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One of the things that Amazon has done well with Alexa is cloud-enabled skills that are directly connected to its Echo products. In my smart home setup, we have a couple of Echo Dots in my kid’s rooms they use for ‘story time’ at night. I love my HomePod, but it can’t replace this functionality at this time. Is it time for Apple to build a HomeKit skills section that runs directly off iCloud?
HomeKit Weekly is a series focused on smart home accessories, automation tips and tricks, and everything to do with Apple’s smart home framework.
From the early days of Alexa, Amazon built all of its ‘skills’ in the cloud. Since Amazon isn’t much of a factor in smartphones, they couldn’t rely on streaming content from it to power audio. From its flash briefings, games, and other types of programming, the Echo lineup stands on its own in terms of ability to work independently of any other devices.
I’d love to see Apple expand HomePod to work in much the same way. For Apple Music and Apple Podcasts, it can play content directly from the internet. Why can’t other apps do the same thing? AirPlay 2 does exist and is a stop-gap to this problem, but it doesn’t solve it. Here are some ways I’d like to be able to use my HomePod:Podcast apps
I want an app like Overcast or Breaker to be able to talk directly to my HomePod from the cloud. The way I envision this working is when I install Overcast, it could request to hook into my HomePod. I could say, ‘Hey Siri, play Apple @ Work podcast on Overcast.’ My HomePod would then, without using another device, talk directly to Overcast’s web infrastructure to stream the podcast.Bedtime apps with HomeKit skills
Like I mentioned at the beginning of this article, one of my most common use cases for our Echo Dots is the ‘Amazon Story Time.’ My kids use it as they fall asleep at night. I’d love to use an app like Moshi: Sleep and Mindfulness directly on my HomePod. Similar to Overcast, I’d love to be able to give Moshi access to run directly from iCloud or whatever system Apple set up for services to talk directly to my HomePod. Right now, I could use AirPlay to make this happen, but it would require the use of a dedicated iOS device.Good morning Siri
I’ve had this idea to be able to say Good morning Siri, and have Siri run a few HomeKit automations and play the latest episode of The Daily and then NPR Up First using the data in Overcast. If I didn’t finish the episode before I paused it, I’d want the Overcast iPhone app to be able to know exactly where it stopped. Another example of how I might use this is that when my abode motion sensor first sees motion in the morning, it would turn on a few Hue lights at 30%, start my morning podcasts (bonus points if I can set a preference on which shows from the ones released on that date are played first). A really fun idea for the future would be if my alarm system could deactivate if it knew either myself or my wife were the ones awake.Wrap-up on HomeKit Skills
What do you think about HomeKit Skills? Would tying in third-party apps into the HomePod experience without needing to launch an iPhone app make sure you want to use your HomePod or purchase one if you don’t have one? I love my HomePod, but I also see the value with how Alexa is a cloud-first operating system. Whatever happens in the future with the HomePod ecosystem, it needs to be open to third-party developers and have a cloud-first approach.Previous HomeKit Weekly Articles
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Wemo Stage Scene Controller Review: Apple HomeKit remote needs refinement
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Sometimes, only a button will do. The smart home and the rise of connected devices like lamps, speakers, locks, and more have brought complex automations within reach of a mainstream audience, but sometimes you don’t want to have to pull up an app – or talk to a smart speaker – just to switch on the lights. For those in the Apple HomeKit ecosystem, then, the Wemo Stage Scene Controller remote could be the answer.
About the size of a car key fob, the $49.99 Wemo Stage remote is a small, white plastic puck. On the front there are three buttons, distinguished by slightly raised dots. Each button supports a short press and a long press, which can be mapped differently, and there’s an LED hidden behind the frontplate to show when a signal has been sent.
Pop the back cover off, and you’ll find a regular CR2032 battery. Wemo says that should last about two years, though I’ve noticed its percentage in the Apple Home app dropping more rapidly than would indicate that sort of longevity. At least the batteries themselves are readily available.
You can leave the remote on your coffee table or nightstand, but Wemo also includes a wall plate with 3M adhesive strips on the back. The remote clings to that magnetically. Or, you can pop out the magnetic dock section, and fit that into a junction box with a standard paddle-style switch trim plate.
