text:
Top 20 Surprising Things Kids Today Will Never Know
Posted by Scott DeNicola | Mar 24, 2020 | Rantings From The Edge | 15 |
Inside: Top 20 Surprising Things Kids Today Will Never Know
I've become my father is so many ways it's scary. I never thought I'd be uttering the words "turn off the lights if you're not upstairs, we don't own stock in the electric company", but I do constantly. In between bad dad jokes I like to "wax poetic" to my kids on the simpler times of when I was a child. From what I can remember, I had a decent childhood. It's hard to tell from pictures because the second child always has fewer pictures of their childhood than the firstborn. My second daughter reminds us of this all the time.
I didn't have all the technology that the children of today have but I seem to have come out the other side unscathed. There are so many items from my childhood that the kids of today will never know and most likely couldn't recognize in a multiple-choice test.
Allow me to take you on a trip back in time to the 1970s, 1980s and even briefly the 1990s. These are the times I grew up in where bell-bottoms and corduroy pants were all the rage and life was less complicated with technology.
The cassette tape was the successor to the 8 track tape which in and of itself was a disastrous form of media. What the cassette tape offered was the flexibility to record your favorite album and take it with you to play in the car. You could also save a ton of money by recording all your friends' albums and they record all of yours. It makes me laugh how nowadays there is such a push against "stealing" music, and rightfully so, but kids today will never know how easy it was to get your hands on any music you wanted with one blank cassette tape.
You haven't lived until you had a tape deck "eat" your cassette tape and you had to wind it back up.
The cassette tape
TV without a remote
Kids today never have to leave the couch when watching television. I remember tv before the invention of the remote control. I also remember tv when it was 5-7 channels but that's another story. When you watched tv at night you watched what was on or you got off the couch and changed the channel. There was no channel surfing in my youth. It took too much energy to channel surf and felt like you were doing squats. Up and down off the couch repeatedly.
Before there was a remote control
Rotary telephones
If you want to have fun for an hour give a teenager a rotary phone and tell them to dial a friend. I guarantee it will be worth the hour it will take for them to figure it. We dialed by electrical impulses, basically sparks. You put a finger in the hole for the first number and rotated the dial clockwise until it reached the guard rail that stopped your finger from going any further. You then pulled out your finger, and the wheel went back to the starting position. This motion was repeated until you dialed each number and you were connected.
I'm old enough to remember when phone numbers had letters in them. Growing up we were AR1-1289. That was Arlington 1-1289.
My friend Jeff had the first-ever VCR that I can recall. It was a giant unit that had knobs on the front that allowed you to tune in the channel you wanted to record. It was a feat of modern technology for 1977. You were able to record your favorite television show when you weren't home and watch it whenever you wanted. Most of us still had VCR's later in the 90's and even 2000's. I still have VCR tapes but I don't have a player to view them on.
This was cutting edge technology at the time
There may still be some fax machines floating around today though most people prefer to send an email attachment instead of a fax. The fact that you could put a piece of paper in a machine, it would scan and send it via a phone line anywhere in the world was pretty miraculous. Faxing still seems to be big in the medical industry for some reason but it is rare to see a fax these days.
In the early days of computing, the floppy disk was our only means of transferring information from one computer to the next. The amount of data that could be held on these disks was 1.44MB or the equivalent of a small document. Compared to today, you can purchase a removable USB drive that is 1 or 2 terabytes in size. 1 terabyte equals 1,000,000 megabytes (MB). You can back up multiple computers on one of these small devices compared to its ancient predecessor.
1.44MB of sheer memory power
Does anyone remember America Online (AOL)? It was the end all be all at the dawn of the internet, but with it came certain challenges. The main one being speed. Kids today will never know what it was like to wait 30 minutes for a picture to download or three minutes to be connected to the internet. My first computer, a Compaq Presario which probably cost $1,500 back then had a dial-up modem. To connect to the internet you had to unplug the phone line and plug it into the back of the computer.
If a call came in during the time you were on the computer there was a good chance you got booted offline. It was a really frustrating experience compared to the blinding speeds that fiberoptic and cable modems reach today. If you want to reminisce a bit click the short video below.
Beepers
Though not from my childhood I did have a beeper at my first real job. At the time the only people who carried beepers or pagers were doctors and drug dealers. For some reason, my job working for a sporting goods store, required me to have a beeper and I loved it. It made me feel important when it went off. Because the cell phone had yet to take over our lives the beeper was the only way to know that someones wanted to speak to you.
Usually kept on your waist, the beeper would vibrate when someone wanted to reach you and you'd see a call back number appear on the screen. You then needed to find a payphone (see below) or borrow a phone to call the person back.
The invention of the cell phone all but made the beeper obsolete. Kids today will never know the excitement of receiving a page or having everyone think you were selling drugs.
Doctor or drug dealer?
Before Netflix, YouTube, Apple TV and every other source of video, we had the neighborhood video store. In my neighborhood that was Blockbuster video. Blockbuster stocked every movie imaginable and when a new one was released they had upwards of 15 copies of each movie. Kids today will never know the feeling of waiting a week for someone to return the movie you wanted to see or that infamous directive "Be Kind. Rewind".
Sony Walkman/Discman
Before the iPod, iPhone, AirPods and all the other fancy Apple devices out there we had the Sony Walkman and Discman. The Walkman and Discman were portable devices that allowed you to listen to your favorite cassette tapes and compact discs while you were out of the house. These devices became useful for people who liked to jog or listen to music when they were out and about. I remember the Discman being especially finicky when you were jogging as sometimes the music would skip if you jostled the device too much.
Kids today have it easy with iPhones and Samsung phones that store thousands of tracks of music at their fingertips. We had to listen to the same album all day or carry around extra cassette tapes or CDs to vary our music selection.
Who didn't want one of these in the day?
Flashbulbs
The first cameras I remember as a child were clunky devices as was most technology back then. Many of these cameras didn't have a flash built-in and as a result, you were required to carry around flashbulbs with you. The bulbs would click into the top of the camera and when you took a picture would almost explode internally emitting a flash of light. It was a very strange invention but it was effective at lighting up a room and making everyone's eyes red in the photo.
the 110 camera equipped with flash
There were no computers when I was growing up which meant there was no Google, Alexa or Siri. If we wanted the answer to a question the only means of research we had was the Encyclopedia. A set of encyclopedias lettered A-Z provided information on many subjects. Every school and library had a set of encyclopedias and people even sold them door to door. Many supermarkets would give them out letter by letter when you spent a certain amount of money in the store. I would sit for hours looking through each volume in amazement.
Before the internet, we had the encyclopedia
Paper maps
If you wanted to travel when I was a kid and you had no idea where you were going, you had to use a paper map. Maps were printed for every state and contained all the roads and highways necessary to take a trip. They would fold up nicely and fit in your glove compartment or trunk. To navigate the map was a bit of a chore because you first had to find out where you were to find out where you were going. Charting a trip using a paper map often caused arguments in many homes besides just mine.
Skate key
If you owned a pair of roller skates (ones that had four wheels) you needed a tool that would help tighten the skate and allow you to get in and out of it. This tool was called a skate key. Without one your skates were useless. Kids today have it easy. Strap on a pair on inline skates and you're out the door. That's if kids today even roller skate at all!
These are in fact roller skates
TV going off the air at night
Television didn't always consist of 24-hour news cycles, infomercials, Friends and The Office reruns and ESPN. There was a time when I was young that television stopped broadcasting and "signed off" for the day usually by playing a series of random test pattern noises followed by the Star-Spangled Banner. From that point on there was no television until the morning. If there were any late-breaking news we didn't hear about it until the next morning. Kids today just hit the remote or open a cell phone and have thousands of channels at their fingertips.
Record/book stores
Record stores and book stores are more a novelty these days. Amazon has all but destroyed the book store the way music streaming services have done with music. There are still some random mom and pop book stores and maybe a few Barnes and Noble but for the most part, gone are the days of spending hours combing the bookshelves for the next great read.
The same is true for music. As a child, I couldn't wait to get to my local music store to search through countless albums. Gone are Sam Goody's, Tower Records, Camelot Music, Coconuts and more. Vinyl is making a comeback of sorts but not to the level it was growing up.
If you were outside your home and needed to make a phone call when I was growing up you had to track down a payphone. It wasn't hard to do and they were everywhere you looked. Outside many stores, you would find rows of payphones. The cost was $.10 to make a phone call until the mid-eighties when it was raised to $.25. When they were introduced it was only $.05. Nowadays you'd be hard-pressed to find a payphone since everyone has a cell phone. New York City is removing it's last remaining payphones as I write this – marking the end of an era.
A real alarm clock
Kids today will never know how annoying it was to be woken up by the basic alarm clock we had as children. I'm not talking about the fancier clock radio that woke you up to music. I'm talking about the standard-issue alarm clock. The one with two bells on top. The one you'd want to throw across the room when it went off. There was no easing into waking up with this alarm clock. It instantly jolted you out of bed.
The Phone book
Growing up, finding a phone number meant scanning through your local white pages name by name until you found the number you needed. Forget it if the person's last name was Smith or Jones. You could be making calls all day until you found the right person. Kids today can just use Google and find out everything you need to know about someone with the click of a thumb in under 10 seconds.
Photo developing
Kids today will never know the struggle of taking a picture and waiting three days to have the picture developed. You had no way to know if the picture you just snapped was good or not. You just clicked and hoped for the best. Advances in film development led to chains like Fotomat, Motophoto and One Hour Photo offering a quicker turnaround but not the immediate gratification of today.
Growing up when I did wasn't necessarily better it was different. Though there are things that kids today will never experience as they aren't necessary anymore I'd like to think they made a simpler time a bit better for us all.
What did I miss on this list? Let me know in the comments below.
Erica (The Prepping Wife) on March 24, 2020 at 12:55 pm
A friend of mine posted a picture on social media just last week of the ancient pay phone. Which surprisingly enough, was not in a museum. It's fun to read this list and know exactly what you're talking about. Does that officially make me old? Because I'm pretty sure it does. I think my favorite memory related to this list was cassette tapes. I remember recording songs off the radio onto tapes to listen to as a kid.
Scott DeNicola on March 24, 2020 at 5:34 pm
I had SO MANY cassette tapes it was ridiculous.
STEPHANIE s. on March 24, 2020 at 8:37 pm
I just loved this post! Oh my goodness the dial up was something I really disliked, haha. I still actually have an AOL account for my mail, but honestly I have so many different email accounts that I rarely use. Now the payphones, that was something my friends and I would use when we would go down to this little store around the corner. And as for paper maps, my husband will still use one. I'll just use the GPS on the phone, or in the car.
Scott DeNicola on March 24, 2020 at 11:52 pm
Glad you enjoyed it, Stephanie!
Nicole Anderson | Camping for Women on March 25, 2020 at 1:58 am
Having read through this, I absolutely can't believe how fast technology has moved/is moving to the point where so many things people of our generation used daily just simply have ceased to exist. It makes me wonder if in 50 years time, kids might also have no clue about many of the things we use today! I am also quite amazed at the research you have done to pull this list together so well. While I know of each of the items you have listed, I don't think I could have thought of all of them to include. This really is a great list to show any current teenager to give some appreciation of what was around in very recent history!
Sarah Emery on March 25, 2020 at 3:51 am
The rotary phone vid! I'm dying! LOL! The picking up & putting down the receiver, I can't stop laughing at. Followed by, "what do you hear?" for the dial tone… which they didn't know what that sound is called.
Oh, this puts me back to my childhood. Man, I'm old 🙂
Thanks for the laughter, which is something we could all use a little more these days. Be safe, healthy and happy! Cheers!
Glad you laughed. I love that video myself.
Alexandra on March 25, 2020 at 3:02 pm
This post was the best!!! It brought back so many memories! I forgot all about those roller skates that attached to the bottom of your shoe. And also those flashbulbs. I think I had seen them before but I don't remember using them. I also do remember my grandmother having a rotary phone and her phone number had letters in it. Man, we used to have so much fun back then. And remember polaroid cameras? I remember that those seemed to be the biggest invention along with the sony walkman.
polaroid cameras had a brief comeback not too long ago. My youngest daughter got one.
Dreams Abroad on March 25, 2020 at 7:39 pm
TV without a remote is so true. I still remember my parents telling me to change the channel or volume. Those were simpler times and now although devices are similar the set up can be a hassle. Smart tv can take hours of set up. I will never forget the noise of Dial-Up too. So many memories.
Britt K on March 26, 2020 at 1:37 am
Haha 🙂 On one hand, I LOVE this – on the other, it made me suddenly feel really old lol! Looking back at a lot of the things mentioned, I can see why they say that today's generation has so little patience… just look at all the 'instant gratification' developments that have become 'the norm' in our society today!
Julia on March 26, 2020 at 3:01 am
Thank you for the sweet and funny post. It brings back such dear memories from childhood and youth. I grew up in a different country, but the memories are very similar.
Every paragraph and every picture in your post immediately brought up an associated story or stories in my memory.
On the other hand, they also remind me of my age… Am I really that old? LOL
Anyway, maybe I'm old in terms of the number of years I'm walking on this planet, but I still have some energy and am young in heart – that's what counts, right?
I've seen several videos when kids were challenged with the rotary phones, but in all of them, they were eventually able to figure out the dialing process but couldn't figure out that they need to pick up the receiver before dialing the number. LOL. These guys in your video figured it out – good for them!
Thank you for the lovely post, Scott!
~ Julia
This video makes me laugh every single time
Lyosha on March 27, 2020 at 2:44 pm
oh yes! A part of me happy it's gone (payphones, phone books, types (because of the jams and waiting for it to be put pack in the beginning) but some are pretty exciting actually. For example chemistry of making photos is something close to magic for me
Subhashish Roy on March 28, 2020 at 7:54 am
What a lovely rewind to my younger days when all these gadgets and tools were a part of my everyday life growing up in the sixty's and beyond. We belong to the same era Scott when the bell-bottoms,corduroy and then denims were the happening things. And yes I do remember getting all the best music recorded on cassettes from friends who owned the originals. Beautiful memories.
PreviousCBD: 7 Surprising Health Benefits According To Research
NextThe Best Advice Anyone Has Ever Given You
The Anti-Bucket List: 10 Things I Never Need To Do
10 COVID-19 Buzzwords And Definitions You Absolutely Need To Know
Pumpkin Spice Season Is Here, Kill Me Now
Plastic Bags: 10 Controversial Facts You Need To Know


