text:
import Popper from 'popper.js'
import BvEvent from '../utils/bv-event.class'
import KeyCodes from '../utils/key-codes'
import warn from '../utils/warn'
import { closest, contains, isVisible, requestAF, selectAll } from '../utils/dom'
import { isNull } from '../utils/inspect'
import clickOutMixin from './click-out'
import focusInMixin from './focus-in'

// Return an Array of visible items
function filterVisibles(els) {
  return (els || []).filter(isVisible)
}

// Dropdown item CSS selectors
const Selector = {
  FORM_CHILD: '.dropdown form',
  ITEM_SELECTOR: ['.dropdown-item', '.b-dropdown-form']
    .map(selector => `${selector}:not(.disabled):not([disabled])`)
    .join(', ')
}

// Popper attachment positions
const AttachmentMap = {
  // Dropup left align
  TOP: 'top-start',
  // Dropup right align
  TOPEND: 'top-end',
  // Dropdown left align
  BOTTOM: 'bottom-start',
  // Dropdown right align
  BOTTOMEND: 'bottom-end',
  // Dropright left align
  RIGHT: 'right-start',
  // Dropright right align
  RIGHTEND: 'right-end',
  // Dropleft left align
  LEFT: 'left-start',
  // Dropleft right align
  LEFTEND: 'left-end'
}

// @vue/component
export default {
  mixins: [clickOutMixin, focusInMixin],
  provide() {
    return {
      bvDropdown: this
    }
  },
  props: {
    disabled: {
      type: Boolean,
      default: false
    },
    text: {
      // Button label
      type: String,
      default: ''
    },
    html: {
      // Button label
      type: String
    },
    dropup: {
      // place on top if possible
      type: Boolean,
      default: false
    },
    dropright: {
      // place right if possible
      type: Boolean,
      default: false
    },
    dropleft: {
      // place left if possible
      type: Boolean,
      default: false
    },
    right: {
      // Right align menu (default is left align)
      type: Boolean,
      default: false
    },
    offset: {
      // Number of pixels to offset menu, or a CSS unit value (i.e. 1px, 1rem, etc)
      type: [Number, String],
      default: 0
    },
    noFlip: {
      // Disable auto-flipping of menu from bottom<=>top
      type: Boolean,
      default: false
    },
    popperOpts: {
      // type: Object,
      default: () => {}
    }
  },
  data() {
    return {
      visible: false,
      inNavbar: null,
      visibleChangePrevented: false
    }
  },
  computed: {
    toggler() {
      const toggle = this.$refs.toggle
      return toggle ? toggle.$el || toggle : null
    },
    directionClass() {
      if (this.dropup) {
        return 'dropup'
      } else if (this.dropright) {
        return 'dropright'
      } else if (this.dropleft) {
        return 'dropleft'
      }
      return ''
    }
  },
  watch: {
    visible(newValue, oldValue) {
      if (this.visibleChangePrevented) {
        this.visibleChangePrevented = false
        return
      }

      if (newValue !== oldValue) {
        const evtName = newValue ? 'show' : 'hide'
        let bvEvt = new BvEvent(evtName, {
          cancelable: true,
          vueTarget: this,
          target: this.$refs.menu,
          relatedTarget: null
        })
        this.emitEvent(bvEvt)
        if (bvEvt.defaultPrevented) {
          // Reset value and exit if canceled
          this.visibleChangePrevented = true
          this.visible = oldValue
          // Just in case a child element triggereded this.hide(true)
          this.$off('hidden', this.focusToggler)
          return
        }
        if (evtName === 'show') {
          this.showMenu()
        } else {
          this.hideMenu()
        }
      }
    },
    disabled(newValue, oldValue) {
      if (newValue !== oldValue && newValue && this.visible) {
        // Hide dropdown if disabled changes to true
        this.visible = false
      }
    }
  },
  created() {
    // Create non-reactive property
    this._popper = null
  },
  deactivated() /* istanbul ignore next: not easy to test */ {
    // In case we are inside a `<keep-alive>`
    this.visible = false
    this.whileOpenListen(false)
    this.removePopper()
  },
  beforeDestroy() {
    this.visible = false
    this.whileOpenListen(false)
    this.removePopper()
  },
  methods: {
    // Event emitter
    emitEvent(bvEvt) {
      const type = bvEvt.type
      this.$emit(type, bvEvt)
      this.$root.$emit(`bv::dropdown::${type}`, bvEvt)
    },
    showMenu() {
      if (this.disabled) {
        /* istanbul ignore next */
        return
      }
      // Ensure other menus are closed
      this.$root.$emit('bv::dropdown::shown', this)

      // Are we in a navbar ?
      if (isNull(this.inNavbar) && this.isNav) {
        // We should use an injection for this
        /* istanbul ignore next */
        this.inNavbar = Boolean(closest('.navbar', this.$el))
      }

      // Disable totally Popper.js for Dropdown in Navbar
      if (!this.inNavbar) {
        if (typeof Popper === 'undefined') {
          /* istanbul ignore next */
          warn('b-dropdown: Popper.js not found. Falling back to CSS positioning.')
        } else {
          // for dropup with alignment we use the parent element as popper container
          let element = (this.dropup && this.right) || this.split ? this.$el : this.$refs.toggle
          // Make sure we have a reference to an element, not a component!
          element = element.$el || element
          // Instantiate popper.js
          this.createPopper(element)
        }
      }

      this.whileOpenListen(true)

      // Wrap in nextTick to ensure menu is fully rendered/shown
      this.$nextTick(() => {
        // Focus on the menu container on show
        this.focusMenu()
        // Emit the shown event
        this.$emit('shown')
      })
    },
    hideMenu() {
      this.whileOpenListen(false)
      this.$root.$emit('bv::dropdown::hidden', this)
      this.$emit('hidden')
      this.removePopper()
    },
    createPopper(element) {
      this.removePopper()
      this._popper = new Popper(element, this.$refs.menu, this.getPopperConfig())
    },
    removePopper() {
      if (this._popper) {
        // Ensure popper event listeners are removed cleanly
        this._popper.destroy()
      }
      this._popper = null
    },
    getPopperConfig() {
      let placement = AttachmentMap.BOTTOM
      if (this.dropup) {
        placement = this.right ? AttachmentMap.TOPEND : AttachmentMap.TOP
      } else if (this.dropright) {
        placement = AttachmentMap.RIGHT
      } else if (this.dropleft) {
        placement = AttachmentMap.LEFT
      } else if (this.right) {
        placement = AttachmentMap.BOTTOMEND
      }
      let popperConfig = {
        placement,
        modifiers: {
          offset: { offset: this.offset || 0 },
          flip: { enabled: !this.noFlip }
        }
      }
      if (this.boundary) {
        popperConfig.modifiers.preventOverflow = { boundariesElement: this.boundary }
      }
      return { ...popperConfig, ...(this.popperOpts || {}) }
    },
    whileOpenListen(open) {
      // turn listeners on/off while open
      if (open) {
        // If another dropdown is opened
        this.$root.$on('bv::dropdown::shown', this.rootCloseListener)
        // Hide the dropdown when clicked outside
        this.listenForClickOut = true
        // Hide the dropdown when it loses focus
        this.listenForFocusIn = true
      } else {
        this.$root.$off('bv::dropdown::shown', this.rootCloseListener)
        this.listenForClickOut = false
        this.listenForFocusIn = false
      }
    },
    rootCloseListener(vm) {
      if (vm !== this) {
        this.visible = false
      }
    },
    show() {
      // Public method to show dropdown
      if (this.disabled) {
        return
      }
      // Wrap in a requestAnimationFrame to allow any previous
      // click handling to occur first
      requestAF(() => {
        this.visible = true
      })
    },
    hide(refocus = false) {
      // Public method to hide dropdown
      if (this.disabled) {
        /* istanbul ignore next */
        return
      }
      this.visible = false
      if (refocus) {
        // Child element is closing the dropdown on click
        this.$once('hidden', this.focusToggler)
      }
    },
    toggle(evt) {
      // Called only by a button that toggles the menu
      evt = evt || {}
      const type = evt.type
      const key = evt.keyCode
      if (
        type !== 'click' &&
        !(
          type === 'keydown' &&
          (key === KeyCodes.ENTER || key === KeyCodes.SPACE || key === KeyCodes.DOWN)
        )
      ) {
        // We only toggle on Click, Enter, Space, and Arrow Down
        /* istanbul ignore next */
        return
      }
      if (this.disabled) {
        /* istanbul ignore next */
        this.visible = false
        /* istanbul ignore next */
        return
      }
      this.$emit('toggle', evt)
      if (evt.defaultPrevented) {
        // Exit if canceled
        return
      }
      evt.preventDefault()
      evt.stopPropagation()
      // Toggle visibility
      this.visible = !this.visible
    },
    click(evt) {
      // Called only in split button mode, for the split button
      if (this.disabled) {
        /* istanbul ignore next */
        this.visible = false
        /* istanbul ignore next */
        return
      }
      this.$emit('click', evt)
    },
    onKeydown(evt) {
      // Called from dropdown menu context
      const key = evt.keyCode
      if (key === KeyCodes.ESC) {
        // Close on ESC
        this.onEsc(evt)
      } else if (key === KeyCodes.DOWN) {
        // Down Arrow
        this.focusNext(evt, false)
      } else if (key === KeyCodes.UP) {
        // Up Arrow
        this.focusNext(evt, true)
      }
    },
    onEsc(evt) {
      if (this.visible) {
        this.visible = false
        evt.preventDefault()
        evt.stopPropagation()
        // Return focus to original trigger button
        this.$once('hidden', this.focusToggler)
      }
    },
    // Document click out listener
    clickOutHandler() {
      if (this.visible) {
        this.visible = false
      }
    },
    // Document focusin listener
    focusInHandler(evt) {
      // If focus leaves dropdown, hide it
      if (
        this.visible &&
        !contains(this.$refs.menu, evt.target) &&
        !contains(this.$refs.toggle, evt.target)
      ) {
        this.visible = false
      }
    },
    // Keyboard nav
    focusNext(evt, up) {
      if (!this.visible || (evt && closest(Selector.FORM_CHILD, evt.target))) {
        // Ignore key up/down on form elements
        /* istanbul ignore next: should never happen */
        return
      }
      evt.preventDefault()
      evt.stopPropagation()
      this.$nextTick(() => {
        const items = this.getItems()
        if (items.length < 1) {
          /* istanbul ignore next: should never happen */
          return
        }
        let index = items.indexOf(evt.target)
        if (up && index > 0) {
          index--
        } else if (!up && index < items.length - 1) {
          index++
        }
        if (index < 0) {
          /* istanbul ignore next: should never happen */
          index = 0
        }
        this.focusItem(index, items)
      })
    },
    focusItem(idx, items) {
      let el = items.find((el, i) => i === idx)
      if (el && el.focus) {
        el.focus()
      }
    },
    getItems() {
      // Get all items
      return filterVisibles(selectAll(Selector.ITEM_SELECTOR, this.$refs.menu))
    },
    focusMenu() {
      this.$refs.menu.focus && this.$refs.menu.focus()
    },
    focusToggler() {
      let toggler = this.toggler
      if (toggler && toggler.focus) {
        toggler.focus()
      }
    }
  }
}


