text:
package com.android.tools.idea.updater;

import com.android.sdklib.repository.descriptors.IPkgDesc;
import com.android.sdklib.repository.descriptors.PkgDesc;
import com.android.tools.idea.sdk.SdkState;
import com.android.tools.idea.sdk.remote.UpdatablePkgInfo;
import com.android.tools.idea.sdk.wizard.SdkQuickfixWizard;
import com.android.tools.idea.welcome.wizard.WelcomeUIUtils;
import com.android.tools.idea.wizard.DialogWrapperHost;
import com.android.utils.HtmlBuilder;
import com.intellij.ide.BrowserUtil;
import com.intellij.ide.IdeBundle;
import com.intellij.openapi.ui.DialogWrapper;
import com.intellij.openapi.updateSettings.impl.AbstractUpdateDialog;
import com.intellij.openapi.updateSettings.impl.UpdateSettings;
import com.intellij.ui.components.JBLabel;
import com.intellij.util.containers.ContainerUtil;
import org.jetbrains.android.sdk.AndroidSdkData;
import org.jetbrains.android.sdk.AndroidSdkUtils;
import org.jetbrains.annotations.NotNull;

import javax.swing.*;
import java.awt.event.ActionEvent;
import java.util.List;
import java.util.Map;

/**
 * Confirmation dialog for installing updates. Allows ignore/remind later/install/show release notes,
 * and links to settings.
 */
public class UpdateInfoDialog extends AbstractUpdateDialog {
  private static final String RELEASE_NOTES_URL = "http://developer.android.com/tools/revisions/index.html";

  private final List<IPkgDesc> myPackages;

  protected UpdateInfoDialog(boolean enableLink, List<IPkgDesc> packages) {
    super(enableLink);
    myPackages = packages;
    getCancelAction().putValue(DEFAULT_ACTION, Boolean.TRUE);
    init();
  }

  @Override
  protected JComponent createCenterPanel() {
    return new UpdateInfoPanel(myPackages).getRootPanel();
  }

  @NotNull
  @Override
  protected Action[] createActions() {
    List<Action> actions = ContainerUtil.newArrayList();

    actions.add(new AbstractAction("Update Now") {
      @Override
      public void actionPerformed(ActionEvent e) {
        SdkQuickfixWizard sdkQuickfixWizard =
          new SdkQuickfixWizard(null, null, myPackages, new DialogWrapperHost(null, DialogWrapper.IdeModalityType.PROJECT));
        sdkQuickfixWizard.init();
        if (sdkQuickfixWizard.showAndGet()) {
          close(0);
        }
      }
    });

    actions.add(new AbstractAction("Release Notes") {
      @Override
      public void actionPerformed(ActionEvent e) {
        BrowserUtil.browse(RELEASE_NOTES_URL);
      }
    });

    actions.add(new AbstractAction(IdeBundle.message("updates.ignore.update.button")) {
      @Override
      public void actionPerformed(ActionEvent e) {
        List<String> ignores = UpdateSettings.getInstance().getIgnoredBuildNumbers();
        for (IPkgDesc desc : myPackages) {
          ignores.add(desc.getInstallId());
        }
        doCancelAction();
      }
    });

    actions.add(getCancelAction());

    return actions.toArray(new Action[actions.size()]);
  }

  @Override
  protected String getCancelButtonText() {
    return IdeBundle.message("updates.remind.later.button");
  }

  private class UpdateInfoPanel {

    private JPanel myRootPanel;
    private JBLabel myPackages;
    private JBLabel myDownloadSize;
    private JEditorPane mySettingsLink;

    public UpdateInfoPanel(List<IPkgDesc> packages) {
      configureMessageArea(mySettingsLink);
      myDownloadSize.setText(WelcomeUIUtils.getSizeLabel(computeDownloadSize(packages)));
      HtmlBuilder packageHtmlBuilder = new HtmlBuilder();
      packageHtmlBuilder.openHtmlBody();
      packageHtmlBuilder.beginList();
      for (IPkgDesc desc : packages) {
        packageHtmlBuilder.listItem().add(desc.getListDescription() + " revision " + desc.getPreciseRevision());
      }
      packageHtmlBuilder.closeHtmlBody();
      myPackages.setText(packageHtmlBuilder.getHtml());
    }

    public JPanel getRootPanel() {
      return myRootPanel;
    }

    private long computeDownloadSize(List<IPkgDesc> packages) {
      AndroidSdkData data = AndroidSdkUtils.tryToChooseAndroidSdk();
      SdkState state = SdkState.getInstance(data);
      // Should already be loaded at this point--just reload to be sure.
      state.loadSynchronously(SdkState.DEFAULT_EXPIRATION_PERIOD_MS, false, null, null, null, false);
      Map<String, UpdatablePkgInfo> sdkPackages = state.getPackages().getConsolidatedPkgs();
      long size = 0;
      boolean preview =
        SdkComponentSource.PREVIEW_CHANNEL.equals(UpdateSettings.getInstance().getExternalUpdateChannels().get(SdkComponentSource.NAME));
      for (IPkgDesc pkg : packages) {
        String iid = pkg.getInstallId();
        UpdatablePkgInfo updatablePkgInfo = sdkPackages.get(iid);
        if (updatablePkgInfo == null && iid.endsWith(PkgDesc.PREVIEW_SUFFIX)) {
          iid = iid.substring(0, iid.indexOf(PkgDesc.PREVIEW_SUFFIX));
          updatablePkgInfo = sdkPackages.get(iid);
        }
        size += updatablePkgInfo.getRemote(preview).getDownloadSize();
      }
      return size;
    }
  }
}

redpajama_set_name: RedPajamaGithub


End of the Year: We look back at how Kabinett fared in 2014 and share our list of highlights from the vintage.
THE sweet spot of the 2014 vintage are the Kabinett wines made from clean grapes. These wines have a lightness not seen in two decades and proved a return to the great Kabinett and Spätlese of the 1990s!
One would expect such classic Estates as Julian Haart, Reinhold Haart, Peter Lauer, Egon Müller, Max Ferd. Richter, von Schubert, Wwe. Dr. H. Thanisch - Erben Thanisch, Daniel Vollenweider or Weiser-Künstler to do well in this style and theses Estates did indeed deliver some little jewels of Kabinett wines in 2014!
A real positive surprise was Weingut Dr. Hermann, an Estate best known for its stunning sweet wines. It crafted a superb Erdener Treppchen Kabinett (AP 6). Also remarkable and heartening is the performance of Weingut von Kesselstatt, which produced a whole range of outstanding wines. Also, the Weingut von Othegraven offered a whole set of deliciously light and elegant Kabinett wines.
Overall, plenty of great Kabinett was produced (more in the Issues) and we would not be surprised if some will actually exceed our high expectations. While still rather shy early on, they are continuously developing rather nicely and are really worth cellaring.


Your health and experience is our priority. To keep and improve our standard of quality health care and patient experience, we need to hear from you.
To measure our care and experience, we participate in the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. The HCAHPS survey measures your experience with the quality of your care. It is a consistent tool for measuring and reporting patient experience across all hospitals in the United States.
After you leave the hospital, you may be contacted by Press Ganey, a consulting firm specializing in measuring quality performance, by mail, to participate in the HCAHPS survey. The survey asks 27 multiple choice questions about your hospital stay. We ask you to please take a few minutes to complete the patient survey and return it to Press Ganey in the enclosed postage-paid envelope.
The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey is backed by the U.S. Department of Health and Human Services. The survey is used to improve the quality of health care. HCAHPS makes survey results public so hospitals are aware of areas that need improvement. The results also enable health care consumers to review and compare hospitals before choosing a health care provider.
Hospital Compare is a federal government website that allows users to compare the quality of care provided by hospitals. The information provided on this website is based on HCAHPS survey results.
The Leapfrog Group rates hospitals that take part in the Leapfrog Hospital Quality and Safety Survey. The Survey measures how well hospitals meet the Leapfrog Group's quality and safety standards. Survey results are reported on Leapfrog's website for users to compare hospitals.
DNV Healthcare is the leading accreditor of United States hospitals integrating ISO 9001 quality compliances with the Medicare conditions of participation. Quality reports for all accredited organizations that meet its high standards are available on its website.
The Quality Transparency Dashboard enables us to keep you informed as you make important choices regarding your health needs. Joining with the Hospital Quality Institute, we are committed to being transparent in providing safe, quality care.