Setup is generally straightforward, mainly because Wemo hands the process over to Apple. The remote will only work with HomeKit devices, including more recent items from Wemo itself, and the broader ecosystem of HomeKit-compatible smart home products like locks and lights. Basically, if you can control it from within Apple’s Home app, the Wemo Stage should be able to control it too, but anything else is left out of the fun.
At its most simple, individual devices – like a lamp or lock – can be registered to one of the Wemo Stage’s buttons. It’s worth noting that, unlike with some remotes, there’s no toggle support here: if you set a short press of the first button to switch on your nightstand lamp, another short press won’t turn it off again. For that, you’ll need to program another button – or maybe a long-press of the same button – for the off command.
Where it gets more capable is when you start registering scenes to the remote’s keys. That could be a group of lamps in a room, or a mixture of bulbs, shades, and more. A single press could shut off the lights, lock the doors, and enable your HomeKit-compatible alarm system, for example, just before you go to bed. Or, it could dim the lights, close the shades, turn on your Apple TV and TV, and get you ready for movie night.
If you’ve already created scenes in the Home app you can assign those to the Wemo Stage straight away. Or, you can build a scene piecemeal as you program each button, first by selecting the appropriate devices and then by customizing the desired settings of each. For a light, for example, you could choose whether you want it to turn on or off, to what brightness level, and to a specific color if the bulb supports it.
It’s flexible, but it does mean that some of HomeKit’s frustrations are shared with the Wemo Stage remote. After picking a selection of lights, for instance, to map to a short-press on one button, it would’ve been convenient to copy that group but have them all switch off again for a long-press of the button. In the Home app, though, you have to select them all individually again.
Different devices can also be finicky. Setting my HomeKit-compatible motorized shades to open or close fully from the remote was easy, but getting them to consistently open partway, or adjust the top rail on a top-down/bottom-up shade, was far less straightforward.
As for media integrations, there you bump up against Apple’s current limitations. While you may be able to set Spotify or other third-party music streaming services as the default with Siri on your iPhone now, for the moment HomeKit only supports Apple Music. That means, if you want the Wemo Stage to start up playback of a certain album or playlist, right now you’ll need to be streaming that through an Apple Music account.
My other big frustration was connectivity. Currently, Wemo Stage links to a HomeKit hub – like a HomePod mini or an Apple TV – via Bluetooth LE. There’s a Thread radio inside the remote, but it’s not enabled yet, and Wemo isn’t saying when that might actually happen.
Come the arrival of Thread, I’d expect range to improve, but for the moment it can be on the short side. Get more than a few walls between your remote and the nearest hub, and the connection can become flaky.
Indeed, there have been more than a few times where pressing a button on the Wemo Stage either didn’t trigger the programmed scene, or only partially triggered it (with some lights remaining off, for instance, while others switched on). There’s also a brief moment after you press a button where a second press isn’t recognized. Adding to the confusion, the remote’s LED glows when you press the button but that doesn’t necessarily mean your scene has actually been triggered.
My gut feeling is that Thread will help address all that. Still, with no public roadmap for its activation, it’s hard to take too much reassurance from that potential.
Natural hazard modelling techniques can be used to more accurately anticipate and prepare for natural disasters like cyclones, earthquakes, floods, and landslides. These instruments can offer crucial information to first responders and legislators during a disaster. Natural hazard modelling systems still need a lot of storage and processing power, which can be problematic for organizations with a restricted budget or available space.
Cloud computing is a great way to simulate complicated models because it can be extended and changed, and cloud computing is a workable answer to this problem. In this essay, we’ll talk about Cloud Computing in natural hazard modelling systems.Cloud Computing Overview
The three main categories of cloud computing services are infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). IaaS offers shared access to various processors, storage, and network tools, making it easier for people to develop and manage their apps.
