text:
Current Students website
Email web access
iExeter (students)
Current staff website
iExeter (staff)
Finance Helpdesk
Coming to campus - info for staff and students
Wellbeing services for students
Wellbeing services for staff
Choose Exeter
Ask a current student
Primary PGCE
Secondary PGCE
About research at Exeter
Research and Innovation blog
Research in the...
College of Engineering, Mathematics and Physical Sciences
College of Social Sciences and International Studies
College of Medicine and Health
We are a globally recognised centre of research excellence, with expertise, facilities and people who can make a real difference to your business
Global Exeter
Pre-University Summer School
Activities between the University of British Columbia and Exeter include a joint research symposium focused on Community, Culture, Creativity, and Wellbeing held at Exeter in May 2018 and a faculty-led, co-funded initiatives in Sport, Exercise and Health Sciences, Climate Change and Digital Humanities.
University of South Florida and Exeter have entered into a 5 year student exchange between the two Universities. They launched the University of Exeter / University of South Florida Research Catalyst Fund to provide grants to support joint research initiatives between the two institutions.
The University of Exeter and Tsinghua University have launched a jointly-awarded PhD degree programme in climate and environmental sciences which supports six students to be co-supervised between Tsinghua's Department of Earth System Science and Colleges at Exeter that conduct research on earth systems and environmental sciences. Read more.
Hong Kong University of Science and Technology (HKUST)
A partnership between Exeter and HKUST will enable students embarking on Engineering or Computer Science undergraduate degrees at HKUST to finish their degree at Exeter, before spending a fifth year at Exeter's Law School, completing either an LLM (Master of Laws) or a Juris Doctor (JD). Read more.
NTU and Exeter are working in partnership to deliver six split-site Biomedical PhD studentships. If successful, you will benefit from expert supervision from researchers in both institutions and have the opportunity to research and live in two great locations, for up to eighteen months in each.
To celebrate, and further extend, the strong relationship of staff within the University of Exeter and the University of Geneva, the two universities have launched a new seed fund to support developing research links.
The University of Exeter and The University of Queensland have partnered to establish the QUEX Institute, a new multi-million pound partnership designed to bolster their joint global research impact. Read more.
Hong Kong University of Science and Technology
Campuses and visitors
Eat and shop
Comedy and academia collide in a special Arts and Culture online event
BA Art History & Visual Culture
Course menu
Course variants
Art History & Visual Culture
Art History & Visual Culture BA
Web: Enquire online
Phone: 0300 555 6060 (UK callers)
+44 (0)1392 723044 (EU/International callers)
A levels: AAB - ABB
IB: 34/665 - 32/655
BTEC: DDD - DDM
View full entry requirements
Streatham Campus
With Study Abroad
With Employment Experience
With Employment Experience Abroad
Develop essential tools to interpret works of arts (including architecture and design) as well as images, objects and practices.
Study traditional fine art and contemporary visual forms including painting, sculpture, illustration, architecture, film, video, performance and digital art.
Learn to interpret visual images to enable your understanding of societies.
Art, history and culture embedded on campus with the Exeter Northcott Theatre, Bill Douglas Cinema Musuem, Lemon Grove and Great Hall as music venues and student theatre Roborough Studios.
Open days and visiting us
Apply via UCAS
Internationally recognised fine art, heritage and film collections on site, including The Bill Douglas Cinema Museum
Opportunities to study 'in the field' at galleries and museums
Top 10 for AHVC in The Complete University Guide 2021
Opportunities for Study Abroad and Employment Experience in the UK or Abroad
Having studied Art History at A-Level, I came to my degree expecting a very traditional course, focusing on old masters, and European works.
However, the breadth of specialism amongst the lecturers is phenomenal. The variety of module choices, despite being challenging, is something to be welcomed and contrasts with the more conventional course I had originally expected. The size of the course has actually been one of the most enjoyable elements of the degree as it enables you to get to know everybody and fosters a great environment that makes you feel at ease very soon after starting at university.
Read more from Alicia
Required grades
Required subjects
AAB - ABB n/a
34/665 - 32/655 n/a
DDD - DDM n/a
C or 4 English Language
Contextual Offer
A-Level: BBB
IB: 30
BTEC: DDM
Specific requirements must still be achieved, therefore where a grade A is required, offers will be ABC or ACC. Find out more about contextual offers.
Other UK, EU and International equivalences
View UK, EU and International equivalences
NB General Studies is not included in any offer.
Placement year (if taken)
Typically, any placement year will take place in Year 3. If you are not taking a placement year please see the Final Year modules for year 3.
BA Art History & Visual Culture with Study Abroad
UCAS code: VW32
Our four-year 'with Study Abroad' degree, offers you the possibility of spending your third year abroad, studying with one of our many partner universities.
Living and studying in a different country is an exciting experience that broadens your academic and cultural horizons, as well as giving you the opportunity to widen your circle of friends. Students who have studied abroad demonstrate initiative, independence, motivation and, depending on where they stay, may also have gained a working knowledge of another language – all key qualities that employers are looking for in today's competitive employment environment.
Where can I Study Abroad?
We have partnership arrangements with many prestigious institutions across the globe. Exactly where you can apply to study will depend on the subjects you are studying at Exeter. For a full list please visit the Study Abroad website.
Does it count towards my degree?
Credit for academic work during your year abroad is arranged by agreement between the University of Exeter and the host institution. These marks are then translated back into your degree at Exeter. If you are Studying Abroad for a semester or full year, your time abroad will count toward your final degree. Please refer to your College Study Abroad co-ordinator for further details.
How does it affect my tuition fee and funding?
For the year that you spend studying abroad you will pay a significantly reduced tuition fee to Exeter, but nothing to your host university – for more information visit our fees pages. If you were previously eligible, you will continue to receive a maintenance loan whilst on your Study Abroad year.
BA Art History & Visual Culture with Employment Experience
Our four-year 'with Employment Experience' degree, offers you the possibility of spending your third year carrying out a graduate-level work placement or placements within the UK as part of your degree.
Why choose to include Employment Experience?
Undertaking graduate-level work during your degree unlocks a world of experience that allows you to develop essential employability and interpersonal skills that relate to your degree and future career. A work placement will dramatically boost your confidence, enhance your CV and develop graduate level skills and competencies that employers are looking for.
Where will I do my work placement?
The sector you choose to work within is very much your choice as you will be responsible for finding and organising your placement. We will provide plenty of guidance and support to help you find a placement, including a three-day employability workshop programme that will prepare you to research and apply for placements. Ultimately, the university will give final approval to your placement to make sure you have a valuable experience.
How does it affect my tuition fees and funding?
For your 'Year In Industry' you will pay a significantly reduced tuition fee to Exeter – for more information visit our fees pages. If you were previously eligible, you will continue to receive a maintenance loan whilst on your year of work placement/s.
Is the work placement paid?
You will be paid at least the National Minimum Wage and if aged over 25 you would receive the National Living Wage.
BA Art History & Visual Culture with Employment Experience Abroad
Our four-year 'with Employment Experience Abroad' degree, offers you the possibility of spending your third year abroad, carrying out a graduate-level work placement or placements as part of your degree.
Why choose to include Employment Experience Abroad?
Spending up to a year living and working in a different country is an exciting experience that broadens your academic and cultural horizons, as well as giving you the opportunity to widen your circle of friends. By carrying out a graduate-level work placement or placements abroad you can demonstrate to employers your adaptability, cultural awareness, independence and resourcefulness and, depending on where you stay, may also have gained a working knowledge of another language.
The sector and country you choose to work within is very much your choice as you will be responsible for finding and organising your placement. We will provide plenty of guidance and support to help you find a placement, including a three-day employability workshop programme that will prepare you to research and apply for placements. Ultimately, the university will give final approval to your placement to make sure you have a valuable experience.
How does it affect my tuition fee?
Is the placement paid?
You will be paid in accordance with the rules of the country you work in and there may be visa restrictions or requirements which you need to consider when applying.
Tuition fees for 2021 entry
UK students: £9,250 per year
International students: £19,500 per year
The University of Exeter is offering scholarships to the value of over £4 million for students starting with us in September 2021. Details of scholarships, including our Global Excellence scholarships for international fee paying students, can be found on our dedicated funding page.
Find out more about tuition fees and funding
Select course variant:
How will I learn?
You will be taught by internationally respected staff, who are leading researchers in their specialisms. We use a wide variety of techniques and approaches including seminars, lectures, study groups and web-based learning, as well as valuable field study. We integrate the latest approaches with traditional learning and teaching to give you a varied and challenging programme. As you study the compulsory modules, you will learn through individual practical and project work, teamwork and a research project, all of which are designed to help you develop key skills for success throughout your degree and into your future career.
You will be assessed in a variety of ways but primarily through exams and coursework. Coursework includes essays, a dissertation and presentation work. The ratio of formal exams to coursework is on average 40:60 (depending on your choice of modules). Your first year doesn't count towards your final degree classification, but you do have to pass it in order to progress. In order to be eligible for 'with Study Abroad' programmes, you will need to attain an average of 60 per cent or more in your first year. The assessments in the second year, year abroad (if applicable) and final year will contribute to your final degree classification.
In your first year, you will receive a minimum of 10 hours of contact with academic staff per week. You will also be expected to attend other activities such as study groups, workshop activities and film screenings. Your total workload should average about 40 hours per week during term time. The exact amount of time spent working independently varies from module to module.
Research-inspired teaching
Teaching that is inspired by research ensures lectures are up-to-date and relevant: you will benefit from access to the latest thinking, equipment and resources. All options are taught by staff with expertise including art history, architecture, film, photography, visual media, art and technology, curation and exhibition, mixed and virtual realities, performance art and visual culture in the UK and abroad.
All students have access to a personal tutor who is available for advice and support throughout your studies. There are also several services on campus where you can get advice and information, including the Students' Guild Advice Unit.
Employer-valued skills this course develops
Studying Art History & Visual Culture equips students with a broad range of highly desirable transferable skills in analysis, critique, research and theoretical and practical creativity. These will prepare you to enter a wide variety of professions. You will also develop specialist knowledge of everything from traditional art forms such as architecture and sculpture, to today's visual practices such as film, video, performance and digital art. You will benefit from productive engagement with collections, institutions and art groups in the area, in tandem with the wider arts community in the South West and beyond.
The Art History & Visual Culture programme prepares its graduates to undertake postgraduate study or training in areas such as education, arts management and journalism, amongst others. Recent graduates have gone on to study:
MA Publishing
MA Multimedia Broadcast Journalism
MA Creative Advertising
CFA (Chartered Financial Analyst)
MA Fine and Decorative Arts
MA Marketing & International Management
Graduate Diploma in Law
MA Stage and Event Management
Career support and pathways
We have a dedicated, award-winning Careers Service, ensuring you have access to careers advisors, mentors and the tools you need to succeed in finding employment in your chosen field on graduation. We offer the Exeter Award and the Exeter Leaders Award which include employability-related workshops, skills events, volunteering and employment which will contribute to your career decision-making skills and success in the employment market. For further information about our Careers Service please visit www.exeter.ac.uk/careers.
Our graduates compete very successfully in the employment market, with many employers targeting the University when recruiting new graduates.
Art History & Visual Culture graduates will be able to succeed in a range of sectors, including:
Art History & Visual Culture and Classical Studies BA
Art History & Visual Culture and English BA
Art History & Visual Culture and Film & Television Studies BA
Art History & Visual Culture and History BA
Art History & Visual Culture and Drama BA
Art History & Visual Culture and Modern Languages BA
Liberal Arts BA
Flexible Combined Honours BA/BSc (Exeter)
View all Art History & Visual Culture courses
Why Exeter?
Fantastic facilities, outstanding teaching, world-leading research and exceptional graduate employability are just a few of the reasons we think you'll love it here. Find out what our students say about choosing Exeter.
Studying at our campuses in Devon or Cornwall means you'll have some of the most beautiful natural scenery in the UK on your doorstep. Find out about student life at Exeter and the huge range of sports, clubs and societies on offer.
One University, two locations
We have around 20,000 students across our three campuses in Devon and Cornwall which means we offer the academic excellence and facilities you'd expect of a major university but also a friendly and welcoming atmosphere.
We warmly welcome international students and work hard to ensure you have the support you need to make the most of your time in the UK. More than 5,000 students from 140 countries contribute to our thriving student community.
iExeter
Find us Contact
St Luke's Campus
Penryn Campus
Truro Campus
Streatham Campus in Exeter
The majority of students are based at our Streatham Campus in Exeter. The campus is one of the most beautiful in the country and offers a unique environment in which to study, with lakes, parkland, woodland and gardens as well as modern and historical buildings.
Find out more about Streatham Campus.
St Luke's Campus in Exeter
Located on the eastern edge of the city centre, St Luke's is home to Sport and Health Sciences, the Medical School, the Academy of Nursing, the Department of Allied Health Professions, and PGCE students.
Find out more about St Luke's Campus.
Penryn Campus near Falmouth, Cornwall
Our Penryn Campus is located near Falmouth in Cornwall. It is consistently ranked highly for satisfaction: students report having a highly personal experience that is intellectually stretching but great fun, providing plenty of opportunities to quickly get to know everyone.
Find out more about Penryn Campus.
Module details