Auhea oukou e na hoa heluhelu o ke ala ulili, mai kahi kihi a kahi kihi o ko kakou kulaiwi o Hawaii, aloha nui kakou. Eia no kakou ke hoomau aku nei i ke kuamoo olelo o ko kakou alii kaulana, o Umi-a-Liloa hoi, a ina ua hele a luhi ke kino i ka loihi o ka hele ana i ke ala ulili, e noho pu oukou i ka lokomaikai o keia alii no Hamakua. Ma keia wahi mahele o ko kakou moolelo, e ike ana kou maka i na hana i kaulana ai keia alii, na hana hoi i hoopaa ia e ke alii maoli, e ke alii pono, a i kiahoomanao kona moolelo no kakou, na mamo e ola nei, i mea e ike ai kakou i ka pono a me ka pono ole o ka noho aupuni ana. I alii no o Umi i na kanaka ana i malama ai, oia hoi, ke kanaka nui a me ke kanaka iki, a oia no ke kumu i olelo ia ai keia olelo noiau e ka poe kahiko, "Hookahua ka aina, hanau ke kanaka. Hookahua ke kanaka, hanau ke alii."
Eia kekahi, mai keia mua aku i ko kakou heluhelu ana i ka moolelo nei no Umi, kakau ia keia mahele o ka moolelo e kekahi haku moolelo, o J. H. Z. Kalunaaina kona inoa. Penei kona olelo, i kona hoomaka ana i ka mahele nei o keia moolelo, "Ma kau noi ana mai e S. K. e i ana, 'E hiki i ka mea i loaa ka moolelo o Umi, ke hookui mai, a i na hoi ua hemahema, a pololei ole paha keia moolelo, e hai mai no ka mea i pau ka moolelo o Umi ma ke akea.' Nolaila, ke hoolawa aku nei au e like me kau noi ana mai." No laila, e ka poe aloha aina o ko Hawaii nei Paeaina, e hoomau kakou me ka mahalo nui no keia kakau moolelo Hawaiʻi naauao o ke au i hala.
Dear reading companions of the ala ʻūlili, from one corner to the other corner of our beloved homelands of Hawaiʻi, aloha to you all. Here we are continuing along the path of tradition of our famous aliʻi, ʻUmi-a-Līloa, and if perhaps your body has become weary from the long journey along the steep trails, then sit and rest here with the generosity of this aliʻi from Hāmākua. In this portion of our moʻolelo, your eyes with bare witness to the deeds that made famous this aliʻi, the deeds that are tended to by a true aliʻi, a pono aliʻi. For this moʻolelo stands as a reminder for us, the living descendants today, so that we may come to know of what establishes pono, or disturbs it, in the work of governance. ʻUmi was an aliʻi by the will of the people he cared for, that is, the "big person" and the "small person," and it is for this reason that the people of old would speak of these wise words: "The ʻāina creates the foundation upon which the people are born. The people create the foundation upon which the aliʻi are born."
Additionally, dear readers, from this point forward in our reading of this moʻolelo of ʻUmi, it is important to note that this portion of the moʻolelo was written by another storyteller by the name of J. H. Z. Kalunaaina. Here is what he said in his introduction to writing this portion of the moʻolelo, "As you had requested, [Simeon Keliikaapuni], 'Someone else who knows the moʻolelo of ʻUmi can surely join it here. And if perhaps this moʻolelo is inaccurate or incorrect in parts, the one who is to finish this moʻolelo of ʻUmi shall announce it publicly.' Thus, I am providing as you have requested." Therefore, dear aloha ʻāina, dear people who love this ʻāina of our Hawaiian Islands, let us continue on with gratitude for this brilliant Hawaiian author of a time now passed.
During ʻUmi's residence there, he remained for a significant period of time. Within that period of residence, a great number of people came to live there. The number of people was equal to that of four kaʻau (x40), that being, 160 houses. Within one kaʻau (group of 40) houses was one lau(400) of people, and so it was for each kaʻau of houses, equalling to four lau of people, that being 1,600 people total. That was how Kaleiokū supplied his aliʻi, with all the necessities to prepare him. For it was Kaleiokū's intention to bring the kingdom under the rule of his aliʻi. That is why he prepared the people with lessons in fighting with spears.
** S. M. Kamakau excerpts from "Ka Moolelo Hawaii," Helu 49. Ke Au Okoa. Nov. 17, 1870.
No ko ʻUmi Noho ʻIlihune ʻana ma Laupāhoehoe. ʻUmi's Life in Destitution at Laupāhoehoe. Illustration by Haley Kailiehu, 2014.
O ka hope keia o ka moolelo o Umi, o ka pau ana keia, ina he nui aku koe, o keia moolelo, e hiki no i ka mea i loaa ia ia ka moolelo o Umi, e hiki no i ka mea i ike ke hoolawa mai, a hookui mai i keia moolelo o Umi, ma keia pepa hou mai. A ina hoi ua hemahema a pololei ole paha keia moolelo, e hai mai no ka mea i pau ia ia ka moolelo no Umi ma ke akea.
Eia kekahi, ma keia hope aku, e hai hou aku ana au i ka Moolelo o ko Kamehameha I., noho aupuni ana maluna o keia Pae Aina o Hawaii nei, mai ka makahiki 1794 a hiki i ka makahiki 1811. A pela hoi i ka wa i hiki mai ai na Misionari mua i Hawaii nei, a me ko Kaahumanu hooponopono Aupuni ana, a me Kamehameha II., a pela'ku, Owau no me ke aloha aku i ka poe e heluhelu ana. S. K.
[This is the end of the moʻolelo of ʻUmi. This is where it will end. If there is much else to tell of this moʻolelo, then someone else who knows the moʻolelo of ʻUmi can surely provide it here, and join it to this moʻolelo of ʻUmi in the coming issues of this paper. And if perhaps this moʻolelo is inaccurate or incorrect in parts, the one who is to finish this moʻolelo of ʻUmi shall announce it publicly.
** S. M. Kamakau excerpt from "Ka Moolelo Hawaii," Helu 49. Ke Au Okoa. Nov. 17, 1870.
Laupāhoehoe, Hilo, Hawaiʻi. Photo by N. Peralto, 2014.
No ka heʻe malu ʻana ʻo Umi mā mai o Hakau aku. The Escape of ʻUmi & his Companions from Hakau. Illustration by Haley Kailiehu, 2014.
I ko lakou hoi hou ana i Hamakua, mai Waipio aku, ma ko lakou ala i hele mua mai ai i ko lakou pii ana'ku ma Koaekea, hiki lakou ma Kukuihaele, alaila, loaa ia lakou o Koi, alaila, hele oia me Umi. I ko lakou hele ana'ku a hiki lakou i Kealakaha, oia ko Umi wahi i hanau ai, aole lakou i kipa i kona makuahine; no ka mea, ua manao lakou e hele kuewa wale aku.
ʻUmi-a-līloa's journey from Waipiʻo to Laumaiʻa and Waipunalei, Hilo, Hawaiʻi. Map by N. Peralto, 2014.


In this time of uncertainty, I feel that we Americans need to remember those who have given their lives in defense of this country. This is not a political statement on the current Middle East situation, but an appreciate of those with their boots on the ground who risk their lives daily. As part of that, my wife and I visited the U.S.S. Arizona (BB-39) Memorial at Pearl Harbor during our trip to Hawaii. Unfortunately, rain prevented us from visiting the U.S.S. Missouri (BB-63) Memorial.
In front of the gift shop that is inside the Visitors Center was a table with gentleman selling Pearl Harbor commemorative medals and sets produced by the Honolulu Mint. I was very intrigued by the items and gravitated to the commemorative set that contains two .999 Silver, 1 Troy Ounce medals, an uncirculated 1943 Steel Lincoln Cent, and a Series 1935A Hawaii issue Silver Certificate. Even though I am not a commemorative collector, I do tend to buy numismatic items from my personal experiences. While standing in the Memorial, and with my wife's urging, I could not pass up buying this set.
The other medal commemorates the Japanese surrender aboard the U.S.S. Missouri. It was prophetic that the articles of surrender were signed on her deck. The obverse of this medal recreates the signing of the document by General Yoshijiro Umezu, Chief of the Army General Staff, with Lieutenant General Richard K. Sutherland standing across the table. The reverse has the inscription on the deck of the Missouri where the articles were signed.
Both medals were designed by the Honolulu Mint's chief engraver, Dave Jones. I had the honor of meeting Mr. Jones when I purchased the set and personally received his autograph on the Artist Proof Certificate of Authenticity. The images on both medals shows Mr. Jones attention to detail and accuracy with creating these medals. The Honolulu Mint executed the designs flawlessly with fantastic strikes on great looking planchets. Both Dave Jones and the Honolulu Mint deserve much praise for their work.
Although everyone should visit the U.S.S. Arizona Memorial and the rest of Pearl Harbor, I know it is not a trip that every can take. So if you are interested in this collectible and other medals honoring those who served and gave the ultimate sacrifice, you can visit the Honolulu Mint's website or the U.S.S. Arizona Memorial Bookstore. Portions of the proceeds benefit the Memorial.


