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I am sitting down to write this article the weekend before it’s due.

Not because I forgot about it or because I’ve been “too busy” to focus on it, but because one of the many lessons I’ve learned from my self-care discovery this year is that I do well with deadlines.

Pending deadlines used to worry me, I’d spend weeks and weeks thinking about things I didn’t need to do yet, only to complete them on time.

This lesson came to me when I was sitting in my therapist’s office telling her how many presentations I’d written the night before the deadline, or in the extreme case that there was no deadline, the night before the speaking engagement.

She smiled and said, “but that’s how you work best, stop worrying about it.”

And with that, I realized I could grant myself permission to meet my deadlines without feeling guilty that I didn’t beat my deadlines.

This is one of the many lessons I’ve allowed myself to learn since opening my agency back in 2023.

I had broken my leg, my role has been “eliminated” from the agency I worked at, and in the midst of it all, opened a business from my bed.

I was learning how to become an agency owner while learning how to walk again.

The very fear of failure that had always prevented me from opening my own business became the fear of not being able to provide for my family.

I look back at the events and the chaos of that year with an immense sense of gratitude. My injury served as a catalyst for every client I serve today.

This year, I’ve finally been able to look back and recognize how much stress and anxiety I endured as a result.

Agency ownership is the job I’ve always wanted, and it took an immense amount of hard work. I now have an amazing team and clients across the globe and I am thankful for each and every one of them.

I’d be remiss if I didn’t say that my learning curve was steep.

Before I was able to start hiring my team, if I was awake, I was working. If I was asleep, I was waking up in the middle of the night thinking about deadlines.

All of this took a toll on my physical and mental well-being. Hindsight is always 20:20 and stopping to think about this journey has given me the chance to consider some of the lessons I’ve learned along the way.

It Takes a Village

I have always found the SEO community to be abundant with friendship, kindness, transparency, help, and knowledge.

There are conferences I attend annually because the camaraderie feels like a reunion.

I’ve found myself my very own “Twitter-village” of friends, peers, and mentors, and we keep in touch between conferences and events.

Twitter has become my social network of choice.

Another way I’ve created a village is by bringing #SEOBeersKC to life. If #SEOBeers isn’t active in your city yet, I’d recommend making it happen.

I can’t take credit for this idea, it’s all Matt Lacuesta, and through this, I’ve made some friends and connections.

I Found a Mechanism for Accountability

Twice a month, I meet with the Agency Owner’s Accountability group that I was invited to.

Some of the group have physical offices, other teams are fully remote, just like mine.

The person who put this group together framed this as being each other’s “Board of Directors” and we hold each other accountable with business and personal goals.

We also have a framework for asking each other questions about strategy. Or if we like our accountant. Or which software works well for a specific goal.

From the outside, we could be seen as competitors, but that’s not what this is about. Our support for one another has lead to growth for everyone that partakes in our meetings.

I Learned What to Delegate

This is the year that my team really took shape.

Massive kudos to them for keeping up with my pace, and keeping me in check.

I learned where my time and energy was best spent.

This was hard for me, but ultimately, I really enjoy strategy and I have a team that can execute it.

In August, I took my youngest daughter to England to visit my family.

For the first time since I opened my business, I was able to step away from the office for 10 days and it was business-as-usual for our clients.

The entire trip felt like a massive trust-fall, and my team didn’t let me down.

I Found Self-Care Regimens That Work for Me

This is probably the biggest thing I needed to learn, and the hardest lesson for me to accept.

It is probably the thing that I needed to pay attention to the most, and still struggle with.

When I was struggling the most with work-life balance, the thing I needed to do was invest my time here.

Until recently, I struggled to place any value in self-care.

My mind races to the next thing on my to-do list.

I’d prioritize client work or requests from other people and assume there’d be time for me later.

Later never came, until I stopped to create it.

I was rarely able to be fully present, walk away from my phone or stop.

As I’ve found ways to “fill my cup,” I have been able to set boundaries to prevent myself from working evenings and weekends. Most notably, I’ve stopped feeling guilty about taking time for me.

So, how did I find a self-care ritual for myself?

Pottery came first. One night a week my hands were on a wheel of clay. If I wanted to touch my phone to check a tweet or an email, I would have to balance that with cleaning my clay-covered hands.

