text:
Brandtone
brandtone.ie
Series D | Alive
Last Raised
$20.88M | 8 yrs ago
About Brandtone
Brandtone is an international company that provides the means for the leading brands in fast-growing markets to conduct mobile marketing campaigns that increase sales, profile engaged consumers and build trusted brands. To date, Brandtone's world leading clients include Unilever, SAB-Miller, Kellogg's, Pepsi, Kraft and BP. The company now has operations in South Africa, Brazil, Russia, Turkey, Kenya, Nigeria, India, China, Indonesia and the US.
51 to 54 Pearse Street
Dublin, 2,
Missing: Brandtone's Product Demo & Case Studies
Missing: Brandtone's Product & Differentiators
Expert Collections containing Brandtone
Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.
Brandtone is included in 1 Expert Collection, including Ad Tech.
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Latest Brandtone News
Seen & Heard: What the Sunday papers reported
O'Reilly's Providence shares, Government's Apple briefings and Brandtone liquidation 51 minutes ago Sir Anthony O'Reilly: Last shares in Providence will be sold as part of bankruptcy process. Photograph: Brenda Fitzsimons Last O'Reilly's shares to be sold The last shares held by Sir Anthony O'Reilly in Providence Resources , the company he founded, will shortly be sold as part of his ongoing bankruptcy process, the Sunday Business Post reports. A US filing in relation to the bankruptcy in the Bahamas also points to ongoing investigation into the former media baron's extensive art collection. Taoiseach briefed Google on Apple ruling Both Google and Facebook were briefed on the implications of the Apple ax ruling by Taoiseach Enda Kenny and Minister for Finance Michael Noonan respectively, the Sunday Independent writes. Google global head of public policy sought a briefing from Mr Kenny on a visit to the State, while Mr Noonan briefed senior Facebook officials during a visit to the United States, documents obtained under Freedom of Information show. Mercury Engineering up for sale The Sunday Independent also reports that the privately held Mercury Engineering, one of the largest engineering businesses in the State, is on the block. The business founded in 1972 by Joe Morgan and Frank O'Kane has annual revenues of about €500 million and employs more than 3,000 people worldwide. Brandtone goes into liquidation Brandtone, a mobile marketing group that raised €41 million from investors, including Unilever, and which won the EY Entrepreneur of the Year award for emerging companies in 2015, has gone into liquidation, according to the Sunday Times. The company went into examinership earlier this month but failed to find necessary funding to survive following a dispute with Accenture of a €1.7 million payment. Version 1 in talks with Volpi Capital The Sunday Times also reports that shareholders in fast-growing IT group Version 1 are in advanced talks to sell a large majority stake to UK private equity group Volpi Capital , which is led by Irishman Crevan O'Grady. Founder Justin Keatinge , who is reported to hold 43 per cent of the equity and recently stepped down from his active role in the business, would be the big winner from any takeover. ADVERTISEMENT Ballymore to sue CRH Developer Sean Mulryan's Ballymore group is to pursue a court action against CRH and its subsidiary Roadstone among others over the alleged supply of pyrite in material used in a high-end housing scheme in Portmarnock , the Sunday Independent reports. More than 90 of the 145 houses in the Drumnigh Wood estate are affected, it is claimed. The case is listed for May 30th. Nama could have made extra €18bn A new report says Nama would have made an additional €18 billion had it held on to and managed its assets. The report by economist Jim Power using numbers validated by Lisney says overseas funds made spectacular profits of more than €317 million in under two years on just 11 deals by "flipping" property assets acquired from Nama at an average return of 47 per cent, the Sunday Business Post reports.
Brandtone forced into examinership over delayed €2m US payment
Brandtone forced into examinership over delayed fee
Mark Sherrington
Brandtone founders claim emerging catergory award
Brandtone Frequently Asked Questions (FAQ)
When was Brandtone founded?
Brandtone was founded in 2009.
Where is Brandtone's headquarters?
Brandtone's headquarters is located at 51 to 54 Pearse Street, Dublin.
What is Brandtone's latest funding round?
Brandtone's latest funding round is Series D.
How much did Brandtone raise?
Brandtone raised a total of $54.36M.
Who are the investors of Brandtone?
Investors of Brandtone include Unilever Ventures, Verlinvest, Syngenta Ventures, Vision Capital Advisors, Ares Capital Corporation and 3 more.


It Ain't Half Hot Mum co-star Melvyn Hayes, who played Gunner/Bombardier Gloria Beaumont, called him "one of the good guys".
Paying tribute to Davies, he said: "I considered him my best friend, even though we hadn't been in contact for many years.
Blackadder star Sir Tony Robinson paid tribute to Davies by quoting lyrics from Whispering Grass, while Downton Abbey actor Paul Putner said he was a "gifted actor" and "one of most generous, lovely blokes you could ever wish to meet".
'Allo 'Allo and Emmerdale star Vicki Michelle shared a picture of herself with Davies and tweeted he was one of her "favourite people", and a "genuinely lovely generous man" with a "huge presence and a huge heart".
In 1978, Davies also featured in one-off BBC drama Grand Slam, which gained cult status and was still fondly remembered years later – gaining a new lease of life with a DVD release.


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e-Models
Facts & Advantages
Charging & Range
Private Charging
Wallboxes
Charging Costs
Charging Times
Optimise Range
THE ELECTRICITY CONSUMPTION OF ELECTRIC CARS.
Find out how affordable it is to charge your electric car, what the advantages are compared to the combustion engine and how you can reduce electricity consumption.
THIS IS HOW EFFICIENT ELECTRIC CARS ARE.
The electric car scores points, among other things, for the efficiency of its drive: the electric motor converts around 80 percent of the energy supplied into motion. If you include the losses that occur when charging the battery and providing the electricity, you get an efficiency of 64 percent. This means that an electric car is about three times as efficient as a vehicle with a conventional combustion engine - the efficiency of a petrol engine is only about 20 percent in normal driving conditions.
ELECTRICITY CONSUMPTION COMPARED TO PETROL CONSUMPTION.
If you can charge your electric car cheaply at home, you can travel 100 kilometres much more cheaply than with a combustion engine. For example, charging an Opel Corsa-e with a 50 kWh battery will cost you around €15.95 at the current electricity prices in 2021 - and is thus significantly cheaper than filling up a combustion engine.
In general, the electric car is much more efficient than a petrol engine. The latter requires about three times the amount of energy for the same distance. This is mainly due to the fact that combustion engines have a much lower efficiency - much of the energy generated during combustion in the car is simply lost.
HOW MUCH ELECTRICITY IS CONSUMED BY YOUR OWN CHARGING STATION?
Charging at your home charging station has numerous advantages, as there is neither an additional basic charge nor metering costs. However, the electric car's share of the household's total electricity consumption cannot be easily tracked on the bill. On the other hand, you can freely choose your electricity provider and keep the costs for charging as low as possible. The total costs to be expected depend not only on the battery in your vehicle, but also on your electricity tariff.
THIS IS HOW MUCH IT COSTS TO CHARGE YOUR ELECTRIC CAR.
If you charge your electric car at your own wallbox, you only pay the average price per kilowatt hour of household electricity, i.e. 31.89 cents (as of 2021). At public charging stations, the price is correspondingly higher: alternating current (AC) costs about 5 to 10 cents per minute, at a direct current fast charging station you have to expect up to 49 cents per minute.
OPTIMIZE YOUR RANGE.
THIS IS HOW YOU CAN REDUCE THE ENERGY CONSUMPTION OF YOUR ELECTRIC CAR.
The consumption of an electric car is given in kilowatt hours (kWh) per 100 km. For the Opel Corsa-e, the average power consumption (WLTP) is 16.8 kWh, for the Opel Mokka-e 18 - 17.4 kWh.
However, the actual consumption depends on various factors: your personal driving style (how often you accelerate), the nature of the route (mountain road or flat country), the outside temperature (winter or summer), the use of heating and air conditioning and any additional weight (how many people are in the car).
The good thing is that you can see the current consumption at any time during the journey - and there are various ways to reduce it and thus save on electricity costs. By the way, the WLTP valid in the EU - or the EPA data in the USA - provide a realistic estimate of the range.
HEATING AND ENERGY CONSUMPTION.
The heating is one of the main energy consumers in an electric car. In winter, the range can therefore be reduced by around 10 to 30%. In extreme cases even more, especially if the cooled electric car has to be heated up several times a day for short trips. However, there are a few tricks you can use to counteract this effect: Use the Opel Connect app to heat up your Opel electric car before you set off, when it is still connected to the mains - then this energy does not come from the drive battery. Use the recirculated air or even just the seat heating while driving - this consumes significantly less energy.
LEARN HOW TO OPTIMISE RANGE
DECISIVE FACTOR: THE BATTERY.
ELECTRICITY CONSUMPTION IN BATTERY PRODUCTION.
In the production of electric cars, the production of the battery is the most important factor. Apart from that, however, they are undeniably more environmentally and climate-friendly than petrol or diesel vehicles: when using the German electricity mix, electric cars can quickly fully exploit their climate advantages over conventional powertrains.
However, as green electricity becomes more widespread, the energy balance in production will become increasingly better in the future. The second life and subsequent recycling of the batteries also contributes to their positive climate balance. And if you charge your Opel electric car exclusively with green electricity, you can even make your own positive contribution.
HIGHEST ENERGY DENSITY: THE LITHIUM-ION BATTERY.
The range of an electric car is determined primarily by the capacity of the battery. All Opel electric cars are equipped with lithium-ion batteries because they have the highest energy density compared to other batteries. This means that they can store the most energy per kilogram of battery. By the way, the capacity of our lithium-ion batteries varies from 50 to 75 kWh.
Since the energy density in electric car batteries has already doubled in the last 10 years, we can expect electric cars to be able to drive even further in the future.
LEARN MORE ABOUT BATTERIES
MORE ON THE TOPIC OF POWER CONSUMPTION.
HOW MUCH DOES AN ELECTRIC CAR CONSUME PER 100 KM?
The energy consumption of your electric car depends on your driving style, the outside temperature, the nature of your route and the load. However, the WLTP value, which is based on everyday driving conditions, gives you a good first impression. The consumption of the Opel Corsa-e is around 16.8 kWh per 100 kilometres (WLTP).
HOW MUCH DOES IT COST TO "FILL UP" WITH ELECTRICITY?
It all depends on where you charge your electric car: you can charge it particularly cheaply at home, because there you pay the current electricity price of 31.89 cents/kWh per kilowatt hour (2021). If you fully charge your 50 kWh battery Opel, it will cost you 15.95 €. Charging at public fast-charging stations is more expensive. Here you have to reckon with costs of up to 49 cents/kWh, which is around €25 for charging the battery. This is still significantly cheaper than filling up your petrol car with fuel.
HOW MANY KW CAN AN ELECTRIC CAR STORE?
How much your electric car can charge depends on the capacity of the battery: Opel offers 50 kW and 75 kW batteries. The fastest way to refuel them is at a fast charging station with direct current. But even with alternating current, there are differences in speed depending on the charging station - and also on the battery charge. If it is already charged to 20-80%, charging is faster. Likewise, if it is not significantly colder or hotter than 20 degrees Celsius.
HOW MANY KW DOES AN ELECTRIC CAR DRAW ON A SINGLE CHARGE?
How many kilowatts an electric car draws when charging depends on the power source and the matching charging cable: if you charge it at a single-phase wallbox, it draws 3.7 or 4.6 kW, at a three-phase wallbox 11 or even 22 kW. At fast charging stations, your electric car draws 50 or even over 100 kW direct current - which means that your battery is charged up to 80 % in around 30 minutes.
NOT FOUND THE RIGHT INFO YET?
ELECTRIC CARS AND CHARGING COSTS
ELECTRIC CARS AND RANGE
E- AND HYBRID MODELS