Anthony Haffner was born in Germany July 6, 1858. His mother Gertrude died in 1864 and in that same year his father Anthony Sr. and children came to America and located in Hennepin County. Some fifteen years later the father, Anthony Sr. and family came to St. Michael and operated a steam saw mill and threshing machine from there. Anthony Jr.
attended schools in Hennepin County. He worked around St. Michael a few years and married Katherine Dronick of Hennepin County in 1884. They resided in the village of St. Michael for about five years. In 1889 he purchased 40 acres of land in section 31 of Frankfort Township. According to the Wright County History Mr. Haffner cleared the land with a grub hoe and planted his first crops. Their house was a board shanty covered with tar paper. For two years they had no horses or oxen and only one cow. He moved the saw mill to this location on a large pond on the north side of his property. The 1879 map does show a pond there with a wagon trail jogging around to the North side. (Anna Duerr in an interview in 2003 recalled the pond as a small lake that just disappeared. It seems that when County Road 34 was built the pond was partially filled in.) He added 33 acres probably in the late 1800's which gave him a total of 75 acres with a splendid water line on Lake Charlotte.
The Wright County History by Franklin Curtis Wedge 1915 states that Mr. Haffner was a very successful farmer and about eleven years after he settled on the place he erected a large barn and a substantial residence. The Haffner's had nine children seven girls and two boys. In 1978 the two remaining Haffner girls, Gertrude age 90 and Millie age 89 were interviewed by a member of the Wright County Historical Society. Listening to the tape it appears that the family lived in the tar paper shanty for several years until the farm house was built in about 1899 or 1900. One of the sisters described the shanty as one large room for the kitchen and dining room. There were two bedrooms added on the sides. One had three double beds and the other had two double beds and a single bed in them. They also talked about the saw mill and Mr. Haffner operating the steam engine threshing machine.
After the farm house was completed from time to time the Haffner's friends came to their place to fish on the lake. Gradually he received requests from strangers for board and room. Finally in 1907 he decided t o go into the summer resort business. When his farm house became too small he erected a five room cottage near the farm house. The house and cottage were several rods from the lake. He selected a slope of land on the North bank of the lake shaded by some nice trees and possessing all the qualifications for a summer resort. He moved the cottage to the new location and started the resort business on a large scale. The main hotel, built in 1910 by the lake, housed the kitchen and dining room. It was cool, airy and well furnished. This is the present home of Terry Rawlings. The history goes on to say that ten cottages gave ample accommodations for a large number of guests. The Haffner sisters in the interviews did not indicate the total number of cabins. They said there were at least one five room cabin, a four room, a three room, and several two room cabins. It appears that the cabins were small with sleeping rooms only. The interviews revealed that Kate Haffner did all the cooking and meals were served in the hotel dining room.
The history also adds that a garage, ice house, dance pavilion, a well of pure water and a large fleet of boats added to the guests comfort and pleasure. It also states that the lake is clear and sparkling one mile long, ½ mile wide and 100 feet deep in the deepest point. The bathing beach in front of the cottages is excellent and the maple grove gave an ideal spot for picnics. Year by year, the place increased in popularity as a fishing, hunting and recreation resort. The resort was open primarily from Decoration Day to Labor Day. In the early days most of the guests came by the railroad. Mr. Haffner would meet them in a horse drawn vehicle at Albertville. Some may have come by way of Rockford. They also hauled supplies and freight from Albertville by wagon team. They operated a small store at the resort which handled gum, candy, tobacco products and a limited amount of canned goods. They did not have electricity for the years that the resort operated. Food refrigeration depended on ice boxes. Mr. Haffner had an ice house and the necessary straw and saw dust to store the blocks of ice for use during the summer months. The frozen lake provided the blocks of ice. Cutting the ice and hauling the huge blocks of ice to the ice house was a necessary winter job. The Haffner sisters said that some of the guests packed their fish in ice to transport them home.
(This brings back some memories for this writer. I recall the process of putting up ice in rural South Dakota as a youth. The ice was cut at the lake using long hand saws. The very large blocks of ice were loaded on a horse drawn sled and taken to the ice house.
Millie said that her brother put on dances in the dance pavilion. We do not have information on what the dances were like however we can bet they were not swing, rock or big band music. The musicians were no doubt local people with drum, violin, accordion and perhaps a horn. There would be a caller for square dances and circle two step. They danced the polkas, old fashioned waltz and in the 20's perhaps the Charleston. Light was provided by gasoline lanterns and kerosene lamps. Dances were generally family affairs as youngsters were not usually left home in rural areas. Dads, Moms and kids all had fun whirling around the dance floor. It was my understanding that when the resort business slowed down the dance pavilion was sold and moved to Buffalo and is still in use as a residential house.
The Haffners generally had one or two hired men to help with the saw mill and threshing machine. Millie said that in the winter time, logs were brought in on a sled to the saw mill. The machine was pretty much enclosed in a shed with one side open to bring in the logs. The saw mill was a thriving business. Millie said in the taped interview that in the summer Mrs. Haffner (Kate), Edith, and Millie stayed at the lake house and did most of the resort work. Gertrude remained unmarried and lived most of her life on the farm.
Edith married Carl Reifler. They lived in the lake house a couple of years before moving to Buffalo. Anne married August Wagner a son of a near neighbor. The 1879 map of Wright County shows that August Wagner was the owner of a portion of section 33 in Frankfort Township on the Northeast side of Lake Walker (Currently Lake Wagner). Since the map was made about 15 years before Anne was born it can be assumed that the land owner was the father of her husband. We also have a copy of a one cent post card dated Sept. 15, 1915 sent from Edith to Mrs. A.G. Wagner, RR #1 Rockford, Minn. The card written in very neat beautiful hand writing reads, "I think I will drop you a card and let you know that we are alive yet. Mamma has not got back from the city yet. I bet she is having a good time. Aug asked me to come down to your place for a few weeks but I can't until the corn is husked. How are the boys? Will be down some time before it gets cold. From your sister Edith". Lena Haffner married a Mr. Lenneman and Alvina married a Mr. Laubner. Theodore Haffner married Regina Bruska a sister of neighbor George Bruska. Millie said that Theodore (Ted) stayed on the farm and Albert the youngest son worked in the cities.
Millie remained unmarried. She worked for Dr. Ludeman in St. Michael for over 20 years. He had built a hospital in St. Michael in 1913. She started working for him in the 20's and stayed with him until he died in the 1950's. He left his property, the hospital, in St. Michael to Millie. It is the present apartment building on HiWay 241 just west of the shopping mall. In 1953 Millie purchased and moved into Lake House (Hotel) on Lake Charlotte. Electricity was installed in the house at that time. (She was still living in the lake house ion 1960-61 when we built a cabin on lot 60. My wife, Pat and my mother visited with her quit often. They called her the flower lady.) Millie died in1987.
Clara Haffner married Nick Duerr in 1912. Their son Arthur was born in 1912 his father, Nick, died when he was only 3 months old. He was raised by his mother, Clara, on the Haffner farm. A picture of Art Duerr and Vernon Haffner on a steam engine is in the Wright County Historical Society files. Vernon (1920-1989) was the son of Theodore and Regina Haffner.
The Haffner resort operated through the 1920's into the 30's until the lake was very low and the bay in front of the resort nearly went dry. Gertrude said that when the lake was so low in the 30's the resort folded, however Millie said even after that people continued to come out and stay at the resort. She said they walked over to the lake on the point. Never-the-less the fate of the resorts on the smaller lakes close to the cities was certain. Automobiles became more common (the tin lizzie, model "T" Ford, came out in 1908) and the roads were improved. Then vacationers and sportsmen began to go to larger lakes farther north. The sisters did tell about selling the saw mill and the threshing machine. The cabins were sold and moved to various locations. One of the cabins still exists at the lake on the Bruska property.
Arthur Duerr married Anna Barthel in 1947. They lived on the farm after they were married. They have three sons. The oldest Tim lives in a house built on the site of the original farm house 6852 10th St. NE. Tony and wife Toni live at 6721 10th St. NE. Greg and wife Mary live at 705 Haug Ave. NE in a house built on the site of the original resort cabins on the lake. The hotel is now the home of Terry Rawlings 671 Haug Ave. N. E. Mr. Anthony Haffner died in 1951 and Mrs. (Kate) died in 1957. Arthur Duerr died in 1986. Anna was a resident of the Park View Retirement Home in Buffalo in 2003 when interviewed. She subsequently has passed away and is interned beside her Husband Arthur in the St. Michael South Cemetery.


text:
Department of Revenue News Release
Home » News » News Item
Maplewood License Office Awarded to Mid-America Transplant Services
JEFFERSON CITY - Mid-America Transplant Services of St. Louis has been named as the new agent for the Maplewood License Office.
Deer Creek License Office Management, Inc. of St. Louis previously received a notice of award for this license office through the state bidding process. However, due to Deer Creek License Office Management, Inc.'s failure to satisfy all of the obligations contained in the proposal, the notice of award for operation of the Maplewood License Office has been withdrawn. The contract will now be awarded to the lowest and best bidder, Mid-America Transplant Services.
Mid-America Transplant Services was founded in 1974, and serves as a non-profit regional organ and tissue procurement organization. The office will remain at its current location, 3238 Laclede Station Road in Maplewood.
Bidders have the option of returning part of the office revenue to the state. The Mid-America Transplant Services bid for the Maplewood License Office includes a 15 percent, estimated to be $62,733, annual return to the state.
The Missouri Department of Revenue is now on Twitter! To follow the Department on Twitter, go to http://twitter.com/#!/MoRevenue.
Anne Marie Moy, Director of Strategy and Communications
Email: Annemarie.Moy@dor.mo.gov

redpajama_set_name: RedPajamaCommonCrawl


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text:
Kryezi is a village situated in the central plains of Albania's Western Lowlands region. It is part of Tirana County. At the 2015 local government reform it became part of the municipality Kavajë. There is one school the Kisha Shen Mehilli.

References

Populated places in Kavajë
Villages in Tirana County

redpajama_set_name: RedPajamaWikipedia


Pink Simple Floral Clusters Scrapbook Paper - 12" x 12"
Fill your paper crafts with a fashionably floral design with the addition of patterned scrapbook paper. Pink Simple Floral Clusters Scrapbook Paper boasts a blush pink background with simple flower blooms arranged in small clusters. Use it as a background for your scrapbook pages, or cut shapes from the paper to fit any and all of your creative needs!


Or is it the new comms bill in work already?
Looks to be just straight down, rather than a bad cert.
Isn't that the same as visiting any of the CACert sites hosted by A&A though? No random CACert installed == Bad SSL warning.
No, that is usually a click through not a try again.
It is working today, but slightly amusing that clicking on the "Security" link from on-line banking went to a page not found.
I've seen it on several occasions where santander's logout link tries to send you to a page within their staging environment.
Shows how well they test their site after making it live!