I tapped into a creative outlet that didn’t need WiFi.

As much as I love this newfound creativity, I ran out of room for all my new plates, cups, and bowls and took a break from this hobby but I know it’s there if ever I want it.

Therapy plays a role, too.

I found a therapist I can relate to. I also found a hypnotherapist that helps me meet goals that I’m struggling with.

I find that hypnotherapy gives me 30-60 minutes of the deepest relaxation I have ever known. When I wake from the short rest, I can breathe deeper, think clearer, and meet goals that I’ve set for myself.

There’s a physical element to self-care, too.

I see a tonal chiropractor to help me try and minimize stress and anxiety. This isn’t the bone-cracking quick adjustment I’ve experienced from other chiropractors.

He specializes in slow movements of the body during a 30-minute session in a meditative environment. He has helped me reduce the symptoms of chronic inflammation from my leg injury, which ended up presenting as severe stress and anxiety.

I’m learning French.

I’d long wanted to learn a third language (English and Hebrew are the first two). I’m using the Duolingo app and I’m almost at a three-month streak.

This had a two-fold effect.

First of all, I am achieving something I’d be thinking about doing for years. The added benefit is rather than mindlessly scrolling through a black-hole of social media, I put the Duolingo app in the space Facebook used to occupy.

Have I Found the Secret to the Perfect Work-Life Balance?

The lessons and tools I’ve outlined here have helped me find a sense of balance.

Each of these has been a valuable piece of the agency ownership journey I’ve embarked on, and if I’m being completely honest, there are many ways I can improve my work-life balance further.

My biggest tools in finding this balance are the permissions to do seek balance itself, the willingness to try something new, and the friends to turn to on the days when everything feels like it’s a little too much.

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Why Am I Not Receiving Weather Notifications On My Iphone?

Modern smartphones have made checking the weather around your place much easier as you can simply open an app and access the information that you need. But what if you want to be notified when rain or thunderstorm is going to affect your area? iPhone users can now set up the Weather app on their devices in such a way that they get notifications when it rains or snows in and around their location. If you’re not receiving weather-related notifications from the native Weather app on your iPhone, the following post will help you understand why that’s happening and how you may be able to fix it. 

iOS version eligibility

For the Weather Notifications to work, the version should say “15.0”. If your iPhone is running iOS 14 or older versions, you won’t be able to access the feature on your iPhone. 

Region eligibility

You will be notified about rain, snow, hail, or sleet through the Weather app on iOS only in a limited number of regions. In its official iOS 15 features page, Apple has revealed that only users in Ireland, the UK, and the U.S. will be able to get next-hour precipitation notifications on their iPhones.

This means, if you reside in a location other than the aforementioned countries, you won’t be able to get precipitation notifications on your iPhone. The only way to check whether or not it will rain or snow at your location if you’re in an unsupported region is by opening the weather app and swiping to the weather card for your preferred location. 

We expect this to change as future beta versions of iOS 15 become available and we might see a longer list of supported regions by the time Apple releases the stable iOS 15 to the masses. 

How to fix Weather Notifications on iOS 15

If you’re at one of the supported locations (Ireland, the UK, and the U.S.) for Weather Notifications but you’re still not able to get weather-related notifications on your iPhone, then you may want to perform the fixes below to fix it. 

Fix #1: Add a location(s) inside the Weather app

One reason you may not be able to get weather notifications at your place may be because you haven’t added your location or your city inside the Weather app. 

To add a location or multiple ones for receiving notifications, open the Weather app on your iPhone and tap on the List icon (the one with bulleted points and three lines) from the bottom right corner. 

This will bring up the list of locations you have enabled weather cards for. On this screen, tap on the search bar at the top and enter the name of the place you want weather results for. 

When you find the desired location from the search results, tap on it. 

You can repeat the steps to see the weather data from other cities and get all weather alerts for them. When you add a location from a support region, you should be able to get Weather Notifications for them without doing anything else. 

Fix #2: Ensure you have enabled notifications for the Weather app

If you have added all the locations you want weather alerts from and you’re still not receiving notifications from the Weather app, then chances are that you have somehow disabled notifications from the app. 

To check and enable weather-related notifications, open the Settings app and select the ‘Notifications’ option. 

