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September 15, 2022 by Amanda MacGregor
Is Social Media Making You Anxious? Me, too, a guest post by Deb Caletti
September 15, 2022 by Amanda MacGregor Leave a Comment
I don't know about you, but in the fall of 2020, I was anxious. If you've read my books (The Nature of Jade and A Heart in a Body in the World, in particular), you can probably guess that anxiety is usually a struggle for me. But add a global pandemic and world events that felt entirely out of control, and, well . . . I felt like I was steering a ship in a storm when I knew nothing about steering a ship in a storm. One thing I had no doubt about: time online and on social media was making this anxiety worse. The message that kept hitting and hitting as I kept scrolling and scrolling was that imminent disaster was everywhere, likely to happen at any moment. Meanwhile, I tried to slap up pretty images on Instagram and Facebook, plugging the leaks and bailing the water of the sinking ship.
Admittedly, my relationship with social media was already rocky. I was certain of this from twelve million articles describing the Ten Signs of Unhealthy Relationship. Was there intensity between me and my partner, aka social media? Yep. Isolation? You betcha. Was my partner, social media, critical of me? Hahaha. Indeed. Manipulative? Can you say "filter"? Was I afraid to say what was really on my mind around them? Could I be myself? Are you kidding?! Did I feel drained when we were together? Did I walk on eggshells? Yes, yes, yes! Did I like who I was when I was around them? HECK NO, unless you like when you feel small and insecure and overwhelmed.
We had a problem, social media and I. I was beginning to wonder if I knew who that woman was, a woman in a square, looking at a square. I wondered if I could care about anything, in the caring about everything. And I was worrying that we all had a problem, most especially young women, who, I kept reading, were feeling the effects of social media to alarming degrees. Social media use was deeply linked to both anxiety and plummeting self-esteem. The scrolling, the noise, the sense that we were in danger all the time (and not measuring up while we're at it) hurt. If I was feeling all of these things, if I was scrolling in the middle of the night, my face lit in a circle of doom-light, a woman who's lived awhile, who's pretty sturdy from heartbreak and failures and trauma and life truths and the force of the storm over years—how might young people feel right now with all of this? How were they managing the constant assault of information? How were they coping with being so seen and so unseen at the same time?
In The Epic Story of Every Living Thing, seventeen-year-old Harper Proulx, at the height of her post-pandemic anxiety, sets out with her half siblings to find their sperm-donor father, a man she believes will tell her who she really is, that girl in the social media squares. The events of that summer and the man they discover—a charismatic deep-sea diver obsessed with solving the mystery of a fragile sunken shipwreck from the 1880s (whose real-life captain, the nineteen-year-old Mary Patten, has an astonishing story of her own)—drop Harper, literally, into the deep, beautiful, and, most of all, quiet waters of identity and connection. In the book, I wanted to explore who we are in dark times, and who we are after them. And I wanted to talk about social media and anxiety—the confusion of looking at that person in the square and wondering who she is. The dangers at the surface, when the real meaning is below it. The whirlwind of noise, where you see a plate of sushi, and a parrot, and then a tragedy. How the noise itself can be its own disaster, its own disease, flattening your land and deadening your empathy.
What I didn't know, not until I did a ton of research for this book, was that this is how we're supposed to feel when we use social media. This is how we're made to feel ON PURPOSE, and BY DESIGN, for PROFIT. Small and insecure, so we'll post and check and like and hope to be liked and click and click and click.
Subtle signs of disease your feet can reveal. Healthy foods that are actually ruining your diet. A kitten licking another kitten. A plate of macaroons. Someone hates a TV show, and someone else hates that person for hating that TV show, and someone else is announcing they are taking a social media break but no one cares. Pretend accidental selfies where the person looks fabulous. A gerbil wearing a tiny hat. Someone's horrible wound, as you innocently eat your scrambled eggs. Tornadoes, wars, hatred. Ten Ways You're Walking Wrong. Ten Signs of Other People's Perfection, make that a hundred. A Thousand Ways You're Not Measuring Up. Forty-Five Thousand Ways Other People's Vacation Is Way Nicer Than Yours. A drawing of a bird. A real bird. A bagel, a muffin, a croissant, a donut, a cronut.
When the mundane sits next to the tragedy, it underscores the sense that disaster can happen at any moment. You can be eating your sushi and an earthquake happens. You can be holding your cronut in a beautiful sunsetty light and a grandmother dies.
And it can all be yours, any time, day or night, because the internet is like a 7-Eleven, open twenty-four hours. You can't get great healthy food there, but you can get hot dogs that have been spinning around for hours. How can we possibly be capable and calm against so much? How can we NOT feel vulnerable and fragile? How can we care and care and care? How many hurricanes, how many shootings, how much cancer, until your heart shuts down? In the book, Harper can almost hear all the hearts, once warm and caring, shut, shut, shutting down, like closed doors, because it's just too much.
Harper needed relief, and so did I, and so, over that summer, Harper connects with her larger family and with a vibrant community. She experiences hope and humor and that timeless thing called love. She goes from a noisy world to the quietest one—underwater. She dives down into the waves that have been waves forever, surrounded by a whoosh of bubbles in that old, old ocean. She goes below the surface, where no one will even see her, where she won't be an object with an object but a real person in the real world. She experiences the ongoing life of astonishing creatures and human beings who have been here since the beginning of time, going through stuff, surviving stuff.
I wanted to say: Dear readers, I hear you. I get it. Me too. I wanted to say: Here we are with our anxiety, hanging out in this book together. I wanted to say: Girls, you know what? That story is a lie, the one you've been told, that you should be afraid all the time, and that you aren't capable of handling hard stuff. Mary Patten, that girl taking Neptune's Car around Cape Horn, she heard the same lie—it's been going on a long time. I wanted to say that caring about everything is exhausting, and impossible besides, but that your empathy is a pure and permanent thing, a cherished gift, a birthright.
And most of all, I wanted to say: You may see yourself in all those squares, but, friend, there is only one you. The you yourself, a you with your own epic story, as every creature and human being who ever lived has an epic story. Step out of square, and look—there you are. Incredible, singular you.
Photo credit: Susan Doupé
DEB CALETTI is the award-winning and critically acclaimed author of over sixteen books for adults and young adults, including HONEY, BABY, SWEETHEART, a finalist for the National Book Award; A HEART IN A BODY IN THE WORLD, a Michael L. Printz Honor Book; and GIRL, UNFRAMED, which received seven starred reviews. Her books have also won the Josette Frank Award for Fiction, the Washington State Book Award, and numerous other state awards and honors, and she was a finalist for the PEN USA Award. Her latest book for young adult readers is THE EPIC STORY OF EVERY LIVING THING, which Kirkus Reviews described as "an epic tale of life, love, and identity." She lives with her family in Seattle.
About The Epic Story of Every Living Thing
From the award-winning author of A Heart in a Body in The World comes a gorgeous and fiercely feminist young adult novel. When a teen travels to Hawaii to track down her sperm donor father, she discovers the truth about him, about the sunken shipwreck that's become his obsession, and most of all about herself.
Harper Proulx has lived her whole life with unanswered questions about her anonymous sperm donor father. She's convinced that without knowing him, she can't know herself. When a chance Instagram post connects Harper to a half sibling, that connection yields many more and ultimately leads Harper to uncover her father's identity.
So, fresh from a painful breakup and still reeling with anxiety that reached a lifetime high during the pandemic, Harper joins her newfound half siblings on a voyage to Hawaii to face their father. The events of that summer, and the man they discover—a charismatic deep-sea diver obsessed with solving the mystery of a fragile sunken shipwreck—will force Harper to face some even bigger questions: Who is she? Is she her DNA, her experiences, her successes, her failures? Is she the things she loves—or the things she hates? Who she is in dark times? Who she might become after them?
Publisher: Random House Children's Books
Age Range: 14 – 17 Years
Filed under: Guest Post
AnxietyGuest postsSocial Media
About Amanda MacGregor
Amanda MacGregor works in an elementary library, loves dogs, and can be found on Twitter @CiteSomething.
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4 Debut YA Authors Center LGBTQIA+ Characters


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Q: How to integrate open ID Login I want to integrate Open ID, Google and Yahoo login in my site, So please anyone can tell me the process of integration in ASP.NET.

A: Google and Yahoo support OpenID, so you just need to OpenID enable your website.
This is a great article which tells you step by step of what you need to do. You will still need an OpenID library for .NET in order to do that.

A: I have actually written a blog post on How to add OpenID authentication to your site using Janrain Engage that might help you along.
It's written with an ASP.NET  MVC 3 application in mind, but there's nothing in there you cannot use in an ASP.NET app.

redpajama_set_name: RedPajamaStackExchange


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Q: DocumentDb CreateDocumentQuery stopped working I have this method:
/// <summary>
/// Gets a list of our documents
/// </summary>
/// <returns></returns>
public List<T> List(string query)
{
    // Return our items
    return this._client.CreateDocumentQuery<T>(UriFactory.CreateDocumentCollectionUri(this._databaseName, this._collectionName), query).ToList();
}

which has been working fine up until today.
Now it throws an error:

Partition routing information cannot be extracted from the query when running in a 32-bit process. To complete your query and avoid this exception, ensure that your host process is 64-bit.\r\nFor Executable applications, this can be done by unchecking the \"Prefer 32-bit\" option in the project properties window, on the Build tab. \r\nFor VSTest based test projects, this can be done by selecting Test->Test Settings->Default Processor Architecture as X64, from Visual Studio Test menu option.\r\nFor locally deployed ASP.NET Web applications, this can be done by checking the \"Use the 64 bit version of IIS Express for web sites and projects\", under Tools->Options->Projects and Solutions->Web Projects.\r\n

which is really odd, because nothing on my end has changed.
I tried doing everything it said, but to no avail, it still has errors.
Does anyone know why?