What would be the expiration / deletion policies for Microsoft Teams?
Is it going to be the same as the Office 365 Groups? How they will be reported in the Admin Portal?
RE: What would be the expiration / deletion policies for Microsoft Teams?


The Navy needs to design new ships capable of accomplishing complex missions at a reduced cost. In order to enable US military response to threats around the world new ships must be designed with improved operational abilities and with capabilities to integrate with other ships and platforms for maximum operational flexibility. New enabling technology is needed for performing systematic design for optimal performance at three different levels: discipline level for optimizing a particular performance metric for a particular ship (i.e. hydrodynamics, survivability, signatures, seakeeping, etc.); improve the system level objectives for a particular ship (i.e. best operational capabilities as a unit at multiple disciplines for minimum cost); optimizing the system-of-systems level operational performance of multiple ships interacting and operating together as a group. Other elements such as the influence of uncertainty on performance metrics, the lack of detailed information at the upper level mission profile, and the impact of engineering decisions and scheduling on cost must be an integral part of the system of systems design process. The proposed SS-MDO-U system provides this new enabling technology. It will provide a systematic manner for performing physics based design for ships, optimizing individual performance metrics for each ship, and ensuring desirable inter-operational characteristics among ships. This design process will be carried in a cost conscious environment, while reliable performance will be secured in the presence of uncertainty. Finally, the SS-MDO-U system will be linked with the current Naval design tools and the new development will be demonstrated through a comprehensive case study on a topic directly related to Naval interests.


text:
The Tax Outlook: Going Up Despite Projected Economic Malaise
Taxes and source deductions will continue to rise despite last week's grim economic forecasts from the Finance Department. Here is what's in store based on forecasting in the Fall Economic Report, released November 3.
In the short term, the reduction in economic activity, noteworthy under the government's downside scenarios, will lead to lower personal and corporate tax revenues for the government, lower excise taxes and duties and lower proceeds from pollution pricing frameworks than anticipated earlier in the year.
Higher interest rates will also result in a short-term decrease in Bank of Canada net profits. Still, taxes will rise across the board:
Personal income tax revenues – the largest revenue line item for government (48%) will increase to $209.4 billion in 2022-23 or 5.6%, before tapering down to a 4.6% growth rate after this.
Corporate income tax revenues – these will increase by 15.4% in 2022-23 in the wake of the post-pandemic reopening and then the growth rate will taper down to 5.1% in 2023-23, due to a projected slow-down in economic growth in 2023. After this, revenues from corporate taxes are projected to grow only 1.5% per year for the remainder of the forecast period. This is a really important metric: how can investors and business owners prepare now for a slow-growth forecast in the 2023-2026 forecast period?
GST revenues – these are projected to grow by 2.2% in 2022-23 and then by an average of 4.9% over the remaining forecast period. This suggests consumer spending growth that is not aligned to the slow-growth forecast on corporate revenues.
EI Premium revenues – these are projected to grow at 11.7% for 2022-23 and then taper down to 4.2% based on built in premium rate increases: $1.58 per $100 of insurable earnings in 2022, $1.63 in 2023 and $1.66 in 2024. These increases are on top of CPP increases in the forecast period and that will make the cost of labor more expensive for still-struggling small business owners.
CPP Premium revenues - For 2023, the CPP contribution rate increases from 5.7% to 5.95%. Starting in 2024, $4,700 of pensionable earnings will be added to the indexed base pensionable earnings (estimated at about $68,000). In 2025, another $5,000 of pensionable earnings will be added so that the additional pensionable earnings will be between the estimated base pensionable earnings of about $70,000 and $79,700. The rate payable on these additional earnings is an 4.0%. Are business owners who pay higher level salaries taking these increases into account? It's an important scenario that requires discussion in business planning now.
Join Evelyn Jacks on November 16 at the Virtual CE Summit when she will highlight these and other significant tax and economic changes affecting your clients in the 2022 tax filing year and in tax planning for 2023 and beyond.
Evelyn Jacks is Founder and President of Knowledge Bureau, holds the RWM™, MFA ™, MFA-P™ and DFA-Tax Services Specialist designations and is the best-selling author of 55 books on tax filing, planning and family wealth management. Follow her on twitter @evelynjacks.
©Knowledge Bureau, Inc. All rights Reserved

redpajama_set_name: RedPajamaCommonCrawl


Enjoy fabulous curry recipes using our yogurt and find out more about our 'Curry Nights at Home' competition.
It's easy. The new 'going out' is cooking curry at home. "Simpler, healthier and tastier" say one pot guru @simplyfoodbymandy and award-winning Greek style yogurt producers ….. us.
To prove our point, and encourage more people to do the same, we are running a campaign through October and November with Mandy's gorgeous curry recipes which use our Greek style bio-live yogurt as an ingredient. There will be a new and tasty recipe idea each week in readiness for Friday night becoming "Curry Night at Home".
Suitable for all the family or for a supper with friends, Mandy's recipes are cost effective, nutritious and very simple to cook. She also loves using our yogurt for anything from tenderizing marinades and making creamy sauces, dips, drinks and desserts; all with an Indian flavour and theme.
Check Mandy's recipe for Chicken (or Tofu) Korma here and enjoy a curry night with friends and family at home.


text:
Two Arrested in Alabama After Brawl Breaks Out at Chinese Buffet Over Crab Legs
By Nicole Rojas On 2/27/19 at 9:40 PM EST
A fight over crab legs broke out at the Meteor Buffet in Huntsville, Alabama, on February 22. The brawl, which including plates being smashed, resulted in the arrest of two people. Google Maps
U.S. Alabama
Two people are facing misdemeanor charges after they allegedly fought over crab legs at a dinner buffet in Huntsville, Alabama.
Huntsville Police Officer Gerald Johnson was eating at the Meteor Buffet on Friday when he said a brawl broke out as diners waited for crab legs. "Literally, as I sat down and maybe took two bites out of my plate," Johnson told WHNT-TV.
Johnson said he witnessed a woman beating a man, plates shattering and diners using service tongs as fencing swords. The fight began after diners were forced to wait for crab legs for more than 10 minutes.
"Everyone was saying, 'They cut me in line. She cut me in line. He cut me in line. I was here first,'" Johnson said.
He added: "They'd been waiting there for the crab legs for a good 10, 20 minutes. When they finally came out, it's very heated. Especially if someone is taking more than their fair share."
Police arrested two diners, identified as John Chapman and Chequita Jenkins, following the fight. Chapman, who suffered a cut to his head, was charged with disorderly conduct.
Meanwhile, Jenkins was not hurt and was charged with third-degree assault. She is scheduled to appear in court on March 21, WHNT-TV reported. Neither Chapman nor Jenkins appear to be jailed by the Madison County Sheriff's Department.
Chapman and Jenkins could be ordered to pay several hundred dollars in fines. Court records were not available to show whether Chapman or Jenkins have a lawyer, the Associated Press reported.
Meteor Buffet, a Chinese restaurant, did not release any statement regarding the incident on its Facebook account.
Man Arrested After Throwing Christmas Ham at Woman
Ohio Woman Dumped Hot Grease on Woman in Argument


