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Non-iterative and uncertainty-aware MRI-based liver fat estimation using an unsupervised deep learning method
No full textLiver proton density fat fraction (PDFF), the ratio between fat-only and overall proton densities, is an extensively validated biomarker associated with several diseases. In recent years, numerous deep learningbased methods for estimating PDFF have been proposed to optimize acquisition and post-processing times without sacrificing accuracy, compared to conventional methods. However, the lack of interpretability and the often poor generalizability of these DL-based models undermine the adoption of such techniques in clinical practice. In this work, we propose an Artificial Intelligence-based Decomposition of water and fat with Echo Asymmetry and Least-squares (AI-DEAL) method, designed to estimate both proton density fat fraction (PDFF) and the associated uncertainty maps. Once trained, AI-DEAL performs a one-shot MRI water-fat separation by first calculating the nonlinear confounder variables, ∗ 2 and off-resonance field. It then employs a weighted least squares approach to compute water-only and fat-only signals, along with their corresponding covariance matrix, which are subsequently used to derive the PDFF and its associated uncertainty. We validated our method using in vivo liver CSE-MRI, a fat-water phantom, and a numerical phantom. AI-DEAL demonstrated PDFF biases of 0.25% and −0.12% at two liver ROIs, outperforming state-of-the-art deep learning-based techniques. Although trained using in vivo data, our method exhibited PDFF biases of −3.43% in the fat-water phantom and −0.22% in the numerical phantom with no added noise. The latter bias remained approximately constant when noise was introduced. Furthermore, the estimated uncertainties showed good agreement with the observed errors and the variations within each ROI, highlighting their potential value for assessing the reliability of the resulting PDFF maps
Fragility curves for unanchored medical equipment accounting for building and content interaction
No full textCurrently, construction codes and standards require nonstructural fragility information to define nonstructural performance objectives and expectations for low- and design-intensity earthquake motions. To address this knowledge gap, this study focuses on the development of analytical fragility curves for unanchored medical equipment commonly found in hospital critical rooms, taking into account the building’s performance, damage progression, and content interaction simultaneously. To achieve this goal, a fully equipped emergency room, intensive care unit, and operating room are simulated on the first, fourth, and fifth floors of a mid-rise hospital building, respectively, and subjected to service, design, and maximum considered earthquake levels under fixed-to-the-base (FB) and base-isolated (BI) support conditions. The building’s floor acceleration responses are used as input motions to assess the performance of several pieces of medical equipment using rolling and sliding nonlinear models. This study has included a comprehensive uncertainty analysis to propagate different sources of uncertainty into the fragility curves. Fragility results indicate that, under FB support conditions, equipment malfunctions and failures are expected to occur during low-intensity earthquake motions, even if the hospital building experiences minor structural damage. Furthermore, knowing the damaged condition of medical equipment (malfunction/failure) is crucial for determining its availability and subsequent use to stabilize critical condition patients or save their lives. Finally, these fragility curves can be used to plan post-disaster recovery and make risk-informed decisions in healthcare facilities
Fluorescent B(III) and Sn(IV) Schiff base: Synthesis, photophysical properties, emergent behavior, applications, and fluorescence mechanism
No full textIn this review, we describe the preparation methods, photophysical properties, and applications of fluorescent Boron(III) and Tin(IV) Schiff base. Although Schiff base have been known for a long time, Boron(III) and Tin(IV) compounds derived from them have gained significant attention in materials chemistry in recent decades. Remarkably, fluorescent organoboron and organotin complexes offer several advantages, such as being synthesized via green methods and exhibiting desirable physical and optical properties. Notably, the low cytotoxicity of organoboron compounds makes them excellent fluorescence dyes for in vitro and in vivo bioimaging. We present the most recent studies of Boron(III) and Tin(IV) compounds and highlight the favorable photophysical and physicochemical properties. Furthermore, we discuss materials that respond to various stimuli -physical, chemical and biological. Some fluorescent organoboron and tin complexes behave as smart materials, showing multi-stimuli responsiveness through processes such as mechanochromism, thermochromism, piezochromism, and vapochromism, most of which are observed in the solid-state. We also include several quantum chemical studies aimed at understanding the electronic structures and optical properties of these fluorescent Schiff base complexes
Integrating Artificial Intelligence in Education: Insights From a Teacher Training Workshop