A: Some information about this error message can be found here.
If you do not use the partition routing feature in DocumentDb you can change your code to provide FeedOptions to disable EnableCrossPartitionQuery.
/// <summary>
/// Gets a list of our documents
/// </summary>
/// <returns></returns>
public List<T> List(string query)
{
    // Return our items
    return
        this
          ._client
          .CreateDocumentQuery<T>(
               UriFactory.CreateDocumentCollectionUri(this._databaseName, this._collectionName),
               query,
               new FeedOptions { EnableCrossPartitionQuery = false };
          ).ToList();
}


I'm wondering if there is a way to quickly stack a large number of clips onto one timeline vertically. As I'm sure you all know, right now when you select multiple clips in a bin and then drag them over to your timeline, the clips are placed back to back to back. Essentially what I'm hoping to do is the opposite... I want to select multiple clips in a bin and drag them onto my timeline, each clip getting their own separate video track. If it helps, audio is not important to me in this scenario. Just picture.
Apologies if this shortcut I seek is right in front of my face already. Any help here is appreciated.
Hero! Thank you so much. This worked perfectly.


In Uganda, there is a small business whose model is based on manufacturing and selling products made from indigenous tree bark. The bark can be sustainably collected as this particular species of tree has the property of easily regrowing bark after being stripped bare. The material is fashioned into bags, accessories and other products where it is sold on the international market for a decent profit.
So, sustainable production of a desirable product fused with BoP ingenuity. Innovative enough for you? The Business in Development (BiD) network apparently agrees. The company is African Skin, and they were the winners of last year's BiD Challenge.The Challenge is back for another year, looking for the best in small sustainable enterprises to provide with startup investment. With this 3rd round opening on April 2nd, BiD will whittle down submitted business plans in an extensive screening process that ends with an awards event in December.
BiD looks for highly creative developing country entrepreneurs that have businesses with a social or environmental component and that will become profitable within three years. Winners receive mentoring services and 20,000 euros to get their idea rolling.
If you are interested in participating, BiD is still accepting business plans through May 31st – visit their homepage for more details. Also check out the seven country-level challenges they have just launched for 2007. If you're a socially minded entrepreneur this is worth looking into – maybe your company will be the next African Skin….


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Mountain FM
Radio staff
Radio media
The Edge Radio Show – Best-selling Authors
by Ronald A. Thiel	| Posted on December 28, 2021 January 2, 2022
Hosts: Ron Tunick and Scott Harries
Subject: Authors of sales
Guest: Harry Spaight
The Edge Radio Show – Best-selling authors – December 28, 2021
In this episode of The Edge on KHTS AM 1220 and FM 98.1, our Coach hosts Ron Tunick and Scott Harries chat with their guest, Harry Spaight. Coach and Scott kick off the series by bringing in Harry to talk about the topic of sales. Harry happens to be a selling author and is well known on social networks such as Instagram. Harry provides insight into successful sales and marketing tips.
Later in the series, Coach, Scott, and Harry talk about sales. Hear Coach, Scott, and Harry talk about sales, marketing, and books.
To listen to the Podcast, Click here!
What Makes a Small Business an Empire? What makes athletes Olympians? What brings artists, from underground musicians to full-fledged rock stars? What can even strengthen your relationships at home, at work, and in the world? The answer is: your 'benefit'. Finding it separates you from the rest of the competition. What does finding your "benefit" even mean? We're here to tell you.
The Edge is a weekly hour-long radio show that describes what it takes to be more successful than the masses. Tune in every Friday at 11:00 a.m. as Scott Harries and Ron Tunick are joined by an engaging guest who will talk about: their paths to prosperity, how they bounced back from their failures (and learned from their mistakes) and discovered the "Edge" which allowed them to fly away.
Be sure to take notes – you'll be sure to gain advice that will help you find the "edge" in your life.
Do you have a news tip? Call us at (661) 298-1220 or email [email protected] Don't miss a thing. Get the latest KHTS Santa Clarita News alerts delivered straight to your inbox. Report a typo or error, email [email protected]
KHTS FM 98.1 and AM 1220 is the only local radio station in Santa Clarita. KHTS mixes a combination of news, traffic, sports, and features with your favorite contemporary adult hits. Santa Clarita News and Features are broadcast throughout the day on our airwaves, on our website and on various social media platforms. Our nationally-awarded daily KHTS newsletters are now read daily by more than 34,000 residents. A dynamic member of the Santa Clarita community, the KHTS broadcast signal reaches throughout the Santa Clarita Valley and parts of the high desert communities located in the Antelope Valley. The station broadcasts its talk shows across the web, reaching potentially global audiences. Follow @KHTSRadio on Facebook, Twitter, and Instagram.
Full stellar actor in "It's a Wonderful Life" radio show
#MUBB radio show debuts Thursday at 6 p.m. CT
Amy Williams Radio Show Tonight
Journalist from Montreal-based online radio station killed on the job in Haiti
Free dental treatment targets Wairoa whānau
The Youth project celebrates young voices on Northampton radio station
Joe Reinagel joins Jason Minnix on local ESPN radio show The Blitz
Tips for the renamed WBB radio show Tuesday
Copyright Mountain FM. All rights reserved.

redpajama_set_name: RedPajamaCommonCrawl


Fans of the New York Knicks are about to snap, but does Carmelo Anthony have a point? The Brooklyn-born ex-Syracuse star forced his way to the New York Knicks, and now he has to deal with the scrutiny and criticism that comes with not performing in the countries biggest media market.
Melo chose to be traded to the Knicks, and made an even bigger decision when he chose to re-sign with the Knicks instead of heading to Chicago.


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Glorious Rock Bottom : 'A shocking story told with heart and hope. You won't be able to put it down.' Dolly Alderton Paperback / softback
by Bryony Gordon
DARK, HONEST, UPLIFTING. THIS IS A SOBRIETY MEMOIR LIKE NO OTHER. >'This is a book that tears down walls.' Marian Keyes'Bryony Gordon is a terrific, compassionate writer whose razor-sharp honesty slices through every sentence of this compelling memoir.' Liz Day'Poetic, raw and very important.' Fearne CottonBryony Gordon is a respected journalist, a number-one bestselling author and an award-winning mental health campaigner.
She is also an alcoholic. In Glorious Rock Bottom Bryony opens up about a toxic twenty-year relationship with alcohol and drugs and explains exactly why hitting rock bottom - for her, a traumatic event and the abrupt realisation that she was putting herself in danger, time and again - saved her life.
Known for her trademark honesty, Bryony re-lives the darkest and most terrifying moments of her addiction, never shying away from the fact that alcoholism robs you of your ability to focus on your family, your work, your health, your children, yourself. And then, a chink of light as the hard work begins - rehab; twelve-step meetings; endless, tedious, painful self-reflection - a rollercoaster ride through self-acceptance, friendship, love and hope, to a joy and pride in staying sober that her younger self could never have imagined. Shining a light on the deep connection between addiction and mental health issues, Glorious Rock Bottom is in turn, shocking, brutal, dark, funny, hopeful and uplifting.
It is a sobriety memoir like no other.
Publisher:Headline Publishing Group
Addiction & therapy
Coping with drug & alcohol abuse
Also by Bryony Gordon | View all
No Such Thing as Normal
Mad Girl


Development of the Bokan Mountain rare earth mine is on hold as the company leading the project focuses on a new processing technology and waits for rare earth metal prices to rebound.
Nova Scotia-based Ucore Rare Metals Inc. finished infill drilling and drilled groundwater monitoring wells in 2014, leaving it at a natural stopping point before moving towards the next steps of development.
Ucore Vice President Randy MacGillivray said in an interview the company has delineated a resource of approximately 5 million tons that is 0.65 percent total rare earth metals.
Bokan, located on the southern part of Prince of Wales Island in Southeast, would be an underground mine.
Rare earth metals are used in small amounts in countless technology applications. Their prices have softened in recent years along with other, more well known metals and commodities.
MacGillivray said because the mine would harvest up to 15 rare earth metals it is hard to set a definitive price point at which Ucore would initiate a full-fledged feasibility study or jump into the environmental impact statement process.
"Certainly a movement towards increased metal values in the rare earth metal sector and or us being able to tie up an end user agreement with defined prices would encourage development," he said.
However, there is no timeline for starting permitting.
Instead, Ucore has invested in molecular recognition separation technology that has been used for other metals but not with rare earths, according to MacGillivray. Generating a revenue stream from that investment could also help the junior mining company fund Bokan, he said.
Based on Ucore's preliminary economic assessment, the mine would cost $220 million to construct over two years and employ up to 300 people during that period. Ucore has "drilled off" a resource base to support operation for 10 years, which would require about 190 jobs, he said.
MacGillivray said Ucore continues to strengthen its baseline environmental work for the project.
In 2014, former Gov. Sean Parnell signed legislation authorizing the Alaska Industrial Development and Export Authority to finance up to $145 million for Bokan's construction.


At SIMSYS we are constantly seeking to find new and innovative solutions for our customers' IT needs. Take a look at some of our innovative ideas and solution services that may suit your organization's needs.
In today's world of new Media, the Internet is the best medium to reach out to target audiences and getting the message out there. A Web Portal is an information medium that allows people to know more; be it a product, an organization or community. It is very much like a book that describes itself.


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Q: get_post_meta image width for lazy load I'm trying to lazy load a page of images in Wordpress - I've tried the plugins but none of them seem to work(I only want to lazy load image on some pages.)
So I'm now trying to do it with a plugin - http://www.appelsiini.net/projects/lazyload
This plugin requires the image width and height in the img tag.

