Dash Activates Sporks 15 and 16, Deterministic Masternodes and InstantSend By Default Dash has activated two sporks finalizing the 0.13 upgrade, locking in deterministic masternodes and activating InstantSend transactions by default.Finally, what expected from DASH community have finished, with deterministic masternodes that operate through three different keys, including collateral key, voting key, and operator key. Additionally, the instant sent will be automatic applied for transactions with 4 inputs or lower as the team described in their reports below. https://dashnews.org/dash-activates-sporks-15-and-16-deterministic-masternodes-and-instantsend-by-default/The system will attempt to “lock” any transaction with 4 or less inputs by default, and remove the additional fee that historically was needed for instant transactions.”
|
|
|
|
It is the list of weeks in descending orders of median of intra-day merits. The minimum median is 616, on the week #2019w1, while the maximum median is 733, on the week #2018w26. . list week merit median q1 q3
+------------------------------------------+
| week merit median q1 q3 |
|------------------------------------------|
1. | 2019w1 4793 616 510 766 |
2. | 2018w52 3278 616.5 509.5 762.5 |
3. | 2019w7 4207 618 519 767 |
4. | 2019w8 4507 618.5 518.5 766.5 |
5. | 2018w51 3753 618.5 515 766.5 |
|------------------------------------------|
6. | 2019w2 6624 618.5 513 773 |
7. | 2018w50 3782 619 517 768 |
8. | 2019w9 4625 619 518 766 |
9. | 2019w6 4318 619.5 517 768 |
10. | 2019w5 4474 620 516 773 |
|------------------------------------------|
11. | 2019w3 5306 620 514 774 |
12. | 2019w4 4659 621.5 516.5 770.5 |
13. | 2018w49 3560 621.5 517 773 |
14. | 2019w10 4901 623 521 764 |
15. | 2019w11 4318 623 521 761 |
|------------------------------------------|
16. | 2019w12 4598 625.5 521 759.5 |
17. | 2018w46 3722 626 521 786 |
18. | 2019w13 6120 626 522 764 |
19. | 2018w48 3750 626 521 774 |
20. | 2018w44 3339 628 521 796 |
|------------------------------------------|
21. | 2018w45 4513 630 522 789 |
22. | 2018w37 5630 634 528 829 |
23. | 2018w41 3800 637 528 808 |
24. | 2018w36 3574 639 528 838 |
25. | 2018w42 4821 639 530 807 |
|------------------------------------------|
26. | 2018w40 4271 639 528 829 |
27. | 2018w43 3945 639 528 801 |
28. | 2018w39 4388 640 531 839 |
29. | 2018w38 7825 641 530 846 |
30. | 2018w35 3065 642 537 844 |
|------------------------------------------|
31. | 2018w34 3789 652 555 848 |
32. | 2018w33 3618 667 559 867 |
33. | 2018w32 3994 675 567 880 |
34. | 2018w31 3798 682 575 891 |
35. | 2018w30 3652 684 577 902 |
|------------------------------------------|
36. | 2018w29 4159 693 589 922 |
37. | 2018w28 4239 707 592 963 |
38. | 2018w27 4253 715 598 979 |
39. | 2018w26 4457 733 609 991 |
|
|
|
|
I finished my newest topic on intra-day merits Observation on interquartile range of intra-day merits with time series plotAs you can see in the time series plot, the medians of intra-day merits have been very stable over time, it has fluctuated in a very narrow range. The range from q1 (p25) to q3 (p75) represents for 50 percent of intra-day merits. Days have intra-day merits below q1 (p25) or q3 (p75) considered as potential or extremely potential outliers |
|
|
|
For the start and end days of each week, you can get them in the quoted post below. The converted datasets in the quote including two parts, one part for 2018, and another part for 2019. Enjoy! Update:Converted intra-day merits for days in 2019. . list id merit date day month2 year week month dofw if year == 2019
+------------------------------------------------------------------------------+
