Dislocation mechanisms and activation barriers of protrusion formation on a near-surface void

The presence of dislocations in metal crystals accounts for the plasticity of metals. These dislocations do not nucleate spontaneously, but require favorable conditions. These conditions include, but are not limited to, a high temperature, external stress, and an interface such as a grain boundary or a surface. The slip of dislocations leads to steps forming on the surface, as atomic planes are displaced along a line. If a void is placed very near a surface, the possibility of forming a dislocation platelet exists. The skip of the dislocation platelet would displace the surface atoms within a closed line. Repeating such a process may form a small protrusion on the surface. In this thesis, the mechanism with which a dislocations displace the surface atoms within a closed loop is studied by using molecular dynamics (MD) simulations of copper. A spherical void is placed within the lattice, and the lattice is then subjected to an external stress. The dislocation reactions which lead to the formation of the dislocation platelet after the initial dislocation nucleation on the void is studied by running MD simulations of a void with the radius of 3 nm under tensile stress. Since the dislocations are thermally activated, the simulation proceeded differently for each run. We describe the different ways the dislocations nucleate, and the dislocation reactions that occur when they intersect to form the platelet. The activation energy of this process was studied by simulating half of a much larger void, with a radius of 8 nm, in order to obtain a more realistic nucleation environment. Formulas connecting the observable and controllable simulation variables with the energies of the nucleation are derived. The activation energies are then calculated and compared with values from literature

Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation

Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natural hazards is an emerging priority for the support of natural disaster relief, risk management, and informing governmental policy decisions. Recent methods to achieve near real-time mapping increasingly leverage deep learning (DL). However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data. Therefore, DL models used to map specific natural hazards struggle with their generalization to other types of natural hazards in unseen regions. In this work, we propose a methodology to significantly improve the generalizability of DL natural hazards mappers based on pre-training on a suitable pre-task. Without access to any data from the target domain, we demonstrate this improved generalizability across four U-Net architectures for the segmentation of unseen natural hazards. Importantly, our method is invariant to geographic differences and differences in the type of frequency bands of satellite data. By leveraging characteristics of unlabeled images from the target domain that are publicly available, our approach is able to further improve the generalization behavior without fine-tuning. Thereby, our approach supports the development of foundation models for earth monitoring with the objective of directly segmenting unseen natural hazards across novel geographic regions given different sources of satellite imagery.Comment: Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023

Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets

Abstract. Identifying weather patterns that frequently lead to extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Here we propose an automated method for recognizing atmospheric rivers (ARs) in climate data using topological data analysis and machine learning. The method provides useful information about topological features (shape characteristics) and statistics of ARs. We illustrate this method by applying it to outputs of 5 version 5.1 of the Community Atmosphere Model (CAM5.1) and reanalysis product of the second Modern-Era Retrospective Analysis for Research &amp;amp; Applications (MERRA-2). An advantage of the proposed method is that it is threshold-free. Hence this method may be useful in evaluating model biases in calculating AR statistics. Further, the method can be applied to different climate scenarios without tuning since it does not rely on threshold conditions. We show that the method is suitable for rapidly analyzing large amounts of climate model and reanalysis output data. </jats:p

Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets

Abstract. Identifying weather patterns that frequently lead to extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Here we propose an automated method for recognizing atmospheric rivers (ARs) in climate data using topological data analysis and machine learning. The method provides useful information about topological features (shape characteristics) and statistics of ARs. We illustrate this method by applying it to outputs of 5 version 5.1 of the Community Atmosphere Model (CAM5.1) and reanalysis product of the second Modern-Era Retrospective Analysis for Research &amp;amp; Applications (MERRA-2). An advantage of the proposed method is that it is threshold-free. Hence this method may be useful in evaluating model biases in calculating AR statistics. Further, the method can be applied to different climate scenarios without tuning since it does not rely on threshold conditions. We show that the method is suitable for rapidly analyzing large amounts of climate model and reanalysis output data. </jats:p

An optical method for rapid examination of check development in decorative plywood panels

Common methods for assessment of surface checking in decorative plywood panels rely on manual handling and visual inspection of specimens, a laborious procedure practically limiting the number of materials and variables that may be considered within one project. In this study, a new automated optical method for detection and measurement of checks has been developed. This method was based on the digital image correlation principle, which allowed identification of checks as small as 0.2 mm wide and 1 mm long. Continuous measurement allowed reliable check counts, and measurement of check dimensions as they develop during exposure to drying conditions. A check severity index has been proposed. The method has been validated in exposure tests conducted in harsh but realistic conditions, to increase the likelihood of checking and reduce the test duration to 4 h. In addition, an innovative test setup allowed near simultaneous monitoring of check development in up to 48 panel specimens sized 30 x 30 cm. The efficiency of the method allows studies to examine an unprecedented number of treatments and replicates

Dynamic changes in ANGUSTIFOLIA3 complex composition reveal a growth regulatory mechanism in the maize leaf

Most molecular processes during plant development occur with a particular spatio-temporal specificity. Thus far, it has remained technically challenging to capture dynamic protein-protein interactions within a growing organ, where the interplay between cell division and cell expansion is instrumental. Here, we combined high-resolution sampling of the growing maize (Zea mays) leaf with tandem affinity purification followed by mass spectrometry. Our results indicate that the growth-regulating SWI/SNF chromatin remodeling complex associated with ANGUSTIFOLIA3 (AN3) was conserved within growing organs and between dicots and monocots. Moreover, we were able to demonstrate the dynamics of the AN3-interacting proteins within the growing leaf, since copurified GROWTH-REGULATING FACTORs (GRFs) varied throughout the growing leaf. Indeed, GRF1, GRF6, GRF7, GRF12, GRF15, and GRF17 were significantly enriched in the division zone of the growing leaf, while GRF4 and GRF10 levels were comparable between division zone and expansion zone in the growing leaf. These dynamics were also reflected at the mRNA and protein levels, indicating tight developmental regulation of the AN3-associated chromatin remodeling complex. In addition, the phenotypes of maize plants overexpressing miRNA396a-resistant GRF1 support a model proposing that distinct associations of the chromatin remodeling complex with specific GRFs tightly regulate the transition between cell division and cell expansion. Together, our data demonstrate that advancing from static to dynamic protein-protein interaction analysis in a growing organ adds insights in how developmental switches are regulated

Altered expression of maize PLASTOCHRON1 enhances biomass and seed yield by extending cell division duration

Maize is the highest yielding cereal crop grown worldwide for grain or silage. Here, we show that modulating the expression of the maize PLASTOCHRON1 (ZmPLA1) gene, encoding a cytochrome P450 (CYP78A1), results in increased organ growth, seedling vigour, stover biomass and seed yield. The engineered trait is robust as it improves yield in an inbred as well as in a panel of hybrids, at several locations and over multiple seasons in the field. Transcriptome studies, hormone measurements and the expression of the auxin responsive DR5(rev): mRFPer marker suggest that PLA1 may function through an increase in auxin. Detailed analysis of growth over time demonstrates that PLA1 stimulates the duration of leaf elongation by maintaining dividing cells in a proliferative, undifferentiated state for a longer period of time. The prolonged duration of growth also compensates for growth rate reduction caused by abiotic stresses