redpajama_set_name: RedPajamaCommonCrawl


This evening Portuguese press reported on speculation linking Sporting Lisbon's William Carvalho with a move to Manchester United.
Carvalho, aged 22, was closely monitored by David Moyes during his reign as United manager but it is now believed that Louis van Gaal has also kept close eye on the Portugal international. Reports say representatives of the club traveled to Lisbon to discuss a possible transfer earlier today.
The player is believed to have a buy-out clause worth £36 million but United are reluctant to spend that amount, so Sporting Lisbon will aim to sell for a figure around that price. There is also a prospect of competition for Carvalho's signature with Jose Mourinho reportedly interested in bringing the midfielder to Chelsea.


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The Jill Roe Prize is awarded annually for the best unpublished article-length work of historical research in any area of historical enquiry produced by a postgraduate student.
The Prize honours the career of Professor Emerita Jill Roe (1940–2017), an eminent and much-loved Australian historian who made a very significant contribution to the writing, teaching and public communication of history in Australia and abroad.
The next Jill Roe Prize will be awarded in July 2022.
Applications will open in January 2022.
The Award will be awarded annually for the best unpublished article-length work (5,000-8,000 words, including footnotes) of historical research in any area of historical enquiry produced by a postgraduate student.
The Award will consist of a cash payment of $150 and a citation, presented annually at the AHA national conference.
In addition to the Prize the winning entry will be considered for publication in History Australia – the journal of the Australian Historical Association. At the time of submission, the article must not be under review by any journal other than History Australia.
Applicants must be enrolled for a higher degree (Masters or PhD) at an Australian university.
Applicants must be members of the AHA at the time of application.
Applicants will be required to provide
a digital copy of their work
a completed application form that contains a statement from the author confirming the originality of the work
Applications should be emailed to the Executive Officer at executive@theaha.org.au. The closing date for applications is 31 March each year.
The Jill Roe Prize will be administered by a judging panel appointed by the AHA, consisting of a Chair selected by the AHA Executive, and two historians. The decision of the panel will be final.


The first problem really is the NFL Draft, it shouldn't exist, but that is for a different time. Lets take away all the outside noise and get down to the root of this. Lets replace Bell, with you. So now you just signed a contract (four years) at a company (the Steelers) as an employee that, lets say, you worked as an intern (college football) for a few years before being hired, so they know you are qualified. After two years at the company, it becomes obvious that you have far and away outperformed your contract because you have access to everyone's contract at every company in the industry.
Rather than reward you for the outstanding work the company decides to keep you on that terrible contract because its economic and because, of course, that is what huge corporations do. Did I mention this job is super dangerous and most employees don't make it more than a year?
Did I mention that this job is super dangerous and any workday could be your last? Now, you work for another year for the company expecting to be compensated correctly at the end of the year because you are an integral part of the company, but the company again uses the loophole that not only robs you of the financial security that you have earned through your service in the form of a long-term contract but also robs you of the opportunity to get paid at a different company. The company is literally holding you hostage because it is what is best for them. They could trade you or they could pay you, but instead, they choose to use this loophole for the company because that is what matters to the company, the money, not the employee.


Tech Summit is a conference held by Microsoft for developers and technical business decision makers that make use of Microsoft technologies. The conference covers a range of topics from Analytics and Machine Learning to Desktop, Compliance, Productivity and Adoption.
AddIn365 CTO, Wes Hackett, will lead two sessions at the event. The first session, 'SharePoint development in the Enterprise' will focus on how SharePoint can be extended to add the tools and customisations every team wants, from small branding tweaks all the way up through full collaboration applications. The second session, 'Build smarter apps with Office using the Microsoft graph' will demonstrate how to use Microsoft Graph to build contextual aware applications and integrate with user data.
AddIn365's invitation to speak follows AddIn365's extensive use of both the SharePoint Framework and Microsoft Graph as part of the AddIn Intranet and AddIn Work Hub products since their release in November 2015.


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Джозеф Уайт Фарнем (; 2 декабря 1884 — 2 июня 1931) — американский драматург и сценарист эпохи немого кино начала 1930-х годов. Один из основателей Академии кинематографических искусств и наук.

Биография
Его карьера в качестве сценариста немых фильмов началась в 1918 году — фильм «Однажды в каждом человеке». За свою карьеру в киноиндустрии он принимал участие в создании 90 фильмов и работал со многими известными режиссёрами немого кино и начала эры звукового кино.

4 мая 1927 Джозеф Фарнем был одним из 36 членов-учредителей Американской академии кинематографических искусств и наук (AMPAS), которые проводили церемонию Оскар 1929 года. Фарнем является единственным, кто получил «Оскар» за лучшие титры, потому что эта награда вручалась лишь один раз, на первой церемонии.

Джозеф Фарнем умер 2 июня 1931 от сердечного приступа в Лос-Анджелесе, штат Калифорния, и был похоронен на кладбище Форест-Лаун в Глендейле, штат Калифорния.

Фильмография
 1918 — Однажды в каждом человеке / Once to Every Man
 1920 — Чудо-человек / The Wonder Man
 1922 — Сельский ветреница / The Country Flapper
 1924 — Алчность / Greed
 1925 — Огни старого Бродвея / Lights of Old Broadway
 1925 — Девичьи посиделки / Pretty Ladies
 1925 — Большой парад / The Big Parade
 1925 — Его секретарь / His Secretary
 1926 — Чёрный дрозд / The Blackbird
 1926 — Монте Карло / Monte Carlo
 1926 — Изящный грешник / Exquisite Sinner
 1926 — Париж / Paris
 1926 — Дорога на Мандалай / The Road to Mandalay
 1926 — Идет улыбаясь / Exit Smiling
 1926 — В глубине сцены / Upstage
 1927 — На бульваре С / On Ze Boulevard
 1927 — Лондон после полуночи / London After Midnight
 1927 — Новобранцы / Rookies
 1927 — Красная мельница / The Red Mill
 1927 — Шоу / The Show
 1928 — Через Сингапур / Across to Singapore
 1928 — Смейся, клоун, смейся / Laugh, Clown, Laugh
 1928 — Четыре стены / Four Walls
 1928 — Кинооператор / The Cameraman
 1928 — Когда город спит / While the City Sleeps
 1928 — Запад Занзибара / West of Zanzibar
 1930 — Месяц Монтаны / Montana Moon
 1930 — Казённый дом / The Big House

Примечания

Ссылки

Лауреаты премии «Оскар»
Похороненные на кладбище Форест-Лаун

redpajama_set_name: RedPajamaWikipedia


NOTE: If upgrading with a configuration file (config.yaml) that has analyzer services enabled from version 0.2.2 or prior, but has no values set for 'endpoint_hostname', 'listen', and 'port', you will need to set these values for analyzer services to come up correctly in 0.2.X. The analyzer service ports do not need to be exposed for normal operation, unless prometheus metrics are enabled and/or other site-specific features are enabled that require analyzer service port access (e.g. for pinging the /heath route, etc.).
The latest release is 0.2.4, which adds support for the new features and capabilities in Anchore Engine 0.2.4.


The ATO want you to be better informed so you don't fall prey to tax scams.
They are reminding you how to recognise, report and protect yourself from identity theft and tax related scams.
They are also working closely with retailers and the police, issuing a variety of scam warning flyers to help educate and protect you.
Help spread the word by having a conversation with your employees, family and friends to raise awareness about tax scams and what to do if you suspect you're being scammed.
If you are ever concerned about correspondence you have received please contact our office to discuss (03) 5561 8618.


