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1,505 records · Page 84

Database development and exploration of process–microstructure relationships using variational autoencoders

The paper demonstrates graphical representation of a large database containing process–microstructure relationships using an unsupervised machine learning algorithm. Correlating microstructural features to processing is an essential first step to answer the difficult problem of process sequence design. Here, a large database of 346,200 orientation distribution functions resulting from a variety of process sequences is constructed, where each sequence comprises up to four stages of tension, compression and rolling along different directions in various permutations. This open-source database is constructed for collaborative development of process design algorithms. The paper demonstrates a novel application of the large database: graphical representation of texture–process relationships. A variational autoencoder is used to reduce the entire database to a two dimensional latent space where variations in processes and properties can be visualized. Using proximity analysis in this latent space, we can quickly unearth multiple process solutions to the problem of texture or property design.

36 MATERIALS SCIENCE↗

Tuning Photoexcited Charge Transfer in Imine-Linked Two-Dimensional Covalent Organic Frameworks

The generation of a long-lived charge-separated state in versatile π-conjugated two-dimensional covalent organic frameworks (2D COFs), a process essential to extending their great potentials in advanced semiconducting applications, is yet fully elucidated. Herein, we report a systematic investigation of the photophysical properties of three highly crystalline imine-linked 2D COFs using steady-state and transient absorption spectroscopy accompanied by time-dependent density functional theory (TDDFT) calculations. The different electron affinity between 5,5',5''-(1,3,5-benzenetriyl)-tris(2-pyridinecarboxaldehyde) (BTPA) and three tunable electron-donating/accepting triamine monomers dominated the formation of the excited state, charge transfer direction and lifetime. A prominent charge transfer from electron-rich 4,4',4''-triaminotriphenylamine (TAPA) to BTPA in COFTAPA-BTPA led to the long-lived charge-separated state, which was attributed to a greater degree of delocalization compared to the two other COFs. Furthermore, these results provide fundamental insight into the importance of structure-property correlation for designing advanced photoactive 2D COF materials with the efficient charge transfer and long-lived charge-separated state.

14 SOLAR ENERGY↗

Chromosome evolution and the genetic basis of agronomically important traits in greater yam

The nutrient-rich tubers of the greater yam, Dioscorea alata L., provide food and income security for millions of people around the world. Despite its global importance, however, greater yam remains an orphan crop. Here, we address this resource gap by presenting a highly contiguous chromosome-scale genome assembly of D. alata combined with a dense genetic map derived from African breeding populations. The genome sequence reveals an ancient allotetraploidization in the Dioscorea lineage, followed by extensive genome-wide reorganization. Using the genomic tools, we find quantitative trait loci for resistance to anthracnose, a damaging fungal pathogen of yam, and several tuber quality traits. Genomic analysis of breeding lines reveals both extensive inbreeding as well as regions of extensive heterozygosity that may represent interspecific introgression during domestication. These tools and insights will enable yam breeders to unlock the potential of this staple crop and take full advantage of its adaptability to varied environments.

59 BASIC BIOLOGICAL SCIENCES↗

Climate-Mediated Changes to Linked Terrestrial and Marine Ecosystems across the Northeast Pacific Coastal Temperate Rainforest Margin

Coastal margins are important areas of materials flux that link terrestrial and marine ecosystems. Consequently, climate-mediated changes to coastal terrestrial ecosystems and hydrologic regimes have high potential to influence nearshore ocean chemistry and food web dynamics. Research from tightly coupled, high-flux coastal ecosystems can advance understanding of terrestrial–marine links and climate sensitivities more generally. In the present article, we use the northeast Pacific coastal temperate rainforest as a model system to evaluate such links. We focus on key above- and belowground production and hydrological transport processes that control the land-to-ocean flow of materials and their influence on nearshore marine ecosystems. We evaluate how these connections may be altered by global climate change and we identify knowledge gaps in our understanding of the source, transport, and fate of terrestrial materials along this coastal margin. Finally, we propose five priority research themes in this region that are relevant for understanding coastal ecosystem links more broadly.