There are many ways to use these resources. There is already an operating system, a language, and tools for making things available. A website offers its readers this material as a service. The apps that end users can access must be stored and maintained by the service provider.Natural Hazard Modeling Systems
Environmental hazard modelling techniques enable us to foresee the effects of natural disasters and take precautions. These systems predict the behaviour of natural events like cyclones, tremors, floods, and collapses using intricate computational processes and modelling models. Tools for modelling natural hazards help emergency personnel, lawmakers and other interested parties decide how to prepare for and respond to natural disasters.The Challenge of Running Natural Hazard Modeling Systems
Gadgets that imitate natural dangers need a lot of memory and processing power, and these systems frequently have significant data handling and big datasets. Systems for modelling natural hazards must be able to run simulations quickly while keeping a high level of accuracy to deliver precise data on the likelihood of natural disasters when needed. For organizations with limited means, this might be challenging.Cloud Computing and Natural Hazard Modeling Systems
Hazard modelling systems may benefit from using cloud computing to address real problems. Thanks to cloud computing, businesses and other organizations can use various flexible and changeable numerical tools on demand. As a result, complex models and models can be executed more quickly. Natural hazard modelling systems operate more affordably thanks to cloud computing because businesses only pay for the resources they use.Benefits of Cloud Computing in Natural Hazard Modelling Systems Scalability
Scalability is one of the benefits of using cloud computing to model environmental disasters. Businesses using cloud computing to meet their specific needs can quickly and easily change the size and number of working tools. This implies that businesses can increase their working capability during periods of high demand, such as those following a natural disaster, and reduce it during periods of low demand. This ensures that companies have the resources to complete complex model tasks quickly and successfully.Flexibility
The versatility of systems that prepare for natural disasters is increased by using cloud computing. Companies can use cloud computing to choose the computational power they need to accomplish their goals. This enables businesses to decide what computing equipment and data storage to run their systems for efficiently modelling environmental hazards. Cloud computing improves efficiency overall and decreases downtime, and allows companies to decide where to locate their work equipment.Cost Savings
Cloud computing can also reduce the cost of running natural hazard modelling systems. Traditional on-premises computing requires organizations to purchase and maintain their own hardware and software infrastructure, which can be expensive.Challenges of Cloud Computing in Natural Hazard Modeling Systems
While there are many benefits to using cloud computing for natural hazard modelling systems, there are also some risks to be aware of. Here are some examples of this kind of problem −Security
Security is one of the most important problems that cloud computing must handle. Because cloud computing requires giving up data control to outside providers, some businesses might be suspicious of it. Because cloud computing services are so extensively used, hackers and data breaches are more likely to happen, which is another possible problem. Due to these aspects, businesses must confirm that the cloud computing service providers they use take the appropriate security measures to safeguard their customers’ data.Data Sovereignty
A problem with cloud computing is figuring out who controls the info. The phrase “data sovereignty” refers to the responsibilities of governments and the judicial system when managing and storing data. Legislation may require businesses to keep data in their home country or within a particular country’s borders. Due to the design of cloud storage, some businesses may find it difficult to adhere to data security laws.Vendor Lock-In
Last, some clients might complain about being restricted to a single cloud service provider. A corporation is “tied in” to a particular cloud computing service supplier if no other source can meet the organization’s processing needs. Because of this, it is challenging for companies to switch to a different seller if they are unhappy with the service or if the company decides to raise prices. By carefully considering their cloud computing options and choosing a service provider that provides independence and scope, businesses can lessen their vulnerability to this risk.Conclusion
cloud computing offers many benefits for natural hazard modelling systems. Businesses and organizations can obtain scalable and flexible processing tools through cloud computing. They can handle intricate models and circumstances more quickly and successfully.
TradeStars has launched a Fantasy Sports Stock Trading platform that leverages some of the finest innovations from the world of blockchain technology to provide an unparalleled gaming experience.
TradeStars allows users to trade Fantasy Sports Stocks whose prices are directly influenced by the real-life performance stats of the athletes they represent.
A NASDAQ built on blockchain, TradeStars allows investors to invest in the Stocks of their favorite players. The Stocks are issued as tokens on the blockchain, which allows TradeStars to provide a host of features that are absent in other Sports gaming platforms.
Users who can use their Sports knowledge to identify early those players who go on to make a name for themselves in the Sports world can expect handsome returns on their investment.
“Olympic Torch”, the Mainnet of TradeStars, was launched on 31st August. Fantasy Stocks of some of the hottest Cricketers from all around the globe can be currently traded on TradeStars.
Blockchain Meets Sports Gaming
TradeStars has been built on Ethereum, which is arguably the first choice for DApp developers all over the globe.
