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Herein, we systematically explored how to tailor heterogeneity in the Ni-Nb-(Y, Gd) phase-separated metallic glasses (PSMGs) by carefully constructing a metastable miscibility gap and investigating its effect on the formation of heterogeneous shear band (SB) nucleation sites. The Ni60Nb40-x(Y, Gd)x PSMGs with either Nb-Y or Nb-Gd atomic pair (ΔHmix=+30 kJ/mol) showed hierarchically phase-separated microstructures at the nanoscale, which can be interpreted based on a metastable miscibility gap. Interestingly, statistical analysis of the initiation of plastic deformation via nanoindentation first pop-in analysis in these PSMGs reveals a unique trimodal distribution, unlike the bimodal distribution in monolithic MGs. This tendency arises from the formation of nanoscale phase boundaries as additional differentiated heterogeneous SB nucleation sites. The homogenously dispersed SB nucleation sites help hinder strain localization and SB propagation. In particular, a PSMG with a nanoscale interconnected structure exhibits homogeneous-like plastic deformation, where neither obvious pop-ins nor distinct surface shear steps are observed in the (nano-)indentation test. We believe this study's results provide an effective guideline for tuning correlative heterogeneity and related mechanical responses in PSMGs, highlighting the importance of tailoring microstructure even in MGs.

Recent advancements have significantly improved the field of segmentation-based change detection, particularly in the context of remote-sensing images. However, change detection datasets generally lack segmentation annotations, and the required labeling process is resource-intensive. We propose an improved change detection method based on segmentation to address this challenge. First, change detection annotations are converted to incomplete segmentation annotations through label matching. During segmentation, we utilize the focused information-guided segmentation method (FIGS) and a greenness index to provide prior information during training, guiding the model using accurately labeled regions. Finally, we generate a change map using pretrained features obtained from the segmentation stage. We demonstrate the robustness of our proposed label-matching process by comparing the results to a correctly matched dataset and show that incorporating FIGS and the greenness index improves the segmentation performance. Our method achieves effective change detection results even in scenarios associated with a shortage of annotations.

Radiometric calibration has become important pre-processing with increasing use of unmanned aerial vehicle (UAV) images in various applications. In order to convert the digital number (DN) to reflectance, vicarious radiometric calibration is widely used including relative radiometric calibration. Some UAV sensor systems can measure irradiance for precise relative radiometric calibration. However, most of UAV sensor systems cannot measure irradiance and therefore precise relative radiometric calibration is needed to produce reflectance map with vicarious calibration. In this study, an optimal image selection method is proposed to improve quality of relative radiometric calibration. The method, relative calibration by the optimal path (RCOP), uses filtered tie points acquired in geometric calibration based on selection optimal image by Dijkstra algorithm. About 100 multispectral images were acquired with a RedEdge-M camera and a fixed-wing UAV. The reflectance map was produced using RCOP and vicarious calibration using ground reference panels. A validation data was processed using irradiance for precise relative radiometric calibration. As a result, the RCOP method showed root mean square error (RMSE) of 0.03–0.10 reflectance to validation data. Therefore, the proposed method can be used to produce precise reflectance map by vicarious calibration.

Abstract Absolute paleointensity (API) of the geomagnetic field can be estimated from volcanic rocks by comparing the natural remanent magnetization (NRM) to a laboratory‐induced thermoremanent magnetization (Lab‐TRM). Plots of NRM unblocking versus Lab‐TRM blocking from API experiments often exhibit nonideal curvature, which can result in biased estimates. Previous work showed that curvature can increase with age; however, selection criteria designed to eliminate such behavior yielded accurate estimates for two‐year‐aged specimens (70.3 ± 3.8 μT; N = 96 specimens out of 120 experiments). API can also be estimated in coercivity space. Here, we use the Tsunakawa‐Shaw (TS) method applied to 20 specimens aged in the laboratory field of 70.0 μT for 4 years, after acquisition of zero‐age (fresh) Lab‐TRM in the same field. Selection criteria for the TS experiment also yielded accurate results (68.5 ± 4.5 μT; N = 17 specimens). In thermal API experiments, curvature is related to internal structure with more single domain‐like behavior having the least curvature. Here we show that the fraction of anhysteretic remanent magnetization demagnetized by low‐temperature treatment was larger for samples with larger thermal curvatures suggesting a magnetocrystalline anisotropy source. We also tested experimental remedies that have been proposed to improve the accuracy of paleointensity estimates. In particular, we test the efficacy of the multi‐specimen approach and a strategy pretreating specimens with low field alternating field demagnetization prior to the paleointensity experiment. Neither yielded accurate results.

Recently, the evidences of climate change have been observed in hydrologic data such as rainfall and flow data. The time-dependent characteristics of statistics in hydrologic data are widely defined as nonstationarity. Therefore, various nonstationary GEV and generalized Pareto models have been suggested for frequency analysis of nonstationary annual maximum and POT (peak-over-threshold) data, respectively. However, the alternative models are required for nonstatinoary frequency analysis because of analyzing the complex characteristics of nonstationary data based on climate change. This study proposed the nonstationary generalized logistic model including time-dependent parameters. The parameters of proposed model are estimated using the method of maximum likelihood based on the Newton-Raphson method. In addition, the proposed model is compared by Monte Carlo simulation to investigate the characteristics of models and applicability.