I'm attaching the images from custom fields like
    <img src="<?php echo get_post_meta($post->ID, 'img1', true); ?>">

Is it possible get the images width and height from the get_post_meta
I've looked at wp_get_attachment_image_src but I can't see how to use it get_post_meta

A: Looks like you have the image URL saved as a custom field and not the attachment id so first get the attachment id using the URL using Philips awesome function and then use
wp_get_attachment_image_src() to get the attachment attributes :
$attch_id = pn_get_attachment_id_from_url(get_post_meta($post->ID, 'img1', true));
$image_attributes = wp_get_attachment_image_src( $attch_id ); // returns an array

//$image_attributes containing
$image_attributes[0] => URL
$image_attributes[1] => width
$image_attributes[2] => Height


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She conquers fear of needles to give back
Ms Asnah Ariffin and her husband, Mr Halim Mohd.TNP PHOTO: ANG HWEE MIN
Two couples strengthen their relationship, one by donating blood and the other by volunteering with the Singapore Red Cross
Ang Hwee Min
As a firefighter, ambulance driver and now part of the service support unit with the Singapore Civil Defence Force (SCDF), Mr Halim Mohd, 51, understands the need for blood donation.
That is why he has been donating blood for over 20 years.
But his wife of 25 years, Ms Asnah Ariffin, also 51, never tried donating blood because needles scared her.
In November last year, Ms Asnah, a housewife, accompanied Mr Halim to the Bloodbank @ Woodlands, run by the Singapore Red Cross.
This time, she decided to donate blood.
"Just seeing the needle made me scared. But when I finally tried it, I didn't feel any pain, it was like an ant bite," she said.
Valentine's fail: Two florists unable to fulfil all orders
She wins V-Day contest to give husband of 50 years new watch
Bus captain couples - keeping love alive on the road
Sweet treats for Valentine's Day
The couple met in 1991, after a friend gave Mr Halim her phone number. Ms Asnah had just come out of a broken engagement.
They decided to meet after talking on the phone for a month.
The couple got married six months later, and now have three sons aged 18, 21 and 23.
Mr Alvin Ee and his wife, Ms Chan Kah Mun.TNP PHOTO: ANG HWEE MIN
Mr Halim said they encourage their sons to donate blood.
He said: "There's always a need for blood. I see it at work."
Another married couple that gives back to the community is Mr Alvin Ee and his wife, Ms Chan Kah Mun, both 30.
They are volunteer instructors with the Singapore Red Cross Academy.
Mr Ee is a senior staff nurse at the Singapore General Hospital (SGH) and Ms Chan is an occupational health nurse.
They were working in the A&E department at SGH when they met on Aug 1, 2012. Mr Ee said he began admiring her from afar after passing her a case.
"It was hard to get to know her because our department has over 200 people," he said.
Ms Chan only remembers becoming friends with Mr Ee after a department outing in March 2013.
She then asked Mr Ee where to fulfil community involvement hours for her degree.
Mr Ee, who has volunteered with the Red Cross since secondary school, immediately suggested she join him.
Ms Chan became a volunteer for First Aid on Wheels at East Coast Park, providing medical assistance to visitors.
They became a couple in November 2013, and married last October.
The couple juggled part-time studies and long shifts at work from 2014 to last year, so they rarely volunteered together.
Mr Ee added: "But we're still much luckier than other couples because we saw each other every day at work."
To volunteer together more often, Mr Ee encouraged Ms Chan to join him as a volunteer instructor with the Red Cross Academy last year. They have taught several classes together.
Ms Chan said: "I was wondering why he is so into education, so I took up the instructor's course last year.
"The students learn from you, and after they pass the course, they pass it on to others. It's very satisfying."
Valentine's DayNURSING (OCCUPATION)COMMUNITY ISSUES
Read articles by Ang Hwee Min


Arthur Cole spoke with Henry Tam, product marketing manager for F5 Networks, about traditional hardware appliance platforms versus virtual-appliance-based platforms.
It makes sense that as the rest of the enterprise converts to virtual/cloud operations, that management platforms should as well. That's why we're seeing a host of appliance-based platforms that are expanding to include virtual appliance configurations. The most recent is F5 Networks, which now offers its Enterprise Manager 4000 as a virtual appliance. While product manager Henry Tam says it provides added flexibility for increasingly dynamic environments, there are still some strong reasons why some users should stick with a traditional hardware appliance.
Cole: I see that the latest version of the F5 Enterprise Manager is available as a virtual appliance. What advantages does this bring to the enterprise, particularly when it comes to managing hybrid environments?
Tam: The virtual appliance offering of F5 Enterprise Manager, Enterprise Manager VE, has exactly the same feature set as the physical appliance, F5 Enterprise Manager 4000. However, customers now have a solution that better aligns with the virtualized data center and the way IT manages their other infrastructure solutions. The advantages of the Enterprise Manager VE is the flexibility of deployment that it offers. Customers now have the option to allocate more memory, CPU and storage to maximize performance and capacity.
Cole: Is there any reason, then, why someone would want to maintain application management, or any kind of management, on a standard hardware configuration? It seems the cost advantages alone are difficult to pass up.
Tam: From an operations point of view, simplifying potential issues is important. In some cases, customers prefer to have single vendor accountability and reduce the complexity of troubleshooting by working with one vendor. For other customers, F5 Enterprise Manager is managed by the network operations group that doesn't necessarily have a hypervisor such as VMware available. In this situation, it is much easier to deploy the Enterprise Manager 4000 than the Enterprise Manager VE.
Cole: Does the virtual appliance model hold any advantages when it comes to managing the cloud? As data environments become more flexible, shouldn't the management layer be able to adapt and respond to changing conditions as well?
Tam: Whether organizations are managing their F5 application delivery infrastructure solutions within the corporate firewall or off premises as part of a managed service or cloud solution, the advantage is the same – Enterprise Manager VE provides a flexible management platform that enables them to manage multiple physical or virtual ADC devices. Enterprises and cloud providers can reduce upfront costs, enable faster deployments and save space in their data centers.


Opengear (www.opengear.com) is a profitable, double-digit annual growth technology provider that enables technical staff to provision, maintain and repair infrastructure from anywhere at any time, as if they were physically present, drastically reducing both operational costs and the risk of downtime.
Opengear solutions deliver true out-of-band management, always-available wired and wireless secure remote access, proactive monitoring and automated response capabilities.
Opengear designs, manufactures, and delivers remote infrastructure management solutions. It offers data center infrastructure management solutions, such as console server solutions that provide secure in-band and out-of-band access to manage data centers and colocation sites; manage serial, USB, and network console ports; monitor environment; control UPS systems and PDUs; and alert and auto respond. The company also provides remote site management solutions that allow users to monitor, access, and control various infrastructure, such as applications and computers at the local or remote site; networking equipment, security cameras, and power supplies; and down to door sensors, and temperature and digital I/O. In addition, it offers environmental monitoring and alerting solutions for managing changes in environmental conditions in computer rooms and remote sites; and central management solutions, including virtual and physical appliances to securely manage distributed infrastructure, and access locations without public IP. The company sells its products through its online stores in the United States, the United Kingdom, Australia, and internationally; and distributors worldwide. Opengear, Inc. was founded in 2004 in Brisbane, Australia and has operations in Sandy, Utah, and sales offices in Slough, United Kingdom. Currently, R&D operations remain based primarily in Brisbane, Australia.
The Director, Strategy and Business Development will spearhead business development initiatives which identify market, technology and partnership opportunities in infrastructure management that match Opengear's strengths, work internally with the executive, product and marketing teams to refine the company's strategic direction, and translate this direction into growth actions that create long-term value.


The Friendship Group meets weekly, prior to Sunday School. This dedicated group's ministry inspires, comforts, and ministers through it's mailing of greeting cards. They send cards with thoughts and prayers of love and care to folks who are experiencing sickness, bereavement, transition, or celebration.


