text:
import React from 'react';
import PropTypes from 'prop-types';

import useBaseUrl from '@docusaurus/useBaseUrl';
import useDocusaurusContext from '@docusaurus/useDocusaurusContext';

import CloseIcon from '../../../../img/chevron-pressed.svg';
import NavMenuIcon from '../../components/NavMenuIcon';
import OpenIcon from '../../../../img/chevron-down.svg';
import PageIndicator from '../../components/PageIndicator';
import PopupMenu from '../../components/PopupMenu';

import styles from './styles.module.css';

const SubnavMobile = ({ activePopupMenu, setPopupMenu }) => {
  const {
    siteConfig: {
      customFields: {
        navbar: { secondaryLinks },
      },
    },
  } = useDocusaurusContext();

  return (

        <PageIndicator />
        <NavMenuIcon
          onClick={() => {
            setPopupMenu('secondary');
          }}
          CloseIcon={CloseIcon}
          isOpen={activePopupMenu === 'secondary'}
          OpenIcon={OpenIcon}
        />

      {activePopupMenu === 'secondary' && (
        <PopupMenu
          links={secondaryLinks}
          onClick={(e) => e.stopPropagation()}
        />
      )}

  );
};

SubnavMobile.propTypes = {
  activePopupMenu: PropTypes.string,
  setPopupMenu: PropTypes.func.isRequired,
};

export default SubnavMobile;


Honestly, I don't have any issues when it comes to revealing my age. Yes, I do join the "let's keep our age a secret" jokes with batchmates, but truth is I don't really mind getting older.
What I do fear is getting older…ungracefully! While we can purchase all the age defying face masks and eye creams offered in the market today, I believe there's really no way of defying ageing! We actually have no option when it comes to growing old. What we do have options for is how to approach it. We either age awkwardly or age gracefully. It's a choice we need to make every time we blow another set of birthday candles and every time we feel like "adulting" is taking a toll on us.
I'm a young parent and wife. Honestly, this new chapter in my life still feels surreal until today because it was just three years ago when I went to bed as a carefree single lady. I traveled when I wanted to, stayed til 10 in the morning on the bed during weekends, and went for a whole body Siatsu massage every other week just coz I wanted to.
Fast forward to NOW, it's suddenly not easy and automatic to get the "me" time I feel I deserve. With a corporate job and a freelancing opp, a household to manage, a child to take care of, a husband to cherish, family relationships to embrace, a regular household help that we can never find, a passion to pursue, a dream to chase, an advocacy to promote, a physical beauty to nourish, a spiritual life to nurture, and an overall personal wellness to take care of (Can you add more to the list, please?), taking a long shower is now a luxury I thank God for everyday. No wonder the carefree young adults like me frown at the thought of growing old.
And so we feel so tired and complain with our former YOLO buddies. "Why is adulting so hard?" Or "I knew it wasn't easy, but noone ever said it's this difficult!" Come on, let's treat ourselves with a cup of our favorite frap with whipped cream on top.
Plus, adulting paints a distracting picture for many of us. Fine lines and wrinkles, bulging tummy, gray hair, CS scars, stretchmarks, eye bags are all part of it, and so we try to conceal each one with good make-up or a good Instagram filter.
Add to that drama is the fear of the unknown. Adulting includes a big collection of monsters that may cause us anxiety, worries, fear, distractions, migraine, and perhaps, a gastroesophageal reflux disease.
We are so concerned about so many things — our future, our health, our business, our relationships, our family, our life. That's why there's a pressure within to work hard, succeed, stay fit, look fab, remain strong and young and pretty. The clock is ticking. We're not getting any younger. It's as if ageing is robbing the best of us.
But if you'll think about it, it isn't ageing or adulting that gets the best of us. It's the negative way we view ageing and the many things that go with it that brings the worst in us. Our pessimistic view of growing old is what robs us of the joy and peace that we should actually be enjoying each fleeting moment of our life.
We have an option to age gracefully. It doesn't mean remaining flawless through the years or achieving every single ounce of success before we reach 40. For me, ageing gracefully means embracing adulthood with all joy and serenity. It means taking care of and loving sincerely our physical beauty despite the wrinkles, the flabs and other flaws. It also means giving our best in all our responsibilities at work, in our families, in our business, in our personal life. It means juggling every ball of responsibility in our life without losing the balance, the poise, the gracefulness. It means being so busy outside yet not losing the peace within.


text:
tel: (718) 761-3888 support@synergycap1.com
Synergy Capital
Notable Deadly Shootings at Churches in Recent Years
Nov 5, 2017 | Bad Credit Loans, Bank Lending, Business Lending, Business Loans, Capital, Charleston, SC, Shooting (2015), Economics, Finance, Murders, Attempted Murders and Homicides, Non-Bank Loans, Pinckney, Clementa C (1973-2015), Roof, Dylann Storm, Samson, Emanuel K, Sutherland Springs, Tex, Shooting (2017), Working Capital
By THE NEW YORK TIMES
The mass shooting at the First Baptist Church of Sutherland Springs, Tex., on Sunday, in which at least 26 people died, was the latest and by far the most deadly shooting at a church in recent years. Here are some notable previous fatal shootings at churches.
Sept. 24, 2017 | Antioch, Tenn.
A gunman carrying two pistols opened fire as Sunday services were ending at the Burnette Chapel Church of Christ, killing one person and wounding seven others; he also pistol-whipped a church usher who confronted him. The gunman, identified as Emanuel K. Samson, shot and wounded himself, the police said; he was taken into custody.
June 17, 2015 | Charleston, S.C.
Nine people who had gathered for Bible study at the Emanuel African Methodist Episcopal Church, a landmark black church in Charleston, were shot and killed at the landmark black church in Charleston by a white gunman, identified by the police as Dylann Storm Roof, who was arrested the next day. A 10th person was wounded. The dead included the Rev. Clementa C. Pinckney, the church's pastor and a prominent state senator.
May 31, 2009 | Wichita, Kan.
A gunman shot and killed an usher in the foyer of the Reformation Lutheran Church as he handed out church bulletins after services. The usher was Dr. George Tiller, one of the few doctors in the country who performed late-term abortions. The assailant, Scott Philip Roeder, an anti-abortion activist, fled the scene and was arrested three hours later; he was convicted of murder in 2010 and sentenced to life in prison.
Dec. 9, 2007 | Colorado
A gunman shot two people at the Youth With a Mission Center at Arvada, near Denver, and then killed five people 12 hours later at the New Life Church in Colorado Springs, before he was shot by a volunteer security guard, the police said. The gunman, identified as Matthew Murray, died at the scene of the second shooting. The police said he was carrying two assault rifles, three handguns and 1,000 rounds of ammunition.
Allya Yourish contributed research.
Copyright © 2016 Synergy Capital. All rights reserved.

redpajama_set_name: RedPajamaCommonCrawl


text:
Mississippi Blues & Civil Rights
Order this guide through the Odyssee-webshop
Order as paperback
Order by chapter
Mississippi Delta 49
Memphis and North Mississippi 42
Jackson and Southwest Mississippi 30
New Orleans and Coastal Mississippi 16
Background information 9
Practical information 8
Tips not to miss
Spotify Playlist Mississippi Blues
Leo Platvoet
Look inside!
Blues & Civil Rights
Mississippi, The Magnolia State, situated in the deep south of the United States, is an intriguing destination. It is a state where African Americans suffered heavily from the slave trade, their exploitation on the countless, hot, cotton plantations, the killings of the Ku Klux Klan, and the government's apartheid policies. It was in Mississippi that the blues was born, the music that has had such a big influence on all the popular music that came after it.
`The Blues had a baby and they called it Rock 'n' Roll' (Muddy Waters)
'I know why the best blues artists come from Mississippi. Because it's the worst state' (John Lee Hooker)
Playlist Mississippi Blues
This Odyssee Travel Guidebook, Mississippi: Blues & Civil Rights considers the state's history of slavery, racism, the struggle for civil rights and the music that bears witness to those struggles. While traveling, you will experience the past, present and future of the blues in museums and historical places, but also at festivals and in clubs, cafes and juke joints where the blues is alive and attracting a new audience.
Civil rights museums, demonstrations, boycotts and sit-ins in small towns and large cities tell the story of resistance and hope, a story that resonates in the blues. This guide also describes New Orleans, Louisiana and Memphis, Tennessee, two other cities associated importantly with both Mississippi's blues and the struggle for civil rights.
by Leo Platvoet
€ 22,50 | $ 27,50 | £ 20,50
Published: 1 January 2021
Post and package: EU, UK, USA and Rest of the World: € 7,50
Er is ook een Nederlandstalige editie van de gids verkrijgbaar.
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Resto Végo …in the heart of the festivities!
The Montreal International Jazz Festival runs from Thursday, June 26 to Sunday, July 6, with a very impressive program!
Add to the pleasure of your downtown visit by discovering our latest summer offerings, like our new teriyaki tempeh burgers or Rawesome's delicious ripe strawberry fondant cake.
Whether for a snack on McGill College before or after a show, or for a drink on the Saint-Denis terrasse, both Resto Végo locations are close to the festivities! Come see us!


text:
Berner Au is a small river of Hamburg, Germany. It flows into the Wandse near Tonndorf (a district of Wandsbek).

See also
List of rivers of Hamburg

Rivers of Hamburg
Rivers of Germany

redpajama_set_name: RedPajamaWikipedia


Visakesa Chandrasekaram's 'Frangipani', touches rapidly changing village life, in a film centered on a love triangle. Frangipani will have screening on November 8.
Nadya Perera's documentary '4th of February', three Sri Lankan women including the high profile case of Rizana Nafeek, a child Sri Lankan housemaid executed by the Saudi Arabian government.
'4th of February' will have a screening on November 3.


text:
Frederik Møller kan henvise til

 Frederik Møller (pianofabrikant) (1839–1917)
 Frederik Møller (fodboldspiller) (født 1993)
 Frederik Møller (landmand) – møller og landmand, ejede Løndal 1839–1850


This lens is a photographic lens with an extremely wide angle classified as "FISHEYE FULLFRAME" one. It can be mounted at any camera of Canon EOS series. Presence of the device of automatic control of the instant-return diaphragm is not obligatory.
Quality of photographs may be significantly improved by the device of controlling diaphragm which is installed on the Peleng 17mm Lens. Multi-coating serves to decrease reflections. Diagonal(fullframe) Fisheye Peleng 17mm is ideal for architecture, landscape, and group pictures.


U.S. index futures are higher this morning, bouncing back from an emerging markets pummeling Monday that saw the iShares MSCI Emerging Markets ETF down by almost 2 per cent.
There are other experts who are far less sanguine, and I've also linked to those stories below. Domestic investors should watch oil prices to gauge market concerns because with 50 per cent of oil demand predicted to come from China and India alone (according to the International Energy Agency), crude demand is an emerging markets story and the possibility of contagion will likely be evident in the commodity price.