Fantasy Stocks can be traded using TSX, which is the in-game currency. TSX is also the governance token of the ecosystem and entitles holders to various utilities and rights, such as earning a portion of the platform’s trading fees as dividends, participating in various platform governance decisions through decentralized voting, and getting early access to special Sports themed NFTs.
All trades on the TradeStars platform are recorded permanently on the blockchain and are visible to everyone. This provides unparalleled security and transparency, completely eliminating the scope of any type of forgery or fraud.
Moreover, TradeStars integrates Polygon’s Layer 2 scaling solution, which helps in massively scaling transactions on TradeStars’ trading platform.
Polygon settles all transactions of the main Ethereum chain without compromising on the security of the underlying blockchain (main chain). The integration ensures that trades on TradeStars are confirmed almost instantly and free of cost, even if gas prices on the Ethereum blockchain are skyrocketing.
Fantasy Stocks as NFTs
The use of blockchain technology also makes it possible to issue the Fantasy Stocks as NFTs (or Non-Fungible Tokens) on the blockchain.
NFTs are being widely used to represent various collector and art items in digital format on the blockchain. Since each piece of art is unique, each NFT is also irreplicable and non-duplicable. In fact, NFTs can be used to represent just about anything, from a tweet to a meme.
In recent times, NFTs have managed to capture the attention of both artists and investors, racking up a media frenzy.
Artists such as Beeple and Grimes have sold NFTs of their digital artworks for millions. Corporations too have jumped onto the NFT bandwagon – Visa purchased a ‘CryptoPunk’ for nearly $0.15 million in Ethereum.
By virtue of their uniqueness, NFTs are a natural fit when it comes to representing the Fantasy Stocks of individual players.
Fractional NFTs, the Game Changer
Fractional NFTs (or F-NFTs) is the next step in the evolution of tokenization. As the name suggests, an NFT is further tokenized into smaller units, with each unit granting its owner partial ownership of the digital asset.
Since ownership is now divided among multiple people, the resulting liquidity is much higher. Instead of purchasing the whole asset, investors can spend any amount of funds and own a portion of the NFT.
With the hype around NFTs soaring, their prices are getting higher with each passing day, making them too costly for the average retail investor to buy. Moreover, selling an NFT in secondary markets also becomes quite difficult, since the owner has to find a buyer who is willing to match the asking price.
Through fractionalization, trading NFTs (in parts) becomes feasible. The services or features, if any, offered by the NFT are also shared between all the owners in a proportionate manner.
NFT Markets through Fractional NFTs
TradeStars is one of the first applications in the blockchain sector which has adopted F-NFTs to offer more utility to users.
The Fantasy Stocks on TradeStars are implemented through Fractional NFTs. For each athlete who is listed on the platform, TradeStars creates an “NFT Market” with a fixed amount of Fantasy Stocks.
Since the NFT Markets comprise of F-NFTs, users have complete flexibility to purchase any amount of stocks they want, including fractions.
On other popular NFT based gaming platforms like Axie Infinity, users have to purchase NFT assets (Axies, in this case) worth hundreds of dollars before they can start playing. The high initial investment puts such games beyond the grasp of a significant portion of the gaming population, especially the school and college-going crowd.
Liquidity on the TradeStars platform is also enhanced significantly through the use of F-NFTs. Users can trade stocks anytime they want, and from any corner of the world.
TradeStars is taking NFTs to the next step, utilizing them in a manner that increases both the affordability and the liquidity of their Fantasy Stock offerings. Combined with the seamless trading experience that the Polygon integration offers, TradeStars is definitely one of the hottest play-to-earn games that have hit the market this year.
With the Cricket season in full swing, TradeStars has seen a steady inflow of users since their Mainnet launch. As TradeStars goes on adding more athletes and more Sports domains on its roster, it’s safe to assume that the Fantasy Sports Stock trading game will continue to attract and enthrall sports fans.
Procedure for deploying a Django application using Heroku Step 1: First of all you are required to execute the command as shown below.
Then, you can execute the command as ‘cat requirements.txt’ for viewing the contents that are present in the ‘requirements.txt’ file.
cat requirements.txtStep 2: Now, go to your Django project and open ‘settings.py’ as shown in the image below. Step 3: At the end of ‘settings.py’ add the following statement.