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Scientific paper "Global and Regional Increase of Precipitation Extremes under Global Warming"
1. Premise
The recent concerns on anthropogenic global warming and climate change stimulated an intensive research activity on climate dynamics during the past decades, which emphasised the key role of future climate predictions to devise mitigation and adaptation strategies. These predictions can be produced with alternative and complementary methods than can be possibly integrated in solutions of different complexity, like for instance statistical extrapolation and/or mathematical models of the physics, chemistry, ecology and anthropogenic impact of the climate system.
The history of climate modeling begun with the development of conceptual and very simple models that helped to identify the relevant processes and their connections. For instance, Greek astronomers and geographers understood that the shape of Earth is spherical and connected climate to the inclination of the terrestrial axis. These simple models were followed in the 19th century by mathematical models of energy balance and radiative transfer. Computer simulation models of the atmosphere were first introduced in the 1950s. From the 1990s, when global warming became more and more a concern, increasingly comprehensive coupled models of the entire climate system were developed by incorporating an increasing number of physical and chemical processes.
2. Climate processes and systems
Climate models are supposed to describe several processes that are linked each others by causal relationships. In fact, climate is governed by a system of physical, chemical, ecological and anthropogenic processes that are linked each other by two-ways connections and feedbacks. One may identify five main subdomains in the climate system:
atmosphere;
cryosphere;
hydrosphere;
biosphere;
technosphere.
The Earth atmosphere is the layer of gases, commonly known as air, retained by the gravity force and surrounding our planet. It is composed by 5 main layers (figure 1): the troposphere is the closet layer to the Earth surface. It is 7 to 20 km thick and contains about half of the whole mass of the atmosphere. The stratosphere is the second layer, which ends about 50 km above ground. The mesosphere extends up to about 85 km. The thermosphere extends up to 1,000 km. The farthest layer from the Earth's ground is the exosphere, where weather satellites fly their orbit. This layer has very few atmospheric molecules and therefore some scientists classify it as a part of outer space. The atmosphere of Earth is composed of nitrogen (about 78%), oxygen (about 21%), argon (about 0.9%), carbon dioxide (0.03%) and other gases in trace amounts.
The cryosphere is the domain of water in solid form, including sea ice, lake ice, river ice, snow cover, glaciers, ice caps, ice sheets, and frozen ground (which includes permafrost). There is a wide overlap between cryosphere and hydrosphere. The cryosphere plays an important role in the Earth's climate. Snow and ice reflect heat from the sun, therefore contributing to regulate planet's temperature. Because polar regions are some of the most sensitive to climate shifts, the cryosphere may be one of the first places where scientists are able to identify changes in climate.
The hydrosphere includes the mass of water found on, under, and above the surface of a planet. Water in the liquid state covers about 70% of the Earth's surface. About 97% of the liquid water is found in the oceans. Only a small portion of the Earth's water is freshwater, found in rivers, lakes, and groundwater (see Figure 2).
The biosphere, also known as the ecosphere, encompasses all ecosystems. It can also be termed the zone of life on Earth and therefore there is a large overlap with other spheres. The biosphere, through photosynthesis, captures solar energy at a rate of around 130 Terawatts/year. It is a system with capacity to self-regulate and is actually close to energetic equilibrium.
The technopshere (sometimes also referred as anthroposphere) is that part of the environment that is made or modified by humans for use in human activities and human habitats. The term was first used by nineteenth-century Austrian geologist Eduard Suess. The technosphere is continuously developing and has reached a level of interaction with the climate system that humans never experienced before. In fact, the technosphere is impacting all the other domains above by inducing global feedbacks. These latter compensate impacts therefore contributing to keep the system in equilibrium. However, the connections between the different subdomains are highly nonlinear and therefore the climate system is characterised by thresholds, possible tipping points and several equilibrium states.
Figure 1. Layers of the atmosphere. Source: NOAA & User:Mysid, Public domain, via Wikimedia Commons
The above sudomains are open systems that are tightly related each other, with exchanges of physical quantities like mass, heat, momentum and energy. The climate system is driven by solar energy. Some of it is reflected back into space, while the rest is absorbed by the climate system and re-emitted as heat. Part of the reflected and re-emitted energy is trapped by the Earth's greenhouse effect. This heat dynamics is what determines the temperature of the Earth.
Figure 2. Sketch of water distribution on Earth. By NASA Official website, [1]., Public Domain, https://commons.wikimedia.org/w/index.php?curid=49654959
Earth is heated by sun in an uneven way, as some parts of the Earth receive more energy therefore originating well known temperature differences that the system tends to reduce by transporting heat from the warm tropics to the cool ice zones of the poles. This heat transportation gives rise to ocean currents, wind, evaporation, precipitation, and global weather patterns. In particular, the hydrosphere is intensively connected with the atmosphere through the hydrological cycle, that includes exchanges of water in fluid phase (liquid and gas) and heat through evaporation and precipitation, among other processes. The ocean-atmosphere system also exchanges gases like carbon dioxide, where the ocean acts as a large carbon sink. These interrelationships are not yet fully known and therefore we need to introduce assumptions when building models.
The above systems are characterised by turbulence (figure 3) and may exhibit chaotic behaviours, which imply a high sensitivity to initial and boundary conditions. In terms of predictability, chaotic systems may be predictable in the short term while in the longer term they exhibit uncertainty, that is due to the inflation of the imprecision with which initial and boundary conditions are defined. The time span that the behavior of a chaotic system can be effectively predicted depends on how accurately its current state can be measured, and a time scale depending on the dynamics of the system, called the Lyapunov time. Lack of predictability unavoidably affects environmental models.
Figure 3. Turbulence in the tip vortex from an airplane wing. Studies of the critical point beyond which a system creates turbulence were important for chaos theory. By NASA Langley Research Center (NASA-LaRC), Edited by Fir0002 - This image or video was catalogued by Langley Research Center of the United States National Aeronautics and Space Administration (NASA) under Photo ID: EL-1996-00130 and Alternate ID: L90-5919., Public Domain, https://commons.wikimedia.org/w/index.php?curid=494937
3. Structure of climate models
Climate models reproduce the functioning of climate with the purpose of gaining a better understanding of how this complex systems works by testing theories and solutions. The second purpose of climate models is to reproduce the distribution in space and time of key variables of the atmospheric system, such as temperature, wind, rainfall and so forth. These variables are affected by significant spatial heterogeneity - with different behaviours depending on the specific variable - and are also quickly varying in time.
Global climate models are mathematical representation of the climate system's dynamics. They range from very simplified mathematical representation, like the so-called box models, to complex emulations of the whole system that apply a set of physical equations at local scale, like the General Circulation Models (GCM). GCMs are frequently applied in climate studies to predict the future climate depending on assigned initial and boundary conditions and assigned input variables and parameters. GCMs have developed rapidly over recent decades in response to increasing interest in climate modeling, major advances in computing power and better understanding of how the climate system works.
GCMs equations describe the evolution in space and time of relevant atmospheric processes, by introducing hypotheses and simplifications that allow a tractable representation of the climate system. In view of their heterogeneity such processes are described at local scale by discretising the atmosphere into a 3-dimensional grid (figure 4). Any grid box is assumed to be a representative elementary volume over which a measurement of the relevant variables is assumed to be representative of the corresponding volume value. The boxes have variable dimension, with increasing computational demand for decreasing box size. Modern models have box size, projected over the land, varying from 100 square kilometers for detailed models to 600 square kilometers for coarser representations. Click here to see an animation presenting the evolution of grid sizes during the progressive development of models. The results of processes modeled in each cell are passed to neighboring cells to model the exchange of quantities over space and time.
Candidates equations to describe the system at local scale are conservation laws which state that a particular measurable property of an isolated physical system within a reference region of space does not change as the system evolves over time. Conserved physical quantities may be, for instance, energy, momentum, mass, and water vapor. A local conservation law is usually expressed as a continuity equation. It is a partial differential equation which gives a relation between the amount of the quantity and its transport. In detail, the continuity equations states that the amount of the conserved quantity within a volume can only change by the amount of the quantity which flows in or out of the volume. The fact that the equation describes "change" justifies its differential form. To simulate the value of the variables we need to sum their changes over the grid and over the infinitesimal time steps, therefore performing an integration over space and time. Integration requires the knowledge of the initial and boundary conditions, which dictate the values to be assumed by the variables at a given time and the boundary of their spatial domain.
Therefore, climate models essentially describe how energy and materials propagate through the climate system, which includes atmosphere, land and oceans. Developing and running a GCMs requires identifying and quantifying Earth system processes, representing them with the above equations, setting variables to represent initial conditions and subsequent changes in climate forcing, and repeatedly solving the equations using powerful supercomputers.
The relevant computational resources required by GCMs are only available at a limited number of computational centers around the world. These required resources are continuously increasing as models evolves. Early generations models relied on simplifying assumptions and solutions which made the coupling of ocean and atmosphere particularly difficult. Recent models solved these problems and also incorporate aerosols and vegetation dynamics and atmospheric chemistry. They also better represent the carbon cycle and a range of CO2 emission scenarios is now employed based on assumptions on the socioeconomic evolution of the global community.
Figure 4. Grid representation of the climate system by GCMs. Processes that are reproduced include physical and chemical phenomena. Image source: NOAA.
4. Input and output
Input data for climate models are the driving forces for the climate itself, which may be collected through observation and future predictions. Solar forcing is one of the main inputs, which is quantified by estimating current and future solar activity and how much of the solar energy is trapped by the atmosphere and absorbed by Earth. Another relevant driving force, which is also related to the human impact, is the concentration of greenhouse gases like CO2, methane (CH4), nitrous oxides (N2O) and halocarbons – as well as aerosols that are emitted when burning fossil fuels. Aerosols reflect incoming sunlight and influence cloud formation. Today, most model projections use one or more of the "Representative Concentration Pathways" (RCPs) of greenhouse gases, which provide plausible descriptions of the future, based on socio-economic scenarios of how global society grows and develops. See here for more information and see Section 5 below for a more extended discussion.
Output data from climate models depict a picture of the Earth's climate, through thousands of different variables across hourly, daily and monthly timeframes. These variables include temperatures and humidity of different layers of the atmosphere from the surface to the upper stratosphere, as well as temperatures, salinity and acidity (pH) of the oceans from the surface down to the sea floor. Models also produce estimates of snowfall, rainfall, snow cover and the extent of glaciers, ice sheets and sea ice. They generate wind speed, strength and direction, as well as climate features, such as the jet stream and ocean currents. A full list of outputs from the climate models being run for the next IPCC report are available from the Coupled Model Intercomparison Project (CMIP) by the World Climate Research Programme. It is a cooperative framework to better understand past, present and future climate changes arising from natural, unforced variability or in response to changes in radiative forcing in a multi-model context. CMIP has been articulated in several phases. The current one is the sixth and is named CMIP6, while the most recently completed phase of the project (2010-2014) is CMIP5. The IPCC Fifth Assessment Report summarizes information of CMIP5 experiments.
A key feature of GCMs output is the time step, which typically is 30 minutes and is influential also on simulation performance. For instance, the partitioning between precipitation determined by large-scale dynamics versus cumulus parameterization is sensitive to model time step, with implications for rainfall extremes (see Yu and Pritchard, 2015). In fact, a finer time scale implies that short time variability in precipitation is better described, but it is not yet clear whether extremes that result from subhourly events are well described. For this reason output of climate models, for certain variables, are presented in terms of longer term statistics. These statistics are often downscaled in space and time using limited area models such as the regional climate models (see the overview by Giorgi (2019)) or stochastic weather generators. The latter may also allow to generate future point rainfall. Both regional climate models and weather generators can be initialised by using statistics generated by GCM.
Weather generators may allow to overcome the problem of the poor fit of GCM models of the statistics of precipitation extremes. For instance, average values for precipitation produced over a given time step may be used to condition the corresponding statistics of the weather generator, while statistics of the extremes at shorter time step may be constrained by using historical data and stochastic projections.
Whatever solution is applied to downscale climate model output in space and time, one needs to take into account that a finer resolution does not necessarily imply improved performances. For instance, the "Guidance: Caveats and llimitations" of the United Kingdom Climate Programme 2018 (UKCP18) says:
Global climate models provide greater confidence for long-term climate averages than extreme events or time series of daily or sub-daily values. All climate models exhibit systematic differences between model results and observations and you need to consider whether to modify the datasets to correct for these. This is called bias-correction and is a popular approach used by many researchers and climate data users. Take care when applying these methods, as debatable assumptionsf are often required and bias-correction may not be appropriate.
Downscaling – the process of generating model data at higher spatial and/or temporal resolution – adds detail but also increases the level of uncertainty. The additional information content can be useful for applications that wish to understand how small-scale features such as mountains and coastlines or land surface features may influence the local climate and their system of interest. However, finer model spatial resolution does not necessarily provide greater confidence in modelling the climate system unless it has been shown to give a better representation of the underlying physical processes.
Therefore, a through appraisal of model performances is needed before using data for technical applications.
5. Examples of climate models
A very simple example of climate model which consider the whole system an a homogeneous entity is based on the concept of radiative equilibrium, which is described by the equation
(1 - a)Sπr2 = 4πr2εσT4
S is the solar constant – the incoming solar radiation per unit area—about 1367 W·m−2;
a is the Earth's average albedo, which is about 0.3;
r is the radius of Earth, which is about 6.371×106m;
σ is the Stefan-Boltzmann constant, that is about 5.67×10−8 J·K−4·m−2·s−1;
ε is the effective emissivity of earth, about 0.612
Solving for the temperature one obtains:
The above equation, which yields an apparent effective average earth temperature of 15 °C, illustrates the effect on average earth temperature of changes in solar constant or change of albedo or effective earth emissivity.
Examples of GCM models are, for instance, those considered by the CMIP project. An introduzione to climate models in the contect of CMIP is given by this video. GCM models considered in CMIP are, for instance, the CSIRO-Mk3-6-0 by the Commonwealth Scientific and Industrial Research Organization/Queensland Climate Change Centre of Excellence, the CanESM2 by the Canadian Centre for Climate Modelling and Analysis, and the HadGEM3 by the Met Office Hadley Centre.
An important input value for climate models is the future concentration of atmospheric CO2. In 2000, the Intergovernmental Panel on Climate Change (IPCC) published the "Special Report on Emissions Scenarios" (SRES), describing four scenario categories corresponding to a range of possible future conditions. Each scenario was based on assumptions on the socioeconomic drivers of greenhouse gas and aerosol emissions and the levels to which those emissions would progress during the 21st century.
In 2013, climate scientists defined a new set of scenarios that focused on the level of greenhouse gases in the atmosphere in 2100. These scenarios are known as Representative Concentration Pathways or RCPs. Each RCP indicates the amount of climate forcing, expressed in Watts per square meter, that would result from greenhouse gases in the atmosphere in 2100. Therefore, there is a key change from the previous approach in that the RCPs focus on concentrations of greenhouse gases, not greenhouse gas inputs, by ignoring the carbon cycle. The reason for such a decision is that large uncertainty is implied by the modelling of terrestrial carbon cycle, which is influenced by both climate change and land use change. The rate and trajectory of the forcing is the pathway. The RCPs scenarios are:
RCP 1.9: a pathway intended to limit global warming to below 1.5 °C,which is the aspirational goal of the Paris Agreement.
RCP 2.6: a very stringent pathway requiring, among the others, that carbon dioxide (CO2) emissions start declining by 2020 and go to zero by 2100.
RCP 3.4: an intermediate pathway between RCP2.6 and the less stringent mitigation efforts associated with RCP4.5.
RCP 4.5: an intermediate scenario. Emissions in RCP 4.5 peak around 2040, then decline.
RCP 6: according to this scenario, emissions peak around 2080, then decline.
RCP 7: a baseline outcome rather than a mitigation target.
RCP 8.5: it is also called the "business as usual scenario", where emissions continue to rise throughout the 21st century. It is considered an unlikely but still possible scenario.
Recently, there have been several discussion on the likelihood of RCP 8.5. For more information please see, for example, Hausfather and Peters (2020) and Schwalm et al. (2020).
Uncertainty assessment is a core topic for all the disciplines involved in environmental modelling. Methods and terminology that are applied in sister disciplines differ a lot. For instance, uncertainty is assessed in climate with a much different perspective with respect, for instance, to hydrology and economics. This is the result of the fact that different level of uncertainty can be tolerated in different applications. What is a unbearable uncertainty for engineering applications may be tolerated in economics and viceversa.
Climate modelers are most concerned about three types of uncertainty:
forcing uncertainty,
model uncertainty and
uncertainty over initial conditions.
Forcing uncertainty mainly arises from emission scenarios that describe future emissions and human activity. These are predictions based on fundamentally different assumptions about future human socio-economic development and political strategies, which are of course very difficult to predict. In fact, they come from complex and often chaotic economic and socio-political processes, that are also influenced by natural or human induced events. The most relevant recent example is the occurrence of COVID-19 which caused a global reduction of CO2 emissions. Forcing uncertainty is probably the largest uncertainty in future climate projections.
Model uncertainty, also termed model structural uncertainty, reflects the fact that models are an approximation of reality, and therefore would be affected by uncertainty even if perfect input variables and initial conditions were used. This is due to the fact that we do not yet have the "correct" climate model available. Relevant uncertainties are due, for instance, to subgrid parameterisation and still imperfect representation of the feedbacks among multiple processes.
Regarding initial conditions, they are relevant to uncertainty as the models exhibit chaotic behaviours (see above). The realization that perfectly deterministic systems may exhibit unpredictable behavior (Lorenz, 1963) is considered one of the greatest scientific discoveries of the twentieth century. In fact, for nonlinear dynamical systems such as atmosphere, ocean and land waters, any uncertainty in the initial state - however small - may grow sufficiently rapidly in time to make impossible to predict the state of the system beyond some finite time horizon.
Climate models are tested by reproducing data that were observed in the past. Testing a model by comparing observed and simulated data is a classical procedure in environmental modelling, which is however applied with different protocols in the various disciplines. For instance, comparison may be carried out by comparing data or their statistics (see figure 5). Model testing includes adjusting model equations and their parameters to improve the fit. Models that do not reproduce well historical climate scenario can be adjusted by correcting their simulation to match selected statistics of the observed climate. One of these procedure is bias correction, which however corrects only selected statistics of the data.
Figure 5. Trends of global temperature from observations, the United Kingdom's Hadley Centre Global Climate Model, and the Canadian Climate Center's Global Climate Model from 1900 to 2000. Trends have been smoothed to remove year-to-year high frequency variations. Public Domain, https://commons.wikimedia.org/w/index.php?curid=1104732
Uncertainty of climate model simulation is often evaluated by ensemble prediction. Instead of making a single prediction by using a single model and a single input information and parameter values, a set (or ensemble) of predictions is produced, by using several models and/or several input data and initial and boundary conditions. This set of predictions is assumed to give an indication of the range of possible future climates. The range of the predictions is expected to encompass two sources of uncertainty that were described above: (1) the errors introduced by the use of imperfect input data and initial conditions, which is amplified by the chaotic nature of the atmosphere; and (2) errors related to model structural uncertainty. Actually, the latter uncertainty is only partially accounted for by ensemble prediction, as the true value of the future climate is not necessarily encompassed by the range of the predictions. An interesting note in this respect is offered by Brown et al. (2012):
