas proteins, especially during Type III-directed interference, and this is consistent with these proteins tending to coevolve with core Cas proteins. Various novel aspects of CRISPR-Cas systems of the Sulfolobales are considered including an alternative spacer acquisition mechanism, reversible spacer acquisition, the formation and significance of antisense CRISPR RNAs, and a novel mechanism for avoidance of CRISPR-Cas defense. Finally, questions regarding the basis for the complexity, diversity, and apparent redundancy, of the intracellular CRISPR-Cas systems are discussed.

Este artículo analiza algunos elementos característicos del mercado OTC de valores colombiano. Usando métricas del análisis de redes e información de las transacciones de títulos de deuda pública, compara el mercado OTC de valores con sus mercados organizados. Los resultados muestran una semejanza estructural entre las redes del OTC y del MEC, que son robustas a choques aleatorios pero frágiles ante choques dirigidos. Aunque el SEN incide en forma decisiva en la estructura jerárquica del mercado de valores de deuda pública y el que capta la mayor proporción del valor de las operaciones, la red del mercado de valores de deuda pública en su conjunto se asemeja a las redes del OTC y del MEC.

Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Machines (SVM), non-deterministic classifiers and Bayesian Networks, but it has been theoretically and empirically demonstrated that combining multiple classifiers can substantially improve the classification performance of the constituent members. An Early Warning System (EWS) for coffee rust disease was therefore proposed based on Error Correcting Output Codes (ECOC) and SVM to compute the binary functions of Plant Density, Shadow Level, Soil Acidity, Last Nighttime Rainfall Intensity and Last Days Relative Humidity.

Spatial visualization and sketching are essential skills for engineers and architects during their training and career. However, successive curricula, in particular those resulting from the European Higher Education Area, have caused a marked decline in spatial skills of students pursuing technical degrees. This paper describes the methodology introduced during the academic year 2012/2013 in several technical degrees at the University of Granada (Spain), designed to enhance the spatial and sketching skills of students by means of a series of complementary activities in Graphic Expression courses. The standardized tests administered to students at the beginning and at the end of the course revealed a major improvement in those who participated in these activities.

El concepto de docencia universitaria tradicional se ha visto modificado por los principios definidos en el Espacio Europeo de Educación Superior. Un cambio metodológico que motiva el concepto de autoevaluación y promueve todas aquellas actividades académicas que faciliten el autoaprendizaje. En este escenario, es fundamental que los estudiantes adquieran nuevos hábitos autoformativos. Nuestro trabajo presenta un método de enseñanza-apren- dizaje para que ésta pase a ser más activa y participativa. Este proyecto permite al alumno disponer de un conjunto de recursos, que favorecen la autoevaluación y autoformación, a la vez que facilita su trabajo personal y en equipo.

The acquisition of data in protected natural environments is subordinated to actions that do not stress the life-forms present in that environment. This is why researchers face two conflicting interests: autonomous and robust systems that minimize the physical interaction with sensors once installed, and complex enough ones to capture and process higher volumes of data. On the basis of this situation, this paper analyses the current state-of-the-art of wireless multimedia sensor networks, identifying the limitations and needs of these solutions. In this sense, in order to improve the trade-off between autonomous and computational capabilities, this paper proposes a heterogeneous multiprocessor sensor platform, consisting of an ultra-low power microcontroller and a high-performance processor, which transfers control between processors as needed. This architecture allows the shutdown of idle systems and fail-safe remote reprogramming. The sensor equipment can be adapted to the needs of the project. The deployed equipment incorporates, in addition to environmental meteorological variables, a microphone input and two cameras (visible and thermal) to capture multimedia data. In addition to the hardware description, the paper provides a brief description of how long-range (LoRa) can be used for sending large messages (such as an image or a new firmware), an economic analysis of the platform, and a study on energy consumption of the platform according to different use cases.

Electric vehicle fleets and smart grids are two growing technologies. These technologies have provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, a comparison of several evolutionary algorithms—namely genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution—is shown, in order to evaluate the proposed architecture. The proposed solution is presented as a means to prevent overload of the power grid.

One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous supply of electricity to their customers. The primary distribution network is a critical part of these facilities because a fault in it could affect thousands of customers. However, the complexity of this network has been increased with the irruption of distributed generation, typical in a Smart Grid and which has significantly complicated some of the analyses, making it impossible to apply traditional techniques. This problem is intensified in underground lines where access is limited. As a possible solution, this paper proposes to make a deployment of a distributed sensor network along the power lines. This network proposes taking advantage of its distributed character to support new approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed network (adapted to the power grid) based on nodes that use power line communication and energy harvesting techniques. In this sense, it also describes the implementation of a real prototype that has been used in some experiments to validate this technological adaptation. Additionally, beyond a simple use for monitoring, this paper also proposes the use of this approach to solve two typical distribution system operator problems, such as: fault location and failure forecasting in power cables.

Intestinal anisakiasis is a rare parasitic disease and difficult to diagnose due to symptoms are not specific, so it is considered an underdiagnosed disease. The clinical suspicion with a correct diagnosis of anisakiasis allows the establishment of a correct treatment; in most cases, the resolution is possible with conservative treatment, avoiding unnecessary surgery to the preoperative differential diagnosis of acute abdomen. We report the case of a patient who required urgent surgery secondary to an exacerbation of chronic anisakiasis.

El levantamiento gráfico de elementos del Patrimonio Cultural tiene una buena parte de su presente y futuro en el Escáner Láser Terrestre. Esta técnica, que aunque no es nueva está todavía en desarrollo, se basa en la adquisición masiva de información geométrica muy precisa de forma semiautomática con un escáner láser, que convenientemente tratada proporciona modelos muy realistas de monumentos y sitios. Con objeto de analizar sus fortalezas y debilidades se ha aplicado esta técnica al levantamiento de la iglesia de San Francisco (Priego de Córdoba), Bien de Interés Cultural perteneciente al Conjunto Histórico-Artístico del casco antiguo de dicha ciudad. Los trabajos realizados y sus resultados muestran que es una técnica muy potente y de gran proyección de futuro, aunque todavía presenta ciertos problemas en el tratamiento de la información y en la caracterización geométrica de los elementos escaneados.