temperate rainforest, coastal margin, climate chan↗

A coupled hydrodynamic (HEC-RAS 2D) and water quality model (WASP) for simulating flood-induced soil, sediment, and contaminant transport

Increased intensity and frequency of floods raise concerns about the release and transport of contaminated soil and sediment to and from rivers and streams. To model these processes during flooding events, we developed an External Coupler in Python to link the Hydrologic Engineering Center-River Analysis System (HEC-RAS) 2D hydrodynamic model to the Water Quality Analysis Simulation Program (WASP). Accurate data transfer from a hydrodynamic model to a water quality model is critical. Our test results showed the External Coupler successfully linked HEC-RAS and WASP and addressed technical challenges in aggregating flow data and conserving mass during the flood event. We ran the coupled models for a 100-year flood event to calculate flood-induced transport of sediment-associated arsenic in Woodbridge Creek, NJ. Change in surface sediment and arsenic at the end of 48-h flood simulation ranged from a net loss of 13.5 cm to a net gain of 11.6 cm, and 16.2 to 2.9 mg/kg, respectively, per model segment, which demonstrates the capability of the coupled model for simulating sediment and contaminant transport in flood.

54 ENVIRONMENTAL SCIENCES↗

Probabilistic Data Association for Orbital-Element Estimation Using Multistage Expectation–Maximization

Tracking space objects is important for managing space traffic and predicting collisions, but is difficult in part due to data association and orbit model uncertainty. Expectation–maximization (EM) is a commonly used tracking method that has not been widely considered for tracking space objects. The technique consists of iteratively computing data association probabilities with a set of current element estimates, and updating estimates of the elements by solving a nonlinear weighted least-squares regression problem where the weights are the data association probabilities. In this paper, we demonstrate the use of EM for probabilistic data association and orbital-element estimation by applying the technique to simulated data from two angles-only tracking scenarios. In both scenarios, EM provides correct data associations and accurate maximum likelihood estimates of orbital elements. One scenario considers tracking a single object in clutter and quantifies the improvement of the orbital-element estimates and data associations as the detection probability increases. However, standard application of EM requires knowing the number of objects or may fail when a large number of objects are present. To address these issues, this paper employs a multistage version of EM that is applicable when there are a large and possibly unknown number of objects.

97 MATHEMATICS AND COMPUTING↗

Localized Defect Detection from Spatially Mapped, In-Situ Process Data With Machine Learning

In powder bed fusion additive manufacturing, machines are often equipped with in-situ sensors to monitor the build environment as well as machine actuators and subsystems. The data from these sensors offer rich information about the consistency of the fabrication process within a build and across builds. This information may be used for process monitoring and defect detection; however, little has been done to leverage this data from the machines for more than just coarse-grained process monitoring. In this work we demonstrate how these inherently temporal data may be mapped spatially by leveraging scan path information. We then train a XGBoost machine learning model to predict localized defects—specifically soot–using only the mapped process data of builds from a laser powder bed fusion process as input features. The XGBoost model offers a feature importance metric that will help to elucidate possible relationships between the process data and observed defects. Finally, we analyze the model performance spatially and rationalize areas of greater and lesser performance.

3D printing↗

High-resolution mapping and genetic characterization of the Lazy-2 gravitropic mutant of tomato

Mutation of the Lazy-2 (Lz-2) gene in tomato (Lycopersicon esculentum mill.) produces a phytochrome-dependent reversal of shoot gravitropism, providing a unique genetic resource for investigating how signals from light modulate gravitropism. We mapped the Lz-2 gene using RFLPs and a PCR-based technique to assess the feasibility of positional cloning. Analysis of a 1338 plant backcross population between L. esculentum and L. pennellii placed Lz-2 within a 1.2 cM interval on chromosome 5, 0.4 cM from TG504-CT201A interval. The inabililty to resolve these markers indicates that Lz-2 resides in a centromeric region in which recombination is highly suppressed. Lazy-2 is tightly linked to but does not encode the gene for ACC4, an enzyme involved in ethylene biosynthesis. We also observed that Lz-2 is partially dominant under certain conditions and stages of development.

Non-NASA Center↗