Cheltenham is a wonderful town in England which is a dwelling place to the celebrated Cheltenham festival. The event is of horse jump racing concluding the Gold Cup takes place for four days at Cheltenham Racecourse. Millions of racegoers will visit the Cheltenham during March for enjoying the national hunt racing. This sort of horse racing festival is considered to be a wonderful one as because of top quality racing. Lots of tremendous action will take place in these four days with line-up seeming mouth watering. Hundreds of horses will take part in Cheltenham festival and every race will be as spirited as ever. Choosing the winners is going to be the hard task in this celebration. Several runners will fly around the race tracks to attain their succeeding point. Best chasers and hurdlers will race against each other in the race meeting. Excitement and anticipation begins enhancing up many weeks beforehand.
Horse racing is a very famous and popular one in all over the world. Cheltenham is a prominent identity for the horse racing term. Thousands of racing face will unite on the Prestbury Park to enjoy the thrilling activities of this festival. Millions of pounds will won and lost during the race course meeting. Bookmakers are interested to involve in this horse race betting to win huge amounts by predicting the winners in advance.
Most of the race meetings will be sub divided into runners who are attempting to win and runners who are getting ready to win. The horse race is just a step in the direction of eventual winning run. But, at cheltenham, each horse that races will have been brought and fine tuned to the crest of robustness by their experienced trainers for this great meeting. This illustrates that all the runners are endeavoring hard to succeed. Further, there will not be horse that is there to formulate up the numbers.
The surging nature of the Cheltenham course can grasp out several horses who do not like the up and downward roller coaster path. Previous good performances are considered to be a good and effective thing to take place in the course for most of the experts. Add the advantage of your experience in horse racing and take part in this course for attaining the success. Fences are not the simplest thing as it will be the complicated one in the race meeting. Some fences are located on the downhill elements of the course. This can simply grab out the reckless horse and jockey. Horse has to be a fast jumper to make more lengths in the air. It requires more efforts than just first-rate jumping to win at Cheltenham festival meeting.


text:
Курятников — русская фамилия.

Известные носители:
 Курятников, Наиль Хабибулович (род. 1959) — советский футболист.
 Курятников, Николай Андреевич (1923—1944) — капитан РККА, Герой Советского Союза.


text:
21September 2012
Theme Park at Oxjam
We chat to Theme Park ahead of their gig amongst the second hand books & bric-a-brac with Graham Coxon to raise money for Oxfam
Theme Park at Oxjam2
Next week, East London's Oxfam store will be throwing its annual festival Oxjam raising money for their charitable cause in its sixth year. Bringing down some of the biggest names to perform shows in their unusual setting, they'll be kick starting with the likes of Graham Coxon, Hot Chip, Totally Enormous Extinct Dinosaurs, and The 2 Bears.
Made up of hundreds of fundraising music events put on by volunteers, this year's Oxjam gigs range will from large-scale festivals to local sponsored busks across cities in the UK from Aberdeen to Bournemouth - meaning the biggest line-up of any music festival in the UK. During October, more than 800 venues, 9,000 volunteers, 7,000 musicians and 150,000 audience members will enable Oxjam to raise at least £350,000 for Oxfam's work.
For the first year ever, Oxfam will be leaving the shop floor exclusively in the hands of Hot Chip. The band will be having their own Oxjam party, inviting friends to donate their music, join them on stage to spin their favourite records and hidden gems uncovered in the shop. The band will also be helping the shop out with some exclusive merchandising by creating some "recommended by Hot Chip" racks of vinyl.
Here we speak to the London-based band Theme Park ahead of their gig next Monday about what's to come...
Dazed Digital: What has been the most exciting thing you've done in the last month?
Miles - Theme Park: Well we just got back from Ibiza Rocks last night which was incredibly exciting. The gig was great and then after we went to this party in the hotel where they filmed Club Tropicana and Queen wrote We Are The Champions, and that was very cool!
DD: What are you most looking forward to next?
Miles - Theme Park: Lots of things! We're going to Jakarta for a few shows next week, which will be the furthest gig from home we've ever done. And also the Bloc Party tour in October will be amazing.
DD: Have you played anything like this before?
Miles - Theme Park: I may be wrong but I'm pretty sure this is our first gig for a charity.
DD: How did you get involved in this project?
Miles - Theme Park: We just got an email out of the blue really, saying that they had enjoyed the songs we'd released so far and would be keen to play at one of their shows. I think everyone was really keen partly because even though we're not yet mega rock stars, it does sometimes feels like lots of people are helping us out with stuff. So it felt nice to do a show where we were helping other people. And also of course we were incredibly excited to be playing a show supporting Graham Coxon!
DD: Why do you think this is a cause that you particularly wanted to be involved in? What do you want the audience to leave with on their minds?
Miles - Theme Park: Well I don't think poverty is a very good thing, and its nice to be able to do something, even if its very small, to try to change that. Hopefully happiness at the music they've heard, and also happy that they did a small deed for charity by buying a ticket and coming to the show.
Monday 24 September: Graham Coxon, Theme Park (acoustic set), plus support
Tuesday 25 September: Totally Enormous Extinct Dinosaurs DJ set, Breach (Aka Ben Westbeech), plus support
Wednesday 26 September: Lucy Rose, Kyla La Grange, plus support
Thursday 27 September: Hot Chip & friends including Hot Chip DJ set, The 2 Bears and special guests TBA.
Oxjam takes place on the weekend of 20th & 21st October. Tickets to the shows go on sale HERE. Tickets for these exclusive one-off performances will raise money for Oxfam's lifesaving work around the world.


text:
Q: PHPUnit outputting whole response on WebTestCase assertion failure I have an app test in which a request is supposed to return 403 and returns 200. That's fine, I haven't implemented access on this route yet. What is bothering me is that the failure outputs the whole response, header and body, which means I have to crawl through a lot of data to access the useful info.
I have looked into PHPUnit's xml conf parameters, cli parameters, ouput handling functions and annotations but I have not found a way to suppress the whole response being dumped on a failed test. I also tried to change monolog's test conf from debug level to critical, and to exclude 403, to no avail. The PHPUnit doc says :

Whenever a test fails PHPUnit tries its best to provide you with as much context as possible

Which is nice except it's actually making it really hard to debug. And that's just one test.
I have found a trick in another answer, so I can just assertSame(), go fetch the response status code myself and be on my way, but as stated it's usually very bad practice.
Any idea on how I might do this better?

redpajama_set_name: RedPajamaStackExchange


Celebrating ingenuity, invention and imagination brought to life on a grand scale, MODERN MARVELS tells the fascinating stories of the doers, dreamers and sometime-schemers who created everyday items, technological breakthroughs and man-made wonders. Nikola Tesla's bizarre vision of the future brought him failure, but his genius electrified the world. Travel to Niagara Falls where, in 1893, Tesla installed his new system of Alternating Electrical Current, known as "AC," the same power we use today. Tour the forgotten ruins of Tesla's dream experiment: a huge tower on Long Island Sound he hoped would wirelessly power the world. Tesla claimed to have created a remarkable array of technologies: radar, death rays, invisibility devices and earthquake machines. More than 100 years ago Tesla foresaw the need for alternative energies like geothermal and solar. He had a lot on his mind, but he was also losing it.


text:
Q: Значение xml тэга объектного типа с использованием XStream Использую библиотеку XStream, появилась проблема, решение которой в документации не нашел.
Какой XML хочу получить на выходе:
<boolProp name="TestPlan.functional_mode">false</boolProp>

Для этого создаю класс BoolProp:
@Data
@NoArgsConstructor
@AllArgsConstructor
@XStreamAlias("boolProp")
public class BoolProp {
    @XStreamAsAttribute
    private String name;
}

Все корректно, остается только добавить значение:
@Data
@NoArgsConstructor
@AllArgsConstructor
@XStreamAlias("boolProp")
public class BoolProp {
    @XStreamAsAttribute
    private String name;
    private String value;
}

Вот тут все и ломается, вывод:
  <boolProp name="TestPlan.functional_mode">
    <value>false</value>
  </boolProp>

Подскажите, пожалуйста, как вместо отдельного тэга value сделать value для тэга boolProp.
Заранее спасибо за помощь.

A: Согласно документации вам нужен аттрибут XStreamConverter
@XStreamAlias("message")
@XStreamConverter(value=ToAttributedValueConverter.class, strings={"content"})
class RendezvousMessage {

    @XStreamAlias("type")
    private int messageType;

    private String content;

    @XStreamConverter(value=BooleanConverter.class, booleans={false}, strings={"yes", "no"})
    private boolean important;

    @XStreamConverter(SingleValueCalendarConverter.class)
    private Calendar created = new GregorianCalendar();

    public RendezvousMessage(int messageType, boolean important, String content) {
        this.messageType = messageType;
        this.important = important;
        this.content = content;
    }
}

Результат
<message type="15" important="no" created="1154097812245">This is the message content.</message>