text:
Wayne Barnett BUTLER COUNTY SHERIFF'S OFFICE
Court docs detail claims against Hamilton man accused of sending sexual material to minor
Lauren Pack, Staff Writer
BUTLER COUNTY —
A Hamilton man is facing a felony charge for allegedly sending and receiving sexual material to and from a juvenile, according to the Butler County Sheriff's Office.
The sheriff's office was contacted on Oct. 23 by an Arizona law enforcement agency concerning an investigation into social media interaction between a 12-year-old girl and Wayne Barnett, 60, according to court documents obtained by the Journal-News.
MORE: Court docs detail 'drug ring' case against Middltown man killed in Cincinnati this week
Between January 1 and August 25, the girl sent several sexually explicit videos to Barnett through Snapchat, the complaint says.
The Arizona detective was able to confirm Barnett received the videos through a search warrant of Barnett's Snapchat account, according to the court documents.
Butler County sheriff's detectives said Barnett initiated the contact with the girl and manipulated her to send the sexual videos and photos.
MORE: Dayton officer shooting: 4 men arrested had drug, violence charges
Barnett, of Fairview Avenue, was interviewed Monday and cooperated with the investigation, according to the sheriff's office. Barnett said he sent the material to an underage child. Barnett was booked into the Butler County Jail and charged with pandering sexually oriented matter involving a minor, a second-degree felony.
Barnett was arraigned Wednesday morning in Hamilton Municipal Court, where bond was set at $50,000. A preliminary hearing has been set for Nov. 13.
Thank you for reading the Journal-News and for supporting local journalism. Subscribers: log in for access to your daily ePaper and premium newsletters.
Thank you for supporting in-depth local journalism with your subscription to the Journal-News. Get more news when you want it with email newsletters just for subscribers. Sign up here.

redpajama_set_name: RedPajamaCommonCrawl


BRAD PAISLEY - "Drive Of Shame"
Drive Of Shame by Brad Paisley (featuring Mick Jagger) is a country song from the album Love and War and was released in the spring of 2017.
The official music video for Drive Of Shame premiered on YouTube at the end of April 2017. Listen to Brad Paisley's song featuring Mick Jagger above.
Brad Paisley's Drive Of Shame lyrics were written by Brad Paisley, Mick Jagger and Matt Clifford.


Thanks everyone for your confidence and support. Need I remind you that you are unique, just like everyone else? Remember, of all the people I know, you are one of them.
Vitale, Mike DS "20110411 Commend Letter"
Many teams go through a life-cycle of stages, identified by Bruce Tuckman as: forming, storming, norming, performing and adjourning.
Henry Ford, American Industrialist, agreed we begin, progress, and succeed by coming together, staying together, and working together.
It was Henry David Thoreau, American Essayist and Poet, believed there are thousands hacking at the branches to one who strikes at the root. Yet no man or woman is an island.
"To exist just for yourself is meaningless," proclaimed Denis Waitley, American Author and Peak Performance Expert, "You can achieve satisfaction when you feel related to some greater purpose in life, something greater than yourself."


text:
Watcher (Watcher #1) by AJ Eversley (Narrated by Chelsea Stephens & Steve Campbell)
YA Dystopian Done Right
Watcher is the first book in The Watcher Series, and it starts off a series that I really enjoyed beginning to end.
Now, I'm not always a fan of when an author includes an "it's like this thing you like meets this thing you like". It always feels like they are trying to link themselves to something more famous even if their books have nothing to do with it (or very little). Fortunately, for Watcher – Eversley was dead on. It was really like Divergent meets Terminator. It had the dystopian groups (the Watchers reminded me a lot of Dauntless). And then there were humanoid robots and Carbons who were out to destroy the remaining humans in pretty much any way they could. I'd say that comparing it to those two films (and books) was pretty dead on.
Sawyer was a slight cookie cutter heroine but it didn't take away from the story at all. She was kind of reckless – putting herself and others in danger when she didn't really need to, wasn't really a rule follower, etc. While these are all the norm for books like this. Sawyer was given a little more to like. The guy she meets later in the story is also a little typical (sort of bad boy but also good for her). In my eyes, in YA books like this, it's as much about the world and the interaction with it as it is the characters. I also assume it's really hard to make a standout and unique character when you have the Katniss and Tris's of the YA Dystopian world without making them a little similar.
I was a big fan of Divergent and I thought that Eversley created a world that was more interesting while also making the story stand on its own. Sawyer was one of those should be forgetful characters but something stuck out to me about her. I think the flashbacks and the "it's been 10 years" parts of the book (taking her from 8 to 18) made me feel for her on a deeper level. Realizing that she was basically raised to be a Watcher and nothing else (no teenager moments, boys, etc).
I've never read anything by Eversley before – but I will be continuing the series. I think that it translated really well to audio especially with Chelsea Stephens at the helm. She was able to give a voice to Sawyer – allowing her to jump off the page. The addition of Steven Campbell was also needed in my opinion. For Chelsea to read his parts (even if they were in the third person) would/could have been jarring. But having a male narrate the major male character (especially while getting a glimpse into his mind) really stood out to me. I think it was needed and allowed Chelsea to focus on moving Sawyer forward without much distraction or interruption.
Overall, a YA Dystopian novel that was a genuine surprise to me. So many books try to be the next YA hit and fail on so many levels – but Eversley including the typical YA fare with brooding teenagers, first loves, and suspense. But also threw in a lot of heart, some betrayal (I guess this is pretty typical too), and freakin' robots!
If you liked Divergent or Terminator – give this one a try. If you like YA Dystopian Trilogies – I couldn't imagine this letting you down.
2019 Independent Audiobook Award winner for Young Adult
Hunt the bots. Avoid the carbons. Stay alive.
Sawyer's life is simple, if not easy. Her home might be in ruins, but she can protect what's left of it.
A city destroyed by the bots – that's home. Humans have been hunted, but Sawyer is one of a few who have turned the tables. She's a watcher. Taking out the bots one by one. Protecting what's left. It's the carbons she needs to be wary of. Faster and stronger than humans, they're dangerous. But Sawyer's smarter. That will keep her alive.
At least, that's what she believed. But the carbons are evolving, and their plans are ever more cunning. Suddenly her friends are dying. Safety can no longer be taken for granted – not even for the watchers. At least she has Kenzie. Love is a strong word, but whatever they have, it's what keeps her going.
Embarking on a desperate mission to save humanity from extinction, Sawyer discovers that the line between friend and foe is no longer easily drawn when one of her own betrays them.
And when a horrific plan comes to fruition, Sawyer will have a choice: who to save, and who to let go.
Divergent meets Terminator in this epic YA trilogy. Get it today.
Watcher by AJ Eversley
Narrator: Chelsea Stephens, Steve Campbell
Series: The Watcher Series #1
Published by Self Published on October 6th 2018
Genres: Dystopian, Science Fiction
Buy from Audible
Also by this author: Carbon, Savior
Also in this series: Carbon, Savior
By AJ Eversley
Audiobook: The Culling by Ramona Finn (N
Spark: (Resistance Series #0.5) by Tracy
Audiobook: Kaijunaut by Doug Goodman (Na
Ice by Kevin Tinto (Narrated by Pamela A
Carbon (Watcher #2) by AJ Eversley (Narr
Savior (Watcher #3) by AJ Eversley (Narr
Posted in Audiobook • No Comments on Watcher (Watcher #1) by AJ Eversley (Narrated by Chelsea Stephens & Steve Campbell)
Daughters of Forgotten Light by Sean Grigsby (Narrated by Morgan Hallett) »
« October 13th New And Notable Audiobooks on Brian's Book Blog