| id merit date day month2 year week month dofw |
|------------------------------------------------------------------------------|
343. | 343 603 01jan2019 1 1 2019 2019w1 2019m1 Tuesday |
344. | 344 526 02jan2019 2 1 2019 2019w1 2019m1 Wednesday |
345. | 345 394 03jan2019 3 1 2019 2019w1 2019m1 Thursday |
346. | 346 1082 04jan2019 4 1 2019 2019w1 2019m1 Friday |
347. | 347 835 05jan2019 5 1 2019 2019w1 2019m1 Saturday |
|------------------------------------------------------------------------------|
348. | 348 783 06jan2019 6 1 2019 2019w1 2019m1 Sunday |
349. | 349 570 07jan2019 7 1 2019 2019w1 2019m1 Monday |
350. | 350 782 08jan2019 8 1 2019 2019w2 2019m1 Tuesday |
351. | 351 1161 09jan2019 9 1 2019 2019w2 2019m1 Wednesday |
352. | 352 987 10jan2019 10 1 2019 2019w2 2019m1 Thursday |
|------------------------------------------------------------------------------|
353. | 353 878 11jan2019 11 1 2019 2019w2 2019m1 Friday |
354. | 354 711 12jan2019 12 1 2019 2019w2 2019m1 Saturday |
355. | 355 978 13jan2019 13 1 2019 2019w2 2019m1 Sunday |
356. | 356 1127 14jan2019 14 1 2019 2019w2 2019m1 Monday |
357. | 357 813 15jan2019 15 1 2019 2019w3 2019m1 Tuesday |
|------------------------------------------------------------------------------|
358. | 358 880 16jan2019 16 1 2019 2019w3 2019m1 Wednesday |
359. | 359 1018 17jan2019 17 1 2019 2019w3 2019m1 Thursday |
360. | 360 611 18jan2019 18 1 2019 2019w3 2019m1 Friday |
361. | 361 643 19jan2019 19 1 2019 2019w3 2019m1 Saturday |
362. | 362 658 20jan2019 20 1 2019 2019w3 2019m1 Sunday |
|------------------------------------------------------------------------------|
363. | 363 683 21jan2019 21 1 2019 2019w3 2019m1 Monday |
364. | 364 618 22jan2019 22 1 2019 2019w4 2019m1 Tuesday |
365. | 365 735 23jan2019 23 1 2019 2019w4 2019m1 Wednesday |
366. | 366 715 24jan2019 24 1 2019 2019w4 2019m1 Thursday |
367. | 367 615 25jan2019 25 1 2019 2019w4 2019m1 Friday |
|------------------------------------------------------------------------------|
368. | 368 587 26jan2019 26 1 2019 2019w4 2019m1 Saturday |
369. | 369 655 27jan2019 27 1 2019 2019w4 2019m1 Sunday |
370. | 370 734 28jan2019 28 1 2019 2019w4 2019m1 Monday |
371. | 371 612 29jan2019 29 1 2019 2019w5 2019m1 Tuesday |
372. | 372 510 30jan2019 30 1 2019 2019w5 2019m1 Wednesday |
|------------------------------------------------------------------------------|
373. | 373 450 31jan2019 31 1 2019 2019w5 2019m1 Thursday |
374. | 374 595 01feb2019 1 2 2019 2019w5 2019m2 Friday |
375. | 375 940 02feb2019 2 2 2019 2019w5 2019m2 Saturday |
376. | 376 571 03feb2019 3 2 2019 2019w5 2019m2 Sunday |
377. | 377 796 04feb2019 4 2 2019 2019w5 2019m2 Monday |
|------------------------------------------------------------------------------|
378. | 378 776 05feb2019 5 2 2019 2019w6 2019m2 Tuesday |
379. | 379 559 06feb2019 6 2 2019 2019w6 2019m2 Wednesday |
380. | 380 548 07feb2019 7 2 2019 2019w6 2019m2 Thursday |
381. | 381 611 08feb2019 8 2 2019 2019w6 2019m2 Friday |
382. | 382 623 09feb2019 9 2 2019 2019w6 2019m2 Saturday |
|------------------------------------------------------------------------------|
383. | 383 559 10feb2019 10 2 2019 2019w6 2019m2 Sunday |
384. | 384 642 11feb2019 11 2 2019 2019w6 2019m2 Monday |
385. | 385 585 12feb2019 12 2 2019 2019w7 2019m2 Tuesday |
386. | 386 671 13feb2019 13 2 2019 2019w7 2019m2 Wednesday |
387. | 387 649 14feb2019 14 2 2019 2019w7 2019m2 Thursday |
|------------------------------------------------------------------------------|
388. | 388 607 15feb2019 15 2 2019 2019w7 2019m2 Friday |
389. | 389 523 16feb2019 16 2 2019 2019w7 2019m2 Saturday |
390. | 390 607 17feb2019 17 2 2019 2019w7 2019m2 Sunday |