A problem with this approach is that GCM projections are relatively poor scenario generators. They describe a "lower bound on the maximum range of uncertainty" (Stainforth et al., 2007). Even a large multimodel ensemble provides relatively few samples in a typical analysis relative to the size possible through stochastically generated risk analysis. Larger ensembles are becoming available, such as via the climate prediction.net experiment (Stainforth et al., 2004) but still face issues of biases that may preclude the discovery of plausible climate risks.
The result from ensemble modelling is often referred to a "multi-model ensemble". The outcomes from ensembles may be averaged to obtain a best estimate that may be needed for practical applications. In such a case, it is important to recall that variability around the mean may be poorly estimated from the ensemble. For example, if an economist was to estimate crop losses due to temperature changes over time and the loss function is nonlinear and concave, the losses calculated using temperatures from a multi-model ensemble may be incorrect if the variance in temperatures around the mean is poorly estimated or the distribution of ensembles around their mean is asymetric.
Figure 6 shows simulations by several climate models under an assigned future emission scenario (SRES-A2).
Figure 6. Climate model predictions for global warming under a given (SRES-A2) emissions scenario relative to global average temperatures in 2000.
Padrón et al. (2018) noted that projections of future water availability vary substantially across different models and found that such disagreement can be reduced by placing more confidence on models that more accurately reproduce observed rainfall amounts. In particular, they showed that previous projections of very extreme future changes in water availability are less likely to occur. Moon et al. (2018) investigated how well current generation models represent drought persistence over the twentieth century and the recent past. They highlighted that the considered models tend to underestimate drought persistence at monthly and annual time scales.
Moreover, downscaling methods such as regional climate models or weather generators introduced additional uncertainty which should be properly considered (see also the previous section 4).
The above uncertainties are the subject of intensive ongoing work by the scientific community. At the present stage, however, what-if scenarios produced by climate model are useful to provide projections of future climate statistics, provided their uncertainty is duly taken into account.
7. Technical applications and solutions for climate change mitigation and adaptation
The impact of climate change can be contrasted through mitigation and adaptation strategies. Climate change mitigation consists of actions to limit the magnitude or rate of climate change. Actions primarily consists of reductions in human emissions of greenhouse gases. Mitigation may also be achieved by increasing the capacity of carbon sinks, e.g., through reforestation or other methods.
Emission reductions have been discussed in several international conferences like the United Nations Framework Convention on Climate Change, which issued the Kyoto Protocol and the United Nations Climate Change conference (COP). The target is to reach international agreements that bind countries to emission reduction targets. In 2010, Parties to the UNFCCC agreed that future global warming should be limited to below 2.0 °C relative to the pre-industrial level. With the Paris Agreement of 2015 this was confirmed, but was revised with a new target laying down "parties will do the best" to achieve warming below 1.5 °C. Current level of warming is recognized around 0.8 °C.
Emission reduction has several impacts on economy and politics and therefore reaching an international consensus is not an easy task. The problem is also complicated by the uncertainty related to the effectiveness of reducing emissions. While the target certainly implies relevant benefits, including reduction of air pollution, it would not lead to significant mitigation of climate change if the latter was mainly due to natural variability. Given the high societal cost associated to emission reduction, the above question is highly relevant to sustainable development.
Climate change adaptation is a response to climate change aiming at reducing the vulnerability of social and biological systems to relatively sudden change and thus offset the effects of global warming. While climate change mitigation is a typical top-down approach, climate change adaptation is an example of the so-called "bottom-up" approach to sustainability, which is discussed here. Climate change adaptation focuses on resilience and sustainability of current societal systems and therefore it is based on "people" rather than politics. Also, climate change adaptation involves actions to be taken at several spatial scales and therefore distributes the responsibility for social security over local, regional and country administrations. Adaptation is especially important in developing countries since those countries have a reduced capacity and potential for humans to adapt (called adaptive capacity).
Scheraga and Grambsch (1998) identify 9 fundamental principles to be considered when designing adaptation policy:
The effects of climate change vary by region;
The effects of climate change may vary across demographic groups;
Climate change poses both risks and opportunities;
The effects of climate change must be considered in the context of multiple stressors and factors, which may be as important to the design of adaptive responses as the sensitivity of the change;
Adaptation comes at a cost;
Adaptive responses vary in effectiveness, as demonstrated by current efforts to cope with climate variability;
The systemic nature of climate impacts complicates the development of adaptation policy;
Maladaptation can result in negative effects that are as serious as the climate-induced effects that are being avoided;
Many opportunities for adaptation make sense whether or not the effects of climate change are realized.
More details can be found in my lecture Climate change and water cycle.
Comprehensive technical guidelines for designing engineering infrastructures for climate change adaptation are still lacking. There is an extended literature discussing non-stationarity and its implications (see also Montanari and Koutsoyiannis, 2014) but it is still not clear if non-stationary models can provide reliable design variables and how non-stationary models should be applied in view of the information provided by climate models.
The literature presented several applications where the impact of climate change is estimated through the application of scenario analysis according to the so-called top down approach: simulations by climate models are used as input variables to assigned environmental models which in turn are tasked to produce the required design variables to address the vulnerability of the given system. The problem with this approach is that using a cascade of models implies a cascade of uncertainties which interact in a non-linear fashion and may therefore produce an inflation of uncertainty itself. As such, the obtained design variables may be not precise enough to support technical applications. One should bear in mind that the uncertainty that may be tolerated for future climate scenario may be too large for technical design, for which ethical and legal responsibilities require that the estimation of design variables is rigorously carried out. To give an example, there is variation in climate across the discrete grid cells of climate models. For example, if one uses a climate model providing output at a monthly time scale, temperatures within the month and among all locations in the grid cell are considered constant, which is of course not the case. The size of grid cell may be small from the perspective of the global climate, but not from the perspective of human systems. This becomes especially relevant if the underlying topography is not flat but mountainous or is located near the ocean. In the absence of an appropriate downscaled dataset for the region and time step of interest, the most common practice is to adopt a downscaling model which may also be used to correct the bias of climate simulations with respect to observed data. If this approach is used, however, only selected statistics will be downscaled and corrected and therefore it is important to estimate the resulting uncertainty. In summary, one should be very careful when simply using GCM output as a direct forecast of future climate in impact estimation relative to a weather station-based baseline climate, even if the forcing scenario on which the simulation is based was accurate (Auffhammer et al., 2011).
If scenario analysis is used, uncertainty of the results should be estimated with a rigorous approach that cannot be limited to an estimation of a scenario envelope. In fact, those envelopes general do not fully account for model structural uncertainty, which is a relevant source of error for both climate and environmental models (see Montanari et al., 2011, for a discussion on model structural uncertainty). On the other hand, if uncertainty is rigorously estimated for a cascade of models the resulting uncertainty envelopes (confidence intervals) often result very large therefore leading to economically and environmentally unsustainable solutions.
With the awareness of the above limitations, the literature recently discussed solutions to support decision making when dealing with the technical design of climate change adaptation solutions. Relevant examples are robust decision making and decision scaling.
Robust Decision Making (RDM) characterizes uncertainty with multiple views of the future given the available knowledge of uncertainty. These multiple views are created by just looking at their possibility, without necessarily taking into account their probability. Then, robustness with respect to those multiple views, rather than optimality, is adopted as a criterion to assess alternative policies. Several different approaches can be followed to seek robustness. These may include, for instance, trading a small amount of optimum performance for less sensitivity to broken assumptions, or performance comparison over a wide range of plausible scenarios. Finally, a vulnerability-and-response-option analysis framework is used to identify robust strategies that minimize the regret that may occur over the different future scenarios. This structuring of the decision problem is a key feature of RDM which has been used in several climate adaptation studies (see, for instance, Bhave et al., 2016 and Daron (2015)).
Details on RDM are given here and briefly summarized here below.
Step 1: identification of future scenarios, systems models and metrics to evaluate success. The first step in RDM is articulated by a joint work among stakeholders, planners, and decision makers. They sit together to identify possible future scenarios, without caring of their probability in this stage. Therefore, uniform sampling may be used rather than basing on a prior distribution, in order to make sure that all possibilities are explored. Metrics to describe how well future goals would be met are also agreed upon. Metrics can be, for instance, water demands or water supplied, or unmet demand. Metrics can also include indexes such as reliability (e.g. the percentage of years in which the system does not fail). Environmental and/or financial metrics can also be considered such as minimum in-stream flows and costs of service provision. Furthermore, in this step candidate strategies for reaching the goals are identified, such as investments or programs. They also agree on the models that will be used to determine future performances of the system.
Step 2: evaluation of system performances. In this step, which is termed as "experimental design", the performances of the alternative strategies are evaluated with respect to the possible future scenarios, by estimating the related metrics of success. This step is typically analytical.
Step 3: vulnerability assessment. Stakeholders and decision makers work together to analyse the results from step 2 to identify the vulnerabilities associated to each strategy. The results from the simulations in Step 2 are first evaluated to determine in which futures the management strategy or strategies do not meet the management targets. Next, a scenario discovery leads stakeholders and decision makers to jointly define a small number of scenarios to which the strategy is vulnerable. The information about vulnerability can help define new management options that can be used to test strategies more robust to those vulnerabilities. Furthermore, they identify tradeoffs among different strategies. The vulnerability analysis helps decision makers recognize those combinations of uncertainties that require their attention and those that can instead be ignored. Visual inspection or more sophisticated statistical analyses can be used depending on the problem and audience.
Step 4: adaptation options to address vulnerabilities. The information on system's vulnerability can then be used to identify the most robust adaptation option. Moreover, suggestions to improve the considered options can also be gained from step 3. For instance, adaptive strategies can be considered, that can evolve over time depending on the observed conditions. Interactive visualizations may be used to help decision makers and stakeholders understand the tradeoffs in terms of how alternative strategies perform in reducing vulnerabilities. This information is often paired with additional information about costs and other implications of strategies.
Step 5: risk management. At this stage decision makers and stakeholders can bring in their assumptions regarding the likelihoods of the future scenarios and the related vulnerable conditions. For example, if the vulnerable conditions are deemed very unlikely, then the reduction in the corresponding vulnerabilities may not be worth the cost or effort. Conversely, the vulnerable conditions identified may be viewed as plausible or very likely, providing support to a strategy designed to reduce these vulnerabilities. Based on this tradeoff analysis, decision makers may finally decide on a robust strategy.
RDM characterizes uncertainty in the context of a particular decision. That is, the method identifies those combinations of uncertainties most important to the choice among alternative options and describes the set of beliefs about the uncertain state of the world that are consistent with choosing one option over another. This ordering provides cognitive benefits in decision support applications, allowing stakeholders to understand the key assumptions underlying alternative options before committing themselves to believing those assumptions.
RDM reverses the order of traditional decision analysis by conducting an iterative process based on a vulnerability-and-response-option rather than a predict-then-act decision framework, which is adaptation based on a single projected future. This is known as a bottom-up analysis and differs from the top-down method that is also widely utilised in decision making (Blöschl et al., 2013).
Decision-scaling (DS) is another bottom-up analysis approach to decision making. It has been introduced in the context of climate change adaptation (Brown et al., 2012). The term "decision scaling" refers to the use of a decision analytic framework to investigate the appropriate downscaling of climate information that is needed to best inform the decision at hand. Here downscaling refers to the identification of the relevant climatic information from the large ensemble of simulations provided by Global Circulation Models (GCMs). DS differs from current methodologies by utilizing the climate information in the latter stages of the process within a decision space to guide preferences among choices.
The analytical heart of DS a kind of "stress test" to identify the factors or combinations of factors that cause the considered system to fail. Thus, in the first step of the analysis vulnerabilities are identified. These vulnerabilities can be defined in terms of those external factors and the thresholds at which they become problematic. The purpose is to identify the scenarios that are relevant to the considered decision which serve as the basis for any necessary scientific investigation.
In the second step of the decision making process, future projections of climate are then used to characterise the relative likelihood or plausibility of those conditions occurring. By using climate projections only in the second step of the analysis, the initial findings are not diluted by the uncertainties inherent in the projections. In the third step of the analysis strategies can be planned to minimize the risk of the system.
The result is a detected 'vulnerability domain' of key concerns that the planner or decision maker can utilise to isolate the key climate change projections to strengthen the respective system against, which differs from the bottom-up analysis featured in RDM (see figure 7).This setup marks DS primarily as a risk assessment tool with limited features developed for overall risk management.
The workflow of DS is compared with the one of RDM in figure 7, where the workflow of the traditional top-down approach is also depicted.
Figure 7. Top-down decision approach versus DS and RDM bottom-up approaches – adapted from Roach (2016), Brown et al. (2012), Hall et al. (2012) and Lempert and Groves (2010). Images are taken from the following sources: NOAA Geophysical Fluid Dynamics Laboratory (GFDL) [Public domain], Mike Toews - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=15592558, James Mason [Public domain], Dan Perry [CC BY 3.0 (https://creativecommons.org/licenses/by/3.0)], Tommaso.sansone91 - Own work, CC0, https://commons.wikimedia.org/w/index.php?curid=77398870, Svjo - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=20327097.
Finally, one may consider to compare the simulations of future climate given by climate models with those provided by stochastic simulation, which may be obtained by using stationary and non-stationary models. Stochastic simulation is based on the assumption that the considered design variable is a random variable. This association is particularly useful in the presence of uncertainty. In fact, randomness is directly associated to the uncertain value of the considered variable. Stochastic simulation is a technique to trace the uncertain evolution of variables that can change stochastically (randomly) with certain probabilities.
The rationale behind stochastic simulation is to characterise the actual dynamics of relevant systems through an inductive approach, namely, through data driven techniques. Recent developments (Montanari and Koutsoyiannis, 2012) elaborated a framework that allows one to take into account physically based knowledge in inductive reasoning. That is, the available knowledge of the system is used to condition stochastic assessment and simulation.
Stochastic simulation is applied in classical water resources management by creating several future scenarios that are obtained by simulating observations that have not been observed, but are equally likely with respect to observed values. Namely, the simulated time series have the same statistics of historical data. These steps are repeated until a sufficient amount of data is gathered. The distribution of the outputs shows the most probable estimates as well as a frame of expectations regarding what ranges of values the variables are more or less likely to fall in.
In the presence of climate change, past and current system conditions may not be representative of what will occur in the future. However, they are still representative of system's behaviors and therefore their assessment is an essential step for deciphering how the system itself may react to change. Therefore, a possible opportunity for estimating design variables under changing condition may be articulated into three main steps which may be identified with the term "stochastic change simulation":
Assessment of current system's conditions through stochastic analysis;
Estimation of the impact of change on system's relevant statistics;
Generation of future scenarios under change through stochastic simulation.
The first step above is carried out by using classical statistical analysis. The second step may be resolved by expert knowledge and/or assessing the output of climate models in statistical terms. A key requirement is uncertainty assessment for the above estimates of the impact of change. The third step can be again carried out through the usual stochastic simulation techniques.
Stochastic change simulation has the advantage of starting from a known situation, which is resembled by the data, rather than starting from assumptions and models whose uncertainty might be difficult to quantify. Also, stochastic change simulation has the advantage that feedbacks and process dynamics are intrinsically considered, as they are resembled by past history and our physical understanding that is incorporated into the stochastic model.
Stochastic change simulation is frequently employed in climate impact studies for generating weather variables. In most of the cases, future climate statistics are estimated by running climate models.
Design of climate change adaptation strategies is a research field that is continuously evolving that opens the doors to exciting perspectives for the near future. In consideration of the on going developments, this lecture is continuously evolving as well. Date of last change is shown at the bottom of the page.
Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A. (2011). Global climate models and climate data: a user guide for economists. Unpublished manuscript, 1, 10529-10530.
Bhave, A. G., Conway, D., Dessai, S., & Stainforth, D. A. (2016). Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world. Climate Risk Management, 14, 1-10.
Blöschl, G., Viglione, A., & Montanari, A. (2013). Emerging approaches to hydrological risk management in a changing world. In: Climate Vulnerability, 3-10, https://doi.org/10.1016/b978-0-12-384703-4.00505-0.
Brown, C., Ghile, Y., Laverty, M., & Li, K. (2012). Decision scaling: Linking bottom‐up vulnerability analysis with climate projections in the water sector. Water Resources Research, 48(9).
Daron, J. (2015). Challenges in using a Robust Decision Making approach to guide climate change adaptation in South Africa. Climatic Change, 132(3), 459-473.
Giorgi, F. (2019). Thirty years of regional climate modeling: where are we and where are we going next?. Journal of Geophysical Research: Atmospheres, 124(11), 5696-5723.
Hall, J. W., Lempert, R. J., Keller, K., Hackbarth, A., Mijere, C., & McInerney, D. J. (2012). Robust climate policies under uncertainty: A comparison of robust decision making and info-gap methods. Risk Anal., 32(10), 1657–1672.
Hausfather, Z., & Peters, G. P. (2020). Emissions–the 'business as usual'story is misleading.
Lempert, R. J., & Groves, D. G. (2010). Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west. Technol. Forecast. Soc., 77(6), 960–974.
Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the atmospheric sciences, 20(2), 130-141.
Montanari, A. (2011), Uncertainty of hydrological predictions, in Treatise of Water Science, vol.2, The Science of Hydrology, edited by P.A. Wilderer, pp. 459–478, Elsevier, Amsterdam, doi:10.1016/B978-0-444-53199-5.00045-2.
Montanari, A., & Koutsoyiannis, D. (2012). A blueprint for process‐based modeling of uncertain hydrological systems. Water Resources Research, 48(9).
Montanari, A., & Koutsoyiannis, D. (2014). Modeling and mitigating natural hazards: Stationarity is immortal!. Water Resources Research, 50(12), 9748-9756.
Padrón, R. S., Gudmundsson, L., & Seneviratne, S. I. ( 2019). Observational constraints reduce likelihood of extreme changes in multidecadal land water availability. Geophysical Research Letters, 46, 736– 744. https://doi.org/10.1029/2018GL080521.
Roach, T. P. (2016). Decision Making Methods for Water Resources Management Under Deep Uncertainty. Available on-line at https://ore.exeter.ac.uk/repository/bitstream/handle/10871/25756/RoachT.... (last
Scheraga, J. D., & Grambsch, A. E. (1998). Risks, opportunities, and adaptation to climate change. Climate research, 11(1), 85-95.
Schwalm, C. R., Glendon, S., & Duffy, P. B. (2020). RCP8. 5 tracks cumulative CO2 emissions. Proceedings of the National Academy of Sciences, 117(33), 19656-19657.
Stainforth, D. A., M. R.Allen, D.Frame, J.Kettleborough, C.Christensen, T.Aina, and M.Collins (2004), Climateprediction.net: A global community for research in climate physics, in Environmental Online Communication, edited by A.Scharl, pp. 101–112, Springer, London, U.K.
StainforthD. A., et al. (2007), Issues in the interpretation of climate model ensembles to inform decisions, Philos. Trans. R. Soc. A, 365, 2163–2177.
Yu, S., & Pritchard, M. S. (2015). The effect of large‐scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM. Journal of Advances in Modeling Earth Systems, 7(4), 1977-1996.
https://www.climate.gov/maps-data/primer/climate-models
https://www.carbonbrief.org/qa-how-do-climate-models-work#regions
www.adaptation-undp.org