Inside Notifications, scroll down and select the Weather app from the list. 

On the next screen, enable the toggle adjacent to ‘Allow Notifications’. 

This should enable all weather notifications from the Weather app. To make sure you get notifications as soon as the Weather app sends them, select the ‘Immediate Delivery’ option inside the ‘Notification Delivery’ section. 

Additionally, you can be sure that you receive notification alerts at the right time by checking the following options inside ‘Alerts’ – Lock Screen, Notification Centre, and Banners. 

Fix #3: Enable Location access for the Weather app

If you want the Weather app to give you rain or snow alerts based on the location that you’re in, then it’s pretty well understood that you give the app access to your location. 

For this, open the Settings app on iOS and go to ‘Privacy’. 

On the next screen, tap on the ‘Location Services’ option. 

If you have entirely disabled location access for your phone, enable the toggle adjacent to ‘Location Services’. 

When you do so, a list of apps that may require location access will load up below. Scroll down this list and select the ‘Weather’ app. 

If you wish to receive notifications for your current whereabouts, you will need to select the ‘Always’ option at the bottom.

We also disabled ‘Precise Location’ on this screen for privacy measure because the Weather app doesn’t need to know your exact location to give you weather info, an approximate location would do. 

The Weather app will only be able to give you the most relevant weather notifications when it knows your current location at all times. 

If you don’t wish to share your location or don’t want the location tracking to affect your battery life, you can choose Fix #1 above to get weather alerts for selected locations only. 

Fix #4: Turn ON Weather Notifications for your Locations

By now, you may have enabled multiple locations and given location access to the Weather app. If you still don’t receive weather alerts on iOS 15, then you may need to enable Weather Notifications for your locations manually inside the Weather app. 

For this, open the Weather app on your iPhone and tap on the List icon (the one with bulleted points and three lines) from the bottom right corner.

On the next screen, tap on the 3-dots icon at the top right corner. 

When an overflow menu appears, select the ‘Notifications’ option.

Inside Notifications, enable the switches adjacent to ‘My Location’ as well as the locations you have added manually to ‘Your Locations’. 

After enabling all notifications for all your locations, tap on ‘Done’. You should now start receiving notifications from all the supported regions in your locations list. 

That’s all we have on Weather Notifications on iOS 15. 

RELATED Namespace Not Found

If you use Visual Studio on your Windows 11 or Windows 10 computer, while working on a portable library class project you may get an error prompt stating Windows.Storage Namespace not found. In this post, we provide the most applicable fixes to this error.

A particular DLL file is missing from your system.

A wrong variable declaration.

You are using the object browser incorrectly.

You have not included the reference to the proper package.

Windows.Storage Namespace not found

If you get Windows.Storage Namespace not found error prompt while working on a portable library class project in Visual Studio on your Windows 11/10 computer, then the suggested fixes presented below should help you resolve the issue on your system.

Declare the variable globally in your code

Use the Visual Studio Object Browser correctly

Manually add the chúng tôi file to your project

Let’s quickly take a look at the description of these fixes. Before you proceed, to rule out the possibility of a missing DLL file, you can re-register all DLL files on your system. If you can identify the missing DLL file (in this case System.Runtime.WindowsRuntime.dll), provided it’s native to the Windows OS, you can head over to Winbindex and download a fresh copy of the file and place it exactly in the same folder as the original.

1] Declare the variable globally in your code

This is a common mistake programmers often commit or most likely hindsight. In the case of Windows.Storage Namespace not found error you may encounter in Visual Studio on PC; you have to declare the variable Windows.Storage.ApplicationData as a global variable on your computer. To do this, include the line below in your code and run all the tests with the build.


If the error is still triggered, then you may use the global object browser as described in the next fix.

2] Use the Visual Studio Object Browser correctly

By default, the object browser is set to All Components, so you need to make sure you’re using the object browser correctly. Do the following:

Load up the build in Visual Studio.

Select Object browser from the menu. Alternatively, you can press the Ctrl+Alt+J key combo.

Next, set the Browse to All Components from the drop-down list.

Next, tap on the Add to References in Selected Project icon in the Object browser to add it to the code.

Repeat the steps above to easily add any other reference object you want to the project library.