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This artist's works throw light on little-known female mythological characters
Published: Jul 12,201908:03 AM by Bhavana Akella
City-based Subirthana Maharaja's art is celebrating the stories of women from the Hindu epics, Ramayana and Mahabharata, who have not been written about much.
A few of the artist's works based on female mythological characters like Aranyani, Mandodari
When one tries to recall the women characters from Hindu mythological texts like Ramayana and Mahabharata, it is often only Sita and Draupadi that cross his or her mind. Hundreds of other prominent women from the texts have simply been forgotten over the years. In an attempt to highlight their bygone stories, Chennai-based artist Subirthana Maharaja has been dedicating a series of paintings to the little-known women from Hindu mythology. Female characters from the Vedic scriptures, including Aranyani (goddess of forests), Ushas (goddess of dawn), Urvashi (celestial maiden), Aditi (mother of the gods) and Saranya (goddess of dusk), are some of the subjects of her paintings.
"I grew up listening to Ramayana and Mahabharata from my mother, who narrated the epics as our bedtime stories. My childhood was also filled with visits to several temples across Tamil Nadu, and noticing the art and architecture in them. From a very young age, I also began painting, and over time was drawn towards representing mythological figures in my works," recalls 25-year-old Subirthana.
The artist says that even though female characters have played important roles in mythological texts, they don't remain in the memory of many as the male characters do. "As a woman, I feel that even though many women have been at the centre of stories from mythology, they have been largely overshadowed by male characters. Even though they would have played significant roles, they didn't receive enough attention for it. I wanted to tell the stories of these lesser known female characters, like the Panchakanyas (five virgins — Ahalya, Draupadi, Kunti, Tara and Mandodari)," she elaborates.
Subirthana has presented the artworks narrating the stories of these female mythological characters through 11 art shows in the city at various art galleries. "Each of the works requires a lot of reading of mythological texts as I try to portray not just their appearances, but also attempt to narrate their stories. It takes me about 5-6 months to read about each of the characters and learn about them in depth. Since we as a society still rely a lot on mythology, I was keen to depict these strong women," asserts Subirthana, who will be presenting her latest paintings from the series through an art exhibition by this year-end.
Pursuing a PhD in political science, art is a passion for the young painter. "I am planning to bring out a book with stories of these female characters. Even though I have an alternative professional career besides art, I have noticed that people in India aren't very perceptive to art and are not ready to spend their money on artworks. I'm holding art workshops and plan to host more so people can learn to appreciate art," she adds.


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Birthday Party Woes
kids, 6 Comments
happy birthday, randomly thinking
I'm feeling super guilty this year…. Porter turns 5 on Monday and we are NOT having a birthday party for him. We decided, instead, to take him to Great Wolf Lodge this coming Thursday-Saturday (we are meeting up with Lori, Don and the girls so he'll get to spend the weekend with family/cousins). I know he's going to be SO excited about Great Wolf Lodge but all he keeps talking about is a birthday party with friends from school! I feel terrible because 1) he's turning 5… which is kind of a big deal birthday to me and 2) he's in school all week so he has friends he is close to at school and he's also been invited to a few of their b-days as well.
I did make plans for Donnie and Aubri (and Lukas, possibly) to come over on Monday (his b-day… and no school b/c of President's Day!) to play. We're going to do a Spiderman Pinata, decorate cupcakes and let the kids play for a bit. He will wake up Monday morning to Birthday Balloons (this year… ALL green!) as goes our newest tradition. And after Ryan gets home I plan to wow him with a GREEN layered cake with GREEN frosting and a GREEN Lego Dump Truck on top, and GREEN plates, GREEN napkins and GREEN cups. Our presents to him are going to be roller blades and a "Surprise: Great Wolf Lodge!" present (a green striped towel, green scuba mask, new swim trunks and a brochure and pictures of Great Wolf Lodge).
In my head I'm thinking "I've done a good job… I've done TONS of things to make him feel special this birthday… I'm not failing because we're not having the normal party decked out with a cute theme." But I can't help but feel I'm letting him down because all he seems to wish for this year is a party with his friends! GRRRR.
So, what do you typically do for birthdays? Do you have a party with friends for your kids? Do you just have family over? Is it quiet and intimate with just immediate family that live in the house? What would you do if you were to replace a party with a trip to a fun place? Is 5 too young for them to realize the "swap"?
I would think he would be so excited about the GWL! Don't be so hard on yourself Nic! I remember those days when the 5's were 'the last year for the last big-hoorah' but, the big parties were so exhausting & it became more focused on celebrating the kids when we just celebrated their birthdays with them with a special event :)You are doing a special event for Porter with his family for his birthday & he will remember that!Great job Mom!!
We did a BIG 5th birthday for Lincoln this year and we were talking about how every year it feels like it should be bigger/better. But we decided that next year we'd let him pick 2-3 friends and do something special. It's such a hard call, but personally I think Porter got the best of all worlds. Celebration at school, one at home with friends and a whole bunch of green stuff AND GWL. Lincoln LOVES it there! Porter will think he's the luckiest guy ever. I think you did great!
This is the first year we did a birthday party with Nathan's entire school class invited and let's just say I wasn't a big fan. Too much chaos, too much $ spent on things that were 'disposable' and frankly, too many toys many of which he hasn't opened yet. We usually just do family and a few friends and that is what I plan on doing next year again. Nathan would pick GWL over a big party any day and I am sure Porter will love it too. Beware of MagicQuest (if you are going to the one in Traverse City).
you are totally being too hard on yourself. first, all of the things you have done sound AWESOME! he will love it. and if he were to ever ask why he didn't get to have friends over – tell him he gets to when he turns SIX! :)for us, tucker had his first 'friend' party last year with three of his friends (and their moms are my friends) over. this year, he's made a couple new friends in preschool and wants to invite them – he has been talking about his birthday for MONTHS. so, he'll have a kid party on the weekend and then on his actual birthday (monday) we'll have some family over. however, i'm only inviting five of his little friends over so … things don't get too crazy!by the way – tucker is obsessed with GREEN too! don't worry about it too much – sounds like his birthday week is going to be awesome!
beanski says:
I'm always wondering where to draw the line with birthday parties. Well, with spoiling my kids in general. I think he'll be happy with whatever you do for him, honestly, but I know how hard it is to feel like you should be doing more because you love him so much.Being a mom is hard.
Yeah. I am waaay late in reading this. But anyway.. I think you did an awesome job making Porter's birthday special. The most important things are not giving him a ton of presents or having a bazillion people over, though those certainly are nice too. The most important thing is that you make him feel loved, and by judging from all of his birthday pictures, you did just that! Happy (belated) Birthday, Porter!! 🙂
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Monday - Friday: 8 a.m. – 4:30 p.m.
Sentara Family & Internal Medicine Physicians is well-equipped to care for a wide variety of medical needs for all members of your family, from children to seniors. Our compassionate staff is dedicated to providing superior patient care through an integrated, coordinated and responsive approach to operations and services. We believe in partnering with our patients to proactively work together toward better health.
Sentara Family & Internal Medicine Physicians is located just off the Prince William Parkway in County Center.


text:
\section{pH-RL - An RL personalization architecture for health practice}

This section introduces our reinforcement learning personalization architecture for mobile applications in e-Health. We start by framing the problem definition. Then we introduce our framework for personalization with RL. Finally, we present our pH-RL framework for personalization in e-Health.

\subsection{The RL architecture for personalisation}

\begin{figure*}[]
\includegraphics[angle=0,origin=c,width=12cm, height=5.5cm, trim=0cm 0cm 0.5cm 3.0cm]{figures/pH-RL_architecture_v4.pdf}
\centering
\caption{pH-RL: A reinforcement learning personalization architecture for mobile applications in e-Health}
\label{overview_system}
\end{figure*}

Figure ~\ref{overview_system} shows the pH-RL framework for personalization of interventions in e-Health applications using RL. This section starts with a definition of the system's different components and the environment it is interacting with to learn personalized policies.

\subsubsection{Users of the mobile applications} Users are the people that utilize the e-Health application to get help achieving a specific health goal. These users own a smart device that can run the e-Health application. They install the e-Health applications and continue to sign-up to be able to use the application. We ask these users to provide information about their demographics, preferences, and goals to form the initial clusters. After the sign-up phase, the users start generating data used by the pH-RL framework to personalize the interventions further. The e-Health applications send these interventions to help the users achieve their goals.

\subsubsection{Smart device} This device can be any computer that can host applications and have an interface to interact with the users. Mobile phones and smartwatches are the most natural types of smart devices used to host e-Health applications. These devices contain sensors that allow the pH-RL framework to obtain potentially very granular data about the users' behavior continuously. We can use this information to infer contextual information about the users' state without having to ask them to provide this information at all times explicitly. Additionally, smart devices contain interfaces in the form of a screen or voice that allows the users to receive interventions from the e-Health applications and provide feedback to the application.

\subsubsection{State} A state contains information about the user at a certain point in time $t$. We capture this information through the e-Health application. To model a real-world problem as an RL problem, one has to design a Markovian state representation. In pH-RL, a state $s \in S$ is made up of features represented by the feature vector representation $\vec{\psi}(s) = \langle\psi_1(s),\psi_2(s),\ldots,\psi_n(s)\rangle^\top$. A feature can indicate the occurrence of a certain activity, the number of times a user performed a certain activity during a specified period, or any other feature that provides useful information about the user's behavior. In pH-RL, we perform binning to transform any continuous feature into a discrete representation.

\subsubsection{Interventions} E-Health applications running pH-RL are used by people to help them with a health-related goal. The application gathers data from the user's smart device and sends it to our pH-RL framework, which returns an action $a \in A$ presented to the user in the form of interventions. The most natural way of providing a user with an intervention is via notifications of mobile applications or smartwatches. The main goal in pH-RL is to provide users with interventions that are relevant at the right moment. This hypothesis is that personalized interventions will lead to a higher long-term adherence level to the user's goal.

\newpage
\subsubsection{Rewards} The pH-RL framework gathers data from the users through the e-Health application. Behavioral change data from the users about the intervention (either direct or indirect) is used as a feedback mechanism used by the RL algorithm in pH-RL as the reward signal to learn personalized policies. For instance, the direct reward can be users providing ratings about their moods a few times per day in a mental health application. Indirect rewards can be the amount of activity measured after receiving the intervention. Rewards play an essential role in pH-RL and should be defined carefully to represent the problem at hand well.