No full textThis chapter explores the impact of an in-person AI training workshop on Chilean in-service teachers across a network of four schools. Through a mixed-methods approach—including pre- and post-surveys with qualitative and quantitative data—the study examines shifts in teacher attitudes, knowledge, and intentions to use AI in educational practice. Results show increased confidence, pedagogical alignment, and ethical awareness, particularly regarding inclusion and differentiated instruction. The chapter also highlights the importance of contextualized training, gender representation, and long-term support to ensure equitable and meaningful AI integration in Latin American classroom
Effects of strong electron correlations and van der Waals interactions in the physical properties of bulk and 2D FeCl2
No full text© 2025 The AuthorsWe conducted a first-principles study of FeCl2, focusing on the significance of strong electron correlations using the GGA+U approximation and van der Waals (vdW) interactions to enhance the description of its physicochemical properties. Our results provide an excellent characterization of both the bulk CdCl2-type structure and the 2D phase 1T crystal structure. We found that both phases were elastically and dynamically stable, showing good agreement with the experimental data from IR, Raman, inelastic neutron scattering, and magnetic measurements. The impact of the FeCl2 dimensionality is discussed in detail. Additionally, we investigated the less-explored distorted 1T phase (1T’), where structural distortions introduce anisotropies that notably affect its properties. Moreover, our analysis of the magnon spectrum aligns with the recently characterized magnetic properties of the FM 1T phase. Simultaneously, magnetic anisotropy calculations revealed that the 1T’ configuration exhibits greater stability in the presence of an external magnetic field.LANCADCONACYT of MexicoCONACYTAgencia Nacional de Investigación y DesarrolloU.S. Department of EnergyOffice of ScienceNSFNSFCONICYT FONDECYTCONICYT FONDECYTIPICYTIPICYTPittsburgh Supercomputer CenterBasic Energy SciencesBasic Energy Science
A simple approach for effective CFD simulation of turbulent pipe transport of shear-thinning, power-law fluids
No full textThis study presents a simple, efficient approach for the CFD simulation of turbulent pipe transport of shear-thinning, power-law fluids. The method is developed within the Newtonian-based Reynolds-Averaged Navier-Stokes (RANS) framework, and it relies on a modification to the Reynolds-averaged apparent viscosity function to compensate for errors induced by the non-decomposition of the instantaneous apparent viscosity, as well as for the use of turbulence models developed for Newtonian fluids. Specifically, the Reynolds-averaged apparent viscosity switches from a power law to a logarithmic function for averaged shear rates below a threshold value, called the “critical shear rate”, which becomes a calibration parameter of the model. The new framework was tested against DNS data reported in the literature for different pipe-flow conditions, covering combinations of flow index and friction Reynolds number , as well as against well-established correlations for the friction factor, with the analysis extended to cases up to and . The analysis was conducted by employing three different turbulence models, namely Lam-Bremhorst k-ε, two-layer k-ε, and k-ω SST, which all rely on a low-Reynolds number treatment to obtain a detailed flow description in the near-wall region. The proposed approach appears attractive from an engineering standpoint, as it allows obtaining reasonably accurate prediction of main features of turbulent pipe transport of shear-thinning, power-law fluids, with a simple mathematical formulation and a robust and easy-to-converge character that can make a difference for the application to more complex flows
METACONE: A scalable framework for exploring the conversion cone of metabolic networks
No full textElementary Conversion Modes (ECMs) – a subset of Elementary Flux Modes (EFMs) – capture the entire production/consumption potential of a metabolic network, providing a more practical view of its interactions with the environment. Despite its reduced size, the set of ECMs is too large for exhaustive enumeration in models reaching genome scale. To address this limitation, we have developed METACONE (METAbolic Conversion cOne for Network Exploration), a scalable algorithm for the computation of a representative linear basis of the conversion cone, the subspace in which all ECMs lie. Two METACONE variants are proposed based on the solution of a series of linear programs following different heuristics. We evaluated the performance of the variants on metabolic models of different sizes, demonstrating their scalability. We further analyzed the resulting basis to explore metabolic capabilities under different environmental conditions in Escherichia coli, identifying metabolic patterns consistent with reported data. Finally, we applied the algorithm to explore metabolic interactions in a microbial consortium of Phocaeicola dorei and Lachnoclostridium symbiosum, recapitulating known cross-feeding interactions and suggesting new possibilities. We envision METACONE as a valuable tool for understanding microbial metabolism in increasingly complex consortia while addressing current scalability challenges
Toward a digital twin for beer quality control: development of a digital model integrating industrial process data and model-based fermentation descriptors
