The present invention relates to a continuous temperature sensor used in a Ruhrstahl-Heraeus (RH) process of mainly performing vacuum degassing in steelworks, and an RH apparatus including the same.
Vacuum level and temperature within a vacuum vessel and additives are important control factors for an RH apparatus that performs a vacuum degassing process.
Although various information for continuous measurement and control of temperature can be obtained through a temperature sensor or a composite sensor using a thermocouple in a batch, the temperature sensor or composite sensor is limited in the continuous provision of information regarding molten steel.
Referring to U.S. Pat. No. 6,235,084 (issued on May 22, 2001), the temperature of an RH apparatus is generally measured with a temperature measurement element-thermocouple using a lance on an upper surface of a ladle exposed to the atmosphere. In this case, however, since temperature measurement is performed using a disposable sensor, the temperature is intermittently measured, thereby making it difficult to achieve continuous temperature management.
Korean Patent No. 10-0816634 (issued on Mar. 18, 2008) discloses a method of measuring temperature for a long period of time by arranging a thermocouple within a protective body. However, this method requires an expensive platinum thermocouple which has a long length like a sensor and, thus, costs are incurred due to a high-priced protection tube and thermal equilibrium takes a long duration when the thermocouple is dipped in a melt to be measured, thereby causing undesirable response performance. Also, degradation rapidly progresses due to characteristics of the vacuum degassing process, in which dipping and recovery of the thermocouple are repeated, thereby making it difficult to use the thermocouple for a long period of time.
U.S. Patent Application Publication No. 2007-0268477 (published on Nov. 22, 2007) discloses a method in which continuous temperature measurement can be performed by dipping an optical fiber in molten metal, while correcting an error of a measurement instrument due to radiant heat. The optical fiber directly contacts the interior of the molten steel while avoiding slag floating on the molten metal, thereby enabling temperature measurement of the molten metal. In this method, since the dipped optical fiber can be lost, continuous dipping of the optical fiber is performed through a feeder supplying optical fibers. Further, this method can suffer from problems caused by heat radiation.
An objective of the present invention is to provide a continuous temperature measuring device which can continuously measure temperature at low cost and can effectively measure the temperature of molten steel in an RH apparatus, and an RH apparatus including the same.


= 0. What is n?
-12, 1, 4, 33
Factor 2*v**2 - 2919384*v + 1065350367432.
2*(v - 729846)**2
Suppose -6*r**5 + 446*r**4 - 25376*r**3/3 + 13424*r**2/3 + 209440*r/3 - 96800 = 0. What is r?
-3, 2, 110/3
Let 2*y**2 - 37648*y + 177171488 = 0. What is y?
9412
Determine x, given that -3*x**2/2 - 7455*x/2 - 11169 = 0.
-2482, -3
Factor -t**5/4 + 32*t**4 - 810*t**3 + 7600*t**2 - 24500*t.
-t*(t - 98)*(t - 10)**3/4
Factor 5*r**3 + 2370*r**2 + 45180*r - 99880.
5*(r - 2)*(r + 22)*(r + 454)
Factor 4*z**2 + 664*z + 2592.
4*(z + 4)*(z + 162)
Suppose 3*g**2 - 15075*g = 0. What is g?
0, 5025
What is s in -s**2 - 18*s - 72 = 0?
-12, -6
Determine m so that 2*m**3/9 + 4*m**2/3 - 2048*m/9 + 4864/3 = 0.
-38, 8, 24
Factor l**4/2 + 35*l**3 + 134*l**2 - 1803*l + 6435/2.
(l - 3)**2*(l + 11)*(l + 65)/2
Suppose 1305*w**4 + 47225*w**3 + 273725*w**2 + 49375*w - 2030 = 0. Calculate w.
-29, -7, -2/9, 1/29
Factor -y**3/5 - 4120*y**2 + 41203*y/5 - 20602/5.
-(y - 1)**2*(y + 20602)/5
Find f, given that -f**3 - 15383*f**2 + 30770*f = 0.
-15385, 0, 2
Factor 2*m**2/3 + 2028*m - 79430/3.
2*(m - 13)*(m + 3055)/3
Find x such that -8*x**4 - 1468*x**3 - 65460*x**2 + 175212*x + 103788 = 0.
-93, -1/2, 3
Determine y, given that -y**2 + 103131*y + 515680 = 0.
-5, 103136
Factor -l**2/5 + 443343*l/5 + 177338.
-(l - 443345)*(l + 2)/5
Let 72*m**5/13 + 9290*m**4/13 - 18826*m**3/13 - 29342*m**2/13 + 274*m/13 + 1572/13 = 0. Calculate m.
-131, -1, -1/4, 2/9, 3
Factor 14*b**3 + 6836*b**2 + 101282*b + 28380.
2*(b + 15)*(b + 473)*(7*b + 2)
Suppose -h**2/4 + 44365*h/2 - 1968253225/4 = 0. Calculate h.
44365
What is k in -14*k**4 + 944*k**3 - 5322*k**2 - 10184*k - 3904 = 0?
-1, -4/7, 8, 61
Find u, given that 3*u**3/2 - 9051*u**2/2 - 347187*u/2 - 3308565/2 = 0.
-19, 3055
Factor -k**3 + 227*k**2 - 6839*k - 7067.
-(k - 191)*(k - 37)*(k + 1)
Determine p, given that -7*p**3/3 + 2005*p**2 + 10336*p/3 - 1148 = 0.
-2, 2/7, 861
Find w such that 2*w**4 + 68*w**3 - 146*w**2 - 68*w + 144 = 0.
-36, -1, 1, 2
Determine f, given that 10*f**3/7 - 104*f**2 - 858*f/7 + 8856/7 = 0.
-4, 3, 369/5
Determine p, given that 352050169*p**2/5 + 37526*p/5 + 1/5 = 0.
-1/18763
Factor 2*d**3 - 42*d**2 - 260*d.
2*d*(d - 26)*(d + 5)
Factor 2*w**3/5 + 602*w**2/5 + 356*w - 2384/5.
2*(w - 1)*(w + 4)*(w + 298)/5
Factor -2*q**2/11 + 11892*q/11 - 47536/11.
-2*(q - 5942)*(q - 4)/11
Solve -21*s**2/5 - 13452*s/5 + 5769/5 = 0.
-641, 3/7
Suppose -4*h**4/9 + 122*h**3/9 + 1288*h**2/9 + 1510*h/9 - 364/3 = 0. What is h?
-7, -2, 1/2, 39
Factor -z**4 - 6554*z**3 + 6561*z**2 + 39330*z.
-z*(z - 3)*(z + 2)*(z + 6555)
Factor -2*y**3 + 1756*y**2 - 492562*y + 44734136.
-2*(y - 412)*(y - 233)**2
Factor 2*r**3 - 140*r**2 + 1266*r + 38808.
2*(r - 49)*(r - 33)*(r + 12)
What is j in 5*j**2 + 505*j - 28200 = 0?
-141, 40
Factor -44*x**2 + 29148*x + 15912.
-4*(x - 663)*(11*x + 6)
Determine c, given that -3*c**5/5 + 43*c**4/5 - 219*c**3/5 + 437*c**2/5 - 138*c/5 - 72 = 0.
-2/3, 3, 4, 5
Suppose -2*j**4 + 58*j**3/3 + 103844*j**2/3 + 420496*j + 136320 = 0. What is j?
-120, -12, -1/3, 142
Let 250*m**4/7 - 3880*m**3/7 + 8462*m**2/7 - 3560*m/7 + 312/7 = 0. What is m?
3/25, 2/5, 2, 13
Determine u, given that u**5/2 - 94473*u**4/2 + 2231050739*u**3/2 + 11156859785*u**2/2 + 8925903528*u + 4463046242 = 0.
-2, -1, 47239
Factor -2*g**4 + 35*g**3 + 8*g**2 - 140*g.
-g*(g - 2)*(g + 2)*(2*g - 35)
Suppose 4*b**4/5 - 2016*b**3/5 - 199984*b**2/5 - 4944576*b/5 - 16708608/5 = 0. Calculate b.
-42, -4, 592
Factor 267*o**3 - 3218*o**2 + 3105*o - 154.
(o - 11)*(o - 1)*(267*o - 14)
Factor 4*u**3 + 1904*u**2 - 60496*u + 113344.
4*(u - 28)*(u - 2)*(u + 506)
Factor -4*q**3/5 + 1452*q**2/5 - 5744*q/5.
-4*q*(q - 359)*(q - 4)/5
Factor 3*p**2/5 - 5184*p/5 - 23100.
3*(p - 1750)*(p + 22)/5
Find t such that -2*t**4/7 + 128*t**3 - 102128*t**2/7 + 56832*t - 394272/7 = 0.
2, 222
Factor 2*q**3/5 + 12252*q**2 + 93819690*q.
2*q*(q + 15315)**2/5
Let -5*m**5 + 45*m**4 + 435*m**3 + 275*m**2 - 750*m = 0. What is m?
-5, -2, 0, 1, 15
Determine b so that -b**3/2 - 173*b**2 - 22431*b/2 - 32103 = 0.
-261, -82, -3
Find m, given that -6825*m**5 - 6*m**4 + 6825*m**3 + 6*m**2 = 0.
-1, -2/2275, 0, 1
Factor 2*q**3/9 + 314*q**2/9 - 956*q/9 + 640/9.
2*(q - 2)*(q - 1)*(q + 160)/9
Let 2*x**2/7 - 74*x/7 - 156/7 = 0. What is x?
-2, 39
Factor y**2/2 - 239*y + 33405/2.
(y - 393)*(y - 85)/2
Determine v so that 3*v**4 + 54*v**3 - 189*v**2 - 4860*v = 0.
-15, -12, 0, 9
Solve -4*d**4 - 1740*d**3 + 3516*d**2 + 8716*d - 10488 = 0.
-437, -2, 1, 3
Suppose -2*a**4 - 94*a**3 + 960*a**2 - 2648*a + 2240 = 0. What is a?
-56, 2, 5
Determine n, given that 5*n**5 - 150*n**4 - 2430*n**3 - 4160*n**2 + 16185*n - 9450 = 0.
-9, -5, 1, 42
Factor 5*o**4/3 + 6435*o**3 + 115470*o**2 + 692100*o + 1383480.
5*(o + 6)**3*(o + 3843)/3
Solve 5*u**3 - 8810*u**2 - 17640*u = 0 for u.
-2, 0, 1764
Factor 3*u**3 - 26226*u**2 - 315036*u - 945432.
3*(u - 8754)*(u + 6)**2
Factor r**2/2 + 449*r/2 + 22922.
(r + 157)*(r + 292)/2
What is j in 4*j**2/7 - 68*j + 1680 = 0?
35, 84
Find c such that -40*c**5/9 + 172*c**4 - 5264*c**3/9 + 980*c**2/3 = 0.
0, 7/10, 3, 35
Factor x**2/6 - 605*x/3 - 44800/3.
(x - 1280)*(x + 70)/6
Suppose -4*h**3/5 - 21428*h**2/5 - 5741632*h - 28686736/5 = 0. Calculate h.
-2678, -1
Factor x**3 - 8*x**2 - 873*x - 2520.
(x - 35)*(x + 3)*(x + 24)
Find w, given that -3*w**4 + 180*w**3 + 42123*w**2 + 437580*w = 0.
-85, -11, 0, 156
Solve -2*r**4/7 - 60*r**3/7 - 206*r**2/7 + 60*r/7 + 208/7 = 0 for r.
-26, -4, -1, 1
Find v such that v**3 - 10833*v**2/5 + 49716*v/5 + 4324 = 0.
-2/5, 5, 2162
Let 2*h**4/5 - 386*h**3/5 + 2262*h**2/5 - 3374*h/5 + 1496/5 = 0. What is h?
1, 4, 187
Solve 12*q**5 - 6140*q**4 - 20600*q**3 - 40*q**2 + 20588*q + 6180 = 0 for q.
-3, -1, -1/3, 1, 515
Find r, given that 5*r**3 - 325*r**2 - 670*r = 0.
-2, 0, 67
What is i in 4*i**3 + 856*i**2 + 11332*i + 26400 = 0?
-200, -11, -3
Let 2*b**2 - 1692*b - 1694 = 0. Calculate b.
-1, 847
Factor 5*v**3 + 315*v**2 - 5550*v - 12320.
5*(v - 16)*(v + 2)*(v + 77)
Suppose -4*h**2 - 11864*h - 8797156 = 0. Calculate h.
-1483
Let -4*z**4 - 345*z**3 - 9914*z**2 - 95769*z - 23328 = 0. Calculate z.
-32, -27, -1/4
Determine z so that -25*z**3/2 + 449690*z**2 + 359758*z + 71952 = 0.
-2/5, 35976
Find r, given that 4*r**4/5 + 5488*r**3/5 + 114072*r**2/5 + 159152*r + 1853572/5 = 0.
-1351, -7
Factor -14*t**2/9 + 608*t/9 - 690.
-2*(t - 27)*(7*t - 115)/9
Solve 2*m**3/3 + 130*m**2/3 - 648*m + 2184 = 0 for m.
-78, 6, 7
Suppose -o**2/5 - 158338*o/5 - 6267730561/5 = 0. Calculate o.
-79169
Factor -2*k**2/9 + 1179026*k/9.
-2*k*(k - 589513)/9
Find n such that 12*n**3 + 129*n**2 + 87*n - 30 = 0.
-10, -1, 1/4
What is w in -33*w**4 + 130*w**3 - 157*w**2 + 56*w + 4 = 0?
-2/33, 1, 2
Find n such that 4*n**2 - 1360*n + 88704 = 0.
88, 252
Solve 4*f**4/5 + 664*f**3/5 + 772*f**2 + 3152*f/5 - 1536 = 0 for f.
-160, -4, -3, 1
Factor -2*k**2/3 - 230586*k.
-2*k*(k + 345879)/3
Determine p so that -20*p**4 + 128*p**3 + 3092*p**2 + 8944*p - 4080 = 0.
-6, -5, 2/5, 17
Find n, given that n**4/5 + 2132*n**3/5 - 14973*n**2/5 = 0.
-2139, 0, 7
What is b in 33208*b**2 - 66428*b + 24 = 0?
3/8302, 2
Suppose -8*m**5 - 155216*m**4 + 155222*m**3 - 38806*m**2 = 0. Calculate m.
-19403, 0, 1/2
Determine a so that -2*a**3/11 + 4118*a**2/11 - 2024048*a/11 - 99603360/11 = 0.
-45, 1052
Solve 65*c**2 + 59890*c + 23025 = 0.
-921, -5/13
Determine b, given that 10985*b**5/2 - 64220*b**4 - 1950195*b**3/2 - 1997225*b**2 - 1468250*b - 367500 = 0.
-7, -10/13, 21
Factor 5*o**2 + 1440*o - 2900.
5*(o - 2)*(o + 290)
Find x such that x**3/7 + 171*x**2 + 359991*x/7 + 1069227/7 = 0.
-597, -3
Solve -p**2/2 - 223*p/2 - 2030 = 0.
-203, -20
Factor -15*y**2 - 79*y/2 + 33/2.
-(y + 3)*(30*y - 11)/2
Let -65*d**3/4 - 362*d**2 - 8261*d/4 - 1089/2 = 0. Calculate d.
-11, -18/65
Determine g so that 3*g**2/4 - 357*g - 2904 = 0.
-8, 484
Factor 3*v**3/4 - 267*v**2/4 - 582*v.
3*v*(v - 97)*(v + 8)/4
Find k, given that -k**5/9 + k**4/9 + 22*k**3 - 196*k**2/9 - 392*k/9 = 0.
-14, -1, 0, 2, 14
Determine z so that -12*z**3/7 + 107523*z**2/7 + 80649*z/7 = 0.
-3/4, 0, 8961
Suppose 10*o**5 + 58*o**4/3 - 808*o**3/3 - 536*o**2 + 2368*o/3 - 640/3 = 0. Calculate o.
-4, 2/5, 2/3, 5
Factor 5*l**3 + 980*l**2 + 1820*l - 23280.
5*(l - 4)*(l + 6)*(l + 194)
What is c in -3*c**5/4 + 1275*c**4/4 + 3861*c**3/4 - 3831*c**2/4