What we can expect in 2012.
This year is coming to a close but a lot of the food trends that rocked 2011 will be carried on for years to come. We here at Entrepreneur.com.ph can\\\'t wait for the 2012 and see what trends we will be seeing in the food industry. To help you prepare for the New Year, we asked Yummy.ph\\\'s Aimee Morales to give us a sneak peek of what 2012 has in store for us.
It's not New Year yet and we're already salivating on what's for dinner next year. See what's on the menu for 2012 and tell us which of these trends do you think will be a hit!
Tell us which trend do you think will be the biggest hit for 2012 in our comments section below.
Related article: The food trends that rocked 2011.


text:
Administrative State
Yes, the President Is Guilty of Obstruction
By Bob Calco
The shocking Justice Department inspector general report confirms our worst suspicions about the president's controversial action last year.
In firing former FBI Director James Comey, President Trump was indeed guilty of obstruction—obstruction of injustice. It wasn't particularly effective if his goal was to stop the Trump-Russia collusion narrative, but it had the effect ultimately of exposing the sinister political origins of that narrative.
Horowitz Pulls a Comey
However cleverly Comey attempts to sell his "higher loyalty" as something other than dedication to his own towering ego, he has it on his conscience, or failing that at least against his reputation, that his leadership profoundly undermined the credibility of the FBI.
No matter who won the 2016 election, Comey deserved to be fired precisely because of his law-unto-himself attitude and consistent pattern of putting his own career calculations over truth and justice in both his prosecutorial indiscretions and his managerial insubordinations. Although these political instincts likely would have served him well under a President Clinton, thank God his political calculations were off.
IG Horowitz appears unwilling to deliver the blunt-force trauma that the public needs on this front, perhaps because he and Comey (like so many in that elite and insular community) are friends who go way back. But the voluminous evidence he lays out to illustrate Comey's insubordination and his staff's hyper-partisanship speaks for itself. It makes the watered-down conclusion that clear and consistently expressed political bias among Comey's team and his own selfish scheming did not actually affect the investigations they ran feel more like gaslighting than an objective assessment of the situation.
Incredibly, in his critique of his friend Comey, Horowitz himself pulled a Comey. In effect, he says: "Nevermind all this overwhelming evidence of political bias I just laid out, no reasonable IG would find that the curious and shocking behavior detailed here was motivated by political bias." Or perhaps that was intended irony, in which case he deserves a Bob Newhart award for the most awkward deadpan joke ever.
The soft-pedaling and white-washing of this damning report of political malfeasance in the FBI isn't working. If anything, it's confirming the political corruption of an institution that to be respected must remain at all times verifiably above petty partisan politics.
The Only Thing We Have to Fear Is . . . the FBI?
Sadly, Americans now have solid, documentary reason to fear the FBI has become a kind of FSB or KGB, working in the service of the party of government (i.e., the Democrats).
If you worked on Trump's campaign even peripherally and without pay, shop at Walmart, or are among the 63 million smelly voters who voted for Donald Trump then you better make sure you never come to the attention of this bunch or that you learn every federal law on the books and never, ever make a mistake or cross an ethical line.
If you do, or can somehow be accused of having done so by anyone in the "correct class," then you can expect trouble. Perhaps you will awaken to a door breaking down at the crack of dawn with armed agents, or to the news that agents have looted the offices of your attorney to find something, anything against you, or throw you in jail without bail on unrelated charges until you agree to cooperate with them.
On the other hand, if you're a Hillary supporter or a Democrat running for high office, fret not. Even if you violate the Espionage Act, all your friends and associates will get blanket immunity in exchange for… silence; your attorneys will be invited to attend every meeting about your case they can possibly permit; and they'll even help you destroy some of the evidence, making it look like a bureaucratic snafu or something totally innocent. You won't even be named formally as a subject of the investigation.
If a new mountain of evidence against you surfaces, they'll sit on it as long as they can, and even if they're forced to announce it, they'll promptly declare that it doesn't change their view of your innocence. If you're running for office as a Democrat it may harm your election chances in a close race, but at least you'll stay out of prison and be able to go on book tours blaming them for your loss.
More Obstruction, Please!
So we can also say, based on this curiously self-contradictory report, that the President has a lot more obstructing of injustice to do before America can once again fully trust the DOJ and FBI to conduct even one non-partisan investigation of a political figure.
As the case studies of Clinton and Trump prove, the fate of any given political figure is entirely dependent on the personal moods, feelings, and political orientation of the lead investigators. And that can't stand.
Given what we know even now, calling the Trump-Russia Collusion narrative a mere "hoax" trivializes the grave nature of the injustice occurring right before our eyes.
Mueller's corrupt, hyper-partisan and arguably unconstitutional investigation is a clear and present danger to our constitutional republic. It owes its very origins to an implausible espionage narrative that was completely fabricated by high-level Obama administration politicos across several agencies and whose sole purpose was and is to use endless innuendo and leaks under the secret veil of an endless investigation into an unproven non-crime in order to affect the political assassination of Donald J. Trump and to disenfranchise the 63 million Americans who voted for him.
Time For Swift Action
A world where Hillary Clinton is allowed not merely to walk free but to run for president after losing state secrets, destroying evidence in a clear-cut case of espionage act violations, all while being credibly suspected of engaging in various pay-to-play schemes through a sham charity while Secretary of State, is a scary one indeed.
It is made even more frightening when you realize that Trump faces possible impeachment for justly firing an insubordinate FBI director over his unwillingness to say publicly what he told the president privately about crazy, unproven accusations of his supposed collusion with a foreign power (again, notwithstanding we have a paper trail of Hillary and the DNC and even Obama's PAC paying foreign spies to generate those unproven accusations and the FBI's enthusiasm in using them as evidence for a FISA warrant).
The sum total of this situation so far is that the rule of law seems barely on life support.
Justice isn't supposed to be this hard. If Sessions, Rosenstein, and Wray can't step up with something more than "unconscious bias" training and a few sternly worded reprimands, it's time for a thorough BleachBit of the upper floors of both the DOJ and the FBI. Starting with them.
Photo credit: Jabin Botsford/The Washington Post via Getty Images
About Bob Calco
Bob Calco is a verified Deplorable who lives in Tampa, Florida, with his wife, two sons, and four cats. A successful software architect by trade, and an inveterate polyglot and world traveler at heart, he studied political philosophy until it was clear that to pay bills he needed a marketable skill. His passion on issues related to trade and economics go back to his formative college days, when he learned to distrust, and independently verify, literally everything he was taught.
Photo: WASHINGTON, DC - JUNE 21 : President Donald J. Trump listens as others speak during a working lunch with governors in the Roosevelt Room of the White House on Thursday, June 21, 2018 in Washington, DC. (Photo by Jabin Botsford/The Washington Post via Getty Images)

redpajama_set_name: RedPajamaCommonCrawl


Rainmaker's VP of Revenue Analytics, Dan Skodol, shares with you a presentation he's showcased at HSMAI ROC & Hotel Data Conference this year.
Gain insight into the aspects of the human brain that drive our decision-making behavior.
Learn how to use bizarre psychological phenomena to your advantage, while remaining in your customers' good favor.
Be armed with specific tactics that you can start using immediately.
Fill out the form to the left to watch the presentation and download a copy of the slides.


text:
Lista protodiakonów Kolegium Kardynalskiego od początków XII wieku aż do teraz:

Do roku 1378

Okres 1378 do 1415 (Wielka schizma zachodnia)

Obediencja rzymska

Obediencja awiniońska

Obediencja pizańska

Od 1415

Bibliografia
 Hüls, Rudolf: Kardinäle, Klerus und Kirchen Roms: 1049–1130. Bibliothek des Deutschen Historischen Instituts in Rom. Max Niemeyer Verlag. Tybinga 1977,
 Zenker, Barbara: Die Mitglieder des Kardinalkollegiums von 1130 bis 1159. Dysertacja doktorska, Uniwersytet w Würzburgu 1964
 Werner Maleczek: Papst und Kardinalskolleg von 1191 bis 1216. Die Kardinäle unter Coelestin III. und Innocenz III. Verlag der Österreichischen Akademie der Wissenschaften. Wiedeń 1984, .
 Martin Souchon: Die Papstwahlen in der Zeit des grossen Schismas, Vol. 1-2, Verlag von Benno Goeritz 1898-1899
 Konrad Eubel, P. Gauchat, R. Ritzler, Hierarchia Catholica, vol. I-VI, Padwa-Ratyzbona 1913-1960
 College of Cardinals GCatholic.org

Kardynałowie

redpajama_set_name: RedPajamaWikipedia


Students love making predictions. And building anticipation for what might happen next is an easy way to make reading fun.
Before Reading- What will the book be about? Who will the main characters be? What big events will happen in the story? Where will the events take place?
During Reading- How will a character respond to the problem? Who might help the character solve the problem?
After Reading- Which predictions were correct? Which predictions were incorrect? Why were the predictions incorrect?
Once students have mastered answering these types of simple questions. It's time to take it up a notch. For predictions to be a truly useful comprehension tool, students need to base their predictions on evidence from the text. So, for example, if your students predict that the main character will meet an alien, ask the students to point to the evidence. If there's a spaceship hovering ominously above the main character, great, the aliens are probably coming! If not, encourage them to take a closer look at the clues left by the author.
Making predictions will quickly become one of your students' favorite reading activities. Here are four ways to help them make better predictions to improve comprehension.
Teach students to support their predictions with evidence from the text by building Prediction Puzzles.
Start by writing a prediction on a puzzle piece. Then help students gather clues from the pictures or the text that support the prediction. Write each clue on a puzzle piece. Then put the puzzle together. By providing readers with a visual representation of how predictions are tied to the text, they will begin to make more logical predictions.
Or, try using this Prediction Puzzle printable.
Who doesn't love a little friendly competition? Before reading or during reading, ask students to stop and make a prediction (e.g., what a character will do, how a character will react, how a situation will turn out). Once you've brainstormed some predictions, have students vote on which one they think will be true. Have students sit in "teams" according to their predictions.
Then read the story and see which group was right. You could let the winning group do a happy dance, or you could give them paper crowns and let them be Prediction Kings and Queens for the remainder of the day.
Bonus: This can be more than just a language arts activity! Use prediction teams to make predictions about the outcome of a science experiment. Or have students make predictions about daily or school events (e.g., which class will collect the most cans for a can drive).
Predicting is a natural link to cause and effect. Use what students know about making predictions to identify cause and effect within a text. For example, ask: What caused this character to be so upset? What do you think will happen if the character misses the bus—will it have a positive or negative effect?
This post is part of a 10-part series. In each post, we share ideas for making comprehension strategy practice more engaging.
Click here to see tips and activity ideas for the other reading comprehension strategies.
Help another teacher by sharing your tips for making predictions with your students.