Recently, a business partner and myself were discussing various methods of sharing and brainstorming business models and ideas in real time.
The tools are very easy to use and the layout and design is very crisp and clean. The intuitive tool palate was so well thought out that I didn't need to ever look at the directions. I was ready to start collaborating with my partners in less than fifteen minutes. One drawback that I didn't like, however it's to be expected with the free account, is that by publishing my mind map it would be visible to the public. The only way I could save and privately view the map would be to sign up for the reasonably priced premium account. Well worth the money and I'm sure that as the company upgrades the software, the features will out perform the fees by a long shot.
I highly recommend this site for anyone needing easy to use, high quality mind-mapping software that can be accessed anywhere and anytime with no downloads.
This entry was posted in Software on December 17, 2015 by Administrator.


text:
Uncategories Tehlka itself becomes Tehlka
Tehlka itself becomes Tehlka
HARISH MONGA November 30, 2013
Is Boss always right or wrong?
I have read this caption in one of the chamber of very senior officer in 80s – "There are two rules. No.1 – Boss is always right. No.2 – if the Boss is wrong, see rule No.1".
In other words, Boss is always right even in the odd circumstances. Such quotes becomes meaningless when Boss himself is involved in controversy and in such a situation but it is not the time to say now that Boss is right or wrong.
Tehlka magazine was first launched in March, 2000 by Taruj J Tehjpal, born on March 15, 1963, an Indian journalist, publisher and novelist. He was the editor-in-chief and publisher of the magazine.
On 20 November 2013, Tehelka magazine informed its staff that Tejpal was stepping down as editor for six months, after a woman colleague alleged that she had been sexually assaulted by him.This received intense public attention and media scrutiny especially because Tejpal and his magazine had previously been involved in highlighting the issue of sexual violence in India, including in a special issue on the topic in February of the year. Police in the state of Goa, where the incident took place, have filed a First Information Report (FIR) which lists charges, including rape, against him. A non-bailable warrant has been issued against him by the Goa Police.
Shoma Chaudhury, Managing Editor of Tehlka magazine, who found herself in the eye of a storm of criticism on social media and television debates for her handling of the case, has also resigned. The details of assault have been reported on two occasions in Goa at Tejpal's hands during the magazine's Think Fest. She sought a probe committee as laid down by the apex court in the 1997 Vishaka judgment. But Shoma failed to set it up immediately and to inform the police of a cognizable offence.
Tehlka, where the recent sexual abuse case of Tarun Tejpal has surfaced, had no internal panel in place despite the same being compulsory under the Act.
With the surfacing of sexual abuse case of Tarun Tejpal, the future of the magazine hangs in balance, with its editor facing a criminal trial, managing editor and key staff leaving, advertisers wary and the brand tarnished.
Even the Alchemist Group founder and Trinamool Congress MP Kanwar Deep Singh, whose companies are the largest shareholders in Tehelka publisher Anant Media, are of the view that the magazine's future appeared dark to him and they too are consulting other investors on the future of the company and how to salvage our investment. We are concerned about the staff there.
Now the ball is in the court of Goa government as the investigation are going on and is seeking non-bailable warrants (NBW) against Tarun Tejpal is his failure to appear before the crime branch.
Another version from police has come that Shoma may also be booked on her failed to inform the police of cognizable offence. However, she announced that Tejpal's unconditional apology and his six-month sabbatical from work was more than what the victim had demanded in the email.
Only the time will tell as to which way the wind blows as the truth is yet to come on surface.
The sponsoring of Think Fest by compromising not to publish certain inside stories of the government and individuals has also been exposed by one of the news channel, the case is like to find a new turn but one questions remains on the front – Can Tehlka survive the debacle brought upon it by its founder? Only then, we can say – Boss is always right or wrong. But, Boss is not always right, he too sometimes wrong which could only be pointed out by others as we are watched minutely by others.


text:
Q: Maximum amount of data to load in browser memory I've got 440 text documents totaling 21.6MB that I'm using as the basis for a simple Javascript app that will analyze word frequency, common phrases, etc. The most important information about common words and phrases is in a concordance instance that loads with the page. It's a manageable 150kB or so. This concordance index maps phrases to the filenames of the documents where they originally appear.
I want to allow users to click on phrases and see them in the original context in the individual documents where they are found. My original plan was just to load the full document (avg 50kB) on demand via AJAX. This would involve a short delay.
I could also lazily load the pages after the app loads, then summon them on demand. I'm wondering if there is a rule of thumb for how much data one can load into memory like this. Is 21MB absurd? This would essentially be a very large JS object.

A: 21 MB is OK. Latest browsers are capable of handling huge documents without much hickups. E.g. our Jenkins (continuous integration) often shows me a 200 MB log, even colored/highligted.
Be careful with regex's over the whole page, traversing whole DOM, and stuff like that.
If you can choose the browser, I recommend Chrome which has much faster JS impl than Mozilla (unfortunately).

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}

A negatively buoyant jet appears during the discharge of heavier effluent in ambient water bodies with smaller densities.
Such phenomena mostly occur during the disposal of wastewater from desalination plants, power plants, industrial factories, and cooling water discharge from liquefied natural gas (LNG) plants.

Nowadays, numerous arid and semiarid coastal regions are encountering freshwater scarcity due to population growth and deficiency of portable water, hence the number of desalination plants is significantly increased.
During the desalination process, using brackish or seawater, drinkable water is obtained, while the by-product is high-salinity concentrated effluents, so-called desalination brine, which is discharged back into the coastal waters using submerged outfalls.
Besides elevated salt concentrations, desalination brine contains traces of chemicals, such as antiscalants, flocculants and coagulants (\cite{panagopoulos2020environmental}, which can lead to environmental degradation (\cite{alameddine2007brine})).
To minimize harmful environmental effects and maximize dilution, the brine is predominantly discharged from diffusers directed upwards at an angle (\cite{de2016bioindicators}), producing negatively inclined buoyant jets.

The design of the discharge systems and condition parameters are based on detailed experimental, mathematical, and numerical investigation in order to determine the characteristics of the jet.

Experimental investigation of inclined buoyant jets was done by numerous researchers: \cite{cipollina2005bench}, \cite{kikkert2007inclined}, \cite{ferrari2010mixing}, \cite{papakonstantis2011inclined}, \cite{papakonstantis2011inclined2}, \cite{abessi2015effect}, \cite{ardalan2018hydrodynamic}.

Mathematical models such as VISJET (\cite{cheung2000visjet}) and CORJET (\cite{doneker2001cormix}) were implemented and compared with experimental data (\cite{jirka2008improved}, \cite{lai2012mixing},  \cite{palomar2012near}). \cite{oliver2013predicting} and \cite{nikiforakis2015modified} developed a modified integral models. \cite{roberts1997mixing} obtained experimental coefficients to calculate the geometrical characteristics of dense jets. \cite{papanicolaou2008entrainment} presented top-hat and Gaussian integral models. \cite{yannopoulos2012escaping} and \cite{bloutsos2020revisiting} used Gaussian model escaping mass approach (EMA). \cite{papakonstantis2020simplified} proposed an analytical model for predicting centerlines, but all models performed similarly poor for a large range of inclination angles.

Numerical simulations provide better predictions and detailed insight into the complexity of the buoyant jet problem than a mathematical model when both were compared to experimental data (\cite{oliver2008k}). Historically, commercial software ANSYS CFX was used  (\cite{vafeiadou2005numerical}, \cite{oliver2008k}) to study inclined buoyant jets, however, most of the newer studies utilized the open-source CFD toolbox OpenFOAM, more specifically the \textit{twoLiquidMixingFoam} multiphase solver (\cite{zhang2017large}, \cite{jiang2019turbulence}, \cite{kheirkhah2021inclined}, \cite{alvir2022openfoam}), solvers supplemented with additional advection-diffusion equations for concentration and saline (\cite{ardalan2019cfd}, \cite{tahmooresi2021effects}, \cite{gildeh2015numerical}), and finally, concentration transport equations (\cite{vafa2021effect}, \cite{tahmooresi2022application}).

In order to model certain complex effects which occur during buoyant jet transport, experimental or numerical data is needed. \cite{shao2010mixing} experimentally investigated the effect of the bed proximity, called the Coanda effect which appears when jet discharge is placed close to an impermeable boundary, which results in reduced mixing. The height limit at which it occurs depends on the flow and nozzle angle. The Coanda effect was successfully investigated using numerical simulation in OpenFOAM by \cite{ramezani2021effect}. \cite{jiang2014mixing}, \cite{abessi2016dense} and \cite{kheirkhah2021inclined} experimentally and numerically investigated the behaviour of inclined buoyant jets in shallow waters.

Lately, machine learning has been proposed as an additional method for understanding and predicting coastal and open channel flow, including hydraulic jumps (\cite{naseri2012determination}), friction factor (\cite{yuhong2009application}), outflow over rectangular side weirs (\cite{bilhan2010application}), flows through backwater bridge constrictions (\cite{pinar2010artificial}), stability number of rubble-mound breakwaters (\cite{gedik2018least}). Also, numerous researchers applied machine learning techniques for predicting the performance, maintenance and control of desalination systems (\cite{behnam2022review}).

More commonly, numerical models previously validated with experimental data, are used to build ML datasets, because it is faster and cheaper way than an experimental approach, and it is possible to investigate a large number of different configurations. Several authors applied machine learning techniques on various CFD problems, including open channel junctions (\cite{sun2014artificial}), mixing in double-Tee pipe junctions (\cite{grbvcic2020efficient}), particle transport and filtration in porous media (\cite{marcato2021computational}), cavitation model parameters prediction (\cite{sikirica2020cavitation}), drag reduction of underwater serial multi-projectiles (\cite{huang2022machine}), large gas engine prechambers (\cite{posch2021development}) and bubble column reactor (\cite{pelalak2021influence}).

Most previous authors applied machine learning to coastal outfalls with a significantly reduced number of variables and small variations of parameters. \cite{abdel2007investigation} applied a neuro-fuzzy approach to predict locations of interest for multiple outfall discharges. \cite{musavi2013simulation} used Artificial Neural Networks (ANN) to predict the upper and inferior limits of the jet trajectory of negatively buoyant jets based on four dimensionless parameters. Multigene genetic-programming (MGGP) was applied for predicting dilution of vertical buoyant jets (\cite{yan2019multigene}) and multiport buoyant discharges (\cite{yan2020evolutionary}, \cite{yan2021simulations}). \cite{yan2022cfd} applied convolutional neural network (CNN) on rosette diffusers for buoyant jets. \cite{jain2022applications} used the ANFIS model coupled with Genetic Algorithm, Particle Swarm Optimization, and Firefly Algorithm for predicting four variables of inclined buoyant jets based on Froude number and angle with numerical simulations.