\subsection{Preliminaries and Problem Statement}

\subsubsection{Reinforcement Learning}
We model a real-world problem $M$ as a Markov Decision Process ($MDP$). Therefore, we consider the task $M$ to be Markovian and therefore it can be modelled using RL algorithms. In this section we borrow the RL terminology from \cite{el2018personalization}. We define $M$ to be $\langle S$, $A$, $T$, $R \rangle$ where $S$ is a finite state space, and $A$ the set of actions that can be selected at each time step $t$. There exists a probabilistic transition function $T$ :: $S \times A \times S \rightarrow$ [0,1] over the states in $S$. When at time $t$ in current state $s$ an action $a$ is selected, a transition is made to a next state $s' \in S$ at $t+1$. $R :: S \times A \rightarrow \mathbb{R}$ is the reward function and outputs a scalar $r = R(s, a)$ to each combination of state  $s \in S$ and action $a \in A$. The feature vector representation $\vec{\psi}(s) = \langle\psi_1(s),\psi_2(s),\ldots,\psi_n(s)\rangle^\top$ defines the features that form the states $s \in S$. Our aim when modelling task $M$ as an RL problem is to learn a policy $\pi$. With the policy $\pi :: S \rightarrow A$ we can determine which action $a \in A$ to take in a state $s \in S$ such that the long term expected cumulative reward is maximized. Selecting action $a = \pi(s)$ will result in a transition to state $s^\prime$. Here, a reward $r = R(s,a)$ will be obtained. Taking an action while in a state and transitioning to a new state forms an an experience $\langle s, a, r, s^\prime \rangle$. A trace is a sequence of experiences in a particular order. Denote a trace by $\zeta$ : $\langle s, a, r, s^\prime, a^\prime, r^\prime, s^{\prime\prime}, a^{\prime\prime}, r^{\prime\prime}, \ldots \rangle$. Multiple transitions over time result in multiple experiences. The combination of experiences over time form a data set $Z \in \langle \zeta_1,\ldots \zeta_k\rangle$.

Reinforcement learning aims at learning the best policy $\pi^*$ out of all possible policies $\Pi$ :: $S \times A \rightarrow$ [0,1]. $\pi^*$ selects actions with the aim of maximizing the the sum of future rewards (at any time $t$). We assign a value for taking action $a \in A$ in state $s$ of policy $\pi$ $\pi(s)=a$ as follows:

\vspace{-15pt}
\begin{equation}
	\label{eq:policyeval}
	Q^\pi(s,a) = E_\pi \large\{ \sum_{k=0}^{K} \gamma^k r^{t+k+1} | s^t=s, a^t=a \large\}
\end{equation}
\vspace{-5pt}

Here $\gamma$ is the discount factor that gives weights to future rewards. $s^t$ and $a^t$ define the states and actions at time $t$. Denote $Q(s,a)$ as the expected long-term value of state $s$ after taking action $a$. If we select the best action $a$ in each possible state $s \in S$, a policy can be derived from the $Q$-function, i.e.

\vspace{-7pt}

\begin{equation}
	\label{eq:argmaxQ}
	\pi^\prime(s) = \arg\max_{a \in A} Q^{\pi}(s,a), \ \forall s \in S
\end{equation}

\newpage
\subsubsection{Cluster-Based Policy Improvement}

One-fits-all approaches are based on the assumption that users belong to one group, and therefore, one policy is learned across all these users. This approach has been shown to perform sub-optimally in e-Health applications because people, in general, have different preferences and are characterized by non-identical transition and reward functions \cite{el2018personalization}.

In the pH-RL framework, we mitigate this issue and propose to group users with similar behavior using clustering techniques \cite{el2018personalization,el2019end}. Clustering algorithms such as K-medoids and K-means have been shown to perform well on similar problems in e-Health \cite{el2018personalization,el2019end,grua2018exploring}. We compare the behavioral traces of users consisting of states and rewards, and we use the Dynamic Time Warping (DTW) algorithm to calculate the distance between two users. Several other distance metrics, such as the Euclidean distance, have been explored in e-Health literature. Although metrics are considered valid approaches for calculating distances between two traces, DTW is more accurate because it measures the similarity between two different users' traces. It does so by finding the optimal match between two potentially similar traces that are out of phase where the Euclidean distance would have found these two traces to be very different. We define the traces of $u$ consisting of states and rewards as:
$\langle s_u, r, s_u^\prime, r^\prime, s_u^{\prime\prime}, r^{\prime\prime}, \ldots \rangle$.

Define the group of users to be targeted by our framework as $U$ and $\Sigma^U$ as all the traces generated by these users. The experiences (excluding the actions) generated by user $i$ during day $d$ are defined as $\Sigma^{\prime u{_{i,d}}}$. We calculated the similarity between two users $u_1$ and $u_2$ as:

\vspace{-15pt}

\begin{equation}
	\label{eq:dtw}
	S_{DTW}(u_1,u_2) = \sum_{d=0}^{D} dtw(\Sigma^{u{_{1,d}}},\Sigma^{u{_{2,d}}}).
\end{equation}

We apply a clustering method to obtain clusters $k$ of users based on their traces where $k$ and $\Sigma^U_1,\ldots,\Sigma^U_k$ is a partitioning of $\Sigma^U$, and let $U_1, \ldots U_k$ be the partitioning of $U$ \cite{el2018personalization}. In a one-fits-all approach, we would utilize all experiences of $U$ to learn one $Q$-function. In pH-RL, we learn a distinct $Q$-function $Q_{\Sigma^U_i}$ and policy $\pi_{\Sigma^U_i}$ for each user set $U_i$ based on all the traces in $\Sigma^U_i$. Note that these steps are done in addition to our previous setup, which allows for a comparison between a policy for $U$ and subgroup policies.

\begin{table}[ht]
\centering
\begin{tabular}{l l }
\hline\hline
Feature & Definition \\ [0.5ex]
\hline
Day Part & A numerical encoding for part of day (0: morning, 1: afternoon, and 2: evening).   \\
Number Rating & The cumulative number of ratings inputted by the user.  \\
Highest Rating & The highest rating inputted by a user during the current day.  \\
Lowest Rating & The lowest rating inputted by a user during the current day.  \\
Median Rating & The median rating inputted by a user during the current day.  \\
SD Rating &  The standard deviation of the ratings inputted by a user during the current day.\\
Number Low Rating &  The number of low (1 and 2) ratings inputted by a user during the current day.\\
Number Medium Rating &  The number of medium (3, 4, and 5) ratings inputted by a user during the current day.\\
Number High Rating &  The number of high (6 and 7) ratings inputted by a user during the current day.\\
Number message Received &  The number of messages received by a user during the current day.\\
Number Message Read &  The number of messages read by a user during the current day.\\
Read All Message &  Indicator if a user reads all messages during the current day.\\
[1ex]
\hline
\end{tabular}
\caption{State features, actions and reward definitions.}
\label{table:features}
\end{table}
\vspace{-4mm}
\subsection{Framework implementation and algorithm setup}
In this section, we discuss the framework implementation and our algorithmic setup. Furthermore, we discuss in detail our design choices for the state, actions, and rewards. Our proposed pH-RL architecture can be applied across many personalization tasks in mobile applications. In this section, we demonstrate an instance implementation of pH-RL for mental health using the MoodBuster platform.

\subsubsection{State}
We designed features (i.e. $\vec{\psi}(s_u)$) to represent the state of user. Table \ref{table:features} shows an overview of the features. These feature were designed to capture the behaviour of the user on the mobile application and their mood ratings.

\newpage
\subsubsection{Action}
We use four actions that the policy can select. Action $0$ represents: "send no message",  action $1$ represents sending an action of type: "encouraging", action $2$ represents sending an action of type: "informing", and action $3$ represents sending an action of type: "affirming". Once the action is selected, we further decide from which sub-group of actions to select based on the user's mood. All messages and categories, including the splits based on mood, can be found in the appendix. To make sure we do not send the same message multiple times during a day, we randomly select a message from the set of possible messages that were not previously selected during the same day.

\subsubsection{Reward}
Adherence can be measured by how often users are using the MoodBuster application. Therefore, the reward function is a combination of two components weighted equally. The first component measures the fraction of messages received during a day up until the current daypart. The second component measures the number of ratings inputted by a user during a day until the current day.

\subsubsection{Least Squares Policy Iteration (LSPI)}
We perform training using (batch) online learning with the LSPI algorithm because of its ability to generalize well on relatively small datasets. Every time the policies for the different clusters of users are updated with a new batch of data, we export a policy for inference of actions. We use the exact basis function transformation of our features by first binning each of the features into four bins, increasing our features by a factor of 4. We use a policy with the LSTDQ Solver, a discount factor of $0.95$, an exploration rate of $0.1$, the tie-breaking strategy first wins, max iterations of 25, and stopping criterion $\epsilon$ of $0.00001$. These hyper-parameters are based on various experiments from earlier work in this area \cite{el2018personalization,el2019end}.

\newpage
\subsubsection{Technical implementation of the pH-RL system}
We utilize Amazon Web Services to run our pH-RL system. Our setup consists of an S3 bucket to store RL experiences safely and securely. pH-RL is implemented as an open-source Python package \cite{ali_el_hassouni_2021_4628543}. The code is deployed on an AWS EC2 instance. The scripts for performing batch training (once a day), clustering (once during the entire experiment), inference, or sending a message (three times every day) are run with a time-based job scheduler (cronjob). We make secure connections with the restful API of MoodBuster to make read calls to retrieve data and post calls to send messages.

\section{Acknowledgements}
We want to thank Mobiquity Inc for providing the AWS instance to run the pH-RL implementation on.

\section{Appendix}

We use personalized motivational feedback messages to improve adherence to an online course for low mood. We define three groups of messages inspired by \cite{mol2018behind}.