3] Manually add the System.Runtime.WindowsRuntime.dll file to your project

The error in view could be triggered if you have not added the reference to the proper package. In this case, to resolve the issue, you can manually add the chúng tôi file to your project by following these steps:

Open Visual Studio.

Go to Solution Explorer.

Select Add Reference from the context menu.

C:Program Files (x86)Reference AssembliesMicrosoftFramework.NETCorev4.5

At the location, select All files from the drop-down list.

Now, find and select the chúng tôi  file.

That’s it!

Now read: IntelliSense not working in VS Code

What does Windows Storage DLL do?

The Windows.Storage DLL file, also known as Microsoft WinRT Storage API, is commonly associated with the Microsoft Windows Operating System. It is an essential component, which ensures that Windows programs operate properly. Essentially, DLL files are necessary to launch a program although they are not used frequently as editorial files. In any case, if the DLL file is corrupted or missing from your system, you could receive a DLL file missing error message.

How do I use Windows Storage?

In Windows 11/10 if configured or set up, Storage Spaces typically store two copies of your data so if one of your drives fails, you still have an intact copy of your data. So, you can use Storage Spaces to group two or more drives together in a storage pool and then use capacity from that pool to create virtual drives called storage spaces.

How To Fixnot Found Or Missing On Windows 11.

If you are using Windows 11 and are having some issues with programs, apps and games failing to launch because you keep getting chúng tôi not found or missing. This article will guide you through several different steps you can take to solve the problem and prevent chúng tôi errors from occurring.

Related: How to fix microphone set up error 0x80004003 on Windows.

Missing .dll errors on Windows 10 and 11 are some of the most frustrating error messages you can get because they always pop up at the most random of times when you haven’t even done anything different on your computer. While most of these error messages are usually caused by file corruption, that can occur for a range of different reasons. They are also quite often caused by Windows updates which occasionally move or delete .dll files that are used by Windows and other programs, apps, games, etc.

This particular .dll file is part of the Microsoft .NET Framework, so is a rather important part of the operating system one that a lot of programs, apps, tools, etc use. The good news is that there are quite a few different solutions available that will help you solve chúng tôi not found or missing on Windows 11.

How do you fix chúng tôi not found or is missing on Windows 11/10?

If you are only getting this error message in one specific program the first and quickest solution is to uninstall and reinstall that program. Just open Control Panel or the Settings app and uninstall it. Just make sure to restart your computer after the uninstall to get the best reinstallation.

Run the SFC (System File Checker) command to double-check Windows files are all good.

The SFC tool is another great tool that will fix any errors in the OS that may be preventing Windows 11 from updating properly. To run the SFC tool to the following.

When you have Command Prompt open type the following command into Command Prompt and press Enter.

sfc /scannow

Now just wait for the process to complete and you should be able to start using the program again without getting the .dll error. If the file checker tool shows any error messages just restart your computer in Safe Mode and try running it again.

Try using the Microsoft .NET Framework Repair Tool to fix chúng tôi not found or missing on Windows 11.

Another really good fix for this problem is to run the dedicated Microsoft .NET Framework Repair Tool. Just download the tool from the Microsoft link below and follow the steps it requires and you shouldn’t have any more issues with chúng tôi not found or missing.

What if this error message comes back?

If you notice that this error message keeps on coming back, the issue is more than likely linked to Windows updates. There have been numerous .dll problems that keep recurring on Windows after Windows updates. Most of which seem to affect Adobe software. If you’re also having problems with Adobe software and missing .dll errors make sure you check out the following article. How to fix Premiere Pro chúng tôi chúng tôi chúng tôi was not found.

How May Bias Be Found In Current Ai Algorithms?

Each stage of the AI process has the potential to inject bias into algorithms in different ways

While businesses cannot completely remove bias from their data, they may greatly minimize bias by putting in place a governance framework and hiring a more diverse workforce. It’s in our human nature to be biased. Each of us has distinct viewpoints, interests, and likes and dislikes. Therefore, it should come as no surprise that these biases can be detected in data.

Biased data can lead to distorted or incorrect machine learning (ML) models if left unchecked. Organizations may better understand their customers, manage their resources, streamline processes, and respond to ongoing market changes with the use of data. This data is more crucial than ever as businesses use AI and ML more and more.