In this paper, a machine learning (ML) approach for negatively inclined buoyant jets reconstruction is proposed. The dataset for the ML model is generated using CFD in order to analyze the behaviour and predict 5 geometrical characteristics of jets.
More specifically, the analysis includes 500 simulations with varied water and pipe heights, a combination of fluid densities, discharge diameters and velocities.  Most of the previous research was focused on a small number of inclination angles, most often 30$^{\circ}$, 45$^{\circ}$ and 60$^{\circ}$. In this paper, results are obtained for a large range of angles varying from 5$^{\circ}$ to 80$^{\circ}$. The CFD model is validated with experimental data from previous research.

Several ML algorithms are used to train the models including Support Vector Regression, XGBoost, CatBoost, LightGBM, Random Forests and ANN, to get full insight into the capabilities of each model. Most of the previous research used a small number of ML algorithms, so in this paper, a comparison of their performance is shown. Prediction of ML models gives a fast and comprehensible perception of the plume behaviour for specific flow conditions and can be used as input data for far field models.

The objective of the detailed investigation and application of ML models is to provide insight into the comprehensive behaviour of buoyant jets for a large number of different flow parameters using the ML feature interpretation framework SHAP. To the best of the authors' knowledge, the SHAP feature explanation is applied for the first time on buoyant jets, coastal flow and outfalls.
Ultimately, an investigation of specific cases is presented including shallow water and the Coanda effect. Results of ML models and SHAP interpretation for the dataset supplemented with past experimental data are shown. Since most of the previous research used a reduced number of variables, the prediction capabilities of ML models for this type of problem are also presented.

\section{Materials and Methods}
\subsection{Problem description}

During the discharge of negatively buoyant jets, brine density $\rho_{b}$ is bigger than ambient density $\rho_{a}$, so momentum forces act in opposite directions than gravity. The side view for the schematic diagram of negatively inclined buoyant jets is illustrated in Fig. \ref{fig:points}. The jet is released through a round nozzle inclined at angle $\theta$ with diameter $d$. Initial velocity $U_0$ is creating the initial vertical momentum flux, the jet is moving upward. After it reaches a maximum height, buoyant forces become more dominant and the jet impinges the bottom, spreading by density current (\cite{roberts1997mixing}). The centre of a coordinate system is placed in the middle of the nozzle.

The dashed curve line presents the centerline or trajectory of buoyant jets, connecting the locus of maximum time-averaged concentration or velocities for the various cross-sections. Previous research has shown a small difference between centerlines created from velocity and contours (\cite{shao2010mixing}), so in this paper, the concentration centerline is used for further investigation, similarly to \cite{zhang2016}. Return point location $x_r$ is defined on the position where the buoyant jet passes the nozzle tap elevation, determined with ($x_r$, 0) coordinates. Horizontal and vertical locations of the centerline peak $x_m$ and $z_m$ represent the maximum location of the centerline. Terminal rise height $z_t$ is defined as a location where the concentration drops to 5 \% of the centerline peak concentration, similarly to \cite{jiang2014mixing}. Additionally, the impact point location with horizontal coordinate $x_i$ represents the point where the centerline hits the bottom.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{fig_points.jpg}
\caption{Schematic diagram of inclined dense jets and characteristic points.}
\label{fig:points}
\end{figure}

The behaviour of negative inclined buoyant jets, assuming Boussinesq approximation ($\rho_0-\rho_a$)<<$\rho_a$, can be described with discharge angle $\theta$,
momentum $M=U_0Q$, buoyancy $B=g_0$'$Q$, jet kinematic fluxes of volume $Q=\pi d^2U_0/4$ and acceleration due to gravity $g_0$'$=g|\rho_b-\rho_a|/\rho_a$.  Previous studies have shown a strong correlation between jet behaviour and densimetric Froude $Fr$ number which can be expressed as:

\begin{equation}\label{eq:1}
Fr=\frac{U_0}{\sqrt{g_0'd}}
\end{equation}

Most of the previous research used the momentum-buoyancy length scale $L_M$ to compare results with different parameters of the flow. It is represented by the distance in which momentum is more significant than buoyancy on the behaviour of the jet, which can be described as follows (\cite{fischer1979mixing}):

\begin{equation}\label{eq:2}
L_M=\frac{M^{3/4}}{B^{1/2}}=\left(\frac{\pi}{4}\right)^{1/4}dFr
\end{equation}

\subsection{Numerical model validation}
To validate the numerical simulation of negatively inclined buoyant jets, experimental data from previous cases were used (\cite{zhang2016}), so numerical settings were set to reproduce experimental conditions. Dimensions of the domain are shown in Fig. \ref{fig:domain}. A nozzle diameter of 5.8 millimetres was angled at 45$^{\circ}$. Pipe height $h_p$ was 70 mm and the distance of the nozzle tip from the water surface $H$ was 150 mm.
The origin of a coordinate system was set in the middle of the nozzle. CfMesh was used to create three meshes with different element numbers and refinement in the zones near the nozzle.

\begin{figure}[H]
\centering
\includegraphics[width=.9\textwidth]{fig_domain.jpg}
\caption{Numerical domain.}
\label{fig:domain}
\end{figure}

To model mixing and effluent behaviour of the jet, the OpenFOAM (\cite{Jasak07openfoam:a}) solver \textit{twoLiquidMixingFoam} was applied. It is based on solving the transient 3D Navier-Stokes equations for fluid flow and an additional phase advection-diffusion equation to represent the mixing of two fluids with different densities. The volume fraction of fluid is represented with an additional variable $\alpha$, which was set to zero in the initial moment throughout the whole domain, while at the pipe inlet, where the brine enters the domain, it was $\alpha$ = 1.

The back, front, right and left boundaries of the domain were represented with the Neumann boundary condition ($zeroGradient$ in OpenFOAM).
The top surface was treated as a free slip boundary condition, while at the nozzle and bottom, the standard turbulence wall functions and the no slip condition were applied. Initial velocity in the domain was set to zero while inlet pipe velocity was set to 0.515 m/s. As suggested by the results from previous studies (\cite{gildeh2015numerical}), a realizable $k-\epsilon$ turbulence model with a turbulence intensity of 10\% was selected.

Courant-Friedrichs-Lewy number (CFL) was set to be always less than 1, to adapt the time step and ensure the stability and convergence of simulation results. Effluent and ambient densities were set to 1034 kg/m$^3$ and 997 kg/m$^3$, respectively. Turbulent Schmidt number, kinematic viscosity and diffusivity were set as 0.7, 10$^{-6}$ m$^{2}$/s and 10$^{-9}$ m$^{2}$/s, respectively. The total simulation time is 120 seconds, while the results were time-averaged from 50 to 120 seconds, as in \cite{zhang2016}.

For Laplacian terms and temporal discretization, the corrected Gauss linear scheme and second-order backward scheme were used. Gauss linear and Gauss VanLeer schemes were used for velocity and $\alpha$, while for the $k$ and $\epsilon$ Gauss upwind schemes were set. The convergence criterion for velocity and pressure was set as 10$^{-6}$. The preconditional conjugate gradient (PCG) method and diagonal incomplete Cholesky (DIC) preconditioner were used to solve the pressure field. For other fields including $U$, $k$ and $\epsilon$, preconditioned bi-conjugate gradient (PBiCGStab) method with diagonal incomplete LU (DILU) preconditioner was used. To reduce calculation time, simulation was performed in an HPC environment on the Bura supercomputer at the Center for Advanced Computing and Modeling (CNRM), University of Rijeka. The total execution time of simulation with medium mesh size performed on four nodes (96 CPU cores) was 22 hours.

To compare results and establish mesh convergence, a simulation of three different mesh sizes was calculated. Coarse, medium and fine mesh consist of $5.76\cdot 10^{5}$, $1.43\cdot 10^{6}$, $3.05\cdot 10^{6}$, respectively. Results of Grid Convergence Index (GCI) (\cite{roache1998verification}), presented in Table \ref{tab:gci_table}, are calculated for horizontal and vertical locations of the centerline peak $x_m$  and $z_m$. From the obtained data, it can be observed that with the increase in the number of elements, the values are converging with the asymptotic range i.e. $GCI_{m,f} / (r^p \cdot GCI_{c,m}) \sim 1$, thus using a medium mesh for comparison with experimental data is adequate.

\begin{table}[!ht]
\centering
\caption{GCI for time-averaged horizontal and vertical locations of the centerline peak $x_m$  and $z_m$.}
\label{tab:gci_table}
\centering
\begin{tabular}{lllllll}
Mesh type& Number of cells& Ratio& $x_{m}${[}m{]}& GCI$_{xm}${[}\%{]} & $z_{m}${[}m{]} & GCI$_{zm}${[}\%{]} \\ \hline
Coarse    & 675852          & -     & 0.11197    & -            & 0.0639     & -            \\ \hline
Medium    & 1431118         & 1.3   & 0.11893    & 0.07211      & 0.07153    & 0.03676      \\ \hline
Fine      & 3047282         & 1.3   & 0.12236    & 0.03396      & 0.07315    & 0.00746
\end{tabular}
\end{table}

Fig. \ref{fig:compare} shows the results of time-averaged centerlines for experimental and LES simulation from the study by \cite{zhang2016} and numerical simulation results from this research. Results are calculated as maximum concentration points along the x direction. The value of root mean squared error (RMSE) between numerical results and experimental data is 0.00277, while the coefficient of determination (R$^2$) is 0.98475. Therefore, it can be concluded that a numerical model can very well predict experimental data.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{comparation_of_results.jpg}
\caption{Comparison of experimental, LES (\cite{zhang2016}) and simulation centerline results.}
\label{fig:compare}
\end{figure}

In Fig. \ref{fig:simulation_results}, the time-averaged velocity and concentration results are shown. It can be observed that the more distant the area is from the jet and centerline, the concentration and velocity rapidly decrease.

\begin{figure}[H]
\centering
    \subfloat[]{\label{a}\includegraphics[width=.5\linewidth, clip]{velocity.jpeg}}\hfill
    \subfloat[]{\label{b}\includegraphics[width=.5\linewidth, clip]{alpha.jpeg}}
    \caption{Time averaged a) velocity (U magnitude) and b) concentration contours at the centre plane for validation case.}
   \label{fig:simulation_results}
\end{figure}

\subsection{ML model and feature selection}
Numerous CFD simulations were used to generate a dataset which was used to create an ML model. The input values were obtained by randomly assigning values (using the Python 3 programming language), however, several conditions had to be met. The density of the effluent had to be greater than the density of the water to create negative buoyant jets and the height of the water had to be at least twice the height of the pipe so that the centerline of the jet could not touch the surface. Furthermore, the set of input values was discarded if they had already been assigned.

