The Real Ph.D. Experience: Now In Digital Form!

Content V rigght the area of your graph where the cart was moving at a constant velocity on the flat pat of the graph which should have a constant negative slope. This is where the cart was not accelerating. 11 12. 13. 14. Use the cursor, tap and release where it begins, then drag to where the run ends and again tap and re You should now have the area highlighted where the cart was moving at a constant velocity. Press menu > 2: Data - 5: Strike Data - 2: Outside Selected Region verify that vou have selected the portion of vour eraph that shows the can mo vita a consrant veocin° n should be a line rising from left to right. If vou need to reselect do that now 15. 16. Press menu -> 4: Analyze -> 6: Curve Fit -> 1: Linear in the window that pops up record the slope (m) value into Table 2, this is the measured velocity. Ignore the slopes negative sign. The sensor measures obiects moving toward it as going in a negative direction. 17. Repeat steps the previous steps for vour other trials 18. Table 2 save your work on the calculator; press doc › 1: File -> 4: Save Run Height Measured v (m/S) 19. Submit your work; press doc -> 1: File -> 6: Send (m) Observations 0.100 20. Did the cart's velocity decrease when it was released from the lower 1.88 marks? It so, why do you think this may have happened? 2 0.075 ,953 21. acce era Use your measured (Table 2) and theoretical (Table 1) values to compute the percent % difference measured. theoretical k 100 ditterence for each run rhonrorical 0.050 4 0.025 .830 .603 Run 2 з 4 Height Measured v (m) (m/s) Table 3 Theoretical v (m/s) Percent Difference 0.100 1.188 ,245 984% 0.075 953 1.187 412% 0.050 830.125 5649 0.025 603.066 99581 Calculations: table 1 22. Were you successful in predicting the velocity of the car at the bottom of the ramp? NO. Absslutely Use the mass of the cart and g = 9.8 m/s? to theoretical gravitational potential enerov (C the 10 cm (0.10 m) height. Use the measur cart for the 10 cm heicht (Run #1\ to calcu energy (KE) of the cart. Record this inforn Calculations: 25. How does the gravitationa potential er 26. Based on vour results. did all of the in 27. If there is a difterence between the caused the ditterence: Synthesize 28. What was the independent varia 29. What did you measure? 30. what was the result when vou Error Analysis 31 What were the sources of err Conclusions 32. Did the initial height of the 33. Do your results support yc Case v2 Case

That circuit looks like what happens when you ask a toddler to recreate the Mona Lisa with spaghetti. The AI promised us a sleek buck converter and delivered what appears to be electronic spaghetti junction instead! Engineers are currently weeping into their coffee mugs everywhere. This is what happens when silicon-based intelligence tries to do what took humans decades to perfect. Next time someone hypes up "AI will replace engineers," just show them this masterpiece of digital confusion. It's the electronic equivalent of putting shoes on backwards and claiming you've revolutionized walking.

Content girl, you better be magnesium iron silicate hydroxide cuz you're.... 12:56 Ballu Bhai Magnesium iron silicate hydroxide is the chemical name for the metamorphic amphibole cummingtonite. It has the chemical composition (Ma.Fe24)2(Ma. Fe24)5Si8022(OH)2 Show more V Cummingtonite 12:57 pm

The scientific aspirations of this feline are both adorable and delusional. What we're witnessing is classic Dunning-Kruger effect in action – but with whiskers. The cat's wide-eyed optimism about matching wits with Einstein and artificial intelligence reveals the beautiful ignorance that comes before understanding the crushing complexity of quantum mechanics or neural networks. I've seen this same expression on first-year physics students before their first thermodynamics exam. Reality is coming, kitty... and it doesn't care how cute your toe beans are.

Control engineers having a field day with this one! The left shooter is decked out with fancy high-tech gear representing complex control algorithms like Model Predictive Control (MPC), Linear Quadratic Regulator (LQR), H-infinity synthesis, and all those neural network goodies. Meanwhile, the right shooter with just a basic pistol represents PID Control - that simple, reliable workhorse that's been keeping our thermostats, drones, and industrial processes running since the 1920s. Despite all our fancy mathematical advancements, sometimes the simple PID controller (Proportional-Integral-Derivative) still gets the job done just as well! It's like bringing a calculator to a math competition while everyone else lugs in supercomputers. Engineering's greatest flex is knowing when simple is better than sophisticated!

Everyone's out here worshipping the complexity of machine learning algorithms, but peek behind the curtain and it's just linear regression in a fancy trench coat. That shocked cat face is all of us realizing we spent years learning calculus and statistics only to find out the AI revolution is built on y = mx + b. The same equation your 8th grade math teacher tortured you with is now worth billions in Silicon Valley. Talk about the ultimate glow-up for the slope-intercept form!