1. Register your interest using this form.
2. Gain exemption approval from the Perth Football League General Manager.
Failure to gain exemption approval before you play will breach the Perth Football League amateur status, and the player and club will be subject to penalties including but not limited to fines, de-registration, loss of premiership points and club grade relegation.
Refer to Perth Football League By-law Part 1, 1.13.
To register or withdraw your interest, CLICK HERE.


text:
Home » News and Features
HDR Throws Away the SDR Technical Rulebook
By Tony Orme,
Monday, 24th April 2017
Example of an HDR image. (Credit: Andrés Nieto Porras)
Thames Valley's late March event in Reading, "Dolby PQ Demystified" was a sell-out as industry expert Prinyar Boon astounded attendees with his knowledge of high dynamic range (HDR).
Firmly putting historic technical myths and legends to rest, Boon, Dolby-Europe director and broadcast systems architect, methodically dissected the assumptions engineers have been making for the past 50 years.
Modern video monitors with 19 stops of range now outclass SDR systems, requiring SDR to be re-defined. HDR, wide gamut, colour space, display mapping and bit depths have all been revisited with Dolby one of the industry leaders in the HDR revolution.
Not all the dynamic range has to be used all the time with the look and feel of an image being firmly in the control of the creatives, for both live as well as post-produced content, in a way that is compatible with existing working practices.
Images with more detail and amazing highlights add vivid, wider colours giving producers new artistic freedom and greater control. No longer constrained by the 100cd/m2 (nits) reference monitor of the past, peak whites can drive compatible televisions to more than 4000nits, giving incredible clarity to the image. The concept of a 100% white reference limit has been abandoned and similar scenes can be represented in many ways, depending on the artistic needs of the director.
With Boon describing in detail the new artistic freedoms available, it was clear that HDR technology demands responsible use in the same way that audio does today. The system can be abused as transients of high-contrast are open to unscrupulous use without constraint.
Following Boon's presentation, the floor was opened to questions, with some delegates pointing out that we need to resolve the issue of UHD lenses before we are too concerned with the HDR revolution. The debate goes on.
Fabio Murra on mobile TV technology
Thames Valley NAB 2017 Review
Event Report: What exactly is Ultra HD?


text:
Nonprofit Sponsorship Opportunities
Around Clarke: Clarke Monthly Calendar
Cauldron, Currents, and Kites
November 20, 2012 /in Banner /by vaobserver
by David Lillard
They started out simply to make a movie. They may have invented a new way to use film, video, and the internet to help conservation groups collaborate and spread their messages wider than ever before.
The Downstream Project got rolling when two Clarke County residents, longtime conservation advocate George Ohrstrom and documentary cameraman George Patterson, teamed up to produce a film about the Shenandoah River. Nearly two years later, Shenandoah: Voices of the River, a 52-minute film exploring the history, ecology, and beauty of the river, debuted to wide acclaim in Charlottesville, Va. Then, like many a documentary film team, they started looking for an audience.
"We did a road show," said Ohrstrom. They showed the film wherever they could, knowing from the response they got that they'd done more than make a movie. They had told the story of the Shenandoah Valley and made a compelling case for the river's restoration and protection.
Still, they had to ask: Now what?
Patterson wanted to make more movies. Ohrstrom was game, but felt they needed a different model—both in terms of how the films were financed and in their sense of purpose.
That's when Bill Howard came aboard. He'd been doing some computer consulting and social media for Downstream to support the film launch. Howard had been a church administrator and run a Mac-based computer consulting firm; in him the two Georges found someone who could help shape the Downstream idea into something purposeful and sustainable.
"Before Bill got involved, we saw ourselves as film producers only," said Patterson. Howard suggested that Downstream could be something bigger, that the group could combine its core production strengths with media and marketing to have an impact on conservation throughout the Chesapeake Bay. Howard pushed the idea of getting Downstream into the current—by providing services to partners.
Ohrstrom was intrigued. He had worked with many conservation organizations over the years, as president of Friends of the Shenandoah River and service on the board of the Piedmont Environmental Council and the Virginia League of Conservation voters. He's also current chair of the Clarke County Planning Commission and vice chair of the county's easement authority. Through this experience, along with his business background, he saw a common challenge among a lot of conservation groups.
"A lot of them do similar work, but they talk about it differently," he said, "and they don't always have the in-house people or expertise to form a message and disseminate it."
So in 2008 Downstream did what many businesses and nonprofits do. They did some strategic planning, looking at the needs of the conservation community throughout the Bay region.
What emerged was a unique nonprofit that grew from the experiences of all three: Patterson's decades of production experience as a television documentary cameraman, Howard's organizational skills and knowledge of internet platforms, and Ohrstrom's understanding of the issues surrounding conservation and his easy going grasp of the intangibles that shape people's attitudes about the environment.
The core of Downstream's approach is what they call OmniMedia. Think of the OmniMedia as an integrated approach to communications. Instead of hiring a web designer to get a site up, a film crew to do a video on a project, a PR firm for press and social media, now conservation groups can turn to Downstream for the package.
At first glance, it looks like a collaborative one-stop shop. But it's more than that. It's a way of building a project from the ground up, knowing how it will be distributed and who is going to watch it.
By way of contrast, go back to the film Shenandoah. Like many documentary filmmakers, Downstream made a film that was roughly an hour long, then figured out how to distribute it, then tried to promote the heck out of it. Doing this utilized many of the same specialties as the OmniMedia approach, but those skills were deployed one at a time—not quite haphazardly, but not necessarily strategically, either.
And now the OmniMedia approach: Take a look at Downstream's effort with Gaining Ground, a partnership of Commonwealth agriculture agencies led by the Natural Resources Conservation Service, or NRCS. They wanted to reach crop producers and livestock farmers throughout the Commonwealth with educational materials on no-till farming and rotational grazing.
In the old days, extension agents and others spent a lot more time handing out leaflets and talking to small groups of farmers than they did offering technical assistance.
As recently as a few years ago, they might have made a video, then spent tens of thousands of dollars mailing it out. Downstream helped change that approach.
Here's how it gets built with the OmniMedia approach:
1. The most effective way to get farmers interested in these new techniques is to hear about them from other farmers—even better, to visit them on their own farms.
2. The best way to hear from other farmers is through a film shot on their own farms.
3. The most cost-effective way to produce and distribute a film throughout the Commonwealth is through short films on the web—which also allows farmers to see it on their own schedule.
4. To get the greatest reach and best stories, involve all the relevant ag-related partners from the beginning: agencies like Virginia Cooperative Extension, Virginia No-Till Alliance, Virginia Forage & Grassland Council, Shenandoah Resource Conservation & Development Council, and New River-Highlands Resource Conservation & Development Council.
5. And, if you're going to build a whole campaign based on web videos, you need to build a website that's designed to show video—and do it with an outfit that's set up with the servers and technology to service it.
Downstream hosts theirs on their own proprietary server, with websites built on a WordPress platform they created especially for these types of projects.
At first, says Howard, NRCS was hesitant to build a campaign around social networking. They wondered whether farmers would take to this idea. So Downstream also produced several thousand DVDs, "to see which platform is more effective," said Howard.
If a picture is worth a thousand words, it only stands to reason that at times that picture has to be taken hundreds of feet above ground. It's the only way to see the forest instead of the trees. But aerial photography can be cost prohibitive. Sometimes, the high-tech people at Downstream rely on old-fashioned low-tech ways to get the shot.
To get aerial shots of Tangier Island for a recent production, Downstream hung a camera on specially equipped kites about 500 feet above the island. "Renting a helicopter is expensive," laughs Howard.
After the Downstream crew participated in a workshop on "balloon mapping," they bought a five-and-a-half foot helium balloon to capture aerials. "Since the workshop, I've been on it like paint," said Howard. "It's an avenue to low-cost aerial photography for a multitude of purposes, whether getting a good aerial shot or mapping a continuous route."
Adding aerial photography to their toolkit has been more than just a way of making better movies. It can provide technical information that would otherwise be out of reach for the organizations and agencies working on the ground.
All this strategic thinking and infrastructure only pays off if the finished product is exceptional. That Downstream's project portfolio is growing indicates people like what the group does. Downstream has produced film projects for the Piedmont Environmental Council and the Chesapeake Bay Foundation, and are working on a new project in Clarke County called CSpout Run, a restoration project focused on the entire Spout Run watershed.
Part of their success grows from the approach that worked with Shenandoah: Voices of the River. Maybe it's all those years Patterson spent behind the camera that creates the style. The films are short on narration—if any, at all—allowing people to speak for themselves, whether it's something heartfelt and passionate, like a connection with land and water, or whether it's a technical topic like "streamside livestock exclusion."
Downstream's cast of characters includes videographer and film editor Nancy Saunders, graphics designer Tom Taylor, and a web building partnership with Studio 105 in Shepherdstown. The board of directors includes scientists and conservation advocates. Howard serves as the producer on Downstream projects, a role that combines his organizing skills and creativity, and ability to herd cats, according to those who work with them.
"I have no doubts about the direction we're headed," said Ohrstrom. He likens Downstream to a cauldron, a kind of big soup pot where people and partners—whether agencies, nonprofits or businesses—can collaborate, hone clearer messages, and benefit from one another. "We'll get a lot more done if we can learn to listen to one another," he said.
After all, says George Ohrstrom, everyone lives downstream of someone.
The Downstream Project is a 501(c)(3) nonprofit organization located on Main Street in Berryville. For information, see www.thedownstreamproject.org.
Share Clarke!
https://clarkeva.com/wp-content/uploads/2017/08/Clarke-Nameplate3-1024x320.jpg 0 0 vaobserver https://clarkeva.com/wp-content/uploads/2017/08/Clarke-Nameplate3-1024x320.jpg vaobserver2012-11-20 15:07:362012-11-20 15:08:57Cauldron, Currents, and Kites
Advertise hereAdvertise here
Around Clarke County
Environment/Nature
© Copyright - Clarke Monthly
In The Saddle Business Briefs