\subsection{Encouraging}
\subsubsection{Positive neutral mood}
\begin{itemize}
  \item It seems like you're on the right track! Keep up the good work!
  \item Good to see that you are doing well. Good luck continuing Moodbuster Lite.
  \item You are making a lot of progress! You can be proud of yourself!
\end{itemize}

\subsubsection{Negative neutral mood}
\begin{itemize}
 \item It is good that you take part in Moodbuster Lite. You can commend yourself for that!
 \item Good that you are still rating your mood on a regular basis! Keep up the good work!
 \item It's great that you are making time for yourself to improve your mood!
\end{itemize}

\subsubsection{Mood unavailable}
\begin{itemize}
  \item It may sometimes be difficult to engage in a training like Moodbuster Lite, but you can do it!
  \item Good that you started with Moodbuster Lite this is already a first step.
  \item It may be difficult to always keep the training and mood ratings on mind, but it's great that you already started.
  \item Don't give up if you haven't rated your mood.
\end{itemize}

\subsection{Informing}
\begin{itemize}
  \item Don't forget to set a reminder in order to not forget about your scheduled pleasant activities.
  \item Do you know you can always review the material of sessions if you need it?
  \item In your calendar, you can see the activities which you planned in the past.
  \item It is good to track what pleasant activities you did.
  \item Do not forget to rate your mood three times per day.
  \item Did you forget what pleasant activities to do? You can always check your notes on the website.
  \item It is sometimes helpful to re-read the content of the training to refresh your knowledge.
  \item Reminders may help you to not forget about the pleasant activities.
  \item It's good to keep track of what pleasant activities you do and how your mood is.
\end{itemize}

\subsection{Affirming}
\subsubsection{Positive neutral mood}
\begin{itemize}
  \item Good to see that you are doing well.
  \item Even if you don't rate your mood at some point, don't worry, it may be hard to always think about it.
  \item I am happy to see that you feel well.
\end{itemize}

\subsubsection{Negative neutral mood}
\begin{itemize}
  \item It is very common to sometimes have low mood, so do not worry if that happens.
  \item It may be difficult to always keep the training and mood ratings on mind, but it is important for your well-being.
  \item Many people sometimes feel sad, this is nothing to worry about.
  \item It must be complicated to engage in this training, so it's completely fine if you sometimes feel that way.
  \item Struggling to find the time to do the scheduled pleasant activities is completely normal, so do not worry if it ever happens to you.
\end{itemize}

\subsubsection{Mood unavailable}
\begin{itemize}
  \item Don't get discouraged if you forget to rate your mood sometimes, it's normal.
  \item It is completely normal to sometimes feel demotivated.
  \item If you keep forgetting to do the pleasant activities? It will be ok! Don't give up!
  \item Do you often feel tired? That can easily happen when doing a training like this! \item It can be hard to think constantly about rating your mood.
  \item Struggling to find the time to do the scheduled pleasant activities is completely normal, so do not worry if it ever happens to you.
  \item It must be complicated to engage in this training, so it's completely fine if you sometimes feel that way.
\end{itemize}

\section{Discussion}
This paper presented a general reinforcement learning architecture for personalization in e-Health mobile applications (pH-RL). This architecture utilizes traces of states and rewards obtained from users to form clusters with k-means and the Dynamic Time Warping distance function. We built and integrated our architecture with the MoodBuster application for mood support. We ran an experiment with human participants for three weeks. Our results show that the pH-RL architecture can learn policies that consistently converge and provide the users with the right actions. Based on the observed reward values, we can conclude that pH-RL leads to increased adherence to the MoodBuster application. We ran our experiment for a period of two weeks. This resulted in a significant amount of user experiences. Given the number of users, we opted for training one policy across these users. In future work, it would be of great importance to experiment with a larger number of participants to find large enough clusters. Furthermore, the experiment can be ran for a longer period of time to evaluate the behaviour of the architecture during a longer period of time. Finally, including user specific features that describe their characteristics and preferences to the state space could result in better performance of the policies.

\newpage
\section{Real-world performance evaluation}

To evaluate the proposed pH-RL framework, we conducted a real-world experiment with a mobile application for mental health. This experiment's main aim is to demonstrate the feasibility of applying the pH-RL framework for personalization in e-Health applications and provide easy-to-follow guidelines for successful integration and deployment of RL models in real-world applications. We integrated our pH-RL framework into the MoodBuster platform to provide personalized messages and answer our first research question.

\subsection{Personalized motivational feedback messages}
We designed a real-world experiment to improve adherence to an online course for low mood with personalized motivational feedback messages. The pH-RL framework is used to select the most appropriate messages to send to a user to maximize adherence to the course. We consider three categories of messages based on existing research \cite{schwebel2018using}. Informing messages aim at providing informative messages to help the user understand the MoodBuster platform and the online course. Encouraging messages try to encourage users to perform a specific action. Affirming messages offer emotional support or encouragement. We further split the encouraging and affirming categories based on the user's mood into the following three groups: positive, neutral mood, negative, neutral mood, and mood unavailable.

\subsection{MoodBuster}
MoodBuster is a research platform that has been developed to treat psychological complaints online. Treatments on MoodBuster take place in connection with research projects. The platform gives access to two types of applications: a web application for patients, a web portal for practitioners, and a mobile application that can measure information such as the Ecological Momentary Assessment (mood and user state measurements) \cite{mikus2018predicting,provoost2020improving}. The platform can be used to treat patients as part of a guided online treatment or as a prevention and self-help tool. Furthermore, MoodBuster can be used as part of guided online treatments or blended treatment (face-to-face therapy combined with online treatment. In this work, we use the cognitive behavioral therapy treatment for depression on the MoodBuster platform. This treatment consists of 6 online modules containing readable and watchable material to guide the user through the module to perform exercises and assignments. The cognitive-behavioral therapy treatment for depression helps the user better understand depression, stimulate positive thinking, stimulate behavioral change through enjoyable activities and physical exercise.

\subsection{Participants}
We conducted this experiment with 30 participants from our research departments at the Vrije Universiteit Amsterdam, students, and friends. All these participants were not selected based on having depression. Furthermore, we informed these users that we performed this study to test our pH-RL framework for personalization. Henceforth, they should use this application with the intent that it is part of a test case.

\subsection{Setup of runs}
We train our policies using batch learning. We set the intervention moments when the application sends messages at 10:00, 14:00, and 21:00. We perform random exploration during the first week of the experiment by sending three random interventions a day. After week one, we perform clustering to find our clusters and train our policies using all available data from the start of the experiment. Phase 2 of the experiment runs for an additional two weeks using actions from the learned policies. We update our policies in batch at the end of every day at 23:59.

\section{Introduction}

Reinforcement learning (RL) has seen tremendous successes in recent years, principally due to the many breakthroughs made in deep learning (DL) \cite{suttonbarto2nd,mnih2013playing,silver2016mastering,komorowski2018artificial,vinyals2019grandmaster}. The field has witnessed these breakthroughs in high-dimensional control tasks, e.g., complex games Atari and Go and continuous control tasks such as MuJoCo, and openAI gym \cite{DBLP:journals/corr/BrockmanCPSSTZ16}. In many of these tasks e.g., Atari and board games such as Go, Chess, and Shogi, superhuman performance was achieved \cite{silver2016mastering,mnih2013playing,mnih2015humanlevel}. We can attribute these successes to the rise of deep reinforcement learning (DRL) fueled by novel algorithms such as deep Q-network (DQN), the availability of powerful computing hardware, and the nature of the problems at hand that allows one to obtain large samples from the task environment and to perform exploration as one wishes.

Many practical limitations arise in traditional domains such as healthcare, making these benefits listed above fade away \cite{dulacarnold2019challenges,el2019end,el2018personalization,den2020reinforcement}. Such limitations are the inaccessibility to large samples of data, the unavailability of environments to train and evaluate algorithms in, the limitations on the data caused by privacy laws, and safety concerns (e.g., unsafe actions and exploration), explainability, and legal responsibility \cite{dulacarnold2019challenges,den2020reinforcement}. As a consequence, the applicability of DRL in many practical tasks remains limited. In many practical tasks where RL has been shown to perform well such as advertisement campaign optimisation, there is ample data available, interactions with users are not costly and safety does not play a big role. In healthcare tasks, all these factors play an equally important role. Therefore, there is a need for structural solutions through standardized frameworks and architectures to overcome the abovementioned obstacles and challenges. Consequently, we pose the following research questions and try to answer them with a real-life experiment: \textbf{1) What are the requirements for integrating RL into mobile applications for e-Health? and 2) Can we learn policies quickly that provide personalized interventions?}

This paper presents a general RL architecture (pH-RL) for personalization with the goal of bringing RL to health practice. We propose an RL architecture that allows for adding a personalisation component to applications in healthcare such as mobile applications for mental health. pH-RL allows for different levels of personalisation, namely: pooled (one-fits-all approach), grouped (cluster-level personalisation) and separate (hyper-personalisation on user level). Furthermore, pH-RL allows for online and offline (batch) learning. We describe a step-by-step guideline for the successful deployment of RL policies in a mobile application.

We implement our open-source RL framework and integrate it with the MoodBuster mobile application for mental health to provide messages to increase daily adherence to the online therapeutic modules. We employ a default control policy approach based on prior knowledge coupled with random exploration in pH-RL. Next, we apply clustering techniques on the traces of states and users' rewards to find an appropriate segmentation of the experiences. Then, we apply online batch RL coupled with exploration driven by the learned policies on each cluster of users. We then perform a comprehensive study with human participants over a sustained period. Our experimental results show that the developed policies learn to select appropriate actions consistently using only a few days' worth of data. Finally, we empirically demonstrate the stability of the learned policies during the study.

\section{Related work}
RL as a solution architecture for real-world problems has seen a significant increase in the last few years ranging from games to advertising and healthcare. This learning paradigm has seen applications in various areas. In this related work section, we discuss RL's real-world applications and architectures in general and Health applications and architectures for personalization in specific.

\newpage
\textbf{Reinforcement Learning Applications} Much effort is put into the use of RL for various applications. A successful application of RL in the last decade was using deep RL to play Atari games \cite{mnih2013playing}. This approach relies on Q-learning with convolutional neural networks to successfully learn control policies for playing Atari games using low-level high-dimensional sensory data as input. These policies surpassed human-level performance in several cases. Also related, \cite{silver2016mastering} combined deep learning with RL to develop policy and value networks that play Go at a superhuman level. These networks learn from extensive amounts of self-play made possible by a well-defined environment with comprehesive rules. In a more recent application, RL has proven to be an effective solution to a real-world autonomous control problem involving the navigation of super-pressure balloon in the stratosphere \cite{bellemare2020autonomous}. This problem is characterized by its complexity, forecast errors, sparse wind measurements, and the need for real-time decision-making. This work uses data augmentation and a self-correcting design to tackle RL issues, usually by imperfect data. As mentioned in Section 1, all these examples do not suffer from the many limitations one encounters with problems in healthcare.