text:
669-454-2046 or rescue.woofdenescue@gmail.com
Welcome To Woof Den
Welcome To Woof Den Welcome To Woof Den
We are a Compassionate, Experienced Nordic and Herding Breed Rescue
We, are committed into giving as many chances for these Nordic and herding breeds before they get placed into their permanent home-- We Don't Euthanize for Space--- at Woof Den Rescue Strive to rescue homeless Nordic and Herding Breeds to give them the life that they deserve.
We are all about providing support, education and assistance as needed , to all owner of the Nordic and Herding Breeds .
We will thoroughly screen each applicant before placing any Nordic or herding breeds into a permanent home and we have the right to refuse Adoption. We hope to educate the public through events and public forums about the Nordic and herding breeds and the need for rescue, and work with others rescues in helping all animals and their humans enjoy a healthy beneficial relationship
We, of Woof Den Rescue, Strive to rescue homeless Nordic and or Herding Breeds . We provide foster homes and medical care until a suitable new forever home is found.
Woof Den Rescue serves California, most of Nevada by rescuing stray, surrendered and endangered Nordic and Herding Breeds and placing them in permanent homes while educating and supporting current and future owners.
Woof Den Rescue strive throughout the states we serve, to have loving forever homes.
Compassionate: Our compassion compels us to save as many Nordic and Herding Breeds as possible.
Impactful: It is important to utilize the resources available to produce real change, awareness and action in the community.
Responsible: We have an obligation to prioritize the welfare of the animals.
Respectful: We must be positive towards one another and hold each other in high esteem, ensuring our actions show we care.
Find out more about Woof Den Rescue
All types of Husky Breeds
THE 3-3-3 RULE OF ADOPTING A RESCUE DOG
This graphic will help you know at at a glance what to expect in the next few months. This will vary from dog to dog and circumstances.
When looking to adopt a new dog, the most important thing to consider is always the energy of the dog and how the dog will fit in with your lifestyle and your family.
A Little About Us the Nordic Breeds
The Siberian Husky
Siberian Husky, breed of working dog raised in Siberia by the Chukchi people, who valued it as a sled dog and companion. It was brought to Alaska in 1909 for sled dog races and soon became established as a consistent winner. In 1925 the breed gained widespread fame by saving Nome, Alaska, during a diphtheria epidemic; teams of Siberian Huskies battled blizzard conditions to relay life-saving serum over 674 miles (1,085 km) to the icebound city.
A graceful dog with erect ears and a dense soft coat, the Siberian Husky stands 20 to 24 inches (51 to 61 cm) tall at the withers and weighs 35 to 60 pounds (16 to 27 kg). It is usually gray, tan, or black and white, and it may have head markings resembling a cap, a mask, or spectacles. The breed, kept pure for hundreds of years in Siberia, is noted for its intelligence and a gentle temperament. It was recognized by the Fédération Cynologique Internationale (International Canine Federation) in 1966 as a member of the spitz and primitive types group (subgroup Nordic sledge dogs) and by the American Kennel Club in 1930 as a member of the working group.
Find out more about the Siberian Husky
The Alaskan Malamute
Alaskan Malamute, sled dog developed by the Malemiut people of Alaska, from whom it takes its name. The Alaskan Malamute is a strongly built dog, with a broad head, erect ears, and a plumelike tail carried over its back. Its thick coat is usually gray and white or black and white, the colours frequently forming a caplike or masklike marking on the head. The Alaskan Malamute stands about 23 to 25 inches (58 to 63.5 cm) tall at the withers and weighs 75 to 85 pounds (34 to 39 kg). Characteristically loyal and friendly, it has been used to haul freight on sleds and has served on expeditions to Antarctica.
Find out more about the Alaskan Malamute
The Samoyed
Samoyed, breed of working dog developed in Siberia, where its ancestors were kept by the Nenets (formerly Samoyed or Yurak) people as a sled dog and companion and as a herding dog for their reindeer. The Samoyed is sturdily built, with erect ears, dark almond-shaped eyes, and a characteristic "smile." Its long heavy coat is white, cream, biscuit (grayish yellow), or white and biscuit. The Samoyed stands 19 to 23.5 inches (48 to 60 cm) tall at the withers and weighs 35 to 65 pounds (16 to 29.5 kg). The breed was formally recognized by the American Kennel Club in 1906.
Find out more about the Samoyed
The Alaskan Husky
Alaskan Husky, The Alaskan husky is an incredibly athletic dog variety, and as a dog crossbreed their appearance can vary markedly, although various lines have been bred for multiple generations and breed very true to that line's type.Some Alaskan husky lines have very traditional husky spitz-like features with pricked ears and curled tails, whilst other lines more closely resemble their Shepherd, hound or gundog heritage with tipped or floppy ears, straight tails and tucked up sighthound-like loins. As they are not bred with conformation in mind, cosmetic features are not a consideration for breeding, and these features instead tend to follow the purpose of the dog's intended style of sled work.
Find out more about the Alaskan husky
Are you ready to Adopt? we have some amazing sweet smart and lovable dog that are looking for a forver home
You want to make a difference in a rescue dogs life? Its a great way for you to help.
Are you looking for somthing new and fresh open your heart and home to help a dog in need? At events, adoption fairs, becoming a foster parent or volunteer with us. Its fun to help a rescue dog in need. You will a Hero
Amazing dogs of the months - Just press the photo to veiw more pictures and Bio
NAME: Nile
AGE: 2 years old
Breed: Siberian Husky
NAME: Harmony
Breed: Husky Mix
NAME: Blossom
If you have any question about Volunteering, Fostering, Rehoming or Interested in Adopting.Send us a message, and we'll get back to you as soon as possible.
Woof Den Rescue
P.O. Box 381 San Juan Bautista, California, United States
669-454-2046 rescue.woofdenrescue@gmail.com
Copyright © 2023 Woof Den Rescue - All Rights Reserved.
Please note all Adoption deposits are Non-Refundable
Non-profit Tax ID # 47-0980964


text:
\section{Introduction}
\IEEEPARstart{W}{ith} wind energy being a driving force, both, onshore and offshore technologies have attracted big investments globally \cite{2016}. Onshore wind farms have an advantage of proximity to the utility grid whereas offshore wind farms need long transmission cables to transmit power from the sea to grid. However, with a strong wind field in the offshore areas compared to onshore, the offshore wind farm installations have risen drastically. Further, with lesser turbulence and more uniform wind speed, the lifetime of wind turbines in offshore scenario increases \cite{Esteban2011}. Offshore wind farms have a disadvantage of higher installation and engineering costs accompanied by the wear and tear of generator and mechanical equipment. Onshore wind farms, on the other hand face the challenge in their acceptance from limited land availability, visual intrusion and damage to wildlife. The defining factor of operation between onshore and offshore wind farms is the surface roughness length. It is observed that, larger the surface length higher the reduction in wind velocity and theoretically the wind speed at ground is zero \cite{Aerodyna75:online}.

Wind energy related installation activity, particularly in the offshore areas, is challenged by the transportation costs owing to large blades and support structures to be placed in deep sea water. Another issue is the uncontrolled vibrations arising from the combination of wind and wave force \cite{Colwell2009}. With a higher wind speed in the sea, the probability of ramp instances increases. Wind power growth has led to detailed study of ramp events, particularly categorized as ramp-up and ramp-down events with each characterized by sudden wind speed change in a short period. Mathematically, a wind power ramp event \cite{Kamath2010} is described as the arithmetic difference between wind power at consecutive time instants:
\begin{eqnarray}
\label{e1}
\Delta P_w^{Ramp}= P_w(t+\Delta t)-P_w(t).
\end{eqnarray}

The occurrence of a wind power ramp event is ascertained by a threshold value of the total wind farm power. Wind power forecasting is an essential market procedure to regulate the electricity markets where the farm operators hold an advantage to derive profit from their generation given an accurate wind forecasting strategy. Predicting these ramp events can suitably prevent intermittent grid failures and for that purpose, proper characterization of ramp events is imperative in avoiding potential risks in the management of a power system. On the contrary, a poor characterisation of the ramp performance inevitably hinders the identification of the processes that cause ramp events.

In Horns Rev wind farm, large variation in wind power is seen in a short duration \cite{Vincent2010}. In a study carried out by Gjerstad et al., the variation in wind power is triggered by atmospheric properties of boundary layer \cite{Gjerstad1995}. The atmospheric factor that influences the wind speed variations is the mean lapse rate ($\partial T/\partial z$), that is, rate of change of air temperature ($T$) with height $z$. In case of offshore wind farms connected to German power grid, within 8 hrs, the wind power transfer between converters increased from 4 GW to 19 GW as a result of a sudden change in wind speed \cite{Kramer2014}.

Further, Nissen studies seasonal variations in wind speed over coastal areas of H{\o}vs{\o}re which shows that the wind speed variation are primarily dominant in the spring and winter season \cite{8220775345684ab4a6201dff3283f31f}.
It is found that the wind power ramp events are characterized based on magnitude error, phase error and location error as pointed out by Potter et al.\cite{Potter2009}. In \cite{Cutler2007}, authors discuss the MesoLAPS and Wind power prediction tool (WPPT) for analyzing and categorizing large wind power ramp events where the problem is modeled as a classification task. Root mean squared error (RMSE) is evaluated over 1 year for two wind speed time series with 5-min and 10-min sampling intervals. Results reveal that the RMSE values are found to be more optimistic than conventional persistence and climatology methods.

Gallego-Castillo et al. reviewed various wind ramp events and have described preliminary definition of a ramp event and the threshold values that various literature have used to formulate a ramp event \cite{GallegoCastillo2015}. Bossavy et al. proposed a reliable forecast methodology to characterize ramps with a derivative filtering edge detection approach and numerical weather prediction ensembles to make probabilistic forecasts of ramp occurrence \cite{Bossavy2012}. Understanding the underlying role of the synoptic weather regimes in triggering the wind power ramp events can help improve and complement the current forecast techniques. Cuoto et al. have identified and classified the weather regimes over mainland Portugal associated with the occurrence of severe wind power ramps \cite{Couto2015}.

Cornejo-Bueno et al. have described the machine learning techniques like, support vector regression (SVR), gaussian process regression (GPR), multi-layer perceptrons (MLP) and extreme learning machines (ELM) to forecast wind power ramp events \cite{CornejoBueno2017}. Cui et al. detect ramp event probability characteristics from scenarios captured bands to evaluate the ramp event forecasting method using a modified genetic algorithm with multi-objective fitness functions \cite{Cui2015}, \cite{Cui2015b}. Gallego et al, formulated a ramp function to describe a ramp event characterised by high power output gradients evaluated under different time scales, and is based on discrete wavelet transform to provide a continuous index related to the ramp intensity \cite{Gallego2014}. In another work, Cornejo-Bueno et al. have considered a neural network based model in tandem with evolutionary algorithm to optimize the parameters of several classifiers such as support vector machines (SVM) and extreme learning machine (ELM) \cite{CornejoBueno2018, Du2019, Zhang2015c}. Three wind speed datasets from Spain are considered to evaluate the accuracy of hybrid method for classifying wind power ramp events. In \cite{Li2017}, the authors have presented a feature extraction method based on Gabor filtering where the atmospheric pressure fields are taken into consideration. Results are compared with   state-of-the-art neural network method and it is found that Gabor method with its change in power as its output gives a better prediction performance.

In a recent study, Ouyang et al. have presented an improved ramp prediction methodology using residual correction through a model that combines the advantages of the auto-regression and Markov chain models \cite{Ouyang2019}. Classification based forecast is inaccurate and causes class imbalance issue in the machine learning problem since the occurrence of a ramp event is typically rare, and so Takahashi et al. have used and experimentally evaluated this sampling approach on a real-world wind power generation dataset \cite{Takahashi2017}. In another work, Fujimoto et al. have presented an altering scheme of an impending ramp event for efficient operational decisions using supervised learning algorithms for better health management and reduced downtime \cite{Fujimoto2019}. Zhang et al. decompose wind speed data and analyze each part using different models to make predictions based on wind speed ramp and residual distribution \cite{Zhang19}. In other ramp event prediction related works, Dhiman et al. have discussed SVR and its variants \cite{Dhiman2018} for predicting wind power ramp events where five datasets are tested for ramp-up and ramp-down events \cite{Dhiman2019}, \cite{harshels}.

The current work addresses following objectives:
\begin{enumerate}
	\item {Ramp prediction models for onshore and offshore wind farms are discussed. The wind powers corresponding to the wind speed time series obtained are calculated for identifying the ramp events. The wind power ramp events are predicted using a hybrid method involving wavelet transform decomposition and machine learning methods. Thus, the underlying problem here is modeled as a regression task with $\Delta t=$ 10 min.}
	\item {The potential capability of random forest regression and gradient boosted machines are checked. Several error metrics for the entire wind speed time series and absolute error for ramp events (ramp-up and ramp-down) are evaluated and further compared with benchmark persistence and SVR models.}
	\item {Log energy entropy based randomness is discussed for onshore and offshore wind farm sites. The randomness in a ramp signal is an important feature to be dealt with. Higher order randomness calls for accurate wind resource assessment and micro-siting.}
\end{enumerate}

This manuscript is organized as follows. Section 2 describes machine learning methods, that is, support vector regression and its variants, random forest regression and gradient boosted machines followed by framework of ramp prediction models in Section 3. In Section 4 outcomes of the proposed models are presented and Discussions are highlighted in Section 5. Section 6 discusses conclusions.