STATIC_ROOT = os.path.join(BASE_DIR, ‘static’)
Django is unable to automatically create a target directory i.e. STATIC_ROOT. Hence, in ‘settings.py’, a variable called ‘STATIC_ROOT’ is responsible for defining the single folder where you are willing to collect all your static files.
Note: Before you carry out the next steps, make sure that you have installed Heroku CLI in your PC and have developed an account at Heroku. For ensuring that Heroku has been correctly installed in the PC, execute the following command, and make sure that you obtain the output as shown in the image below.
heroku -hStep 4: A Procfile is essential in the Heroku application. Therefore, requires a ‘nano’ command is used to edit/create the ProcFile. Hence, first of all, you are required to execute the following command.
After the files open, provide the following statement in the file.
web: gunicorn myproject.wsgi
Here, the created ProcFile requires a Gunicorn as it is one of the most preferred production web server for Django applications.
After you have provided the statement, press ‘Ctrl+X’ then ‘Enter’ to save the changes made in the ProcFile.Step 5: Now, after you have successfully created the ProcFile, open the given file in the text editor. Then, you are required to update ‘web: gunicorn myproject.wsgi’ to ‘web: gunicorn weatherapp.wsgi’. The updated statement that is to be updated in the ProcFile is provided below.
web: gunicorn weatherapp.wsgi
Here, myproject.wsgi is updated as chúng tôi using a text editor (Sublime text) as shown in the image below.
Note: If you want to learn more about Web Server Gateway Interface (WSGI) in detail, please visit the link provided below.Step 6: Now, you are required to install Gunicorn.
Hence, at first, you are required to execute the following queries before carrying out further tasks.
pip install gunicorn
‘pip install gunicorn’ is used to install Gunicorn for your project. Finally, now you can execute the following commands.Step 7: Here, you are required to install ‘django-heroku’ package. So, in order to install the package, you need to run the following command.
pip install django-heroku
Django application should be configured in order to work on Heroku. Hence, ‘django-heroku’ package is able to carry out the configurations part automatically which allows your Django application to work Heroku.
Then, you can execute the following commands.Step 8: Now, you need to import ‘os’ and ‘django-heroku’ package in chúng tôi as shown in the image below. The code for importing the packages has also been provided below.
import os import django_herokuStep 9: Now, to activate ‘django_heroku’ you are required to go to the end of ‘settings.py’ and add in the following lines of code.
# Activate Django-Heroku. django_heroku.settings(locals())
Note: Here, in this blog, we are not using our GitHub repository with the application which is going to be deployed in Heroku. Therefore, if you have cloned your application from your remote repository, move your application into a fresh new folder as shown in the image below.Step 10: Since, you are now moving onto the production site, the ‘DEBUG’ in ‘settings.py’ should be set as ‘FALSE’ as shown in the image below.
DEBUG = False
Let us obtain an understanding of — Why do we need to set DEBUG as False?
First of all, let us understand what happens when DEBUG is set as True. DEBUG = True keeps details regarding the error pages. Hence, Django provides a stacktrace of what had gone wrong which can be quite helpful when you are debugging your application. Furthermore, Django also keeps track of all the SQL queries that had been executed in DEBUG mode.
Now, let on move onto the outcome when DEBUG is set as False. Keeping the DEBUG mode as True keeps track of all the SQL queries which had been executed in DEBUG mode which is not convenient in the production site. Hence, you are now required to provide ALLOWED_HOSTS in the chúng tôi file which identifies where you are hosting your Django application.
Note: If you require guidance for configuring ALLOWED_HOSTS, you can go to step 23 of this blog. Also, you should have created an app in Heroku which has been carried out in step 18 before you configure the ALLOWED_HOSTS.
Note: If you have not installed Git Bash yet and require guidance for installing Git bash then you can view my previous blog which consists of a detailed procedure for installing Git.
A combination of data types allows marketers to identify and build out the segment of ideal and high-value customers
Segmentation helps boost the value that marketing teams bring to an organization. I am a big fan of the trusted segmentation toolkit, but for most companies, it can do with a much-needed upgrade.
