Abstract: Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions. Resumen: La entropía de transferencia (TE) se ha utilizado para identificar y cuantificar las interacciones entre sistemas fisiológicos. Existen diferentes métodos para estimar la TE, pero no hay consenso sobre cuál funciona mejor en aplicaciones específicas. En este estudio, se compararon cinco métodos (lineal, k-nearest neighbors, fixed binning with ranking, kernel density estimation, and adaptive partitioning). La comparación se realizó en tres modelos de simulación (lineal, no lineal y dinámicas lineales + no lineales). A partir de las simulaciones, se encontró que el mejor método para cuantificar las diferentes interacciones fue el adaptive partitioning. Este método se aplicó luego a datos de un estudio de polisomnografía, específicamente a las señales de ECG y respiratorias (flujo nasal y esfuerzo respiratorio alrededor del tórax). Se probó la hipótesis de que los componentes lineales y no lineales de las interacciones cardiorespiratorias durante el sueño ligero y profundo cambian con la etapa del sueño. Diferencias significativas, después de realizar un análisis de subrogados, indican un aumento en la TE durante el sueño profundo. Sin embargo, se encontró que estas diferencias dependían del tipo de señal respiratoria y la frecuencia de muestreo. Estos resultados resaltan la importancia de seleccionar las señales apropiadas, el método de estimación y el análisis de subrogados adecuados para el estudio de las interacciones cardiorespiratorias lineales y no lineales.

Abstract: There are too many reinforced concrete (RC) columns built before mid-1970s without sufficient transverse reinforcement. By now, we understand quite well the importance of transverse reinforcement in allowing a column to maintain its integrity under large displacement reversals in the nonlinear range of response. Poorly confined RC columns undergo a fast decay in resistance due to formation of criss-crossing inclined cracks, which can cause an abrupt failure or more gradual disintegration and trigger collapse of the structure. Those columns need to be strengthened to increase their drift capacity. Although there are several alternatives to retrofit RC columns, they often require specialized workmanship and equipment, and involved installation procedures. An easy-todesign and easyto-implement retrofit technique is examined here. It consists of external posttensioned clamps fastened around the column. Results of tests on full-scale RC columns furnished with the proposed clamps suggest the clamps can be effective in increasing column shear strength and drift capacity. Resumen: Muchas columnas de concreto reforzado construidas antes de mediados de la década de 1970 tienen insuficiente refuerzo transversal. Hace solo un par de décadas que se entendió la importancia del refuerzo transversal para permitir que una columna mantenga su integridad bajo grandes desplazamientos en el rango de respuesta no lineal. Las columnas de concreto reforzado mal confinadas sufren una rápida disminución de la resistencia debido a la formación de grietas, que pueden causar una falla abrupta o una desintegración más gradual y desencadenar el colapso de la estructura. Es necesario reforzar dichas columnas para aumentar su capacidad de deriva. Aunque existen varias alternativas para reforzar columnas de concreto reforzado, a menudo requieren mano de obra y equipo especializados, además de procedimientos de instalación complicados. Aquí se examina una técnica de reforzamiento fácil de diseñar e implementar. La técnica consta de abrazaderas presforzadas externas fijadas alrededor de la columna. Los resultados de laboratorio de las columnas de concreto reforzado a gran escala equipadas con las abrazaderas propuestas sugieren que las abrazaderas pueden ser efectivas para aumentar la resistencia al corte y la capacidad de deriva de la columna.