I'm having some troubles with a slider, that isn't using the 'slide'effect in IE. In other browsers it's working fine (chrome, firefox etc).
What version of IE are you on? Try turning the compatibility on and see if it works.
Yeah I know... They say IE could die within 3 years, i'll hope it disappear quicker.
lol. Rest in Pieces IE. Amen!


On the ninth day of the Traverse Family epic trip, they stop by Frogmore Plantation. No the did not see any frogs, but they did see plenty of cotton.
The bots learned that the first step in cotton was planting the cotton and watching it grow.
Then you trick Posey into removing all the seeds from the cotton.
Lastly, the cotton is bailed up and ship out to make t-shirts and other items. Did you know they make cotton oil for cooking?


Take a deep dive into a topic or era. Exhibitions are groups of items curated and interpreted by librarians, historians, and other experts. Join a civil rights protest. Marvel at the engineering of an early car. Get a glimpse of what life was like for people throughout history.


text:
« Going for Gold XXXI | Main | Flashback: Arctic Monkeys at First Avenue, Minneapolis (25 Sep 2009) »
Flashback: Ladyhawke at Fine Line Music Cafe, Minneapolis (21 Sep 2009)
Ladyhawke Setlist
Professional Suicide
Dusk Till Dawn
Manipulating Woman
Love Don't Live Here
Danny & Jenny
Better Than Sunday
Back of the Van
My Delirium
Ladyhawke is back with her third studio album Wild Things set for release on June 3 via Polyvinyl Records. If you can't wait to hear it - the good news is that if you buy it on 180-Gram Yellow vinyl or CD, directly from Polyvinyl …
In 2009 popular celebrity blogger Perez Hilton organized a tour of his favorite bands – including Ida Maria and rising star Ladyhawke. Unfortunately, Ida Maria, called in sick at the eleventh hour. Ladyhawke was now the headliner, and local booking for Fine Line Music Café quickly called in some last-minute support acts. What's interesting was that ticket holders were allowed to get a refund on their tickets, and the show was opened as a free show. Unfortunately, once you checked in the venue, you were not allowed to leave (and come back).
Pip Brown, aka Ladyhawke, is the cute-shy unsuspecting headliner for Perez Hilton tour, when it was announced that, due to illness, that main headliner, Ida Maria will not be playing the Fine Line Cafe in Minneapolis 9/22/09.
The good news was that the show was free, so it was still a pretty decent turnout, lots of young underage students (you can tell by the sea of black "X" on the back of their hands. It also seems to me that this night is possibly the best night of their lives.
Sliimy opened up the whole thing. I thought he was joking when he said he was French ("with a bad English accent"), but he was telling the truth. He also reminded me of Prince, but with glasses and a gold lamé jacket. I expected Sliimy (incidentally pronounced "slimmy", not "slimey") to sound like Lady Gaga or something fitting on Perez Hilton's record label - but they don't sound quite as poppy.
In replacement for Maria was Natalie Portman's Shaved Head, which you may know is a reference to V For Vendetta. They are a young, energetic band, singing songs about "Sophisticated Side Ponytail" and "Holding Hands in the Shower" (and possibly with your daughter). You half expect them to burst out singing, "I'm on a motherfucking boat".
Speaking of energetic, Semi Precious Weapons are unforgettable. I first saw this band at SXSW but didn't know who they were. I thought to myself, "this band is going to be huge", never thinking I would actually see them randomly at some point in the future. They are definitely a band to catch live, the bassist is insane!
When Ladyhawke took the stage, she's nervous happy. Between songs, she was at a loss of words, or when she did speak they sound nonsensical to me. However, she did manage to sneak in the story about seeing a man on a unicycle. She called it the dumbest thing she's ever seen. I suppose growing up in a small town in New Zealand, you don't see too many weird stuff like that.
Ladyhawke seems more comfortable when she's playing guitar, and as much as I like "Dust Till Dawn", it didn't sound as good as some of her other songs - particularly "Love Don't Live Here", "Paris Is Burning", and "My Delirium". Yes, the hits keeps on coming.
You'll be happy to know that Ladyhawke is continuing her tour through America until early October, before heading back to Australia. She did mention that she'll be back in the US "very soon", which I get the impression that means three months.
Her self-titled album was released last year, but there is a re-issue with extra acoustic tracks, according to amazon.
Ladyhawke at Fine Line Music Cafe, Minneapolis (21 Sep 2009)
vu (vu@weheartmusic.com) ♥weheartmusic.com♥facebook.com♥twitter.com/weheartmusic
Posted at 12:00 AM in Fine Line Music Cafe, Flashback, Ida Maria, Ladyhawke, Minneapolis, Natalie Portmans Shaved Head, Perez Hilton, Semi Precious Weapons, Sliimy | Permalink


text:
using System;
using System.Reflection;
using Weasel.Postgresql;
using Marten.Schema.Identity;
using Marten.Storage;
using Weasel.Core;

namespace Marten.Schema
{
    internal interface IDocumentMapping
    {
        IDocumentMapping Root { get; }

        MemberInfo IdMember { get; }

        Type DocumentType { get; }

        Type IdType { get; }
        DbObjectName TableName { get; }
    }

}

redpajama_set_name: RedPajamaGithub


Life-style || Fashion Photoshoot with Chachi Gonzales and World of Dance. See blog for more details.
L.A. Times Southern California Moments Features My Second Photo.
My photo featured in L.A. Times Socal Moments "Stealing a picture from a picture"
My photo used for promo in Brazil, for Chachi Gonzales workshop.


I love cooking with apples!
I love your recipes, especially the cookies recipes. I was wondering, is there a way to make the cholesterol buster oatmeal cookies a little more moist or chewy?
Jon, try eliminating the flaxseed meal and use all-purpose flour. You'll still get good fiber from the oats but they should be more moist. You could also try adding another tablespoon of butter. Oh, and store them in a plastic bag with all the air removed – that makes a big difference.
Anyone with non-stick pots will be happy to see what I found. It's a new silicone steamer that won't scratch your pots, it's flexible so it fits into almost any size pot, and it has silicone handles for easy lifting. Steaming vegetables retains most of their nutritional benefits so even if you don't have a steamer, cook them in a covered pot with very little water.
If you like a super crispy pizza crust like I do, then a pizza stone is the way to go. Last night I was craving a plain cheese pizza but it had to be well done and crispy so I used my favorite pizza stone (yes, I have more than one!). This one is black so it absorbs heat really well. I placed it in the oven and preheated it as high as it would go – 500 degrees. Those commercial ovens go a lot higher than that but I preheated the oven and stone for almost an hour and put my pizza in there using a wooden peel. I used to sprinkle the peel with cornmeal but now I just use parchment paper. I cover the peel with parchment, place my pizza dough on top and spread the sauce and cheese. Then I slide it onto the stone, paper and all, and later on when it's almost done, I remove the paper and let it finish. In this photo I had just removed the paper and I still baked it another couple of minutes for an even more well done crust. Then… no fork, no plate… just slice, pick up, and into the pie hole!


text:
Q: Stop Rack session setting a cookie for all pages In my Sinatra app I am using rack sessions
use Rack::Session::Cookie, :secret => ENV['SECRET']

It seems to be setting a cookie for every page load, even if I am not setting anything in a session. Is there a way to change it to only set a cookie if a session is set?

A: Rack::Session::Cookie keeps sessions into a marshaled Ruby Hash.
Basically it is marshaling your session and persist it into cookies.
And it does not care your session is an empty Hash,
it will be marshaled and persisted into cookies anyway.
Do you really need to keep your sessions in cookies?
Perhaps use Rack::Session::Pool to keep your sessions in a memory pool?
Then it will set a cookie only once.

redpajama_set_name: RedPajamaStackExchange


Sonoma-Marin Rail Transit (SMART) is coming to Novato. The City of Novato is coordinating efforts with SMART as they make improvements to their facilities to allow for passenger trains. More info on SMART and construction updates.
Following the joint session, the San Rafael City Council met and discussed the timing of the Quiet Zone implementation, and voted to request approval for the start of the Quiet Zone for San Rafael, Novato, and Marin County as soon as possible.
Quiet Zones for the City of Novato, the City of San Rafael and the County of Marin were implemented on Friday, May 12, 2017.
Please be aware that the train horn will not sound at railroad crossings in those areas, unless there is an emergency. SMART urges the public to be alert and aware at all crossings.
For the past few years, the City of Novato has been actively working on establishing quiet zones throughout SMART's crossings in Novato. In mid-2016, it was discovered that the Hamilton Pedestrian Crossing next to the Hamilton SMART station could not be included in Novato's independent quiet zone for technical, regulatory reasons. After researching a variety of options that were not successful, the City of Novato approached the City of San Rafael to explore the possibility of creating a comprehensive quiet zone for San Rafael, Marin County and Novato. This comprehensive quiet zone could include the Hamilton Pedestrian Crossing and two private crossings in Marin County (between Novato and San Rafael) in addition to providing Novato a more streamlined application process for quiet zones.
The City Council of the City of San Rafael was supportive of assisting Novato in this effort, but needed confirmation that a revised quiet zone could be implemented before the start of revenue service for SMART. On Wednesday, October 19, the SMART Board of Directors met and confirmed that the launch date for commercial service would be moved to late spring 2017. SMART's change of timing allows us enough time to complete the comprehensive Quiet Zone process prior to SMART's commercial operations.
Based on this, a partnership has been created between three jurisdictions - the City of San Rafael, Marin County and the City of Novato - to create one comprehensive quiet zone. The City of San Rafael agreed to serve as the lead agency for applying for the comprehensive quiet zone. The City of Novato is covering the costs for the consultant's time to resubmit the notice process. Both San Rafael and Novato have now rescinded each agency's prior individual Notice of Intent and as of Thursday, October 20, a new Notice of Intent to implement a comprehensive Marin quiet zone was submitted to the Federal Railroad Administration, the railroad owners/operators and other required regulatory agencies, together with private crossing owners. The purpose of the Notice of Intent is to allow a 60 day period for railroad owners/operators, regulatory agencies, and owners of private crossings to comment. Once the comment period is complete, the City of San Rafael will review, consider and potentially respond to any comments received on behalf of the three jurisdictions in the quiet zone.
The City Council of Novato had been considering back-up options for the Hamilton Pedestrian crossing including retaining the crossing with a local wayside horn, retaining the crossing with horn noise, and closing the crossing. City staff had held a neighborhood meeting in August and solicited neighborhood input via an on-line survey in September/October 2016. On October 25, the City Council heard an update on the comprehensive quiet zone and agreed that no further action is needed on the back-up options based on the active support for the comprehensive Marin quiet zone with San Rafael.
Following the joint session, the San Rafael City Council met and discussed the timing of the Quiet Zone implementation, and voted to request approval for the start of the Quiet Zone for San Rafael, Novato, and Marin County as soon as possible. Quiet Zones were implemented in Novato, San Rafael and Marin County on Friday, May 12, 2017.
On June 6, 2016, staff sent out a Notice of Intent to support the City's proposal to create a new contiguous Quiet Zone along the SMART railroad extending from the North Hamilton Parkway crossing to the Rush Creek Place crossing.
On May 3, 2016, staff provided a Quiet Zone update to the City Council (video of presentation and PowerPoint presentation).
On Sept. 23, 2014, the City Council discussed Quiet Zones (video of meeting and associated staff report). The City supports the establishment of Quiet Zones, and is currently researching the extent of the City's liability if the zones were to be established. Staff plan to come back with more information be or before the end of March 2015.
Staff presentation to City Council on Sept. 25, 2012 and the related Powerpoint presentation; SMART info on Quiet Zones; City FAQ on Quiet Zones for Novato.
At their October 25 meeting, the City Council was updated that the partnership of the City of Novato, City of San Rafael, and County of Marin to apply for a comprehensive Quiet Zone is moving forward. Given that the proposed comprehensive Quiet Zone will include the State Access Road Pedestrian Crossing, no action will be taken on the back-up options at this time.
At their October 11 meeting, the City Council received neighborhood feedback via an online survey and all other feedback regarding the Hamilton State Access Road Pedestrian Crossing back-up options--which included closing the pedestrian crossing, retaining the crossing and installing Federal Railroad Administration approved "wayside horns", or retaining the crossing and allowing the horns to sound.
More information on the Hamilton State Access Road Pedestrian Crossing Back-up options and survey.
A community meeting was held on Wednesday, August 24 at 6:30pm at the Hamilton Community Center, 503 Palm Drive.
At the June 14, 2016 Council meeting, Council directed staff to conduct a workshop to provide residents an opportunity to learn more about the proposed Quiet Zones and the impact on the Hamilton area, federal regulations on train horns, and to provide feedback on the pedestrian crossing options at the State Access Road, including: A) retain the pedestrian crossing and install "wayside horns", B) retain the pedestrian crossing and no southerly quiet zone, or C) remove the pedestrian crossing.
UPDATE: Council approved an initial phase of a Downtown SMART station.
On March 10, 2015, the City Council discussed two station design options and names for the two Novato stations. See staff report.
On Sept. 9, 2014, the City Council discussed the design for two proposed SMART stations, and compiled the collective feedback from the public and Council and sent a response to SMART on Sept. 15, 2014.
Sign up below to receive email notifications on this topic.


