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The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value. [ABSTRACT FROM AUTHOR]

Abstract: In the present work, new, differential cross-section values are presented for the natK(p, p 0) reaction in the energy range E lab =3000–5000keV (with an energy step of 25keV) and for detector angles between 140° and 170° (with an angular step of 10°). A qualitative discussion of the observed cross-section variations through the influence of strong, closely spaced resonances in the p+ 39K system is also presented. Information has also been extracted concerning the 39K(p,α 0) reaction for E lab =4000–5000keV in the same angular range. As a result, more than ∼500 data points will soon be available to the scientific community through IBANDL (Ion Beam Analysis Nuclear Data Library –http://www-nds.iaea.org/ibandl/) and could thus be incorporated in widely used IBA algorithms (e.g. SIMNRA, WINDF, etc.) for potassium depth profiling at relatively high proton beam energies. [Copyright &y& Elsevier]

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale. [ABSTRACT FROM AUTHOR]

Studies on domain adaptation (DA) for remote sensing (RS) imagery analysis lack consistency in selection and description of evaluation scenarios. Without properly characterizing datasets, model assumptions, and evaluation scenarios, it is difficult to objectively compare DA methods and reach conclusions about their suitability across different applications. With this motivation, this work seeks to empirically assess to which extent the interaction between data characteristics and model assumptions influences the effectiveness of DA methods. Using the widely explored task of building footprint segmentation as a case study, we perform a large-scale study across over 200 DA scenarios that include variations across view angles, areas observed, and sensors used for data acquisition. Rather than adopting different model architectures or optimization criteria, we contrast the performances of two DA methods based on adversarial learning that differ only in their assumptions about source and target domains. Informed by metadata and data characteristics unveiled using traditional computer vision (CV) techniques as well as pretrained deep models, we provide a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks. As demonstrated by a “cherry-picking” exercise, different claims regarding which model is best could be made by selecting different subsets of evaluation scenarios. While well-calibrated assumptions can be beneficial, mismatching assumptions can lead to negative biases in DA applications. This study intends to motivate the community toward more consistent evaluation protocols while providing recommendations and insights toward creating novel benchmark datasets, documenting data characteristics, application-specific knowledge, and model assumptions. [ABSTRACT FROM AUTHOR]

Infill-frame interaction constitutes a still open question both in research and in practicing engineering. Computational models used to predict this interaction are, in most cases, addressing the estimation of the response of the infilled frames when subjected to actions parallel to their plane. However, the observation of the post-earthquake damage has demonstrated that infills, weakened by the in-plane actions, may fail out-of-plane increasing the risks associated to the earthquake scenarios. In spite of this, different studies have shown that infills, if properly designed and supported by the frame, exhibit a significant strength and displacement capacity when called to resist to out-of-plane actions, offering the possibility to develop an arching mechanism in their deformed configuration. The prediction of the combined in-plane out-of-plane response prefigures the new goal of the seismic assessment of masonry infilled frames. This paper presents an in-depth literature review of the capacity models developed for the prediction of the out-of-plane response of infilled frames, from the first flexural based computational models to the models implementing the arching action theory in their formulation. A comparison between the results obtainable is provided in order to compare the models reliability against the results of different experimental tests. A final discussion is devoted to the effectiveness the recent integrated in-plane/out-of-plane macromodels used in 3D structural models. A new promising approach, based on the use nonlinear fiber-section elements, is also outlined providing a numerical testing of the capacity of such elements to naturally account for the out-of-plane arching mechanism. [ABSTRACT FROM AUTHOR]