Abstract: Soil available water capacity (AWC) is a key function for human survival and well-being. However, its direct measurement is laborious and spatial interpretation is complex. Digital soil mapping (DSM) techniques emerge as an alternative to spatial modeling of soil properties. DSM techniques commonly apply machine learning (ML) models, with a high level of complexity. In this context, we aimed to perform a digital mapping of soil AWC and interpret the results of the Random Forest (RF) algorithm and, in a case study, to show that digital AWC maps can support agricultural planning in response to the local effects of climate change. To do so, we divided this research into two approaches: In the first approach, we showed a DSM using 1857 sample points in a southeastern region of Brazil with laboratory-determined soil attributes, together with a pedotransfer function (PTF), remote sensing and DSM techniques. In the second approach, the constructed AWC digital soil map and weather station data were used to calculate climatological soil water balances for the periods between 1917–1946 and 1991–2020. The result showed the selection of covariates using Shapley values as a criterion contributed to the parsimony of the model, obtaining goodness-of-fit metrics of R2 0.72, RMSE 16.72 mm m?1, CCC 0.83, and Bias of 0.53 over the validation set. The highest contributing covariates for soil AWC prediction were the Landsat multitemporal images with bare soil pixels, mean diurnal, and annual temperature range. Under the current climate conditions, soil available water content (AW) increased during the dry period (April to August). May had the highest increase in AW (?17 mm m?1) and decrease in September (?14 mm m?1). The used methodology provides support for AWC modeling at 30 m resolution, as well as insight into the adaptation of crop growth periods to the effects of climate change.

Architecture and Urban planning have traditionally been viewed through an adult-centric lens, focusing on adult-scale and experience. However, this approach ignores the valuable insights and perspectives that children can bring to the transformation of our cities. Assemble story began from the desire to make children active participants of society´s transformation and its platform is an instrument to realize it. This is a sample of the process and results in 10 workshops (fall 2022), the collective carried out in Berlin with children between 9-14 years old.

Abstract: The decarbonization of energy matrices is crucial to limit global warming below 2°C this century. An alternative capable of enabling zero or even negative CO2 emissions is bioenergy with carbon capture and storage (BECCS). In this sense, the Brazilian sugar–energy sector draws attention, as it would be possible to combine the production of fuel and electricity from renewable biomass. This paper is the final part of a study that aimed to research carbon capture and storage (CCS) in energy systems based on sugarcane. The case studied is CCS in thermal power plants considering two different technologies: the steam cycle based on the condensing–extraction steam turbine (CEST) and the combined cycle integrated to biomass gasification (BIG-CC). The results for the thermal power plant indicate that the CO2 capture costs may be lower than those in cogeneration systems, which were previously studied. The main reasons are the potential scale effects and the minimization of energy penalties associated with integrating the CCS system into the mills. In the best cases, capture costs can be reduced to EUR 54–65 per ton of CO2 for the CEST technology and EUR 57–68 per ton of CO2 for the BIG-CC technology.

Abstract: Residency programs face overwhelming numbers of residency applications, limiting holistic review. Artificial intelligence techniques have been proposed to address this challenge but have not been created. Here, a multidisciplinary team sought to develop and validate a machine learning (ML)-based decision support tool (DST) for residency applicant screening and review. Results The ML model areas under the receiver operating characteristic and precision recall curves were 0.95 and 0.76, respectively; these changed to 0.94 and 0.72, respectively, with removal of USMLE scores. Applicants’ medical school information was an important driver of predictions—which had face validity based on the local selection process—but numerous predictors contributed. Program directors used the DST in the 2021 application cycle to select 20 applicants for interview that had been initially screened out during human review. Conclusions The authors developed and validated an ML algorithm for predicting residency interview offers from numerous application elements with high performance—even when USMLE scores were removed. Model deployment in a DST highlighted its potential for screening candidates and helped quantify and mitigate biases existing in the selection process. Further work will incorporate unstructured textual data through natural language processing methods.

Abstract: The emergence of digital twin technologies offers a promising avenue for improving decision-making through the integrated use of up-to-date physical or synthetically simulated data. Nevertheless, the practical implementation of digital twins in the built environment remains a significant challenge. This paper describes a system that seamlessly integrates data into digital twins of built assets. The system uses a knowledge graph to achieve data integration, which is designed to be modular, flexible, and interoperable. The graph includes BIM models, metadata from an external IoT platform, and processrelated information. The system is microservice-based and revolves around a graph database housing the knowledge graph. It employs dynamic operations to update the knowledge graph and is tested using civil engineering infrastructure examples. Results from this work can be used to create pipelines that extract and operate with data connecting computational agents integrated into the system as microservices or connected through the system API.