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a GPS in a connected watch for less than 70 euros
Amazfit has just unveiled two new entry-level watches, but with a "Pro" model with GPS tracking for sports enthusiasts.
Amazfit Bip 3 Pro // Source: Amazfit
The products had already been awkwardly launched in Brazil earlier this month, now they are official. Amazfit has just officially announced its two new entry-level connected watches, Amazfit Bip 3 and Bip 3 Pro.
Recall that the Amazfit Bip range corresponds to the most accessible watches of the Chinese manufacturer, at much cheaper prices than its high-end models such as GTS 3, GTR 3 or GTR 3 Pro. Logically, therefore, we do not expect a particularly refined design, exemplary finishes or sporty measures… whatever.
In fact, on its new Amazfit Bip 3 Pro, the manufacturer announces a whole series of sanitary measures. Of course, it includes step tracking, heart rate measurement, and even blood oxygen saturation (SpO2) measurement. Recall that this measurement indicates whether you are breathing well enough and can eventually be used, with some manufacturers, to identify cases of sleep apnea.
A "Pro" model with integrated GPS tracking
But where the Bip 3 Pro surprises is not the integration of GPS tracking directly into the watch, without the need to carry the smartphone with you during running or cycling sessions. Amazfit thus announces compatibility with "Four satellite positioning systems".
Among other features of the Amazfit Bip 3 Pro, it is equipped with a 1.69-inch diagonal square TFT LCD touch screen with a definition of 240 x 280 pixels – and therefore a density of 218 pixels per inch. It is also 5 ATM certified and can therefore be used in shallow water.
A button is located on the right side to allow navigation in the menus. Also, as is often the case with Amazfit watches, the Bip 3 Pro boasts a remarkable range with a 280 mAh battery and up to 14 days of use in typical use – 7 days in heavy use.
We also remember that Amazfit watches work with the RTOS system, the same as the OnePlus Watch or the Xiaomi Watch S1. It is therefore impossible to install third-party applications on it, even if they can be used to view notifications.
The Amazfit Bip 3 Pro is already available in the United States, at a price of 69.99 euros, or 67 euros excluding taxes. A classic version of Bip 3, without GPS, is displayed for 59.99 euros, or 57 dollars excluding taxes. For now, we still don't know at what price the two watches will be marketed in France.
Connected watches are becoming more and more powerful, but their limited autonomy prevents them from taking advantage of certain functions. Fortunately, there are alternatives and they are sufficient for most users.
To follow us, we invite you to download our Android and iOS application. You can read our articles, files and watch our latest YouTube videos.


