#Maths And Science

[12-toads-in-a-trench-coat]

GUYS GUYS GUYS I GOT INTO THE SCHOOL I APPLIED TO

[mitchipedia]

[mysharona1987]

[sub-at-omicsteminist]

Astronomy I-

[viejospellejos]

[nasa]

LaRue Burbank, mathematician and computer, is just one of the many women who were instrumental to NASA missions.

4 Little Known Women Who Made Huge Contributions to NASA

Women have always played a significant role at NASA and its predecessor NACA, although for much of the agency’s history, they received neither the praise nor recognition that their contributions deserved. To celebrate Women’s History Month – and properly highlight some of the little-known women-led accomplishments of NASA’s early history – our archivists gathered the stories of four women whose work was critical to NASA’s success and paved the way for future generations.

LaRue Burbank: One of the Women Who Helped Land a Man on the Moon

LaRue Burbank was a trailblazing mathematician at NASA. Hired in 1954 at Langley Memorial Aeronautical Laboratory (now NASA’s Langley Research Center), she, like many other young women at NACA, the predecessor to NASA, had a bachelor's degree in mathematics. But unlike most, she also had a physics degree. For the next four years, she worked as a "human computer," conducting complex data analyses for engineers using calculators, slide rules, and other instruments. After NASA's founding, she continued this vital work for Project Mercury.

In 1962, she transferred to the newly established Manned Spacecraft Center (now NASA’s Johnson Space Center) in Houston, becoming one of the few female professionals and managers there.  Her expertise in electronics engineering led her to develop critical display systems used by flight controllers in Mission Control to monitor spacecraft during missions. Her work on the Apollo missions was vital to achieving President Kennedy's goal of landing a man on the Moon.

Eilene Galloway: How NASA became… NASA

Eilene Galloway wasn't a NASA employee, but she played a huge role in its very creation. In 1957, after the Soviet Union launched Sputnik, Senator Richard Russell Jr. called on Galloway, an expert on the Atomic Energy Act, to write a report on the U.S. response to the space race. Initially, legislators aimed to essentially re-write the Atomic Energy Act to handle the U.S. space goals. However, Galloway argued that the existing military framework wouldn't suffice – a new agency was needed to oversee both military and civilian aspects of space exploration. This included not just defense, but also meteorology, communications, and international cooperation.

Her work on the National Aeronautics and Space Act ensured NASA had the power to accomplish all these goals, without limitations from the Department of Defense or restrictions on international agreements. Galloway is even to thank for the name "National Aeronautics and Space Administration", as initially NASA was to be called “National Aeronautics and Space Agency” which was deemed to not carry enough weight and status for the wide-ranging role that NASA was to fill.

Barbara Scott: The “Star Trek Nerd” Who Led Our Understanding of the Stars

A self-described "Star Trek nerd," Barbara Scott's passion for space wasn't steered toward engineering by her guidance counselor. But that didn't stop her!  Fueled by her love of math and computer science, she landed at Goddard Spaceflight Center in 1977.  One of the first women working on flight software, Barbara's coding skills became instrumental on missions like the International Ultraviolet Explorer (IUE) and the Thermal Canister Experiment on the Space Shuttle's STS-3.  For the final decade of her impressive career, Scott managed the flight software for the iconic Hubble Space Telescope, a testament to her dedication to space exploration.

Dr. Claire Parkinson: An Early Pioneer in Climate Science Whose Work is Still Saving Lives

Dr. Claire Parkinson's love of math blossomed into a passion for climate science. Inspired by the Moon landing, and the fight for civil rights, she pursued a graduate degree in climatology.  In 1978, her talents landed her at Goddard, where she continued her research on sea ice modeling. But Parkinson's impact goes beyond theory.  She began analyzing satellite data, leading to a groundbreaking discovery: a decline in Arctic sea ice coverage between 1973 and 1987. This critical finding caught the attention of Senator Al Gore, highlighting the urgency of climate change.

Parkinson's leadership extended beyond research.  As Project Scientist for the Aqua satellite, she championed making its data freely available. This real-time information has benefitted countless projects, from wildfire management to weather forecasting, even aiding in monitoring the COVID-19 pandemic. Parkinson's dedication to understanding sea ice patterns and the impact of climate change continues to be a valuable resource for our planet.

Make sure to follow us on Tumblr for your regular dose of space! 

[swaglet]

[catscomputerscreativewriting]

It's so funny to me that people think of Math/Mathematicians as being hyper-logical and rational. Like, have you seen some of the wild things hiding in the Math?

Did you know there are non-computable numbers?? (https://en.wikipedia.org/wiki/Chaitin%27s_constant)

Did you know that there are things that are true, but we can't prove them??? (https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_theorems)

Did you know that we can prove that something exists, and yet never actually figure out what that thing is?? (https://mathworld.wolfram.com/NonconstructiveProof.html)

Math is crazy. Math is wild. Math hardly makes sense, and when you think you understand the weirdest parts of it, everyone who hears you explain it to thinks you're a gibbering lunatic.