\section{Methods for Wind Power Ramp Prediction}
Next, we discuss various methods employed to predict ramp events in onshore and offshore wind farms. Wavelet decomposition based machine learning methods are used. A hybrid model based on  Wavelet decomposition serves the purpose of eliminating stochastic trends in wind speed time series. Support vector regression and its variants like $\varepsilon$-SVR, Least square support vector regression, Twin support vector regression and $\varepsilon$-Twin support vector regression are discussed. Further, prediction models based on regression trees like Random forest and Gradient boosted machines are also discussed for predicting ramp events.

\subsection{Support Vector Regression \& its variants}
Support vector regression (SVR) which is coined from support vector machines (SVMs) \cite{Vapnik2000} is a popular machine learning method used in several branches of forecasting like solar radiation forecasting \cite{Gala2013}, wind forecasting \cite{LIU2018} and hydrological time series \cite{Sahoo2018}. The basic idea of SVR is to map a set of non-linear features to a higher dimension where the data is linearly separable,  mathematically expressed as
\begin{equation}
g(x)=w^Tx+b,
\end{equation}
where $w$ is a weight vector, $x$ represents set of input features and $b$ is the bias term. SVR operates on historical data which plays an important role in predicting the future values of the response variable. Consider a set $X\in R^n$, $X=[x_1, x_2,\dots,x_n]$ as the set of input features. The weights for the input features are obtained by solving an optimization problem described in Table \ref{T}. Further, Suykens penned Least square support vector regression (LSSVR), where the objective function for minimization incorporates the squared error term \cite{Suykens1999}. The computation time for LSSVR is fast compared to classical SVR owing to its smaller sized lagrangian multiplier matrix. In 2010, Peng derived Twin support vector regression (TSVR) where two smaller sized optimization problems are solved to arrive at resultant regressor \cite{Peng2010}. The optimization problem along with resultant regressor $g(x)$ is depicted in Table \ref{T}. Further, in 2012, Shao et al. presented a $\varepsilon$-Twin support vector regression that incorporates an extra regularization factor and arrives at the final regressor using successive over relaxation technique which is a fast convergence algorithm \cite{Shao2012}.
However, with larger datasets, the regression analysis can be computationally expensive and can lead lead to large errors in predicting ramp events. Thus, the ramp event prediction is extended for two more methods, that is, random forest regression (RFR) and gradient boosted machines (GBM) categorically for onshore and offshore wind farm sites, and specifically for offshore wind farms, the dominating strong wind field poses a forecasting challenge for operators.

	\begin{table}[h]
		\begin{center}

			\caption{$\varepsilon$-SVR, LS-SVR, TSVR and $\varepsilon$-TSVR regression models} \vspace{0.2cm}\label{T}

			\begin{tabular}{|c|}\hline
				Optimization Problem  \\ \hline
				Model: $\varepsilon$-SVR,~~ Regressor: $g(x)=w^{T}x+b$ \\
				$\text{min} \hspace{0.1cm} \frac{1}{2} \parallel w\parallel^2 + C(e^T\xi+e^T\xi^*)$ \\
				$ \textbf{s.t.}~~ y-w^{T}x-eb \le e\varepsilon +\xi, \xi \ge 0,$ \\
				$w^{T}x+eb-y \le e\varepsilon +\xi^{*}, \xi^* \ge 0$, \\
				\hline
				Model: LS-SVR\\
				Regressor: $g(x)=\sum_{i=1}^{n}\alpha_i k(x,x_i)+b$\\
				$\text{min} \hspace{0.2cm} \frac{1}{2} \parallel w \parallel^2 + \frac{1}{2}\gamma\sum_{i=1}^{n}\varepsilon_i^2$\\
				$\textbf{s.t.} \hspace{0.2cm} y_i= w^T\phi(x_i)+b+\varepsilon_i$ \\ \hline
				Model: TSVR\\
				Regressor: $g(x)=\frac{1}{2}((w_1+w_2)^Tx+(b_1+b_2))$\\
				$\text{min} \hspace{0.2cm} \frac{1}{2}\parallel(y-e\varepsilon_1-(xw_1+eb_1))\parallel^2+ C_1e^T\sum_{i=1}^{n}\xi_i$ \\
				$\text{min} \hspace{0.2cm} \frac{1}{2}\parallel(y-e\varepsilon_2-(xw_1+eb_2))\parallel^2+ C_2e^T\sum_{i=1}^{n}\eta_i$ \\
				$\textbf{s.t.} \hspace{0.2cm} y_i-(x_iw_1+eb_1)\ge e\varepsilon_1-\xi_i$ \\
				$\textbf{s.t.} \hspace{0.2cm} (x_iw_2+eb_2)-y_i\ge e\varepsilon_2-\eta_i$ \\ \hline
				Model: $\varepsilon$-TSVR\\
				Regressor: $g(x)=\frac{1}{2}((w_1+w_2)^Tx+(b_1+b_2))$ \\
				$\text{min} \hspace{0.2cm} \frac{1}{2}C_3(w_1^Tw_1+b_1^2)+\frac{1}{2}\xi^{*T}\xi+C_1e^T\xi$ \\
				$\text{min} \hspace{0.2cm} \frac{1}{2}C_4(w_2^Tw_2+b_2^2)+\frac{1}{2}\xi^{*^T}\xi+C_2e^T\eta$ \\
				$\textbf{s.t.} \hspace{0.2cm} Y-(Xw_1+eb_1)=\xi^*$
				,\\ $Y-(Xw_1+eb_1)\ge -e\varepsilon_1-\xi, \xi \ge 0$ \\
				$\textbf{s.t.} \hspace{0.2cm} Y-(Xw_2+eb_2)=\eta^*$
				 , \\ $Y-(Xw_2+eb_2)\ge -e\varepsilon_2-\eta, \eta \ge 0$ \\
				\hline
				Model: Kernel, $\varepsilon$-TSVR\\
				Regressor: $g(x)=\frac{1}{2}((w_1+w_2)^TK(X,X^T)+(b_1+b_2))$  \\
				$\text{min} \hspace{0.2cm} \frac{1}{2}C_3(w_1^Tw_1+b_1^2)+\frac{1}{2}\xi^{*T}\xi+C_1e^T\xi$ \\
				$\text{min} \hspace{0.2cm} \frac{1}{2}C_4(w_2^Tw_2+b_2^2)+\frac{1}{2}\xi^{*^T}\xi+C_2e^T\eta$ \\
				$\textbf{s.t.} \hspace{0.2cm} Y-(K(X,X^T)w_1+eb_1)=\xi^*$, \\
				$Y-(K(X,X^T)w_1+eb_1)\ge -e\varepsilon_1-\xi, \xi \ge 0$\\
				$\textbf{s.t.} \hspace{0.2cm} Y-(K(X,X^T)w_2+eb_2)=\eta^*$,\\
				$Y-(K(X,X^T)w_2+eb_2)\ge -e\varepsilon_2-\eta, \eta \ge 0$ \\
				\hline
			\end{tabular}
			\vspace{-0.25cm}

		\end{center}
	\end{table}

\subsection{Random forest regression}
Proposed by Brieman, random forest is an ensemble method that generates something, akin to a forest of trees from a given training sample \cite{Breiman2017}. The ensemble based models are far more accurate than a single method owing to advantages like capturing linearity and non-linearity of time-series obtained from individual methods. Random forest begins with splitting the input features into a group of subsets that essentially form a tree. Similar to hyperparameters ($\sigma$ and $C$) in SVR, in random forest regression, the number of trees and number of random features in each tree decomposition are the parameters that decide the performance of regression. At each decision tree, a fitting function is created which acts on the random features selected. Finally, at the end of the training process, a random forest model is created. It is worthwhile to note that during training process, each tree is created from randomly selected input vectors and thus it is called `random' forest. The estimated output of a random forest regression is given as
\begin{equation}
\hat{A}=\frac{1}{k}\sum_{i=1}^{k}\hat{r}\Big(X,V_i\Big),
\end{equation}
where $\hat{r}\Big(X,V\Big)$ is the representative tree at the end of training process, $X$ is the set of input feature vectors and $T$ is the collective set representing input-output pair $V_i=(x_1,y_1),(x_2,y_2),\dots(x_n,y_n)$.

A particular tree is characterized by a node that leads to number of branches, as depicted in Fig. \ref{rfr_wavelet}.
\begin{figure}[h]
	\centering

	\includegraphics[width=1\linewidth]{rfr}
	\vspace{-0.25cm}
	\caption{Block diagram for random forest regression model}\vspace{-0.15cm}
	\label{rfr_wavelet}
\end{figure}

The predicted output is averaged over $k$ decision trees. An added advantage of random forest regression is its insensitivity to noise due to uncorrelated trees via differential sampling of inputs. Figure \ref{fd} illustrates a generic block diagram of the wavelet based ramp prediction model for onshore and offshore wind farms.

\begin{figure*}
	\centering
	\includegraphics[width=0.9\linewidth]{fundamental_design}
	\caption{Block diagram for ramp event prediction and randomness in ramp signal}
	\label{fd}
\end{figure*}

The input feature set or predictors are a set of approximation signal (A5) and detail signals (D1, D2, D3, D4 and D5) obtained from wavelet decomposition of wind speed. A common problem of over-fitting persists in machine learning regression models when a well trained model captures the noise component as well. To reduce the complexities posed by over-fitting, random forest makes a compromise between a flexible and an inflexible model. In the training phase, each regression tree draws a sample from the feature set and is drawn repeatedly. This ensures that even though the tree might possess a high variance but the overall variance of forest will be low. Random forest work on the principle of bagging that combines the predictions from different tree models to give an overall insight to the data under training. This also helps to reduce the potential over-fitting caused by supervised machine learning models.

\subsection{Gradient boosted machines }
Gradient boosted machines (GBM) is an ensemble based regression method that takes into account the loss of the previously fit decision tree. In a GBM model, various weak learners combine together to arrive at an accurate model. Boosting based regression trees are effective in predicting accurate response values. A particular boosting algorithm strengthens the tree model by minimizing the inaccuracies of weak models in form of loss function \cite{Solomatine}. The main task is to reduce the error posed by weak learners in each iteration. The final predicted response is the linear combination of fit trees. The learning rate of each decision tree determines the predictive performance. A higher learning rate indicates less number of trees required for prediction and vice versa \cite{Elith2008}. Based on a cross-validation experiment the ideal number of trees required along with learning rate is obtained.