391. | 391 565 18feb2019 18 2 2019 2019w7 2019m2 Monday |
392. | 392 637 19feb2019 19 2 2019 2019w8 2019m2 Tuesday |
|------------------------------------------------------------------------------|
393. | 393 696 20feb2019 20 2 2019 2019w8 2019m2 Wednesday |
394. | 394 504 21feb2019 21 2 2019 2019w8 2019m2 Thursday |
395. | 395 509 22feb2019 22 2 2019 2019w8 2019m2 Friday |
396. | 396 657 23feb2019 23 2 2019 2019w8 2019m2 Saturday |
397. | 397 608 24feb2019 24 2 2019 2019w8 2019m2 Sunday |
|------------------------------------------------------------------------------|
398. | 398 896 25feb2019 25 2 2019 2019w8 2019m2 Monday |
399. | 399 736 26feb2019 26 2 2019 2019w9 2019m2 Tuesday |
400. | 400 553 27feb2019 27 2 2019 2019w9 2019m2 Wednesday |
401. | 401 707 28feb2019 28 2 2019 2019w9 2019m2 Thursday |
402. | 402 508 01mar2019 1 3 2019 2019w9 2019m3 Friday |
|------------------------------------------------------------------------------|
403. | 403 412 02mar2019 2 3 2019 2019w9 2019m3 Saturday |
404. | 404 1001 03mar2019 3 3 2019 2019w9 2019m3 Sunday |
405. | 405 708 04mar2019 4 3 2019 2019w9 2019m3 Monday |
406. | 406 677 05mar2019 5 3 2019 2019w10 2019m3 Tuesday |
407. | 407 787 06mar2019 6 3 2019 2019w10 2019m3 Wednesday |
|------------------------------------------------------------------------------|
408. | 408 711 07mar2019 7 3 2019 2019w10 2019m3 Thursday |
409. | 409 712 08mar2019 8 3 2019 2019w10 2019m3 Friday |
410. | 410 723 09mar2019 9 3 2019 2019w10 2019m3 Saturday |
411. | 411 656 10mar2019 10 3 2019 2019w10 2019m3 Sunday |
412. | 412 635 11mar2019 11 3 2019 2019w10 2019m3 Monday |
|------------------------------------------------------------------------------|
413. | 413 680 12mar2019 12 3 2019 2019w11 2019m3 Tuesday |
414. | 414 687 13mar2019 13 3 2019 2019w11 2019m3 Wednesday |
415. | 415 804 14mar2019 14 3 2019 2019w11 2019m3 Thursday |
416. | 416 580 15mar2019 15 3 2019 2019w11 2019m3 Friday |
417. | 417 482 16mar2019 16 3 2019 2019w11 2019m3 Saturday |
|------------------------------------------------------------------------------|
418. | 418 428 17mar2019 17 3 2019 2019w11 2019m3 Sunday |
419. | 419 657 18mar2019 18 3 2019 2019w11 2019m3 Monday |
420. | 420 758 19mar2019 19 3 2019 2019w12 2019m3 Tuesday |
421. | 421 651 20mar2019 20 3 2019 2019w12 2019m3 Wednesday |
422. | 422 720 21mar2019 21 3 2019 2019w12 2019m3 Thursday |
|------------------------------------------------------------------------------|
423. | 423 674 22mar2019 22 3 2019 2019w12 2019m3 Friday |
424. | 424 625 23mar2019 23 3 2019 2019w12 2019m3 Saturday |
425. | 425 594 24mar2019 24 3 2019 2019w12 2019m3 Sunday |
426. | 426 576 25mar2019 25 3 2019 2019w12 2019m3 Monday |
427. | 427 726 26mar2019 26 3 2019 2019w13 2019m3 Tuesday |
|------------------------------------------------------------------------------|
428. | 428 1249 27mar2019 27 3 2019 2019w13 2019m3 Wednesday |
429. | 429 927 28mar2019 28 3 2019 2019w13 2019m3 Thursday |
430. | 430 725 29mar2019 29 3 2019 2019w13 2019m3 Friday |
431. | 431 655 30mar2019 30 3 2019 2019w13 2019m3 Saturday |
432. | 432 851 31mar2019 31 3 2019 2019w13 2019m3 Sunday |
|------------------------------------------------------------------------------|
433. | 433 987 01apr2019 1 4 2019 2019w13 2019m4 Monday |
434. | 434 700 02apr2019 2 4 2019 2019w14 2019m4 Tuesday |
435. | 435 616 03apr2019 3 4 2019 2019w14 2019m4 Wednesday |
+------------------------------------------------------------------------------+
For days in 2018, please get it there |
|
|
|
INTERQUARTILE RANGE OF INTRA-DAY MERITS WITH TIME SERIES PLOT
(1) Updates will be published on weekly basis.