Data can, however, also inject biases into ML models, and these biases might be challenging to identify. Each stage of the AI process has the potential to inject bias into algorithms in different ways. From data gathering efforts to data processing, analysis, and modeling, each stage brings a unique set of difficulties and chances for unintentional bias introduction into an ML model, training data set, or analysis.

Businesses need to be aware of the various biases in their data that could find their way into their machine-learning models. Organizations may identify and possibly fix some of the problems causing skewed, erroneous, or unsuitable outcomes for the machine learning models by understanding the different types of bias that may present.

Many contemporary businesses gather data in both organized and unstructured formats, in a variety of formats or modalities, including numerical, graph, text, image, and audio data. Bias can be introduced into the data collection process employed by businesses, and it can also be present in the language used in each of these many data forms. For instance, erroneous input data from a mislabeled graph may result in skewed results from a machine learning model.

Data collection frequently contains biases that cause some groups or categories to be overrepresented or underrepresented. This is particularly true when several data sets are merged to be used in aggregate. For smaller datasets, anomalies can be detected, but for larger datasets with millions or billions of data points, anomalies are very challenging to detect.

As a result, the models have bias, preferring or disfavoring particular data categories. When some data types are overrepresented in the data or, conversely, when other datasets are undervalued relative to their actual incidence in real-world data collection, modeling bias can happen.

How to identify data bias?

Even when factors like gender, color, locality, and sexual orientation are eliminated, AI systems learn to make conclusions based on training data, which can include biased human decisions or reflect historical or social imbalances.

Businesses can more effectively identify and remove bias in their data by recognizing common data biases. In all stages of their data pipeline, organizations should consider ways to minimize the likelihood of skewed data sets.

Since not all data have an equal representation of the data pieces, there are various opportunities for bias to be introduced during the data collection process. Some sources might offer data that is insufficient, while others might not accurately reflect the real world or your modeling data set.

Biases can also be introduced during data processing, including data preparation and data labelling. The replacement of outdated or duplicate data is a part of data preparation. Businesses run the danger of unintentionally eliminating critical data, even though this might assist remove unnecessary data from training sets. Data anonymization, which removes personally identifiable information like a person’s ethnicity or gender, contributes to privacy protection and makes it more challenging to identify or correct bias based on those variables.

Adding labels to unstructured data enables a computer to interpret and comprehend it. This technique is known as data labelling. Data labelling, however, depends on both people and technology. A human data labeler may add bias to the data if they incorrectly label a picture or use their own judgment when translating or tagging. Organizations should make sure they have established checks and balances and don’t rely only on one system or data labeler for all human-based data labelling decisions in order to reduce errors.

Google Bert Vs Smith: How They Work & Work Together

Last month here on Search Engine Journal, author Roger Montti covered the Google research paper on a new Natural Language Processing algorithm named SMITH.

The conclusion? That SMITH outperforms BERT for long documents.

Before we dive in, as of right now, SMITH is not live in Google’s algorithms. If my Spidey senses are right though, it’ll be rolling out with passage indexing, or preceding it.

Regular readers will know I have an interest in Machine Learning as it relates to search, and so I had to dive into the research paper for myself.

I also had to revisit some of the BERT docs to really wrap my brain about what was going on.

Is BERT about to be replaced?

Aren’t most documents on the web that aren’t thin content therefore long, and thus better for SMITH?

I’m going to take you to the conclusion first.

SMITH can do both jobs, and a bazooka can open a door. But you are still better off bringing your key, in many cases.

Why BERT or SMITH to Begin With?

What we’re really asking with this question is why a search engine would want to use Natural Language Processing (NLP).

The answer is quite simple; NLP assists in the transition from search engines understanding strings (keywords) to things (entities).

Where Google once had no idea what else should be on a page other than the keywords, or whether the content even made sense, with NLP it learned to better understand the context of the words.

The tone.

That “bank account” and “riverbank” are referring to different banks.

That the sentence, “Dave met up with Danny for a beer, beers, pint, glass, drink, ale, brew…” is not natural.

As an SEO professional, I miss the old days.

As someone who needs to find things on the internet, I do not.

Enter BERT

BERT is the best current NLP model we have for many, if not most, applications including understanding complex language structures.

The biggest leap forward with BERT in my opinion was in the first character, Bidirectional.