The aim for a individual model is to reduce the error generated by a loss function. Commonly used loss function is $\mathcal{L}_2$ function that minimizes the sum of squared errors between predicted and actual value. Consider $k$ such weak models that account for prediction of a variable $y$ given feature set $x=[x_1,\dots,x_n]$, mathematically it can be expressed as
$\hat{y}=\sum_{i=1}^{k}f_i(x),$
where $f_i(x)$ represents each weak learner that is collectively trained to improve the prediction. The loss function is given as\vspace{-0.15cm}
\begin{equation}
    \mathcal{L}_2 =\frac{1}{N}L(y_i,\hat{y}_i),~~
    L(y_i,\hat{y}_i) =\sum_{i=1}^{N}(y_i-\hat{y}_i)^2,
\end{equation}
where $L(y_i,\hat{y}_i)$ is a loss function based on squared errors for $N$ observations. The aim of a GBM regression technique is to minimize the $\mathcal{L}_2$ loss function. However, the $\mathcal{L}_2$ loss function is more sensitive to outliers and can reduce the robustness of the model. For optimizing the hyper-parameters in GBM, a gradient descent algorithm is used which minimizes the cost function taking into account the negative of the gradient. Let us consider a $\mathcal{L}_2$ loss function, the gradient of this function with respect to predicted sample $\hat{y}_i$ is given as
\begin{equation}
    \frac{\partial L(y,\hat{y})}{\partial \hat{y}_i}=\frac{\partial}{\partial \hat{y}_i}\sum_{i=1}^{N}(y_i-\hat{y}_i)^2 = -2(y_i-\hat{y}_i).
\end{equation}
The gradient reflects that while tracking the minimum point of the loss function, the GBM actually tracks the residual vector $(y-\hat{y})$. Similarly, for $\mathcal{L}_1$, the MAE can be tracked by finding the gradient of loss function. The essence of a GBM model lies in the boosting technique that combines all the weak models in a stage-wise manner than in a parallel manner as seen in random forest model. The GBM model in recursive form is given as $F_m(x)=F_{m-1}(x)+\eta\Delta_m(x)$,
where $\eta$ represents the learning rate and $\Delta_m$ refers to a regression model fitted to the residuals.

Figure \ref{fig:XGBM} illustrates the \textit{XBoost} algorithm used for obtaining the best regressor.
\begin{figure}[h]\vspace{-0.25cm}
\centering
   \includegraphics[width=0.99\linewidth]{GBM_algo}\vspace{-0.25cm}
   \caption{XBoost algorithm for Gradient boosted machines}\vspace{-0.25cm}
    \label{fig:XGBM}
\end{figure}

\section{Datasets \& Framework of prediction models}
Wind speed variation depends on the terrain under study. For all the wind farms operating in neutral atmospheric boundary layer, the wind speed follows a logarithmic profile with height. For onshore wind farms, the surface roughness length ($z_0$), is around 0.005 m while for offshore wind farms it is 0.0002 m. The current hybrid prediction model is based on a combination of wavelet transform and a machine learning algorithm. Wavelet transform is widely used for defragmentation of a time-series signal into low and high frequency sub-signals called as approximate and detail signals respectively. A 5-level db4 wavelet transform is used to decompose the wind speed time series. The approximate signal (A5) along with detail signals (D1, D2, \dots, D5) form input feature set for all the prediction models. The datasets collected for the month of March 2019 are wind speed time-series at a height of 10 m above ground averaged over a time interval of 10 minutes. For an accurate wind power ramp event study, it is desirable to transform the available wind speed to a hub height of 90 m using logarithmic law \cite{Irwin1979}. In this work, we consider all the wind farms with identical wind turbines having rotor diameter of 120 m with a hub height of 90m. The datasets (onshore and offshore) with their site coordinates and statistical parameters are depicted in Table \ref{TCR7}. Mean represents the mean of the wind speed time-series and SD indicates the standard deviation. The variability in wind speed is high as the changes in wind speed on minute to minute scale are likely to cause large power changes.

\begin{table}[h]
	\caption{Description of wind farm datasets for the month March 2019}\label{TCR7}\vspace{0.2cm}
	\centering
	\begin{tabular}{|c|c|c|}\hline
		Onshore Wind farm  & Mean & SD
		\\
		(Site coordinates) & &
		\\
		\hline
		Amakhala Emoyeni, SA (a) & 6.264 & 3.198 \\
		($-32.17^\circ$ N, $25.95^\circ$ E) & &\\
		Clyde, Scotland (b) & 3.829 & 1.626\\
		($55.46^\circ$ N,  $03.65^\circ$ W) & &\\
		Gansu, China (c) & 4.000 &2.477\\
		($40.20^\circ$ N, $96.90^\circ$ E) & &\\
		McCain Foods, UK (d) & 6.491 & 3.519\\
		($52.56^\circ$ N, $0.172^\circ$ W) & & \\
		Shephards Flat, USA (e) & 6.074 & 2.618\\
		($45.70^\circ$ N, $120.06^\circ$ W) & &\\
		Akhfenir, Morocco (f) & 3.096 & 1.505\\
		($27.95^\circ$ N, $12.00^\circ$ W) & &\\
		\hline
		Offshore Wind farm & Mean & SD  \\
		(Site coordinates) & &\\
		\hline
		Amrumbank, Germany (A) & 11.176 & 4.962 \\
		($54.50^\circ$ N, $7.80^\circ$ E) & &\\
		Anholt, Denmark (B) & 8.999 & 3.537\\
		($56.60^\circ$ N, $11.21^\circ$ E) & &\\
		Gemini, Netherlands (C) & 7.577 &4.174\\
		($54.03^\circ$ N, $5.96^\circ$ E) & &\\
		HornsRev 2, Denmark (D) & 11.183 & 4.546\\
		($55.60^\circ$ N, $7.59^\circ$ E) & &\\
		Veja Mate, Germany (E) & 11.490 & 4.685\\
		($54.31^\circ$ N, $5.87^\circ$ E) & &\\
		Walney, UK (F) & 11.342 & 5.015\\
		($54.04^\circ$ N, $5.92^\circ$ W) & &\\
		\hline
	\end{tabular}
\end{table}

Ramp events occur when the wind power increases or decreases suddenly in a short duration of time which is typically in the range from 5 minutes to 6 hours \cite{GallegoCastillo2015}.  For a given wind turbine, let us say the ramp threshold power is $\alpha_{th}\%$ of the nominal wind power. Then we can define two ramp thresholds, that is,\vspace{0.1cm}
\begin{eqnarray}
\Delta P_w^{ramp}&=&\left \{
\begin{aligned}
&+\alpha_{th}\%~~ \text{of}~~ P_{nom} &=& P^{up},\\
&-\alpha_{th}\%~~ \text{of}~~ P_{nom} &=& P^{down},
\end{aligned}
\right.
\end{eqnarray}
where $P^{up}$ and $P^{down}$ are the upper and lower ramp thresholds respectively depicting ramp-up and ramp-down events in a short period of time. The rated wind speed considered for ramp event study is 12 m/s. Further, in order to forecast the ramp events in the wind speed time series, wind power is calculated for the given sample of data.

The ramp event is identified when the wind power generated exceeds lower ($P^{down}$) or upper ($P^{up}$) threshold values. To evaluate the forecasting performance, several error metrics such as Root mean squared error (RMSE), normalized mean squared error (NMSE), coefficient of performance ($R^2$) and Theil's U1 and U2 metrics and are given by (\ref{e7})-(\ref*{e11}):
\begin{eqnarray}
\label{e7}
\text{RMSE} &=& \Bigg[\frac{1}{n}\sum_{i=1}^{n}(\hat{s_i}-{s_i})^2\Bigg]^{1/2} \\
\label{e8}
R^2&=&\frac{\sum_{i=1}^{n}(\hat{s_i}-\bar{s})^2}{\sum_{i=1}^{n}({s_i}-\bar{s_i})^2} \\
\label{e9}
\text{NMSE}&=&\frac{\sum_{i=1}^{n}(\hat{s_i}-{s_i})^2}{\sum_{i=1}^{n}({s_i}-\bar{s})^2} \\
\label{e10}
\text{Theil U1}&=&\frac{\sqrt{\frac{1}{n} \sum_{i=1}^{n}\left(\hat{s}_{i}-s_{i}\right)^{2}}}{ \left(\sqrt{\frac{1}{n}  \sum_{i=1}^{n} s_{i}^{2}}+\sqrt{\frac{1}{n} \sum_{i=1}^{n} \hat{s}_{i}^{2}}\right)}\\
\label{e11}
\text{Theil U2}&=&\frac{\sqrt{\frac{1}{n} \sum_{i=1}^{n}\left(\left(s_{i+1}-\hat{s}_{i+1}\right) / s_{i}\right)^{2}}}{\sqrt{\frac{1}{n} \sum_{i=1}^{n}\left(\left(s_{i+1}-\hat{s}_{i}\right) / s_{i}\right)^{2}}}
\end{eqnarray}
where $\hat{s_i},s_i,\bar{s}$ are the predicted, actual and mean values of the $n$ testing samples.

\section{Results and Discussions}
In this Section, we discuss the forecasting performance of the prediction models (Persistence, $\varepsilon$-SVR, LSSVR, TSVR, $\varepsilon$-TSVR, RFR and GBM). The ramp-up and ramp-down events are identified for a threshold of 10\% of nominal wind power. The forecasting process is carried out by splitting the entire dataset into training (80\%) and testing (20\%) sets. For persistence model, the forecast is carried out using two previous dispatch windows. Further, for SVR and its variants, the hyperparameters, that is, RBF bandwidth ($\sigma$) and regularization parameter ($C$) are tuned from the set $[2^{-10},2^{-9},...2^9,2^{10}]$. For random forest regression (RFR) and gradient boosted machines (GBM), the simulations are carried out in R studio using randomForest package. The number of trees used in training phase are 1000 for RFR, while for GBM the learning rate is kept at 0.05 and number of trees are 10000. The input feature set to all the prediction models is a matrix consisting of approximation signal A5 and detail signals (D1,D2, $\dots,$ D5) obtained from wavelet decomposition of wind speed time series. Further, we evaluate, absolute error for ramp-up and ramp-down events as $R^{up}$ and $R^{down}$ respectively.