No full textBeer production consists of a series of complex chemical, physical, and biological transformations. Although modern industrial production protocols are highly standardized, external and process disturbances often lead to degradation in beer quality. While models are available to predict beer quality, their widespread use is currently limited. Typically, these models rely on input variables from products that are almost complete; therefore, corrective measures cannot be implemented on time. Advanced modeling tools, such as digital twins, are effective alternatives to tackle this limitation, as they can integrate real-time process data into a digital model of the physical system to provide online predictions. To advance the development of such a tool, we have developed a hybrid model for beer quality prediction by combining different modeling frameworks and industrial process data. First, a dynamic model of industrial beer fermentation was calibrated that satisfactorily captured the kinetics of primary (extract) and quality-associated volatile compounds (pentanedione and butanedione). Second, a Naïve Bayes classifier for predicting beer quality was trained using physicochemical variables that identified the most critical attributes in a high-quality beer (ethyl acetate, total esters, foam stability, bitterness, and isoamyl acetate). Lastly, a hybrid regression model was constructed using fermentation model descriptors and external process data to predict the latter attributes with high prediction fidelity (less than 10% relative root mean squared error). The model identified yeast handling—including storage and propagation—and wort preparation as critical determinants of final product quality. Overall, this work represents a step toward developing a digital twin that can provide real-time process descriptors and integrate industrial data to optimize production and enhance beer quality.ANIDCENIA; Folio: FB21001
Developing temporal clustering for identifying solar radiation zones to improve separation models
No full text© 2025 Elsevier LtdAccurate solar-plant design requires detailed measurement campaigns to determine the site's radiative conditions. In the absence of empirical data, researchers employ separation models to estimate solar radiation components by calibrating polynomial coefficients with local meteorological data. Previous studies have adjusted these coefficients for various climate zones using the Köppen & Geiger classification, originally devised to demarcate regions based on plant distributions. Consequently, applying this classification to solar radiation may merge areas with different radiative characteristics, resulting in flawed assessments. This study describes a clustering technique that treats solar radiation as temporal data through the Discrete Fourier Transform and the Time Series Feature Extraction Library. By selecting input variables based on atmospheric attenuation and sky conditions, the K-means algorithm identified six clusters as the optimal solution, validated with a widely used separation model adjusted for the new clusters. The results of the estimation for the Cluster-adjusted model were then compared to the same separation model adjusted to the Köppen & Geiger classification. The Cluster-adjusted model showed superior performance in 34 of 50 stations, which shows that grouping meteorological stations according to their radiative characteristics achieves better results than dividing them on the basis of climate.ANIDSERCSubdirección de Capital HumanoFONDAPFONDAPDoctorado Naciona
Networked inequality: The role of changes in network heterogeneity and network size in attitudes towards inequality
No full textExisting research on attitudes towards inequality has predominantly focused on individual class or socioeconomic position, with little attention paid to the role of personal networks. The limited existing research has primarily focused on the influence of specific class ties, while overlooking a crucial dimension: network size. Moreover, the lack of quantitative data containing information about socioeconomic standing, network configuration and attitudes over time for a group of the same individuals has hindered the accurate testing of the influence of personal networks on attitudes towards inequality. To address these gaps, the main goal of this paper is to examine the extent to which changes in the size and heterogeneity of acquaintanceship networks affect attitudes towards inequality in Chile – a country with high levels of income and wealth inequality. We utilise quantitative data from two waves (2016–2018) of a representative panel survey for the urban Chilean population, provided by the Chilean Longitudinal Social Survey (ELSOC). Our cross-sectional analyses indicate that network heterogeneity and network size both enhance perceptions of income inequality and preferences for equality, while decreasing perceptions of meritocracy. In the fixed effects regression models, however, network size is more closely linked to an increased perception of inequality, while network heterogeneity is more strongly associated with greater preferences for equality. Moreover, increases in network size tend to reduce meritocratic perceptions. These findings suggest that network size and network heterogeneity are complementary network characteristics in explaining attitudes towards inequality