\textbf{Reinforcement Learning for Clinical Applications} A literature review has shown that the number of applications of RL has been increasing \cite{den2020reinforcement}. Applications in healthcare range from treating patients with Sepsis at the Intensive Care Unit to sending personalized messages in e-Health mobile applications.
There is strong evidence that suggests that current practices at the ICU are not optimal while the best treatment strategy remains unknown. (Komorowski et al.) used RL to develop policies that are on average more reliable than human clinicians \cite{komorowski2018artificial}. This approach provides individualized treatment decisions that are interpretable by clinicians. Similarly, \cite{saria2018individualized} used RL to develop policies for individualized treatment strategies to correct hypotension in Sepsis. More recent work showed that these developed policies for optimizing hemodynamic treatment for critically ill patients with Sepsis are transferable across different patient populations \cite{roggeveen2020transatlantic}. Furthermore, this work proposes an in-depth inspection approach for clinical interpretability. In a strictly regulated area such as healthcare, these example are considered very innovative. However, structural solutions are needed. Our pH-RL standardized architecture and the corresponding generic framework allow us to bring online and offline RL for personalization to health practice.

\textbf{Reinforcement Learning for Personalization in e-Health Applications} More and more real-world applications using RL for personalization in e-Health are found in literature \cite{den2020reinforcement}. Work by \cite{el2018personalization} focuses on developing RL policies coupled with clustering techniques for personalizing health interventions. They show that clustering using traces of states and reward and developing policies based on these clusters leads to improved personalization levels while speeding-up the learning time of the approach. In a later work, \cite{el2019end} demonstrates that this approach leads to improved personalization levels when applied on state representations consisting of raw sensor data obtained from mobile apps. Similarly, k-means clustering and RL were combined to develop policies across similar users for the purpose of learning better policies \cite{zhu2018group}. Clustering methods were also used to effectively learn personalized RL policies in health and wellbeing \cite{grua2018exploring}.

\newpage

\textbf{Personalization Architectures for e-Health Mobile Applications} A wealth of mobile apps exist that support people in their daily lives. We can use these apps for mental coaching, health interventions, fitness apps, and various other purposes. Although these applications can take various types of information into account, such as location, and historical behavior, they still rely on rule-based approaches and do not achieve high effectiveness and efficaciousness of treatment. Reinforcement learning-driven personalization has proven to be a practical approach for many health settings, including e-health and m-health \cite{den2020reinforcement}.
Furthermore, clinical support systems could rely on the same techniques to achieve high effectiveness and officiousness of treatments. Research shows a lack of publications that propose RL architectures for personalization. \cite{grua2020reference} presents a reference architecture that enables self-adaptation of mobile apps for e-Health. Although this architecture proposed a non-rule-based approach for self-adaptation, it does not specify machine learning techniques to achieve personalization. This architecture relies on MAPE loops that operate at different levels of granularity meant for different purposes. (Hoffman et al.) proposed a research framework for distributed RL (Acme) \cite{hoffman2020acme}. This framework aims at simplifying the process of developing RL algorithms in academia and industry.

\section{Results}

In this section, we present the results related to the experiment laid out in section 4. We experimented with 27 participants to test our integration of the pH-RL framework for personalization in e-Health with the MoodBuster mobile application for depression. We instructed the participants to use and interact with the application to generate data
We do not assume any of the participants to have any symptoms of depression, and therefore we do not expect the app to lead to any significant changes in the participants' mood. Our main aim is to present, implement and test the pH-RL architecture in an e-Health setting and demonstrate that we can learn policies quickly that provide personalized interventions.

\subsubsection{Inactive participants}
During the experiment, a fraction of all participants were entirely dormant and showed no activity. Inactive users reflect actual real-life usage stats of mobile applications. The experiences of these users are still included in the data set provided used to develop the models. During the whole experiment, 6 participants showed barely any activity and were excluded (entered a rating or read a message at least once for maximum of two days). Furthermore, an additional eight users became inactive during weeks 2 and 3 of the experiment. During the data analysis, we reported different results that include and exclude these users. In this experiment users becoming inactive is not the results of the interaction with the application.

\subsection{Exploration phase}
Developing RL policies requires a data set consisting of many experiences. At the start of our experiment, we lack such data. As a solution, we implemented a default policy to be used during the experiment's initial phase for one week. During this phase, random exploration was applied, resulting in the data presented in figure 2. From the figure on the right, we can see that around 70\% of the messages were read throughout the first week. Furthermore, we observed that around 75\% of the users rated their mood at least once per day during the first week with a drop on Friday and Saturday.

\begin{figure}[H]
    \centering
    \begin{adjustbox}{max width=\linewidth}
    \begin{tabular}{c c c c}
    \includegraphics[width=\textwidth]{figures/Week_1_action_distribution_random_exploration.pdf}&
    \includegraphics[width=\textwidth]{figures/Week_1_number_ratings.pdf}&
    \includegraphics[width=\textwidth]{figures/Week_1_read_all_messages.pdf}&

    \end{tabular}
    \end{adjustbox}
    \caption{Week 1 action distributions (left), number of ratings per day (middle) and fraction of messages read (right).}
\label{figure3}
\end{figure}

\subsection{Learning phase}
After phase 1, we trained the first policy and started using it. We update the policy with new data at the end of every day. Here we do include data from all 8 participants that became inactive after week 1. Using the obtained results we answer the second research question in this section. Figure 3 (middle) shows how often users rated their mood per day. On average, at least 45\% of the participants rated their mood at least once per day.  Finally, we see from figure 3 (right) that around 50\% of the users read all their messages per day. Figure 3 (left) shows the distribution of actions for all dayparts. We can see that the policy started favoring "Informing" messages around 50\% of the time. As we move forward in time, the policy starts favoring encouraging messages more often. This is in line with what we hypothesized based on existing research \cite{bailoni2016healthy,schwebel2018using}. On day 7 of the second week, the policy decides not to send a message in around 40\% of the cases.  Overall the policy favors the actions in the following order: "encouraging", "informing", "affirming", and "sending no message".

\begin{figure}[H]
    \centering
    \begin{adjustbox}{max width=\linewidth}
    \begin{tabular}{c c c c}
    \includegraphics[width=\textwidth]{figures/week_2_3_action_dist_all.pdf}&
    \includegraphics[width=\textwidth]{figures/week_2_3_number_ratings.pdf}&
    \includegraphics[width=\textwidth]{figures/week_2_3_read_all_messages.pdf}&

    \end{tabular}
    \end{adjustbox}
    \caption{Week 2 and 3 action distributions (left), number of ratings per day (middle) and fraction of messages read (right).}
\label{figure3}
\end{figure}

To further understand how the policy is personalizing towards specific attributes in the state space, we created the visualization that shows the action distributions of the policies per daypart in figure 4. In figure 4 (left), we can see that the policy started favoring "informing" messages in the morning but quickly changed to a strategy with "encouraging" messages. In the afternoon, we see similar behavior with more "affirming" messages. On day 14 of the experiment, the policy does not send any messages in the afternoon. Finally, the evening strategy is to mostly send "encouraging" messages during the first two days and then switch to a strategy dominated by "informing" messages in the evening. These findings are in line with existing research \cite{schwebel2018using}.

\begin{table}
\centering
\small
\begin{tabular}{c|c|c|c|c|c}
\hline
\multicolumn{1}{c}{} & \multicolumn{1}{c}{\textbf{Week 1} } & \multicolumn{1}{c}{\textbf{Week 2} } & \multicolumn{1}{c}{\textbf{Week 2*} } & \multicolumn{1}{c}{\textbf{Week 3} } & \textbf{Week 3*}       \\
\hline
\textbf{All day parts}     & 0.98 ± 0.79                          & 0.83 ± 1.02                          & \textbf{1.20 ± 1.06}                  & 0.77 ± 0.98                          & 1.16 ± 1.01            \\
\hline
\textbf{Morning}     & 0.66 ± 0.57                          & 0.62 ± 0.78                          & \textbf{0.87 ± 0.81}                  & 0.54 ± 0.64                          & 0.81 ± 0.65            \\
\hline
\textbf{Afternoon}   & 1.1 ± 0.80                           & 0.87 ± 1.0                           & 0.87 ± 0.81                           & 0.83 ± 1.01                          & \textbf{1.25 ± 1.03}   \\
\hline
\textbf{Evening}     & 1.2 ± 0.87                           & 1.00 ± 1.19                          & \textbf{1.46 ± 1.22}                  & 0.93 ± 1.16                          & 1.40 ± 1.19            \\
\hline
\end{tabular}

\caption{The average reward per user per daypart for weeks 1, 2, and 3. Week 2* and Week 3* are with inactive users excluded. }
\label{table:results}
\end{table}

\begin{figure}[H]
    \centering
    \begin{adjustbox}{max width=\linewidth}
    \begin{tabular}{c c c c}
    \includegraphics[width=\textwidth]{figures/week_2_3_action_dist_morning.pdf}&
    \includegraphics[width=\textwidth]{figures/week_2_3_action_dist_afternoon.pdf}&
    \includegraphics[width=\textwidth]{figures/week_2_3_action_dist_evening.pdf}&

    \end{tabular}
    \end{adjustbox}
    \caption{Week 2 and 3 action distributions morning (left), afternoon (middle) and evening (right).}
\label{figure3}
\end{figure}

\newpage

\subsubsection{Rewards}
Figure 5 and Table \ref{table:results} show the observed rewards during weeks 1, 2, and 3. Furthermore, we also consider statistics and make comparisons after excluding all inactive users after week 1. We can see that the average reward per day part per user drops as time proceeds. One of the main reasons that a certain number of users stopped using the app after week 1 was because some were instructed to do so. Furthermore, other users stopped using the app once they felt they have performed enough testing. When we exclude users that dropped out, the average reward increases again. Furthermore, we observe that users obtain higher rewards consistently in the evening. Our hypothesis has to do with the fact that people have more time to check their phones during these moments of the day. From Figure 5, we also see that the maximum reward per user per day part increased after week 1.

\begin{figure}[H]
    \centering
    \begin{adjustbox}{max width=\linewidth}
    \begin{tabular}{c c c c}
    \includegraphics[width=\textwidth]{figures/Week_1_rewards_boxplot.pdf}&
    \includegraphics[width=\textwidth]{figures/week_2_3_rewards_boxplot.pdf}&
    \includegraphics[width=\textwidth]{figures/week_2_3_rewards_boxplot_active_users.pdf}
    \end{tabular}
    \end{adjustbox}
    \caption{The box plots of the average daily reward per day part for weeks 1, 2 and 3.}
\label{figure3}
\end{figure}

redpajama_set_name: RedPajamaArXiv


text:
Gorjani este o comună în cantonul Osijek-Baranja, Croația, având o populație de  de locuitori (2011).