#ifndef BOOST_MPL_VECTOR_VECTOR50_C_HPP_INCLUDED
#define BOOST_MPL_VECTOR_VECTOR50_C_HPP_INCLUDED

// Copyright Aleksey Gurtovoy 2000-2004
//
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
//
// See http://www.boost.org/libs/mpl for documentation.

// $Id: vector50_c.hpp 49267 2008-10-11 06:19:02Z agurtovoy $
// $Date: 2008-10-10 23:19:02 -0700 (Fri, 10 Oct 2008) $
// $Revision: 49267 $

#if !defined(BOOST_MPL_PREPROCESSING_MODE)
#   include <boost/mpl/vector/vector40_c.hpp>
#   include <boost/mpl/vector/vector50.hpp>
#endif

#include <boost/mpl/aux_/config/use_preprocessed.hpp>

#if !defined(BOOST_MPL_CFG_NO_PREPROCESSED_HEADERS) \
 && !defined(BOOST_MPL_PREPROCESSING_MODE)

#   define BOOST_MPL_PREPROCESSED_HEADER vector50_c.hpp
#   include <boost/mpl/vector/aux_/include_preprocessed.hpp>

#else

#   include <boost/mpl/aux_/config/typeof.hpp>
#   include <boost/mpl/aux_/config/ctps.hpp>
#   include <boost/preprocessor/iterate.hpp>

namespace boost { namespace mpl {

#   define BOOST_PP_ITERATION_PARAMS_1 \
    (3,(41, 50, <boost/mpl/vector/aux_/numbered_c.hpp>))
#   include BOOST_PP_ITERATE()

}}

#endif // BOOST_MPL_CFG_NO_PREPROCESSED_HEADERS

#endif // BOOST_MPL_VECTOR_VECTOR50_C_HPP_INCLUDED


Q:

ffmpeg timestamp information using fps filter isn't aligned with ffprobe

I'm using ffmpeg to extract images (thumbnails) from a video using the filter fps so that I get an image every 0.5 seconds. This is the command I use:
ffmpeg -i video.mp4 -f image2 -filter:v fps=1/0.5 -y out_%3d.png

I want to know the timestamp for these images and I've found out that ffmpeg behaves differently than ffprobe.
First of all, I haven't found a way to get the timestamps as metadata (log files or whatever) but I got to overlay the timestamp in the images themselves using ffmpeg:
ffmpeg -i video.mp4 \
-vf "fps=fps=1/5,drawtext=fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf: text='%{pts\:hms}': x=(w-tw)/2: y=h-(2*lh): fontcolor=white: box=1: boxcolor=0x00000000@1" \
out_with_timestamp_%03d.png

However, using ffprobe you can simulate the use of the same fps filter and ffprobe allows you to get some information from the frames. This command is supposed to simulate the ffmpeg one and provides some metadata where you can extract the timestamps from:
fprobe -hide_banner \
-i "movie=video.mp4,fps=fps=1/0.5[out0]" \
-f lavfi -show_frames -show_entries frame=pkt_pts_time -of csv=p=0

The problem is that the timestamps that ffmpeg prints into the images are different than the ones that ffprobe gives, and ffprobe ones are wrong while the ones given by ffmpeg are right.
The timetamps given by ffmpeg are in the middle of the period of time while the ones given by ffprobe are in the very begining of the window.
Is there any way to extract thumbnails and its timestamp from a video using ffmpeg?
You can find all the steps to be able to reproduce this behaviour here:
https://www.joseoc.com/en/video/ffmpeg/extract-images-from-video/#getting-the-timestamp-for-the-images

A:

ffmpeg and ffprobe do yield the same timestamps.
However, you want to know the source timestamps, for which you have to apply the fps filter afterwards. What the fps filter does is to drop and/or duplicate frames from the source stream to generate a constant frame-rate output. It also regenerates smooth timestamps at the given rate. So you have to draw the text earlier to imprint the source TS.

Here's a crude workaround to get source TS to file.
Run,
ffmpeg -i video.mp4 -vf "showinfo,fps=fps=1/5,showinfo" out_%03d.png 2> showinfo.log

showinfo.log, will have lines like these
[Parsed_showinfo_0 @ 000000000051fe40] n:  56 pts:  28672 pts_time:2.33333 pos:   159155 fmt:yuv420p sar:0/1 s:1280x720 i:P iskey:0 type:P checksum:E16BA92B plane_checksum:[DDD39061 7EA5EE0E B9642AAD] mean:[188 113 134 ] stdev:[35.2 24.8 6.3 ]
[Parsed_showinfo_0 @ 000000000051fe40] n:  57 pts:  29184 pts_time:2.375   pos:   161402 fmt:yuv420p sar:0/1 s:1280x720 i:P iskey:0 type:P checksum:A363FB5E plane_checksum:[9BBF72F4 87165D47 ADB32B23] mean:[188 113 134 ] stdev:[35.3 24.7 6.3 ]
[Parsed_showinfo_0 @ 000000000051fe40] n:  58 pts:  29696 pts_time:2.41667 pos:   173158 fmt:yuv420p sar:0/1 s:1280x720 i:P iskey:0 type:B checksum:5EE879C4 plane_checksum:[55EA6029 A578E90A 4F463082] mean:[188 113 134 ] stdev:[35.3 24.6 6.3 ]
[Parsed_showinfo_0 @ 000000000051fe40] n:  59 pts:  30208 pts_time:2.45833 pos:   170104 fmt:yuv420p sar:0/1 s:1280x720 i:P iskey:0 type:B checksum:EE8A0C3A plane_checksum:[A90276D4 620C6744 F5012E13] mean:[188 114 134 ] stdev:[35.4 24.5 6.3 ]
[Parsed_showinfo_0 @ 000000000051fe40] n:  60 pts:  30720 pts_time:2.5     pos:   175138 fmt:yuv420p sar:0/1 s:1280x720 i:P iskey:0 type:B checksum:8E112769 plane_checksum:[9A02FB68 1FF2EC88 1E573F5B] mean:[188 114 134 ] stdev:[35.5 24.4 6.3 ]
[Parsed_showinfo_2 @ 0000000004947540] n:   0 pts:      0 pts_time:0       pos:   170104 fmt:yuv420p sar:0/1 s:1280x720 i:P iskey:0 type:B checksum:EE8A0C3A plane_checksum:[A90276D4 620C6744 F5012E13] mean:[188 114 134 ] stdev:[35.4 24.5 6.3 ]
[Parsed_showinfo_0 @ 000000000051fe40] n:  61 pts:  31232 pts_time:2.54167 pos:   164026 fmt:yuv420p sar:0/1 s:1280x720 i:P iskey:0 type:P checksum:BB465233 plane_checksum:[EAEAE802 57E310BE 62275964] mean:[187 114 134 ] stdev:[35.7 24.4 6.3 ]
[Parsed_showinfo_0 @ 000000000051fe40] n:  62 pts:  31744 pts_time:2.58333 pos:   187333 fmt:yuv420p sar:0/1 s:1280x720 i:P iskey:0 type:B checksum:9D4E3B24 plane_checksum:[D5D19959 05A4FDBE 88B7A3EF] mean:[187 114 134 ] stdev:[35.8 24.4 6.3 ]