Rather than simply “reading” from left-to-right, it could also understand context going the other way around.

An overly simplified example might be in understanding the following sentence:

A car has lights.

If you can understand only left to right, when you hit the word “lights” you would classify the car as something that has lights because you have encountered the word car prior to it and could make the association.

But, if you were wanting to classify things on cars, lights may be missed because they had not been encountered prior to “car.”

It’s hard to learn in one direction only.

Additionally, the “under the hood” of BERT is remarkable and allows for processing language effectively with lower resource costs than previous models – an important consideration when one wants to apply it to the entire web.

One additional leap forward with BERT was its application of tokens.

In BERT, there are 30,000 tokens and each represents a common word with some leftover for fragments and characters in case a word is outside the 30,000.

Through the token processing and transformers, the way BERT was able to understand content gave it the ability I alluded to above, to understand that in the sentence:

“The man went to the bank. He then sat on the river bank.”

The first and last instances of “bank” should be assigned different values as they are referring to different things.

What About SMITH?

So now SMITH swaggers in, with better numbers and resource use in processing large documents.

BERT taps out at 512 tokens per document. After that, the computing cost gets too high for it to be functional, and often just isn’t.

SMITH, on the other hand, can handle 2,248 tokens. The documents can be 8x larger.

To understand why computing costs go up in a single NLP model, we simply need to consider what it takes to understand a sentence vs. a paragraph.

With a sentence, there is generally only one core concept to understand, and relatively few words meaning few connections between words and ideas to hold in memory.

Make that sentence a paragraph and the connections multiply exponentially.

Processing 8x the text actually requires many more times that in speed and memory optimization capacity using the same model.

SMITH gets around this by basically batching, and doing a lot of the processing offline.

But interestingly, for SMITH to function, it still leans heavily on BERT.

At its core, SMITH takes a document through the following process:

It breaks the document into grouping sizes it can handle, favoring sentences (i.e., if the document would allocate 4.5 sentences to a block based on length, it would truncate that to four).

It then processes each sentence block individually.

A transformer then learns the contextual representations of each block and turns them into a document representation.

The diagram of the process looks like:

You can see a similarity between the bottom four rows, and the BERT process above. After that, we move to sentence-level representations and transforming that to a document level.

A Bit of Side Tech

Interestingly, to train the SMITH model, we take from BERT in two ways:

1. To train BERT they would take a word out of a sentence and supply options.

The better trained BERT was the more successful in choosing the right option. For example, they might give it the sentence:

Options 2 – fox

The better trained, the more likely it is to pick Option 2.

This training method continues with SMITH, as well.

2. Because they’re training for large documents, they also take passages and remove sentences.

The more likely the system is at recognizing the omitted sentence, the better trained.

Same idea, different application.

I find this part interesting as an SEO pro, as it paints a world with Google generated content pieced together into walled-in SERPs. Sure, the user can leave, but why would they if Google can piece together short and long-form content from all the best sources in one place?

Think that won’t happen? It’s already starting and it looks like:

Though they’re still doing it poorly, as evidenced by this example from the Ryerson site:

This next stage will just make it less blatant they’re just ripping off content.

Sounds Like SMITH Is Better…

It sure sounds like SMITH is better, doesn’t it?

And at many tasks, it will be.

But think of how you use the internet.

“What’s the weather?”

“Play a song.”

“Directions to a restaurant.”

Many queries are satisfied not just with short answers, but with limited and often uncomplicated data.

Where SMITH gets involved is in understanding long and complex documents, and long and complex queries.

This will include the piecing together of documents and topics to create their own answers.

This will include determining how content can be broken apart (dare I say… into passages) so Google knows what to surface.

It will help each one to better understand how pages of content are related to each other, how links may be valued, and more.

So, each serves a purpose.

SMITH is the bazooka. It will paint the understanding of how things are. It is more costly in resources because it’s doing a bigger job, but is far less costly than BERT at doing that job.

BERT will help SMITH do that, and assist in understanding short queries and content chunks.

That is, until both are replaced, at which time we’ll move another leap forward and I’m going to bet that the next algorithm will be:

Bidirected Object-agnostic Regresson-based transformer Gateways.

The Star Trek nerds like me in the crowd will get it. 😉

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Image Credits

All screenshots taken by author, January 2023

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