#md-108

[hatecrimesmd-rests-my-soul]

The healer with his magic powers

[canonmisogynyvictimstournament]

After a brutal preliminary round, we have reached the tournament proper. Round 1 will consist of 64 matchups, with 1 through 32 in Week 1 and 33 through 64 in Week 2, and those groups will continue to alternate through the rounds until we reach the Finals, which will consist of both the final matchup for first place and a matchup between the two losers of the semifinals for third place.

This tournament has concluded, and the winners can be found here.

Links to a google sheet with the original submissions and a google doc with all propaganda for all submissions with content warnings can be found here.

If you have any trigger warnings you want me to include (you can check against what is already being tagged here), please submit them here.

And if you want to create a prediction for the whole tournament, you can create and submit it here because why not, right? Let's see who gets it right! Go here, scroll down, and click the green button saying "Create a Prediction"!

NOTE: You can use a phony email address when they ask for one.

Now, for some convenient links to the matchups!

The matchups were as follows:

ROUND 7 - FINALS AND 3RD PLACE

WEEK 12

127.) Bumble (Warrior Cats) vs Cordelia Chase (Buffy the Vampire Slayer/Angel the Series) 128.) Asuna (Sword Art Online) vs Misa Amane (Death Note)

ROUND 6 - SEMIFINALS

WEEK 11

125.) Bumble (Warrior Cats) vs Asuna (Sword Art Online) 126.) Cordelia Chase (Buffy the Vampire Slayer/Angel the Series) vs Misa Amane (Death Note)

ROUND 5

WEEK 10

123.) Katherina Minola (The Taming of the Shrew) vs Cordelia Chase (Buffy the Vampire Slayer/Angel the Series) 124.) Misa Amane (Death Note) vs Abbie Mills (Sleepy Hollow)

WEEK 9

121.) Bumble (Warrior Cats) vs Alex DeWitt (DC Comics) 122.) Asuna (Sword Art Online) vs Sakura Haruno (Naruto)

ROUND 4

WEEK 8

117.) Katherina Minola (The Taming of the Shrew) vs Talia al Ghul (DC Comics) 118.) Sansa Stark (Game of Thrones) vs Cordelia Chase (Buffy the Vampire Slayer/Angel the Series) 119.) Misa Amane (Death Note) vs Casca (Berserk) 120.) Elektra Natchios (Marvel Comics) vs Abbie Mills (Sleepy Hollow)

WEEK 7

113.) Starfire (DC Comics) vs Bumble (Warrior Cats) 114.) Alex DeWitt (DC Comics) vs Leafpool (Warrior Cats) 115.) Kamala Khan (Marvel Comics) vs Asuna (Sword Art Online) 116.) Sakura Haruno (Naruto) vs Daenerys Targaryen (Game of Thrones)

ROUND 3

WEEK 6

105.) Katherina Minola (The Taming of the Shrew) vs Orihime Inoue (Bleach) 106.) Talia al Ghul (DC Comics) vs Athena Cykes (Ace Attorney) 107.) Sansa Stark (Game of Thrones) vs Mary Winchester (Supernatural) 108.) Natasha Romanoff (MCU) vs Cordelia Chase (Buffy the Vampire Slayer/Angel the Series) 109.) Misa Amane (Death Note) vs Madison Paige (Heavy Rain) 110.) Casca (Berserk) vs Hélène Kuragina (War and Peace) 111.) Elektra Natchios (Marvel Comics) vs Susan Pevensie (The Chronicles of Narnia) 112.) Mikaela Banes (Transformers) vs Abbie Mills (Sleepy Hollow)

WEEK 5

97.) Starfire (DC Comics) vs Marwa (What We Do in the Shadows (TV Series)) 98.) Chi-Chi (Dragon Ball) vs Bumble (Warrior Cats) 99.) Alex DeWitt (DC Comics) vs Marinette Dupain-Cheng (Miraculous Ladybug) 100.) Leafpool (Warrior Cats) vs Lisa Cuddy (House MD) 101.) Kamala Khan (Marvel Comics) vs Stephanie (EverymanHYBRID) 102.) Malty S Melromarc (Rising of the Shield Hero) vs Asuna (Sword Art Online) 103.) Sakura Haruno (Naruto) vs Pussy Galore (Goldfinger) 104.) Daenerys Targaryen (Game of Thrones) vs Flora Reinhold (Professor Layton)

ROUND 2

WEEK 4

81.) Kendra Young (Buffy the Vampire Slayer/Angel the Series) vs Katherina Minola (The Taming of the Shrew) 82.) Naomi Misora (Death Note) vs Orihime Inoue (Bleach) 83.) Talia al Ghul (DC Comics) vs Grelle Sutcliff (Black Butler) 84.) Athena Cykes (Ace Attorney) vs Kotori Mizuki/Tori Meadows (Yu-Gi-Oh! ZEXAL) 85.) Kairi (Kingdom Hearts) vs Sansa Stark (Game of Thrones) 86.) Mary Winchester (Supernatural) vs Chloe von Einzbern (Fate/kaleid liner PRISMA ILLYA) 87.) Natasha Romanoff (MCU) vs Gwen Stacy (Marvel Comics) 88.) Eve (Paradise Lost) vs Cordelia Chase (Buffy the Vampire Slayer/Angel the Series) 89.) Misa Amane (Death Note) vs Nezuko Kamado (Demon Slayer) 90.) Margaret Houlihan (MASH (Movie 1970)) vs Madison Paige (Heavy Rain) 91.) Casca (Berserk) vs Stephanie Brown (DC Comics) 92.) Dahlia Hawthorne (Ace Attorney) vs Hélène Kuragina (War and Peace) 93.) Sonia Hedgehog (Sonic Underground) vs Elektra Natchios (Marvel Comics) 94.) Susan Pevensie (The Chronicles of Narnia) vs River Tam (Firefly) 95.) Mikaela Banes (Transformers) vs Momo Yaoyorozu (My Hero Academia) 96.) Amy Rose (Sonic the Hedgehog) vs Abbie Mills (Sleepy Hollow)

WEEK 3

65.) Starfire (DC Comics) vs Gwen (BBC Merlin) 66.) Marwa (What We Do in the Shadows (TV Series)) vs Azula (Avatar the Last Airbender) 67.) Chloe Bourgeois (Miraculous Ladybug) vs Chi-Chi (Dragon Ball) 68.) Bumble (Warrior Cats) vs Irene Adler (BBC Sherlock) 69.) Alex DeWitt (DC Comics) vs Amber Volakis (House MD) 70.) Marinette Dupain-Cheng (Miraculous Ladybug) vs Jade (Dragon Quest 11) 71.) Leafpool (Warrior Cats) vs Holy Kujo (JoJo's Bizarre Adventure: Stardust Crusaders) 72.) Mikan Tsumiki (Danganronpa 2: Goodbye Despair) vs Lisa Cuddy (House MD) 73.) Kaede Akamatsu (Daganronpa V3) vs Kamala Khan (Marvel Comics) 74.) Teresa (Maze Runner Series) vs Stephanie (EverymanHYBRID) 75.) Ochako Uraraka (My Hero Academia) vs Malty S Melromarc (Rising of the Shield Hero) 76.) Asuna (Sword Art Online) vs Tasha Yar (Star Trek: The Next Generation) 77.) Sakura Haruno (Naruto) vs Hinata Hyuuga (Naruto) 78.) Quiet (Metal Gear Solid: The Phantom Pain) vs Pussy Galore (Goldfinger) 79.) Ann Takamaki (Persona 5) vs Daenerys Targaryen (Game of Thrones) 80.) Flora Reinhold (Professor Layton) vs Deanna Troi (Star Trek: The Next Generation)

ROUND 1

(Due to limits on links per post, Round 1 links are here.)