For each Parsed_showinfo_2 line, you have to extract the Parsed_showinfo_0 line whose pos is the same. The pts_time in Parsed_showinfo_0 is your source TS.


1. Incorporating Clipper native oct interface instead of mex.
2. Adding new algorithm as per the paper of F. Martínez, A.J. Rueda, F.R. Feito
3. Improve the already implemented scripts ispolyccw, poly2ccw, poly2cw, polyjoin, polysplit, and ensure that they are compatible with MATLAB.
4. Devise an automated way to ensure that the functions which are in geometry package repo are synced with those in matGeom.

This section describes the overall idea of implementation and the workplan for the implementation of the new algorithmalgorithm which is based on the paper by F. Martínez, A.J. Rueda, F.R. Feito.

Basic structure of the program for boolean operations :

1. Take the polygons in standard input form of Octave (NaN separated or Cell arrays) and convert it into the required input format of the algorithm (the way it is implemeted). This part will be handled by m script.

2. Implement .cc file which will return the resultant polygons. This will also contain input validation.

3. oct-interface will be created for this file.

4. The output of the cpp code will be passed onto the m script which will handle the necessary processings before returning it as final result.

Expected Challenges :

1. Handling overlapping edges seems to be a bit tedious with this algorithm. It will surely be a challenge.

The goal is to devise a somewhat automated way to ensure that the functions which are there in geometry are synced with those in matGeom without manually checking the edits.

To achieve this, first a workaround is implemented on a dummy repository dummyMatGeom. Its master branch is matGeom (dummy) and there is another branch (named geometry) is created which contains geometry package (dummy).
To test the entire procedure, go to the dummy repository dummyMatGeom , pull both branches in different folders, say "dummyMatGeom" for master branch and "dummyGeom" for geometry branch.
Then follow the given steps.

1. go to the local dummyMatGeom directory (named dummyMatGeom) and pull upstream (dummy matGeom) using these commands :

- git fetch origin master
- git reset --hard FETCH_HEAD
- git clean -df
This will ensure that your local repo is exactly same as your current version of matGeom. Here, we are doing hard reset because when we apply folder structure change script (next step), it might have changed its structure.

2. run the following octave command.

- rmdir("dummyGeom/deprecated","s");
- copyfile("dummyMatGeom/matGeom/deprecated","dummyGeom/");
- copyfile("dummyMatGeom/matGeom", "dummyGeom/");
- rmdir("dummyGeom/inst","s");
- rename("dummyGeom/matGeom","dummyGeom/inst");
This will simply replace the scripts of geometry with those of matGeom. Don't worry, we are not going to push this whole matGeom script folder onto our geometry repo.We'll now commit and push only those scripts which have been modified and the rest of scripts will remain as it is, in the remote repository(geometry branch).

3. Now, go to local geometry directory (dummyGeom) and run following command.

- git diff --name-only --diff-filter=M | xargs git add
- git commit -m "synced with matGeom"
- git push origin master:geometry
This will sync the scripts which were already present in the geometry branch with those of the matGeom branch without disturbing other scripts.

4. Now, update your local geometry repo by resetting it from the remote geometry branch.

- git fetch origin geometry
- git reset --hard origin/geometry

Challenges :

Clearly, the above procedure will only sync the script of the function, not it's tests and demo, which are in separate folders in a Matlab package structure. Even if we try to concatenate their corresponding test/demo scripts with the function scripts (as it is in an octave package structure), there will be discrepancies because the notion or writing tests for octave and matlab packages are quite different. The way octave allows tests to work is unique to octave as explained here. SO, we can't simply concatenate the Matlab test scripts with the functions.

On benchmarking the polyunion function in oct interface vs the mex interface, it is found that oct interface had twice as better performance as mex. As it doesn't ensure much advantage, it is not worthed to rewrite the package with oct.


Criteria and Concurrent Validity of Adult ADHD Section of the Psychiatry Research Interview for Substance and Mental Disorders.
Prevalence of ADHD in adults is around 2% to 4%. Comorbidity is frequent in ADHD; 75% of patients develop a comorbid disorder across life span, substance use disorder (SUD) being one of the most prevalent. A case-control study was performed to check the criteria and concurrent validity of psychiatric research interview for substance and mental disorders (PRISM) adult ADHD section. Validation was done comparing PRISM with the Conners' Adult ADHD Diagnostic Interview for Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.). A convenient sample (N = 80) participated, 40 had diagnosis of SUD and ADHD and 40 had diagnosis of SUD without ADHD. The statistics hypothesis was bivariant, and the confidence level was 95%. Kappa index concordance was .78, sensitivity of PRISM adult ADHD module was 90%, specificity was 87.5%, positive predictive value was 87.8%, and the negative predictive value was 89.7%. PRISM has good psychometric properties to detect ADHD associated with SUD.


Sun Life Financial’s Asia Strategy

8/29/2013 3:07AM

Competition is heating up between foreign insurance companies hoping to get a piece of Asia’s growing middle class. The WSJ’s Deborah Kan speaks to Kevin Strain, Asia President of Sun Life Financial, about staying innovative to gain market share in the region.

This transcript has been automatically generated and may not be 100% accurate.

... the ... for insurers in Asia ... as for the PC ... market share ideas ... and joining me now is a constraint is the isthe president ... did ... they get to get a little bit of a bird's eye perspective ... here's a strategy here in Asia alot of foreign companies in here aggressively ... I've tried to get their customer base so what is so much ... there were were in some markets in Asia ... were in the Philippines ... Indonesia Vietnam and Malaysia in the ... region ... however home based in Hong Kong and when to be to bring ventures ... India and China ... ay ay ay between a recently acquired at CIMB didn't Malaysia Aval ... and TCI to actively seeking more apt to acquisitions an orgy the tie up so ... they have expanded ... our focus right now is on expanding in some markets and in particular with Malaysia and with Ian on the new getting those up and running in a way that's successful and is adding value to our claims these two markets divisions ... into our part so we were out were very focused on ... getting those too often going to continue or sense because in the Philippines and the staff ... and growing our existence of this so ... it's that it's a market that data were were were really focused on getting those ones right ... again a lot of people thought that distribution in Asia ... lot of thinking and that the chances here ... so it is that the model I mean t think that is that they predominate model has a lot of these countries develop and and become more use to adventures ... I think in Asia and across the world you have to borrow multi channel distribution awful bancassurance is obviously Billy important it's about half of our still from Asia to a sixty percent of the industry sales in Hong Kong ... sea can ignore bancassurance ... but you also want to get agency ready you when it appropriate for a weren't available in one of the group right ... so you wanna have a distribution model that covers each of those areas ... and that our products tied to the one that distribution ... ok speaking of products they are what are you seeing in Asia body was popular products a delegate at targeting yet ... so we see a full so to set a price that reach from life insurance health insurance the wealth ... and increasingly we want to find ways to tie tie them together so we were very strong wealth portfolio ... a very strong life portfolio the one to find ways to integrate them ... and now we're trying to make sure we add more and more health insurance into the mix and we think for clients of the gonna have waste time financial security ... they have tablet could plan backup encompasses ... white ... health well ... Caroline is a country is that what's that you're targeting we're seeing a lot of but but it's the volatility in plenary ... what makes him as nervous ... about that BJ and at what types of things I are you looking out for going to write each each country's different and obviously India has had a lot of economic challenges recently currency ... as well as in the beach ... and so those two are having are experiencing a lot but in general you were seeing interest rates interest rate shifting around or something that we keep a very close eye on so ... low interest rates make it very difficult for insurers so ... we we watch that closely ... and then matching our assets to our liabilities is also very important so we did a very long-term view on the economy's ... we take a very long-term view on investor plus a fee ... aam ... but we do we do make sure that we're were monitoring sort of interest rates the equity markets the overall economics of circumstances at any countries at a show that that is just to get into that are so there is that you know I think there's opportunity across the region and doubt ... I will say no to a new the country's but they are focusing Alice on most of the markets in the onthe as each ... day and the effect is a sign that the ad ... I think it's different a lot of a foreign insurance companies that want ... to get a more market share in China and particularly given the ... rising middle class in the numbers in China ... a lot of people finding a lot of frustration there so what is your strategy on the ground ... in China so in China China receives a significant opportunity we have enjoyed lunch with Everbright Bank ... every banks from the top ten banks in China state owned enterprise ... has over forty million customers over twenty five million credit card holders ... your focus for us is getting that distribution partner right ... adding value to Everbright pink ... and adding value to the claims of or break so we can get that piece right ... we think that's unique because we are there or in part so that bag getting that ever for a relationship right really import ... and ... sale of people they say it's just that he even played felt so to speak at the high place a bid to take money ... that its output you weren't domestic company there we own just under twenty five percent and that ... we we think that there is significant opportunity inside of Everbright think ... I as well as the eye to the inner ... okay thanks Fran that's shitty weekend


Time of day and caffeine influence some neuropsychological tests in the elderly.
We report that performance on neuropsychological tests used in the diagnosis of dementia can be influenced by external factors such as time of day (TOD) and caffeine. This study investigates TOD effects on cognitive performance in the elderly. The optimal TOD at which an individual is at his or her maximal arousal alters with age, and in the elderly, typically occurs in the morning. Neuropsychological test scores from healthy elderly participants were analyzed to determine whether TOD affected performance. Interactions between caffeine and TOD were also investigated. Across 2 data sets that were analyzed, significant TOD effects were noted for Pattern-Comparison Speed (PCS), Letter-Comparison Speed (LCS; Salthouse & Babcock, 1991), Trail Making Test Part A (Reitan, 1958), Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975) and the Graded Naming Test (GNT; McKenna & Warrington, 1980), revealing a decline in test scores as TOD increases. Significant interactions between TOD, age, and the PCS, LCS, and Trail Making Part A were noted in Data Set 1. In Data Set 2, caffeine intake had been controlled for, and significant interactions between caffeine, TOD, and scores on the MMSE and GNT were found. The TOD and caffeine effects highlight the need to control for these external factors when scoring the assessments. This conclusion has implications for the clinical procedure of diagnosis and treatment of dementia and Alzheimer's.