The Level 5 NVQ in Occupational Health and Safety Practice is suitable for those who are working in the area of health and safety practice or managing the health and safety function.
Allowing learners to understand the risks associated with manual handling and to make recommendations to manage those risks and how to apply them.
The qualification has been designed to meet the needs of those who are entering the workplace for the first time by providing an awareness of key health and safety issues.


And… introducing our new baby shower gifts! The adorable mini collection includes a photo frame, tooth and curl boxes, and a money box. All perfect gifts to celebrate the arrival of a new baby.
Engrave your pieces to make them extra special by adding a name, date or thoughtful message that will last forever.
Join us on a journey through childhood: Beginning with gorgeous gifts for mums to be, celebrate the arrival of the new baby with our unique baby shower gifts and sentimental keepsakes. Engrave your pieces to make them extra special by adding a name, date or thoughtful message that will last forever.
From their first steps to first words, the new collection embraces playfulness, learning and growth. Our adorable jewellery captures precious memories and milestones for you to treasure – because they just grow up so fast!


text:
Q: Vertically Center Icon with CSS I am building a web site. My web site will have blocks of content. Each block will have a title, some text, and an icon. My challenge is, I'm trying to vertically center each icon in the block. Currently, I have the following HTML or you can look at the jsfiddle.

The Block Title

        Some small blurb

Here is a paragraph relating to this block. It may include a couple of sentences. These sentences will wrap. Ultimately, the heart needs to be vertically centered.

In this code block, I'm using Zurb Foundation to help with layout. However, I can't seem to get the heart icon to center vertically in my right column. Does anyone know how I can do this with CSS?
Thank you

A: Add a line-height property to your inline style for  with a value equal to the font-size:
font-size:4em; line-height: 4em