WEEK 2

33.) Kendra Young (Buffy the Vampire Slayer/Angel the Series) vs Sylvanas Windrunner (Warcraft) 34.) Katherina Minola (The Taming of the Shrew) vs Barbarara Gordon (DC Comics) 35.) Arcee (Transformers) vs Naomi Misora (Death Note) 36.) Orihime Inoue (Bleach) vs Julia (Hellraiser) 37.) Talia al Ghul (DC Comics) vs Amy Amanda Allen (The A-Team (TV Series)) 38.) Britta Perry (Community) vs Grelle Sutcliff (Black Butler) 39.) Athena Cykes (Ace Attorney) vs Carmelita Montoya Fox (Sly Cooper) 40.) Kotori Mizuki/Tori Meadows (Yu-Gi-Oh! ZEXAL) vs Iris Sagan (AI: the Somnium Files) 41.) Kairi (Kingdom Hearts) vs Clarke Griffin (The 100) 42.) Gamora (MCU) vs Sansa Stark (Game of Thrones) 43.) Mary Winchester (Supernatural) vs Konan (Naruto) 44.) Jade Harley (Homestuck) vs Chloe von Einzbern (Fate/kaleid liner PRISMA ILLYA) 45.) Natasha Romanoff (MCU) vs Sylvia (Two Gentlemen of Verona) 46.) Dragona Joestar (JoJo's Bizarre Adventure: The JOJOLands) vs Gwen Stacy (Marvel Comics) 47.) Katara (Avatar the Last Airbender) vs Eve (Paradise Lost) 48.) Jiang Yanli (Mo Dao Zu Shi) vs Cordelia Chase (Buffy the Vampire Slayer/Angel the Series) 49.) Misa Amane (Death Note) vs Nami (One Piece) 50.) Rey (Star Wars) vs Nezuko Kamado (Demon Slayer) 51.) Tetra (The Legend of Zelda: Wind Waker) vs Margaret Houlihan (MASH (Movie 1970)) 52.) Amy Pond (Doctor Who) vs Madison Paige (Heavy Rain) 53.) Casca (Berserk) vs Sweet-P (The Caligula Effect) 54.) Stephanie Brown (DC Comics) vs Aki Izayoi/Akiza Izinski (Yu-Gi-Oh! 5D's) 55.) Dahlia Hawthorne (Ace Attorney) vs Ophiuchus Shaina (Saint Seiya) 56.) Hélène Kuragina (War and Peace) vs Laurel Lance (Arrow (CW)) 57.) Sky (Lost in Blue) vs Sonia Hedgehog (Sonic Underground) 58.) Elektra Natchios (Marvel Comics) vs Celica (Fire Emblem Echoes: Shadows of Valentia) 59.) Charlie Bradbury (Supernatural) vs Susan Pevensie (The Chronicles of Narnia) 60.) Mildred "Millie" Knolastname (Helluva Boss) vs River Tam (Firefly) 61.) Allura (Voltron: Legendary Defender) vs Mikaela Banes (Transformers) 62.) Ophelia (Hamlet) vs Momo Yaoyorozu (My Hero Academia) 63.) Amy Rose (Sonic the Hedgehog) vs Alys Brangwin (Phantasy Star IV) 64.) Abbie Mills (Sleepy Hollow) vs Misaki Unasaka (Buddy Daddies)

WEEK 1

1.) Starfire (DC Comics) vs Nya Smith (Lego Ninjago) 2.) Natasha Rostova (War and Peace) vs Gwen (BBC Merlin) 3.) Marwa (What We Do in the Shadows (TV Series)) vs Mikoko Sakazaki (Kaiji) 4.) Azula (Avatar the Last Airbender) vs Jennifer Lopez (John Dies at the End) 5.) Chloe Bourgeois (Miraculous Ladybug) vs Milla Maxwell (Tales of Xillia) 6.) Kallen Kouzuki (Code Geass) vs Chi-Chi (Dragon Ball) 7.) Padmé Amidala (Star Wars) vs Bumble (Warrior Cats) 8.) Irene Adler (BBC Sherlock) vs Leia Organa (Star Wars) 9.) Alex DeWitt (DC Comics) vs Elya Musayeva (Топи/The The Swamps (2021)) 10.) Is (Kamen Rider 01) vs Amber Volakis (House MD) 11.) Marinette Dupain-Cheng (Miraculous Ladybug) vs Throné Anguis (Octopath Traveler 2) 12.) Jane Crocker (Homestuck) vs Jade (Dragon Quest 11) 13.) Leafpool (Warrior Cats) vs Katherine Pierce (The Vampire Diaries) 14.) Holy Kujo (JoJo's Bizarre Adventure: Stardust Crusaders) vs Poppy Pipopapo (Kamen Rider Ex-Aid) 15.) Mikan Tsumiki (Danganronpa 2: Goodbye Despair) vs Ochette (Octopath Traveler 2) 16.) Lisa Cuddy (House MD) vs Brunhilda/Mym (Dragalia Lost) 17.) Kaede Akamatsu (Danganronpa V3) vs Juvia Lockser (Fairy Tail) 18.) Agent Texas (Red vs Blue) vs Kamala Khan (Marvel Comics) 19.) April O'Neil (Teenage Mutant Ninja Turtles (2012)) vs Teresa (Maze Runner Series) 20.) Stephanie “Steph” Nocanonlastname (EverymanHYBRID) vs Megaera (Hades) 21.) Ochako Uraraka (My Hero Academia) vs Julia Wicker (The Magicians) 22.) Ran Mouri (Detective Conan) vs Malty S Melromarc (Rising of the Shield Hero) 23.) Magne (My Hero Academia) vs Asuna (Sword Art Online) 24.) Tasha Yar (Star Trek: The Next Generation) vs Yan Hui (Back from the Brink) 25.) Sakura Haruno (Naruto) vs Ada Vessalius (Pandora Hearts) 26.) Hinata Hyuuga (Naruto) vs Niki Nihachu (Dream SMP) 27.) Pyrrha Nikos (RWBY) vs Quiet (Metal Gear Solid: The Phantom Pain) 28.) Lucy Heartfilia (Fairy Tail) vs Pussy Galore (Goldeneye) 29.) Ann Takamaki (Persona 5) vs Mikuru Asahina (The Melancholy of Haruhi Suzumiya) 30.) Daenerys Targaryen (Game of Thrones) vs Kes (STar Trek: Voyager) 31.) Flora Reinhold (Professor Layton) vs Agent South Dakota (Red vs Blue) 32.) Deanna Troi (Star Trek: The Next Generation) vs Nemu Kurotsuchi (Bleach)

[mossy-rainfrog]

hello everyone, I made a series of doodles to put inside of my copy of MobyDick and I would like to share them :3 pls enjoy:

[ID: Two traditional drawings. Ahab shows off his new pegleg with a flourish. Ishmael infodumps about whales to a fond Queequeg. End ID.] [More detailed ID in ALT.]

New Leg Goofin is for chapters 108-9, when Ahab gets fitted with his new leg! it's right before a super devastating chapter so i needed to make myself laugh lmao

Wikipedia Page About Every Whale is for the whole goddamn book, honestly, but I chose to put it at the beginning of ch.32, Cetology, where Ishmael really does try to explain every whale ever

[ID: A comic of Stubb and Flask bursting into Ahab's quarters, thinking the Captain is in danger, only to find Ahab and Fedallah playing a game of cards. End ID.] [More detailed ID in ALT.]

This gem belongs to p.344 where Stubb wonders if Fedallah means to kidnap Ahab, which was such a baffling ridiculous concept that I couldn't help but make fun of it. literally Ahab snuck this man on board bro, what the fuck is fedallah going to do to him. they're playing uno. shut up

[ID: Two drawings, with the first showing Pip after being cast away, haunted and alone on the deck of the Pequod. The other shows Ishmael and Queequeg homoerotically grasping hands while processing whale spermaceti. End ID.] [More detailed ID in ALT.]

this goes out to chapters 93-95, because the UNREAL whiplash from "a child was just abandoned at sea" to "hey let's be horny about whale oil" is still the most insane transition of all time. Ishmael, what the fuck

[ID: A simply doodled meme diagram for how to greet a fellow amputee. The "wrong" answer shows Captains Ahab and Boomer shaking hands, while the "right" answers show Ahab in a handstand and then kicking his leg up high, both times to cross his prosthetic with Boomer's. End ID.] [More detailed ID in ALT.]

this goes out to p. 454. every interaction between these two absolutely delighted me but my mental image for the specific line about them "crossing ivory limbs" got. very silly.