ElemenElemen, located in Genting Highlands, is really a calm residential inside a well known place.

Elemen

Elemen, located in Genting Highlands, is really a calm residential inside a well known place. Becoming surrounded with plenty of services, residents of Elemen will not need to drive much to gain access to Pedas Springs Spa and Balai Bomba dan Penyelamat @ Genting Highlands. . There is a total of 673 units which come in different dimensions. The project that was led by NCT Group & Galeri Tropika Sdn Bhd is actually a freehold development equipped with facilities for example Multi-purpose hall, Spa, Pavilion, Playground, Recreation Park, Garden, 24-hour Security, Gym, Covered Carpark, Sky Lobby.


1. Introduction {#sec1-ijerph-13-00042}
===============

Cardiovascular disease (CVD) is the leading cause of death in the United States. Recent health guidelines reinforce the importance of cardiovascular health promotion and primary prevention since it is a major cause of disability, contributes to lower life expectancy, and a burden on the health care system \[[@B1-ijerph-13-00042],[@B2-ijerph-13-00042],[@B3-ijerph-13-00042]\]. National estimates indicate CVD prevalence rates are highest for Non-Hispanic blacks (NHBs) (45% for both sexes), compared to Non-Hispanic whites (NHWs) (38% for men and 33% for women) and Hispanics (26% for men and 32% for women). However, there is variation by Hispanic subgroup where Mexican Americans have the highest CVD risk \[[@B4-ijerph-13-00042],[@B5-ijerph-13-00042],[@B6-ijerph-13-00042]\].

Increasing evidence demonstrates that biological risk indicators, such as C-reactive protein (CRP), can predict the presence, susceptibility, and physiologically related assessments for CVD \[[@B7-ijerph-13-00042],[@B8-ijerph-13-00042],[@B9-ijerph-13-00042]\]. CRP, an acute phase protein considered a marker of systemic inflammation, has been shown to be an independent predictor of stroke, myocardial infarction, atherosclerosis, peripheral vascular disease and sudden cardiac death \[[@B10-ijerph-13-00042],[@B11-ijerph-13-00042],[@B12-ijerph-13-00042],[@B13-ijerph-13-00042],[@B14-ijerph-13-00042]\]. Known differences in CRP levels have been reported by race/ethnicity \[[@B15-ijerph-13-00042]\] with higher CRP levels for NHBs and Hispanics \[[@B16-ijerph-13-00042],[@B17-ijerph-13-00042]\] compared to NHWs, but the etiology of the risk factors driving these differences is unclear \[[@B18-ijerph-13-00042]\].

Multiple social, behavioral and biological pathways are hypothesized as important predictors of increased CRP levels for race/ethnic groups. However, socioeconomic status (SES) as a predictor of CRP levels has shown inconsistent positive associations. SES, a dynamic social pathway, includes income, education, and occupation that possibly mediate health risks. Education is hypothesized to have a unique impact on health because it's unaffected by health impairments that may emerge in adulthood (which impact income) and it allows one to move up the wealth gradient by obtaining access to occupations that pay higher wages. Since non-Whites tend to have lower educational attainment and income than Whites, SES as measured by educational attainment, can reasonably be considered a potential mediating pathway in the association between race/ethnicity and elevated CRP levels \[[@B19-ijerph-13-00042]\]. To date, the association of SES and CRP has been assessed in NHBs in comparison to NHWs and have produced mixed, empirical results \[[@B20-ijerph-13-00042],[@B21-ijerph-13-00042],[@B22-ijerph-13-00042]\]. For example, Koster *et al.* \[[@B20-ijerph-13-00042]\] report similar SES-inflammation associations in NHB and NHW adults whereas other studies find no SES related Black/White differences \[[@B21-ijerph-13-00042],[@B22-ijerph-13-00042]\]. Educational attainment is an important SES component that has not been fully explored for race/ethnic groups. Of the few studies that have investigated racial/ethnic group outcomes, an inverse relationship between educational attainment and CRP levels was shown for NHWs but not NHBs \[[@B15-ijerph-13-00042]\], while higher levels of education were associated with increased CRP levels for Hispanics \[[@B23-ijerph-13-00042]\]. Investigations of educational pathways with elevated CRP for multiple race/ethnic groups in one investigation have found NHWs and NHBs with higher educational attainment have lower inflammation levels but not Hispanics \[[@B24-ijerph-13-00042]\]. Another study found that educational attainment was not associated with elevated CRP levels for NHBs compared to NHWs and Hispanics \[[@B25-ijerph-13-00042]\].

Further examination of the educational pathway in the race/ethnicity--CRP association may inform our understanding of the biological pathways through which education impacts health in different demographic groups \[[@B15-ijerph-13-00042]\]. Findings can add to the existing literature on racial/ethnic disparities as these groups have a greater burden of diseases in which inflammatory processes play a role (*i.e.*, obesity, hypertension, diabetes) and may engage in adverse health behaviors that increase inflammation levels (*i.e.*, smoking and heavy drinking) \[[@B26-ijerph-13-00042],[@B27-ijerph-13-00042]\]. However, smoking and heavy drinking have not consistently explained elevated CRP levels for NHBs or Hispanics \[[@B28-ijerph-13-00042],[@B29-ijerph-13-00042]\].

This study aims to test whether hypothesized educational pathways underlie disparities in CRP levels for NHWs, NHBs and Mexican Americans (MAs) in the National Health and Nutrition Examination Survey (NHANES). Increasing evidence shows that SES indicators such as income and education have different associations with health \[[@B30-ijerph-13-00042],[@B31-ijerph-13-00042]\] such that the bulk of the relationship for income is concentrated in low income categories experiencing the highest burden of adverse health whereas there might be an educational gradient where greater differences in health appear at higher educational levels in comparison to lower levels \[[@B32-ijerph-13-00042]\]. We expect similar patterns of associations of lower educational attainment and elevated CRP levels for race/ethnic groups understudy. In addition, we focus our analysis on MAs who are the largest U.S. Hispanic subgroup represented in NHANES. In this paper, we examine SES as a primary exposure rather than adjusting for SES and build on prior studies that have found conflicting associations between SES and elevated CRP in race/ethnic groups. Given their disproportionate representation in lower SES categories, we hypothesize that NHBs and MAs will have higher CRP levels compared to NHWs by level of inflammation (*i.e.*, intermediate or high risk). Furthermore, we expect that NHBs and MAs will have elevated CRP levels as educational levels surpass high school diploma.

2. Methods {#sec2-ijerph-13-00042}
==========

Pooled data were obtained from the 2001 to 2008 NHANES, a cross-sectional survey designed to assess health and nutritional status of adults in the United States. The survey includes information on demographic, socioeconomic, dietary, medical, dental, physiological measurements, and laboratory tests administered by trained medical personnel \[[@B33-ijerph-13-00042]\]. This study examines data from respondents who underwent medical examinations and completed a battery of laboratory tests including blood specimens for testing CRP. The study uses a complex sampling design consisting of sampling at the county, household and individual levels. The NHANES oversamples race/ethnic groups for a representative sample. The NHANES protocol was approved by a governmental Institutional Review Board. All respondents were compensated with a financial incentive for their participation.

The sample includes 13,262 NHWs, 7,696 NHBs, and 4,545 MAs born in one of the 50 states or Washington D.C. over 20 years of age. Analyses were weighted with the Mobile Examination Center sampling weight to account for the complex design of pooled data. We adjusted the weight by dividing by the number of survey years to obtain the average U.S. civilian non-institutionalized population to generalize our findings to the U.S. population.

CRP level is the dependent variable, measured by a high sensitivity assay using latex-enhanced nephelometry, with a lower limit of detection of 0.1 mg/L. Blood specimens were processed, stored, and shipped to the Johns Hopkins University Lipoprotein Analytical Laboratory for analysis. Details of sample collection, measurement procedures, quality control, and quality assurance have been described elsewhere \[[@B33-ijerph-13-00042]\]. We chose to focus on multiple CRP levels since most studies have relied upon a continuous or dichotomous measure to represent variations in CVD risk that may be related to different sources of risk with either chronic or acute implications \[[@B34-ijerph-13-00042]\]. These levels are used in clinical settings to determine inflammation risk, and intervene at critical stages to prevent the risk and progression of CVD and its sequele, therefore CRP was coded into 3 clinically relevant categories: low ≤1 mg/L, intermediate = 1--3 mg/L, and high ≥3 mg/L \[[@B35-ijerph-13-00042],[@B36-ijerph-13-00042]\]. Participants with CRP levels ≥10 mg/L (*n* = 10) were excluded from the analysis since this level typically signals acute illness.

Race, demographic, SES, health behavior and health condition variables were obtained directly from the NHANES and not recoded. Self-reported race/ethnicity was ascertained from respondents (*i.e.*, NHWs, NHBs, MAs). Since race/ethnicity is a multidimensional category, we are not measuring race/ethnicity as an independent variable, but rather assessing how racial stratification exposes these groups to additional risk factors that induce health risks \[[@B37-ijerph-13-00042]\]. Demographic variables included age, (20--34, 35--44, 45--54, 55--64, or 65+ years) and sex (men, women). Income was categorized as 0--\$24,999, \$25,000--\$44,999, \$45,000--\$74,000, or \$75,000 and above. Educational attainment includes \<9 years of schooling, 9 to 11 years, a high school diploma/GED, some college/AA degree, or post-secondary degree or higher. Health behaviors include current smokers based on self-reporting (yes, no), and heavy drinkers consuming five or more alcoholic beverages per day (yes, no). Health conditions were measured by a question that asked respondents "Ever been told by a doctor or health professional that {you have/{he/she/SP} has} Diabetes or sugar Diabetes (1 = yes, 0 = no) or high blood pressure or high blood sugar (1 = yes, 0 = no). To assess obesity, waist circumference was measured using a fiberglass tape crossing over the umbilicus. Waist circumference has been shown to be a more robust measure of CVD risk for NHBs and MAs \[[@B38-ijerph-13-00042],[@B39-ijerph-13-00042],[@B40-ijerph-13-00042]\]. Cut-off points are 88 cm for women and 102 cm for men then transformed into a dichotomous variable (yes, no). Statin use was obtained by a questionnaire that asked respondents "In the past month, have you used or taken medication for which a prescription is needed?" If the respondent answered "yes" then the respondent provided the name of the medication that was verified by the interviewer. NHANES provides drug codes for all medications and we selected drugs used in NHANES for statins. Selected drug names were verified by a cardiologist (*i.e.*, atorvastatin, cerivastatin, fluvastatin, lovastatin, pravastatin, and simvastatin) then transformed into a dichotomous variable to indicate statin use (1 = yes, 0 = no). Although more recent data from NHANES is available, the prescription file for 2010 did not include statins for public use. Therefore due to data constraints, we use pooled data from 2001 to 2008. Interaction terms tested associations between race/ethnicity and SES predictors.