redpajama_set_name: RedPajamaStackExchange


text:
Hong Gi Yeom
Chosun University, Republic of Korea
Saugat Bhattacharyya
Ulster University, United Kingdom
Dalin Zhang
Data Availability Statement
Front. Hum. Neurosci., 13 January 2021
Sec. Brain-Computer Interfaces
Volume 14 - 2020 | https://doi.org/10.3389/fnhum.2020.604639
Recognition of Consumer Preference by Analysis and Classification EEG Signals
Mashael Aldayel1,2, Mourad Ykhlef2 and Abeer Al-Nafjan3*
1Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
2Information System Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
3Computer Science Department, College of Computer and Information Sciences, Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia
Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.
Neuromarketing or consumer neuroscience is an emerging disciplinary area that connects the affective and cognitive aspects of customer behavior utilizing neuroimaging tools such as brain-computer interfaces (BCIs). BCIs play the role of a communication tool between humans and computer systems without any external devices or muscle intervention to issue commands, control, or complete an interaction. BCI research and development initially considered as an assistive technology aimed to help individuals with physical disabilities in various aspects such as communication, control, and mobility. In recent times, alternative BCI applications for healthy humans have been developed, and an increasing number of these re-searches target fields such as neuromarketing (Al-Nafjan et al., 2017a). Electroencephalography (EEG) is a practical, versatile, affordable, portable, and non-invasive technique for performing repetitive sessions, tasks, and observations. EEG-based BCIs have gained increasing interest in the literature from various scientific disciplines (Al-Nafjan et al., 2017a).
In neuromarketing, EEG-based preference detection seeks to provide insights into an individual's experience with a variety of products and media as well as his responses to market stimuli. It is a well-known fact that consumer emotions impact decision-making. On the other hand, consumer's emotions can strongly be influenced by many internal and external factors. The detection and recognition of a consumer's emotional state thus reveal true consumer preferences (Aldayel et al., 2020). Although several studies have been conducted on EEG-based emotion recognition (Ramadan et al., 2015), EEG-based studies for detecting preferences in consumers are in a very early phase. Furthermore, only a few preference-recognition studies have evaluated passive BCIs compared to the number of active BCIs. Additional research that employee BCIs to assess unconscious customer preferences is therefore needed, as opposed to research on BCIs for direct control actions (van Erp et al., 2012).
An EEG-based preferences detection system helps us understanding consumer preferences and behavior to understand how one makes a buying decision. It will help marketers and organizations acting upon them to increase customer satisfaction, positive customer experiences, consumer loyalty, and revenue. (Aldayel et al., 2020).
Although the neuromarketing field has evolved significantly in the last decade; it still has not been fully implemented in the separated academic fields in marketing research. This is because marketing researchers lack training on systematic cognitive practices in neuroscience. In addition, marketing researchers have previously doubted the implications of violating ethical rules and the privacy of consumers when using neuroscience technologies for commercial purposes. However, there are still reservations against the use of neuromarketing to extract specific knowledge of customers (Ait Hammou et al., 2013). Consequently, the potential use of EEG data during passive observations to derive product preferences remains an open debate (Telpaz et al., 2015). Accordingly, only a few neuromarketing research on advertising efficiency (Morin, 2011) were reported. This research aims to thoroughly examine the preference detection in neuromarketing using EEG indices. We chose these EEG indices based on an analysis of neural correlations of the preference that was explained in our previous research (Aldayel et al., 2020). We employed two approaches for the extraction of EEG features, namely, discrete wavelet transform (DWT) and power spectral density (PSD).
These approaches were used to measure the EEG-based preference indices. The preference indices enhance the accuracy of preference prediction. In fact, to the best of our knowledge, this is the first study that examines in detail the effect of preference indicators in enhancing the performance of classification algorithms. Furthermore, we analyzed the performance of deep learning with other conventional classification algorithms, such as k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).
The remainder of this paper is arranged as follows: section 2 introduces the main concepts of this study with background details; section 3 presents the related works; section 4 describes the research methodology, i.e., the experiments with EEG data; section 5 discusses the evaluation results; and, finally, section 6 presents the conclusion.
In this section, we provide an overview of BCI-based preference detection and examine EEG-based preference indices.
2.1. BCI-Based Preference Detection
This section explains the design process of neuromarketing experiments for anticipating customer preferences and choices. First, a customer places a BCI device on his/her head. Then, the customer looks at the products while EEG data are recorded at the same time on the BCI. Next, the customer rates his/her preference on each product using a nine-point subjective ranking scale. After viewing all products, the subjective ranks need to be manually labeled as "preferred" or "unpreferred." Next, the recorded EEG signals go through preprocessing and feature extraction. The training and prediction of the classifier are based on the consumer's choice (subjective ranks). The proposed BCI system for preference detection is shown in Figure 1. This system has three fundamental modules: signal preprocessing, feature extraction, and classification modules.
Figure 1. EEG-based BCI for preference recognition.
2.2. EEG-Based Preference Indices
This section explains the preference indicators based on EEG signals. Based on our literature review (Aldayel et al., 2020), we defined the following four EEG indices to measure people's responses to marketing stimuli: the approach-withdrawal (AW) index, valence, choice index, and effort index. Such indices help marketers in realizing the reactions of consumers to products (Cartocci et al., 2017; Cherubino, 2018).
2.2.1. AW Index
The AW index measures the frontal alpha asymmetry reflected the difference between the left and right hemispheres; that is, the percentage of participation of the left hemisphere compared to the right one in the frontal alpha band (Cartocci et al., 2017; Touchette and Lee, 2017; Cherubino, 2018; Ramsøy et al., 2018). Several studies have shown the efficacy and precision of frontal alpha asymmetry as an essential determinant in emotion and neuromarketing research (Cartocci et al., 2017; Touchette and Lee, 2017; Al-Nafjan et al., 2017b; Cherubino, 2018; Modica et al., 2018; Ramsøy et al., 2018).
2.2.2. Effort Index
This measure is described as the activity level of the frontal theta in the prefrontal cortex. Higher theta activity has been associated with higher levels of task difficulty and complexity in the frontal area. It is an indication of cognitive processing arising from mental exhaustion (Modica et al., 2018) and has been frequently studied in neuromarketing research (Vecchiato et al., 2010, 2011; Boksem and Smidts, 2015; Telpaz et al., 2015; Modica et al., 2018). This reveals the significance of handling emotional changes for the formation of sustainable memory in commercials (Cartocci et al., 2017).
2.2.3. Choice Index
The choice index measures the frontal irregular fluctuations in beta and gamma, frequently associated with the actual stage of decision-making. It has been the most associated marker of willingness to pay for assessing customer desire and preferences, particularly in the gamma band. Higher gamma and beta implied greater neural activity of the left frontal area, while smaller amounts are associated with greater neural activity of the right area (Ramsøy et al., 2018).
2.2.4. Valence
Asymmetrical activation of the frontal hemisphere was correlated to preferences interpreted as valence, that is, the orientation of affective status of a consumer). Activation of the right and left prefrontal area is related to negative and positive values of valence, respectively. A large number of studies support the theory that frontal EEG asymmetry can be a measure of valence (Al-Nafjan et al., 2017b).
EEG-based preference classification normally includes the spectral conversion of waveforms into features exploited by data-mining algorithms, which are trained on labeled data to forecast whether preferences are presently being detected. The preference classification of EEG varies from binary labels such as ("like" vs. "dislike") and (most favored vs. least favored) to multiple ordinal labels in the form of ranks, such as the nine-scale rank or five-scale rank. Several preference studies have used more than two algorithms of classification to find tuned classifiers for a set of features (Hwang et al., 2013). Chew et al. (Ramadan et al., 2015) evaluated user preferences of aesthetics displayed as virtual three-dimensional objects. The frequency bands were used as features for the EEG classification into two classes—"like" and "dislike"—using SVM and KNN and achieving an accuracy of 75 and 80%, respectively. These results, however, are not considered credible since the authors used a relatively low dataset of five subjects. In their extended research (Teo et al., 2017, 2018b), the authors raised the number of subjects to 16 but did not obtain better results.
By integrating EEG measures with questionnaire measures, Hakim et al. (2018) obtained an accuracy of 68.5% using the SVM to determine the most and least preferred items. Combining classifiers, such as boosting, voting, or stacking, can be used to gather multiple classification algorithms by integrating their outcomes and/or training them to complement each other and improve their performance (Lotte et al., 2018). The choice of classifiers in a BCI system is mainly dependent on both the type of mental signals acquired and the setting in which the application is used. LDA and SVM, however, are the most widely used classification algorithms and were used in over half of the EEG-based BCI experiments. Some works employed graph-based deep learning to study attention behavior (Zhang et al., 2019, 2020). Table 1 summarizes several studies in neuromarketing in which various classifiers were used to achieve the most accurate accuracy in predicting customer preferences.
Table 1. Classification algorithms employed for preferences detection in neuromarketing.
Our review in Aldayel et al. (2020) highlighted the need to use further features and fusion of classifiers to boost the accuracy of the prediction. In this study, we used a publicly available neuromarketing dataset (Yadava et al., 2017) that was previously used (Yadava et al., 2017) in building a predictive model for consumer product choice from EEG data. By using a passive BCI, researchers studied the influences of gender and age on consumer preferences in terms of like/dislike. However, all indices of EEG-based preference recognition have not been combined in any study. To the best of our knowledge, this is the first in-depth investigation of the effect of preference indicators in enhancing the performance of classification algorithms.
The outcome of preference detection is dependent on the choices of algorithms for feature extraction and classification. In this study, we examined the probability that two affective levels, namely, "like" and "dislike," could be identified employing different feature combinations of EEG indices as well as different approaches of feature extraction and classification algorithms. We chose these EEG indices based on an analysis of neural correlations of the preference that was explained in our previous research (Aldayel et al., 2020). For EEG feature extraction, we used DWT and PSD. Then, the PSD features were used to calculate the EEG-based preference indices. We applied deep learning classification to identify approaches of using intelligent computational modeling in the form of classification algorithms as these approaches can effectively reflect the subjects' preferred states. Moreover, we compared the efficiency of deep learning with other traditional classifiers, such as SVM, RF, and KNN. We developed our model in Python programming language using the Scikit-Learn, SciPy, and MNE and Keras packages for machine learning, EEG preprocessing and filtration, and signal processing and deep learning, respectively.
In this section, we present our methods and describe the architecture of the proposed EEG-based preference recognition. First, we examine the neuromarketing benchmark dataset and labeling of preferences states. Then, we illustrate how to extract features from EEG signals. Lastly, we explain the DNN classifier for preference detection. Figure 2 presents the methods used in the consumer preference prediction system.
Figure 2. Architecture of the consumer preference prediction system.
4.1. Dataset
This section describes a publicly available EEG dataset (Table 2) that has been used (Yadava et al., 2017) in neuromarketing experiments. The Emotiv EPOC+ headset was used to record EEG data. Twenty-five users participated, and their EEG data were recorded while they watched products on a computer screen. The age of the users ranged from 18 to 38 years. A set of 14 diverse products, each with three variations, were selected. A total of 42 (= 14 × 3) diverse product pictures were then generated, and 1050 (= 42 × 25) EEG data were therefore logged for all users. The EEG data were downsampled to 128 Hz and preprocessed to 14 channels, resulting in 25 documents or one document per user. The EEG features were collected from 14 channels placed at AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, and AF4 locations. Responses in the form of either "like" or "dislike" were collected from the users for each product. Each product was presented for 4 s, and EEG data were logged simultaneously. After each image was presented, the preferred choice of the user was collected.
Table 2. Affective dataset description.
Since consumers may not be able to express their preferences when asked to clearly articulate them, their subjective labeling is not sufficient. We extracted true hidden preferences (i.e., the ground truth table) from EEG signals. We used two methods to identify preference labels ("like" or "dislike"): (1) subjective self-assessment labels collected during the experiment; and (2) valence-based labels to identify the objective preference states. In this experiment, we used different types of preference labeling to obtain more accurate results. We used the valence index as the determinant of preference to match the target preference state—"like" or "dislike." Valence rates were categorized to lower rates if values ranged from one to five and higher rates if values ranged from six to nine. A lower valence rate is an indicator of a "dislike" preference state, while a higher valence rate is an indicator of a "like" preference state.
We used Cohen's kappa to test the agreement level between two types of labeling, namely, subjective self-assessment and valence-based labels determined from EEG. The kappa score was 0.03, which can be interpreted as a slight agreement between these labels. We also noticed there were differences in 513 of the 1050 trials, which in line with the main goal of this neuromarketing research: real and more accurate identification of preferences using EEG signals.
4.2. Signal Preprocessing
We first averaged the EEG signals and then resampled the frequency to 128 Hz per channel. From prior knowledge of EEG, the correlated signal frequency ranges produced by the brain during preferences states are mainly concentrated below 45 Hz. The useful frequency band in EEG signal data is therefore between 4 and 45 Hz. We used a bandpass filter ranging from 4.0 to 45.0 Hz. Subsequently, we used ICA and Savitzky–Golay filters to remove artifacts. We considered only the following electrodes in the preference calculation: AF3, F3, AF4, and F4.
4.3. Feature Extraction
Feature extraction aims to find important and relevant information from EEG signals. We extracted EEG frequency bands using two approaches: DWT and a PSD method named Welch. Then, we used the resulting frequency bands to calculate the preference indices. The first approach extracts a set of statistics-based features for each frequency band (details [D2-D5] and approximation [A5]) computed by DWT. The second approach stacks the features computed by PSD into a single array over the raw EEG of the channels.
4.3.1. Discrete Wavelet Transform
The DWT is a time-frequency domain analysis method that decomposes signals into different coefficients. It can be defined as multi-resolution or multi-scale analysis, where each coefficient is a unique representation of mind signals. The convolution operation is a two-function multiplication process (Chen et al., 2015; Vega-Escobar et al., 2016). Each inner product results in a wavelet coefficient. Therefore, the DWT can be expressed using the following Equation (1):
W(j,k)=∑N=0M-1f(n)·ψj,k*(n) (1)
where f(n) is a signal (sequence) of length n, and ψj,k*(n) is scaling wavelet function. DWT decomposition can be implemented as a group of high- and low-pass filters in a filter bank. The outputs of the low-pass filters are called approximation coefficients, and those of the high-pass filters are called wavelet detail coefficients. After the filtering, the signal is down-sampled by a factor of two based on the Nyquist Theorem, resulting in a frequency band ranging between fn/2 and fn. Assuming fs sampling frequency and L decomposition level, every detail coefficient frequency is related to the sampling frequency rate fs of the raw signals, given by fn = fs/2L + 1. The number of wavelet decomposition levels and the selection of a proper wavelet technique are critical to achieving DWT analysis accuracy (Chen et al., 2015; Vega-Escobar et al., 2016; Yadava et al., 2017).
Since the sampling frequency in the present study was 128 Hz, we used four levels of Daubechies (db4) wavelets to decompose EEG signals into five coefficients, namely, A4, D4, D3, D2, and D1. Each coefficient is approximately correlated to the basic frequency bands, namely, (1–4 Hz) delta, (4–8 Hz) theta, (8–13 Hz) alpha, (13–22 Hz) beta, and (22–64 Hz) gamma. The decomposed details D1-D4 and approximation A4 for each of the 14 channels are shown in Figure 3, and their correlated ranges of frequency are listed in Table 3.
Figure 3. Different levels of DWT decomposition by low-pass (LP) and high-pass (HP) filtering yield A4, D4, D3, D2, and D1 corresponding to delta, theta, alpha, beta, and gamma, respectively, with 128 Hz sampling frequency (SF).
Table 3. Frequency bands correlated to decomposed coefficients.