[ID: A small comic of Ahab asking the Harpooners to give him blood to temper his harpoon in. They stare back at him with varying expressions of confused, uncomfortable disbelief. End ID.]

the last one, for p.504. yknow that feel when your boss just walks up and asks you to bleed on his custom made harpoon??? yeah uh. normal workday things

anyways thank you for reading, I had a delightful time making these and am so very fond of them all, so yea :3

credit as always for the designs goes to the darling @pocketsizedquasar , as well as credit for pricelessly annotating my copy of MD and thus getting me to actually read it, love youuuu💙💙

[renytherat]

tagged by @thevampywolf <3

relationship status: single...

fav colour: yall know it; YELLOW

fav food: noodles my guy. or dumplings. i really like rice tho

song stuck in my head: LA DI DA by everglow

last song i listened to: bon bon chocolat by everglow

dream trip: anywhere in europe with my sister

last tv/movie: epic (2013) and house md. wait no tmnt(2003 tv series)

spicy/sweet/savoury: savoury. i like salt.

fun fact abt me: i have 108 stuffed animals

tagging @userelv @hanniiesuckle17 @strawbsandkisses @bringinsexybackk69

[iwannakissasopwithcamel]

Fischer Poll Results

Once this is posted, all the playbook polls will be up! But for now, here are the poll results for the Fischers-

Most Smashable Plane: It's our first 'close enough Tumblr says it' tie! The König-Werke S4 and Teicher Möwe 13S-J both ended up with a 79.6% smash vote, though for the S4 that only means 39 smash votes to the 13S-J's 86. They're tied for second place!

Least Smashable Plane: The König-Werke S1 had a 35.4% pass vote, meaning 17 pass votes. It's in third place!

Most Popular Plane: By over double the Teicher Möwe 13S-J, with 108 votes- more than any aircraft other than the Theler Kobra MD, and for the same reason.

Least Popular Plane: The populace's absurd anti-rotary bias is demonstrated further with the Ritter Model D ‘SeePfau’ getting the least votes, at 44. As things head towards a general status quo, I'd expect to see a lot around that level.

Most Average Plane: Nothing particularly close this time for the Fischer-only part, but the Ritter Model D 'SeePfau' is somewhat close. Among all the planes, the most average is currently... again the Ritter Model D 'SeePfau', at 70.5%- just off the average 70.3%.

[dailyanarchistposts]

Footnotes, 101-150

[101] Joost A. M. Meerloo, Mental Seduction and Menticide: The Psychology of Thought Control and Brain-Washing (London: Jonathan Cape, 1957), 163–164, 184.

[102] B. A. Robinson, “Promise Keepers, Pro and Con: Part 1,” Ontario Consultants on Religious Tolerance, November 2, 2003, www.religioustolerance.org.

[103] Jena Recer, “Whose Promise Are They Keeping?” National NOW Times, August 1995, www.now.org.

[104] James Dobson, “Building Moral Character in Kids,” radio broadcast, Focus on the Family International, February 8, 2006, www.oneplace.com =2/8/2006.

[105] Tony Kushner, Angels in America (New York: Theatre Communications Group, 1995), 46.

[106] James Dobson, Marriage under Fire: Why We Must Win This Battle (Sisters, OR: Multnomah, 2004), 41.

[107] “Focus on the Family,” Citizen Magazine January 2003, quoted in Jeff Lutes, A False Focus on My Family (Lynchburg, VA: Soulforce, 2004), 8.

[108] Dobson, Marriage Under Fire, 49.

[109] James Dobson, Bringing Up Boys (Wheaton, IL: Tyndale House, 2001), 127.

[110] Robert Knight, “The Homosexual Agenda in Schools,” Family Research Council, quoted in Matthew Shepard, “Nazi Anti-Jewish Speech vs. Religious Right Anti-Gay Speech,” Hatecrime.org, www.hatecrime.org.

[111] P. Gibson, “Gay Males and Lesbian Youth Suicide,” in M. R. Feinleib, ed., Report of the Secretary’s Task Force on Youth Suicide, Volume 3: Prevention and Interventions in Youth Suicide(Rockville, MD: U.S. Department of Health and Human Services; Public Health Service; Alcohol, Drug Abuse, and Mental Health Administration, 1989; DHHS publication ADM 89–1623), 110.

[112] Pat Robertson, quoted in Richard K. Fenn, Dreams of Glory, 8.

[113] Kavan Peterson, “Washington Gay Marriage Ruling Looms,” Stateline.org, March 7, 2006, cms.stateline.org; “Same-Sex Marriage Measures on the 2004 Ballot,” National Conference of State Legislatures, November 2004, www.ncsl.org.

[114] Mel White, Stranger at the Gate (New York: Penguin, 1995), 25.

[115] Ibid., 22–23.

[116] Ibid., 29.

[117] Ibid., 14.

[118] Ibid., 49–50.

[119] Ibid., 96.

[120] Ibid., 107.

[121] Ibid., 142.

[122] Hannah Arendt, The Origins of Totalitarianism(New York: Harcourt, 1979), 353.

[123] Scott LaFee, “Local Scientists, Doctors and Professors Talk About ‘Intelligent Design,’” San Diego Union Tribune, June 8, 2005, F-1.

[124] Frank Newport, “Third of Americans Say Evidence Has Supported Darwin’s Evolution Theory,” Gallup Poll, November 19, 2004, poll.gallup.com.

[125] Keith Graham, Biology: God’s Living Creation (Pensacola, FL: A Beka, 1986), 404.

[126] Alfred M. Rehwinkel, The Wonders of Creation (Minneapolis, MN: Bethany House, 1974), in Graham, Biology, 133.

[127] Graham, Biology, 163.

[128] Graham, Biology, 351.

[129] Carl Wieland, “Darwin’s Bodysnatchers: New Horrors,” Creation 14:2 (March 1992), 16–18.

[130] Carl Wieland, “Apartheid and ‘The Cradle of Humankind,’” Creation 26:2 (March 2004), 10–14.

[131] “What Happened When Stalin Read Darwin?” Creation 10:4 (September 1998), 23.

[132] Jerry Bergman, “Darwinism and the Nazi Race Holocaust,” Technical Journal 13:2, 101–111.

[133] “Evolution and the Hutu-Tutsi Slayings,” Creation 21:2 (March 1999), 47.

[134] Graham, Biology, 347.

[135] Jerry Bergman, “Was Charles Darwin Psychotic? A Study of His Mental Health,” Impact (January 2004).

[136] Raymond Hall, “Darwin’s Impact—The Bloodstained Legacy of Evolution,” Creation 27:2 (March 2005), 46–47.

[137] Graham, Biology, 347.

[138] Ibid., 349.

[139] Hannah Arendt, Origins of Totalitarianism, 371.

[140] “Intelligence Report,” Southern Poverty Law Center (Spring 2005), 4. www.splcenter.org.

[141] Union of Concerned Scientists, “Scientific Integrity in Policy Making: An Investigation into the Bush Administration’s Misuse of Science,” March 2004, 2; 32, www.ucsusa.org.

[142] This lecture was taped and transcribed by Timothy Nunan of Princeton University.

[143] Karl Popper, The Open Society and Its Enemies (Princeton: Princeton University Press, 1971), 1:96.

[144] Max Blumenthal, “Justice Sunday Preachers,” The Nation, May 9, 2005 (Web edition only), www.thenation.com.

[145] Ibid.

[146] Ibid.

[147] David Kirkpatrick, “Club of the Most Powerful Gathers in Strictest Privacy,” The New York Times, August 28, 2004.

[148] Ibid.