SAS version 9.2 is used to perform the weighted analyses (SAS Institute, Inc., Cary, North Carolina). Descriptive statistics are presented as weighted proportions. Chi-square tests are used to test levels of significance in descriptive analyses. In our sensitivity analyses, we treated the response variable as dichotomous, placing those with CRP levels ≥3 mg/L into the "high" category. Our results showed MA's were more likely to have "high" CRP levels, earning an SC/AA degree was associated with being in a "High" CRP category, and low income was positively associated with high CRP. In the current analysis, we employed weighted multinomial logistic regression models assessing the relative risk of being in an intermediate or high CRP level relative to low risk. These relative risks are also referred to as odds ratios and calculate the log odds that a member of group, falls into a category, as opposed to the reference group. Model 1 assesses the direct association between race/ethnicity on CRP levels with NHWs as the reference category. This step tests whether there are race/ethnic differences in CRP levels and the magnitude of these differences. Model 2 incorporates income to examine if this pathway explains or attenuates the effect for NHBs and MAs. Model 3 introduces education. Income and education were tested in different steps in order to parcel out the different impact of these two important measures of SES on CRP levels. Educational attainment is entered in Model 3 since we wanted to control for known factors that could confound the model for education since it is our main SES mechanism understudy. The full model (model 4) includes controls for health behaviors, health status, statin use and significant interactions to assess the best model that may explain race/ethnic disparities in elevated CRP levels. To investigate whether an educational gradient exists, we estimated predicted probabilities over educational attainment by race/ethnicity for low, intermediate and high CRP levels. This step allows for the estimation of predicted probabilities for each educational level while holding income, sex, age, health behaviors, health status and statin use constant.

3. Results {#sec3-ijerph-13-00042}
==========

[Table 1](#ijerph-13-00042-t001){ref-type="table"} shows weighted descriptive statistics of the sample. Three quarters of NHWs and over 60% of NHBs were over 35 years old, whereas half of MAs were in the youngest age category (20--34 years). The sample was advantaged by females for all three race/ethnic groups. In important SES categories, income disparities were evident between race/ethnic groups. The majority of NHWs were represented in the highest category compared to the majority of NHBs and MAs who made less than \$25,000 per year.

Surprisingly, educational disparities were less pervasive. Over one third of NHWs, NHBs and MAs had some college training or an Associate's Degree. However, more NHWs (29%) compared to NHBs (16%) and MAs (12%) had a bachelor's degree, which is an important SES indicator associated with higher income, more stable job opportunities and sustainable lifestyles. Results also show that over half of NHBs were smokers compared to NHWs (41%) and MAs (36%). Low percentages of heavy drinkers were observed for all three groups. Mexican Americans represent the group with the highest percent of normal waist circumference (49%) compared to 34% of NHWs and 41% of NHBs. For health conditions, nearly 20 percent of NHBs had hypertension, and 10% had diabetes. The majority of NHWs, NHBs and MAs were not using statins but more NHWs (11%) were using statins compared to 6% of NHBs and 4% of MAs. Although the percent distribution on CRP shows the majority of race/ethnic groups fall in the low level category, NHBs have higher representation in both the intermediate and high CRP levels.

ijerph-13-00042-t001_Table 1

######

Weighted sample descriptive statistics by race/ethnicity, national health and nutrition examination survey, United States, 2001--2008.

  Variable                      Non-Hispanic White *n* = 13,362   Non-Hispanic Black *n* = 7696   Mexican American *n* = 4545   *p*-Value
  ----------------------------- --------------------------------- ------------------------------- ----------------------------- -------------
  Age
   20--34                       26.7%                             36.8%                           49.6%                         *p* ≤ 0.001
   35--44                       19.2                              20.3                            17.5
   45--54                       21.7                              19.8                            14.7
   55--64                       13.6                              11.7                            9.3
   65+                          18.8                              11.3                            8.9
  Sex
   Female                       50.9%                             54.0%                           51.3%                         *p* ≤ 0.001
   Male                         49.1                              46.0                            48.7
  Income
   Less than 25,000             18.7%                             37.9%                           31.1%                         *p* ≤ 0.001
   25,000--44,999               21.3                              26.0                            28.1
   45,000--74,000               25.1                              19.3                            22.2
   75,000+                      34.9                              16.8                            18.5
  Education
   Less than 9 years            3.6%                              5.3%                            8.5%                          *p* ≤ 0.001
   9--11 years                  9.4                               22.7                            19.5
   High school diploma/GED      26.8                              24.2                            26.1
   Some college/AA degree       31.6                              32.3                            33.5
   Post Secondary or higher     28.7                              15.5                            12.4
  Health Behaviors
   Non Smoker                   59.4%                             45.9%                           63.9%                         *p* ≤ 0.001
   Smoker                       40.6                              54.1                            36.1
   Not Heavy Drinker            83.9                              83.7                            85.2
   Heavy drinker                16.1                              16.3                            14.8
  Health Status
   Normal Waist Circumference   34.1%                             40.6%                           49.6%                         *p* ≤ 0.001
   High Waist Circumference     65.9                              59.4                            50.4
   Normal Blood Pressure        84.2                              81.0                            90.8
   High Blood Pressure          15.8                              19.0                            9.2
   Diabetic                     6.8                               9.6                             5.7
   Statin User                  11.2                              6.1                             3.5
  C-Reactive Protein
   Low                          92.2%                             88.0%                           90.0%                         *p* ≤ 0.001
   Intermediate                 6.7                               10.6                            7.9
   High                         1.2                               1.4                             1.3

Note: All statistics represent column percent. Statistics between race/ethnicities are significant at the *p* \< 0.001 level.

Weighted descriptive statistics of the predictor variables with CRP levels are shown in [Table 2](#ijerph-13-00042-t002){ref-type="table"}. More females than males were in intermediate and high CRP level categories. Non-Hispanic Blacks had higher representation in both elevated CRP categories compared to NHWs and MAs. Ten percent of respondents aged 55--64 were represented in the intermediate CRP category. For important indicators of socioeconomic status, a higher percentage of respondents in the lowest income category were represented in both intermediate and high CRP level categories. However, for respondents with 9--11 years of schooling, 13.2% had intermediate CRP and 2.2% had high CRP levels. Although the majority of the sample that practiced adverse health behaviors such as cigarette smoking and heavy drinking had low CRP levels, almost 10% of smokers were represented in the intermediate CRP category and 2% had high CRP levels. In contrast to smokers, a higher percent of respondents that were not heavy drinkers (9%) had intermediate CRP levels. Lastly, the distribution of health conditions show 10% of respondents with HWC and HBP had intermediate CRP levels, whereas 15% of the population with diabetes were in this category. Only 2% of diabetics had high CRP levels.

ijerph-13-00042-t002_Table 2

######

Weighted descriptive statistics by C-reactive protein level.

  Variable              Low CRP   Intermediate CRP   High CRP   *p*-Value
  --------------------- --------- ------------------ ---------- -------------
  Sex
   Female               88.8%     9.8%               1.4%       *p* ≤ 0.001
   Male                 94.4      4.6                1.0
  RACE
   NHW                  92.2%     6.7%               1.2%       *p* ≤ 0.001
   NHB                  88.0      10.6               1.4
   MA                   90.9      7.9                1.3
  AGE
   20--34               92.0%     6.8%               1.2%       *p* ≤ 0.001
   35--44               91.6      7.3                1.1
   45--54               88.9      9.8                1.2
   55--64               88.4      10.3               1.4
   65+                  88.8      9.1                2.2
  Income
   less than 25,000     88.8%     9.7%               1.6%       *p* ≤ 0.001
   25,000--44,999       90.9      7.6                1.4
   45,000--74,000       91.4      7.4                1.2
   75,000+              93.8      5.4                0.8
  Education
   less than 9 years    86.8%     10.8%              2.4%       *p* ≤ 0.001
   9--11 years          84.6      13.2               2.2
   HS                   88.8      10.0               1.2
   Some College         90.5      8.1                1.4
   Post-Secondary       90.1      8.5                1.4
  Health Behaviors
   Non smokers          89.7%     8.9%               1.4%       *p* ≤ 0.001
   Smokers              88.6      9.7                1.7
   Not Heavy Drinkers   89.9      8.8                1.3
   Heavy Drinkers       91.3      7.3                1.4
  Health Condition
   Normal waist         97.0%     2.2%               0.7%       *p* ≤ 0.001
   HWC                  88.9      9.7                1.4
   Normal Bldpres       92.0      6.8                1.1
   High Bldpres         88.3      10.4               1.3
   Non Diabetic         92.2      6.7                1.1
   Diabetic             82.4      15.2               2.4

Abbreviations: NHW, Non-Hispanic White; NHB, Non-Hispanic Black; MA, Mexican American; HWC, High waist circumference; Bldpres, Blood Pressure; HS/GED, High School/GED; SC/AA, Some College/Associate's Degree. All row statistics are significant at the *p* \< 0.001 level.

[Table 3](#ijerph-13-00042-t003){ref-type="table"} shows results from multinomial logistic regression assessing the association between race/ethnicity and intermediate and high CRP levels relative to low CRP adjusting for age, sex, health behaviors, health status and statin use. The relative risk for being in an intermediate CRP level is 1.66 times higher for NHBs compared to NHWs (Model 1). Similar to NHBs, MAs had higher intermediate CRP levels (RR = 1.20; *p* \< 0.001) compared to NHWs. Also, NHBs (RR = 1.38, *p* \< 0.001) and MAs (RR = 1.10, *p* \< 0.001) had greater high CRP levels compared to NHWs (Model 1). When income was included in the model (Model 2), the relative risk of being in an intermediate CRP level slightly decreased for NHBs (RR = 1.52; *p* \< 0.001) but marginally increased for MAs (RR = 1.12; *p* \< 0.001). Respondents with incomes ≤\$25,000 had the highest intermediate inflammation levels in comparison to respondents making over \$75,000 per year. For high CRP levels (Model 2), relative risk ratios decreased from the previous model for NHBs (RR = 1.20; *p* \< 0.001) and MAs (RR = 1.05; *p* \< 0.001). Similar to intermediate levels, respondents with incomes ≤\$25,000 had the greatest risk for high CRP levels. When educational attainment was entered into the additive model (Model 3), NHBs (RR = 1.69; *p* \< 0.001) and MAs (RR = 1.47; *p* \< 0.001) showed higher intermediate CRP levels compared to NHWs and the magnitude of those associations strengthened from the previous model (Model 2). In addition, the predicted odds of intermediate CRP levels were 1.58 times higher for respondents with 9--11 years of schooling. In the full model (Model 4) that adjusts for health behaviors, health status, age, and statin use, findings show NHBs had higher intermediate CRP levels (RR = 1.31; *p* \< 0.001*)* compared to NHWs, but the strength of that association decreases from the previous model. However, the effect for MAs disappeared (RR = 0.74; *p* \< 0.001). For high CRP, Model 4 shows the predicted odds of high CRP was 1.14 times higher for NHBs (RR = 1.14; *p* \< 0.001) but lower for MAs (RR = 0.38: *p* \< 0.001). Interactions reveal race/ethnic effects are dependent on education for intermediate CRP levels in NHBs, and lower income for intermediate CRP levels in MAs. Interactions for high CRP level show the race/ethnicity effect for NHBs is contingent on educational attainment, particularly the 9--11 years of schooling category.