Moreover, we computed the (Shannon) entropy values as measures of signal complexity and extracted the statistical features that are most commonly used for signals, such as variance, standard deviation, mean, median, 25th and 75th percentile values, root mean square of the average amplitude values, zero and mean crossing rates, and the mean of the signal derivatives. These 10 statistical features and entropy and coefficient values were calculated for the five coefficients for the 14 channels. Thus, the number of DWT features was 12 × 5 × 14 = 840.
4.3.2. Power Spectral Density
The PSD is an indicator of power in a certain signal in terms of frequency (Xie and Oniga, 2020). PSD is one of the most common feature extraction approaches in neuromarketing research, based on frequency domain analysis. Previous studies (Ohme et al., 2009, 2010; Khushaba et al., 2013) have demonstrated that the PSD obtained from EEG signals is suitable for determining consumer preferences. The PSD approach transforms the data from the time domain to the frequency domain, and vice versa. This conversation is focused on the fast transformation of Fourier, measuring the discrete transformation of Fourier and its opposite. In addition to DWT, we applied the PSD technique in this study to divide each EEG signal into four different frequency bands: theta θ (4–8 Hz), alpha α (8–13 Hz), beta β (13–30 Hz), and gamma γ (30–40 Hz). The MNE package for signal processing was employed for computing PSD and the average power across the frequency ranges.
4.4. Calculation of Preference Indices
We implemented various equations to measure the following EEG-based preferences indices (Section 2.2): the AW index, effort index, choice index, and valence. The AW index (frontal alpha asymmetry), measures motivation and desire as higher activation of alpha in the left frontal cortex. We used (Equation 2) stated by Touchette and Lee (2017) to measure the AW scores using electrodes F4 and F3 to find the difference between the right and left PSD divided by their amounts.
AW index=α(F4)-α(F3)α(F4)+α(F3) (2)
The effort index measures effort and cognitive processing as higher theta activation in the prefrontal cortex. We used the following equation to calculate the effort index:
Effort Index=θ(F4)-θ(F3)θ(F4)+θ(F3) (3)
The choice index measures choice possibility in decision making as higher gamma and beta activation in the frontal cortex (Ramsøy et al., 2018). We used Equation (4) reported by Ramsoy et al. to calculate the choice index for each band individually (gamma and beta) using electrodes AF3 and AF4:
Choice index=log(AF3)-log(AF4)log(AF3)+log(AF4) (4)
The valence measures positive emotion as left frontal activation in alpha and beta bands. We applied different valence equations and investigated the relationships between the self-assessment and different valence measurements. We computed the values of valence using Equations (5), (6), (7), and (8), which are well-explained in literature (Al-Nafjan et al., 2017b).
Valence=β(AF3,F3)α(AF3,F3)-β(AF4,F4)α(AF4,F4) (5)
Valence=ln[α(Fz,AF3,F3)]-ln[α(Fz,AF4,F4)] (6)
Valence=α(F4)-β(F3) (7)
Valence=α(F4)β(F4)-α(F3)β(F3) (8)
For all preference indices, we used the PSD to extract frequency band powers from the neuromarketing data because the PSD is based on frequency analysis, unlike DWT, which is based on time and frequency analysis.
4.5. Preference Classification Algorithms
In our study, two preference states ("like" or "dislike") were detected from EEG neuromarketing data. Mainly, we proposed a DNN classifier and compared its performance with those of the KNN, RF, and SVM classifiers. We applied four classifiers, namely, DNN, KNN, RF, and SVM, to discover the optimal preference index and a well-matched classifier with the best accuracy.
RF is an ensemble learning used for classification and regression problems. It consists of a combination of several decision trees where the final outcome class is the mode of all outcome classes of individual trees. Such advantage resulted in low error rates and robustness against over-fitting while preserving computational efficiency (Al-Nafjan et al., 2017b; Teo et al., 2018a). We used the default hyper-parameters of RF in an sklearn package and adjusted the number of trees in the foreset to 500, which all processed in parallel.
4.5.1. DNN Classification
There is an explosive growth of deep learning in machine learning due to its capacity to learn good feature representations from the raw input. DL was able to provide optimal solutions to many problems in natural language processing, image, and speech. With EEG-based BCI, DL has been proven an effective tool to analyze EEG signals (Roy et al., 2019). We aim to investigate the possibility to detect two preference states in the EEG data. We proposed a DNN classifier and compared its performance with the performances of KNN and RF classifiers. The proposed DNN classifier block diagram is shown in Figure 4. The extracted features were first normalized using minimum-maximum normalization (Equation 9) and then fed into the DNN classifier.
x-scaled=(x-min)/(max-min) (9)
Figure 4. Block diagram of the DNN classifier.
In our work, we experimented various techniques and architectures. The optimal DNN architecture and properties are as follows:
• Fully connected feed-forward neural network comprised of three hidden layers.
• The input layer consisted of 2,367 units, and each hidden layer consisted of 50% units from its predecessor layer.
• Rectified Linear Unit (ReLu) as activation functions.
• Cross entropy (cost function) to compute the output of the softmax layer.
• The dimension(s) of the output layer was related to the number of target preferences state (2) units.
We used the Adam gradient descent with three objective loss functions: the binary cross-entropy, categorical cross-entropy, and hinge cross functions for training the DNN classifier with the following properties:
• Learning rate was set to 0.001.
• Dropout rate for the input and hidden layers was set to 0.5.
• Stopping criterion, to prevent over-fitting, was determined according to the model performance on a testing set.
Then, we tested our classifier on a test set, which contained approximately 20% of the data samples in the dataset. In our work, we used different approaches to prevent over-fitting including regularization ( such as L1 regularization, L2 regularization, and Gaussian noise), early stopping, and dropout. Adding noise to the DNN model in a relatively small dataset can improve its robustness with regularizing effect and decrease over-fitting.
We detected the preference states ("like" or "dislike") of the subjects using two different feature extraction methods (PSD and DWT) and four classifiers: DNN, KNN, RF, and SVM. For validation and evaluation, we used various measurements, namely precision, recall, and accuracy. The precision was the percentage of the prediction of "like" states, which was correct. The recall was the percentage of actually expected "like" states. To evaluate the efficiency of the classification algorithms, we split the data into train and test sets with holdout cross-validation.
The proposed DNN classifier was compared with three traditional classifiers for EEG signals: KNN, RF, and SVM using PSD and DWT feature extraction methods as well as various preference indices. Tables 4, 5 list the results of recall, accuracy, and precision results of the KNN, RF, SVM, and DNN algorithms using various activation functions in the DNN: hinge and cross-entropy (categorical and binary) functions. To show the importance of the feature extraction (DWT and PSD), Figure 5 presents the accuracy results of KNN, RF, SVM, and DNN (hinge activation) using raw EEG signals with and without feature engineering (DWT and PSD).
Table 4. Classification results of PSD-based feature extraction with/without preference indices and the valence index (V) and different classifiers: KNN, RF, SVM, and DNN using various activation functions in the DNN: hinge, and cross-entropy (categorical and binary) functions.
Table 5. Classification results of DWT-based feature extraction with/without preference indices and the valence index (V) and different classifiers: KNN, RF, SVM, and DNN using various activation functions in the DNN: hinge, and cross-entropy (categorical and binary) functions.
Figure 5. Accuracy results of KNN, RF, SVM, and DNN (hinge activation) using raw EEG signals with and without feature engineering (DWT and PSD).
When using PSD-based features, the KNN and SVM classifiers yielded enhanced accuracies of 80 and 87% with the valence index, whereas RF and DNN (binary cross-entropy function) achieved the highest accuracy of 93% with all preference indices. Similar results were achieved with the valence index. Using DWT-based features, the best results were achieved with all preference indices for all classifiers. The KNN and SVM classifiers led to enhanced accuracies of 73 and 81%, respectively. The highest accuracy was 87% with RF and the second-highest accuracy was 83% with DNN and the hinge loss function.
Figure 6 analyzes the results from the viewpoint of preference indices. We consider the DNN results with hinge loss function as it achieved the best accuracy result compared with other loss functions. About EEG features that exclude preference indices, the best accuracy results reached 83% with RF and DWT-based features.
Figure 6. Classification results of the SVM, RF, KNN, and DNN (hinge function) of different combination of preference indices. (A) Accuracy. (B) Recall. (C) Precision.
A DNN model is proposed for detecting subject preferences from EEG signals using the benchmark neuromarketing dataset. Two kinds of features—PSD and DWT—have been generated from the EEG to obtain a set of 2367 interesting attributes, which demonstrate the EEG task in each experiment. We used various evaluation measures (recall, accuracy, and precision) to test the performance of the classifiers. We built four classifiers, namely, DNN, KNN, RF, and SVM.
The results demonstrated that RF reached the best results in PSD-based and DWT-based features with either valence or all preference indices, however, RF obtained comparable outcomes to DNN. PSD-based features achieved better results in preference detection than DWT-based features. Moreover, combining preference indices leads to better results with either PSD or DWT-based features. This is perhaps the first study that examines in detail the effect of preference indicators in enhancing the performance of classification algorithms.
Publicly available datasets were analyzed in this study. This data can be found here: https://link.springer.com/article/10.1007/s11042-017-4580-6.
MA conceived, designed, and performed the experiment, analyzed and interpreted the data, and drafted the manuscript. MY supervised the analysis and reviewed the manuscript. AA-N co-supervised this study, and contributed to the discussion. All authors have read and approved the submitted version of the manuscript.
The authors would like to thank the deanship of scientific research for funding and supporting this research through the initiative of DSR Graduate Students Research Support (GSR) at King Saud University.
Ait Hammou, K., Galib, M. H., and Melloul, J. (2013). The contributions of neuromarketing in marketing research. J. Manage. Res. 5:20. doi: 10.5296/jmr.v5i4.4023
CrossRef Full Text | Google Scholar
Aldayel, M., Ykhlef, M., and Al-Nafjan, A. (2020). Deep learning for EEG-based preference classification in neuromarketing. Appl. Sci. 10, 1–23. doi: 10.3390/app10041525
Al-Nafjan, A., Hosny, M., Al-Ohali, Y., and Al-Wabil, A. (2017a). Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review. Appl. Sci. 7:1239. doi: 10.3390/app7121239
Al-Nafjan, A., Hosny, M., Al-Wabil, A., and Al-Ohali, Y. (2017b). Classification of human emotions from electroencephalogram (EEG) signal using deep neural network. Int. J. Adv. Comput. Sci. Appl. 8, 419–425. doi: 10.14569/ijacsa.2017.080955
Boksem, M. A. S., and Smidts, A. (2015). Brain responses to movie trailers predict individual preferences for movies and their population-wide commercial success. J. Market. Res. 52, 482–492. doi: 10.1509/jmr.13.0572
Cartocci, G., Caratu, M., Modica, E., Maglione, A. G., Rossi, D., Cherubino, P., et al. (2017). Electroencephalographic, heart rate, and galvanic skin response assessment for an advertising perception study: application to antismoking Public Service Announcements. J. Visual. Exp. 126:e55872. doi: 10.3791/55872
Chen, L. L., Zhao, Y., Zhang, J., and Zou, J. Z. (2015). Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Syst. Appl. 42, 7344–7355. doi: 10.1016/j.eswa.2015.05.028
Cherubino, P. (2018). "Application of Neuro- Marketing techniques to the wine tasting experience," in 11th Annual Conference of the EuroMed Academy of Business (Malta), 290–298.
Chew, L. H., Teo, J., and Mountstephens, J. (2016). Aesthetic preference recognition of 3D shapes using EEG. Cogn. Neurodyn. 10, 165–173. doi: 10.1007/s11571-015-9363-z
Hadjidimitriou, S. K., and Hadjileontiadis, L. J. (2012). Toward an EEG-based recognition of music liking using time-frequency analysis. IEEE Trans. Biomed. Eng. 59, 3498–3510. doi: 10.1109/TBME.2012.2217495
Hadjidimitriou, S. K., and Hadjileontiadis, L. J. (2013). EEG-Based classification of music appraisal responses using time-frequency analysis and familiarity ratings. IEEE Trans. Affect. Comput. 4, 161–172. doi: 10.1109/T-AFFC.2013.6
Hakim, A., Klorfeld, S., Sela, T., Friedman, D., Shabat-Simon, M., and Levy, D. J. (2018). Pathways to consumers minds: using machine learning and multiple EEG metrics to increase preference prediction above and beyond traditional measurements. bioRxiv. Available online at: https://www.biorxiv.org/content/10.1101/317073v2
Hwang, H.-J., Kim, S., Choi, S., and Im, C.-H. (2013). EEG-based brain-computer interfaces: a thorough literature survey. Int. J. Hum. Comput. Interact. 29, 814–826. doi: 10.1080/10447318.2013.780869
Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., and Townsend, C. (2013). Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst. Appl. 40, 3803–3812. doi: 10.1016/j.eswa.2012.12.095
Kim, Y., Kang, K., Lee, H., and Bae, C. (2015). "Preference measurement using user response electroencephalogram," in Computer Science and Its Applications, eds J. Park, I. Stojmenovic, H. Jeong, and G. Yi (Berlin; Heidelberg: Springer) 1315–1324. doi: 10.1007/978-3-662-45402-2_183
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., et al. (2018). A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J. Neural Eng. 15:aab2f2. doi: 10.1088/1741-2552/aab2f2
Modica, E., Cartocci, G., Rossi, D., Martinez Levy, A. C., Cherubino, P., Maglione, A. G., et al. (2018). Neurophysiological responses to different product experiences. Comput. Intell. Neurosci. 2018,1—10. doi: 10.1155/2018/9616301
Moon, J., Kim, Y., Lee, H., Bae, C., and Yoon, W. C. (2013). Extraction of user preference for video stimuli using eeg-based user responses. ETRI J. 35, 1105–1114. doi: 10.4218/etrij.13.0113.0194
Morin, C. (2011). Neuromarketing: the new science of consumer behavior. Society 48, 131–135. doi: 10.1007/s12115-010-9408-1
Ohme, R., Reykowska, D., Wiener, D., and Choromanska, A. (2009). Analysis of neurophysiological reactions to advertising stimuli by means of EEG and galvanic skin response measures. J. Neurosci. Psychol. Econ. 2, 21–31. doi: 10.1037/a0015462
Ohme, R., Reykowska, D., Wiener, D., and Choromanska, A. (2010). Application of frontal EEG asymmetry to advertising research. J. Econ. Psychol. 31, 785–793. doi: 10.1016/j.joep.2010.03.008
Pan, Y., Guan, C., Yu, J., Ang, K. K., and Chan, T. E. (2013). "Common frequency pattern for music preference identification using frontal EEG," in International IEEE/EMBS Conference on Neural Engineering, NER (San Diego, CA), 505–508. doi: 10.1109/NER.2013.6695982
Ramadan, R. A., Refat, S., Elshahed, M. A., and Ali, R. A. (2015). Brain-Computer Interfaces. Cham: Springer International Publishing. doi: 10.1007/978-3-319-10978-7
Ramsøy, T. Z., Skov, M., Christensen, M. K., and Stahlhut, C. (2018). Frontal brain asymmetry and willingness to pay. Front. Neurosci. 12:138. doi: 10.3389/fnins.2018.00138
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., and Faubert, J. (2019). Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16:051001. doi: 10.1088/1741-2552/ab260c
Telpaz, A., Webb, R., and Levy, D. J. (2015). Using EEG to predict consumers' future choices. J. Market. Res. 52, 511–529. doi: 10.1509/jmr.13.0564
Teo, J., Chew, L. H., Chia, J. T., and Mountstephens, J. (2018a). Classification of affective states via EEG and deep learning. Int. J. Adv. Comput. Sci. Appl. 9, 132–142. doi: 10.14569/IJACSA.2018.090517
Teo, J., Hou, C. L., and Mountstephens, J. (2017). Deep learning for EEG-based preference classification. AIP Conf. Proc. 1891, 020141. doi: 10.1063/1.5005474
Teo, J., Hou, C. L., and Mountstephens, J. (2018b). Preference classification using Electroencephalography (EEG) and deep learning. J. Telecommun. Electron. Comput. Eng. 10, 87–91.
Touchette, B., and Lee, S. E. (2017). Measuring neural responses to apparel product attractiveness: an application of frontal asymmetry theory. Cloth. Tex. Res. J. 35, 3–15. doi: 10.1177/0887302X16673157
van Erp, J., Lotte, F., and Tangermann, M. (2012). Brain-computer interfaces: beyond medical applications. Computer 45, 26–34. doi: 10.1109/MC.2012.107
Vecchiato, G., Astolfi, L., Fallani, F. D. V., Cincotti, F., Mattia, D., Salinari, S., et al. (2010). Changes in brain activity during the observation of TV commercials by using EEG, GSR and HR measurements. Brain Topogr. 23, 165–179. doi: 10.1007/s10548-009-0127-0
Vecchiato, G., Toppi, J., Astolfi, L., Fallani, F. D. V., Cincotti, F., Mattia, D., et al. (2011). Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements. Med. Biol. Eng. Comput. 49, 579–583. doi: 10.1007/s11517-011-0747-x
Vega-Escobar, L., Castro-Ospina, A., and Duque-Munoz, L. (2016). "DWT-based feature extraction for motor imagery classification," in 6th Latin-American Conference on Networked and Electronic Media (LACNEM 2015) (Medellin). doi: 10.1049/ic.2015.0309
Xie, Y., and Oniga, S. (2020). A review of processing methods and classification algorithm for EEG signal. Carpathian J. Electron. Comput. Eng. 13, 23–29. doi: 10.2478/cjece-2020-0004
Yadava, M., Kumar, P., Saini, R., Roy, P. P., and Dogra, D. P. (2017). Analysis of EEG signals and its application to neuromarketing. Multimedia Tools Applicat. 76, 19087–19111. doi: 10.1007/s11042-017-4580-6
Zhang, D., Chen, K., Jian, D., and Yao, L. (2020). Motor imagery classification via temporal attention cues of graph embedded EEG signals. IEEE J. Biomed. Health Inform. 24, 2570–2579. doi: 10.1109/JBHI.2020.2967128
Zhang, D., Yao, L., Chen, K., Wang, S., Haghighi, P. D., and Sullivan, C. (2019). A graph-based hierarchical attention model for movement intention detection from EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 2247–2253. doi: 10.1109/TNSRE.2019.2943362
Keywords: deep learning, feature extraction, customer neuroscience, classification, signal processing, neuromarketing
Citation: Aldayel M, Ykhlef M and Al-Nafjan A (2021) Recognition of Consumer Preference by Analysis and Classification EEG Signals. Front. Hum. Neurosci. 14:604639. doi: 10.3389/fnhum.2020.604639
Received: 10 September 2020; Accepted: 23 November 2020;
Published: 13 January 2021.
Hong Gi Yeom, Chosun University, South Korea
Saugat Bhattacharyya, Ulster University, United Kingdom
Dalin Zhang, Aalborg University, Denmark
Copyright © 2021 Aldayel, Ykhlef and Al-Nafjan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Abeer Al-Nafjan, nnafjan@imamu.edu.sa
Deep Learning in Brain-Computer Interface