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ITHS Accelerator
DCDR
ITHS › Investigators › ITHS Resource Directory
The ITHS Resource Directory connects researchers with tools, technologies, laboratories, and services offered by institutions and organizations throughout the five-state WWAMI region. This centralized directory allows you to quickly search for resources that were previously scattered across numerous different websites.
Please note that most of the resources listed in this directory are independent of ITHS.
Search or filter by institution, category, keyword, or location to begin.
Select category Analytical / Bioanalytical (32) Animal Models (20) Behavioral Science (11) Biobank (13) Bioinformatics (15) Biophysical (4) Cell Culture (17) Clinical (23) Computational (3) Computational Services (20) Crystallography (3) Data & Software (9) Database (9) Exercise / Body Composition (3) Fabrication (1) Flow Cytometry (8) Genomics (22) Imaging (31) Immunohistochemistry (9) Materials Synthesis and/or production (7) Metabolomics (6) Microscopy (23) Monoclonal antibody facility (2) Nutrition (4) Other (27) Population Assessment (11) Proteomics (15) Sleep Study Facility (2) Stem Cell Therapy (2) University (10)
Allen Institute for Brain Science
Benaroya Research Institute at Virginia Mason
Kaiser Permanente Washington Health Research Institute
Institute for Systems Biology
McLaughlin Research Institute
Cancer Research and Biostatistics (CRAB)
Computational Services
CRAB, 1730 Minor Ave, STE 1900, Seattle, WA 98101
Resources at CRAB have been developed to maintain the highest standards for the collection, manag...
Cardiovascular Research and Training Center, University of Washington
University of Washington, 4311 11th Ave NE, Seattle, WA 98105
We reconstruct body organs in 3D from multiple image views. The focus has been on the heart but w...
CEEH Facility Core #4: Bioinformatics & Biostatistics, University of Washington
Bioinformatics Computational Services
Department of Environmental and Occupational Health Sciences, 4225 Roosevelt Way NE, Suite 100, Seattle, WA 98105
Transcriptional profiling and other high throughput genomic technologies (e.g. high density SNP g...
CEHS Molecular Histology and Fluorescence Imaging Core, University of Montana
Analytical / Bioanalytical
Skaggs Building - rooms SB 070 (Histology Lab), SB 060C ( FV1000), and SB 052A (Nikon/Nuance), University of Montana - Missoula, Missoula, MT 59812
The MHFI Core is a fully equipped histology and imaging lab designed to provide support for exper...
Cell Analysis Facility - Flow Cytometry, University of Washington
University of Washington Medical Center, 1959 NE Pacific Street, Rm E-103, Seattle, WA 98195
The facility provides a broad range of instrumentation and expertise for addressing experimental ...
CENPA (Center for Experimental Nuclear Physics and Astrophysics)
Center for Experimental Nuclear Physics and Astrophysics, University of Washington, 4311 Mason Place NE, Seattle, WA 98195
CENPA supports a broad program of experimental physics research in fundamental symmetries, neutri...
Center for Aids and STD/CFAR
Bioinformatics Cell Culture
University of Washington Center for AIDS and STDs, 325 Ninth Avenue, Seattle, WA 98104
(206) 744-2238 or (206) 744-3785(206) 744-2238 or (206) 744-3785
STI CRC Core Facilities provide biostatistical, laboratory, study design, and other services that...
Center for Biomedical Statistics, Institute of Translational Health Sciences
Center for Biomedical Statistics, UW Tower, 4333 Brooklyn Ave NE, 15th Floor, Seattle, WA 98105
The Center for Biomedical Statistics (CBS) provides statistical collaboration at every stage of a...
Center for Clinical and Translational Research, Seattle Children's Hospital
Analytical / Bioanalytical Clinical
CCTR, 2001 8th Ave, Suite 400, Seattle, WA 98121
The Center for Clinical and Translational Research is located at Seattle Children's Hospita...
Center for Ecogenetics and Environmental Health, University of Washington
The Center for Ecogenetics and Environmental Health (CEEH) is dedicated to contributing to scienc...
Center for Environmental Genomics
Genomics Proteomics
Center for Environmental Genomics, University of Washington, 616 NE Northlake Place, Seattle, WA 98195
Marine Bacteria and Archaea play critical roles in global nutrient cycles by incorporating and re...
Center for Environmental Health Sciences, University of Montana
Flow Cytometry Imaging Clinical
Center for Environmental Health Sciences, University of Montana - Missoula, 32 Campus Drive, Missoula, MT 59812
The primary research mission of the Center is to advance knowledge of environmental impacts on hu...
Center for Infectious Disease Research
Center for Infectious Disease Research, 307 Westlake Ave N, Suite 500, Seattle, WA 98109
We believe that targeted research is the first step toward conquering any disease. Our world-clas...
Center for Innovation in Sleep Self-Management (CISSM)
Behavioral Science Biobank Bioinformatics Clinical Data & Software Database Other Sleep Study Facility
Biobehavioral Nursing and Health Informatics Univeristy of Washington, School of Nursing Seattle, WA 98195 United States
The mission of the Center for Innovations in Sleep Self-Management (CISSM) is to advance nursing ...
Center for Intracellular Delivery of Biologics, University of Washington
UW Department of Engineering, William H. Foege Building, 3720 15th Ave NE, Seattle, WA	98195
Department of Bioengineering: Center for Intracellular Delivery of Biologics includes, analytical...
Center for Process Analytical Chemistry (CPAC), University of Washington
Center for Process Analysis & Control, 616 NE Northlake Place, Seattle, WA 98105
CPAC, established at the University of Washington in 1984, is a consortium of Industrial, Nationa...
Center for Reproductive Biology
Analytical / Bioanalytical Animal Models Genomics
Center for Reproductive Biology, Washington State University, Pullman, WA 99164
The Center for Reproductive Biology is an inter-institutional program involving 18 departments an...
Center for Rural Health Research & Education (CRHRE), University of Wyoming
Database Population Assessment
CRHRE, University of Wyoming, 1000 E. University Ave, Department 3432, Laramie, WY 82071
The CRHRE is recognized throughout Wyoming as a leader in using technology to support health care...
Center for Social Science Computation and Research (CSSCR), University of Washington
Bioinformatics Behavioral Science Computational Services
Center for Social Science Computation and Research, University of Washington, Seattle, WA 98195
(CSSCR) is a computer resource center providing facilities and support for social science departm...
Center for Statistics and the Social Sciences, University of Washington
Behavioral Science Computational Services
Center for Statistics and Social Sciences, UW, Padelford Hall, C 14, University of Washington, Seattle, WA 98195
The Center for Statistics and the Social Sciences promotes collaborative interdisciplinary resear...
The Institute is supported by grants UL1 TR002319, KL2 TR002317, and TL1 TR002318 from the NIH National Center for Advancing Translational Sciences through the Clinical and Translational Science Awards Program (CTSA).
Please help us continue to support your research by citing our grant number(s) in publications we supported.