[149] Max Blumenthal, “Who Are Justice Sunday’s Ministers of Ministry?” Talk To Action, January 6, 2006, www.talk2action.org.

[150] Quoted in Daniel Lev, The Terrorist Next Door (New York: Thomas Dumae/St. Martin, 2002), 27.

[aryburn-trains]

5 More of Roger Puta Playing With Light by Marty Bernard Via Flickr: B&O RDC, Train 108, during the Blue Hour at Laurel, MD on March 10, 1971.

Enjoy Roger's work!

[jymeia]

All the elements of the periodic table in the Kichwa language

Jose E. Andino-Enríquez, Manuel A. Andino-Enríquez, Francis E. Hidalgo-Báez, Sisa P. Chalán-Gualán, Santiago D. Gualapuro-Gualapuro, Simone Belli and Michelle B. Chicaiza-Lema recently published in the Journal of Chemical Education a proposal to adapt the names of the 118 elements of the periodic table to Kichwa (Adaptation of the Periodic Table to Kichwa: An Ecuadorian Native Language; J. Chem. Ed., 2022 99 (1), 211-218, DOI: 10.1021/acs.jchemed.1c00383).

The authors explain that the Kichwa lacks scientific tools to respond to educational needs, triggering the gradual loss of intercultural diversity. To adapt the periodic table to Kichwa, they took into account the different linguistic variations of the language and the opinion of the speakers. This is the adaptation compared to the Spanish version, the language most widely spoken in Ecuador:

Lo autores explican que que el kichwa carece de herramientas científicas para responder a las necesidades educativas, desencadenando la pérdida gradual de la diversidad intercultural. Para adaptar la tabla periódica al kichwa tuvieron en cuenta las diferentes variaciones lingüísticas del idioma y la opinión de los hablantes. Así quedó la adaptación:

Kichwa Spanish

1 H Yakutiksi Hidrógeno 2 He Hilyu Helio 3 Li Lityu Litio 4 Be Wirilyu Berilio 5 B Puru Boro 6 C Killimsa Carbono 7 N Nitrohinyu Nitrógeno 8 O Wayrasamay Oxígeno 9 F Flur Flúor 10 Ne Nyun Neón 11 Na Sutyu Sodio 12 Mg Maknisyu Magnesio 13 Al Aluminyu Aluminio 14 Si Silisyu Silicio 15 P Puspuru Fósforo 16 S Salliy Azufre 17 Cl Kluru Cloro 18 Ar Arkun Argón 19 K Putasyu Potasio 20 Ca Kalsyu Calcio 21 Sc Iskantyu Escandio 22 Ti Titanyu Titanio 23 V Panatyu Vanadio 24 Cr Krumyu Cromo 25 Mn Mankanisyu Manganeso 26 Fe Hirr Hierro 27 Co Ankasi Cobalto 28 Ni Nikyl Níquel 29 Cu Anta Cobre 30 Zn Zink Zinc 31 Ga Kalyu Galio 32 Ge Hirmanyu Germanio 33 As Arsyniku Arsénico 34 Se Silinyu Selenio 35 Br Prumyu Bromo 36 Kr Kriptun Kriptón 37 Rb Rupityu Rubidio 38 Sr Instrunsyu Estroncio 39 Y Itryu Itrio 40 Zr Zirkunyu Zirconio 41 Nb Nyupyu Niobio 42 Mo Muliptinyu Molibdeno 43 Tc Tiknisyu Tecnecio 44 Ru Rutinyu Rutenio 45 Rh Rutyu Rodio 46 Pd Palatyu Paladio 47 Ag Kullki Plata 48 Cd Katmyu Cadmio 49 In Intyu Indio 50 Sn Istanyu Estaño 51 Sb Antimonyu Antimonio 52 Te Tiluryu Telurio 53 I Iwtyu Yodo 54 Xe Sinun Xenón 55 Cs Sisyu Cesio 56 Ba Paryu Bario 57 La Lantanyu Lantano 58 Ce Siryu Cerio 59 Pr Prasyutimyu Praseodimio 60 Nd Nyutimyu Neodimio 61 Pm Prumisyu Prometio 62 Sm Samaryu Samario 63 Eu Yurupyu Europio 64 Gd Katulinyu Gadolinio 65 Tb Tirpyu Terbio 66 Dy Tisprusyu Disprosio 67 Ho Hulmyu Holmio 68 Er Irpyu Erbio 69 Tm Tulyu Tulio 70 Yb Itirpyu Iterbio 71 Lu Lutisyu Lutecio 72 Hf Hafnyu Hafnio 73 Ta Tantalyu Tántalo 74 W Ulpramyu Wolframio 75 Re Rinyu Renio 76 Os Usmyu Osmio 77 Ir Irityu Iridio 78 Pt Platinyu Platino 79 Au Kuri Oro 80 Hg Mirkuryu Mercurio 81 Tl Talyu Talio 82 Pb Antaki Plomo 83 Bi Pismutyu Bismuto 84 Po Polunyu Polonio 85 At Astatyu Astato 86 Rn Ratun Radón 87 Fr Fransyu Francio 88 Ra Ratyu Radio 89 Ac Aktinyu Actinio 90 Th Turyu Torio 91 Pa Prutaktinyu Protactinio 92 U Uranyu Uranio 93 Np Niptunyu Neptunio 94 Pu Plutonyu Plutonio 95 Am Amerisyu Americio 96 Cm Kuryu Curio 97 Bk Pirkilyu Berkelio 98 Cf Kalifurnyu Californio 99 Es Instinyu Einstenio 100 Fm Firmyu Fermio 101 Md Mintilipyu Mendelevio 102 No Nupilyu Nobelio 103 Lw Lawrinsyu Lawrencio 104 Rf Rutirfurtyu Rutherfordio 105 Db Tupnyu Dubnio 106 Sg Syapurhyu Seaborgio 107 Bh Puhryu Bohrio 108 Hs Hasyu Hasio 109 Mt Mitniryu Meitnerio 110 Ds Tarmastatyu Darmstatio 111 Rg Runtihinyu Roentgenio 112 Cn Kupirnisyu Copernicio 113 Nh Nihunyu Nihonio 114 Fl Flirupyu Flerovio 115 Mc Muskupyu Moscovio 116 Lv Lipirmuryu Livermorio 117 Ts Tinisyu Teneso 118 Og Ukanisun Oganesón

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Reprinted with permission from “Adaptation of the Periodic Table to Kichwa: An Ecuadorian Native Language. Author: Jose E. Andino-Enríquez, Manuel A. Andino-Enríquez, Francis E. Hidalgo-Báez, et al. Publication:  Journal of Chemical Education Publisher: American Chemical Society. Date: Jan 1, 2022. Copyright © 2022, American Chemical Society”.

(For an individual, for non-commercial purposes, permission is granted at no charge for a cumulative total of 4 or fewer figures, tables, or micrographs, or an excerpt of 400 or fewer words for a single article. Appropriate credit should be given. Appropriate credit should read: "Reprinted with permission from {COMPLETE REFERENCE CITATION}. Copyright {YEAR} American Chemical Society." Insert appropriate information in place of the capitalized words. Please save this page for your records and for publication in print provide a copy to your publisher).

[the-eternal-quest-for-balance]

Here is a list of the US July temperatures in 1936.

On July 9, temperature’s spiked, with many all-time record highs being set in both the Great Lakes and Northeast United States. The recap of temperatures are as follows for July 9th.

Rockford, IL: 101 °F (38 °C)[22]

Pittsburgh, PA: 101 °F (38 °C)

Syracuse, NY: 102 °F (39 °C)

Rochester, NY: 102 °F (39 °C)

Detroit, MI: 102 °F (39 °C)[28]

Philadelphia, PA: 103 °F (39 °C)

Albany, NY: 103 °F (39 °C)[29]

Baltimore, MD: 103 °F (39 °C)

Scranton, PA: 103 °F (39 °C)

Washington DC: 104 °F (40 °C)

Johnstown, PA: 104 °F (40 °C)

Columbus, OH: 105 °F (40.6 °C)

Warren, OH: 105 °F (40.6 °C)

Williamsport, PA: 106 °F (41.1 °C)

Trenton, NJ: 106 °F (41.1 °C)

Central Park, New York City: 106 °F (41.1 °C)

On July 10, the heat peaked in Mid-Atlantic and Northeast, with some areas setting all-time record highs in parts of the South and most of the Midwest. The recap is as follows.