How Do Elliptical Machines Compare With Other Exercise Equipment in 2019?
"Fitting Exercise Into a Busy Schedule"
How Do Elliptical Machines Compare With Other Exercise Equipment?
The options for exercise equipment are quite overwhelming. It seems as though every month a "cutting-edge, must-have, fat-burning, muscle-toning" piece equipment hits the market, promising fast results. So what makes the elliptical machine stand above the rest?
The elliptical machine has so many advantages that can't be found in other equipment. It is a high cardiovascular workout with extremely low impact to your joints and body. No other exercise equipment offer such a low impact workout, protecting the body from more injury due to arthritis, ankle, knee, hip or back problems. It also can exercise both your upper and lower body at the same time.
Elliptical machines combine the natural movement of a treadmill with the simple ease-of-use of a stair climber. Because you stand straight in a natural position, your back does not have the unwanted stress from pulling when using a rowing machine, or leaning over with some bicycles.
Elliptical vs Treadmill – This is probably one of the most compared pieces of equipment. The elliptical wins out. With the ease of use using the elliptical machine, you are actually burning more calories and fat in less time and with less of a feeling of exertion that you would with a treadmill. You are actually exercising harder than you perceive. This can be more encouraging because you are seeing results that keep you motivated to stick with your workout program. While running on a treadmill is a little less jarring than running on pavement, there is still a very high impact that can easily injure your joints. One of the best benefits of the elliptical is it's very low impact. Also elliptical machines have the moveable upper handles that allow you to exercise both your upper and lower body at the same time.
Elliptical vs Exercise Bike – The Elliptical is a weight-bearing exercise while the stationary bicycle is not. Weight-bearing exercises are important to increase bone density and prevent the onset of osteoporosis.
Elliptical vs Running – Running isn't a piece of equipment for comparison, but since it is many people's go-to cardiovascular exercise we wanted to examine this comparison as well. Every step that you take jogging or running jolts your body. It is suggested that runners impact their body up to 2.5 times their body weight. No matter your size, this is a lot of pressure that affects the joints in your legs and your lower back. As stated above, the low-impact, high results of the elliptical is one of the best selling points. If you use the machine correctly, you are protecting your body from injury.


Information technology (IT) is defined as an application of computers and telecommunications to the collection, processing, storage, and dissemination of voice, graphics, text and numerical information (Tansey, 2003).
For business, IT is the use of knowledge to make and implement commercial decisions. Efficient organizations require established systems to empower them to make the best possible decisions in the situations they are likely to meet. Therefore, an organizational information system (IS) is a set of interrelated components that gather, control, store and distribute data and information and deliver a corrective reaction to meet organization's objectives or goals (Stair & Reynolds, 2008).
The rising complexity of the business industries in which the companies are operating, their internal processes and also, jointly with the emergence of the digital economy have displayed the importance and necessity of an organization's to determine its competitive advantage in the digital economy and to implement strategies to acquire its information system infrastructure. The 'right' strategic information system supports the competitive position and strategies of a business enterprise, plays strategic roles and provides effective support in an organization's effort for achieving competitive advantage (Grant, Hackney & Edgar, 2010).
Under section 2 of this paper, an assessment of the engineering and technology industry is done using Porter's Competitive Model (1979). The section also covers the importance of IS/IT to the engineering and technology industry.
Companies' perspective of using strategic IS/IT to achieve and/ or enhance competitive advantages is covered under section 3 and 4. The successful examples selected for discussion are the implementations from Siemens AG and M+W Group. As for the unsuccessful example, the case study was about a difficult CRM (Customer Relationship Management) implementation in OSLO-based Institute of Technology.
The structured analysis of IS/IT, in section 5, emphasizes the internal and external factors that influence the success or failure in the attempts at gaining and enhancing competitive advantage, lessons learned, and factors influencing the use of IS and the internet to achieve strategic success) are comprised in section 5.
As a final point, the 'Conclusion' section wraps up the analysis whether the success of business strategy and IS/ IT can effectively position any organizations for the future.
In today's business setting, almost all organizations will recognize that their information systems or information technologies play an important role in contributing to their success. The three companies that I have selected for discussion are Siemens AG (Siemens), M+W Group (M+W) and OSLO-based Institute of Technology (INTSOK) because all of them are project-based solution providers and the use of information technologies and/or information systems to manage and harness their knowledge management is part of the companies' core business and competitive intelligence strategy.
Michael Porter's five forces model is the best-known framework for identifying a company's position in a given industry and analyzing competitiveness (Rainer & Cegielski, 2009). This model helps to avoid inspecting the competition too narrowly by breaking the industry into logical parts; intensity of rivalry, threat of new entrants, bargaining power of suppliers, bargaining power of buyers and threat of substitute. The diagram below illustrated that Engineering and Technology is overall an attractive industry.
In general, the interaction process is divided into 4 distinct sub-processes: Sales and Marketing, Design, Supply Chain Management (SCM) and, Support and Maintenance.
However, it is no longer that business is run with 'standard product' concept. Instead, a value innovator thinks in terms of the total solution customers seek, i.e. an engineering company looks into its asset and competence to satisfy customers. According to a study by Professor Carl Fornell (2001), satisfied customers lead the way to excellent business performance.
Value Engineering is a long term business strategy applicable to Siemens, M+W & INTSOK.
It is a professionally applied, function-oriented, systematic team approach used to analyze and improve value in a product, facility design, system or service. By enhancing value characteristics, Value Engineering increases customer satisfaction and adds to the company's investment.
By looking at the current size and type of the engineering and technology industry, including the consideration of value engineering element, a supportive IT environment that supports the business and global strategy of a company is vital. The information systems must be able to support a seamless flow of information over process and organization boundaries.
The Value Chain of the engineering and technology industry (below) shows the common components that support the daily business operations. Information Technology support is throughout the value chain. For the purpose of this paper, the diagram illustrates on Marketing, Design, Development, Installation, Support and Maintenance and, Consultancy. For instance, CRM (Customer Relationship Management) system can support Marketing and Consultancy and, ERP (Enterprise Resource Planning) system can support the rest of the processes.
"Information Technology" OtherPapers.com. 10 2011. 2011. 10 2011 <https://www.otherpapers.com/essay/Information-Technology/12599.html>.
"Information Technology." OtherPapers.com. OtherPapers.com, 10 2011. Web. 10 2011. <https://www.otherpapers.com/essay/Information-Technology/12599.html>.
"Information Technology." OtherPapers.com. 10, 2011. Accessed 10, 2011. https://www.otherpapers.com/essay/Information-Technology/12599.html.