Growing your current database with high-value customer acquisition seems just the way to get there. A new form of segmentation and targeting can be found with the use of Customer Data Platforms (CDP). A combination of data types allows marketers to identify and build out the segment of ideal, high-value customers.Moving away from “blunderbuss marketing” from the start
Look at the Pillars of Segmentation marketing segmentation framework for a minute and you will realize that your data is at the heart of all segmentation efforts. Purchase data is a good starting point to identify interesting groups. What your a good segmentation sets look like will differ from company to company, so they need a bit of Segment discovery (more about that later).
The typical use of marketing segmentation is to divide your customer file into several semi-static groups of customers with similarities and differentiate marketing communication to them. These groups or “segments” are the basis of your marketing segmentation.
But now imagine your marketing data to be more effective from day one. Especially for businesses with big customer databases, the returns of segmented messaging overshadows any non-targeted “blunderbuss marketing” method. Segmentation has a strong foothold in two main use-cases:
Resonating Content: Play into the differences and buying reasons of your target groups so they “feel that it is for them”, if well executed it is a form of resonating messaging; relevant, interesting and persuasive. This brings the kind of targeting and personalization that your customers are growing to expect in their messages. It is especially crucial in direct (one-to-one) channels like email marketing and in lead nurturing funnels.The ROI of newfound segmented customer sets
Realistically, not every single campaign can be completely individualized to the specific customer needs if you have too broad a mix of potential clients.
“Small segments in your database can represent big audiences outside of your current database.”
It might seem like this is something only big companies need to do, but an added benefit of all of this data is the bigger insight into your customer base. Startups, scale-ups and small businesses can benefit as well, particularly to pinpoint the segments to go after when faced with scarce resources.Discover your segmentation set and create the best customer view
Now we are left with the how. A strong example comes from Spyder Snow Gear, a US sports retailer. Overcoming Data and MarTech challenges with the help of a customer data platform to make the data accessible.
Every company has pockets of Ideal clients, the clients that you love to have and that love your products and services. Now imagine all of your customers are like that. Profitable, premium, the best customer you could wish for. Even if we could get a few more of these, it could make all the difference.
Seems great, but that is not where most businesses are at the moment. For most businesses, the Pareto principle (or 80/20 rule) is what today looks like. The top 20% of your file is likely to present 80% of revenue. Same story for Spyder, but with the twist that they could now identify the patterns in that 20%.
To identify the most profitable customer segments you need to combine the purchase and behavioral data available with the customer profile. It is extremely important not to let this go to waste.
Spyder built a “best customer view” and was able to derive high-value customer personas and their profiles, where they weren’t before. Think of it as a mix of segmentation, RFM and targeting to create identifiable acquisition personas.
According to the study, the new prospect acquisition tactics resulted in:
Email list growth of 85%, while the Cost of Acquisition went down.
Revenue increase of 94% from targeting in subsequent sales campaigns
Content-focused EDMs generate 52% more revenue
Here are two (out of three) of their high value-customer segments:
You can imagine the potential gains and probably also potential problems if these segments aren’t treated correctly. The acquisition campaign, in this case, is targeted through taking our seed list of most profitable clients and creating look-a-like audiences on Facebook. Each segment got a tailored prize draw competition in exchange for their details and an enriched opt-in. The real long-term value, of course, is in identifying the correct seed list(s) and follow-up sequences to refine after an initial win.Get your structure in shape for the next step in Data Activation
The benefits of Data centralization are well documented and yet marketers aren’t doing it. Reasons for this tend to be quite similar, with tech, data or the prospect of extra work-load holding them back. But it doesn’t need to be like that. Smart marketers are employing segmentation for the right type of campaigns and in combination with keen MarTech.
Gathering and transforming customer data is the all-important step. Most marketers will agree that we are just at the beginning of what is possible. The technology to use customer data and predictive analytics will soon be at the point where a combination of understandable segmentation-driven campaigns will be augmented with programmatically driven (often black box) targeting that use even more of the data and independently learn what works for who.
In that mode, you can drive smaller and smaller selections on a bigger scale – and identify the opportunities that our own logic could never. The smart marketers today are the citizen data scientists of tomorrow and something to prepare your MarTech infrastructure for.
A great customer segmentation/acquisition campaign is a beautiful use-case for bringing together your data and starting with more refined analytics.
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