Atlanta, GA: 100 °F (37.8 °C)

Pittsburgh PA: 101 °F (38.3 °C)

Detroit, MI: 102 °F (38.9 °C)[28]

Grand Rapids, MI: 102 °F (38.9 °C)[26]

Central Park, New York City: 102 °F (38.9 °C)[5]

Youngstown, OH: 103 °F (39.4 °C)

Philadelphia, PA: 104 °F (40.0 °C)

Richmond, VA: 105 °F (40.6 °C)

Washington DC: 105 °F (40.6 °C)

Lynchburg, VA: 106 °F (41.1 °C)

Rockford, IL: 106 °F (41.1 °C)[22]

Bowling Green, KY: 106 °F (41.1 °C)

St. Cloud, MN: 106 °F (41.1 °C)[30]

Baltimore, MD: 107 °F (41.7 °C)

Lexington, KY: 108 °F (42.2 °C)

Xenia, OH: 108 °F (42.2 °C)

Cumberland & Frederick, MD: 109 °F (42.8 °C)

Runyon, NJ: 110 °F (43.3 °C)

Phoenixville, PA: 111 °F (43.9 °C)

Martinsburg, WV: 112 °F (44.4 °C)

Aberdeen, SD: 114 °F (45.6 °C)

They lie, they lie, they lie.

The only way to move their agenda forward is to lie.

And peoples fear, their ignorant, selfish fear, is what makes the lies work.

[wqp88888]