Basic input data of ML models consist of flow parameters: angle $\theta$, water $H$ and pipe height $h_p$, nozzle diameter $d$, effluent velocity $U_0$, brine $\rho_b$ and ambient $\rho_a$ density. The interval of considered values for the basic input data is presented in Table \ref{tab:limit}, with a large range of Froude numbers from 1 to 94 and Reynolds numbers from 413 to 1390980. A total of 500 simulations with different flow conditions were calculated. Geometry was created based on Fig. \ref{fig:domain}. The mesh sizes for all cases ranged from $8\cdot 10^{5}$ to $2\cdot 10^{6}$. All other numerical settings were set the same as in the validation case.

\begin{table}[!ht]
\caption{The minimum and maximum range for all ML model input variables.}
\label{tab:limit}
\centering
\begin{tabular}{cccccccc}
        & $\theta$ ($^{\circ}$)& $h_p$ (m) & $H$ (m) & $d$ (m) & $U_0$ (m/s) &  $\rho_b$ (kg/m$^3$) &  $\rho_a$ (kg/m$^3$)   \\ \hline
Min         & 5        & 0.02    & 0.11 & 0.002 & 0.059   & 1000       & 980        \\ \hline
Max         & 80       & 5.89    & 9.8  & 0.39  & 4.96    & 1059       & 1030       \\ \hline
Average     & 51.2     & 1.39    & 5.04 & 0.15  & 2.37    & 1036       & 1006       \\ \hline
Std dev     & 14.5     & 1.02    & 2.03 & 0.09  & 1.31    & 14.36      & 11.52
\end{tabular}
\end{table}

The ML model had a total of 5 output features, including return point location $x_r$, impact point location $x_i$, terminal rise height $z_t$, horizontal and vertical locations of the centerline peak $x_m$ and $z_m$. The range of output variables are presented in Table \ref{tab:limit2}. For ML prediction, the multi-target regression model was applied, with the relationship as follows:

\begin{equation}\label{eq:3}
x_m, z_m, z_t, x_r, x_i = f(\theta, h_p, H, d, U_0, \rho_b, \rho_a )
\end{equation}

\begin{table}[!ht]
\caption{The minimum and maximum range for ML model output variables.}
\label{tab:limit2}
\centering
\begin{tabular}{cccccc}
& $x_{m}$ & $z_{m}$ & $z_{t}$ & $x_{r}$ & $x_{i}$        \\ \hline
Min     & 0.018   & 0.001   & 0.008   & 0.016   & 0.048  \\ \hline
Max     & 9.046   & 5.619   & 7.350   & 11.662  & 12.036 \\ \hline
Average & 2.042   & 1.514   & 2.267   & 3.676   & 4.194  \\ \hline
Std dev & 1.436   & 1.046   & 1.462   & 2.500   & 2.633
\end{tabular}
\end{table}

Furthermore, Pearson's correlation matrix was calculated for input and output data and is shown in Fig. \ref{fig:heatmap} to exhibit the linear relationship strength between variables. It can be concluded that all input variables have a positive correlation except the $\theta$ and $\rho_a$. According to this graph, $U_0$ has the greatest influence, but in order to obtain a more detailed analysis, the SHAP feature interpretation method will be applied.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{heatmap.jpg}
\caption{Correlation heatmap displaying the relationship between the features based on Pearson's correlation coefficient.}
\label{fig:heatmap}
\end{figure}

In Fig. \ref{fig:diagram} a flowchart for creating the ML model is shown. Firstly, after a numerical model validation, simulations with different input variables were obtained. Secondly, an array of machine learning algorithms were used for ML model training based on the data. The final ML model is trained and validated to predict the geometrical characteristics of negatively buoyant jets (based on Equation \ref{eq:3}).

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{flowchart.jpg}
\caption{ML model flowchart which includes data inputs, algorithms and the ML model output.}
\label{fig:diagram}
\end{figure}

\subsection{Machine learning algorithms}

Different machine learning models have been applied for predicting geometrical characteristics for negatively inclined buoyant jets to give a full insight into the capabilities and best prediction performance of the different models.

Support Vector Regression (SVR) is a variant of support vector machines (SVM) for regression problems (\cite{drucker1996support}). The main principle is mapping input features to high-dimensional space separated by a hyperplane to minimize error and maximize the margin using a kernel function.

Tree-based ensemble methods are a group of machine learning algorithms based on combining several decision trees to produce better performances than a single decision tree.
The most popular tree-based ensemble method is Random Forests (RF) which applies averaging on several estimators built independently to improve prediction and control overfitting (\cite{breiman2001random}).
Boosting methods are a group of tree-based ensemble methods which build estimators sequentially, along with the reduction of bias from combined estimators.
XGBoost (XGB) is a scalable machine learning system for tree boosting proposed by \cite{chen2016xgboost}. It utilises a sparsity-aware algorithm and weighted quantile sketch procedure for approximate learning.
Catboost (CB) is another type of tree boosting method established on ordered boosting, permutation-driven alternative and a new algorithm for processing categorical features (\cite{prokhorenkova2018catboost}).
Another boosting method used is LightGBM (LGB) (\cite{ke2017lightgbm}) which includes two novel techniques, Gradient-based One-Side Sampling (GOSS) to find the best split value and Exclusive Feature Bundling (EFB) to reduce the feature space complexity.

Finally, an Artificial Neural Network (ANN) is a machine learning model made according to the structure of a biological neural network. Most of the previous studies used ANN for the prediction of various parameters in desalination systems (\cite{behnam2022review}). In this paper, Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) is applied. The basic structure consists of three types of layers: input, hidden, and output with neurons interconnected with links. Each neuron has an activation function calculated from input data resulting in weights, additionally adjusted based on the accuracy of prediction (\cite{nielsen2015neural}).

All algorithms were implemented in Python 3.8. programming language. SVR, RF and ANN implementations in scikit-learn 1.1.1 Python module (\cite{pedregosa2011scikit}) were used, CB from the catboost 1.0.0 module, XGB from the xgboost 1.6.0 module and LGB from lightgbm 3.3.2.
The input data for SVR and ANN were standardized between 0 and 1 using MinMaxScaler function from the scikit-learn module. This standardization procedure did not affect the tree-based ensemble methods including RF, XGB, CB and LGB.
To build a model for all geometrical characteristics simultaneously, the MultiOutputRegressor function implemented in the scikit-learn model was used for SVR, RF, CB and LGB.
XGB and ANN support multi-output regression, so an additional function is not needed. Hyperparameters of all ML algorithms were tuned using a cross-validated grid search algorithm over a parameter grid, and are presented in Table \ref{tab:hyper}. Other parameters were set as default.

\begin{table}[!ht]
\caption{Hyperparameters for ML models.}
\label{tab:hyper}
\centering
\begin{tabular}{ll}
ML model & Hyperparameters                                                            \\ \hline
SVR      & Kernel: rbf, $C$: 1000, $\epsilon$: 0.005, $\gamma$: 0.5                             \\
RF       & Estimators: 250, maximum depth: 90                                             \\
XGB      & Estimators: 300, maximum depth: 2                                              \\
CB       & Estimators: 300, maximum depth: 5                                              \\
LGB      & Estimators: 250, maximum depth: 25, number of leaves: 7                               \\
ANN      & Hidden layers: 3, neurons 25, solver: lbfgs, activation: tanh, $\alpha$=0.05
\end{tabular}
\end{table}

For comparison of predicted ($y_{i, predicted}$) and observed ($y_{i, observed}$) results, the root mean squared error (RMSE) and coefficient of determination (R$^2$) were used, which can be calculated according to:
\begin{equation}\label{eq:4}
RMSE = \sqrt{\frac{1}{n}\sum_{i=1}^{n}[(y_{i, observed}-y_{i, predicted})]^2}
\end{equation}

\begin{equation}\label{eq:5}
R^2 = 1-\frac{\sum_{i=1}^{n}[(y_{i, observed}-y_{i, predicted})]^2}{\sum_{i=1}^{n}[(y_{i, observed}-\overline{y})]^2}
\end{equation}

where $\overline{y}$ and $n$ represent the average of observed data and the number of data points.

\subsection{Shapley additive explanations}
SHapley Additive exPlanations (SHAP) is a game theoretic-based method for interpreting feature importance from machine learning models (\cite{lundberg2017unified}). In contrast to Pearson's correlation matrix, which assessed only a single linear relationship between two features, SHAP assessed both linear and nonlinear multivariable relationships, giving full insight into variable importance. SHAP values were calculated using shap 0.39.0 Python module based on a multi-output ANN model using the KernelExplainer function.

\section{Result and discussion}
\subsection{Results analysis}

In order to rigorously validate the ML models, the dataset was randomly split 90-10 for train$\backslash$test and validation.
Firstly, on the train$\backslash$test dataset, average and standard deviation were calculated for a K-fold cross validation procedure with K=5 with results presented in Table \ref{tab:result1}.
Secondly, the 90\% train dataset was used to train the model, and the 10\% dataset was used for model validation, with results presented in Table \ref{tab:result2}. The bolded value indicates the best result for each geometric characteristic. It can be observed that the best results are obtained using the ANN algorithm. Tree-based ensemble methods predict results with lower accuracy, especially RF. The best results from tree-based ensemble models are obtained by CB.