Demografie

Conform recensământului din 2011, comuna Gorjani avea  de locuitori. Din punct de vedere etnic, majoritatea locuitorilor (%) erau croați. Apartenența etnică nu este cunoscută în cazul a % din locuitori. Din punct de vedere confesional, majoritatea locuitorilor (%) erau catolici, cu o minoritate de ortodocși (%). Pentru % din locuitori nu este cunoscută apartenența confesională.

Note

Comune din cantonul Osijek-Baranja


text:
Pipeline rupture sends 40,000 litres of oil into Alberta creek
The incident happened Aug. 15 on a Bonterra Energy Corp. line 14 kilometres south of Drayton Valley, Alta.
DRAYTON VALLEY, Alta. — The Alberta Energy Regulator says a pipeline has spilled 40,000 litres of crude oil into a creek.
The regulator says on its website that the incident happened Aug. 15 on a Bonterra Energy Corp. line 14 kilometres south of Drayton Valley, Alta.
It says the line was shut in and depressurized, and that containment booms were installed.
The AER says no impacts to wildlife were reported.
Calgary-based Bonterra says in a news release that it began investigating a problem with the pipeline at 8 a.m. Thursday, and at 1 p.m. it discovered a rupture that was leaking into Washout Creek.
It says it is removing the oil with booms and vacuums, and is mitigating the effect on wildlife by setting up barriers as well as visual deterrents that include having people present.
Washout Creek flows into the North Saskatchewan River, which is the source of Edmonton's water supply, and Bonterra says it has placed additional booms where the creek meets the river as an extra precaution.
The company says cleanup and recovery is expected to continue for the next three weeks.
 PEI to withdraw as intervener in Saskatchewan's carbon tax reference  Scheer blasts Trudeau on day of damning SNC-Lavalin report


Many students do not have a clue that they need health insurance. Some of them know how important this is but still prefer to rely on the college or university clinic when they are ill.
Being a student doesn't mean you are not likely to get seriously ill or injured and for that reason a proper medical insurance plan is a must. Of course one of the reasons why students do not have medical insurance is that they cannot afford the typical health insurance policies. However there are other options available.
There are medical insurance providers that have special plans for students only. Moreover some colleges and universities have health insurance programs that feature many providers. All a student should do is to check what the options are and choose a plan that suits his or hers needs.
This is the second part of the problem. How could I know what is best when it comes to medical insurance? This is a though questions for those who have been working for many years and have significant experience with different heath insurance policies, let alone for e student newbie. The good thing is that you can ask questions to determine which policy will work for you.
The first thing to ask is what your health insurance plan will cover. Some student insurance policies do not cover sport injuries for example. Check if there is a restriction on the physicians you will have to use and if you will need an approval from the insurance company before you go for a check up. Of course ask about the expenses.
Since many students travel a lot, the insurance conditions for traveling should also be checked carefully. This is in two directions if you are traveling abroad and if you are traveling within the country. In many cases if you will be traveling abroad you will need an additional health insurance.
Check how long your health insurance will last. Some student health insurance plans cover only under graduates. What this means is that when you graduate you will have to seek another individual health insurance policy. So if you are close to graduation you can start looking for a good individual policy instead of going for a student medical insurance plan. Another option is to get a short term student insurance.
Finally you can check if you cannot get medical care under your parents' health insurance plan. Of course some policies would not allow you to stay covered if you are over a specific age, but other policies allow this. If you are going to become a student soon, discuss this issue with your parents and plan ahead.


text:
Q: customize View class adding an imageView dynamically I have inherited a View class from an example zbar source code, but I need to add an ImageView to this View class, I don't have any xml layout file for this view, so how I can add this ImageView dynamically from code?
public class AndroidBarcodeView extends View
{
    static Context mContext;
    public AndroidBarcodeView(Context context) {
        super(context);
        this.mContext = context;
    }

@Override
protected void onDraw(Canvas canvas) {
    super.onDraw(canvas);
     AndroidBarcodeTestActivite myActivity = (AndroidBarcodeTestActivite) getContext();
        String id=myActivity.getString();

    try {

} catch (Exception e) {
        e.printStackTrace();
    }
}
}

A: You cannot add a View to a View. You should extend from a RelativeLayout, LinearLayout, FrameLayout or anything that extends from ViewGroup. There you can call addView(), where you can pass in an ImageView.
Regarding lay-outs, you might want to read this page from the docs: http://developer.android.com/guide/topics/ui/declaring-layout.html. That also explains what all those *Layout classes do.
That looks something like this (I copied your code and adapted it):
public class AndroidBarcodeView extends FrameLayout
{
    // static Context mContext;
    // Please do not do this with a Context, as it might leak and you'll get memory problems
    // Also, the Context is available via getContext()

    private ImageView img;

    public AndroidBarcodeView(Context context) {
        super(context);

        ImageView img = new ImageView(context);
        addView(img); // This adds the ImageView to the ViewGroup
    }

    @Override
    protected void onDraw(Canvas canvas) {
        super.onDraw(canvas);

        AndroidBarcodeTestActivite myActivity = (AndroidBarcodeTestActivite) getContext();
        String id=myActivity.getString();

        try {

        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

redpajama_set_name: RedPajamaStackExchange


CHURCH ISN'T CANCELLED…BECAUSE YOU ARE THE CHURCH!
We're bummed that we don't get to gather for services this weekend but that doesn't mean church won't happen. You, the people of MCC, are what make up the Church. And in the midst of this crazy winter weather, we've been given a unique opportunity. It's an opportunity to worship God in our living rooms and around our kitchen tables. It's an opportunity to serve our neighbors by helping shovel their driveway. It's an opportunity to pray for those who are working to clear roads and restore power lines.
We want to encourage you to take time this Sunday to be with the Lord. We've provided some resources below for you and your family to do just that. You'll find an abbreviated video message from Aaron, links to give online and submit prayer requests, tools for elementary through high school students, and more.
We may be snowed in but we don't have to miss out!
It's one thing to believe that there's no limit to what God can do. It's another thing entirely to believe that there's no limit to what God can do in and through YOU.
No matter what life throws at you—with God, you can experience LIMITLESS ENDURANCE!
Sing a few songs of worship. If you can sing to yourself in the shower at home, you can sing to the Lord in your living room! Click here for a playlist of worship songs on Youtube or, if someone in your family plays a guitar, have them get it out and lead you in a few songs. Or, just sing a cappella to the Lord. However you do it, make a joyful noise to the Lord!
Take communion. You may not have any of those fancy trays with pre-cut Communion wafers and mini juice cups in your kitchen cabinets but a few saltine crackers broken up on a paper plate and some grape juice in a few solo cups works just as well. The point is to be reminded by the symbols, whatever they are, that Jesus died for you. Take some time to reflect on that sacrifice and thank God for it!
Read Scripture. Open up the YouVersion Bible app on your phone or crack open your real-life paper and ink Bibles and read this week's main text—Hebrews 12:1-3 (NLT). Here are a few questions you can reflect on individually or discuss as a family: 1) The "cloud of witnesses" mentioned in verse 1 is a reference to the great examples of faithful people discussed in chapter 11. Who are some people in your life that have been great examples of faith to you? 2) What are some "weights" that have slowed you down in the past (or are slowing you down right now) from doing what God has called you to do? 3) What does "the race God has set before you" look like for you right now? What is He calling you to pursue? 4) What does "keeping our eyes on Jesus" look like in everyday life and how does that help us run with endurance?
Pray. God meets us right where we're at. Whether you're in the worship center at MCC or at your dining room table, it makes no difference. He invites us to make our needs/praises/hurts/questions known to Him. Take some time to do that during your time of worship at home.
The ministry of MCC continues in good weather and bad. Thank you for partnering with us financially to carry out our vision of developing lifelong followers of Jesus!
Do you have what it takes? Probably not, and that's Okay.
Sometimes just being a human can make us feel inadequate. Whatever it is, all of us have this thing—something about us that keeps us from feeling confident.
Moses had that one thing he couldn't quite get over about himself. So when God, through a burning bush, told Moses that He was sending him to deliver the Hebrew people from the Egyptians, Moses didn't think he had what it took.
But here's the truth—the thing you think is keeping you from being qualified for the job may be the very thing God can use anyway to accomplish what He wants.
this month's life app is self-control!
In week three, we discover more about self-control in Proverbs 12:18. Solomon talks about the power of our words. We want kids to learn that when they choose the right words in a difficult situation they can actually bring healing to the situation rather than destruction. Bottom Line: Choose your words carefully. We want kids to think about the words they use throughout different situations, because our words can help point others to Jesus!
One of the best ways to stay up-to-date with what's happening at MCC is to sign up for (AND READ!) our weekly e-newsletter. Click here to check out this week's edition and sign up at the bottom of this page to have this resource delivered to your inbox weekly!