For onshore wind farm sites, the datasets labeled are \textbf{a, b, c, d, e} and \textbf{f}. For dataset \textbf{a}, GBM outperforms all the prediction models in terms of RMSE and NMSE. For predicting ramp-up and ramp-down events, TSVR outperforms GBM and RFR. In terms of $R^2$, TSVR gives the best fit close to 1.00 whereas $\varepsilon$-SVR gives a value greater than 1.00 causing over-fitting. The over-fitting in prediction is avoided by using RFR and GBM.
Similarly, for offshore wind farm datasets (\textbf{A, B, C, D, E \& F}), TSVR outperforms all the models in terms of RMSE and NMSE for predicting wind speed, while for predicting ramp events TSVR gives the best result in terms of $R^2$. Since the datasets are large sized, TSVR, $\varepsilon$-TSVR, RFR and GBM take less CPU time (which is the time spent by the regression method to predict the ramp events) than conventional $\varepsilon$-SVR. The detailed performance metrics for onshore and offshore wind farm sites are depicted in Table \ref{T3} and  \ref{T5}. It is worthwhile to note that, ramp prediction performance for GBM is significantly closer to TSVR in case of offshore wind farms.

\begin{table*}
		\begin{center}
			\caption{Performance metrics for Onshore wind farm sites}\label{T3}
			\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\hline
				Dataset & Model & RMSE &  NMSE & $R^2$& U1 & U2 & $R^{up}$ & $R^{down}$& CPU time \\
				& & (m/sec) & & & &  & & & (secs) \\ \hline
				& LSSVR & 0.3602 &  0.0177 &  0.9856 & 0.0024& 0.5678 & 0.0236& NA & 1.46082 \\
				& TSVR & 0.3525 &  0.0177 & 0.9899 & 0.0001& 0.0519&0.0016& NA & 57.41689 \\
				\textbf{a}& $\varepsilon$-TSVR & 0.3475 & 0.0178  &  0.9701 & 0.0046& 0.6538 & 0.0036& NA & 7.3946  \\
				& RFR & 0.3436 & 0.1251  &  0.9506 & 0.5633& 0.6276 & 0.0789& NA & 4.2693  \\
				& GBM & 0.1197 & 0.0044  &  0.9705 & 0.0115& 0.1539 & 0.0043& NA & 0.7119  \\
				& {Persistance} & 0.4399 & 0.0221  &  1.0145 & 0.0259 & 0.6296& 0.2800& NA & 0.02545  \\
				& $\varepsilon$-SVR & 0.3620 &  0.0178 &  1.0004 & 0.0029& 6.6035 &0.0862 & NA & 238.8274 \\
				\hline
				& LSSVR & 0.0403 &  0.00057 &  0.9748 & 0.0044 & 0.5716 & 0.1169 & 0.1498 & 0.76059 \\
				& TSVR & 0.0268 &  0.0002 & 0.9832 & 0.0029 & 0.444 & 0.0881 & 0.1228 & 23.9192 \\
				\textbf{b}& $\varepsilon$-TSVR & 0.0486 & 0.0008  &  0.9758 & 0.0053 & 0.6978 & 0.1098 & 0.1332 & 3.3806  \\
				& RFR & 0.06991 & 0.00121  &  0.9730 &  0.0660& 0.7191 & 0.1112 & 0.1421 & 32.11  \\
				& GBM & 0.0651 & 0.0017  &  0.9986 & 0.0078 & 0.0219 & 0.0235 & 0.0307 & 45.93  \\
				& {Persistance} & 0.4399 & 0.0108  &  1.0068 & 0.0190 & 0.6429& 0.2350& 0.2000 & 0.04356  \\
				& $\varepsilon$-SVR & 0.0593 &  0.0013 &  0.9556 & 0.0065 & 0.6517 & 0.1876 & 0.2367 & 84.4812 \\ \hline
				& LSSVR & 0.1806 &  0.0026 &  0.9263
				& 0.0141 & 0.2834 & 0.0600 & 0.5072 & 1.41105 \\
				& TSVR & 0.0284 &  0.00002 & 0.9884 & 0.0022 & 0.0034 &0.0014 & 0.1119 & 61.54305 \\
				\textbf{c}& $\varepsilon$-TSVR & 0.1964 & 0.0031  & 0.9252 & 0.0154 & 0.5499 & 0.0793 & 0.5588 & 7.82425  \\
				& RFR & 1.7407 & 0.2461  &  0.4191 & 0.1478 & 1.3082 & 1.7112 & 5.0778 & 8.3472  \\
				& GBM & 1.2148 & 0.1239  &  0.6060 & 0.1740 & 0.2787 & 0.1661 & 3.4109 & 0.6997  \\
				& {Persistance} & 0.3989 & 0.0129  &  1.0051  & 0.0309 & 0.6207 & 0.3250 & 0.1950 & 0.03783  \\
				& $\varepsilon$-SVR & 0.1937 &  0.0030 &  0.9253  & 0.0152 & 0.4438 & 0.0563 & 0.5227 & 251.86508 \\ \hline
				& LSSVR & 0.0263 &  0.000009 &  0.9927 &  0.0024 & 0.4229 & 0.0300 & 0.0109 & 0.519493 \\
				& TSVR & 0.0040 &  0.00002 & 1.000 & 0.0003 & 0.0662 &0.0004 & 0.00002 & 15.967091 \\
				\textbf{d}& $\varepsilon$-TSVR & 0.0732 & 0.0007  &  0.9814 & 0.0067 & 0.6507 & 0.1065 & 0.0471 & 2.067738  \\
				& RFR & 0.6014 & 0.0489  &  0.7966 & 0.0543 & 0.9854 & 0.0204 & 0.1416 & 5.187355  \\
				& GBM & 0.2758 & 0.0103  &  0.9585 & 0.0252 & 0.1458 & 0.1001 & 0.1444 & 0.505211  \\
				& {Persistance} & 0.2154 & 0.0063  &  1.0023 & 0.0197 & 0.5417& 0.3550 & 0.3500 & 0.0616  \\
				& $\varepsilon$-SVR & 0.0382 &  0.0001 &  0.9951 & 0.0035 & 0.6014 & 0.0594 & 0.0345 & 33.06520 \\
				\hline
				& LSSVR & 0.0232 &  0.000006 &  0.9935 & 0.0017 & 0.3551 & 0.0626 & 0.0689 & 1.022718 \\
				& TSVR & 0.0042 &  0.00002 & 0.9994 & 0.00004 & 0.004 & 0.0134 & 0.0125 & 43.252339 \\
				\textbf{e}& $\varepsilon$-TSVR & 0.0344 & 0.0001  &  0.9921 & 0.0025 & 0.7606 & 0.1058 & 0.1035 & 5.18525  \\
				& RFR & 0.09609 & 0.00137  &  0.9607 & 0.0016& 0.2612 & 0.1527 & 0.093129 & 15.87  \\
				& GBM & 0.07739 & 0.0008  &  0.9968 & 0.0059 & 1.3718 & 0.0822 & 0.0160 & 36.72  \\
				& {Persistance} & 0.2517 & 0.0077  &  1.0065 & 0.0185 & 0.6629 & 0.5450 & 0.1700 & 0.002612  \\
				& $\varepsilon$-SVR & 0.0400 &  0.0001 &  0.9974 &  0.0029 & 0.4500 & 0.0603 & 0.0981 & 253.34162 \\
				\hline
				& LSSVR & 0.0216 &  0.0001 &  0.9971 & 0.0022 & 0.4651 & 0.0475 & 0.1055 & 0.700270 \\
				& TSVR & 0.0030 &  0.000002 & 0.9993 & 0.0003 & 0.0112 &0.0076 & 0.0003 & 17.6919 \\
				\textbf{f}& $\varepsilon$-TSVR & 0.0467 & 0.0005  & 0.9922 & 0.0049 & 0.8286 & 0.1660 & 0.0980 & 4.27225  \\
				& RFR & 0.085906 & 0.00175  &  0.9552 & 0.0057 & 0.8316 & 0.03032 & 0.08309 & 15.83  \\
				& GBM & 0 .07239 & 0.001219  &  0.9998 &  0.0051 & 0.7216 & 0.01447 & 0.1298 & 44.66 \\
				& {Persistance} & 0.2250 & 0.0118  &  1.0040 & 0.0234 & 0.5833 & 0.3050 & 0.2250 & 0.0494  \\
				& $\varepsilon$-SVR & 0.0440 &  0.0004 &  1.0129 & 0.0046 & 0.7638 & 0.0518 & 0.0776 & 63.81193 \\
				\hline
			\end{tabular}
		\end{center}
	\end{table*}

	\begin{table*}
		\begin{center}
			\caption{Performance metrics for Offshore wind farm sites}\label{T5}
			\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\hline
				Dataset & Model & RMSE &  NMSE & $R^2$& U1 & U2 & $R^{up}$ & $R^{down}$& CPU time \\
				& & (m/sec) & & & & &  & & (secs) \\ \hline

				& LSSVR &0.0225&	0.00004&	0.9899&	0.0013&	0.352&	0.004&	0.0221&	0.507431\\
				& TSVR&0.0037&	0.00001&	0.9998&	0.0002&	0.0745&	0.0034&	0.0038&	11.28299\\
				\textbf{A}& $\varepsilon$-TSVR&0.0429&	0.0001&	0.9891&	0.0025&	0.5738&	0.0162&	0.0777&	2.0435\\
				& RFR &0.0989&	0.00104&	0.9799&0.0036&	0.5161&	0.1032&	0.05638&	31.2\\
				& GBM&0.0967&	0.0075&	0.9904&	0.0032&	0.5016&	0.1052&	0.0423&	50.59\\
				& Persistance &0.324&	0.0084&	1.0001&0.019&	0.6&	0.185&	0.345&	0.051183 \\
				& $\varepsilon$-SVR &0.0367&	0.0001&	1.0009&	0.0022&	0.5024&	0.0167&	0.0375&	84.006\\
				\hline

				&LSSVR &0.0198&	0.00006&	0.9912&	0.0013&	0.3908&	0.008&	0.031&	0.4999\\
				& TSVR&0.0038&	0.000002&	0.9998&	0.0002&	0.0115&	0.0043&	0.0028&	12.5415\\
				\textbf{B}&$\varepsilon$-TSVR &0.0434&	0.0003&	0.9998&	0.0028&	0.7492&	0.1064&	0.0771&	1.9274\\
				& RFR &0.7733&	0.00105&	0.9718&	0.0034&	0.0081&	0.18005&	0.07037&	31.0600\\
				& GBM &0.06248&	0.00068&	0.9994&	0.003&	0.0071&	0.180035&	0.07988&	44.5800\\
				& Persistance&0.1655&	0.0048&	1.0067&	0.0106&	0.6389&	0.38&	0.3&	0.1496\\
				& $\varepsilon$-SVR&0.0367&	0.0002&	1.0001&	0.0024&	0.0074&	0.0186&	0.0849&	27.9990\\
				\hline

				&LSSVR&0.0232&	0.00006&	0.9944&	0.0021&	0.3838&	0.0217&	0.0305&	0.514049\\
				& TSVR &0.0064&	0.000004&	0.9993&	0.0005&	1.0497&	0.0001&	0.0012&	13.034972\\
				\textbf{C}& $\varepsilon$-TSVR&0.0463&	0.0002&	0.9871&	0.0043&	1.0083&	0.056&	0.076&	1.90010\\
				&RFR&0.07823&	0.00724&	0.9763& 0.0051&	1.0091&	0.7805&	0.0896&	30.58\\
				& GBM&0.07146&	0.0039&	0.9863&	0.0041&	1.0012&	0.1687&	0.04527&	43.47\\
				&Persistance&0.2577&	0.0079&	1.0015&	0.0238&	0.5&	0.55&	0.555&	0.04261\\
				&$\varepsilon$-SVR&0.0626&	0.0004&	0.9875&	0.0058&	1.0347&	0.0765&	0.0866&	39.806\\ \hline
				&LSSVR&0.0244&	0.000004&	0.9987&0.0014&	0.057&	0.0139&	0.0119&	0.5023\\
				&TSVR&0.0032&	0.00007&	0.9999&		0.0001&	0.0213&	0.0024&	0.0027&	12.7748\\
				\textbf{D}&$\varepsilon$-TSVR&0.0352&	0.00009&	0.9994&		0.002&	1.5215&	0.0338&	0.0229&	1.9140\\
				&RFR&0.09762&	0.00072&	0.9758&	0.0042&	0.7166&	0.09793&	0.04394&	38.7200\\
				&GBM &0.0793&	0.001004&	0.9868&	0.0056&	0.8166&	0.00686&	0.0029315&	55.4600 \\
				&Persistance&0.4252&	0.0138&	1.0038&	0.0236&	0.75&	0.055&	0.45&	0.0492\\
				&$\varepsilon$-SVR&0.0379&	0.0001&	1.0105&	0.0021&	0.6116&	0.0136&	0.0123&	80.1100\\ \hline