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Vol. XXXIII, No. 3, Spring 2017
Algorithm of the Enlightenment
By Ed Finn
In 2010, two researchers at Cornell University hooked up a double pendulum system to a machine learning algorithm, instructing it to seek patterns in the seemingly random data emerging from the movements of the machine. Within minutes, the algorithm constructed its own version of Newton's second law of motion. After hearing of their work, a biologist asked the researchers to use their software to analyze his data of a complex system of single-cell organisms. Again the system spit out a law that not only matched the data but effectively predicted what cells would do next. Except this time, while everyone agreed the equation worked, nobody understood what it meant. As early as 1976, mathematicians turned computers loose on the famous four-color map theorem, using brute force to check a huge number of possible scenarios and prove the theorem correct. Increasingly, we are using computational systems to assist us not just in the retention or management of information but in identifying patterns, comparing disparate information sources, and integrating diverse observations to create a new holistic understanding of the problem in question.
The ambition reflected in these scientific projects pales in comparison with the dreams of Silicon Valley, which hopes to render just about everything tractable by algorithm, from investing in stocks to driving a car, from eliminating genetic disorders to finding the perfect date. Thanks in large part to these enterprises, the world is now covered with a thickening layer of computation built on our smartphones, ubiquitous wireless networking, constellations of satellites, and the proliferation of cloud-based computing and storage solutions that allow anyone to tap sophisticated high-end processing power anywhere, for any problem. When we combine this technical platform for computation with the growing popularity of machine learning, it is possible to spin up a cutting-edge neural network based on open source tools from field leaders such as Google, link it to large repositories of public and private data stored on the cloud, and develop scarily effective pattern-seeking systems in a matter of hours. For example, over the course of 24 hours at a hackathon in 2016 a programmer coded a web camera, Amazon's cloud computing, and Google's TensorFlow machine learning tool kit to learn how to recognize San Francisco's traffic enforcement vehicles and send him an instant message in time to move his car before getting a ticket.
In short, we are entering a new age of computational insight, one that extends beyond the self-driving Uber vehicles that are currently circling my office in Tempe, Arizona. Tools such as IBM's Watson artificial intelligence system are not just good at Jeopardy! but also at medical diagnosis, legal analysis, actuarial work, and a range of other tasks they can be trained on for specific applications. The rising tide of automation is already affecting a tremendous range of jobs that demand intellectual judgment as well as specific physical capacities, and we're only getting started.
Algorithms are also beginning to surprise us. Consider the trajectory of computational systems playing complex, effectively unbounded games such as chess and Go. Commentators studying the performance of the artificial intelligence system AlphaGo in its historic matches with world champion Lee Sedol often struggled to understand the system's moves—sometimes describing them as programming failures, other times as opaque but potentially brilliant decisions. When a previous IBM system, Deep Blue, defeated world chess champion Garry Kasparov in 1997 in a six-game series, it did so in part by playing a strange, unpredictable move near the end of the first game. As Nate Silver tells it in The Signal and the Noise, that move was a bug, but Kasparov took it as a sign of unsettling depth and intelligence; it distressed him enough to throw off his play for the second game and opened the door to his ultimate defeat. We assume that the algorithm is perfectly rational and all-knowing, and that gives it an advantage over us.
This uncertainty in interpreting the performance of algorithms extends to far more nebulous cultural problems, such as the project Netflix undertook a few years ago to organize its vast catalog of films and television shows into a set of 76,897 predictable categories. The system, behaving more like something out of Futurama than a computer algorithm, demonstrated an inexplicable fondness for Perry Mason. Its chief (human) architect waxed philosophical about the implications of this output that he could not explain: "In a human world, life is made interesting by serendipity. The more complexity you add to a machine world, you're adding serendipity that you couldn't imagine. Perry Mason is going to happen. These ghosts in the machine are always going to be a by-product of the complexity. And sometimes we call it a bug and sometimes we call it a feature."
Like so many other algorithmic systems, the Netflix algorithm was creating knowledge without understanding. Steven Strogatz, an applied mathematician at Cornell, has provocatively called this the "end of insight"—the era when our machines will deliver answers that we can validate but not fully comprehend. This may not seem so different from the typical doctor's visit or lawyer's advice today, where an ordained expert gives you a solution to a problem without giving you new insight or context. But of course you can pester that person to explain his or her thinking, and you can often learn a great deal from the emotional affect with which he or she delivers the judgment. It's not so easy to do that with a black box machine that's specifically designed to keep that decision-making apparatus secret. Nevertheless, this kind of intellectual outsourcing is a central function of civilization, a deferral to our tools and technologies (and experts) that happens for each of us on a daily basis. We already trust computational systems to perform a range of tasks using methods most of us do not understand, such as negotiating a secure connection to a wireless network or filtering spam e-mail. But the gap between result and analysis becomes stark, perhaps existentially so, when we are taking about scientific research itself.
The replication crises currently plaguing a number of scientific disciplines stem in part from this lack of insight. Scientists use complex computational tool kits without fully understanding them, and then make inferences based on the results that introduce various kinds of bias or false equivalencies. In 2016, for example, a team of researchers from Sweden and the United Kingdom uncovered bugs and error rates with the statistical sampling of three leading software applications used in functional magnetic resonance imaging, a technique for measuring brain activity, that may cast the findings of 40,000 research papers into doubt. A more pernicious form of this problem emerges from the fact that so much scientific work depends entirely on computational models of the universe, rather than on direct observation. In climate science, for example, we depend on models of incredibly complex air and water systems, but it is sometimes extremely difficult to disentangle the emergent complexity of the models themselves from the emergent complexity of the systems they are intended to represent.
The science fiction author Douglas Adams envisioned a magnificent reduction ad absurdum of this argument, when an advanced civilization constructs a supercomputer called Deep Thought to find the "Answer to the Ultimate Question of Life, The Universe, and Everything." After 7.5 million years of calculation, Deep Thought returns its answer: 42. Precise but meaningless answers such as 42 or Perry Mason can easily be generated from incoherent questions.
This is an old problem. Philosophers have dreamed for centuries of what Gottfried Wilhelm von Leibniz called the mathesis universalis, a universal language built on mathematics for describing the scientific universe. He and other philosophers of the seventeenth century were laying foundation stones for the Enlightenment, imagining a future of consilient knowledge that was defined not just by a wide-ranging set of rational observations but by a rational structure for reality itself. A grammatically correct statement in the language of Leibniz would also be logically sound, mathematically true, and scientifically accurate. Fluency in that imagined tongue would make the speakers godlike masters of the space-time continuum, capable of revealing truths or perhaps even writing the world into being by formulating new phrases according to its grammatical rules.
Leibniz was among the thousands of scientists and philosophers who contributed to our modern understanding of scientific method. He imagined a set of instructions for building and extending a structure of human knowledge based on systematic observation and experimentation. According to his biographer, Maria Rosa Antognazza, Leibniz sought an "all-encompassing, systematic plan of development of the whole encyclopaedia of the sciences, to be pursued as a collaborative enterprise publicly supported by an enlightened ruler."
I call Leibniz's project, this recipe for the gradual accumulation and cross-validation of knowledge, the algorithm of the Enlightenment. Structured scientific inquiry is, after all, a set of high-level instructions for interpreting the universe. The ideal version of this idea is that the progress of civilization depends on a method, a reproducible procedure that delivers reliable results. Leibniz was echoed in the eighteenth century by Denis Diderot and Jean d'Alembert, the French creators of the first modern encyclopedia, who pursued their work not just to codify existing knowledge but to cross-validate it and accelerate future research. As these ambitious projects were realized, they articulated a vision of the world that unseated the prevailing orders of religiosity and hereditary feudalism. There is a reason many people credit Diderot and d'Alembert's Encyclopédie française with helping to ignite the French Revolution. The Enlightenment has always contained within it a desire, a kind of utopian aspiration, that one day we will understand everything.
Leibniz was one of the chief advocates of that vision, not as a means of overthrowing a deity but of celebrating The Creation through human understanding. He was also a strong advocate of binary numerals and made an early attempt at building a calculating machine. In this effort, he attempted to represent existence and nonexistence, truth and falsehood, through ones and zeros: little wonder that so many of Leibniz's ideas resurfaced in computation, where mathesis universalis is possible in the controlled universes of operating systems and declarative programming languages. The modern notion of the algorithm as a method for solving a problem or, more technically, a set of instructions for arriving at a result within a finite amount of time emerges from an extension of the Enlightenment quest for universal knowledge on the part of mathematicians and logicians.
The emergence of modern computer science in the 1940s and 1950s began with the proofs of computability advanced by Alan Turing, Alonzo Church, and others. It was, in many ways, a conversation about the language of mathematics, its validity and symbolic limits. This work created the intellectual space for computation as researchers such as Stephen Wolfram articulate it today, arguing not only that we can model any complex system given enough CPU cycles but that all complex systems are themselves computational. They are best understood, as Wolfram, a wildly inventive British-American scientist, mathematician, and entrepreneur, suggests in his book A New Kind of Science, as giant computers made up of neurons or molecules or matter and antimatter—and therefore they are mathematically equivalent. This is the "hard claim" for effective computability implicit in the most promethean work at the forefront of artificial intelligence and computational modeling: the brain, the universe, and everything in between is a computational system, and will ultimately be tractable with enough ingenuity and grant funding.
So the algorithm of the Enlightenment found its most elaborate expression in the language of code, and it has become increasingly commonplace, often necessary, to encode our understanding of the universe into computational systems. But as these systems grow more complex, and our capacity to represent data expands exponentially, we are starting to say things, computationally speaking, that we don't fully understand—and that may be fundamentally wrong without us knowing it.
Consider the advanced machine learning systems Google has used to master Go, natural language translation, and other challenges: these algorithms depend on large quantities of data and training sets that reveal a correct answer or desired output. Rather than figuring out how to directly analyze input and deliver the required output, we are now starting with the input and a sampling of the desired output and black-boxing the middle. Simulated neural nets iterate millions of times to create a set of connections that will reliably match the training data, but offer little insight to researchers attempting to understand how it works. Just as with the algorithm analyzing the single-cell complex system, we can see that a law has been derived, but we don't know how—we are on the outside of the laboratory, looking in.
Like computational algorithms, critical scientific inquiry depends on some important assumptions: that the universe is inherently causal, and relatively uniform in the application of that causality. But as we encode the method into the space of computation, our assumptions must change. The causality of computational systems is more specific and delimited than the causality of the universe at large: rather than a system of quantum mechanical forces, we have a grammatical system of symbolic representations. It is the difference between an equation that describes the curve of a line through infinite space, and a model that calculates the line's position for a discrete number of points. (Want more points? Buy a better graphics card.) Or to take another example, it is the difference between a model of the weather that divides the sky into one-kilometer cubes and the actual weather, in all of its weird, still unpredictable mutability. When we encode our scientific work into computational representations, we introduce a new layer of abstraction that may warp our future understanding of the results.
The stakes are high because we are growing increasingly dependent on computational spectacles to see the world. The basic acts of reading, writing, and critical thinking all flow through algorithms that shape the horizons of our knowledge, from the Google search bar to the models assessing our creditworthiness and job prospects. For scientists, the exponentially increasing flood of information means that computational filters and abstractions are vital infrastructure for the mind, surfacing the right ideas at the right time. Experiments have shown how trivial it is to manipulate someone's emotional state, or political position, using targeted computational messages, and the stakes only grow when we confront the desire inherent in computational thinking to build a black box around every problem. Right now we are accepting these intellectual bargains like so many "click-wrap" agreements, acting with very little understanding of what kind of potential knowledge or insights we might trade away for the sake of convenience or a tantalizingly simple automated solution.
In fact, we are far more to blame than our computational systems are, because we are so enamored of the ideal, the romance of perfect computation. I have seen many parents repeat sentences to their smartphones with more patience than they have for their children. We delight in the magic show of algorithmic omniscience even when we are bending over backwards to hide the flaws from view.
