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This study develops an automated crop growth detection APP, with the functionality to access the cadastral data for the target field, that was to be used for a satellite-imagery-based field survey. A total of 735 ground-truth records of the cabbage cultivation areas in Yunlin were collected via the implemented APP in order to train a deep learning model to make accurate predictions of the growth stages of the cabbage from 0 to 70 days. A regression analysis was performed by the gradient boosting decision tree (GBDT) technique. The model was trained on multitemporal multispectral satellite images, which were retrieved from the ground-truth data. The experimental results show that the mean average error of the predictions is 8.17 days, and that 75% of the predictions have errors less than 11 days. Moreover, the GBDT algorithm was also adopted for the classification analysis. After planting, the cabbage growth stages can be divided into the cupping, early heading, and mature stages. For each stage, the prediction capture rate is 0.73, 0.51, and 0.74, respectively. If the days of growth of the cabbages are partitioned into two groups, the prediction capture rate for 0–40 days is 0.83, and that for 40–70 days is 0.76. Therefore, by applying appropriate data mining techniques, together with multitemporal multispectral satellite images, the proposed method can predict the growth stages of the cabbage automatically, which can assist the governmental agriculture department to make cabbage yield predictions when creating precautionary measures to deal with the imbalance between production and sales when needed.

The Earths solid inner core is a highly attenuating medium. It consists mainly of iron. The high attenuation of sound wave propagation in the inner core is at odds with the widely accepted paradigm of hexagonal close-packed phase stability under inner core conditions, because sound waves propagate through the hexagonal iron without energy dissipation. Here we show by first-principles molecular dynamics that the body-centered cubic phase of iron, recently demonstrated to be thermodynamically stable under the inner core conditions, is considerably less elastic than the hexagonal phase. Being a crystalline phase, the body-centered cubic phase of iron possesses the viscosity close to that of a liquid iron. The high attenuation of sound in the inner core is due to the unique diffusion characteristic of the body-centered cubic phase. The low viscosity of iron in the inner core enables the convection and resolves a number of controversies. ; Funding Agencies|Swedish Research Council (VR) [2013-5767, 2014-4750, 2017-03744]; National Natural Science Foundation of China [11804175]; Olle Engkvist Byggmastare Foundation; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-MatLiU) [2009 00971]; Spanish Ministry of Economy and Competitiveness [CGL2013-41860-P, CGL2017-86070-R]

The Earths solid core is mostly composed of iron. However, despite being central to our understanding of core properties, the stable phase of iron under inner-core conditions remains uncertain. The two leading candidates are hexagonal close-packed and body-centred cubic (bcc) crystal structures, but the dynamic and thermodynamic stability of bcc iron under inner-core conditions has been challenged. Here we demonstrate the stability of the bcc phase of iron under conditions consistent with the centre of the core using ab initio molecular dynamics simulations. We find that the bcc phase is stabilized at high temperatures by a diffusion mechanism that arises due to the dynamical instability of the phase at lower temperatures. On the basis of our simulations, we reinterpret experimental data as support for the stability of bcc iron under inner-core conditions. We suggest that the diffusion of iron atoms in solid state may explain both the anisotropy and the low shear modulus of the inner core. ; Funding Agencies|Swedish Research Council (VR) [2013-5767, 2014-4750]; National Magnetic Confinement Fusion Program of China [2015GB118000]; China Scholarship Council; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-Mat-LiU) [2009 00971]