\begin{table}[H]
\caption{Results of R$^2$ and RMSE with standard deviation (std) of ML models on train$\backslash$test dataset for K-fold cross validation.}
\label{tab:result1}
\centering
\begin{adjustbox}{width=1\textwidth}
\begin{tabular}{cccccccc}
                         &       & SVR         & RF          & XGB         & CB          & LGB         & ANN         \\ \hline
\multirow{2}{*}{$x_{m}$} & R$^2$$\pm$std & 0.944$\pm$0.016 & 0.883$\pm$0.418 & 0.911$\pm$0.027 & 0.932$\pm$0.011 & 0.923$\pm$0.022 & \textbf{0.955$\pm$0.011} \\ \cline{2-8}
                         & RMSE$\pm$std  & 0.337$\pm$0.035 & 0.481$\pm$0.055 & 0.423$\pm$0.069 & 0.365$\pm$0.019 & 0.397$\pm$0.059 & \textbf{0.298$\pm$0.053} \\ \hline
\multirow{2}{*}{$z_{m}$} & R$^2$$\pm$std & 0.962$\pm$0.004 & 0.855$\pm$0.007 & 0.917$\pm$0.024 & 0.928$\pm$0.026 & 0.925$\pm$0.013 & \textbf{0.971$\pm$0.004} \\ \cline{2-8}
                         & RMSE$\pm$std  & 0.208$\pm$0.022 & 0.393$\pm$0.025 & 0.295$\pm$0.027 & 0.278$\pm$0.052 & 0.281$\pm$0.022 & \textbf{0.176$\pm$0.012} \\ \hline
\multirow{2}{*}{$z_{t}$} & R$^2$$\pm$std & 0.956$\pm$0.009 & 0.861$\pm$0.010 & 0.926$\pm$0.012 & 0.941$\pm$0.014 & 0.926$\pm$0.016 & \textbf{0.971$\pm$0.007} \\ \cline{2-8}
                         & RMSE$\pm$std  & 0.311$\pm$0.021 & 0.537$\pm$0.036 & 0.394$\pm$0.012 & 0.352$\pm$0.052 & 0.380$\pm$0.039 & \textbf{0.246$\pm$0.037} \\ \hline
\multirow{2}{*}{$x_{r}$} & R$^2$$\pm$std & 0.959$\pm$0.013 & 0.894$\pm$0.026 & 0.921$\pm$0.021 & 0.941$\pm$0.012 & 0.930$\pm$0.006 & \textbf{0.973$\pm$0.006} \\ \cline{2-8}
                         & RMSE$\pm$std  & 0.501$\pm$0.069 & 0.825$\pm$0.112 & 0.702$\pm$0.095 & 0.590$\pm$0.086 & 0.656$\pm$0.048 & \textbf{0.402$\pm$0.049} \\ \hline
\multirow{2}{*}{$x_{i}$} & R$^2$$\pm$std & 0.946$\pm$0.014 & 0.902$\pm$0.011 & 0.915$\pm$0.020 & 0.941$\pm$0.020 & 0.935$\pm$0.010 & \textbf{0.956$\pm$0.009} \\ \cline{2-8}
                         & RMSE$\pm$std  & 0.606$\pm$0.067 & 0.826$\pm$0.071 & 0.761$\pm$0.065 & 0.619$\pm$0.076 & 0.673$\pm$0.083 & \textbf{0.540$\pm$0.019}
\end{tabular}
\end{adjustbox}
\end{table}

\begin{table}[H]
\caption{Results of R$^2$ and RMSE of ML models for validation.}
\centering
\begin{adjustbox}{width=1\textwidth}
\label{tab:result2}
\begin{tabular}{ccccccccccccc}
        & \multicolumn{2}{c}{SVR} & \multicolumn{2}{c}{RF} & \multicolumn{2}{c}{XGB} & \multicolumn{2}{c}{CB} & \multicolumn{2}{c}{LGB} & \multicolumn{2}{c}{ANN}  \\ \hline
        & R$^2$      & RMSE       & R$^2$      & RMSE      & R$^2$      & RMSE       & R$^2$      & RMSE      & R$^2$      & RMSE       & R$^2$      & RMSE        \\ \hline
$x_{m}$ & 0.960      & 0.291      & 0.890      & 0.488     & 0.924      & 0.374      & 0.936      & 0.352     & 0.924      & 0.413      & \textbf{0.963}      & \textbf{0.259}      \\ \hline
$z_{m}$ & 0.971      & 0.160      & 0.868      & 0.331     & 0.930      & 0.277      & 0.932      & 0.241     & 0.948      & 0.241      & \textbf{0.980}      & \textbf{0.156}       \\ \hline
$z_{t}$ & 0.962      & 0.255      & 0.880      & 0.498     & 0.926      & 0.390      & 0.957      & 0.271     & 0.935      & 0.364      & \textbf{0.984}      & \textbf{0.200}       \\ \hline
$x_{r}$ & 0.969      & 0.482      & 0.910      & 0.715     & 0.934      & 0.644      & 0.951      & 0.557     & 0.948      & 0.679      & \textbf{0.987}      & \textbf{0.290}       \\ \hline
$x_{i}$ & 0.951      & 0.635      & 0.902      & 0.853     & 0.924      & 0.737      & 0.951      & 0.587     & 0.935      & 0.652      & \textbf{0.962}      & \textbf{0.491}       \\ \hline
Average & 0.962      & 0.364      & 0.890      & 0.577     & 0.928      & 0.484      & 0.945      & 0.402     & 0.938      & 0.470      & \textbf{0.975}      & \textbf{0.279}
\end{tabular}
\end{adjustbox}
\end{table}

Considering that ANN gave the best results for the prediction of all geometrical characteristics, it is used for further detailed investigation. In Fig. \ref{fig:predsim}, a comparison between simulated and predicted results is shown. It can be observed that most of the train and validated data are located near the 1:1 line, indicating well model performance for all geometrical characteristics.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{predicted_simulation.jpg}
\caption{Comparison of predicted and simulated data for ANN ML model.}
\label{fig:predsim}
\end{figure}

Fig. \ref{fig:learncurve} shows the learning curve for the  ANN model. It represents the dependency between cross validated R$^2$ and training/test size. The shaded part shows the standard deviation of the results. It can be observed, that accuracy of K-fold cross validation is growing significantly until the training set size reaches 150, while further, a stagnant growth is achieved at around 300. For the whole dataset, training and cross-validation score are converged. Therefore, it can be concluded that this size of the dataset is sufficient for analysis and it is not necessary to generate more data because the accuracy of the model would not increase.

\begin{figure}[H]
\centering
\includegraphics[width=0.8\textwidth]{learining_curve.jpg}
\caption{The learning curve for all output data and ANN ML model.}
\label{fig:learncurve}
\end{figure}

\subsection{SHAP feature interpretation}
In Fig. \ref{fig:shapbarplot} SHAP feature importance bar plots are presented. The input features are ranked by their importance in descending order based on their contribution.
The highest importance has $U_0$, followed by $d$ as in Fig. \ref{fig:heatmap} for all geometrical characteristics. Other SHAP variable importance order is different from Pearson correlation, implying the nonlinear relationship between variables.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{barplot.jpg}
\caption{SHAP features an important bar plot for geometrical characteristics.}
\label{fig:shapbarplot}
\end{figure}

To get the full insight of input feature influence, in Fig. \ref{fig:shapsummary}, a SHAP summary plot which shows the positive and negative relationships with geometrical characteristics is presented. On the x axis, feature contribution on the ML model output is presented. The colour of the dots presents the value of each observation, red implies higher values of input characteristics, while blue lower ones. Most of the input parameters show a positive correlation, which means that by increasing e.g. $U_0$, the value of geometrical characteristics will also be increased. A negative correlation can be observed for $\rho_b$ for all geometrical characteristics. Insignificantly low SHAP values are recorded for $H$ and $h_p$ indicating the small importance of geometrical characteristics.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{summaryplot.jpg}
\caption{SHAP summary plot on geometrical characteristics.}
\label{fig:shapsummary}
\end{figure}

In Fig. \ref{fig:shapdepp}, a more detail influence of $\theta$ and $U_0$ on SHAP values is presented. Many previous works have discussed which angle is the best to place the desalination nozzle in order to get the best performances, mostly mentioning angles of 45$^{\circ}$ and 60$^{\circ}$. According to the upper images, a horizontal distance of geometrical characteristics will be most influenced by the 35$^{\circ}$, while vertical distance will increase with the angle. For 50$^{\circ}$ the impact of velocity will be the smallest. Therefore, it is not possible to provide only one best angle, but other flow parameters including $d$ and densities should be taken into account.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{deppendplot.jpg}
\caption{SHAP partial dependence plots for $\theta$ and $U_0$.}
\label{fig:shapdepp}
\end{figure}

\subsection{SHAP analysis of Coanda and Shallow Water Effects}

As can be observed from Fig. \ref{fig:shapbarplot} and \ref{fig:shapsummary} water and pipe heights have the smallest importance values among all input variables summarized for the entire dataset. To observe specific cases, SHAP waterfall diagrams are plotted for one prediction result to identify if the impact of $h_p$ and $H$ increases in some flow conditions. Diagrams represent the positive and negative SHAP values, similar to points in Fig. \ref{fig:shapsummary}. However, due to the scale and general influence on the summary plot, it is not possible to investigate specific cases without waterfall diagrams.

For Coanda effect, the controlling parameter by \cite{shao2010mixing} and \cite{ramezani2021effect} is $h_p$/$L_M$. Results from previous research presented a strong effect for $h_p$/$L_M$<0.2 and an angle of 30$^{\circ}$. For the angle of 45$^{\circ}$, the effect was weaker. Therefore, to understand the effect of the bed proximity and the Coanda effect more clearly, the SHAP waterfall diagram presented in Fig. \ref{fig:waterfall1} is used. Results are presented for smaller values in the dataset $h_p$/$L_M$= 0.038 and 50$^{\circ}$. It can be observed that $d$ and $U_0$ have the greatest influence on the results, while $h_p$ has a bigger influence than in previous results (Fig. \ref{fig:shapbarplot} and \ref{fig:shapsummary}) with overall results.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{waterfallplot_9.jpg}
\caption{SHAP waterfall plots case with $h_p$/$L_M$= 0.038 and 50$^{\circ}$. At the bottom of the plot, the value for  E(f(x)) represents the average predicted score for each output data of the dataset, while on the top, f(x) presents the predicted value of the output variables for this specific case.}
\label{fig:waterfall1}
\end{figure}

Previous literature (\cite{shao2010mixing}) indicates a larger influence of the Coanda effect on the horizontal variables, translating them downstream, while there was no influence on the vertical variables, which can be confirmed from these results. $\theta$ and $\rho_a$ show a positive impact on prediction, while other variables have a negative. However, this influence is still small and in order to increase the influence, $L_M$ should be further reduced to allow for $h_p$ to have a greater impact on the output results, which is the opposite of the desired environmental effect.

For the shallow water effect, the most important parameter is $d$ $Fr$/$H$ (\cite{abessi2016dense}). The effect occurs when the value of $d$ $Fr$/$H$ equals 0.95, 0.65 for inclinations, 30$^{\circ}$, 45$^{\circ}$, respectively (\cite{jiang2014mixing}). Larger angles yield a stronger shallow water effect.

To investigate this behaviour, the case with $d$ $Fr$/$H$=0.66 and 45$^{\circ}$ is investigated and presented in Fig. \ref{fig:waterfall2}. For this value, there is a change from the full submergence regime to the plume contact regime. In contrast to previous results (Fig. \ref{fig:shapsummary}), $H$ has a much greater influence. The biggest influence is on $x_m$, $x_r$ and $x_i$, moving them downstream due to surface attachment, as expected from previous literature (\cite{jiang2014mixing}). In contrast to the Coanda effect, the shallow water effect creates a negative influence of $H$, i.e. its reduction increases the value of the output variable. For the angle of 45$^{\circ}$, SHAP analysis gave the least importance to $\theta$, indicating that the angle has no positive or negative impact on results.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{waterfall_10.jpg}
\caption{SHAP waterfall plots case with $d$ $Fr$/($H$)= 0.65 and 45$^{\circ}$.}
\label{fig:waterfall2}
\end{figure}

\subsection{Analysis based on previous research}

Results from previous research have been analyzed using the SHAP method and compared with CFD simulations. Flow parameters of the experiments and the result of geometrical characteristics from several studies were used (\cite{cipollina2005bench}, \cite{abessi2016dense}, \cite{azizi2020experimental}, \cite{jiang2014mixing}). To the authors' knowledge, this dataset contains all available data from previous experiments which hold all input information used in this ML model (most of the literature omits values such as water and pipe height or densities).