But of course there is no un-ringing the bell of computation, just as there is no reversing the Enlightenment. So what can we do to become more insightful users and architects of algorithms? The first thing is to develop a more nuanced sense of context about computation. The question of how the stakes of causality and observation change in computational models is fundamental to every branch of research that depends on these systems. More than once during the Cold War, US and Soviet officers had to see through a computer model showing enemy nukes on the wing and perceive what was really behind them: a flock of birds, perhaps, or a software glitch. As humans, we consistently depend on metaphor to interpret computational results, and we need to understand the stakes and boundary conditions of the metaphors we rely on to interpret models and algorithmic systems. That's doubly true when the metaphors we are being sold are alluring, flattering, and simplifying. In short, we need a kind of algorithmic literacy, one that builds from a basic understanding of computational systems, their potential and their limitations, to offer us intellectual tools for interpreting the algorithms shaping and producing knowledge.
The second thing we need is to create better mechanisms for insight into computational systems. In a recent conversation on the topic someone half-jokingly proposed the example of the fistulated cow from veterinary science: a living animal with a surgically created hole in its flank that allows students to directly examine the workings of its stomach. There is a blunt analogy here to the "black box" metaphor so beloved of technologists and intellectual property lawyers—society needs new rules and procedures for prying open these black boxes when their operation threatens to perpetuate injustice or falsehoods. Elements of this are common sense: having a right to see what data a company has collected about us and how that data is used, and having easy access to a robust set of privacy and "opt-out" features that delineate what kinds of bargains we are making with these services. Perhaps more ambitiously, we need more open data standards so that models with real human significance—models of health care used to determine insurance benefits, for example—use definitions and calculations that are open to public review.
But on a deeper level, we need to think about fistulated algorithms as a means of regaining our capacity for insight and understanding. The collective computational platforms we have created, and are rapidly linking together, represent the most complicated and powerful achievement of our species. As such, it is a worthy object of study in its own right, and one we must attend to if we want to enjoy the thrill of discovery as well as the comfort of knowledge. These systems are not really black boxes floating in computerland—they are culture machines deeply caught up in the messy business of human life. We need to understand that causality works differently with algorithms, and that what you're getting with a Google search is not just a representation of human knowledge about your query but a set of results tailored specifically for you. Our tools are now continuously reading us and adapting to us, and that has consequences for every step on the quest for knowledge.
Ed Finn is the author of What Algorithms Want: Imagination in the Age of Computing and a coeditor of Frankenstein: Annotated for Scientists, Engineers and Creators of All Kinds (forthcoming May 2017) and Hieroglyph: Stories and Visions for a Better Future. He is the founding director of the Center for Science and the Imagination at Arizona State University, where he is an assistant professor in the School of Arts, Media & Engineering and the Department of English.
Finn, Ed. "Algorithm of the Enlightenment." Issues in Science and Technology 33, no. 3 (Spring 2017).
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Top 12 Surprising Celebrity Teachers
By: Amber Johnson
Amber Johnson
To be a great teacher, you've got to know how to handle a crowd. Maybe that's why these celebrities commanded their classrooms before finding fame.
Here are the Top 12 Surprising Celebrity Teachers:
Jon Hamm: The dashing Mad Men star was breaking high school hearts as a drama teacher at John Burroughs High School in St. Louis. In fact, he taught The Office's Ellie Kemper, who now stars with him in Bridesmaids. Showing off his teacher skills and lifelong memory of students, he even commented Ellie's success in a Sundance interview: "It's really cool to see them blossom and find their own way in."
Sylvester Stallone: One of the most celebrated action/adventure figures of all time, Sylvester Stallone is also a director, producer, screenwriter and former teacher. The Rocky and Rambo star survived boarding schools and a special education school for "problem children" as a child, and then enrolled in beauty school after high school graduation. Stallone dropped out of beauty school to attend the American College of Switzerland, where he studied drama and worked as a gym teacher.
Sting: Back when the legendary Sting was known as Gordon Matthew Thomas Sumner, the Grammy award-winner grew up in Wallsend, in northeast England. He worked odd jobs after high school until he enrolled in the Northern Counties College of Education. As he pursued local music gigs, Sting also worked as a teacher for two years at St. Paul's First School in Cramlington, England.
Barack Obama: The 44th President of the United States, Barack Obama, worked as a community organizer, lawyer and law school lecturer after graduating from Columbia University and Harvard Law School. In fact, up until just four years before running for president, Obama was a constitutional law professor and senior lecturer at the University Chicago.
Mr. T: In the 1980s, actor and professional wrestler Mr. T also gained popularity among children for appearing in commercials and animated TV specials that encouraged children to "stay in school" and live safe, healthy lives. Mr. T was born Laurence Tureaud in Chicago as the youngest of 12 children. In Chicago, Mr. T attended the Paul Lawrence Dubar Vocational Career Academy and worked as a gym teacher, before winning a scholarship to Prairie View A&M Univeristy and working as a military policeman for the U.S. Army.
J.K. Rowling: J.K. Rowling has made a fortune off of her fictional world of the Hogwarts boarding school for witches and wizards, and the Harry Potter writer was actually a teacher herself. Rowling, who has been named the 12th richest woman in Britain, admits to having based some Harry Potter characters on her own past teachers, and in the early 1990s, Rowling moved to Portugal after her mother died to teach English as a foreign language. Rowling was already writing Harry Potter at that time.
Sheryl Crow: Sheryl Crow started her path to fame in front of the classroom. She taught music at Kellison Elementary school in Fenton, Missouri while working on her music. After selling a "back-to-school" jingle and other songs, she moved up as a songwriter for larger brands, then sang backup for Michael Jackson before gaining stardom in her own right with "All I Wanna Do Is Have Some Fun" and other hits.
Art Garfunkel: One-half of the immensely popular and socially-critical folk singing group Simon and Garfunkel, Art Garfunkel was born and raised in Queens, New York. Paul Simon and Art Garfunkel met in sixth grade in Queens, though they did not release their first official record as Simon and Garfunkel until 1964, after Garfunkel had earned a graduate degree in mathematics. Garfunkel taught math in Connecticut even as he enjoyed success as a singer.
Robert Frost: The iconic poet of works like "The Road Not Taken" and "Fire and Ice" was born in San Francisco, CA, in 1874. After graduating from Dartmouth College, Frost returned home to work and for a time, helped his mother as an assistant teacher. Years later, after attending Harvard for two years and working as a farmer, Frost worked as an English teacher in New Hampshire. For the rest of his life, Frost would write poetry and teach English at various schools, including at Amherst College in Massachusetts and the University of Michigan, Ann Arbor.
Roberta Flack: This singer, with a star on the Hollywood Walk of Fame, may have taught Lauren Hill a thing or two with her original "Killing Me Softly," but she also taught English and music to students in North Carolina and Maryland. After graduating from Howard University at 19, Flack turned to teaching after her father's death. She move from the front of the classroom to centerstage a few years later.
Gene Simmons: Gene Simmons, also known as "The Demon" during the prime of his career as a bassist with rock band Kiss, is known for his controversial lifestyle, performance style and music. But before he made it big, Simmons taught sixth grade in Harlem, New York. Simmons also speaks four languages: Hungarian (his mother's native language), Hebrew, English and German.
Billy Crystal: Comedian, actor and presenter Billy Crystal is known for such pop culture classics as When Harry Met Sally, City Slickers, and Saturday Night Live. Crystal grew up in Long Beach, NY, and attended college at Marshall University in West Virginia, Nassau Community College on Long Island, and New York University, from which he graduated with a B.F.A. While on Long Island, Crystal worked as a substitute teacher for classes like girls' gym.
Who did I miss? What celebrity qualities do you think teacher possess? Share in the comments section!
Edited version republished with permission of Amber Johnson.
Original source: 10 Surprising Celebs Who Used to Be Teachers
Additional Reference: Famous Celebrity Teachers
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text:
Sources and Methods
A blog of the History and Public Policy Program
India's Nuclear Policy: China, Pakistan, and Two Distinct Nuclear Trajectories
By Harsh V. Pant & Yogesh Joshi on May 14, 2018
Nuclear HistoryAsiaChina MainlandIndia
In May 1998, India and Pakistan conducted the twentieth century's last nuclear tests.
The events of May 1998 were seminal insofar as they created a triangular matrix of nuclear weapon states in South Asia – India, China and Pakistan – which shared not only disputed territorial borders but also deep historical animosities vis-à-vis one another. However, the India-China nuclear equation is much more stable than the India-Pakistan nuclear dyad. India and China have never issued a veiled or overt nuclear threat to each other. Nuclear blackmail does not figure in their military strategies vis-à-vis one another.
On the other hand, all military crises between India and Pakistan have suffered from a nuclear overhang. If Pakistan's penchant for nuclear risk-taking is apparent in its nuclear doctrine of full spectrum deterrence, India has been equally vocal in calling Pakistan's bluff. It stands to reason, therefore, that the India-Pakistan rather than the India-China nuclear dyad is a nuclear flashpoint.
The difference in these two nuclear equations is not simply a matter of their nuclear and military strategies. It is equally a product of their unique nuclear histories.
India's nuclear history disproves the linear model of nuclear weapons proliferation where insecurity vis-à-vis a bigger and hostile nuclear power is the principal source of a state's motivation to pursue nuclear weapons (as was the case with the Soviet Union, China and to a certain extent both the United Kingdom and France). Pakistan rather than China was the most important reason for India to go nuclear.
India's reactions to the Chinese nuclear threat in the 1960s and the Pakistani nuclear threat in the 1980s were markedly different. The Chinese nuclear test in October 1964 presented India with its first nuclear adversary. It took India a decade to conduct its first nuclear test in May 1974. However, by that time, Indian decision-makers were convinced that China "will not use nuclear weapons against India." After the 'peaceful nuclear explosion' of 1974, Indian decision-makers abstained from weaponizing its nascent nuclear weapons capability nor did they seek a nuclear weapon status. Until the mid-1980s, India practiced a policy of 'nuclear refrain.'
As the Pakistani nuclear program matured into an existential threat, India prepared for 'catalytic' response. In 1988, India decided to acquire a deliverable nuclear arsenal. Within a decade, it had not only operationalized the first leg of its nuclear delivery capability based on fighter aircrafts but had also declared itself a nuclear weapon state with a series of nuclear tests in May 1998.
This 'differential response' can only be understood by taking into account not only the variation in India's perceptions of the Chinese and the Pakistani nuclear threat, but also the methods which Indian decision-makers employed to counter them.
China's acquisition of a nuclear weapon capability did not fundamentally threaten Indian security as was the case with Pakistani nuclear weapons.
First, rather than perceiving Chinese nuclear capability as a direct threat, New Delhi situated China's quest for nuclear weapons in the great power nuclear rivalry of the Cold War. The threat perception vis-à-vis Pakistan was entirely different: Islamabad's nuclear drive was considered as an existential threat given its historical penchant for revisionism in South Asia.
Second, in the aftermath of the 1962 War, India learned that a robust conventional defense against China would help preserve the status quo on the border. Nuclear weapons were not essential for maintaining this territorial status quo. Even today, Indian military strategy vis-à-vis China remains largely conventional. Pakistan, on the other hand, had consistently challenged the territorial status quo even when though it was a relatively inferior military power in the India-Pakistan dyad. In New Delhi's calculus, therefore, nuclear weapons in Pakistani possession would have only exacerbated the latter's inclination for a military resolution of the territorial conflict.
This difference in New Delhi's threat perceptions of China and Pakistan is borne out in various military crises on the Sino-Indian and Indo-Pakistani border. Neither China nor India have ever issued a veiled or overt nuclear threat to each other in any of their military crises along the Himalayan border; it has remained purely conventional. However, since the Brasstacks crisis of 1986-1987, Pakistan has always leveraged its nuclear arsenal to influence any Indian decision to pursue conventional operations across the border.
India's threat perceptions of Chinese and Pakistani nuclear capability differed substantially, and so have the methods which India employed to counter them.
Against China, India primarily relied on implicit nuclear security guarantees from the two great powers during the Cold War, the US and the Soviet Union. New Delhi's calculation was simple but profound: any use or threat of use of nuclear weapons by Beijing against India involved a risk of nuclear retaliation from the great powers. This minimal risk rather than absolute credibility of great power response was sufficient to deter the Chinese decision-makers. This assumed both rationality and restraint on Beijing's part.
India's historical experiences vis-à-vis Pakistan however betrayed both these assumptions. Pakistani nuclear capability was solely directed against New Delhi. Given its penchant for risk-taking, it also posed a fundamental threat to the Indian state. Therefore, to deter Pakistan, an indigenous nuclear capability was a must.
India's threat perceptions vis-à-vis China and Pakistan explain the gradual evolution of India's nuclear weapons program in the first two decades after the Chinese nuclear tests in 1964 and the catalytic response to the Pakistani nuclear program thereafter. Not all nuclear adversaries are the same after all.
Two decades after Pokhran-II, New Delhi's nuclear policy is still coming to terms with two different kinds of adversaries on its frontiers, despite occasional talks of a 'two front' war.
Harsh V. Pant and Yogesh Joshi are co-authors of a forthcoming book, Indian Nuclear Policy, by Oxford University Press. This piece appeared previously on the website of the Observer Research Foundation.
Harsh V. Pant
Distinguished Fellow and Head of Strategic Studies, Observer Research Foundation
Yogesh Joshi
Post-Doctoral Nuclear Security Fellow, Center for International Security and Cooperation (CISAC), Stanford University
New Zealand's Anti-Nuclear Legislation and the United States in 1985
By Henry Cronic on August 26, 2020


