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Abstract Bayesian inference based on computational simulations plays a crucial role in model-informed damage diagnostics and the design of reliable engineering systems, such as the miter gates studied in this article. While Bayesian inference for damage diagnostics has shown success in some applications, the current method relies on monitoring data from solely the asset of interest and may be affected by imperfections in the computational simulation model. To address these limitations, this article introduces a novel approach called Bayesian inference-based damage diagnostics enhanced through domain translation (BiEDT). The proposed BiEDT framework incorporates historical damage inspection and monitoring data from similar yet different miter gates, aiming to provide alternative data-driven methods for damage diagnostics. The proposed framework first translates observations from different miter gates into a unified analysis domain using two domain translation techniques, namely, cycle-consistent generative adversarial network (CycleGAN) and domain-adversarial neural network (DANN). Following the domain translation, a conditional invertible neural network (cINN) is employed to estimate the damage state, with uncertainty quantified in a Bayesian manner. Additionally, a Bayesian model averaging and selection method is developed to integrate the posterior distributions from different methods and select the best model for decision-making. A practical miter gate structural system is employed to demonstrate the efficacy of the BiEDT framework. Results indicate that the alternative damage diagnostics approaches based on domain translation can effectively enhance the performance of Bayesian inference-based damage diagnostics using computational simulations. 

Miter gates are vital civil infrastructure components in inland waterway transportation networks. To provide risk-informed insights for decisions related to repair and maintenance, sensors have been installed on some miter gates for monitoring. Despite the monitoring system's ability in collecting a large volume of monitoring data, accurately diagnosing damage state in such large structures remains challenging due to the lack of labeled monitoring data, since these structures are designed with high reliability and for a long operation life. This paper addresses this challenge by proposing a damage diagnostics approach for miter gates based on domain adaptation. The proposed approach consists of two main modules. In the first module, Cycle-Consistent generative adversarial network (CycleGAN) is employed to map monitoring data of a miter gate of interest and other similar yet different miter gates into the same analysis domain. Subsequently, a normalizing flow-based likelihood-free inference model is constructed within this common domain using data from source miter gates whose damage states are labeled from historical inspections. The trained normalizing flow model is then used to predict the damage state of the target miter gate based on the translated monitoring data. A case study is presented to demonstrate the effectiveness of the proposed method. The results indicate that the proposed method in general can accurately estimate the damage state of the target miter gate in the presence of uncertainty. 

Abstract Plant diversity effects on community productivity often increase over time. Whether the strengthening of diversity effects is caused by temporal shifts in species-level overyielding (i.e., higher species-level productivity in diverse communities compared with monocultures) remains unclear. Here, using data from 65 grassland and forest biodiversity experiments, we show that the temporal strength of diversity effects at the community scale is underpinned by temporal changes in the species that yield. These temporal trends of species-level overyielding are shaped by plant ecological strategies, which can be quantitatively delimited by functional traits. In grasslands, the temporal strengthening of biodiversity effects on community productivity was associated with increasing biomass overyielding of resource-conservative species increasing over time, and with overyielding of species characterized by fast resource acquisition either decreasing or increasing. In forests, temporal trends in species overyielding differ when considering above- versus belowground resource acquisition strategies. Overyielding in stem growth decreased for species with high light capture capacity but increased for those with high soil resource acquisition capacity. Our results imply that a diversity of species with different, and potentially complementary, ecological strategies is beneficial for maintaining community productivity over time in both grassland and forest ecosystems. 

Abstract Picocyanobacteria make up half of the ocean’s primary production, and they are subjected to frequent viral infection. Viral lysis of picocyanobacteria is a major driving force converting biologically fixed carbon into dissolved organic carbon (DOC). Viral-induced dissolved organic matter (vDOM) released from picocyanobacteria provides complex organic matter to bacterioplankton in the marine ecosystem. In order to understand how picocyanobacterial vDOM are transformed by bacteria and the impact of this process on bacterial community structure, viral lysate of picocyanobacteria was incubated with coastal seawater for 90 days. The transformation of vDOM was analyzed by ultrahigh-resolution mass spectrometry and the shift of bacterial populations analyzed using high-throughput sequencing technology. Addition of picocyanobacterial vDOM introduced abundant nitrogen components into the coastal water, which were largely degraded during the 90 days’ incubation period. However, some DOM signatures were accumulated and the total assigned formulae number increased over time. In contrast to the control (no addition of vDOM), bacterial community enriched with vDOM changed markedly with increased biodiversity indices. The network analysis showed that key bacterial species formed complex relationship with vDOM components, suggesting the potential correspondence between bacterial populations and DOM molecules. We demonstrate that coastal bacterioplankton are able to quickly utilize and transform lysis products of picocyanobacteria, meanwhile, bacterial community varies with changing chemodiverisity of DOM. vDOM released from picocyanobacteria generated a complex labile DOM pool, which was converted to a rather stable DOM pool after microbial processing in the time frame of days to weeks. 

Several variants of the subgraph isomorphism problem, e.g., finding, counting and estimating frequencies of subgraphs in networks arise in a number of real world applications, such as web analysis, disease diffusion prediction and social network analysis. These problems are computationally challenging in having to scale to very large networks with millions of vertices. In this paper, we present SAHAD, a MapReduce algorithm for detecting and counting trees of bounded size using the elegant color coding technique developed by N. Alon et al. SAHAD is a randomized algorithm, and we show rigorous bounds on the approximation quality and the performance of it. SAHAD scales to very large networks comprising of 107-108 edges and tree-like (acyclic) templates with up to 12 vertices. Further, we extend our results by implementing SAHAD in the Harp framework, which is more of a high performance computing environment. The new implementation gives 100x improvement in performance over the standard Hadoop implementation and achieves better performance than state-of-the-art MPI solutions on larger graphs. 

Precise fabrication of semiconducting carbon nanotubes (CNTs) into densely aligned evenly spaced arrays is required for ultrascaled technology nodes. We report the precise scaling of inter-CNT pitch using a supramolecular assembly method called spatially hindered integration of nanotube electronics. Specifically, by using DNA brick crystal-based nanotrenches to align DNA-wrapped CNTs through DNA hybridization, we constructed parallel CNT arrays with a uniform pitch as small as 10.4 nanometers, at an angular deviation <2° and an assembly yield >95%.