Country Codes List

CountryAlpha 2Alpha 3 CodeUN Code

A

Afghanistan AF AFG 004

ALA Aland Islands AX ALA 248

Albania AL ALB 008

Algeria DZ DZA 012

American Samoa AS ASM 016

Andorra AD AND 020

Angola AO AGO 024

Anguilla AI AIA 660

Antarctica AQ ATA 010

Antigua and Barbuda AG ATG 028

Argentina AR ARG 032

Armenia AM ARM 051

Aruba AW ABW 533

Australia AU AUS 036

Austria AT AUT 040

Azerbaijan AZ AZE 031

B

Bahamas BS BHS 044

Bahrain BH BHR 048

Bangladesh BD BGD 050

Barbados BB BRB 052

Belarus BY BLR 112

Belgium BE BEL 056

Belize BZ BLZ 084

Benin BJ BEN 204

Bermuda BM BMU 060

Bhutan BT BTN 064

Bolivia BO BOL 068

Bosnia and Herzegovina BA BIH 070

Botswana BW BWA 072

Bouvet Island BV BVT 074

Brazil BR BRA 076

British Virgin Islands VG VGB 092

British Indian Ocean Territory IO IOT 086

Brunei Darussalam BN BRN 096

Bulgaria BG BGR 100

Burkina Faso BF BFA 854

Burundi BI BDI 108

C

Cambodia KH KHM 116

Cameroon CM CMR 120

Canada CA CAN 124

Cape Verde CV CPV 132

Cayman Islands KY CYM 136

Central African Republic CF CAF 140

Chad TD TCD 148

Chile CL CHL 152

China CN CHN 156

Hong Kong, SAR China HK HKG 344

Macao, SAR China MO MAC 446

Christmas Island CX CXR 162

Cocos (Keeling) Islands CC CCK 166

Colombia CO COL 170

Comoros KM COM 174

Congo (Brazzaville) CG COG 178

Congo, (Kinshasa) CD COD 180

Cook Islands CK COK 184

Costa Rica CR CRI 188

Côte d'Ivoire CI CIV 384

Croatia HR HRV 191

Cuba CU CUB 192

Cyprus CY CYP 196

Czech Republic CZ CZE 203

D

Denmark DK DNK 208

Djibouti DJ DJI 262

Dominica DM DMA 212

Dominican Republic DO DOM 214

E

Ecuador EC ECU 218

Egypt EG EGY 818

El Salvador SV SLV 222

Equatorial Guinea GQ GNQ 226

Eritrea ER ERI 232

Estonia EE EST 233

Ethiopia ET ETH 231

F

Falkland Islands (Malvinas) FK FLK 238

Faroe Islands FO FRO 234

Fiji FJ FJI 242

Finland FI FIN 246

France FR FRA 250

French Guiana GF GUF 254

French Polynesia PF PYF 258

French Southern Territories TF ATF 260

G

Gabon GA GAB 266

Gambia GM GMB 270

Georgia GE GEO 268

Germany DE DEU 276

Ghana GH GHA 288

Gibraltar GI GIB 292

Greece GR GRC 300

Greenland GL GRL 304

Grenada GD GRD 308

Guadeloupe GP GLP 312

Guam GU GUM 316

Guatemala GT GTM 320

Guernsey GG GGY 831

Guinea GN GIN 324

Guinea-Bissau GW GNB 624

Guyana GY GUY 328

H

Haiti HT HTI 332

Heard and Mcdonald Islands HM HMD 334

Holy See (Vatican City State) VA VAT 336

Honduras HN HND 340

Hungary HU HUN 348

I

Iceland IS ISL 352

India IN IND 356

Indonesia ID IDN 360

Iran, Islamic Republic of IR IRN 364

Iraq IQ IRQ 368

Ireland IE IRL 372

Isle of Man IM IMN 833

Israel IL ISR 376

Italy IT ITA 380

J

Jamaica JM JAM 388

Japan JP JPN 392

Jersey JE JEY 832

Jordan JO JOR 400

K

Kazakhstan KZ KAZ 398

Kenya KE KEN 404

Kiribati KI KIR 296

Korea (North) KP PRK 408

Korea (South) KR KOR 410

Kuwait KW KWT 414

Kyrgyzstan KG KGZ 417

L

Lao PDR LA LAO 418

Latvia LV LVA 428

Lebanon LB LBN 422

Lesotho LS LSO 426

Liberia LR LBR 430

Libya LY LBY 434

Liechtenstein LI LIE 438

Lithuania LT LTU 440

Luxembourg LU LUX 442

M

Macedonia, Republic of MK MKD 807

Madagascar MG MDG 450

Malawi MW MWI 454

Malaysia MY MYS 458

Maldives MV MDV 462

Mali ML MLI 466

Malta MT MLT 470

Marshall Islands MH MHL 584

Martinique MQ MTQ 474

Mauritania MR MRT 478

Mauritius MU MUS 480

Mayotte YT MYT 175

Mexico MX MEX 484

Micronesia, Federated States of FM FSM 583

Moldova MD MDA 498

Monaco MC MCO 492

Mongolia MN MNG 496

Montenegro ME MNE 499

Montserrat MS MSR 500

Morocco MA MAR 504

Mozambique MZ MOZ 508

Myanmar MM MMR 104

N

Namibia NA NAM 516

Nauru NR NRU 520

Nepal NP NPL 524

Netherlands NL NLD 528

Netherlands Antilles AN ANT 530

New Caledonia NC NCL 540

New Zealand NZ NZL 554

Nicaragua NI NIC 558

Niger NE NER 562

Nigeria NG NGA 566

Niue NU NIU 570

Norfolk Island NF NFK 574

Northern Mariana Islands MP MNP 580

Norway NO NOR 578

O

Oman OM OMN 512

P

Pakistan PK PAK 586

Palau PW PLW 585

Palestinian Territory PS PSE 275

Panama PA PAN 591

Papua New Guinea PG PNG 598

Paraguay PY PRY 600

Peru PE PER 604

Philippines PH PHL 608

Pitcairn PN PCN 612

Poland PL POL 616

Portugal PT PRT 620

Puerto Rico PR PRI 630

Q

Qatar QA QAT 634

R

Réunion RE REU 638

Romania RO ROU 642

Russian Federation RU RUS 643

Rwanda RW RWA 646

S

Saint-Barthélemy BL BLM 652

Saint Helena SH SHN 654

Saint Kitts and Nevis KN KNA 659

Saint Lucia LC LCA 662

Saint-Martin (French part) MF MAF 663

Saint Pierre and Miquelon PM SPM 666

Saint Vincent and Grenadines VC VCT 670

Samoa WS WSM 882

San Marino SM SMR 674

Sao Tome and Principe ST STP 678

Saudi Arabia SA SAU 682

Senegal SN SEN 686

Serbia RS SRB 688

Seychelles SC SYC 690

Sierra Leone SL SLE 694

Singapore SG SGP 702

Slovakia SK SVK 703

Slovenia SI SVN 705

Solomon Islands SB SLB 090

Somalia SO SOM 706

South Africa ZA ZAF 710

South Georgia and the South Sandwich Islands GS SGS 239

South Sudan SS SSD 728

Spain ES ESP 724

Sri Lanka LK LKA 144

Sudan SD SDN 736

Suriname SR SUR 740

Svalbard and Jan Mayen Islands SJ SJM 744

Swaziland SZ SWZ 748

Sweden SE SWE 752

Switzerland CH CHE 756

Syrian Arab Republic (Syria) SY SYR 760

T

Taiwan, Republic of China TW TWN 158

Tajikistan TJ TJK 762

Tanzania, United Republic of TZ TZA 834

Thailand TH THA 764

Timor-Leste TL TLS 626

Togo TG TGO 768

Tokelau TK TKL 772

Tonga TO TON 776

Trinidad and Tobago TT TTO 780

Tunisia TN TUN 788

Turkey TR TUR 792

Turkmenistan TM TKM 795

Turks and Caicos Islands TC TCA 796

Tuvalu TV TUV 798

U

Uganda UG UGA 800

Ukraine UA UKR 804

United Arab Emirates AE ARE 784

United Kingdom GB GBR 826

United States of America US USA 840

US Minor Outlying Islands UM UMI 581

Uruguay UY URY 858

Uzbekistan UZ UZB 860

V

Vanuatu VU VUT 548

Venezuela (Bolivarian Republic) VE VEN 862

Viet Nam VN VNM 704

Virgin Islands, US VI VIR 850

W

Wallis and Futuna Islands WF WLF 876

Western Sahara EH ESH 732

Y-Z

Yemen YE YEM 887

Zambia ZM ZMB 894

Zimbabwe ZW ZWE 716

[saintkey]

I posted 8,495 times in 2022

That's 6,003 more posts than 2021!

290 posts created (3%)

8,205 posts reblogged (97%)

Blogs I reblogged the most:

@seilon

@jonghyuny

@sister-cage

@taemmin

@minjungismygodess

I tagged 3,368 of my posts in 2022

#q - 398 posts

#it speaks - 237 posts

#q: dead cells misery party - 233 posts

#yuzuru hanyu - 209 posts

#personal - 196 posts

#shinee - 152 posts

#rina - 109 posts

#d20 - 108 posts

#key - 77 posts

#md - 67 posts

Longest Tag: 140 characters

#'gee well shes a minor and the tests came at such a bad time!! :( i'd feel bad if she couldnt compete just because of a failed drug test :((

My Top Posts in 2022:

#5

easy (g.o.a.t. in the keyland)

247 notes - Posted October 23, 2022

#4

Alysa Liu skating to Loco by ITZY at the 2022 Beijing Olympics Figure Skating Exhibition Gala

Fun Fact: Alysa was put in the lineup last minute due to another skater being injured (Correction: Alysa was originally invited, I mixed it up with another skater!) Alysa didn’t have a gala performance prepared. She learned this program just this week and had help from other skaters with her hair, makeup, and costume.

371 notes - Posted February 20, 2022

#3

something in the scene of ally being so concerned and asking if they can help pinocchio because he’s a sweet child in mother goose’s eyes meanwhile pinocchio is presently squaring up to stephan and calling him a “stupid motherfucker”

381 notes - Posted December 1, 2022

#2

here’s xinyu spinning yuzu around in a princess carry for posterity

467 notes - Posted February 20, 2022

My #1 post of 2022

Yuzuru Hanyu skating to Haru Yo, Koi (Come, Spring) for the 2022 Beijing Olympics Figure Skating Exhibition Gala

604 notes - Posted February 20, 2022

Get your Tumblr 2022 Year in Review →

[peters385]

Labtron MD-RPW-1001 Reclining Power Wheelchair features a brushless controller and dual 24V 250W motors. It has a lithium 10.8Ah battery offering a range of 16 to 20 km. With a max speed of 6 km/h it charges in 6 hours. The wheelchair includes an electromagnetic braking system and measures 108 × 64 × 110 cm unfolded.

[snehagoogle]

Since childhood

Since childhood I have known that Jupiter is ten times bigger than Earth.

Recent research shows from the perspective of NASA's Juno Spacecraft

that Jupiter has normal water in its atmosphere.

Now Jupiter is a planet in which thousands of Earths can fit.

Earth has water at three levels on its entire surface.

So Jupiter's normal water content means how much water is there there?

Jupiter's atmosphere contains about 0.25% water molecules at the equator, which is almost three times the amount of water in the Sun's atmosphere. 

Here's some more information about water on Jupiter: 

Measuring water

The Juno mission's Microwave Radiometer (MWR) measures the amount of water in Jupiter's atmosphere by detecting how water absorbs certain wavelengths of microwave radiation. 

Water in the atmosphere

The MWR can collect data from deeper into Jupiter's atmosphere than the Galileo probe, allowing it to measure water in greater detail. 

Water in the ocean

Jupiter's large ocean is made of hydrogen, not water. The pressure and temperature increase deep in the atmosphere, compressing the hydrogen gas into a liquid.

Relatively speaking, there's not much water on Jupiter. It's gasseous atmosphere is made up primarily of helium and hydrogen, the same gasses that the Sun is made of. Together they make up about 99% of the planet.30 Apr 2020

Jupiter: Facts

NASA Science (.gov)

https://science.nasa.gov › jupiter › jupiter-facts

This gives Jupiter the largest ocean in the solar system – an ocean made of hydrogen instead of water.

Jupiter Fact Sheet - the NSSDCA

NASA (.gov)

https://nssdc.gsfc.nasa.gov › planetary › jupiterfact

11 Jan 2024 — Jupiter Mean Orbital Elements (J2000). Semimajor axis (AU) 5.20336301 ... Water (H2O) - 4 (varies with pressure) Aerosols: Ammonia ice

Jupiter/Earth Comparison

Bulk parameters 

                              Jupiter                        Earth   Ratio

(Jupiter/Earth)

Mass (1024 kg) 1,898.13 5.9722 317.83

Volume (1010 km3) 143,128 108.321 1321.33

Equatorial radius (1 bar level) (km) 71,492 6,378.1 11.209

Polar radius (km) 66,854 6,356.8 10.517

Volumetric mean radius (km) 69,911 6,371.0 10.973

Ellipticity 0.06487 0.00335 19.36

Mean density (kg/m3) 1,326 5,513 0.241

Gravity (mean, 1 bar) (m/s2) 25.92 9.82 2.640

Acceleration (eq., 1 bar) (m/s2) 23.12 9.78 2.364

Acceleration (pole, 1 bar) (m/s2) 27.01 9.83 2.748

Escape velocity (km/s) 59.5 11.19 5.32

GM (x 106 km3/s2) 126.687 0.39860 317.83

Bond albedo 0.343 0.294 1.17

Geometric albedo 0.538 0.434 1.24

V-band magnitude V(1,0) -9.40 -3.99 -

Solar irradiance (W/m2) 50.26 1361.0 0.037

Black-body temperature (K) 109.9 254.0 0.433

Moment of inertia (I/MR2) 0.254 0.3308 0.768

J2 (x 10-6) 14,736 1082.63 13.611

Number of natural satellites 95 1  

Planetary ring system Yes No

Jovian Atmosphere

Surface Pressure: >>1000 bars  

Temperature at 1 bar: 165 K (-108 C)