Keeping it in check is an urgent priority: Even though Asia is mostly known for rice growing, maize is an important staple crop and a crucial source of feed for poultry and livestock.
Organic farmers don't use the usual farm chemicals so they can sell the waste corn stalks as cattle fodder. Spraying with biomaterials like fungi and threadworms that may be parasites of the worms can help, but some fields still lost about a third of the first baby corn harvest.
The best-case scenario, says Fredrix, would be farmers successfully scouting for and finding the worms early on, so they can keep them under control. The FAO has developed an app that teaches the basics of how to find and deal with fall armyworms.
"This new pest needs to be managed in years to come and farmers will need to develop skills to do so in a sustainable manner," Fredrix said.
At first glance, the 6.5-foot-high stalks look vigorous, with good-sized ears that are nearly picking size. But a closer look shows holes in the stalks where the worms have worked their way inside, munching ear after ear into yellowish-brown mush.
The family has other sources of income, a small eco-hostel set in their tropical garden. But income from that is unstable and the corn crop will be missed, Yodsapon says.
A good harvest from the field would have netted 2,000-3,000 baht ($65-$100), he says. He expects no income from this one and spraying repeatedly to try to vanquish the worm could triple his costs.


a little danish squirrel stove by morso, custom cottage-style doors by our friend + contractor sam koch.
our friend ben gilliom made the concrete countertops, which seemed like the most utilitarian option for a kids' craft space. paint color on cabinets and wainscoting is "gatherings" by magnolia.
faucet is from ikea. sink basin was a $50 amazon find.
my sewing + office space is to the left. i'll store two sewing machines + two printers beneath the desk, tucked away behind linen curtains.
sam is building carriage doors that will open out (and have windows up top). this area will have a sofa and most of our homeschool things.


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Xanthisma spinulosum is a highly variable species ranging from four inches to three feet tall; shorter plants often are dense with an abundance of leaves and showy flowers and taller plants are often sparsely leaved and sparsely flowered; the plants can be completely herbaceous or woody at the base; flower heads can be less than a half inch wide to over an inch wide; leaves and stems can be smooth or densely hairy; leaves can be deeply cut or entire; and the plant can be found blooming in the spring, summer, or fall. Xanthisma spinulosum grows in dry, sandy hot openings and in cooler woodlands. It sometimes grows in large colonies over many hundreds of square feet and at other times it is scattered widely.
This multiplicity of characteristics gives rise to nomenclatural confusion: Intermountain Flora and Utah Flora call the plant Haplopappus spinulosus. Intermountain notes that "this widespread species is divisible, with some difficulty, into several geographic varieties..., variety paradoxus [shorter plants] and variety gooddingii [taller plants]".
Colorado Flora calls this plant Machaeranthera pinnatifida and Weber states that it is a "plant with many races" but Weber does not provide characteristics or names for these races.
The Synthesis of the North American Flora and The Flora of North America indicate that Machaeranthera pinnatifida should be called Xanthisma spinulosum, as proposed in 2003 by Morgan and Hartman. FNA recognizes five varieties of Xanthisma spinulosum: gooddingii, paradoxum, glaberrimum, chihuahuanum, and spinulosum.
FNA indicates that Xanthisma spinulosum
"is an exceedingly complex, variable taxon. It is doubtful that any 'absolute' key can be constructed so as to recognize unequivocally the infraspecific entities proposed here. Instead, we have composed a key that attempts to recognize character trends that serve, in combination, to distinguish a given taxon from another, but the occasional (if not frequent) specimen will be found that keys to a given regional variety but belongs to yet another. However, if one accepts the exception and recognizes our account as an attempt to portray variable, regional populations and not as a key to individuals, then little trouble should be experienced in pinning a varietal name on this or that collection".
Finally, Flora of the Four Corners Region indicates that only two varieties are found in the Four Corners region, Machaeranthera pinnatifida variety gooddingii and variety paradoxum.
Some of the overlap in names does seem to indicate that the various authors are referring to the same plants, but range maps for some plants with similar names show that different plants are probably being discussed.
Whatever the name, this is an attractive plant. I especially like the high desert growth form shown in the first six photographs below.
Xanthisma is from the Greek "xanth" meaning "yellow".
Xanthisma spinulosum. Synonyms: Machaeranthera pinnatifida, Haplopappus spinulosus. (Woolly Goldenweed)
Asteraceae (Sunflower Family)
Semi-desert. Openings, hillsides. Spring, summer, fall.
Above and left: Mc Elmo Canyon, Canyons of the Ancients National Monument, April 27, 2005 and April 22, 2016.
The plant at left (and in the next three photographs below) has numerous, thin, deeply lobed, intermeshed leaves. Last year's dried flower stalks are often present. Once new flowers appear, the plant becomes a lovely, vibrant mass of gold floating on top of silvery green leaves. Flowers are over an inch across and the plant is quite hairy.
This plant is probably what the Synthesis and The Flora of North America call Xanthisma spinulosum variety spinulosum and what Flora of the Four Corners Region calls Machaeranthera pinnatifida variety gooddingii. (See the discussion about names at the top of this page.)
"Machaer" is Greek for "sword" and "anther" is Greek for "flower". Together they refer to the sword shape of the anther tips.
In 1804 Meriwether Lewis collected the first specimen of this species on the prairies of what is now South Dakota.
The Xanthisma genus was named by Augustin de Candolle but in 1836 he put the species shown on this page in the genus Haplopappus (H. spinulosus). In 1814 Pursh named the species Amellus spinulosus, in 1841 Nuttall named it Dieteria spinulosa, in 1964 Shinners named it Machaeranthera pinnatifida, and in 2003 Morgan and Hartman named it Xanthisma spinulosum.
Mc Elmo Canyon, Canyons of the Ancients National Monument, March 27, 2005.
The silvery appearance is due to innumerable soft, short, fine hairs.
Mc Elmo Canyon, Canyons of the Ancients National Monument, April 27, 2005 and
Lower Cross Canyon, May 21, 2016.
The symmetry of the plant is apparent in the top photograph at left. Also note the other Xanthisma spinulosum plants at top left and top center. It is common to have scores of plants near each other.
When flowers fade they are followed by a variety of lovely shapes and colors as seeds develop.
Yellowjacket Canyon, Canyons of the Ancients National Monument, October 27, 2007.
This sprawling plant with stems up to fourteen inches long is a different subspecies of Xanthisma spinulosum from the plants shown above. As this and the two photographs below indicate, flower heads are smaller and cylindrical rather than spherical (see the flower heads of the other subspecies above); stems and leaves have scattered fine hairs rather than a dense woolliness; and although a few leaves are deeply cut, most have smooth (entire) margins. The leaves of both plants are tipped in small, stiff hairs.
This plant is probably what the Synthesis and The Flora of North America call Xanthisma spinulosum variety paradoxum and what Flora of the Four Corners Region calls Machaeranthera pinnatifida variety pinnatifida.
Phyllaries of both subspecies shown on this page are in 5-7 rows. The phyllaries at left are somewhat glandular (sticky) as you can see by the dots of dirt adhering to them.
The leaf shape at upper left is the same as the leaf shape on the subspecies at the top of the page, but most other leaves on the subspecies pictured at left have no indentations. Leaves of either shape have very fine hairs (especially noticeable on their edges).
Range map for Xanthisma spinulosum