"In mathematics you don’t understand things. You just get used to them." - von Neumann

(please share more unhinged math with me, i want to see more scary math)

[certifiedcoffeeaddict]

good afternoon,

[ravings-of-a-mad-scientist]

Math

Geologist: I do more math than you might think

Chemist: I mean, chemical equations are basically mathematical equations. If you think about it (I also do math math)

Physicist: Oh, yeah, it’s all math but we just handwave it

Mathematician: YOU DO WHAT!?

Quantum Physicist: *regularly does math that is literally beyond human comprehension* *now resides in a higher plane of existence*

Engineer: If I don’t do this math correctly PEOPLE WILL DIE

Military Scientist: If I don’t do this math correctly PEOPLE WILL SURVIVE

Topologist: If I don’t do this math correctly PEOPLE WILL BE MOSTLY UNAFFECTED

Philosopher: But what even IS math, really? No seriously, what is it?

Organic Chemist: I kinda forgot how to do math, to be honest

Biologist: I literally only chose this field so I wouldn’t have to do as much math. I love stamp collecting

Biostatistician: wtf

[minisciencecentre]

What Are Teaching Models In Mathematics?

Mathematics is not only a subject but it is the language with some different symbols and relations. Mathematics simplifies all the things easily but in different manner. Math teaching models are just like a bridge which associates mathematics with real life event or a problem. The basic purpose of those models is how to make mathematics education interesting and students enjoy doing mathematics, not only for their academic progress but discovers new tricks, methods and mainly they can be able to relate all the math problems or content of text book to real life problems.

Education defines mathematical modeling as a way of solving problems in the maths and science, if modeling is carried out taking into account the real characteristics of the processes or systems that are being modeled.

The difference is that mathematical modeling can be part of Realistic Mathematics Education (RME) is a domain-specific instruction theory for mathematics. In other words, it is hardly possible to educate mathematical modeling by itself, since it is most often associated with an understanding of processes and systems studied in other sciences (physics & etc.).

Therefore, RME is an innovative learning approach that emphasises mathematics as a human activity that must be associated with real life using real world context as the starting point of learning.

Maths teaching models or mathematical modellings are used in the natural sciences (such as physics, biology, earth science, chemistry) and engineering disciplines (such as computer science, electrical engineering), as well as in non-physical systems such as the social sciences (such as economics, psychology, sociology, political science).

Mathematical models can take many forms, including dynamical systems, statistical models, differential equations, or game theoretic models etc. Mathematical models are of different types:

Linear vs. nonlinear:- If all the operators in a mathematical model exhibit linearity, the resulting mathematical model is defined as linear. A model is considered to be nonlinear otherwise. The definition of linearity and nonlinearity is dependent on context, and linear models may have nonlinear expressions in them.

Static vs. dynamic:- A dynamic model accounts for time-dependent changes in the state of the system, while a static model calculates the system in equilibrium, and thus is time-invariant. Dynamic models typically are represented by differential equations or difference equations.

Explicit vs. implicit:- If all of the input parameters of the overall model are known, and the output parameters can be calculated by a finite series of computations, the model is said to be explicit. But sometimes it is the output parameters which are known, and the corresponding inputs must be solved for by an iterative procedure, such as Newton's method or Broyden's method. In such a case the model is said to be implicit.

Discrete vs. continuous:- A discrete model treats objects as discrete, such as the particles in a molecular model or the states in a statistical model; while a continuous model represents the objects in a continuous manner, such as the velocity field of fluid in pipe flows, temperatures and stresses in a solid, and electric field that applies continuously over the entire model due to a point charge.

Deterministic vs. probabilistic:- A deterministic mathematical model is meant to yield a single solution describing the outcome of some ""experiment"" given appropriate inputs. A probabilistic model is, instead, meant to give a distribution of possible outcomes.

Deductive, inductive, or floating:- A deductive model is a logical structure based on a theory. An inductive model arises from empirical findings and generalization from them. The floating model rests on neither theory nor observation, but is merely the invocation of expected structure.

The creation and use of maths teaching models can help students develop new concepts or relationships and make connections between symbols and concepts. Because different models show different aspects of the maths and science concept. Through model, students can express how they understand the mathematical concept or relationship.

Syndication URL on What Are Teaching Models In Mathematics?

[facts-i-just-made-up]

Though most non-integers are not round numbers, Pi (3.14159etc) is considered round because of circles or something.

I dunno. My back hurts.

[engineersthoughts]

A GOAT doesn‘t care about physics. 🐐

[lucidloving]

Joshua Moritz, 'Is There a God-Shaped Hole at the Heart of Mathematics?' // Bluepoch Games, Reverse 1999 // Li C. Tien, "Right Triangle" // Proclus Diadochus // @lothmoth // Lisa Rosenberg, "Introduction to Methods of Mathematical Physics" // Wassily Kadinsky, watercolour abstract // Albert Einstein // see 4

[marinotcurie]

january this january that

[typhlonectes]