Archive Layout with Content

A variety of common markup showing how the theme styles them.

Header one

Header two

Header three

Header four

Header five
Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

EntryItem 
John Doe2016Description of the item in the list
Jane Doe2019Description of the item in the list
Doe Doe2022Description of the item in the list
Header1Header2Header3
cell1cell2cell3
cell4cell5cell6
cell1cell2cell3
cell4cell5cell6
Foot1Foot2Foot3

Definition Lists

Definition List Title
Definition list division.
Startup
A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
#dowork
Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.
Do It Live
I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

Ordered List (Nested)

  1. List item one
    1. List item one
      1. List item one
      2. List item two
      3. List item three
      4. List item four
    2. List item two
    3. List item three
    4. List item four
  2. List item two
  3. List item three
  4. List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you strikeout text.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H2O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC2, which should lift the 2 up.

Variable Tag

This allows you to denote variables.

Artificial Intelligence for Climate Change Mitigation

Event date:

Webinar with Alp Kucukelbir, Columbia University. Artificial intelligence (AI) has the potential to make very significant contributions to climate change mitigation. The complexity and scale of the challenge is broad. In this talk, I break down opportunities for AI to effect incremental and transformational change across multiple sectors, focusing on industries with large carbon footprints. I highlight barriers and risks to the adoption of AI, including the carbon footprint of AI usage worldwide. I focus on the multiple definitions (and ultimate importance) of "trust in AI" and its impact on the integration of AI into complex workflows. This talk is for AI practitioners looking to understand how AI fits into the bigger picture of climate change. I highlight opportunities and challenges in each sector that I hope will motivate collaboration across academia and industry.

Estimation of water quality parameters using remote sensing data and machine learning models

Event date:

Webinar with Alireza Taheri Dehkordi, Lund University. The global decline in water quality, exacerbated by climate change and population growth, underscores the need for continuous and accurate monitoring of water quality parameters (WQPs). Remote sensing (RS) data, especially from multispectral satellites like Sentinel-2 and Landsat-8, offers large-scale, periodic observations for tracking WQPs. However, deriving accurate estimates solely from RS data is complex due to the intricate relationships between spectral bands and water quality indicators. This talk presents two novel machine learning approaches that leverage advanced RS data processing to enhance water quality monitoring.