				&LSSVR&0.0263&	0.00005&	0.9994&	0.0015&	0.4724&	0.0032&	0.0626&	0.50647\\
				&TSVR&0.004&	0.00001&0.9997&0.0002&	0.0309&	0.0004&	0.0018&	12.743\\
				\textbf{E}&$\varepsilon$-TSVR&0.0437&	0.0001&	0.9994&	0.0024&	0.633&	0.004&	0.1313&	1.90508\\
				&RFR&0.1059&	0.0008&	0.9761&0.0059&	0.8372&	0.2281&	0.3498&	37.43\\
				&GBM&0.08278&	0.0004&	0.9951&0.0096&	1.8164&	0.08923&	0.0112&	57.2\\
				&Persistance&0.3866&	0.0109&	1.0057&	0.0215&	0.6&	0.19&	0.105&	0.052525\\
				&$\varepsilon$-SVR&0.0338&	0.00008&	1.0041&		0.0019&	0.1171&	0.0885&	0.0112&	90.5178\\
				\hline

				&LSSVR&0.0206&	0.00003&0.9991&	0.0012&	0.3587&	0.0015&	0.044&	0.58391\\
				&TSVR&0.0029&	0.000006&	0.9995&	0.0001&	0.173&	0.0003&	0.0065&	12.78991\\
				\textbf{F}&$\varepsilon$-TSVR&0.037&	0.0001&	0.9912&0.0022&	0.419&	0.0034&	0.0408&	1.87901\\
				&RFR&0.1059&	0.00084&	0.9774&0.0062&	1.2043&0.1264& 0.2182&	33.01\\
				&GBM&0.0929&	0.00064&	0.9976& 0.0054&	0.3996&	0.04356&	0.07129&	48.38\\
				&Persistance&0.3244&	0.0079&	1.0036&0.0139&	0.5&	0.21&	0.405&	0.05162\\
				&$\varepsilon$-SVR&0.033&	0.00008&	1.0001&	0.0019&	35.36&	0.0358&	0.0535&87.76891\\
				\hline
			\end{tabular}
		\end{center}
	\end{table*}

Results obtained in aforementioned section depict that SVR based regressors have an upper hand over RFR and GBM based prediction methods. While it is worthy to note that, to study the ramp events the time interval between consecutive samples plays an important role in deciding the threshold value. In a study carried out by Ouyang et al., the wind speed data collected is sampled every 15 minute for a wind farm site in China and ARIMA model is used to forecast wind power \cite{Ouyang2019}.

Further, swinging door algorithm is used to detect ramp events. The RMSE values for the ramp event prediction are found to be in range of 32\%. Using machine learning techniques such as classical SVR, GPR, MLP and ELM, the ramp events are predicted for time interval 6hr and RMSE values are found to be in range of (5-7 MW). However, wind speed time-series of onshore and offshore wind farm has a lot of variability in terms of magnitude. Thus, a ramp event study with 10 minute as time interval poses critical impositions in market clearing and day-ahead scenarios.

The current work deals with predicting wind power ramp events with 10 minute sampling interval for onshore and offshore wind sites. With offshore wind farms, the wind speed being high and variable, the probability of ramp event increases. Thus, in the current work, we extend the ramp event prediction study by incorporating advanced machine learning algorithms like variants of SVR, random forest regression and gradient boosted machines. Results reveal that TSVR based prediction model yield lowest error for ramp-up and ramp-down events.

\subsection*{Uncertainties in Ramp events}
Ramp event in onshore and offshore wind farms is a challenging issue which could
adversely affect the associated power systems.
The randomness in wind speed stimulated by turbulent air flow can cause unwanted vibrations in turbine blade and tower thus questioning its structural stability. Randomness in a signal or time-series can often be expressed by calculating the log energy entropy. In order to critically examine a ramp event signal, expressed as (\ref{e1}), we decompose the obtained signal by two signal processing techniques such as wavelet transform decomposition (WT) and empirical model decomposition (EMD). While wavelet transform decomposes a signal into low-frequency and high-frequency components, EMD on the other hand is capable of extracting oscillatory features from the signal for a dominant frequency. The ramp signal is decomposed using db4 wavelet filter using wavelet transform and into 5 intrinsic mode functions (IMFs) using EMD.

The log energy entropy for a signal $h(t)$ is given as
\begin{eqnarray}
E\{h(t)\}=\sum_{t=0}^{T}\log(h(t)^2).
\end{eqnarray}
A typical ramp event signal and its signal decomposition using wavelet transform and EMD is illustrated in Figure \ref{nokia} which shows the low frequency (LF) signals obtained from wavelet transform and EMD and are used to assess the randomness in the ramp event signal by calculating the log energy entropy of predicted ramp signals based on TSVR, RFR and GBM are depicted in Table \ref{T8}.

\begin{figure}[H]
	\centering
	\hspace*{-0.3cm}\includegraphics[width=1.2\linewidth]{rampevent1}
	\vspace{-1.3cm}
	\caption{Ramp event signal and its decomposition using wavelet transform and EMD}
	\label{nokia}
\end{figure}

\begin{table}[H]
	\caption{Log energy entropy for ramp event signals}\label{T8}\vspace{0.2cm}
	\centering
	\begin{tabular}{|c|c|c|c|}\hline
		\multirow{1}{*}{Dataset} & \multirow{1}{*}{Model} & \multicolumn{2}{c|}{Log energy entropy}\\ \cline{3-4}
		& & WT & EMD \\ \hline
		\textbf{a} & TSVR &  6.6094$\times$10$^3$ & 5.6985$\times$10$^3$ \\
		& RFR  &  6.2866$\times$10$^3$ & 5.5948$\times$10$^3$\\
		& GBM  &  6.7926$\times$10$^3$ & 5.9943$\times$10$^3$ \\ \hline
		\textbf{A} & TSVR &  1.7259$\times$10$^4$ & 1.5646$\times$10$^4$ \\
		& RFR  &  2.0676$\times$10$^4$ & 1.2297$\times$10$^4$\\
		& GBM  &  2.0776$\times$10$^4$ & 1.4621$\times$10$^4$ \\ \hline
	\end{tabular}
\end{table}
We observe that for a particular prediction model, the log energy entropy based on WT is more than that of EMD suggesting higher order randomness in ramp event signal. As far as uncertainties are concerned, the decomposition based prediction models yield better ramp prediction than single methods.

\section{Conclusion}
In the current work, wind power ramp events are studied for 6 onshore and 6 offshore wind farm sites. Wind speed data for the month of March 2019 with 10 minute sampling interval is collected. The available data is transformed to a hub height of 90m for all the datasets. A threshold value for change in wind power is chosen as 10\% of nominal wind power and the ramp event points are assessed for absolute error. Machine learning based prediction models are compared with benchmark persistence model. TSVR based model gives minimum absolute error for both, ramp-up and ramp-down events. The coefficient of performance ($R^2$) for RFR and GBM in case of offshore wind farm datasets is found to be close to 1.00 which indicates a good agreement between predicted and actual ramp values. Further, it is observed that error in predicting ramp-down event is more than in ramp-up event. Overall, machine learning based prediction models are found to be good approximators for analyzing 10-min wind ramps. The randomness in wind power ramp series in case of TSVR, RFR and GBM is evaluated using signal decomposition techniques and with RFR the randomness is found to be minimum. Thus, an accurate machine intelligent model like TSVR and GBM in this case, affirms a stable grid operation in presence of large ramp-up and ramp-down events.

\bibliographystyle{IEEEtranTIE}

redpajama_set_name: RedPajamaArXiv


text:
Antoine Butaud-Dupoux (né le  à Saint-Benoît-du-Sault, mort le  à Paris) est un ecclésiastique qui fut évêque constitutionnel de l'Allier de 1798 à 1801.

Biographie
Antoine Butaud-Dupoux est originaire du diocèse de Bourges. Il fait ses études aux Missions étrangères de Paris et devient docteur en théologie de l'université de Bourges. Curé de Clion en 1755 puis de Bourbon-l'Archambault en 1759, il passe dans le diocèse d'Autun en 1767, devient archiprêtre en 1769 et obtient la cure de Saint-Pierre de Moulins. Il prête alors le serment civique.

Pendant la Révolution française, François-Xavier Laurent le premier évêque constitutionnel de l'Allier avait apostasié et s'était marié pendant la Terreur. Antoine Butaud-Dupoux  est d'abord choisi le  comme archiprêtre par les prêtres assermentés du diocèse, puis comme successeur de Laurent. Ce fut un évêque « sans église et sans clergé ». Il assiste au Concile de Paris en 1801 et après la signature du Concordat, il se démet en septembre. Il se retire à Paris, où il tombe malade en 1802. Il accepte de se confesser et de signer une déclaration de rétractation avant de mourir le . L'archevêque concordataire de Paris lui fait rendre les honneurs religieux.

Notes et références

Voir aussi

Bibliographie
  Tableau des évêques constitutionnels de France de 1791 à 1801, Paris, 1827.

Liens externes

Naissance dans la Marche
Naissance en avril 1730
Décès en août 1802
Décès à Paris
Évêque de Moulins
Évêque constitutionnel
Décès à 72 ans


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Budgetary travails of the auditor-general
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It is very unfortunate that we have not noticed the concerted efforts, determination to undermine the office of the auditor-general of the federation (through the recent budgetary downplaying of the 2023 budgetary appropriation for the office of the auditor-general)
Oluwole Oke, Chairman, House of Representatives' Committee on Public Accounts
Ordinarily, a nation's determination, even sense of mission, on any particular subject or policy issue is best understood as a derivative of the weight of financial commitment accruing to that policy. Hence, for instance, a nation's spending of a substantial amount of resources rearming or re-kitting its military is read off as putting teeth to their bite. Rhetoric, however elegant, cannot substitute for resolution and consistent spending on particular policy goals. Consequently, the recent controversy regarding the comparatively paltry amount of allocation to the Office of the Auditor-General of the Federation speaks volumes, as pointed out in the opening quote sourced from Oke.
Recently, Oke lamented that the budgetary appropriation for that office was slashed somewhat drastically to less than half of what was demanded, leaving it at a slim N5.112bn, making it almost equal to what was appropriated for local and foreign trips of the President, Major General Muhammadu Buhari (retd.), and the Vice President, Professor Yemi Osinbajo (SAN). That is why Oke concluded that we are dealing with a case of subversion of an institution, which is critical to the success of the anti-corruption programme.
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FAIRFAX, Va. (CBS.MW) -- Shares of Xybernaut Corp. gained 14 percent Tuesday morning after the company entered a joint marketing agreement with Hewlett-Packard Co.
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Xybernaut's Mobile Assistant products are portable computers featuring speech-activation and head-mounted displays. Xybernaut shares rose 9/16 to 4 1/2 on the news.
HP executives expect the partnership to bolster its line of mobile computing products, which includes palmtop and notebook PCs.
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Dub Jana Ámose Komenského je památný strom v Karlových Varech v městské čtvrti Drahovice. Roste v těsné blízkosti křižovatky ulic Kollárova a Stará Kysibelská, nedaleko základní školy Jana Ámose Komenského. Solitérní strom je ve velmi dobrém zdravotním stavu, měřený obvod kmene činí 188 cm, výška stromu je 22 m (měření 2014). Za památný byl vyhlášen v roce 2014 jako strom s významným vzrůstem.