text:
Q: lme4 or other open source R package code equivalent to asreml-R I want to fit mixed model using lme4, nlme, baysian regression package or any available.
Mixed model in Asreml- R  coding conventions
before going into specifics, we might want to have details on asreml-R conventions, for those who are unfamiliar with ASREML codes.
y = Xτ + Zu + e ........................(1) ;

the usual mixed model with, y denotes the n × 1 vector of observations,where τ is the p×1 vector of fixed effects, X is an n×p design matrix of full column rank which associates observations with the appropriate combination of fixed effects, u is the q × 1 vector of random effects, Z is the n × q design matrix which associates observations with the appropriate combination of random effects, and e is the n × 1 vector of residual errors.The model (1) is called a linear mixed model or linear mixed effects model. It is assumed

where the matrices G and R are functions of parameters γ and φ, respectively.
The parameter θ is a variance parameter which we will refer to as the scale parameter.
In mixed effects models with more than one residual variance, arising for example in the
analysis of data with more than one section or variate, the parameter θ is
fixed to one. In mixed effects models with a single residual variance then θ is equal to
the residual variance (σ2). In this case R must be correlation matrix. Further details on the models are provided in the Asreml manual (link).
Variance structures for the errors: R structure and Variance structures for the random effects: G structures can be specified.

variance modelling in asreml() it is important to understand the formation of variance structures via direct products. The usual least squares assumption (and the default in asreml()) is that these are independently and identically distributed (IID). However, if the data was from a field experiment laid out in a rectangular array of r rows by c columns, say, we could arrange the residuals e as a matrix and potentially consider that they were autocorrelated within rows and columns.Writing the residuals as a vector in field order, that is, by sorting the residuals rows
within columns (plots within blocks) the variance of the residuals might then be

 are correlation matrices for the row model (order r, autocorrelation parameter ½r) and column model (order c, autocorrelation parameter ½c)
respectively. More specifically, a two-dimensional separable autoregressive spatial structure
(AR1 x  AR1) is sometimes assumed for the common errors in a field trial analysis.
The example data:
nin89 is from asreml-R library, where different varities were grown in replications / blocks in rectangular field. To control additional variability in row or column direction each plot is referenced as Row and Column variables (row column design). Thus this row column design with blocking. Yield is measured variable.
Example models
I need something equivalent to the asreml-R codes:
The simple model syntax will look like the follows:
 rcb.asr <- asreml(yield ∼ Variety, random = ∼ Replicate, data = nin89)
 .....model 0

The linear model is specified in the fixed (required), random (optional) and rcov (error
component) arguments as formula objects.The default is a simple error term and does not need to be formally specified for error term as in the model 0.
here the variety is fixed effect and random is replicates (blocks). Beside random and fixed terms we can specify error term. Which is default in this model 0. The residual or error component of the model is specified in a formula object through the rcov argument, see the following models 1:4.
The following model1 is more complex in which both G (random) and R (error) structure are specified.
Model 1:
data(nin89)

 # Model 1: RCB analysis with G and R structure
     rcb.asr <- asreml(yield ~ Variety, random = ~ idv(Replicate),
      rcov = ~ idv(units), data = nin89)

This model is equivalent to above model 0, and introduces the use of G and R variance model. Here the option random and rcov specifies random and rcov formulae to explicitly specify the G and R structures. where idv() is the special model function in asreml() that identifies the variance model. The expression idv(units) explicitly sets the variance matrix for e to a scaled identity.
# Model 2: two-dimensional spatial model with correlation in one direction
  sp.asr <- asreml(yield ~ Variety, rcov = ~ Column:ar1(Row), data = nin89)

experimental units of nin89 are indexed by Column and Row. So we expect random variation in two direction - row and column direction in this case. where ar1() is a special function specifying a first order autoregressive variance model for Row. This call specifies a two-dimensional spatial structure for error but with spatial correlation in the row direction only.The variance model for Column is identity (id()) but does not need to be formally
specified as this is the default.
# model 3: two-dimensional spatial model, error structure in both direction
 sp.asr <- asreml(yield ~ Variety, rcov = ~ ar1(Column):ar1(Row),
 data = nin89)
sp.asr <- asreml(yield ~ Variety, random = ~ units,
 rcov = ~ ar1(Column):ar1(Row), data = nin89)

similar to above model  2, however the correlation is two direction - autoregressive one.
I am not sure how much of these models are possible with open source R packages. Even if solution of any one of these models will be of great help. Even if the bouty of +50 can stimulate to develop such package will be of great help !
See MAYSaseen has provided output from each model and data  (as answer)  for comparision.
Edits:
The following is suggestion I received in mixed model discussion forum:
" You might look at the regress and spatialCovariance packages of David Clifford.  The former allows fitting of (Gaussian) mixed models where you can specify the structure of the covariance matrix very flexibly (for example, I have used it for pedigree data).  The spatialCovariance package uses regress to provide more elaborate models than AR1xAR1, but may be applicable.  You may have to correspond with the author about applying it to your exact problem."

A: You can fit this model with AD Model Builder. AD Model Builder is free software for building general nonlinear models including general nonlinear random effects
models. So for example you could fit a negative binomial spatial model where
both the mean and over dispersion had an ar(1) x ar(1) structure.
I built the code for this example and fit it to the data.
If anyone is interested it is probably better to discuss this on the list at
http://admb-project.org
Note: There is an R version of ADMB, but the features available in the R package are a subset of the standalone ADMB software.
For this example it is easier to create an ASCII file with the data,
read it into the ADMB program, run the program, and then read the parameter
estimates etc back into R for whatever you want to do.
You should understand that ADMB is not a collection of packages, but rather
a language for writing nonlinear parameter estimation software. As I said before it is better to discuss this on the ADMB list where everyone knows about the software.  After it is done and you understand the model you can post the results here.  However here is a link to the ML and REML codes I put together
for the wheat data.
http://lists.admb-project.org/pipermail/users/attachments/20111124/448923c8/attachment.zip

A: Model 0
ASReml-R
rcb0.asr <- asreml(yield~Variety, random=~Rep, data=nin89, na.method.X="include")
summary(rcb0.asr)
$call
asreml(fixed = yield ~ Variety, random = ~Rep, data = nin89,
    na.method.X = "include")

$loglik
[1] -454.4691

$nedf
[1] 168

$sigma
[1] 7.041475

$varcomp
                gamma component std.error  z.ratio constraint
Rep!Rep.var 0.1993231  9.882911  8.792829 1.123974   Positive
R!variance  1.0000000 49.582368  5.458839 9.082951   Positive

attr(,"class")
[1] "summary.asreml"

summary(rcb0.asr)$varcomp
                gamma component std.error  z.ratio constraint
Rep!Rep.var 0.1993231  9.882911  8.792829 1.123974   Positive
R!variance  1.0000000 49.582368  5.458839 9.082951   Positive

