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Do Bong-soon (Park Bo-young) was born with superhuman strength. Her strength is hereditary and passed along only to the women in her family. Her dream is to create a video game with herself as the main character. She desperately wants to become a delicate and elegant woman, which is the ideal type of her crush, In Guk-doo (Ji Soo), a police officer. Thanks to her strength, she gets a job as bodyguard to rich heir Ahn Min-hyuk (Park Hyung-sik), the CEO of a gaming company, Ainsoft. A series of kidnapping cases happen in Dobong-dong, the district Bong-soon lives in, and she is determined to catch the culprit, who targeted her best friend. With help and training from Min-hyuk, she manages to control her strength to use it for good causes. Min-hyuk and Bong-soon find their relationship growing into something more. Their relationship at work and in pursuit of the kidnapper creates comical and dangerous situations, which bring them closer.
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A hierarchical nanostructured Al-based alloy is studied by MD simulation and an extraordinary grain-flattening based plastic deformation behavior is successfully predicted. On one hand, dislocations nucleate more difficultly from the glass/crystal interface compared with the GB, which contribute to ultrahigh strength. On the other hand, a series of detailed MD studies help us to understand the origin of the exceptional properties of this hierarchical nanostructured Al-based alloy. The shear deformation is constrained at the glass/crystal interface and cannot cross the MG interior. Furthermore, the dislocation-based mechanism dominates the plastic deformation of the crystalline phase, which contributes to the large plasticity. The strong MG phase in the hierarchical nanostructured Al-based alloy impedes the large shear failure and well coordinate the plasticity by plastic flow.
The line-of-sight observables code is called to compute the Doppler velocity and LoS magnetic field strength, as well as the Fe I line width, line depth, and continuum intensity, from the Stokes parameters at each time step. The LoS observables are calculated with the MDI-like algorithm detailed in Couvidat et al. (2012). Briefly, discrete estimates of the first and second Fourier coefficients of the solar neutral iron line are calculated from the six wavelengths, separately for the IV polarizations. The Doppler velocity is proportional to the phase of the Fourier coefficients; currently, only the first Fourier coefficient is used to compute the velocity. Certain approximations made in this calculation introduce errors in the results: the HMI filter profiles are not delta functions, the discrete estimate of the Fourier coefficients is not completely reliable due to the small number of wavelength samples, and the spectral line profile is not Gaussian (an assumption made to relate the phase to the velocity). Hence Doppler velocities returned by the basic algorithm must be corrected. This is accomplished in two steps.
The eleven HARPs present on the Sun at 00:00 TAI on 1 July 2012, four in the North and seven in the South, are shown with their lifetime maximum bounding boxes. Each box is labeled with the HARP number in the upper right corner. The colored patch encloses the active region (with active pixels shown in black) associated with the HARP bitmap at the time of the image. White + signs mark the reported locations of NOAA Active Regions; the NOAA number appears at the same longitude near the equator for identification purpsoses. HARPs may be associated with one or more NOAA active regions (e.g. HARP 1807 is associated with NOAA AR #11515 and #11514), but most are not. HARP bounding boxes may overlap, as 1819 and 1806 do in this image, or a small HARP may even be completely enclosed inside another, e.g. 1822 inside 1811. Colored patches denoting unique HARPs never overlap. Definitive HARPs are tracked both before emergence (e.g. 1822) and after decay (e.g. 1786), as indicated by the parenthesis character to the left of the HARP number in the legends in the two right corners. The key for the HARP information appears in the lower left. Additional information can be found at jsoc.stanford.edu/data/hmi/HARPs_movies/movie-note .
The HMI noise level varies with location on the disk and with relative velocity between the spacecraft and Sun (see Section 7.1 for a discussion of the temporal and large-scale spatial variations found in the data). A constant is added to the derived noise value, and pixels with a transverse field strength above this noise estimate are assigned to the disambiguation mask. The mask is then eroded to eliminate isolated pixels above the noise, and this defines the high-confidence pixels. Intermediate confidence is assigned to a buffer zone surrounding the clusters of pixels with a well-determined field. All pixels within that grown mask are included in minimizing the energy.