Each researcher studied different geometrical characteristics, so the number of data for each output value is different and presented in Table \ref{tab:combined_database}. Since most of those points include values with inclination 30$^{\circ}$, 45$^{\circ}$ and 60$^{\circ}$ and the same values of some parameters such as $\rho_{a}$, for ML models and SHAP analysis, the data from those experiments were mixed with CFD simulation data.

Results were calculated on the same principle as in Tables \ref{tab:result1} and \ref{tab:result2}, splitting data on 90-10 for train$\backslash$test and validation, along with a 5-fold cross validation. The number of variables is different, therefore, in this case, the multioutput regression function was not utilized. For calculation, the same tuned ANN model was used as in the previous analysis. Similar results in Table \ref{tab:combined_database} can be observed as in Tables \ref{tab:result1} and \ref{tab:result2}.

\begin{table}[!ht]
\centering
\caption{Results of ML models for a large dataset including previous experimental data.}
\label{tab:combined_database}
\begin{tabular}{cccccc}
        & Number of       & \multicolumn{2}{c}{R$^2$} & \multicolumn{2}{c}{RMSE} \\ \cline{3-6}
        & additional data & K-fold         & Val.     & K-fold         & Val.    \\ \hline
$x_{m}$ & 130             & 0.965$\pm$0.007    & 0.977    & 0.278$\pm$0.022    & 0.203   \\ \hline
$z_{m}$ & 44              & 0.972$\pm$0.004    & 0.977    & 0.177$\pm$0.009    & 0.185   \\ \hline
$z_{t}$ & 80              & 0.977$\pm$0.002    & 0.981    & 0.232$\pm$0.016    & 0.215   \\ \hline
$x_{r}$ & 124             & 0.981$\pm$0.006    & 0.988    & 0.355$\pm$0.055    & 0.301   \\ \hline
$x_{i}$ & 88              & 0.971$\pm$0.006    & 0.977    & 0.470$\pm$0.053    & 0.471
\end{tabular}
\end{table}

In Fig. \ref{fig:shapsum2} SHAP summary plot for the combined dataset is presented. The order of importance of the variables is similar to Fig. \ref{fig:shapsummary}. $x_i$ and $z_t$, $\theta$ have smaller importance which can be attributed to more cases with 45$^{\circ}$. Regarding heights, $H$ and $h_p$ have different importance, which is probably due to the dataset with a focus on the shallow water effect or Coanda effect, depending on geometrical characteristics. The importance of the rest input parameters is the same as in the initial dataset.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{summaryplot2.jpg}
\caption{SHAP summary plot for a combined dataset on geometrical characteristics.}
\label{fig:shapsum2}
\end{figure}

\subsection{Reduced ML model}

Most of the previous research analysed geometrical characteristics of negatively buoyant jets with 3 parameters: $\theta$, $Fr$ and $d$. An ANN model could also be trained with the three mentioned input parameters. Results for such model are presented in Table \ref{tab:table_3var}. Predicting technique is the same as in the previous steps. The dataset is built from the same 500 numerical simulations, except input data are compressed. The ANN architecture was tuned for this dataset and the only difference is that it consists of 20 neurons in each of 3 hidden layers, instead of 25 (as seen in Table \ref{tab:hyper}).

It can be seen that the accuracy is similar to that in Table \ref{tab:result2}, therefore it can be concluded that the use of the number of variables is justified and their application to ML models is also possible. Slightly worse results (average R$^2$ decreased from 0.975 to 0.957 and RMSE increased from 0.279 to 0.369) can be attributed to the influence of the remaining variables that are not compressed and the specificity of each case. This is also a possible explanation for some discrepancy in results from previous literature, considering that they mainly observed a reduced number of variables while comparing results. More importantly, this approach does not provide a detailed insight into the importance of each feature using the SHAP method, reducing the explainability of the models essential for understanding the flow behaviour of inclined dense jets.

\begin{table}[!ht]
\caption{Results of ML models for testing (K-fold cross validation) and validation for a reduced number of input variables.}
\centering
\label{tab:table_3var}
\begin{tabular}{ccccc}
        & \multicolumn{2}{c}{R$^2$} & \multicolumn{2}{c}{RMSE} \\ \cline{2-5}
        & K-fold         & Val.     & K-fold         & Val.    \\ \hline
$x_{m}$ & 0.939$\pm$0.014    & 0.946    & 0.351$\pm$0.057    & 0.310   \\ \hline
$z_{m}$ & 0.952$\pm$0.013    & 0.966    & 0.232$\pm$0.041    & 0.164   \\ \hline
$z_{t}$ & 0.965$\pm$0.009    & 0.968    & 0.276$\pm$0.046    & 0.241   \\ \hline
$x_{r}$ & 0.960$\pm$0.019    & 0.975    & 0.477$\pm$0.084    & 0.413   \\ \hline
$x_{i}$ & 0.930$\pm$0.021    & 0.931    & 0.669$\pm$0.121    & 0.719
\end{tabular}
\end{table}

In Fig. \ref{fig:shapdepp2}, the partial dependence plots are shown for the geometrical characteristics of inclined dense jets. It can be seen that the relationship between $\theta$, SHAP value for $\theta$ and $d$ $Fr$ is similar to previously plotted dependence on $U_0$ in Fig. \ref{fig:shapdepp}. This is consistent with scientific knowledge that the greatest influence on the Froude number is the velocity $U_0$. Therefore, this is a confirmation that it has the most impact on the form and behaviour of inclined dense jets.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{deppendplot_dFr.jpg}
\caption{SHAP partial dependence plots with a reduced number of variables for $\theta$ and $d$ Fr.}
\label{fig:shapdepp2}
\end{figure}

\section{Conclusion}

In this paper, a detailed analysis of negatively buoyant jets with interpretable ML algorithms was presented. Numerical simulation provides better prediction than mathematical models compared to experimental data and they allow simulation of different phenomena and flow conditions, but require more time and computational resources. Therefore, in this paper, a dataset of ML models was built based on a large number of CFD simulations with numerical setup validated and compared with previous experimental data with high accuracy. The calculation time of one numerical simulation is long, so complex consolidated databases on which ML models are trained may replace these simulations in the future. The main contributions of this work are:

\begin{itemize}
\item Different types of ML algorithms were tested. The best performance was obtained with ANN for all geometrical points.

\item To get more detailed insight into the performance of negatively inclined jets, the SHAP algorithm was used to analyze the negative or positive contribution of input variables on geometrical characteristics. The most significant parameters were $U_0$, while $H$ and $h_p$ had shown very small importance.

\item By investigating specific cases where Coanda and shallow water effect appear, an increase in the influence of $H$ and $h_p$ on horizontal coordinates are detected.

\item Prediction of ML models based on a dataset with incorporated previous experimental results indicates similar results and behaviour. For some points, the influence of $\theta$ was decreased due to similar angles of experimental data.

\item Using a reduced number of variables for the prediction of ML models resulted in slightly poorer prediction capabilities, due to neglecting certain effects.
\end{itemize}

\section*{Acknowledgements}

This research article is a part of the project Computational fluid flow, flooding, and pollution propagation modelling in rivers and coastal marine waters--KLIMOD (grant no. KK.05.1.1.02.0017), and is funded by the Ministry of Environment and Energy of the Republic of Croatia and the European structural and investment funds.
The authors also acknowledge the support of the Center of Advanced Computing and Modelling (CNRM), the University of Rijeka for providing supercomputing resources for numerical simulations. The work of doctoral student Marta Alvir has been fully funded by the Croatian Science Foundation.

\bibliographystyle{elsarticle-harv}

redpajama_set_name: RedPajamaArXiv


Rue de Vaugirard, Paris, France, 1968.
May '68: All Power to the Slogans!
Whether scribbled on tables, put up on posters in the streets, or chanted during protests, the slogans from May '68 have become a part of French popular culture. As part of the 50th anniversary of the "events," a collection of the slogans has been translated into English and published by the MIT Press.
The spirit of May '68 may have faded, but the rallying cries can still be heard — etched into our collective memory thanks to the work of Julien Besançon. When the first tremors of unrest were felt in May 1968, the journalist was 36 and had already reported on the Algerian War, J.F.K.'s funeral, and the Six-Day War for the French radio station Europe n°1. He captured the processions, the barricades, and the charging of the CRS police force, and also recorded the protestors' slogans. The resulting collection, Les murs ont la parole : Mai 68, was published less than three weeks after order was restored.
The MIT Press chose to translate the work into English and republish it in its original format. American translator Henri Vale is the man behind the project, having already translated several pages of Julien Besançon's collection in 1998 when he was a student at the State University of New York at Buffalo. He remembers comparing the austerity of his campus with that of Nanterre where the Parisian uprising began, and was inspired by the protestors' optimism.
Henri Vale has since translated works by Gérard de Nerval, Raymond Queneau, and Georges Perec. But the expressions from 1968 are still a challenge, as the objective is to reflect the urgency and creativity of phrases born of a specifically French political and cultural context during the 1960s. Some of the slogans have been translated literally (Sous les pavés la plage: Under the paving stones, the beach; L'imagination au pouvoir: All power to the imagination; Il est interdit d'interdire: Banning is banned), while others have remained in French. This is the case of Contestation, mais con d'abord (Contestation, but con before anything) and the untranslatable Des veaux, dévots, des votes (Dopes, believers, votes).
The Walls Have the Floor: Mural Journey, May '68 by Julien Besançon, translated from French by Henri Vale, MIT Press, 2018. 202 pages. 14.95 dollars.
=> Every day throughout May, discover a new English slogan from the May '68 Paris riots on the France-Amérique Facebook and Twitter accounts!