> anova(rcb0.asr)
Wald tests for fixed effects

Response: yield

Terms added sequentially; adjusted for those above

              Df Sum of Sq Wald statistic Pr(Chisq)
(Intercept)    1   12001.6        242.054    <2e-16 ***
Variety       55    2387.5         48.152    0.7317
residual (MS)         49.6
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> coef(rcb0.asr)$fixed
                    effect
Variety_ARAPAHOE    0.0000
Variety_BRULE      -3.3625
Variety_BUCKSKIN   -3.8750
Variety_CENTURA    -7.7875
Variety_CENTURK78   0.8625
Variety_CHEYENNE   -1.3750
Variety_CODY       -8.2250
Variety_COLT       -2.4375
Variety_GAGE       -4.9250
Variety_HOMESTEAD  -1.8000
Variety_KS831374   -5.3125
Variety_LANCER     -0.8750
Variety_LANCOTA    -2.8875
Variety_NE83404    -2.0500
Variety_NE83406    -5.1625
Variety_NE83407    -6.7500
Variety_NE83432    -9.7125
Variety_NE83498     0.6875
Variety_NE83T12    -7.8750
Variety_NE84557    -8.9125
Variety_NE85556    -3.0500
Variety_NE85623    -7.7125
Variety_NE86482    -5.1500
Variety_NE86501     1.5000
Variety_NE86503     3.2125
Variety_NE86507    -5.6500
Variety_NE86509    -2.5875
Variety_NE86527    -7.4250
Variety_NE86582    -4.9000
Variety_NE86606     0.3250
Variety_NE86607    -0.1125
Variety_NE86T666   -7.9000
Variety_NE87403    -4.3125
Variety_NE87408    -3.1375
Variety_NE87409    -8.0625
Variety_NE87446    -1.7625
Variety_NE87451    -4.8250
Variety_NE87457    -5.5250
Variety_NE87463    -3.5250
Variety_NE87499    -9.0250
Variety_NE87512    -6.1875
Variety_NE87513    -2.6250
Variety_NE87522    -4.4375
Variety_NE87612    -7.6375
Variety_NE87613    -0.0375
Variety_NE87615    -3.7500
Variety_NE87619     1.8250
Variety_NE87627    -6.2125
Variety_NORKAN     -5.0250
Variety_REDLAND     1.0625
Variety_ROUGHRIDER -8.2500
Variety_SCOUT66    -1.9125
Variety_SIOUXLAND   0.6750
Variety_TAM107     -1.0375
Variety_TAM200     -8.2000
Variety_VONA       -5.8375
(Intercept)        29.4375
> coef(rcb0.asr)$random
          effect
Rep_1  1.8795997
Rep_2  2.8432659
Rep_3 -0.8712739
Rep_4 -3.8515918

lme4
> rcb0.lmer <- lmer(yield~Variety+(1|Rep), data=nin89)
> print(rcb0.lmer, corr=FALSE)
Linear mixed model fit by REML
Formula: yield ~ Variety + (1 | Rep)
   Data: nin89
  AIC  BIC logLik deviance REMLdev
 1334 1532 -608.9     1456    1218
Random effects:
 Groups   Name        Variance Std.Dev.
 Rep      (Intercept)  9.8829  3.1437
 Residual             49.5824  7.0415
Number of obs: 224, groups: Rep, 4

Fixed effects:
                  Estimate Std. Error t value
(Intercept)        29.4375     3.8556   7.635
VarietyBRULE       -3.3625     4.9791  -0.675
VarietyBUCKSKIN    -3.8750     4.9791  -0.778
VarietyCENTURA     -7.7875     4.9791  -1.564
VarietyCENTURK78    0.8625     4.9791   0.173
VarietyCHEYENNE    -1.3750     4.9791  -0.276
VarietyCODY        -8.2250     4.9791  -1.652
VarietyCOLT        -2.4375     4.9791  -0.490
VarietyGAGE        -4.9250     4.9791  -0.989
VarietyHOMESTEAD   -1.8000     4.9791  -0.362
VarietyKS831374    -5.3125     4.9791  -1.067
VarietyLANCER      -0.8750     4.9791  -0.176
VarietyLANCOTA     -2.8875     4.9791  -0.580
VarietyNE83404     -2.0500     4.9791  -0.412
VarietyNE83406     -5.1625     4.9791  -1.037
VarietyNE83407     -6.7500     4.9791  -1.356
VarietyNE83432     -9.7125     4.9791  -1.951
VarietyNE83498      0.6875     4.9791   0.138
VarietyNE83T12     -7.8750     4.9791  -1.582
VarietyNE84557     -8.9125     4.9791  -1.790
VarietyNE85556     -3.0500     4.9791  -0.613
VarietyNE85623     -7.7125     4.9791  -1.549
VarietyNE86482     -5.1500     4.9791  -1.034
VarietyNE86501      1.5000     4.9791   0.301
VarietyNE86503      3.2125     4.9791   0.645
VarietyNE86507     -5.6500     4.9791  -1.135
VarietyNE86509     -2.5875     4.9791  -0.520
VarietyNE86527     -7.4250     4.9791  -1.491
VarietyNE86582     -4.9000     4.9791  -0.984
VarietyNE86606      0.3250     4.9791   0.065
VarietyNE86607     -0.1125     4.9791  -0.023
VarietyNE86T666    -7.9000     4.9791  -1.587
VarietyNE87403     -4.3125     4.9791  -0.866
VarietyNE87408     -3.1375     4.9791  -0.630
VarietyNE87409     -8.0625     4.9791  -1.619
VarietyNE87446     -1.7625     4.9791  -0.354
VarietyNE87451     -4.8250     4.9791  -0.969
VarietyNE87457     -5.5250     4.9791  -1.110
VarietyNE87463     -3.5250     4.9791  -0.708
VarietyNE87499     -9.0250     4.9791  -1.813
VarietyNE87512     -6.1875     4.9791  -1.243
VarietyNE87513     -2.6250     4.9791  -0.527
VarietyNE87522     -4.4375     4.9791  -0.891
VarietyNE87612     -7.6375     4.9791  -1.534
VarietyNE87613     -0.0375     4.9791  -0.008
VarietyNE87615     -3.7500     4.9791  -0.753
VarietyNE87619      1.8250     4.9791   0.367
VarietyNE87627     -6.2125     4.9791  -1.248
VarietyNORKAN      -5.0250     4.9791  -1.009
VarietyREDLAND      1.0625     4.9791   0.213
VarietyROUGHRIDER  -8.2500     4.9791  -1.657
VarietySCOUT66     -1.9125     4.9791  -0.384
VarietySIOUXLAND    0.6750     4.9791   0.136
VarietyTAM107      -1.0375     4.9791  -0.208
VarietyTAM200      -8.2000     4.9791  -1.647
VarietyVONA        -5.8375     4.9791  -1.172
> anova(rcb0.lmer)
Analysis of Variance Table
        Df Sum Sq Mean Sq F value
Variety 55 2387.5  43.409  0.8755
> fixef(rcb0.lmer)
      (Intercept)      VarietyBRULE   VarietyBUCKSKIN    VarietyCENTURA
          29.4375           -3.3625           -3.8750           -7.7875
 VarietyCENTURK78   VarietyCHEYENNE       VarietyCODY       VarietyCOLT
           0.8625           -1.3750           -8.2250           -2.4375
      VarietyGAGE  VarietyHOMESTEAD   VarietyKS831374     VarietyLANCER
          -4.9250           -1.8000           -5.3125           -0.8750
   VarietyLANCOTA    VarietyNE83404    VarietyNE83406    VarietyNE83407
          -2.8875           -2.0500           -5.1625           -6.7500
   VarietyNE83432    VarietyNE83498    VarietyNE83T12    VarietyNE84557
          -9.7125            0.6875           -7.8750           -8.9125
   VarietyNE85556    VarietyNE85623    VarietyNE86482    VarietyNE86501
          -3.0500           -7.7125           -5.1500            1.5000
   VarietyNE86503    VarietyNE86507    VarietyNE86509    VarietyNE86527
           3.2125           -5.6500           -2.5875           -7.4250
   VarietyNE86582    VarietyNE86606    VarietyNE86607   VarietyNE86T666
          -4.9000            0.3250           -0.1125           -7.9000
   VarietyNE87403    VarietyNE87408    VarietyNE87409    VarietyNE87446
          -4.3125           -3.1375           -8.0625           -1.7625
   VarietyNE87451    VarietyNE87457    VarietyNE87463    VarietyNE87499
          -4.8250           -5.5250           -3.5250           -9.0250
   VarietyNE87512    VarietyNE87513    VarietyNE87522    VarietyNE87612
          -6.1875           -2.6250           -4.4375           -7.6375
   VarietyNE87613    VarietyNE87615    VarietyNE87619    VarietyNE87627
          -0.0375           -3.7500            1.8250           -6.2125
    VarietyNORKAN    VarietyREDLAND VarietyROUGHRIDER    VarietySCOUT66
          -5.0250            1.0625           -8.2500           -1.9125
 VarietySIOUXLAND     VarietyTAM107     VarietyTAM200       VarietyVONA
           0.6750           -1.0375           -8.2000           -5.8375
> ranef(rcb0.lmer)
$Rep
  (Intercept)
1   1.8798700
2   2.8436747
3  -0.8713991
4  -3.8521455

nlme
> rcb0.lme <- lme(yield~Variety, random=~1|Rep, data=na.omit(nin89))
> print(rcb0.lme, corr=FALSE)
Linear mixed-effects model fit by REML
  Data: na.omit(nin89)
  Log-restricted-likelihood: -608.8508
  Fixed: yield ~ Variety
      (Intercept)      VarietyBRULE   VarietyBUCKSKIN    VarietyCENTURA
          29.4375           -3.3625           -3.8750           -7.7875
 VarietyCENTURK78   VarietyCHEYENNE       VarietyCODY       VarietyCOLT
           0.8625           -1.3750           -8.2250           -2.4375
      VarietyGAGE  VarietyHOMESTEAD   VarietyKS831374     VarietyLANCER
          -4.9250           -1.8000           -5.3125           -0.8750
   VarietyLANCOTA    VarietyNE83404    VarietyNE83406    VarietyNE83407
          -2.8875           -2.0500           -5.1625           -6.7500
   VarietyNE83432    VarietyNE83498    VarietyNE83T12    VarietyNE84557
          -9.7125            0.6875           -7.8750           -8.9125
   VarietyNE85556    VarietyNE85623    VarietyNE86482    VarietyNE86501
          -3.0500           -7.7125           -5.1500            1.5000
   VarietyNE86503    VarietyNE86507    VarietyNE86509    VarietyNE86527
           3.2125           -5.6500           -2.5875           -7.4250
   VarietyNE86582    VarietyNE86606    VarietyNE86607   VarietyNE86T666
          -4.9000            0.3250           -0.1125           -7.9000
   VarietyNE87403    VarietyNE87408    VarietyNE87409    VarietyNE87446
          -4.3125           -3.1375           -8.0625           -1.7625
   VarietyNE87451    VarietyNE87457    VarietyNE87463    VarietyNE87499
          -4.8250           -5.5250           -3.5250           -9.0250
   VarietyNE87512    VarietyNE87513    VarietyNE87522    VarietyNE87612
          -6.1875           -2.6250           -4.4375           -7.6375
   VarietyNE87613    VarietyNE87615    VarietyNE87619    VarietyNE87627
          -0.0375           -3.7500            1.8250           -6.2125
    VarietyNORKAN    VarietyREDLAND VarietyROUGHRIDER    VarietySCOUT66
          -5.0250            1.0625           -8.2500           -1.9125
 VarietySIOUXLAND     VarietyTAM107     VarietyTAM200       VarietyVONA
           0.6750           -1.0375           -8.2000           -5.8375

Random effects:
 Formula: ~1 | Rep
        (Intercept) Residual
StdDev:     3.14371 7.041475