Abstract: The need for measurable data from physical assets to actively feed a living Digital Twin (DT) is paramount. The requirements and needs that the gathered data should fulfill in order to be practically implemented in the stream data pipeline are heterogeneous, some of them general and other case-specific. This article summarizes a set of identified challenges and requirements for a seamless infusion of well-established Structural Health Monitoring (SHM) systems within DT platforms, without the objective of solve all of them. This identification is performed based on a review of traditional SHM systems with a vast array of information sources as well as on the review of techniques for the systematic digitalization of existing assets. On the other hand, ten real demo cases belonging to Ashvin, an H2020 Research and Innovation project, are providing real world testing beds for an active development of SHM infused in DT systems. Multiple information sources are studied in those sites, which also enriches with more realism, the identification of requirements and challenges presented herein. These assets provide a perspective to researchers about practical implications of these needs.

Abstract: The generation of genetic diversity via mutagenesis is routinely used for protein engineering and pathway optimization. Current technologies for random mutagenesis often target either the whole genome or relatively narrow windows. To bridge this gap, we developed CoMuTER (Confined Mutagenesis using a Type I-E CRISPR-Cas system), a tool that allows inducible and targetable, in vivo mutagenesis of genomic loci of up to 55 kilobases. CoMuTER employs the targetable helicase Cas3, signature enzyme of the class 1 type I-E CRISPR-Cas system, fused to a cytidine deaminase to unwind and mutate large stretches of DNA at once, including complete metabolic pathways. The tool increases the number of mutations in the target region 350-fold compared to the rest of the genome, with an average of 0.3 mutations per kilobase. We demonstrate the suitability of CoMuTER for pathway optimization by doubling the production of lycopene in Saccharomyces cerevisiae after a single round of mutagenesis. Resumen: La generación de diversidad genética mediante mutagénesis es utilizada habitualmente para la ingeniería de proteínas y la optimización de vías metabólicas. Las tecnologías actuales para realizar mutagénesis aleatoria a menudo se dirigen a todo el genoma o a secuencias relativamente estrechas de ADN. Para cerrar esta brecha, desarrollamos CoMuTER (mutagénesis confinada utilizando un sistema CRISPR-Cas tipo I-E, por sus siglas en inglés), una herramienta que permite la mutagénesis in vivo inducible y dirigida de loci genómicos de hasta 55 kilobases. CoMuTER emplea la helicasa Cas3, enzima distintiva del sistema CRISPR-Cas clase 1 tipo I-E, fusionada a una citidina desaminasa para desenrollar y mutar grandes extensiones de ADN a la vez, incluidas vías metabólicas completas. La herramienta aumenta en un 350 veces el número de mutaciones en la región objetivo en comparación con el resto del genoma, con un promedio de 0,3 mutaciones por kilobase. Demostramos la idoneidad de CoMuTER para la optimización de vías al duplicar la producción de licopeno en Saccharomyces cerevisiae después de una sola ronda de mutagénesis.

Abstract: This article presents a case study with various developments of digital twinning of a sample of load tests performed on several railways bridges. The case study is located in Extremadura, South Western Spain and its aim is the generation of a validated, multi-layered information construct in the form of a digital twin as the result of a load test. This result is conceived, not only to verify the assumptions of the design of the bridge but also, to optimize future maintenance plans of the network. This particular case study is framed within a vaster European effort on digitization of the construction sector. Research and Innovation Actions within this demo case are aimed at integrating routine requirements and procedures of load tests with cutting edge digital technologies for the generation of validated virtual replica of these physical bridges. The generated twins during these load tests behaviourally match the obtained response during loading and as such, represent an ideal model for future simulations and behavioural predictions. Different data-gathering techniques and numerical models are integrated within a Common Data Environment (CDE). All efforts related to measurement, simulation, 3D modelling, assessment and validation can be wrapped up systematically for further use during regular operation of the asset.