text:
Turkish court issues life sentences in trial of 2016 coup leaders
in Foreign
Agency Reporter
A Turkish court handed down dozens of life sentences on Thursday for some of the nearly 500 defendants, including army commanders and pilots, accused of leading a 2016 coup attempt from an airbase near the capital Ankara.
More than 250 people were killed on July 15, 2016, when rogue soldiers commandeered warplanes, helicopters, and tanks and sought to take control of state institutions and overthrow the government of President Tayyip Erdogan.
The trial was the highest-profile of dozens of court cases targeting thousands of people accused of involvement in the coup attempt, which Ankara blamed on supporters of the U.S.-based Muslim preacher Fethullah Gulen.
Former air force commander Akin Ozturk and others at the Akinci air base near Ankara were accused of directing the coup and bombing government buildings, including parliament, and attempting to kill Erdogan.
Four ringleaders, dubbed "civilian imams" over ties to Gulen's network, were given 79 aggravated life sentences for charges of attempting to assassinate the president, murder, and seeking to overthrow the constitutional order, state-owned Anadolu news agency said.
READ ALSO: Turkish central bank raises interest rates to 15 percent
F-16 warplane pilots were also among those given aggravated life sentences – the severest punishment in Turkish courts – meaning there is no possibility of parole.
Turkey's then-military chief and now defence Minister Hulusi Akar and other commanders were held captive for several hours at the base on the night of the coup. A total of 475 people were on trial, 365 of them in custody.
Sustained Crackdown
The 79-year-old cleric Gulen, who was once an ally of Erdogan and has denied any role in the coup, was one of six defendants being tried in absentia. Their dossiers were separated from the main trial, media reports said.
The government declared a state of emergency in Turkey – a NATO member and candidate for European Union membership – after the failed coup and carried out a large-scale crackdown which alarmed Ankara's Western allies.
Some 292,000 people have been detained over alleged links to Gulen, nearly 100,000 of them jailed pending trial, Anadolu cited Interior Minister Suleyman Soylu as saying.
Some 150,000 civil servants were sacked or suspended after the coup, with some 20,000 expelled from the military. Courts have handed down more than 2,500 life sentences.
Four years on, police operations targeting suspects accused of links to Gulen continue on a regular basis.
The government has said the crackdown was needed given the security challenges which Turkey has faced to root out a network of Gulen supporters deeply embedded in the state apparatus.
(www.newsnow.co.uk)
Ex-Sudan PM Sadiq Al-Mahdi dies of COVID-19
Hoodlums hack security personnel to death in Ariaria Int'l market