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Tag Archives: Jason Bateman
TV's Greatest Straight Men (and Women)
Posted on July 6, 2012 by jimbo
With the passing of Andy Griffith earlier this week, it seems only fitting to devote a List to the best straight men and women in TV history. It's such a delicate balance: being strong enough comedically to stand up to your zany co-stars, yet grounded enough for the audience to relate to you.
HAL LINDEN
BARNEY MILLER (1974-82)
At the center of one of my favorite shows, Linden's compassionate cop, Barney Miller, presided over an absurd, smart, feisty circus of humanity. He was the moral compass, but with enough personal quirks to keep things interesting.
ARRESTED DEVELOPMENT (2003-06)
Bateman gave a virtuoso performance as the put-upon Michael Bluth in "Arrested Development." His timing is fantastic, every episode, every line. Plus, he had some great scenes with Attorney Bob Loblaw.
GREEN ACRES (1965-71)
Of course, no character was more put-upon than Oliver Wendell Douglas, played by Eddie Albert. This surreal sitcom about city folks living in the country had daffy Eva Gabor, sneaky Pat Butram and a brainy pig among the ensemble. And in the middle of it all was Albert, who gamely tried to regain control – but thankfully never did.
THE MARY TYLER MOORE SHOW (1970-77)
This one is a tough call, particularly since I'm not including Dick Van Dyke on this List. Mary's character was incredibly funny in her own right, but I always felt she played it best when she was reacting to Lou, Ted, Rhoda, etc.
NEWSRADIO (1995-99)
Here was a terrific show that featured a great cast, with Foley as the straight-arrow guy running the radio station. He was witty, while being at his wit's end. Not a bad game plan when your co-stars include Phil Hartman and Andy Dick.
DAN ROWAN
ROWAN & MARTIN'S LAUGH-IN (1967-73)
Dan Rowan had one job to do on "Laugh-In" and he did it exceedingly well. Amid all the groovy goings-on, with sketches coming and going at lightning speed, Rowan simply looked tan and dapper while cleanly setting up punchline after punchline. You can look it up in your Funk & Wagnalls. I dare you.
THE GEORGE BURNS AND GRACIE ALLEN SHOW (1950-58)
Although a lot of their material is now dated, it's amazing how much of the Burns & Allen formula still works in today's scripted TV comedies. Gracie had all the best lines, of course. But George was smooth as silk.
NORTHERN EXPOSURE (1990-95)
Rob Morrow sort of came out of nowhere as Joel, the New York City doctor stuck in a crazy Alaskan town. The concept burned itself out after a few years, but it was great while it lasted. Joel's frustration kept the show on its toes.
ISABEL SANFORD
THE JEFFERSONS (1975-85)
It took a very strong personality to hold the screen with Sherman Hemsley as George Jefferson. Enter Isabel Sanford as Louise. By turns she would yell, plead, cajole and guilt trip George to keep pace. It was a classic sitcom combination.
GEORGE FENNEMAN
YOU BET YOUR LIFE (1950-61)
Fenneman is a unique figure in television history. As the announcer for the Groucho Marx quiz show, "You Bet Your Life," he was verbally pummeled from here to Tuesday by one of the funniest human beings who ever lived. Groucho could be merciless, and George routinely was left speechless. Oddly enough, it made Fenneman all the more endearing.
THE OFFICE (2005-PRESENT)
As Jim Halpert, Krasinski carries his show's "regular guy" role without the benefit of being the lead character. It's a tricky thing to calibrate, but Krasinski has been all over it from the first episode. He gets his own jokes in, too.
THE BOB NEWHART SHOW (1972-78)
How great was this dude as a straight man? Well, he often was HIS OWN straight man, in scenes on the phone. No one does a thoughtful stammer like Mr. Newhart. Perfection.
ALICE (1976-85)
This wasn't Gilbert & Sullivan, but it was solid comedy. Lavin played things straight as working class mom Alice, adding a touch of drama here and an earthy zinger there. And she held her own around characters willing to shout "Kiss my grits!" at the drop of a hat.
THE TONIGHT SHOW WITH JOHNNY CARSON (1962-92)
Gotta give it up to the most durable sidekick in talk show history. Ed was Carson's Rock of Gibraltar: quick with a laugh, a comment or a helping hand. He was a joke target when need be, as well. And the show was never funnier than when Ed got in a funny line at Johnny's expense.
THE BEVERLY HILLBILLIES (1962-71)
Ebsen's Jed Clampett had a way of seeming smart and not-so-smart at the same time. He never, ever veered out of character. One of his main responsibilities was to bear witness to the twin whirlwinds of Granny and Jethro, which he did with his trademark country squint.
THE DEAN MARTIN COMEDY HOUR (1965-74)
Dino went from being Jerry Lewis' straight man in movies to being everybody's straight man on television. He was superb at it. Effortlessly, he made singers, actors and comedians who came on his show look good. Here was a guy who was the butt of nearly every joke, yet he was clearly the coolest guy in the room.
DEMOND WILSON
SANFORD AND SON (1972-77)
Absolutely an unsung hero of TV sitcoms. Wilson was the all-too-human counterpoint to Redd Foxx, who gloriously chewed the scenery like it was a danish from the craft service table. Without Wilson, the show doesn't work nearly as well.
I DREAM OF JEANNIE (1965-70)
Looking back, maybe this wasn't the most enlightened comedy premise in the world: an astronaut finds a bottle containing a beautiful genie who becomes his servant. But that's not Hagman's fault. He did every ridiculous facial contortion known to man during his years as Major Nelson, as if he knew most people would later know him as J.R. Ewing.
THE BIG BANG THEORY (2007-PRESENT)
He's a nerd's version of a straight man, but it still applies. Galecki expertly reels in audience sympathy while lobbing up softballs for Jim Parsons to knock out of the park. Well done, sir.
THE STEVE ALLEN SHOW (1956-60)
A TV pioneer, Steve Allen was a genius at conducting "interviews" with his stable of comic actors. They included Don Knotts, Louis Nye and Tom Poston. They'd show up as crazy characters and Allen would gently skewer them with puns and wry observations. David Letterman and Jon Stewart have been doing a version of this for years, but Allen got there first.
HAPPY DAYS (1974-84)
Though not a huge fan of "Happy Days," I very much respected the way Ron Howard gave the show a likable, viable central character. I also admired the way he didn't quit the show when the Fonzie character took over the world.
THE MUNSTERS (1964-66)
Yes, it is possible to be a great straight woman while dressed as a lady vampire. Despite her appearance, De Carlo basically played a suburban housewife dealing with a sarcastic father and a childish husband. They got most of the one-liners and she was the rational, head of the household.
JUDD HIRSCH
TAXI (1978-83)
Hirsch was the voice of reason on "Taxi." Sometimes that meant giving a pep talk; sometimes it meant taking someone to task. Usually, it meant trying not to crack up while the likes of Danny DeVito and Andy Kaufman brought the funny.
F TROOP (1965-67)
Very little about "F Troop" made sense, except that it was disarmingly funny. Tucker played his Sgt. O'Rourke like he was the lead in "The Music Man," which was exactly the right approach.
DICK YORK/DICK SARGENT
BEWITCHED (1964-72)
The wonderful duo of Darrins did their level best to hold their heads high, no matter what incantation or evil twin scenario they were up against. That's what happens when you marry a witch with a big family.
THE ABBOTT AND COSTELLO SHOW (1952-53)
Abbott was a legendary straight man from the stage and the big screen, but he also qualifies for The List because of his popular, but brief TV series with the great Lou Costello. Abbott was like a professional hit man of comedy. He was sharp and he was confident and you almost never knew he was there.
THE ANDY GRIFFITH SHOW (1960-68)
Thank goodness for Andy Griffith. His show was heartbreaking, human and hilarious. At its best, it was as funny as any TV program before or since, and it did so with a firm belief that humor could come out of everyday life and common foibles.
Is it possible I've missed anyone? Add them to The List!
Posted in TV and tagged Andy Griffith, Bob Newhart, Dean Martin, Demond Wilson, Isabel Sanford, Jason Bateman, Judd Hirsch, Mary Tyler Moore, Ron Howard, Steve Allen, The Office, TV. Bookmark the permalink.


Brown channa is commonly is used in curries and used to make besan flour. It is consumed boiled, sprouted and fried.
Brown channa is a rich source of mineral such as iron, potassium, magnesium, calcium, phosphorous and importantly, molybdenum. Copper, zinc and manganese are present in traces. Vitamins A, C, E and K also constitute as one of the nutrients. It possesses antioxidant properties and aids weight-loss as it helps reduce cholesterol levels and relieve "constipation", and also promotes "digestive health" and healthy skin.