Temperature at 0.1 bar: 112 K (-161 C)

Density at 1 bar: 0.16 kg/m3

Wind speeds

   Up to 150 m/s (<30 degrees latitude)

   Up to  40 m/s (>30 degrees latitude)

Scale height: 27 km

Mean molecular weight: 2.22 

Atmospheric composition (by volume, uncertainty in parentheses)

    Major:       Molecular hydrogen (H2) - 89.8% (2.0%); Helium (He) - 10.2% (2.0%)

    Minor (ppm): Methane (CH4) - 3000 (1000); Ammonia (NH3) - 260 (40);

                 Hydrogen Deuteride (HD) - 28 (10); Ethane (C2H6) - 5.8 (1.5);

                 Water (H2O) - 4 (varies with pressure)

    Aerosols:    Ammonia ice, water ice, ammonia hydrosulfide 

Author/Curator:

Dr. David R. Williams, dave.williams@nasa.gov

NSSDCA, Mail Code 690.1

NASA Goddard Space Flight Center

Greenbelt, MD 20771

+1-301-286-1258

Translate Hindi

बचपन में से ही मैं जानते आए है बृहस्पति ग्रह पृथ्वी दश गुणा है

अभी अभी की रिसर्च यह समझा रहा है नासा की जूनो स्पेस क्रैफ्ट की दृष्टिकोण से

की बृहस्पति ग्रह की वातावरण में सामान्य पानी का मौजूदगी है

अब वो है बृहस्पति ग्रह जिसमें हजारों पृथ्वी जो समा सकता है

पृश्वी की तो पानी है संपूर्ण सतह की तीन स्तर

तो फिर बृहस्पति ग्रह की वो सामान्य पानी का मतलब कितना पानी है वहां

बृहस्पति के वायुमंडल में भूमध्य रेखा पर लगभग 0.25% जल अणु हैं, जो सूर्य के वायुमंडल में मौजूद जल की मात्रा से लगभग तीन गुना है। बृहस्पति पर पानी के बारे में यहाँ कुछ और जानकारी दी गई है: पानी को मापना जूनो मिशन का माइक्रोवेव रेडियोमीटर (MWR) बृहस्पति के वायुमंडल में पानी की मात्रा को मापता है, यह पता लगाकर कि पानी माइक्रोवेव विकिरण की कुछ तरंग दैर्ध्य को कैसे अवशोषित करता है। वायुमंडल में पानी गैलीलियो जांच की तुलना में MWR बृहस्पति के वायुमंडल में गहराई से डेटा एकत्र कर सकता है, जिससे यह पानी को अधिक विस्तार से माप सकता है। महासागर में पानी बृहस्पति का विशाल महासागर हाइड्रोजन से बना है, पानी से नहीं। वायुमंडल में गहराई में दबाव और तापमान बढ़ता है, जिससे हाइड्रोजन गैस एक तरल में संपीड़ित होती है। तुलनात्मक रूप से, बृहस्पति पर बहुत अधिक पानी नहीं है। इसका गैसीय वायुमंडल मुख्य रूप से हीलियम और हाइड्रोजन से बना है, वही गैसें जिनसे सूर्य बना है। साथ में वे ग्रह का लगभग 99% हिस्सा बनाते हैं।30 अप्रैल 2020

बृहस्पति: तथ्य

NASA विज्ञान (.gov)

https://science.nasa.gov › jupiter › jupiter-facts

यह बृहस्पति को सौर मंडल का सबसे बड़ा महासागर बनाता है - पानी के बजाय हाइड्रोजन से बना एक महासागर।

बृहस्पति तथ्य पत्रक - NSSDCA

NASA (.gov)

https://nssdc.gsfc.nasa.gov › planetary › jupiterfact

11 जनवरी 2024 — बृहस्पति माध्य कक्षीय तत्व (J2000)। अर्ध-प्रमुख अक्ष (एयू) 5.20336301 ... पानी (H2O) - 4 (दबाव के साथ बदलता रहता है) एरोसोल: अमोनिया बर्फ

बृहस्पति/पृथ्वी तुलना

बल्क पैरामीटर

बृहस्पति पृथ्वी अनुपात

(बृहस्पति/पृथ्वी)

द्रव्यमान (1024 किग्रा) 1,898.13 5.9722 317.83

आयतन (1010 किमी3) 143,128 108.321 1321.33

भूमध्यरेखीय त्रिज्या (1 बार स्तर) (किमी) 71,492 6,378.1 11.209

ध्रुवीय त्रिज्या (किमी) 66,854 6,356.8 10.517

आयतन माध्य त्रिज्या (किमी) 69,911 6,371.0 10.973

अण्डाकारता 0.06487 0.00335 19.36

औसत घनत्व (किलोग्राम/मी3) 1,326 5,513 0.241

गुरुत्वाकर्षण (औसत, 1 बार) (मी/सेकेंड2) 25.92 9.82 2.640

त्वरण (समतुल्य, 1 बार) (मी/सेकेंड2) 23.12 9.78 2.364

त्वरण (ध्रुव, 1 बार) (मी/सेकेंड2) 27.01 9.83 2.748

पलायन वेग (किमी/सेकेंड) 59.5 11.19 5.32

जीएम (x 106 किमी3/सेकेंड2) 126.687 0.39860 317.83

बॉन्ड एल्बेडो 0.343 0.294 1.17

ज्यामितीय एल्बेडो 0.538 0.434 1.24

V-बैंड परिमाण V(1,0) -9.40 -3.99 -

सौर विकिरण (W/m2) 50.26 1361.0 0.037

ब्लैक-बॉडी तापमान (K) 109.9 254.0 0.433

जड़त्व आघूर्ण (I/MR2) 0.254 0.3308 0.768

J2 (x 10-6) 14,736 1082.63 13.611

प्राकृतिक उपग्रहों की संख्या 95 1

ग्रहीय वलय प्रणाली हाँ नहीं

जोवियन वायुमंडल

सतह दबाव: >>1000 बार

1 बार पर तापमान: 165 K (-108 C)

तापमान 0.1 बार: 112 K (-161 C)

1 बार पर घनत्व: 0.16 kg/m3

हवा की गति

150 m/s तक (<30 डिग्री अक्षांश)

40 m/s तक (>30 डिग्री अक्षांश)

पैमाने की ऊँचाई: 27 किमी

औसत आणविक भार: 2.22

वायुमंडलीय संरचना (आयतन के अनुसार, कोष्���क में अनिश्चितता)

मुख्य: आणविक हाइड्रोजन (H2) - 89.8% (2.0%); हीलियम (He) - 10.2% (2.0%)

मामूली (पीपीएम): मीथेन (CH4) - 3000 (1000); अमोनिया (NH3) - 260 (40);

हाइड्रोजन ड्यूटेराइड (HD) - 28 (10); ईथेन (C2H6) - 5.8 (1.5); पानी (H2O) - 4 (दबाव के साथ बदलता रहता है)

एरोसोल: अमोनिया बर्फ, पानी की बर्फ, अमोनिया हाइड्रोसल्फाइड

लेखक/क्यूरेटर:

डॉ. डेविड आर. विलियम्स, dave.williams@nasa.gov

NSSDCA, मेल कोड 690.1

NASA गोडार्ड स्पेस फ़्लाइट सेंटर

ग्रीनबेल्ट, MD 20771

+1-301-286-1258

[kayvanh123]

Cognitive behavioral therapy strengthens brain circuits, helping to alleviate depression.

Cognitive behavioral therapy (CBT), a widely used treatment for depression, helps individuals develop coping skills, reinforce healthy habits, and challenge negative thoughts. But can changing thought patterns and behaviors lead to lasting changes in the brain?

New research from Stanford Medicine suggests it can—when tailored to the right patients. In a study of adults with both depression and obesity, a challenging combination to treat, problem-solving therapy (a type of CBT) reduced depression in a third of the participants. These patients also experienced positive changes in their brain circuitry.