text:
Items: 1 |
Total: $18.00 Cart
Home / Humorous Prints
Welcome to our collection of vintage and antique images depicting the...
Vintage Irish Prints
Humorous Prints
Irish Proverbs
Other Helpful Pages
Welcome to CelticPosters.com
We are the first online store devoted exclusively to prints representing Ireland, Scotland and Wales.
View Cart "Keep Calm and Party On" was successfully added to your cart.
Do Not Mess With Saint Patrick
"Behave Yourself: What I did it to the snakes, I can do to you."
Grand Irish Lady with Proverb
"Here's to me and here's to you, and here's to love and laughter. I'll be true as long as you, and not one moment after."
Irish Boys
"Irish Boys, building a better world through clean living and proper manners."
Irish Fighter
"The Irish: You're welcome to start a fight, just know that we'll be finishing it!"
Irish Girls Movie Poster
"Good girls by day, shameless hussies by night. This daring exposé reveals the shocking truth..."
Irish Lass with an Attitude
"Excuse me... what was that you said about the Irish?"
An Irish play on the famous "Keep Calm and Carry On" motivational poster produced by the British Ministry of Information in 1939.
Keep Calm and Throttle the Oppressor
An Irish play on the famous "Keep Calm and Carry On" poster produced by the British in 1939.
©2012 CelticPosters.com. All rights reserved worldwide. | (952) 473-4959


text:
Home/Current Affairs/Over 6,000 gather in Houston to pay respects to George Floyd before burial
Current AffairsWorld
Over 6,000 gather in Houston to pay respects to George Floyd before burial
Houston: Over 6,000 mourners turned out to bid a farewell to George Floyd at the last official public memorial in honour of the African American, whose in-custody death sparked nationwide protests against police brutality and racial discrimination.
His gilded casket was on display on Monday for six hours at the The Fountain of Praise church in Houston, Texas, where Floyd grew up and will be interred on Tuesday after a private service.
"Let me just verify. Yes, it's 6,362 [visitors]. It was very peaceful and dignified," the ceremony manager told Sputnik as the motorcade with a casket was about to leave the venue.
Shuttle buses transported mourners to a heavily guarded event from several rally points. People were allowed inside the building in small groups to observe social distancing. Mindful of coronavirus risks, organisers carried out sweeping temperature checks at the entrance and made sure everyone has a face mask.


Dehradun: Prime Minister Narendra Modi on Wednesday celebrated Diwali with the personnel of Army and Indo-Tibetan Border Police (ITBP), mandated to guard 3,488 km Sino-India border, in Uttarakhand and said that India was taking great strides forward in the defence sector.
"Diwali is the festival of lights which spreads the light of goodness and dispels fear. The jawans, through their commitment and discipline, are also helping to spread the sense of security and fearlessness among the people," Modi said.
The Prime Minister has taken a personal interest in the reconstruction and has visited the shrine twice in the last six months.


text:
RCW 28B.15.545
Grants for undergraduate coursework for recipients of the Washington award for vocational excellence.
Students named by the workforce training and education coordinating board after June 30, 1994, as recipients of the Washington award for vocational excellence under RCW 28C.04.520 through 28C.04.550 shall be eligible to receive a grant for undergraduate coursework as authorized under RCW 28B.76.670.
[ 2015 c 55 § 220; 2004 c 275 § 50; 1995 1st sp.s. c 7 § 7; 1993 sp.s. c 18 § 20; 1992 c 231 § 18; 1987 c 231 § 1; 1985 c 390 § 31; 1984 c 267 § 6.]
Part headings not law—2004 c 275: See note following RCW 28B.76.090.
Severability—1995 1st sp.s. c 7: See note following RCW 28C.04.520.
Effective date—1993 sp.s. c 18: See note following RCW 28B.12.060.
Effective date—1992 c 231: See note following RCW 28B.10.016.


text:
<?php require dirname(__FILE__) . '/../header.php'; ?>
<link href="static/css/sidebar_dev_link.css" rel="stylesheet" type="text/css">
<link href="static/css/sidebar_statistics.css" rel="stylesheet" type="text/css">
<link href="static/css/go_top.css" rel="stylesheet" type="text/css">

<?php require dirname(__FILE__) . '/../content_start.php'; ?>

主题

                <?php foreach($subjects as $subject){ ?>

                        <a href="topics/<?php echo $subject['id'] ?>"><?php echo $subject['name'] ?>
                        <a class="btn btn-default pull-right btn-xs" href="topic/create/<?php echo $subject['id'] ?>" role="button">新的话题
                        <?php if($subject['topic_count']){ ?>

                            <?php echo $subject['topic_count'] ?>个话题
                                 •  <?php echo $subject['last_alter_on'] ?>前有更新

                        <?php } ?>

                <?php } ?>

        <?php require dirname(__FILE__) . '/../sidebar_dev_link.php'; ?>
        <?php require dirname(__FILE__) . '/../sidebar_statistics.php'; ?>

<?php require dirname(__FILE__) . '/../go_top.php'; ?>
<?php require dirname(__FILE__) . '/../content_end.php'; ?>

<?php require dirname(__FILE__) . '/../footer.php'; ?>

redpajama_set_name: RedPajamaGithub