Secondary analyses examining whether an educational gradient exists for race/ethnic groups on CRP levels are represented in [Figure 1](#ijerph-13-00042-f001){ref-type="fig"} and [Figure 2](#ijerph-13-00042-f002){ref-type="fig"}. For intermediate CRP level, NHWs have more favorable profiles at every educational category compared to NHBs and MAs ([Figure 1](#ijerph-13-00042-f001){ref-type="fig"}). Both NHBs and MAs with ≤9 years of schooling display a 16% higher probability of being in an intermediate CRP level. However, 9--11 years of schooling proves to be an important marker of intermediate CRP level since the probability of risk increases for NHBs for this educational category whereas for MAs, intermediate CRP levels decline with more educational attainment. Overall, MAs have the highest probability of high CRP at all levels of educational attainment ([Figure 2](#ijerph-13-00042-f002){ref-type="fig"}). Instead of a clear educational gradient, trajectories show the probability of high CRP levels decrease for MAs that have ≤ a high school diploma, then increases for MAs with some college or an Associate's degree. NHWs and NHBs show similar high CRP patterns across educational attainment.

![Predicted Probability of Intermediate C-Reactive Protein by Education and Race/Ethnicity. Note: Differences Across Groups Statistically significant at the *p* \< 0.001 level. Model controls for age, diabetes, heavy drinker, HBP, HWC, income, sex, smoker, and statin use.](ijerph-13-00042-g001){#ijerph-13-00042-f001}

ijerph-13-00042-t003_Table 3

######

Multinomial regression models for intermediate and high levels of C-reactive protein by race/ethnicity, national health and nutrition examination survey, United States, 2001--2008.

                                    Model 1             Model 2             Model 3             Model 4
  --------------------------------- ------------------- ------------------- ------------------- ------------------- ------------------- ------------------- ------------------- -------------------
  **Race/ethnicity**
  NHW(ref.)
  NHB                               1.66 (1.65--1.66)   1.31 (1.30--1.31)   1.52 (1.52--1.52)   1.20 (1.19--1.21)   1.69 (1.69--1.70)   1.36 (1.35--1.36)   1.31 (1.30--1.32)   1.14 (1.13--1.15)
  MA                                1.20 (1.19--1.20)   1.10 (1.09--1.11)   1.12 (1.11--1.12)   1.05 (1.05--1.06)   1.47 (1.46--1.47)   1.51 (1.50--1.52)   0.74 (0.73--0.75)   0.38 (0.37--0.39)
  **Income**
  Less than 25,000                                                          1.76 (1.76--1.77)   1.99 (1.98--2.00)   1.49 (1.48--1.49)   2.06 (2.05--2.07)   1.24 (1.23--1.24)   3.51 (3.47--3.54)
  25,000--44,999                                                            1.41 (1.40--1.41)   1.80 (1.78--1.80)   1.16 (1.15--1.16)   1.91 (1.90--1.92)   1.06 (1.05--1.06)   3.54 (3.51--3.57)
  45,000--74,000                                                            1.39 (1.39--1.39)   1.48 (1.47--1.49)   1.22 (1.22--1.22)   1.58 (1.57--1.58)   1.11 (1.11--1.12)   1.75 (1.73--1.76)
  75,000+ (ref.)
  **Education**
  Less than 9 years                                                                                1.32 (1.36--1.37)   1.48 (1.47--1.49)   1.91 (1.90--1.92)   1.13 (1.12--1.15)
  9--11 years                                                                                1.58 (1.57--1.59)   1.30 (1.29--1.30)   1.49 (1.49--1.50)   0.70 (0.69--0.71)
  HS/GED                                                                                1.54 (1.53--1.54)   0.84 (0.83--0.84)   1.55 (1.54--1.55)   0.77 (0.77--0.78)
  SC/AA degree                                                                                1.25 (1.25--1.26)   0.99 (0.98--0.99)   1.06 (1.05--1.06)   1.05 (1.03--1.05)
  Post-Secondary or Higher (ref.)
  **[Controls]{.ul}**
  **Sex** (1 = male)                                                                                0.45 (0.45--0.46)   0.58 (0.57--0.58)
  **Age**
  20--34                                                                                0.91 (0.91--0.92)   0.39 (0.39--0.40)
  35--44                                                                                0.94 (0.97--0.98)   0.33 (0.32--0.33)
  45--54                                                                                1.52 (1.52--1.53)   0.54 (0.53--0.55)
  55--64                                                                                1.04 (1.03--1.04)   0.61 (0.60--0.61)
  65+ (ref.)
  **Health behaviors**
  Smokers (Yes)                                                                                1.20 (1.19--1.20)   1.66 (1.65--1.67)
  Heavy drinkers (Yes)                                                                                0.89 (0.88--0.89)   1.08 (1.07--1.09)
  **Health Status**
  HWC                                                                                3.22 (3.20--3.22)   1.31 (1.30--1.32)
  HBP                                                                                1.04 (1.03--1.04)   0.72 (0.72--0.74)
  Diabetes                                                                                1.78 (1.77--1.79)   0.82 (0.81--0.82)
  Statin (Yes)                                                                                1.01 (1.00--1.01)   1.01 (0.99--1.01)

Abbreviations: NHW, Non-Hispanic White; NHB, Non-Hispanic Black; MA, Mexican American; HWC, High waist circumference; HS/GED, High School/GED; SC/AA, Some College/Associates Degree; CI, Confidence Interval; Ref, Reference Group. Model 1: Race/Ethnicity; Model 2: Race/Ethnicity, Income; Model 3: Race/Ethnicity, Income, Education; Model 4: Race/Ethnicity, Income, Education, Health Behaviors, Health Status, Statin Use, Interactions, Age, Sex.

![The predicted probability of high C-reactive protein by education and race/ethnicity. Note: Differences across groups statistically significant at the *p* \< 0.001 level. Model controls for age, sex, income, education, smokers, heavy drinkers, HBP, HWC, diabetes, statin use and interactions.](ijerph-13-00042-g002){#ijerph-13-00042-f002}

4. Discussion {#sec4-ijerph-13-00042}
=============

Using a nationally representative cohort sample in the United States, significant racial/ethnic disparities in CRP levels were found to be strongly associated with educational attainment. In the current study, education consistently strengthened the relationship between race/ethnicity and CRP level, showing more explanatory power compared to income. In support of our first study expectation and consistent with a growing body of research, our data extend and confirm prior findings of elevated CRP levels for NHBs and Hispanics that were explained by lower educational attainment \[[@B24-ijerph-13-00042],[@B25-ijerph-13-00042]\] but extend this area to include MAs, an important Hispanic subgroup. In contrast to previous studies that suggest similar \[[@B20-ijerph-13-00042]\] or no race/ethnic differences \[[@B15-ijerph-13-00042],[@B21-ijerph-13-00042],[@B22-ijerph-13-00042]\] in education-inflammation associations, our results confirm the strength of directly measuring education level, over income, among minorities to enhance its analytic predictive power. Future studies should account for the importance of the educational component in the SES pathway to understand race/ethnic disparities in inflammation burden.

In this study, educational attainment as a SES indicator, represents an important dynamic social mechanism that shows a unique association with CRP levels. Therefore, education may be a more stable and reliable social pathway relative to income since the majority of Americans have had some exposure to schooling; are unaffected by health impairments that may emerge in adulthood; and have the potential to increase income to purchase health-enhancing goods such as health care services, nutritious food, residence in cleaner environments, reduce health risk behaviors, and decrease stress \[[@B41-ijerph-13-00042],[@B42-ijerph-13-00042]\]. Our findings confirm these differential effects by showing that low educational attainment is associated with intermediate (*i.e.*, 9--11 years of schooling) and high CRP levels (*i.e.*, less than 9 years of schooling) in the SES-race/ethnicity pathway for both NHBs and MAs.

Our results show that income has an attenuating effect on intermediate and high CRP levels for both NHBs and MAs. Many correlational studies of income and health that are stratified by race/ethnicity normally report stronger associations with income for Whites \[[@B43-ijerph-13-00042]\]. Explanations for this effect for our race/ethnic groups might suggest that income is not strongly associated with all indicators of health as previously assumed.

Our results show health behaviors such as cigarette smoking were positively associated with both intermediate and high CRP levels, whereas high CRP levels were greater for heavy drinkers. Positive associations were found between HWC and both intermediate and high CRP levels. In addition, HBP and diabetes were associated with intermediate CRP levels. However, these health behavior and health condition pathways did not explain race/ethnic disparities in CRP levels as the strength of the association lessened for NHBs and disappeared for MAs when entered into the full models. Similar to our findings, Paalani *et al.* \[[@B44-ijerph-13-00042]\] found high CRP levels were attenuated for NHBs when controlling for SES, exercise, diet, smoking, alcohol consumption, waist circumference, diabetes, stroke and sleep apnea. This study shows that health behaviors and health conditions lessen the effect for NHBs at both intermediate and high CRP levels, making a significant contribution to the literature. Lastly, our findings challenge studies that report high waist circumference as a significant predictor of elevated CRP levels for Mexicans \[[@B45-ijerph-13-00042]\] as the present study showed health conditions may not fully explain elevated inflammation levels for this important population considering the overwhelming evidence that educational attainment does.