(2) Updates will be posted in the last thread of the topic (not in the OP) at specific point of time.
Will edit this part later
In short, again, interquartile range represents for 50% of observed data points.
For instance, if you have 100 data points (100 days over weeks), the interquartile range will represents for 50 datapoints in the 'middle range', that ranges from the 25th quartile to the 75th quartile. Interquartile range has its role to exclude outliers, such as maximum, minimum and some extreme large and small data points closely with max and min.
In the same way, median represents nearly the true mean (average) of observed data. It is better than what we usually use, mean or average.
Both median and interquartile range exclude effects from outliers.
Time series plot of median and interquartile rangeNotes: - p25 ~q1; - p75 ~ q3. The interquartile range (IQR) ranges from p25 (q1) to p75 (q3), and the IQR represents 50% of observed days. For example, with the week #2019w13, 50% of of days observed till the end of #2019w13 have their total intra-day merits change from 522 (p25 ~ q1) to 764 (p75 ~ q3). The median of the same period is 626, it means that there are 50% of observed days have intra-day merits below 626, while the rest 50% of observed days have intra-day merits above 626. . list week merit median q1 q3
+------------------------------------------+
| week merit median q1 q3 |
|------------------------------------------|
39. | 2019w13 6120 626 522 764 |
Dataset for median, interquartile range of intraday merits. list week merit median q1 q3
+------------------------------------------+
| week merit median q1 q3 |
|------------------------------------------|
1. | 2018w26 4457 733 609 991 |
2. | 2018w27 4253 715 598 979 |
3. | 2018w28 4239 707 592 963 |
4. | 2018w29 4159 693 589 922 |
5. | 2018w30 3652 684 577 902 |
|------------------------------------------|
6. | 2018w31 3798 682 575 891 |
7. | 2018w32 3994 675 567 880 |
8. | 2018w33 3618 667 559 867 |
9. | 2018w34 3789 652 555 848 |
10. | 2018w35 3065 642 537 844 |
|------------------------------------------|
11. | 2018w36 3574 639 528 838 |
12. | 2018w37 5630 634 528 829 |
13. | 2018w38 7825 641 530 846 |
14. | 2018w39 4388 640 531 839 |
15. | 2018w40 4271 639 528 829 |
|------------------------------------------|
16. | 2018w41 3800 637 528 808 |
17. | 2018w42 4821 639 530 807 |
18. | 2018w43 3945 639 528 801 |
19. | 2018w44 3339 628 521 796 |
20. | 2018w45 4513 630 522 789 |
|------------------------------------------|
21. | 2018w46 3722 626 521 786 |
22. | 2018w48 3750 626 521 774 |
23. | 2018w49 3560 621.5 517 773 |
24. | 2018w50 3782 619 517 768 |
25. | 2018w51 3753 618.5 515 766.5 |
|------------------------------------------|
26. | 2018w52 3278 616.5 509.5 762.5 |
27. | 2019w1 4793 616 510 766 |
28. | 2019w2 6624 618.5 513 773 |
29. | 2019w3 5306 620 514 774 |
30. | 2019w4 4659 621.5 516.5 770.5 |
|------------------------------------------|
31. | 2019w5 4474 620 516 773 |
32. | 2019w6 4318 619.5 517 768 |
33. | 2019w7 4207 618 519 767 |
34. | 2019w8 4507 618.5 518.5 766.5 |
35. | 2019w9 4625 619 518 766 |
|------------------------------------------|
36. | 2019w10 4901 623 521 764 |
37. | 2019w11 4318 623 521 761 |
38. | 2019w12 4598 625.5 521 759.5 |
39. | 2019w13 6120 626 522 764 |
- From LoyceV's weekly data dumps. - From my converted datasets in the topic: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly) |
|
|
|
It is better to get $10 per week instead of waiting months just to get $40 worth of tokens.
It is good and acceptable if eventually participants can get payments at promised payment rates. However, for month-lasting bounties, there are risks to join bad projects, that might end with ICO fails or scam exits. When such bad cases occur, participants won't get anything for all their works, and their time over months. That's one of the most terrible scenario for bounty participants. Since the day I joined the forum, I have never joined campaigns of ICOs, because I don't want to waste my time in worst cases.