Remarkably, these neural changes appeared within just two months of therapy and could predict which patients would benefit from long-term treatment.

The findings add to growing evidence that matching depression treatments to the neurological profiles of patients—since these differ between individuals—increases the chances of success.

This concept is already routine in other medical fields. As Leanne Williams, PhD, professor of psychiatry and behavioral sciences and director of Stanford Medicine's Center for Precision Mental Health and Wellness, explained, "If you had chest pain, your doctor would run tests—like an ECG or a heart scan—to find the cause and determine treatment. But in depression, we have no tests for what’s happening in the brain, so treatment selection is often trial-and-error."

Williams and Jun Ma, MD, PhD, professor at the University of Illinois at Chicago, co-led the study, which was published in Science Translational Medicine on Sept. 4. Their work is part of a larger trial, RAINBOW (Research Aimed at Improving Both Mood and Weight).

Problem-solving therapy, the CBT approach used in the trial, is designed to enhance cognitive skills like planning, troubleshooting, and ignoring irrelevant information. Therapists help patients identify real-life problems, brainstorm solutions, and choose the best course of action. These skills rely on a network of neurons known as the cognitive control circuit.

Earlier research from Williams’ lab found that about 25% of people with depression experience dysfunction in their cognitive control circuits, either with too much or too little activity.

The new study focused on adults with both major depression and obesity—symptoms often linked to cognitive control issues. These patients tend to respond poorly to antidepressants, with a low success rate of 17%.

Of the 108 participants, 59 received a year of problem-solving therapy along with their usual care (medications, primary care visits), while the other 49 continued with their usual care alone. Brain scans (fMRI) were conducted at the study’s start and at two, six, 12, and 24 months. During the scans, participants performed tasks designed to engage their cognitive control circuits, allowing researchers to track changes in brain activity.

"We wanted to know if this specific therapy could modulate the cognitive control circuit," said Xue Zhang, PhD, the study’s lead author.

Alongside the brain scans, participants completed surveys that measured problem-solving ability and depression symptoms.

As with any treatment, problem-solving therapy didn’t work for everyone, but 32% of participants saw their symptoms decrease by at least half—double the 17% response rate for antidepressants.

In the usual care group, cognitive control circuit activity declined over time, which correlated with worsening problem-solving ability. In contrast, the therapy group showed a reversal: decreased brain activity corresponded with better problem-solving. Researchers believe this reflects the brain learning to process information more efficiently.

"Before therapy, their brains were working harder. Now, they’re working smarter," Zhang said.

Across both groups, overall depression improved, but therapy’s impact on "feeling everything is an effort"—a key symptom of cognitive impairment—stood out, highlighting its real-world benefits. Participants reported clearer thinking, resuming work, and re-engaging in social activities.

Importantly, brain scans showed changes in cognitive control circuit activity after just two months of therapy, signaling early brain plasticity. "Real-world problem solving is literally changing the brain in a couple of months," Williams noted.

Moreover, these early changes predicted which patients would benefit long-term, offering a potential tool for precision psychiatry—using brain scans to match patients with the most effective treatments.

"This is advancing the science and transforming lives," Zhang said.

Researchers from the University of Washington, University of Pittsburgh School of Medicine, and The Ohio State University contributed to the study.

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Massive Biomolecular Shifts Occur in Our 40s and 60s, Stanford Medicine Researchers Find

Time marches on predictably, but biological aging is anything but constant, according to a new Stanford Medicine study.

— August 14, 2024 | By Rachel Tompa

We undergo two periods of rapid change, averaging around age 44 and age 60, according to a Stanford Medicine study. Ratana21/Shutterstock.com

If it’s ever felt like everything in your body is breaking down at once, that might not be your imagination. A new Stanford Medicine study shows that many of our molecules and microorganisms dramatically rise or fall in number during our 40s and 60s.

Researchers assessed many thousands of different molecules in people from age 25 to 75, as well as their microbiomes — the bacteria, viruses and fungi that live inside us and on our skin — and found that the abundance of most molecules and microbes do not shift in a gradual, chronological fashion. Rather, we undergo two periods of rapid change during our life span, averaging around age 44 and age 60. A paper describing these findings was published in the journal Nature Aging Aug. 14.

“We’re not just changing gradually over time; there are some really dramatic changes,” said Michael Snyder, PhD, professor of genetics and the study’s senior author. “It turns out the mid-40s is a time of dramatic change, as is the early 60s. And that’s true no matter what class of molecules you look at.”

Xiaotao Shen, PhD, a former Stanford Medicine postdoctoral scholar, was the first author of the study. Shen is now an assistant professor at Nanyang Technological University Singapore.

These big changes likely impact our health — the number of molecules related to cardiovascular disease showed significant changes at both time points, and those related to immune function changed in people in their early 60s.

Abrupt Changes in Number

Snyder, the Stanford W. Ascherman, MD, FACS Professor in Genetics, and his colleagues were inspired to look at the rate of molecular and microbial shifts by the observation that the risk of developing many age-linked diseases does not rise incrementally along with years. For example, risks for Alzheimer’s disease and cardiovascular disease rise sharply in older age, compared with a gradual increase in risk for those under 60.

The researchers used data from 108 people they’ve been following to better understand the biology of aging. Past insights from this same group of study volunteers include the discovery of four distinct “ageotypes,” showing that people’s kidneys, livers, metabolism and immune system age at different rates in different people.

Michael Snyder

The new study analyzed participants who donated blood and other biological samples every few months over the span of several years; the scientists tracked many different kinds of molecules in these samples, including RNA, proteins and metabolites, as well as shifts in the participants’ microbiomes. The researchers tracked age-related changes in more than 135,000 different molecules and microbes, for a total of nearly 250 billion distinct data points.

They found that thousands of molecules and microbes undergo shifts in their abundance, either increasing or decreasing — around 81% of all the molecules they studied showed non-linear fluctuations in number, meaning that they changed more at certain ages than other times. When they looked for clusters of molecules with the largest changes in amount, they found these transformations occurred the most in two time periods: when people were in their mid-40s, and when they were in their early 60s.

Although much research has focused on how different molecules increase or decrease as we age and how biological age may differ from chronological age, very few have looked at the rate of biological aging. That so many dramatic changes happen in the early 60s is perhaps not surprising, Snyder said, as many age-related disease risks and other age-related phenomena are known to increase at that point in life.

The large cluster of changes in the mid-40s was somewhat surprising to the scientists. At first, they assumed that menopause or perimenopause was driving large changes in the women in their study, skewing the whole group. But when they broke out the study group by sex, they found the shift was happening in men in their mid-40s, too.

“This suggests that while menopause or perimenopause may contribute to the changes observed in women in their mid-40s, there are likely other, more significant factors influencing these changes in both men and women. Identifying and studying these factors should be a priority for future research,” Shen said.

Changes May Influence Health and Disease Risk

In people in their 40s, significant changes were seen in the number of molecules related to alcohol, caffeine and lipid metabolism; cardiovascular disease; and skin and muscle. In those in their 60s, changes were related to carbohydrate and caffeine metabolism, immune regulation, kidney function, cardiovascular disease, and skin and muscle.

It’s possible some of these changes could be tied to lifestyle or behavioral factors that cluster at these age groups, rather than being driven by biological factors, Snyder said. For example, dysfunction in alcohol metabolism could result from an uptick in alcohol consumption in people’s mid-40s, often a stressful period of life.

The team plans to explore the drivers of these clusters of change. But whatever their causes, the existence of these clusters points to the need for people to pay attention to their health, especially in their 40s and 60s, the researchers said. That could look like increasing exercise to protect your heart and maintain muscle mass at both ages or decreasing alcohol consumption in your 40s as your ability to metabolize alcohol slows.

“I’m a big believer that we should try to adjust our lifestyles while we’re still healthy,” Snyder said.

— The Study was Funded by the National Institutes of Health and the Stanford Data Science Initiative.

[schoje]

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