There is growing scientific literature that demonstrates that Hispanics and Non-Hispanic Blacks are more likely to have elevated inflammatory biomarkers compared to non-Hispanic whites where differences in CRP levels by racial/ethnic group are not entirely explained by traditional CVD risk factors, suggesting that environmental or genetic influences may also be operative \[[@B17-ijerph-13-00042]\]. In our predicted probability models, our hypothesis that NHBs and MAs will have greater elevated CRP levels compared to NHWs as educational attainment surpasses a high school diploma was partially supported for NHBs with intermediate and MAs with high CRP levels. Education's unique impact on health is hypothesized to have a differential effect compared to income, where there may be greater differences between college graduates and high school graduates relative to those with a high school diploma or below \[[@B46-ijerph-13-00042]\]. Our study showed NHBs had greater intermediate CRP levels beginning with 9--11 years of schooling and beyond, which contradicts previous findings that suggest an inverse relationship exists between educational attainment and inflammation \[[@B32-ijerph-13-00042]\]. A likely explanation for this finding is that the returns of educational attainment work differently in NHB populations such that education does not operate as a buffer to protect against disease risk. Moreover, a novel finding that MAs are more vulnerable to high CRP at all educational levels is a noteworthy contribution. Prior studies have found inconsistent SES-health gradients in Hispanics, sometimes showing a flattened or even a reversed pattern that may vary by national origin, nativity, or acculturation \[[@B30-ijerph-13-00042],[@B47-ijerph-13-00042],[@B48-ijerph-13-00042]\]. The existing studies elucidating the relationship between ethnicity and inflammation have called for researchers to disaggregate the Hispanic category and to investigate the predictive value of multiple CRP levels that may vary by national origin, nativity, and acculturation \[[@B24-ijerph-13-00042]\]. We contribute to this body of evidence by providing data that show variations in the educational predictors associated with intermediate and high CRP levels for MAs who represent 65% of Hispanics, and are concentrated in environments which may increase exposure to higher rates of depression, discrimination, racism, and other sources of psychological stress; each of these factors has been independently shown to affect CRP levels and subsequent cardiovascular disease risk \[[@B49-ijerph-13-00042]\]*.* Importantly, our paper makes the important contribution that education levels appear to moderate the levels of CRP, opening the possibility for future studies.

Although these results yielded important conclusions, several limitations warrant consideration. The cross sectional nature of the data did not allow for establishing causality. A longitudinal design would support investigations that examine multiple factors implicated in transitions in intermediate and high CRP. Unfortunately, we were not able to test the heritability of plasma concentrations of CRP. Due to data limitations and restrictions on public use data, we were not able to include other variables that might confound the models such as genetics, neighborhood environments such as residential segregation and other health risk behaviors including diet. Despite these limitations, findings highlight multiple potential analytic associations between race/ethnicity, education and elevated CRP levels.

5. Conclusions {#sec5-ijerph-13-00042}
==============

In conclusion, the present study extends research that seeks to understand the SES factors underlying race/ethnic disparities in inflammation. Our findings suggest the intersectional relationships among race/ethnicity and educational attainment are key associations that require scholarly attention to assure we address the measures that profoundly contribute to health disparities. As a significant body of work has identified the impact of stratification on our biomarkers, future studies should assess the role of neighborhoods as distal measures of SES that may play an integral part in elevating CRP and measure, prospectively, the role of oxidative stress to better understand how racial/ethnic stratification plays an important role in differentiating CRP risk. We provided empirical evidence of the importance of the education-inflammatory pathway for both NHBs and MAs that signals the need to focus on preventive detection of CVD risk for these populations that are over-represented in low SES environments, experience persistent chronic strains, and lack access to adequate health care.

We gratefully acknowledge Michael Wagner for data analysis, Shannon Winakur for verification of drug codes used in the analysis and Laura A. Logie for her careful review and editing of the manuscript. We acknowledge funding support from the ADVANCE Program for Inclusive Excellence funded by the National Science Foundation under Grant No. HRD-1008117 to Dinwiddie and Zambrana. López acknowledges the RWJ Foundation Harold Amos Faculty Development Program and NIDDK 1K23DK098280-01 for his support. Funding sources had no involvement in the conduct and/or preparation of the article.

Gniesha Y. Dinwiddie designed the overall study, conceptualized the statistical analysis, interpreted the findings and drafted the manuscript, which was approved by all co-authors. Ruth E. Zambrana contributed to the literature review and interpretation of the findings. Lauren A. Doamekpor assisted with the literature review and contributed to the discussion. Lenny Lopez served as senior author and contributed to the interpretation of the data, discussion, and study design. All authors reviewed and revised all sections of the article for intellectual content.

The authors declare no conflict of interest.


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b
What is the second biggest value in -3/5, -0.08, 4, 0.1, 2/9729?
0.1
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d
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f
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c
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-641/6
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e
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b
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a
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b
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d
What is the third smallest value in 8, 56.5, 2/299, 0.3, 2/7?
0.3
Which is the smallest value?  (a) 3/5  (b) -66  (c) -38  (d) -11894
d
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-1
Which is the sixth biggest value?  (a) -0.02  (b) -46  (c) 30  (d) 3  (e) -3/5  (f) 0.098  (g) 0.02
e
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b
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e
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c
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b
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-0.3
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3/7
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2
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d
Which is the second smallest value?  (a) -1/2  (b) -2/3  (c) 21  (d) 55  (e) 15  (f) 1  (g) 0.8
a
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-3
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0.06
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-0.0586
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-4
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-3/5
Which is the fifth smallest value?  (a) -0.07  (b) 3/5  (c) -4/15  (d) -2/3  (e) -1  (f) 676
b
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0
Which is the fourth smallest value?  (a) 241  (b) -14/5  (c) -2/15  (d) -0.7  (e) -2/241  (f) 0.1  (g) 0.5
e
Which is the biggest value?  (a) -1  (b) -0.3  (c) -773719
b
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c
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f
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Which is the fourth biggest value?  (a) -2  (b) 2/13  (c) 0  (d) 1.057  (e) 2  (f) -82
c
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c
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f
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Which is the fifth smallest value?  (a) 36  (b) 4  (c) -0.15  (d) -2  (e) -0.4  (f) 89
a
Which is the smallest value?  (a) -0.79  (b) -2/11  (c) -5  (d) 196280  (e) -0.5
c
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d
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f
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e
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c
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498503
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-0.01
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-170
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c
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-4/13
Which is the sixth smallest value?  (a) 4/5  (b) -2  (c) -4  (d) -0.26383  (e) -2/7  (f) -1/4
a
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10/3
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b
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b
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-4090
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f
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-2
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-4
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2/13
Which is the second biggest value?  (a) 1  (b) -22165552.6  (c) -0.3
c
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c
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-1/5
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63
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b
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-0.23
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2027
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b
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a
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-9972223
Which is the smallest value?  (a) 607  (b) 91  (c) 14/5  (d) -1.7
d
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c
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e
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-0.4
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10
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-1
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g
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c
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1
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353
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-0.3
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e
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d
Which is the second biggest value?  (a) 3  (b) 232.5716  (c) 24
c
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a
Which is the smallest value?  (a) -0.2  (b) 23  (c) 0.4  (d) 9/74
a
What is the sixth biggest value in -1, -977/2, -4/7, -5, -5/2, -20, -5.7?
-20
Which is the biggest value?  (a) 5  (b) 2  (c) 2/7  (d) -2/9  (e) 4/16493  (f) 0.4
a
Which is the sixth smallest value?  (a) 0.3  (b) 2/7  (c) 0.0218  (d) -8  (e) 0  (f) -1/52
a
What is the second biggest value in 3/5, 4, -1/97, -1/9, 3/2?
3/2
Which is the second biggest value?  (a) -1  (b) -6176  (c) 1  (d) -0.1  (e) -1/3  (f) 1/2  (g) 19
c
What is the smallest value in -0.4, 1879, -3/4, 1/3, 1, -42.4?
-42.4
Which is the biggest value?  (a) -169  (b) 0.3  (c) -2215288
b
Which is the second smallest value?  (a) -2/9  (b) -4984713  (c) -5
c
Which is the fifth biggest value?  (a) 3  (b) -6  (c) 7/5  (d) -1002  (e) 0.1
d
What is the third smallest value in -20/17, 0, 21501?
21501
Which is the fourth smallest value?  (a) 0.1  (b) 28/9  (c) -4  (d) -216  (e) -5/3
a
Which is the fourth biggest value?  (a) 16460  (b) -4  (c) -1/3  (d) 1.89
b
What is the third smallest value in 12, -391, 3/215?
12
Which is the smallest value?  (a) 3/4  (b) 147305.7  (c) -3
c
Which is the biggest value?  (a) 40351.05  (b) -1/5  (c) 0.02
a
What is the smallest value in 10004, -1/4, -1/169, 0.3, 8, -2?
-2
What is the fourth smallest value in -1.7, 44, 4, 3/2, -31618, -1/6?
3/2
Which is the third biggest value?  (a) -5  (b) -4  (c) -52817  (d) -2
a
What is the fifth biggest value in -0.5, -12.48, -4, -0.655, 2/7, -5, 5?
-4
What is the second biggest value in -1743, -5, -255, -0.29?
-5
Which is the smallest value?  (a) 5  (b) 0.13  (c) -20  (d) 39  (e) -


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Inspired by Time’s Person of the year, the Rampage decided to take a closer look at it’s own community to find someone who fits the criteria of a Person of the Year. The Rampage considered people with deep roots in the community who have bettered students’ lives and become a figurehead in the community.

Football coach and child development teacher Jason Lomax (´97) returned to the RHS community at the beginning of the 2018-19 school year and is known to many students as the head coach who reinvented the 0-10 football team in a single season. Lomax prides himself on being a ¨Rockville man¨ and has impacted the community in ways beyond the football field.

After coaching football at Springbrook High School for three years, Lomax became the head coach of the Rams football team this past fall. The football team improved from their winless 2016-2017 season to a 4-6 record this year. Many players said they gained new perspectives on how to carry themselves outside of the game as well.

“[Lomax] taught me the importance of grades while being a student athlete,” sophomore varsity kicker Eric Giron said, “He also taught me what being a team player means.”

While not on the field, Lomax teaches child development 1 and stresses to his students the importance of family and pride in developing their characters.

“I always try and help my players and students as if they were my own children,” Lomax said. “I’m trying to get these guys on the right track… and live a better life than I have and than their parents have. That’s what it’s all about, trying to push them into the next generation, being Rockville alum, my family being rockville alum, us always living in this community that’s all we want–because everytime another Rockville kid does good, it looks good on all of us.”

Even though Lomax’s current impact is prevalent, he said that in the future he plans to create a youth football program with elementary and middle school teams from the Rockville cluster. This way, by the time they begin freshman year, they are already a part of the Rockville football family.

Athletic director Michael Hayes hired Lomax for his athletic background and his plans to continue building the RHS community spirit.

Fior now football and his classes hold a lot of importance, but it’s the lasting impact Lomax intends to leave which he feels most passionate about.

“Wins and losses are awesome I always want to win, I’m a super competitive person, but at the end of the day when they put me in the ground if a bunch of people can come back and say you know what, coach helped me get on the path of doing better in my life then that’s my state championship,” Lomax said.


Dame Judi Dench has revealed she cried when Bond producers told her that M was being killed off.

The veteran actress played the MI6 chief in seven Bond films but her character met her demise in Skyfall in 2012.

She said she “laughed through her tears” when Bond producers told her the news over lunch in London.

“They told me gently and I laughed through my tears. Seven [Bond] films is a long time. But MI6 would have given her the push by now, don't you think?”, she told Radio Times magazine.

The actress, who turns 80 today, said that her late husband, actor Michael Williams, encouraged her to sign up for the Bond films.