It is also a recommendation, from my experience, for bounty hunters, and for newbies: If you can not find campaigns to join or get acceptance from campaigns' managers to join, you should forget about bounties, campaigns. Instead of spending your time on shit campaigns, you should spend your time to read forum rules, structures, and fundamental topics about the forum, blockchain that will help you become more comprehensively knowledgable; then try to help others if you can help them to solve their questions. This is the way you build up your knowledge, skills, and your account. Then, some day, you will have better chance to join better quality (non-scam, better payment rates) for sure. Believe me! |
|
|
|
|
TryNinja gave a topic that might help you, but I suggest you to give the name or link to the profile of your locked account, so someone might help you with more details, such as potential reasons of the account lock. You should make should that your account got locked or got banned.
Furthermore, for any cases, when you logged in your account, you will see message that shows potential reasons, that you also should post it here.
If you need help, you should show proof here, there is no reason to hide them all when you need help from others.
Best,
|
|
|
|
There is abstract of my last week analyses, on both intraday and intraweek merits. ABSTRACT
Intra-day merits:
Notes:
- The part of the asbstract describes figures of intraday merits over the period from 19/2/2018 to 01/4/2019 (truncated dataset);
- Days from 24/1/2018 to 18/2/2018 truncated due to highly potential outliers; and days after 01/4/2019 truncated as well due to incomplete week (the 2019w14);
- Statistics presented in the post are for truncated dataset
(1) Potential outliers are days that have intraday total merits beyond 159 or 1127;
(2) Median of intraday merits over the period is 626;
(3) 50% of observed days have their intra-day merits range from 522 to 764 (the interquartile range);
(4) Friday [in GTM time] is the day over weeks has lowest intraday merits in terms of both median and mean, at 569, and 616, respectively.
(5) Monday [in GTM time] is the day over weeks has highest intraday merits in terms of mean, at 742.
(6) There is a flippening between Wednesday and Monday in terms of highest intra-day merits, with figures for Wednesday and Monday are 660 and 658, respectively;
(7) There are 26 potential outliers in total, and there is only three potential outlier days happened in early weeks of 2019, on 09/01/2019, 14/01/2019, and 27/3/2019, at 1161, 1127, and 1249, respectively.
<8> Minimum and maximum of intraday merits (full dataset) are 312 and 13018, on 11/2/2019 and 24/1/2018 respectively.
Intra-week merits:
Notes:
The part of the abstract use full dataset, only dropped last two days due to incomple week (2019w10).
(1) The median of intra-week merits is 4510;
(2) 50% of observed weeks (62 weeeks in total), have total merits in the range from 3854 to 5487 (the interquaritle range of intra-week merits);
(3) Minimum and maximum of intraweek merits are 3065 and 30949, in 2018w35, and 2018w4, respectively;
(4) Seven potential outliers [beyond 1405 or 7937], all of them occurred in the year 2018.
More details can be found there: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly)As we can see in the abstract, with truncated dataset, there are only three extremely potential outliers, that happened on 09/1/2019, 14/1/2019, and 27/3/2019, range from 1161 to 1249. |
|
|
|
|
ABSTRACT
Intra-day merits:
Notes:
- The part of the asbstract describes figures of intraday merits over the period from 19/2/2018 to 01/4/2019 (truncated dataset);
- Days from 24/1/2018 to 18/2/2018 truncated due to highly potential outliers; and days after 01/4/2019 truncated as well due to incomplete week (the 2019w14);
- Statistics presented in the post are for truncated dataset
(1) Potential outliers are days that have intraday total merits beyond 159 or 1127;
(2) Median of intraday merits over the period is 626;
(3) 50% of observed days have their intra-day merits range from 522 to 764 (the interquartile range);
(4) Friday [in GTM time] is the day over weeks has lowest intraday merits in terms of both median and mean, at 569, and 616, respectively.
(5) Monday [in GTM time] is the day over weeks has highest intraday merits in terms of mean, at 742.
(6) There is a flippening between Wednesday and Monday in terms of highest intra-day merits, with figures for Wednesday and Monday are 660 and 658, respectively;
(7) There are 26 potential outliers in total, and there is only three potential outlier days happened in early weeks of 2019, on 09/01/2019, 14/01/2019, and 27/3/2019, at 1161, 1127, and 1249, respectively.
<8> Minimum and maximum of intraday merits (full dataset) are 312 and 13018, on 11/2/2019 and 24/1/2018 respectively.
Intra-week merits:
Notes:
The part of the abstract use full dataset, only dropped last two days due to incomple week (2019w13).