text:
You are here: Home Page > Arts & Humanities > History > Intellectual History > Chosen Peoples
360 Pages | 12 halftones & 6 maps
Chosen Peoples
Sacred Sources of National Identity
Anthony D. Smith
Anthony Smith explores a phenomenon which has taken on increasing importance in today's society
Author is an undisputed expert in his field
Presents a controversial case for reappraising the way we view national identity and nationalist movements
'Unto thy seed I have given this land.' From the moment of God's covenant with Abraham in the Old Testament, the idea that a people are chosen by God has had a central role in shaping national identity.
Chosen Peoples argues powerfully that sacred belief remains central to national identity, even in an increasingly secular, globalized modern world. In this important new study, Anthony D. Smith goes in search of the deep Judeo-Christian roots of the many manifestations of national identity.
This rich and timely contribution to current debates about nationalism explains the complex historical reasons behind often violent modern conflicts around issues of land, culture, religion, and politics. Tracing the development of individual nations over many centuries, it offers fascinating insights into the religious and cultural foundations of countries such as Great Britain, the United States, Israel, France, and Germany. The argument draws on a wide range of examples from historic landscapes in Ireland, Switzerland and Egypt, myths of Arthurian Britain, Holy Russia, and Byzantium, through memories of a 'Golden Age', to the modern commemoration of the 'Glorious Dead', and of victims of war.
1. Nationalism and Religion
2. The Nation as a Sacred Communion
3. Election and Covenant
4. Peoples of the Covenant
5. Missionary Peoples
6. Sacred Homelands
7. Ethno history and the Golden Age
8. Nationalism and Golden Ages
9. The Glorious Dead
Anthony D. Smith is Professor of Ethnicity and Nationalism at the London School of Economics, vice-president of the Association for the Study of Ethnicity and Nationalism. He is the author of ublications include Nationalism(1994) and Ethnicity (1996) (both co-edited with John Hutchinson) from the Oxford Readers series.
"This is a big book: not in size, but in ideas. Anthony D. Smith reads history well and widely, reflects on it respectfully, and then produces a series of conclusions much bigger than most historians would dare to entertain...This sort of study is apt to give hard work and common sense a good name."--American Historical Review
The Nation Made Real
Gandhian Nonviolent Struggle and Untouchability in South India
Mary Elizabeth King
Caretaking Democratization
Renaud Egreteau
The Oxford Handbook of Religion and Violence
Mark Juergensmeyer, Margo Kitts, and Michael Jerryson
Myths and Memories of the Nation
Harmful and Undesirable
Guenter Lewy
Approaches to Politics
Pierre Elliott Trudeau, Ivon Owen, and Jacques Hebert
G.A. Bremner and Jonathan Conlin
Aristocracy: A Very Short Introduction
Religion and the Philosophy of Life
Gavin Flood
The World Come of Age
Lilian Calles Barger
Manu Bhagavan
Holy Ignorance
Roy Olivier
Thomas Wylton
Lauge O. Nielsen, Cecilia Trifogli, and Gail Trimble
Arts & Humanities > History > Intellectual History
Social Sciences > Politics > Political Theory
Arts & Humanities > Religion > Religious Studies > Comparative Religion


Bake Sale Deadline Extended to April 1!
You are here: Home / News / Bake Sale Deadline Extended to April 1!
You can now order your items through April 1, 2019. Click here to fill out your order form and get more information.


text:
Treaty Chiefs Join to Oppose Alberta Sovereignty Act and Saskatchewan First Act
(ANNews) –The Confederacy of Treaty Six First Nations and the G4 in Treaty 7 Territory have joined Vice Chief Aly Bear of the Federation of Sovereign Indigenous Nations to put forward an emergency resolution at the Special Chiefs Assembly in Ottawa. The resolution rejects the Alberta Sovereignty Within a United Canada Act and the Saskatchewan First Act.
After working with lawyers and policy experts, concerns about both Acts have spurred the Treaty Chiefs to put forward an emergency resolution at the Assembly of First Nations. These Acts function as a mechanism to challenge any federal law or policies the Provincial government deem constitutional or harmful and is a blatant disregard of the rule of law.
"This isn't a First Nation issue – this impacts everyone in Canada" says Chief Tony Alexis of Alexis Nakota Sioux First Nation. "If this law goes forward in Alberta, it will set a dangerous precedent – that laws can be made behind closed doors. What would keep other provinces from following suit? We have seen similar attempts in Quebec and Saskatchewan and have very good reason to be deeply concerned when three provinces are trying to extend their jurisdiction."
"We will not give up our Treaty rights to our land, resources, and water," says Stoney Nakoda Tsuut'ina Tribal Council representative and Bearspaw First Nation Chief Darcy Dixon. "We have never relinquished, ceded, nor surrendered the rights to our natural resources. Through Treaty, we agreed to share the land, not give exclusive jurisdiction to the Government of Alberta or the Government of Saskatchewan. We will not allow these provinces to assert their control over Treaty lands."
The Chiefs emphasize that "both of these Acts are without rigour and alarmingly open for interpretation-their uncertainty may have harmful, permanent and widespread impacts. There has been no consultation or dialog with First Nations Peoples to discuss these Acts, their details and implications. This lack of transparency runs against the ideals of respectful relations and Reconciliation."
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The exotic flavors of southeast Asia and the tropical paradise of Bali come together in this very Texas rendition of shrimp and noodles. It's like making the ultimate street food in your own kitchen.
Pan-fry in olive oil or grill the shrimp just until pink, seasoning with lemon pepper, crushed red pepper and garlic powder.
Coat generously with about ½ the Bali BBQ Sauce and remove from heat.
In a large pan, skillet or wok, heat another 2 tablespoons of olive oil and stir-fry the onion, red bell pepper, carrots, broccoli florets and baby corn until lightly caramelized. Remove from the pan.
Combine the green onions with the beaten eggs and pour into the pan, scrambling the eggs until cooked. Chop lightly with the spatula while cooking.
Return the caramelized vegetables along with the cooking noodles, stirring to heat through.
Pour in the remaining Bali BBQ, lime juice and fish sauce. Bubble over medium-high heat for a moment until slightly thickened.
Cover the bottom of a serving platter with noodles and vegetables.
Top with shrimp and garnish with cilantro.


Marketing agencies that demonstrate value as delivered to their clients will see significantly more growth than those that simply execute campaigns.
Want to hear a pretty frightening statistic? Less than five-percent of some of the marketing entities we have made contact with are in a position to track true marketing ROI (Return on Investment) for their own clients – in other words, the return on each incremental dollar spent in marketing "vehicle A" versus "B" versus "C." If you represent a marketing entity and are reading this, you first need to ask yourself how you define "value": Is it something you believe can be measured? What are your clients' products and services actually worth to their customers? Remarkably, very few suppliers in business markets are able to answer those questions and yet the ability to pinpoint the value of a product or service for one's customer has never been more important.
But demonstrating value can be a challenge for agencies for a multitude of reasons: Marketing companies take advantage of many different online tools, and pulling reports from each tool and compiling results is a painstakingly time-consuming process. Further, using disparate online tools means they don't communicate with one another, and so it ultimately becomes tough to paint a clear picture of overall campaign results; a good example of this would be an agency that's promoting a new product using social media, email campaigns, video marketing and more, and studying which advertising mediums are paving the way toward leads, conversions and sales.
Demonstrating value is also a challenge to marketing agencies because such agencies typically report once a month due to the labor involved in creating the reports; reporting in real-time with weekly and/or monthly summaries significantly strengthens the agency's relationship with the client.
Now that we understand the challenges, what is the solution? Integrated marketing automation platforms – such as the type offered by ActiveDEMAND – can automate reporting, but more significantly will calculate the return on marketing campaigns based on the goals an agency sets. These goals can be anything from new introductions or the number of White Paper downloads to the number of event registrations, purchases or coupon downloads.
Still, the point of this particular article isn't to sell the merits of using an integrated marketing automation platform – we want to express the importance of demonstrating value as delivered to clients. The bottom line is that agencies that want to grow their existing retainer business with less effort need to start demonstrating value delivered, not just execute a campaign.


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We are currently looking to recruit enthusiastic, highly motivated band 5 Children's trained nurses who can deliver exceptional levels of care alongside a commitment to enhancing your personal and professional development.
You will have excellent organisational, communication and team working skills and the ability to build strong working relationships with children and their families. We provide extensive staff support and value staff for the individual qualities that they can bring to our service. We understand that changing jobs or embarking on your career can be daunting, but we will support you all the way.
This is an exciting time to help shape the vision of children's services within a top teaching hospital.
We are happy to accept newly qualified nurses.
Nicholls Ward – 19 beds with a focus on Paediatric Surgery and Neurosciences.
Frederick Hewitt Ward – 17 beds with a focus on Paediatric Medicine, Gastroenterology, Respiratory and Hematology.


text:
Webinar Series: Making Communications Accessible for Remote Audiences
In this time of social distancing, critical activities of daily living have gone virtual for many – we shop, bank, work, attend classes, and get.
Marketing In Accessibility Best Practices, Accessibility Best Practices (New), Captioning (New), Electronic Documents, Electronic Documents (New), Image Accessibility, Web Accessibility 101 (New), Webinar (New), Webinars
Scalable Vector Graphics (SVG) Accessibility Basics Webinar Q&A
Last week, Level Access Accessibility Consultant Brian McNeilly presented a webinar on Accessibility Basics for Scalable Vector Graphics (SVG). We had such a great turnout.
Kristin Heineman In Image Accessibility, Webinars
Tagging Complex Tables
Complex Tables are to be avoided at all cost, as I stated in yesterday's post, The Trouble with Tables: A Brief Introduction. The reason is that.
Gershon Blackmore In Assistive Technology, Image Accessibility, JAWS
Thoughts on the HTML 5 Working Draft
The last call for comments on the working draft of the HTML 5 specification has been made and comments are due by today. Over the.
Jonathan Avila In ARIA, Image Accessibility
CSS Background Images and Accessibility
There have been a spattering of articles discussing background images and their accessibility issues. There does not seem to be a clear solution for all.
Jonathan Avila In Accessibility Best Practices, CSS (Cascading Style Sheets), CSS (New), Image Accessibility, WCAG
Classifying Images for More Accurate Automated Results
Introduction In order to provide a higher level of accuracy to the automated results generated by InFocus, SSB Bart Group needed a way to classify images.
Timothy Stephen Springer In Accessibility Best Practices, Image Accessibility
On the Accessibility of Links
There are many in the usability community who regard accessibility as part of usability. I believe this to be a misunderstanding of accessibility. At its.
Timothy Stephen Springer In Accessibility Best Practices, Image Accessibility, Links


text:
A Venezuela competiu na 10º edição dos Jogos Pan-Americanos, realizados na cidade de Indianápolis, nos Estados Unidos. Conquistou 26 medalhas nesta edição.

Ver também
 Venezuela nos Jogos Olímpicos
 Venezuela nos Jogos Pan-Americanos

Países nos Jogos Pan-Americanos de 1987
1987
Desporto na Venezuela em 1987