Bond on screen through the years Show all 8 1 /8 Bond on screen through the years Bond on screen through the years Barry Nelson The first ever Bond on screen, Nelson played James Bond in 1954 in Climax! Casino Royale Hulton Archive/Getty Images Bond on screen through the years Sean Connery The Scottish actor first played Bond in the 1962 'Dr. No'. He portrayed the secret agent from 1962 until 1967, although he reprised the role twice: once in 1971 for 'Diamonds Are Forever' and again in 1983 in 'Never Say Never Again' Hulton Archive/Getty Images Bond on screen through the years David Niven Niven was 007 the time of one film; the 1967 'Casino Royale' Roy Jones/Hulton Archive/Getty Images Bond on screen through the years George Lazenby Lazenby played Bond in the 1969 'On Her Majesty's Secret Service' Larry Ellis/Hulton Archive/Getty Images Bond on screen through the years Roger Moore Moore's Bond started with 'Live and Let Die', the 1973 film directed by Guy Hamilton. He was the face of 007 for 12 years, when his tenure finished with the 1985 'A View to a Kill' Keystone/Hulton Archive/Getty Images Bond on screen through the years Timothy Dalton Timothy Dalton was James Bond in 'The Living Daylights' and 'Licence to Kill' Mychele Daniau/AFP/Getty Images Bond on screen through the years Pierce Brosnan Brosnan took over the role in 1995 when he played the spy in 'GoldenEye'. He played in a total of four films, and his last time playing 007 was in 2002 'Die Another Day' Hulton Archive/Getty Images Bond on screen through the years Daniel Craig Daniel Craig was announced as the new James Bond in 2005. His first appearance as the secret agent was in the 2006 'Casino Royale'. He has since starred as Bond in 'Quantum of Solace' and 'Skyfall'. He is expected to appear once again on screen in 2015 for 'Bond 24' Greg Williams/Getty Images

“He wanted to live with a Bond woman. 'Oh God, oh crikey,' he said, 'you've got to do it.'"

Despite being killed off from the Bond franchise, the actress, who stars opposite Dustin Hoffman in the BBC’s adaptation of Esio Trot this Christmas, said she had no intention of retiring soon.

She is due to star in the Best Exotic Marigold Hotel sequel next year, and also features in the forthcoming period drama Tulip Fever.

The cast for the next Bond film, Spectre, was unveiled last week and will include Monica Bellucci, Lea Seydoux and Andrew Scott as new faces to the franchise.


http://tvtropes.org/pmwiki/pmwiki.php/Main/DrosteImage

Various people, upon seeing a droste image "I can see FOREVER!"

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An image that contains a smaller replica of itself, which (being a replica of the image) contains a smaller replica of itself, which contains ... well, you get the idea. Like a fractal, but without all the complicated math. The more formal name for this is "infinite regression".

For instance, take this cover of Spoof which shows the characters holding a copy of that very comic, whose cover depicts them holding ... a copy of the very same comic (with the same cover). Theoretically, there could be infinite recursion of that comic book in the image, though it's safe to assume that the printer doesn't have enough resolution to reproduce them all. (It's the thought that counts!)note Actually, the Barnabas Collins parody is holding his hand in front of where the third repetition of the cover would be, but that's just nitpicking.

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A similar effect can be set up in a Hall of Mirrors by standing between two mirrors facing each other only for real. Of course you have to turn slightly to see it so even then the effect has limits.

See also Nested Stories and Dream Within a Dream, which has the layering but not the self-similarity. Also see Recursive Reality which is this trope on a cosmic level.

Examples:

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Advertising

The Trope Namer is Droste cocoa powder, made by a Dutch food company that was famous for using this trope in their ads. See the Wikipedia article.

Seen on several cereal packages. You know, the type that feature a picture of somebody having breakfast. Said breakfast of course centers on the cereal, with a package proudly displayed. On that package, we see somebody having said cereals for breakfast. And so on...

Royal Baking Powder. ◊

Land O'Lakes Butter. ◊

Nippon Paint used to have this ◊ on cans of Pylox spray paint, ◊ where what appears to be a hand belonging to a woman is holding a can of Pylox, whose can shows the same thing over and over. Newer cans did away with this, although a vector drawing of a hand holding the paint can be seen.

on cans of Pylox spray paint, where what appears to be a hand belonging to a woman is holding a can of Pylox, whose can shows the same thing over and over. Newer cans did away with this, although a vector drawing of a hand holding the paint can be seen. This cover ◊ of the German magazine "Amiga Joker".

of the German magazine "Amiga Joker". La Vache qui rit. French cartoonist Gotlib parodied this for his series of exhibitionism gags: the cow opens her cloak to show a cow which opens her cloak etc.

French cartoonist Gotlib parodied this for his series of exhibitionism gags: the cow opens her cloak to show a cow which opens her cloak etc. Science mag "Nature" (issue #7476) illustrates an article about science replication with a Droste image.

Mackó sajt, a Hungarian cheese brand.

a Hungarian cheese brand. Almost all covers of the chess series by GM Csaba Balogh. Example

Anime & Manga

Urusei Yatsura 2: Beautiful Dreamer had a scene where Ataru is in a room in the school building, looks through the doorway, and sees on the other side of the door the same room that he's in, complete with himself and the doorway, and past that doorway is the same room again, etc. (It seems that space itself is forming loops; earlier there was a scene where we see Mendo run up the stairs past the "camera", only to reappear coming up the same stair and go past the camera again.)

The final shot ◊ of Owarimonogatari's first opening.

of Owarimonogatari's first opening. Thoth from JoJo's Bizarre Adventure: Stardust Crusaders is a comic book which depicts events in the near future that are guaranteed to happen, but heavily subject to Prophecy Twists. The zenith of this occurs when it shows what appears to be bullets going through Jotaro's head, but turns out to be bullets going through a picture of Jotaro's head—the same picture its user just saw. Turns out any of Thoth's prophecies might be depicting its own pages in the future.

The inside front cover for volume 3 of Kaguya-sama: Love Is War has a scene where the inside covers of the previous two volumes are depicted as two schoolgirls (the pages are wearing Sailor Fuku). Naturally, the inside back cover is the inside back cover as another girl. ''Volume 3 Inside Back Cover Sempai!"

In Cells at Work! episode 5, Red Blood Cell lets out one of her typical scream upon seeing a cedar pollen. There is a zoom on her eye, where the pollen is reflected, zooming further on its eye, where Red Blood Cell is reflected, with again another zoom on her eye, etc., etc.

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Arts

M. C. Escher's "Print Gallery" ◊ is a very unusual take on this concept: It only shows one copy of the picture, but implies an infinite recursion all the same, using uneven magnification to make the contents of the picture merge with their real-world analogues. The Dutch mathematicians Bart de Smit and Hendrik Lenstra calculated that the blank area at the center would contain a copy of the original picture (scaled down by a factor of 22.5 and rotated by 158 degrees), and created a version where the blank area is filled (as well as many variants thereof with different curvatures).

is a very unusual take on this concept: It only shows one copy of the picture, but implies an infinite recursion all the same, using uneven magnification to make the contents of the picture merge with their real-world analogues. Cassandra Calin's drawing of her stealing her drawing. Though sharp-eyed viewers will note that the second iteration of the image is incorrect — the burglar is not holding the drawing consistently with the others.

Comic Books

Comic Strips

Garfield: The title panel of this Sunday comic from 1992 shows a picture of Garfield reading the comics...with a picture of himself reading the comics, etc.

from 1992 shows a picture of Garfield reading the comics...with a picture of himself reading the comics, etc. Subverted with this ◊ New Yorker cartoon by Charles Addams.

Fan Works

Films — Animation

In Incredibles 2, Elastigirl sees a screencap from her bodycam of her battle with the Screenslaver in his lair and notices that one of his monitors has the exact same image from the feed, which brings her one step closer to finding out the Screenslaver's true identity.

Films — Live-Action

Literature

Live-Action TV

Music

The last scene of The Smashing Pumpkins' "Ava Adore" video.

The New Pornographers use TV monitors to create this effect in their video for "Letter From An Occupant."

for "Letter From An Occupant." The cover of Pink Floyd's Ummagumma uses a variation: a photo of the band with a smaller photo on the wall containing a smaller photo containing a smaller photo. However, each successive photo shows the various band members occupying each other's places.

The album cover for Best of Friends - The Smurfs.

Used on the cover for Big Country's 2001 covers album Undercover

The album cover for Amber Gambler by Gorky's Zygotic Mynci.

The album cover for Swing to the Right by Utopia.

The video for The White Stripes' Seven Nation Army.

The video feedback method referenced below is one of the special effects used in the music video for Queen's Bohemian Rhapsody.

The music video for "I'm Gonna Always Love You" (Baby Piggy's song from The Muppets Take Manhattan) has a number of moments where a scene revealed to be on a TV the Muppet Babies are watching. Inevitably, this leads to a moment when the scene of them watching TV does this, and they turn to the camera trying to figure out what's going on.

for "I'm Gonna Always Love You" (Baby Piggy's song from The Muppets Take Manhattan) has a number of moments where a scene revealed to be on a TV the Muppet Babies are watching. Inevitably, this leads to a moment when the scene of them watching TV does this, and they turn to the camera trying to figure out what's going on. The original cover art for Brian Eno's and David Byrne's My Life in the Bush of Ghosts was created by pasting small cutouts of humanoid figures onto a TV screen, displaying a live feed from a video camera on said screen, then pointing the camera at that same screen. The recursion got distorted enough that the resulting picture isn't immediately obvious as a droste image.

Electronic indie rock band from Portland, called Starfucker (also stylized as STRFKR for obvious reasons), released a mash-up mixtape titled STARFUCKER: MIXTAPE #1, whose cover reflects a Gucci Mane image on an infinite loop.

Pinballs

The scene in the backglass of Capcom's Breakshot pinball machine has a Breakshot pinball machine in the background.

Pinball Magic has a Pinball Magic machine shown in the backglass, complete with another pinball machine on its backglass.

Print Media

Pro Wrestling

Dean Ambrose finds a wired, fully turned-on flat-screen monitor under the ring at Tables, Ladders, and Chairs 2014. Before he uses it as a weapon, he stares at it, grinning like a maniac, while a cameraman looks over his shoulder at Dean and the monitor, intentionally invoking this kind of image. But then the scene cuts to the array of giant ladders towards the stage, giving him an insidious idea. He'd use that monitor later, but it was gimmicked to explode when the cord was pulled on too hard, eventually costing him the match .

Sports

Every year, ESPN The Magazine has an issue called "Revenge of the Jocks" where a famous athlete takes over as editor for that issue. The front cover features the athlete in question tearing up last year's magazine, which features last year's cover athlete tearing up the previous year's magazine, etc. etc.

Tabletop Games

The card Super Secret Tech from Magic: The Gathering's parody expansion set Unhinged depicts itself in its illustration.

Video Games

Web Animation

Web animator Cyriak employs this trope with a number of his animations, especially his earlier .gifs, alongside Body Horror, Soundtrack Dissonance, and plenty of other things.

Webcomics

Web Original

Web Videos

Western Animation

Real Life


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    <varlistentry>
      <term>Verder in geschiedenis...</term>

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