The black curves in the left column in the top three panels are four-day temporal profiles for AR #11084 of the mean magnetic-field strength determined for the umbra, penumbra, and quiet Sun, respectively. The red curves are third-order polynomial fits. Residuals are plotted in the top three panels on the right. The residual is the difference between the mean field strength and the polynomial fit. For reference, the mean LoS velocity observed in the quiet-Sun region is plotted in the two bottom panels. For this analysis the umbra includes pixels where, compared with the quiet Sun, the continuum intensity, I c
Scatter plots on the left show the relationship between the residual of the mean field strength and LoS velocity in the AR #11084 umbra (top), penumbra (middle), and nearby quiet Sun (bottom). Note the scale change for the quiet Sun. Linear fits are described in the text. The right panels show the power spectra of the mean field strength residuals for the sunspot umbra (top), penumbra (middle), and quiet Sun (bottom).
Two types of bad pixels. Top left: magnetic-field strength of AR #11476 at 12:00 TAI 8 May 2012 at N10 E34. The erroneous values are denoted by an arrow. The field strengths are much higher than in adjacent pixels. Bottom left: field strength along a horizontal row including a bad pixel. Note that 5000 G is the highest possible value allowed by the VFISV code. Top right: field strength of AR #11515 at 02:24 TAI 1 July 2012 at S17 E29. Bad pixels are denoted by an arrow. The field strengths at bad pixels are much lower than in adjacent pixels. Bottom right: field strength along a horizontal row including a bad pixel.
In addition to having abnormal field strengths, these pixels typically have out-of-the-ordinary values in other quantities computed with VFISV. This provides a way to identify them. Figure 14 shows a cluster of anomalous inversion values in AR #11476. They are clearly seen in the error of the field strength, and are also visible in the errors of inclination and azimuth and the LoS field. Some also appear in the confidence maps, although they do not always show up in χ 2. A description of the data confidence map is given in Section A.1.
CCD cut-outs of inverted data for a small area in HARP 1638, AR #11476 showing bad pixels. Panels from left to right and top to bottom are field strength, inclination, azimuth of magnetic field, their errors, χ 2 for the least-squares fit, the confidence map, and LoS magnetic field. Erroneous values can be clearly seen in field strength, error of field strength, and LoS field. They are also visible in errors of inclination and azimuth. Units along the x- and y-axis are pixels.
Closeup of magnetic field and currents in AR #11158 for two consecutive time steps on 16 February 2011 at 04:36 and 04:48 TAI. The red and blue contours show the vertical magnetic field and the arrows indicate the strength and direction of the horizontal field component. Yellow and green contours show where the computed vertical current is strong. Axes are in pixels. The upper panel at 04:36 TAI shows a fairly normal field configuration. The central region of the lower panel at 04:48 TAI with diamond-shaped contours shows a case where the disambiguation module gives conflicting results in adjacent pixels. Certain areas of an active region may be more susceptible to the instability, which will come and go from one time step to the next. See text for details.
Figure 17 shows a more detailed comparison between the velocity and magnetic fields. The inverted velocity, V Inv, and inverted LoS magnetic field, B Los, are determined using the fd10 inversion. The Doppler velocity, V Dop, and LoS magnetic field, M Los, are computed from LCP and RCP using the MDI-like algorithm (see Section 2.3). The data are all observed with the same HMI camera. Four pairs of observations, taken 12 February 2011 at 00:48, 06:48, 12:48, and 18:48 TAI are used. The magnetic comparison is shown in the left column of Figure 17 and the velocity comparison is shown in the right column. The MDI-like LoS magnetogram and Dopplergram values are shown on the x-axis versus the fd10 magnetic and velocity values on the y-axis. Results for different parts of the disk and for strong-field regions are shown. Note that the color scales in the left and right columns are different to better show the details of the distribution of scatter-plot points. For reasons discussed in Section 2.3 and by Couvidat et al. (2012) and Liu, Norton, and Scherrer (2007), the MDI-like algorithm is not as accurate in strong-field regions and underestimates the field strength. The LoS velocities from the two algorithms agree very well.
First, there is no treatment of scattered light, either as part of the data reduction or the inversion procedure. This can lead to systematically lower inferred field strengths and incorrect inferred angles of the magnetic vector (LaBonte 2004b). It will also provide simply incorrect relative photometry between different solar structures. In the future a treatment of scattered light may be performed; this is a feature available in the original VFISV scheme. 2ff7e9595c
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