Number of Observations: 224
Number of Groups: 4
> anova(rcb0.lme)
            numDF denDF   F-value p-value
(Intercept)     1   165 242.05402  <.0001
Variety        55   165   0.87549  0.7119
> fixef(rcb0.lme)
      (Intercept)      VarietyBRULE   VarietyBUCKSKIN    VarietyCENTURA
          29.4375           -3.3625           -3.8750           -7.7875
 VarietyCENTURK78   VarietyCHEYENNE       VarietyCODY       VarietyCOLT
           0.8625           -1.3750           -8.2250           -2.4375
      VarietyGAGE  VarietyHOMESTEAD   VarietyKS831374     VarietyLANCER
          -4.9250           -1.8000           -5.3125           -0.8750
   VarietyLANCOTA    VarietyNE83404    VarietyNE83406    VarietyNE83407
          -2.8875           -2.0500           -5.1625           -6.7500
   VarietyNE83432    VarietyNE83498    VarietyNE83T12    VarietyNE84557
          -9.7125            0.6875           -7.8750           -8.9125
   VarietyNE85556    VarietyNE85623    VarietyNE86482    VarietyNE86501
          -3.0500           -7.7125           -5.1500            1.5000
   VarietyNE86503    VarietyNE86507    VarietyNE86509    VarietyNE86527
           3.2125           -5.6500           -2.5875           -7.4250
   VarietyNE86582    VarietyNE86606    VarietyNE86607   VarietyNE86T666
          -4.9000            0.3250           -0.1125           -7.9000
   VarietyNE87403    VarietyNE87408    VarietyNE87409    VarietyNE87446
          -4.3125           -3.1375           -8.0625           -1.7625
   VarietyNE87451    VarietyNE87457    VarietyNE87463    VarietyNE87499
          -4.8250           -5.5250           -3.5250           -9.0250
   VarietyNE87512    VarietyNE87513    VarietyNE87522    VarietyNE87612
          -6.1875           -2.6250           -4.4375           -7.6375
   VarietyNE87613    VarietyNE87615    VarietyNE87619    VarietyNE87627
          -0.0375           -3.7500            1.8250           -6.2125
    VarietyNORKAN    VarietyREDLAND VarietyROUGHRIDER    VarietySCOUT66
          -5.0250            1.0625           -8.2500           -1.9125
 VarietySIOUXLAND     VarietyTAM107     VarietyTAM200       VarietyVONA
           0.6750           -1.0375           -8.2000           -5.8375
> ranef(rcb0.lme)
  (Intercept)
1   1.8795997
2   2.8432659
3  -0.8712739
4  -3.8515918

A: Model 1
ASReml-R
> rcb.asr <- asreml(yield~Variety, random=~idv(Rep), rcov=~idv(units), data=nin89, na.method.X="include")
> summary(rcb.asr)
$call
asreml(fixed = yield ~ Variety, random = ~idv(Rep), rcov = ~idv(units),
    data = nin89, na.method.X = "include")

$loglik
[1] -454.4691

$nedf
[1] 168

$sigma
[1] 1

$varcomp
                gamma component std.error  z.ratio constraint
Rep!Rep.var  9.882911  9.882911  8.792823 1.123975   Positive
R!variance   1.000000  1.000000        NA       NA      Fixed
R!units.var 49.582368 49.582368  5.458839 9.082951   Positive

attr(,"class")
[1] "summary.asreml"
> summary(rcb0.asr)$varcomp
                gamma component std.error  z.ratio constraint
Rep!Rep.var 0.1993231  9.882911  8.792829 1.123974   Positive
R!variance  1.0000000 49.582368  5.458839 9.082951   Positive
> anova(rcb.asr)
Wald tests for fixed effects

Response: yield

Terms added sequentially; adjusted for those above

              Df Sum of Sq Wald statistic Pr(Chisq)
(Intercept)    1   242.054        242.054    <2e-16 ***
Variety       55    48.152         48.152    0.7317
residual (MS)        1.000
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> coef(rcb.asr)$fixed
                    effect
Variety_ARAPAHOE    0.0000
Variety_BRULE      -3.3625
Variety_BUCKSKIN   -3.8750
Variety_CENTURA    -7.7875
Variety_CENTURK78   0.8625
Variety_CHEYENNE   -1.3750
Variety_CODY       -8.2250
Variety_COLT       -2.4375
Variety_GAGE       -4.9250
Variety_HOMESTEAD  -1.8000
Variety_KS831374   -5.3125
Variety_LANCER     -0.8750
Variety_LANCOTA    -2.8875
Variety_NE83404    -2.0500
Variety_NE83406    -5.1625
Variety_NE83407    -6.7500
Variety_NE83432    -9.7125
Variety_NE83498     0.6875
Variety_NE83T12    -7.8750
Variety_NE84557    -8.9125
Variety_NE85556    -3.0500
Variety_NE85623    -7.7125
Variety_NE86482    -5.1500
Variety_NE86501     1.5000
Variety_NE86503     3.2125
Variety_NE86507    -5.6500
Variety_NE86509    -2.5875
Variety_NE86527    -7.4250
Variety_NE86582    -4.9000
Variety_NE86606     0.3250
Variety_NE86607    -0.1125
Variety_NE86T666   -7.9000
Variety_NE87403    -4.3125
Variety_NE87408    -3.1375
Variety_NE87409    -8.0625
Variety_NE87446    -1.7625
Variety_NE87451    -4.8250
Variety_NE87457    -5.5250
Variety_NE87463    -3.5250
Variety_NE87499    -9.0250
Variety_NE87512    -6.1875
Variety_NE87513    -2.6250
Variety_NE87522    -4.4375
Variety_NE87612    -7.6375
Variety_NE87613    -0.0375
Variety_NE87615    -3.7500
Variety_NE87619     1.8250
Variety_NE87627    -6.2125
Variety_NORKAN     -5.0250
Variety_REDLAND     1.0625
Variety_ROUGHRIDER -8.2500
Variety_SCOUT66    -1.9125
Variety_SIOUXLAND   0.6750
Variety_TAM107     -1.0375
Variety_TAM200     -8.2000
Variety_VONA       -5.8375
(Intercept)        29.4375
> coef(rcb.asr)$random
          effect
Rep_1  1.8795997
Rep_2  2.8432658
Rep_3 -0.8712738
Rep_4 -3.8515916

nlme
See the trick
> nin89$Int <- 1
> rcb.lme <- lme(yield~Variety, random=list(Int=pdIdent(~Rep-1)), data=na.omit(nin89))
> print(rcb.lme, corr=FALSE)
Linear mixed-effects model fit by REML
  Data: na.omit(nin89)
  Log-restricted-likelihood: -608.8508
  Fixed: yield ~ Variety
      (Intercept)      VarietyBRULE   VarietyBUCKSKIN    VarietyCENTURA
          29.4375           -3.3625           -3.8750           -7.7875
 VarietyCENTURK78   VarietyCHEYENNE       VarietyCODY       VarietyCOLT
           0.8625           -1.3750           -8.2250           -2.4375
      VarietyGAGE  VarietyHOMESTEAD   VarietyKS831374     VarietyLANCER
          -4.9250           -1.8000           -5.3125           -0.8750
   VarietyLANCOTA    VarietyNE83404    VarietyNE83406    VarietyNE83407
          -2.8875           -2.0500           -5.1625           -6.7500
   VarietyNE83432    VarietyNE83498    VarietyNE83T12    VarietyNE84557
          -9.7125            0.6875           -7.8750           -8.9125
   VarietyNE85556    VarietyNE85623    VarietyNE86482    VarietyNE86501
          -3.0500           -7.7125           -5.1500            1.5000
   VarietyNE86503    VarietyNE86507    VarietyNE86509    VarietyNE86527
           3.2125           -5.6500           -2.5875           -7.4250
   VarietyNE86582    VarietyNE86606    VarietyNE86607   VarietyNE86T666
          -4.9000            0.3250           -0.1125           -7.9000
   VarietyNE87403    VarietyNE87408    VarietyNE87409    VarietyNE87446
          -4.3125           -3.1375           -8.0625           -1.7625
   VarietyNE87451    VarietyNE87457    VarietyNE87463    VarietyNE87499
          -4.8250           -5.5250           -3.5250           -9.0250
   VarietyNE87512    VarietyNE87513    VarietyNE87522    VarietyNE87612
          -6.1875           -2.6250           -4.4375           -7.6375
   VarietyNE87613    VarietyNE87615    VarietyNE87619    VarietyNE87627
          -0.0375           -3.7500            1.8250           -6.2125
    VarietyNORKAN    VarietyREDLAND VarietyROUGHRIDER    VarietySCOUT66
          -5.0250            1.0625           -8.2500           -1.9125
 VarietySIOUXLAND     VarietyTAM107     VarietyTAM200       VarietyVONA
           0.6750           -1.0375           -8.2000           -5.8375

Random effects:
 Formula: ~Rep - 1 | Int
 Structure: Multiple of an Identity
           Rep1    Rep2    Rep3    Rep4 Residual
StdDev: 3.14371 3.14371 3.14371 3.14371 7.041475

Number of Observations: 224
Number of Groups: 1
> anova(rcb.lme)
            numDF denDF   F-value p-value
(Intercept)     1   168 242.05402  <.0001
Variety        55   168   0.87549  0.7121
> fixef(rcb.lme)
      (Intercept)      VarietyBRULE   VarietyBUCKSKIN    VarietyCENTURA
          29.4375           -3.3625           -3.8750           -7.7875
 VarietyCENTURK78   VarietyCHEYENNE       VarietyCODY       VarietyCOLT
           0.8625           -1.3750           -8.2250           -2.4375
      VarietyGAGE  VarietyHOMESTEAD   VarietyKS831374     VarietyLANCER
          -4.9250           -1.8000           -5.3125           -0.8750
   VarietyLANCOTA    VarietyNE83404    VarietyNE83406    VarietyNE83407
          -2.8875           -2.0500           -5.1625           -6.7500
   VarietyNE83432    VarietyNE83498    VarietyNE83T12    VarietyNE84557
          -9.7125            0.6875           -7.8750           -8.9125
   VarietyNE85556    VarietyNE85623    VarietyNE86482    VarietyNE86501
          -3.0500           -7.7125           -5.1500            1.5000
   VarietyNE86503    VarietyNE86507    VarietyNE86509    VarietyNE86527
           3.2125           -5.6500           -2.5875           -7.4250
   VarietyNE86582    VarietyNE86606    VarietyNE86607   VarietyNE86T666
          -4.9000            0.3250           -0.1125           -7.9000
   VarietyNE87403    VarietyNE87408    VarietyNE87409    VarietyNE87446
          -4.3125           -3.1375           -8.0625           -1.7625
   VarietyNE87451    VarietyNE87457    VarietyNE87463    VarietyNE87499
          -4.8250           -5.5250           -3.5250           -9.0250
   VarietyNE87512    VarietyNE87513    VarietyNE87522    VarietyNE87612
          -6.1875           -2.6250           -4.4375           -7.6375
   VarietyNE87613    VarietyNE87615    VarietyNE87619    VarietyNE87627
          -0.0375           -3.7500            1.8250           -6.2125
    VarietyNORKAN    VarietyREDLAND VarietyROUGHRIDER    VarietySCOUT66
          -5.0250            1.0625           -8.2500           -1.9125
 VarietySIOUXLAND     VarietyTAM107     VarietyTAM200       VarietyVONA
           0.6750           -1.0375           -8.2000           -5.8375
> ranef(rcb.lme)
    Rep1     Rep2       Rep3      Rep4
1 1.8796 2.843266 -0.8712739 -3.851592