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Horcajo Medianero – gmina w Hiszpanii, w prowincji Salamanka, w Kastylii i León, o powierzchni 53,37 km². W 2011 roku gmina liczyła 250 mieszkańców.

Przypisy

Gminy w Kastylii i León

redpajama_set_name: RedPajamaWikipedia


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Accounting at University of Illinois at Urbana-Champaign
374 Bachelor's Degrees
3 Master's Degrees
0 Doctor's Degrees
Accounting is one of the majors in the business, management & marketing program at University of Illinois at Urbana-Champaign.
Here, you'll find out more about the major, including such details as the number of graduates, what degree levels are offered, ethnicity of students, average salaries, and more. We've also included details on how UIUC ranks compared to other colleges offering a major in accounting.
Accounting Degrees Available at UIUC
Bachelor's Degree in Accounting
UIUC Accounting Rankings
Ranked at #10 in College Factual's most recent rankings, UIUC is in the top 1% of the country for accounting students pursuing a bachelor's degree. It is also ranked #1 in Illinois.
Most Popular Bachelor's Degree Colleges for Accounting 7
Best Accounting Bachelor's Degree Schools 9
Most Focused Doctor's Degree Colleges for Accounting 14
Most Popular Doctor's Degree Colleges for Accounting 14
Best Value Bachelor's Degree Colleges for Accounting 284
Most Focused Bachelor's Degree Colleges for Accounting 445
Most Focused Master's Degree Colleges for Accounting 523
Most Popular Master's Degree Colleges for Accounting 523
Popularity of Accounting at UIUC
During the 2019-2020 academic year, University of Illinois at Urbana-Champaign handed out 374 bachelor's degrees in accounting. Due to this, the school was ranked #18 in popularity out of all colleges and universities that offer this degree. This is an increase of 1% over the previous year when 370 degrees were handed out.
In 2020, 3 students received their master's degree in accounting from UIUC. This makes it the #481 most popular school for accounting master's degree candidates in the country.
How Much Do Accounting Graduates from UIUC Make?
$62,700 Master's Median Salary
Salary of Accounting Graduates with a Bachelor's Degree
The median salary of accounting students who receive their bachelor's degree at UIUC is $61,400. This is great news for graduates of the program, since this figure is higher than the national average of $43,900 for all accounting bachelor's degree recipients.
Salary of Accounting Graduates with a Master's Degree
Accounting majors graduating with a master's degree from UIUC make a median salary of $62,700 a year. This is a better than average outcome since the median salary for all accounting graduates with a master's is $56,600.
How Much Student Debt Do Accounting Graduates from UIUC Have?
$28,986 Master's Median Debt
Student Debt of Accounting Graduates with a Bachelor's Degree
While getting their bachelor's degree at UIUC, accounting students borrow a median amount of $18,000 in student loans. This is not too bad considering that the median debt load of all accounting bachelor's degree recipients across the country is $25,000.
The typical student loan payment of a bachelor's degree student from the accounting program at UIUC is $187 per month.
Student Debt of Accounting Graduates with a Master's Degree
At UIUC, a typical accounting student working on their master's degree borrrows about $28,986 in student loans. This is not favorable since the median amount of student debt for all accounting master's graduates is $27,303.
Those master's students from UIUC on a 10-year student debt repayment plan end up paying a median amount of $338 per month.
UIUC Accounting Students
Take a look at the following statistics related to the make-up of the accounting majors at University of Illinois at Urbana-Champaign.
Is UIUC a Good Match for You?
UIUC Accounting Bachelor's Program
During the 2019-2020 academic year, 374 students graduated with a bachelor's degree in accounting from UIUC. About 51% were men and 49% were women.
The following table and chart show the ethnic background for students who recently graduated from University of Illinois at Urbana-Champaign with a bachelor's in accounting.
Hispanic or Latino 25
Other Races 10
Most Popular Majors Related to Accounting
Management Sciences & Quantitative Methods 631
Finance & Financial Management 256
Management Information Systems 161
View All Accounting Related Majors >
Image Credit: By Beyond My ken under License