text:
MarketScreener Homepage > Equities > Nyse > Realty Income Corporation O
REALTY INCOME CORPORATION
58.65 USD +1.42%
01/14 REALTY INCOME : 607th Consecutive Common Stock Monthly Dividend Declared By Realty Income
01/14 REALTY INCOME CORP : Other Events, Financial Statements and Exhibits (form 8-K)
01/14 REALTY INCOME : Closes 12.075 Million Share Common Stock Offering
Realty Income : Appoints Christie Kelly As Chief Financial Officer
SAN DIEGO, Oct. 13, 2020 /PRNewswire/ -- Realty Income Corporation (Realty Income, NYSE: O), The Monthly Dividend Company®, today announced that Christie Kelly will become Executive Vice President, Chief Financial Officer and Treasurer. Ms. Kelly joined the company's Board of Directors in November 2019 and currently serves as a member of the Audit Committee. The appointment of Ms. Kelly to Chief Financial Officer will take effect January 19, 2021, at which time Ms. Kelly will resign from the company's Board of Directors.
Ms. Kelly is the former Global Chief Financial Officer of Jones Lang LaSalle Incorporated ("JLL"), a publicly traded financial and professional services firm specializing in real estate. She worked with JLL from 2013 – 2018, bringing with her 25 years of experience in financial management, international real estate, mergers and acquisitions, information technology and investment banking. From 2009 – 2013, Ms. Kelly was the Executive Vice President and Chief Financial Officer of Duke Realty Corporation, a publicly traded industrial REIT. Prior to that, she was a Senior Vice President, Global Real Estate, with Lehman Brothers, where she led real estate equity syndication in the United States and Canada. Ms. Kelly holds a B.A. degree in economics from Bucknell University.
"Christie has been a valuable addition to our company since joining the board last year, and we look forward to her continued contributions as Chief Financial Officer," said Michael D. McKee, Chairman of the Board of Directors. "Her extensive industry experience as both a senior executive and director, as well as her service on Realty Income's Audit Committee, will allow for a seamless transition."
Commenting on Ms. Kelly's appointment to Chief Financial Officer, Sumit Roy, President and Chief Executive Officer, said, "We are honored to welcome Christie to our senior management team. It has been my pleasure to work with Christie on our Board of Directors, and I am excited to be a partner with her in continuing to advance Realty Income's strategy and objectives. Her breadth of knowledge and experience with leading real estate companies will be immediately additive to our team."
"I have witnessed the extent and depth of Realty Income's team while serving as a director throughout the past year, and I look forward to joining the company as Chief Financial Officer," added Christie Kelly. "Realty Income's strength in the market as a leading S&P 500 REIT is impressive and I am excited to continue working with the team to drive the business forward."
Realty Income, The Monthly Dividend Company®, is an S&P 500 company dedicated to providing stockholders with dependable monthly income. The company is structured as a REIT, and its monthly dividends are supported by the cash flow from over 6,500 real estate properties owned under long-term lease agreements with commercial tenants. To date, the company has declared 603 consecutive common stock monthly dividends throughout its 51-year operating history and increased the dividend 108 times since Realty Income's public listing in 1994 (NYSE: O). The company is a member of the S&P 500 Dividend Aristocrats® index. Additional information about the company can be obtained from the corporate website at www.realtyincome.com.
View original content to download multimedia:http://www.prnewswire.com/news-releases/realty-income-appoints-christie-kelly-as-chief-financial-officer-301150825.html
SOURCE Realty Income Corporation
© PRNewswire 2020
All news about REALTY INCOME CORPORATION
01/14 REALTY INCOME : 607th Consecutive Common Stock Monthly Dividend Declared By Real..
01/12 TWITTER, TESLA, REALTY INCOME : What to Watch When the Stock Market Opens Today
01/12 TWITTER, TESLA, REALTY INCOME, CANTE : What to Watch When the Stock Market Opens..
01/11 REALTY INCOME : Announces Pricing Of 10.5 Million Share Common Stock Offering
01/11 REALTY INCOME CORP : Other Events (form 8-K)
01/11 REALTY INCOME : Announces Commencement of Common Stock Public Offering
2020 REALTY INCOME CORPORATION : Ex-dividend day for
2020 Goldman Unit Makes New Push Into Real Estate with Stake in Sale-Leaseback Fir..


The Clean Team is equipped with professional tools and equipment that suffer a lot of wear and tear. Maintaining the equipment is costly and replacing it is even more expensive. You can help us by contributing to our cause.
Your donation helps formerly homeless men who are working their way to self-sufficiency. It also helps keep our community clean. If you would like to donate items, please fill out the Donations Inquiry Form below.
Want to make a monetary donation? Click on the Donate Now link to make a secure online donation today!


As we told you earlier today, Apple has taken the wraps of its newly updated 9.7-inch iPad. The screen features a resolution of 1536 x 2048, and under the hood is 2016's A10 Fusion chipset, which is a bump up from the A9 SoC powering last year's 9.7-inch model. The biggest change is that the new iPad carries support for the Apple Pencil. It also features an 8MP rear-facing camera and a front-facing FaceTime HD camera. The slate includes Touch ID (you can stare at the tablet for hours, but it won't unlock) and Apple says it will run for 10 hours on a single charge.
This afternoon, a tweet from T-Mobile president and CEO John Legere revealed that the carrier will be offering the new Apple iPad sixth generation, as the device is officially called. While the tweet didn't contain any other information, Apple has already released pricing details. The Wi-Fi only model with 32GB of storage will be priced at $329 ($299 for schools), while the 128GB slate will set you back $429. The Wi-Fi + Cellular models will cost $459 for 32GB of storage, and $559 for the 128GB tablet. The Apple Pencil does not come with the tablet out of the box, and requires a separate $99 purchase ($89 for schools).
Legere says that more details will be coming. Considering that T-Mobile sold last year's model for $27.99 down and $18 per month for 24 months ($459.99 full retail price), T-Mobile customers should probably expect something similar for the new 9.7-inch Apple iPad (2018).
Its almost exactly the same as last years model! It has a new processor and pencil support, that costs an additional $99. Greedy Apple of course doesn't include the pencil with it. Just like with the $1000 Iphone X and they don't even include the fast charger.
Many are dropping the stylus from the box these days. My original Surface Book came with the stylus, so when I sold it I included the stylus. When I picked up my 15" Surface Book 2, there wasn't any stylus in the box. It was another $100 too. When I got my iPad Pro 10.5", I was lucky enough to get a deal on the pencil for $85 at least. Only my Samsung Chromebook Pro came with the stylus. Before I picked up the Surface Book 2, I was looking at Lenovo, Dell, Samsung, HP and any other 2-in-1 I could find. Most didn't come with the Stylus, or had a stylus included in one certain model that cost more anyway. Only Samsung came with the stylus on all their models that supported it.


Look sharp behind the wheel of this polished 2018 Hyundai Elantra SEL Sedan in Lakeside Blue! Powered by a robust 2.0 Liter 4 Cylinder generating 147hp while tethered to a smooth shifting 6 Speed Shiftronic Automatic transmission with Active ECO. Our Front Wheel Drive Sedan achieves near an incredible 37mpg on the open road. As you observe the exterior of this Elantra, the well-placed curves and muscular stance of this elegant sedan are mesmerizing.
Inside the SEL cabin, you are surrounded with a variety of amenities such as plush, premium cloth seats, Bluetooth hands-free phone system, Android Auto and Apple CarPlay, an impressive audio system with MP3, HD radio, available satellite radio, and USB and auxiliary input jacks, keyless entry, steering wheel mounted audio, and cruise controls.
Our Hyundai also rewards you with peace of mind thanks to the rearview camera, brake assist, traction control, and multiple airbags. Safe and efficient, our stylish Elantra SEL is an intelligent choice for you! Print this page and call us Now... We Know You Will Enjoy Your Test Drive Towards Ownership!


text:
Quick Facts About Miguel Rejala
Miguel Ángel Almirón Rejala
Paraguayan footballer
Paraguayan
Rubén Almirón
Paraguayan football star is MLS who plays as an attacking midfielder for Atlanta United and for representing his country Paraguay in international football
Alexia Notto
5feet 9inches
Who is Miguel Almiron?
Miguel Almirón is a Paraguayan footballer who plays as an attacking midfielder for Newcastle United as of 2020 and the Paraguay national team. He was named in the MLS Best XI for both of his seasons in Major League Soccer, as well as MLS Newcomer of the Year for 2017. He is also widely known as the longtime player for Cerro Porteno previously. So far, he has represented clubs as Lanus and Atlanta united as well.
What is Miguel Almiron famous for?
Miguel Almiron is best known as a Paraguayan football star is MLS who plays as an attacking midfielder for Atlanta United and for representing his country Paraguay in international football.
What nationality is Miguel Almiron?
Miguel Almiron is a Paraguayan by nationality. He was born on 10 February 1994 as Miguel Ángel Almirón Rejala in Asunción, Paraguay. His ethnicity is currently not known as his parent's information is still not traced. Also, his sibling's information is also under review. Almirón grew up in an impoverished family where his father worked 18-hour shifts as a security guard and his mother worked in a supermarket. The seven members of his household slept in three bedrooms, and he had to share a bed with his mother until he was 18. Further information on his education background is currently not available.
What position is Almiron?
Almiron started his football journey with the club Nacional when he gave his first trial at the age of 14. He started with the club Cerro Porteno as a youth player in 2008 at the age of 18. Miguel eventually made his professional debut with Cerro Porteño's first team on 10 March 2013. Almiron's first match was against Deportivo Capiata. Almiron scored his first goal against club Rubio Nu on 19 April 2013. He then signed for Club Atlético Lanús in the Argentine Primera División in August 2015. He won the 2016 Argentine Primera División. Thereafter he signed for Atlanta United FC for $8 million on 5 December 2016. He was named in the MLS Best XI for both of his seasons in Major League Soccer, as well as MLS Newcomer of the Year for 2017. After helping Atlanta to the MLS Cup 2018 he signed for Newcastle for £21 million, a club record and the highest fee for an MLS player. Almirón made his international debut for Paraguay in 2015 and represented the country at the Copa América in 2016 and 2019.
Who is Miguel Almiron's girlfriend?
Almiron is currently dating a girl named Alexia Notto. There is much less information about how they first met and how long they have been in a relationship. All that is known is the couple are dating happily as Miguel keeps posting glimpses of their time together on his social media account mostly on Instagram.
What is Miguel Almiron's salary?
As of 2019, Miguel Almiron has a base salary like any other top-end player as per MLS union which is estimated to be $1,912,500.00. His major source of income is his football career. Miguel Almiron is listed in the highest-paid Atlanta united player and a 12th highest-paid player in Major Soccer League (MLS) history. Almiron's current estimated net worth value is about $9 million. In addition, he earns from sponsorships and endorsements as well. further information regarding his cars and houses are currently under review.
How tall is Miguel Almiron?
The Paraguayan MLS star, Miguel has a tall height of 5feet 9inches. He has a very athletic body build. However, his weight is currently not known. He has a black hair color and black eyes. Further information regarding his body statistics, dress and shoe size are still not known and are going under review. He has a straight sexual orientation.