A: Model 2
ASReml-R
sp1.asr <- asreml(yield~Variety, rcov=~Column:ar1(Row), data=nin89, na.method.X="include")

> summary(sp1.asr)
$call
asreml(fixed = yield ~ Variety, rcov = ~Column:ar1(Row), data = nin89,
    na.method.X = "include")

$loglik
[1] -408.1412

$nedf
[1] 168

$sigma
[1] 7.975127

$varcomp
               gamma  component  std.error   z.ratio    constraint
R!variance 1.0000000 63.6026561 11.3182328  5.619486      Positive
R!Row.cor  0.7795799  0.7795799  0.0406026 19.200245 Unconstrained

attr(,"class")
[1] "summary.asreml"
> summary(sp1.asr)$varcomp
               gamma  component  std.error   z.ratio    constraint
R!variance 1.0000000 63.6026561 11.3182328  5.619486      Positive
R!Row.cor  0.7795799  0.7795799  0.0406026 19.200245 Unconstrained
> anova(sp1.asr)
Wald tests for fixed effects

Response: yield

Terms added sequentially; adjusted for those above

              Df Sum of Sq Wald statistic Pr(Chisq)
(Intercept)    1   24604.3         386.84 < 2.2e-16 ***
Variety       55    7974.4         125.38 2.048e-07 ***
residual (MS)         63.6
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> coef(sp1.asr)$fixed
                        effect
Variety_ARAPAHOE     0.0000000
Variety_BRULE       -2.4048816
Variety_BUCKSKIN     7.8064972
Variety_CENTURA     -1.6997427
Variety_CENTURK78   -1.3829446
Variety_CHEYENNE    -1.1113084
Variety_CODY        -6.7461911
Variety_COLT        -1.7963394
Variety_GAGE        -3.4539524
Variety_HOMESTEAD   -5.5877510
Variety_KS831374    -0.8589476
Variety_LANCER      -2.8418476
Variety_LANCOTA     -5.9394801
Variety_NE83404     -3.4112613
Variety_NE83406     -1.9057358
Variety_NE83407     -3.2563922
Variety_NE83432     -5.4594311
Variety_NE83498      0.6446010
Variety_NE83T12     -4.0071361
Variety_NE84557     -4.2005181
Variety_NE85556      1.4836395
Variety_NE85623     -2.7617129
Variety_NE86482     -1.4309381
Variety_NE86501     -2.2287462
Variety_NE86503     -0.4557866
Variety_NE86507     -0.6983418
Variety_NE86509     -3.9215624
Variety_NE86527      0.5294386
Variety_NE86582     -5.4653632
Variety_NE86606     -0.7291575
Variety_NE86607     -0.1265536
Variety_NE86T666   -12.1437291
Variety_NE87403     -7.4623631
Variety_NE87408     -3.3586380
Variety_NE87409     -1.0360336
Variety_NE87446     -4.9030958
Variety_NE87451     -3.2836149
Variety_NE87457     -3.5244583
Variety_NE87463     -3.8427658
Variety_NE87499     -4.6298393
Variety_NE87512     -5.3760809
Variety_NE87513     -5.5656241
Variety_NE87522     -7.6500899
Variety_NE87612     -2.7225851
Variety_NE87613     -0.8793319
Variety_NE87615     -4.0089291
Variety_NE87619      0.7975626
Variety_NE87627    -10.1315147
Variety_NORKAN      -7.1804945
Variety_REDLAND      0.6753066
Variety_ROUGHRIDER  -0.9637487
Variety_SCOUT66      0.7088916
Variety_SIOUXLAND   -1.1998807
Variety_TAM107      -3.7160351
Variety_TAM200      -9.0340942
Variety_VONA        -2.7970689
(Intercept)         28.3487457

nlme
Working on, yet not figured out. Could be something like this. Still could not figure out how to do rcov=~Column:ar1(Row) with nlme
nin89$Int <- 1
sp1.lme <- lme(yield~Variety, random=~1|Int, data=na.omit(nin89))

A: Model 3
ASReml-R
sp2.asr <- asreml(yield~Variety, rcov=~ar1(Column):ar1(Row), data=nin89, na.method.X="include")

> summary(sp2.asr)
$call
asreml(fixed = yield ~ Variety, rcov = ~ar1(Column):ar1(Row),
    data = nin89, na.method.X = "include")

$loglik
[1] -399.3238

$nedf
[1] 168

$sigma
[1] 6.978728

$varcomp
                 gamma  component  std.error   z.ratio    constraint
R!variance   1.0000000 48.7026395 7.15527571  6.806536      Positive
R!Column.cor 0.4375045  0.4375045 0.08060227  5.427943 Unconstrained
R!Row.cor    0.6554798  0.6554798 0.05637709 11.626704 Unconstrained

attr(,"class")
[1] "summary.asreml"
> summary(sp2.asr)$varcomp
                 gamma  component  std.error   z.ratio    constraint
R!variance   1.0000000 48.7026395 7.15527571  6.806536      Positive
R!Column.cor 0.4375045  0.4375045 0.08060227  5.427943 Unconstrained
R!Row.cor    0.6554798  0.6554798 0.05637709 11.626704 Unconstrained
> anova(sp2.asr)
Wald tests for fixed effects

Response: yield

Terms added sequentially; adjusted for those above

              Df Sum of Sq Wald statistic Pr(Chisq)
(Intercept)    1   16165.6         331.93 < 2.2e-16 ***
Variety       55    5961.7         122.41 4.866e-07 ***
residual (MS)         48.7
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> coef(sp2.asr)$fixed
                         effect
Variety_ARAPAHOE     0.00000000
Variety_BRULE        0.03029321
Variety_BUCKSKIN     8.89207227
Variety_CENTURA     -0.68979639
Variety_CENTURK78    0.16461970
Variety_CHEYENNE     0.50267820
Variety_CODY        -3.26960093
Variety_COLT        -0.51826695
Variety_GAGE        -0.95824999
Variety_HOMESTEAD   -4.57873078
Variety_KS831374     0.27843476
Variety_LANCER      -2.95379384
Variety_LANCOTA     -4.67006598
Variety_NE83404     -1.32290865
Variety_NE83406     -1.66351994
Variety_NE83407     -2.64471830
Variety_NE83432     -4.42828427
Variety_NE83498      1.80418738
Variety_NE83T12     -2.11789109
Variety_NE84557     -2.34685080
Variety_NE85556      2.78001120
Variety_NE85623     -1.42164134
Variety_NE86482     -1.63334029
Variety_NE86501     -2.94339063
Variety_NE86503     -0.95747374
Variety_NE86507      0.46223383
Variety_NE86509     -3.27166458
Variety_NE86527      1.86588098
Variety_NE86582     -3.87940069
Variety_NE86606      0.22753741
Variety_NE86607      0.60702026
Variety_NE86T666   -10.27005825
Variety_NE87403     -7.43945904
Variety_NE87408     -3.10433009
Variety_NE87409      1.29746980
Variety_NE87446     -4.15943316
Variety_NE87451     -1.85324718
Variety_NE87457     -2.31156727
Variety_NE87463     -4.47086114
Variety_NE87499     -1.85909637
Variety_NE87512     -4.06473578
Variety_NE87513     -3.99604937
Variety_NE87522     -5.52109215
Variety_NE87612     -1.95543098
Variety_NE87613     -0.83160454
Variety_NE87615     -1.92104271
Variety_NE87619      2.98322047
Variety_NE87627     -7.33205188
Variety_NORKAN      -5.78418023
Variety_REDLAND      1.75249392
Variety_ROUGHRIDER  -0.97736288
Variety_SCOUT66      2.13126094
Variety_SIOUXLAND   -2.54195346
Variety_TAM107      -1.59083563
Variety_TAM200      -6.54229161
Variety_VONA        -1.52728371
(Intercept)         27.04285175

nlme
Working on, yet not figured out. Could be something like this. Still could not figure out how to do rcov=~ar1(Column):ar1(Row) with nlme
nin89$Int <- 1
sp1.lme <- lme(yield~Variety, random=~1|Int, data=na.omit(nin89))

I could not figured out how to fit Model 2 and 3 with nlme. I'm working on it and will update the answer when get it done. But I've included the output from ASReml-R for Model 2 and 3 for comparison purposes. Kevin has good experience of analyzing such models and Ben Bolker has wonderful authority on Mixed Models. I hope they can help us on Models 2 and 3.


text:
North Yorkshire Council Elections 5 May 2022
Elections for North Yorkshire's new unitary authority (North Yorkshire Council, NYC) were held on 5 May 2022. This new council will replace the current county and the seven district and borough councils from 1 April 2023. There are 90 new councillors representing 89 divisions across the county. Each will serve for a five-year term (1 year on the current NYCC, then four years on the new NYC unitary authority). Click here to see an interactive map showing the new council divisions, here for a district-by-district list of electoral divisions for the new council and here for a full list of results.
Our new councillors will have to address urgent environmental (and of course other) challenges during their term of office. Crucially, that includes responsibility for steering the county's decarbonisation plans. As the latest report by the UN's Intergovernmental Panel on Climate Change makes clear, climate breakdown is accelerating rapidly, many of the impacts will be more severe than predicted and we must take urgent action now to switch away from fossil fuels towards a clean, green economy powered by renewables, such as solar and wind.
Opinion polls show that more and more people see climate change and the damage being caused to our natural world – here in North Yorkshire, nationally and globally – as key concerns that will influence the way they vote. That goes for young people in particular. A YouGov poll published on 1 November 2021 showed that 43% of 18 to 24-year-olds in the UK believe "The Environment" is the most important issue facing the country.
Before the election, we wrote to all candidates inviting them to complete a survey (ten questions on climate and environmental issues) so that voters would know where they stand and can be confident that their chosen candidates would help to drive the urgent and ambitious actions needed. The survey was of course entirely voluntary. We received completed surveys from around 30% of those candidates who went on to be elected. We understand there may be perfectly good reasons why other candidates were unable to take part.
To make room on the website, the main section showing the candidate responses for each division has now been condensed into a single page of responses from the newly elected councillors in each district. Simply choose your district from the 'New councillors' tab at the top of this page and select your division. If your councillor submitted a response, her/his name will be linked to the completed survey.