Stromy v okolí
 Dub pod rozvodnou
 Dub Moudrosti
 Mozartův dub
 Hrušeň v Drahovicích

Odkazy

Reference

Související články
 Seznam památných stromů v okrese Karlovy Vary

Externí odkazy

Památné duby letní v Česku
Památné duby v okrese Karlovy Vary
Památné stromy v Karlových Varech
Památné stromy ve Slavkovském lese
Památné stromy osobností


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A. J. Davis
Alexander Jackson Davis
A. J. Davis (cornerback, born 1983)
A&J
A. J. Baker
A. J. Davis may refer to:
Alexander Jackson Davis (1803–1892), architect
Andrew Jackson Davis (1826–1910), an American Spiritualist
A. J. Davis (cornerback, born 1983), American football player
This page contains text from Wikipedia, the Free Encyclopedia -	https://wn.com/A._J._Davis
Alexander Jackson Davis, or A. J. Davis (July 24, 1803 – January 14, 1892), was one of the most successful and influential American architects of his generation, known particularly for his association with the Gothic Revival style.
Davis was born in New York City to Cornelius Davis, a bookseller and editor of theological works, and Julia Jackson. He spent his early years in New Jersey and attended elementary school in upstate New York. In 1818, Davis went to Alexandria, Virginia, to learn the printing trade from a half-brother. Living mostly in New York City from 1823 onward, he studied at the American Academy of Fine Arts, the New-York Drawing Association, and from the Antique casts of the National Academy of Design. Dropping out of school, he became a respectable lithographer and from 1826 he worked as a draftsman for Josiah R. Brady, a New York architect who was an early exponent of the Gothic revival style: Brady's Gothic 1824 St. Luke's Episcopal Church is the oldest surviving structure in Rochester, New York.
This page contains text from Wikipedia, the Free Encyclopedia -	https://wn.com/Alexander_Jackson_Davis
A. J. Davis (born May 29, 1983) is former American football cornerback who was drafted by the Detroit Lions in the fourth round of the 2007 NFL Draft. He played college football at North Carolina State.
Davis was also a member of the Cleveland Browns, Kansas City Chiefs, Indianapolis Colts and Houston Texans.
Davis attended Northern Durham High School. He played at both corner and wide receiver. He made 13 career interceptions, three of which were returned for touchdowns. As a senior he made 42 receptions for 709 yards and nine touchdowns. He played in the 2002 U.S. Army All-American Bowl following his high school career. Davis was a Parade All-American cornerback at North Durham High School during his senior year, where he also competed as a receiver. Tom Lemming's Prep Football Report, a scouting service that also awarded Davis with first-team All-American honors, ranked him the top cornerback in the nation. The North Carolina High School Athletics Association named him North Carolina's Male High School Athlete of the Year.
This page contains text from Wikipedia, the Free Encyclopedia -	https://wn.com/A._J._Davis_(cornerback,_born_1983)
AJ, Aj, aJ, or aj may refer to:
AJ Alexander, American model and Playboy Playmate
A. J. Balaban (1889–1962), co-founder of Balaban and Katz
A. J. Allmendinger (born 1981), American race car driver
A. J. Burnett (born 1977), American Major League Baseball pitcher for the Pittsburgh Pirates
A. J. Calloway (born 1974), American television personality
A. J. Cook (actress), a Canadian actress
A. J. Croce (born 1971), American singer and songwriter
A. J. Cronin (1896–1981), Scottish novelist
A. J. Foyt (born 1935), American race car driver
A.J. Gibson (architect) (1862–1927), American architect
A. J. Haglund (born 1983), American football player
A. J. Hawk (born 1984), American football linebacker for the Green Bay Packers
Andrew Jackson (1767–1845), American general and politician, seventh President of the United States
A. J. Jacobs (born 1968), American journalist and author
Ananta Jalil, Bangladeshi actor
Adrienne Janic (born 1974), American actress and television host
AJ Junior, real name Achraf Janussi, songwriter
This page contains text from Wikipedia, the Free Encyclopedia -	https://wn.com/AJ
A&J may refer to:
Adam and Joe, British comedy performers
Arlo and Janis, comic strip
A. & J. Inglis, shipbuilding firm founded in 1862 by Anthony Inglis and his brother John in Glascow, Scotland
All pages beginning with "A&J"
All pages with titles containing A&J
AJ (disambiguation)
This page contains text from Wikipedia, the Free Encyclopedia -	https://wn.com/A&J
A. J. (Jim) Baker (born 1923) is an Australian philosopher who is best known for systematising the realist philosophy of John Anderson.
He studied under Anderson at Sydney University and has taught philosophy in Scotland, New Zealand, the United States and Australia. He was a prominent member of the Sydney Libertarians and the Sydney Push.As of 2012, he continued to contribute occasional articles to The Sydney Realist.
Anderson's Social Philosophy: The Social Thought and Political Life of Professor John Anderson, Sydney: Angus and Robertson (1979)
Australian Realism: The Systematic Philosophy of John Anderson, Cambridge University Press (1986)
Social Pluralism: A Realistic Analysis, Wild and Woolley, Glebe NSW (1997).
This page contains text from Wikipedia, the Free Encyclopedia -	https://wn.com/A._J._Baker
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Latest News for: a.j. davis
Longtime Ruston sportswriter Buddy Davis was more than O.K. — he was a legend
The Town Talk 17 Jul 2019
Buddy Davis was every bit the legend in Lincoln Parish as the coaches and players he chronicled in his 50 years at the Ruston Daily Leader ... ....
Hear Davis High School Madrigals sing at International Choir Festival
Sacramento Bee 17 Jul 2019
The Davis High School Madrigals participated in the third annual Leonardo Da Vinci International Choral Festival in Florence, Italy. Click to Continue .......
Xbox Announces Special Edition Xbox One X NBA� 2K20 Console Bundle Featuring Basketball Superstar Anthony Davis
Business Wire 17 Jul 2019
CULVER CITY, Calif.--(BUSINESS WIRE)--Xbox Announces Special Edition Xbox One X NBA� 2K20 Console Bundle Featuring Basketball Superstar Anthony Davis ... ....
Davis carries on as Hornell BOE president
The Evening Tribune 17 Jul 2019
School board leader answers questions on academics, Facility Study and district progress ... ....
fifth 60t Liebherr for Davies
Vertikal 17 Jul 2019
UK rental company Davies Crane Hire has taken delivery of a 60t Liebherr LTM 1060-3.1 All Terrain crane on its 40th anniversary ... ....
NBA Rumors: Lakers GM Rob Pelinka Considers Anthony Davis As 'Pillar Of This Franchise For ...
Inquisitr 17 Jul 2019
One of the Lakers' biggest acquisitions this summer is All-Star center Anthony Davis, whom they acquired from the New Orleans Pelicans in exchange for Brandon Ingram, Lonzo Ball, Josh Hart, and three future first-round picks. Click here to continue and read more... ....
Davis County blanks EBF
Fort Madison Daily Democrat 17 Jul 2019
DONNELLSON — Trevor Slayton tossed a four-hit shutout to help Davis County defeat Eddyville-Blakesburg-Fremont Tuesday ... ....
John Davis: Kaepernick and Rapinoe offend me
Lewiston Sun Journal 17 Jul 2019
LETTERS: George Davis statue is a tribute to slavery
Star News 17 Jul 2019
Statue honors slaveryEDITOR ... But it seems clear, at least in the case of the statue of George Davis (Third and Market streets in Wilmington), statues are allowed to change the past. There is no mention on his statue of Davis's support for slavery.Davis was not a soldier, but a lawyer appointed attorney general ... ....
Joseph "Joey" H. Davis
The Lewiston Tribune 17 Jul 2019
Joseph "Joey" H. Davis, 70, of Lewiston, passed away Sunday, July 14, 2019, at St. Joseph Regional Medical Center in Lewiston, from cancer ... ....
Is It Already Time For The Lakers To Worry About Anthony Davis?
Anthony Davis is the newest Lakers superstar. But how long he will be in Los Angeles is somewhat uncertain ... ....
Decked in costumes befitting the birthplace of the Renaissance, the Davis High School Madrigals brought tears to the eyes of those in the Teatro Verdi in Florence, Italy, last week Click to Continue .......
Video games often come under fire for their violent and potentially addictive properties. But a new study from a UC Davis researcher and a Swiss colleague has found that they Click to Continue .......
The Times of India | 17 Jul 2019


James Teitsma was elected to his first term as the MLA for Radisson on April 19, 2016. He is a lifelong Manitoban who has lived and been an active community member in the Radisson constituency for over 20 years.
James attended Murdoch MacKay Collegiate and went on to complete a First Class Honours Degree in Computer Science from the University of Manitoba. After graduation, James joined EISI, a small software development company. James' career grew along with the company as it expanded from a staff of 7 to a leading, global provider of financial and tax planning software with over 300 employees serving a number of major financial institutions. As EISI's Chief Development Officer, James led a team of over 100 software developers. Known today as Advicent, James worked in a senior leadership role as the company's Chief Architect prior to his election to the Manitoba Legislature.
As immigrants to Canada, James' parents instilled in him the strong values of hard work and giving back to the community. A lifelong volunteer, James has provided leadership with a number of organizations and is a member of the Transcona Legion and the Transcona Rotary Club. He is also an active member of his church and is proud to have played a leading role in converting the District #4 Police and Fire Station into a new church building that maintains the building's historic architecture.
James is the youngest of six children and grew up in the family home across the street from the CN Transcona Shops. He and his wife Joanne have been married for 19 years and are the proud parents of 6 children and share in their family's love of sports including hockey, basketball, and cycling. James also enjoys playing piano whenever he gets the chance.
James is dedicated and passionate about the issues that affect our community and our province. As an MLA, he is committed to building a stronger Manitoba that provides better education and economic opportunities for our young people, a health care system that puts patients first, and a government that respects the hard-earned tax dollars of Manitobans.


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