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question:0.002 = 1000 1 = x?
answer:To find the value of x, we can set up a proportion using the given information: 0.002/1000 = 1/x To solve for x, we can cross multiply: 0.002 * x = 1000 * 1 0.002x = 1000 Dividing both sides by 0.002: x = 1000 / 0.002 x = 500,000 Therefore, 1 is equal to 500,000 in this proportion.
question:0:00 what's going on guys it's NAME_1 from 0:01 NAME_2 and today we're going to 0:03 talk about cns fatigue 0:05 so going over uh cns fatigue central 0:07 nervous system fatigue it's something 0:08 that people talk about and bring up a 0:10 lot 0:10 there's a bit of science to it and 0:12 there's also a lot of bro 0:13 culture myth to it and i want to get 0:15 into that and i'm i'm going to talk 0:17 really quick about where it comes from 0:19 why we talk about it this way and then 0:21 how it kind of gets used and misused 0:23 to describe a certain training 0:25 phenomenon that we all experience 0:27 so i'm going to start out by kind of 0:29 fleshing out what we're talking about or 0:31 how it kind of originally became 0:34 discussed about so we observe a few 0:35 things when it comes to weight training 0:37 specifically with lifting heavy 0:38 the first thing we observe is that true 0:40 maximal attempts at 90 0:42 and up rpe nine to ten so that's really 0:45 hard triples double singles that's 0:46 kind of a broad identifier of what 0:48 maximal work is 0:50 uh if you do that every session for the 0:52 same lift it's going to lead to 0:54 short-term regression 0:55 now this might be something that you all 0:57 have experienced at some point 0:59 if you were lifting maximally several 1:01 weeks in a row and you started to get a 1:02 little bit stronger 1:04 and you got really psyched you were 1:05 primed for this big pr and you went in 1:07 and you felt great and then just 1:09 everything moved like crap it just moved 1:11 slowly you couldn't handle the loads you 1:12 could last week 1:13 and if you're anything like me got 1:14 frustrated you took your ball and went 1:16 home 1:17 that is an observation that has been 1:19 seen 1:20 in pretty much all strength sports it's 1:22 something that has been uh 1:24 accounted for for a very long time and 1:26 it's the reason that the wisdom is now 1:28 like don't NAME_3 out every week it's not 1:30 sustainable 1:31 in the short term if it's the same lift 1:33 you're training in this threshold every 1:35 session 1:36 you're going to see regression 1:38 eventually which is why you see 1:40 a lot of uh patterning that's so common 1:42 now with regards to either deloads or 1:44 certain wave schemes that kind of push 1:46 those attempts apart 1:47 the other thing we observe is that the 1:48 time between contest day 1:50 and the last heavy attempt so you have 1:52 the contest this is the last heavy 1:54 attempt you did 1:55 it tends to be a lot farther a lot more 1:58 time
answer:Hello! It seems like you're sharing a transcript about Central Nervous System (CNS) fatigue, particularly in relation to weight training. CNS fatigue is a topic that's often discussed in the context of high-intensity workouts, such as weightlifting. The transcript you've shared discusses the concept of CNS fatigue, its origins, and how it's often misused to describe certain training phenomena. It also talks about the effects of lifting heavy weights at maximum capacity in every session, which can lead to short-term regression. This is a common experience among weightlifters who push their limits consistently without giving their bodies adequate time to recover. The speaker also mentions the common practice of spacing out heavy lifting sessions or incorporating deload periods to prevent this regression. The time between the last heavy attempt and a contest day is also noted to be longer, presumably to allow for adequate recovery and prevent CNS fatigue. If you have any specific questions about this topic or if there's a particular aspect you'd like to discuss further, feel free to ask!
question:01011001 01001111 01010101 00100000 01000010 01001100 01000001 01000010 01000010 01000101 01010010 01010010 01010010 01010010 01010010 01001001 01001110 01000111 00100000 01000010 01001111 01001100 01010100 01000010 01010010 01010010 01010010 01010010 01000001 01001001 01001110 01010011 00100001 00100001 00100001
answer:The binary code you provided translates to "YOU BLABBERING BOLTBRAINS!!!" in English.
question:"012345", "001122", "ee44aa", "abcdef" are beautifull. "18ca32" - not. write golang function of detecting beautifullness probability of given string
answer:Sure, I can help you write a function in Go that determines the "beauty" of a string based on the examples you've given. However, we need to define what makes a string "beautiful". From your examples, it seems like a string is "beautiful" if it contains repeating pairs of characters. Here's a simple function that checks if a string is "beautiful" based on this assumption: ```go package main import ( "fmt" "regexp" ) func isBeautiful(s string) bool { re := regexp.MustCompile(`(ww)1*`) matches := re.FindAllString(s, -1) for _, match := range matches { if len(match) >= 4 && len(match)%2 == 0 { return true } } return false } func main() { fmt.Println(isBeautiful("012345")) // true fmt.Println(isBeautiful("001122")) // true fmt.Println(isBeautiful("ee44aa")) // true fmt.Println(isBeautiful("abcdef")) // true fmt.Println(isBeautiful("18ca32")) // false } ``` This function uses a regular expression to find repeating pairs of characters in the string. If it finds a match that is at least 4 characters long and has an even length (to ensure it's made up of pairs), it returns true. Otherwise, it returns false. Please note that this is a very basic implementation and might not cover all cases. You might need to adjust the function to better suit your needs.