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FIRST DRAFT: Georgia's only absinthe, International Women's Day, and more beer news
Local craft brews and news
Photo credit: Jaz Jarzewiak
THE GREEN FAIRY: Jetty Absinthe from Lilburn, Georgia's Hope Springs Distillery is the only commercial absinthe in the state.
By Alex Patton Thursday March 7, 2019 02:56 pm EST
Truth, beauty, freedom and love — the famous Bohemian absinthe spirit is shrouded in mystery, popularized by its association with artists and writers from Hemingway to Van Gogh, and its portrayal in films like Moulin Rouge! Mystical potion, or just a tasty beverage? Georgia drinkers can decide for themselves now that we have our own Jetty Absinthe, distilled at Lilburn's Hope Springs Distillery. At only 24 years old, Jaz Jarzewiak is the youngest master distiller in the United States, but he brings an undeniable millennial ambition that might just be the creative kick the local distilling industry needs. It's not all green fairies and magic — absinthe is a culinary-driven beverage with a misunderstood history and hopefully a brighter future.
Jetty Absinthe is an herb-forward spirit, made by carefully distilling neutral wheat alcohol with anise seeds, fennel seeds, and a bitter herb called wormwood, which absinthe is famous for. Jarzewiak further colors and flavors the absinthe with another herb called hyssop, as well as peppermint, thyme, and whole black peppercorn. The finished product is a light and refreshing, yet deceptively boozy, spirit similar to gin, with a spiced licorice-like flavor produced by the anise. "Jetty is perfect to drink outdoors, particularly on a hot day," Jarzewiak says. "It is inspired by the wide blanket of forest covering Georgia, one of the most wooded states in the South. The thyme is intended to capture the bright lemon note of pine in the morning, while the peppercorn adds the spice of the forest floor."
The botanical ingredients of absinthe are difficult and expensive to acquire, and it's easy to mess up the distilling process. For small businesses, that's a lot of wasted effort and resources for a product that many consumers might be reluctant to try, but Jarzewiak wants to change that perspective. "Absinthe is a connoisseur-driven beverage, so the artistry of the craft is worth the risk of failure to me," he says. "I'm trying to demystify the distilling world so that people will be knowledgeable about the spirits they are drinking, and hopefully they'll be more willing to try something new."
It's a common misconception that the wormwood in absinthe is dangerous to consume, but Jarzewiak says this basically comes down to bad marketing. Wormwood has traditionally been used in medicinal and culinary applications by various cultures around the world for thousands of years because it contains an essential oil called thujone, which is thought to boost creativity and general cognitive function. This made absinthe popular among European artists and writers in the 1800s, and it all but replaced wine as the most popular beverage in Europe when aphid infestations destroyed many major vineyards. In response, the Big Wine industry of the time led a government-backed smear campaign against thujone, and it remains a regulated substance to this day.
Jetty Absinthe contains a minimal amount of thujone, but Jarzewiak says that his recipe maintains the artistic inspiration of traditional absinthe, intended to bring the consumer closer to nature. "No one else distills absinthe in Georgia because our recent history has not been familiar with it, but I can't think of a spirit Atlantans could love more," he says. " Absinthe isn't for everyone, but if you love wild nature, and you find peace among the pines in Georgia, this is a spirit for you."
Jetty Absinthe is available now in major liquor stores across Georgia, and directly from Hope Springs Distillery. In the near future, Jarzewiak plans to produce a gin inspired by American West Coast IPA's, flavored with juniper and hops, as well as a cannabis-inspired bourbon with added terpenes for a dank aroma.
More Beer News
On March 8, members of the Georgia chapter of Pink Boots Society will meet at Second Self Beer Company from 8 a.m. - 3 p.m. to brew a collaborative beer in honor of International Women's Day. The brew day will serve as a meeting for the chapter as well as an open opportunity for any women in the brewing industry to network and learn about the process of brewing. Sufferin' Till Suffrage is a hoppy Double New England IPA, officially a collaboration between Second Self and New Realm Brewing Company. It will be sold in-house, and distributed into the market. Proceeds benefit education and programming for Pink Boots Society Georgia chapter. Attendance to the brew day is free, but attendees should RSVP on the PBS website. From 5-7 p.m., a pre-brewed batch of Sufferin' Till Suffrage will be tapped at Second Self for anyone to try.
Steady Hand Beer Co. will officially open its doors Saturday, March 9, with a grand opening party at the brewery from 2-8 p.m. General admission tickets for $25 get you four 10oz beers of your choice plus a branded souvenir glass, and access to the party with food trucks, raffles and live music from local Phish tribute band Skyballs. To learn more about Steady Hand's story and beer selection, check out First Draft's recent feature story on the brewery.
Friends in the Atlanta craft beer community: If you have a cool event or new beer release in need of coverage, drop a line to Creative Loafing's beer correspondent, Alex Patton, at alex.patton at creativeloafing.com
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You are here: Home / May Hashem Comfort their families
Israel, News and politics
May Hashem Comfort their families
Twelve IDF reservists were killed by a Katyusha rocket near Kibbutz Kfar Giladi as they were preparing to enter Lebanon On Sunday. All have been named:
Sgt. Gregory Aharonov, 34, of Or Akiva, moved to Israel from the Ukraine in 1991. Despite concerns about serving, Aharanov obliged when he was called up. Aharanov was named after his grandfather who died, also at the age of 34, in World War One. Aharanov was the manager of a cosmetics factory and is survived by his wife, two children, parents and older sisters. He was laid to rest at 4 p.m. on Monday.
Sgt.-Maj. Marian Berkowitz, of Ashdod, was called up Wednesday and has a younger brother who is currently serving in Lebanon. Friends described him as fun loving and that he "loved challenges." Berkowitz is survived by his parents and two brothers. He was laid to rest at 4 p.m. on Monday in the Ashdod military cemetery.
CWO Yosef Karkash, 41, of Afula, met with Shlomo Buchris, his cousin and fellow reservist, early Sunday. Later that day, both cousins were killed by the Katyusha. Relatives are devastated, and were quoted as saying that they "don't know which family to visit and console first." Karkash is survived by his wife and two daughters. He was laid to rest at 5 p.m. in the Afula military cemetery.
Sgt.-Maj. Ro'i Yaish, 27, of Herzliya
got his call up order last week. "He loved his motorcycle," friends said. "You couldn't touch his bike or his helmet. Whenever we heard his bike, we knew he was coming and everyone would get excited." Ro'i is survived by his parents and three brothers. Ro'i was laid to rest at 5 p.m. in his hometown.
Despite being recently hurt in a field trip to the Judean Desert, St.-Sgt. Yehuda Greenfeld, 27, of Maale Michmas, was called to duty. Greenfeld leaves behind a two and a half year old daughter and a four month old son. Greenfled is survived by his wife and two kids, along with his parents and five brothers. He was laid to rest at 5 p.m. in the Herzliya cemetery.
St.-Sgt. Shaul Shai Michlowitz, 21, of Netanya, had finished his army service three months ago, and was waiting for his request to serve additional time as a career soldier to be authorized. Instead, he received an emergency call-up order last week. Shaul is survived by his parents and two sisters. He was buried at 5 p.m. in the Netanya military cemetery.
St.-Sgt.Maj. Daniel Ben-David, 38, of Moshav Ahituv, volunteered to join fellow paratroopers in Lebanon, despite his family's objections. Ben-David was described by a neighbor as "always laughing and hugging" and as someone who "loved to help people." Ben-David is survived by his wife and three children. He was buried at 5:30 p.m. in the Ahitub cemetery.
Warr.Ofc. Shmuel Halfon, 41, of Bat Yam, was called up two weeks ago, only to be told that he could return home last week. One day after he went home, Halfon was called up again. Family members said that Halfon loved the army and liked serving reserve duty. Halfon left behind three sons, one of whom is 11 months old. Halfon is survived by his wife and three sons. He was laid to rest at 7 p.m. in the military section of the Holon cemetery.
Sgt.-Maj. Ziv Balali, 28, of Kfar Sava, was about to celebrate his 29th birthday next month. Ziv recently completed a degreee in Middle East Studies. He is survived by his parents and sister. He was laid to rest at 7 p.m. in the military cemetery in his hometown.
St.-Sgt.Maj. Shlomo Buchris, 36, of Moshav Sde Yitzchak, r
eassured his brother that while other troops had gone into Lebanon, he had not yet entered and was fine. Just a short time later, Buchris was killed. Buchris was named after his father, who fell in the Six Day war.
He was buried at 7:15 p.m. in the Sde Yitzhak cemetery.
F.-Sgt. Mordechai Abutbul, 28, of Shlomi. He was buried at 10 p.m. in the military section of the Shlomi cemetery.
Captain Eliyahu Elkariaf, 34, of Moshav Granot. He will be laid to rest on Tuesday at 5 p.m. in the military section of the Kfar Ata cemetery.
August 7, 2006 /0 Comments/by rabbiyonah
http://rabbiyonah.com/wp-content/uploads/2013/04/cropped-RabbiYonahbookCover11.jpg 0 0 rabbiyonah http://rabbiyonah.com/wp-content/uploads/2013/04/cropped-RabbiYonahbookCover11.jpg rabbiyonah2006-08-07 19:15:572006-08-07 19:15:57May Hashem Comfort their families
OK OK Go – Here It Goes Again & Million Ways Hizbollywood 3: Fauxtography


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package org.terasology.world.chunks.blockdata;

import org.terasology.module.sandbox.API;

import java.lang.annotation.ElementType;
import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

/**
 * Annotation for classes which register extra per-block data fields.
 * Methods for this purpose must also be annotated with {@link org.terasology.world.chunks.blockdata.RegisterExtraData}.
 */
@API
@Retention(RetentionPolicy.RUNTIME)
@Target(ElementType.TYPE)
public @interface ExtraDataSystem {
}


We are pleased to announce our Distinguished Business Awards recipients at this event.
Please join us in congratulating these members for their contributions to the Bremerton Chamber of Commerce and our community. Nominate a business. Nominations!


Regardless, The Buzzcocks were among the most influential bands to emerge from the UK punk – new wave era with their deft combination of pop melodies as penned by Pete Shelley, stripped down arrangements, and boundless energy.
Employing no frills four-to-the-bar roots passages to creative counter-melodic motifs, the bassist during the band's glory years was Steve Garvey, who anchored some of their finest singles along with The Buzzcocks' essential early canon: Another Music in a Different Kitchen (1978), Love Bites (1978), and A Different Kind of Tension (1979).
Garvey, who also moonlighted with pop punks The Teardrops (which also included members of The Fall and PIL) and drummer John Maher were a ferocious rhythm section which grooved mightily at a frenetic pace.
After The Buzzcocks' initial split in 1981, Garvey worked with Steve Diggle (50 Years of Comparative Wealth / 1981), toiled in various ensembles, produced, lectured, and returned to the band briefly in the early 1990s for one of their numerous reunions.


See the latest Swarovski Xirius 2088 flat back colors & effects to help you decide which one you need! Over 100 different colors and effects. Also includes the 2080 flat back cabochons.
The back includes technical information on the crystal, a size chart with a representative sample of each size mounted in place, as well as a sample of each of the current shapes available in both flat back and hotfix flatback.
Comes on a heavy plastic easel that be displayed on a flat surface or hung on the wall.