(1) The median of intra-week merits is 4510;
(2) 50% of observed weeks (62 weeeks in total), have total merits in the range from 3854 to 5487 (the interquaritle range of intra-week merits);
(3) Minimum and maximum of intraweek merits are 3065 and 30949, in 2018w35, and 2018w4, respectively;
(4) Seven potential outliers [beyond 1405 or 7937], all of them occurred in the year 2018.
|
|
|
|
Update on intra-week merits (from 24/1/2018 to 01/4/2019)Converted dataset:. list merit week
+-----------------+
| merit week |
|-----------------|
1. | 30949 2018w4 |
2. | 19958 2018w5 |
3. | 13304 2018w6 |
4. | 11722 2018w7 |
5. | 8758 2018w8 |
|-----------------|
6. | 8806 2018w9 |
7. | 7253 2018w10 |
8. | 7309 2018w11 |
9. | 6941 2018w12 |
10. | 6707 2018w13 |
|-----------------|
11. | 6415 2018w14 |
12. | 5487 2018w15 |
13. | 4631 2018w16 |
14. | 4585 2018w17 |
15. | 4953 2018w18 |
|-----------------|
16. | 4753 2018w19 |
17. | 4346 2018w20 |
18. | 3854 2018w21 |
19. | 4183 2018w22 |
20. | 4527 2018w23 |
|-----------------|
21. | 3818 2018w24 |
22. | 4921 2018w25 |
23. | 4457 2018w26 |
24. | 4253 2018w27 |
25. | 4239 2018w28 |
|-----------------|
26. | 4159 2018w29 |
27. | 3652 2018w30 |
28. | 3798 2018w31 |
29. | 3994 2018w32 |
30. | 3618 2018w33 |
|-----------------|
31. | 3789 2018w34 |
32. | 3065 2018w35 |
33. | 3574 2018w36 |
34. | 5630 2018w37 |
35. | 7825 2018w38 |
|-----------------|
36. | 4388 2018w39 |
37. | 4271 2018w40 |
38. | 3800 2018w41 |
39. | 4821 2018w42 |
40. | 3945 2018w43 |
|-----------------|
41. | 3339 2018w44 |
42. | 4513 2018w45 |
43. | 3722 2018w46 |
44. | 4558 2018w47 |
45. | 3750 2018w48 |
|-----------------|
46. | 3560 2018w49 |
47. | 3782 2018w50 |
48. | 3753 2018w51 |
49. | 3278 2018w52 |
50. | 4793 2019w1 |
|-----------------|
51. | 6624 2019w2 |
52. | 5306 2019w3 |
53. | 4659 2019w4 |
54. | 4474 2019w5 |
55. | 4318 2019w6 |
|-----------------|
56. | 4207 2019w7 |
57. | 4507 2019w8 |
58. | 4625 2019w9 |
59. | 4901 2019w10 |
60. | 4318 2019w11 |
|-----------------|
61. | 4598 2019w12 |
62. | 6120 2019w13 |
+-----------------+
- 50% of observed weeks (62 weeks) have total intra-week merits above 4510, whilst the rest 50% of them have total intra-week merits below 4510. 4510 is the median - p50. - 50% of observed weeks have total intra-week merits fluctuated in the range from 3854 to 5487 (the interquartile range, from p25 to p75, in raw statistics below). - Min - max: 3065 - 30949. . tabstat merit, s(n mean sd p50 p25 p75 min max)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 62 5696.177 4211.547 4510 3854 5487 3065 30949
----------------------------------------------------------------------------------------------
Potential outliers: . di 5487-3854
1633
. di 1633*1.5
2449.5
. di 5487+2449.5
7936.5
. di 3854-2449.5
1404.5
It means that potential outliers are weeks that have intra-week merits beyond 1405 or 7937. How many weeks are potential outliers? . count if (merit >= 7937 | merit < 1405) & merit != .
6
weeks are outliers, in total. List of those seven weeks: . list merit week if merit >=7937 | merit <= 1405
+----------------+
| merit week |
|----------------|
1. | 30949 2018w4 |
2. | 19958 2018w5 |
3. | 13304 2018w6 |
4. | 11722 2018w7 |
5. | 8758 2018w8 |
|----------------|
6. | 8806 2018w9 |
+----------------+
All of them occured in the year 2018. 
|
|
|
|
Medians and means of intra-day merits over days of weeks.Colors: - Green: highest.- Red: Lowest.- In median, the highest days are Wednesday, Monday, and Thursday at 660, 658, and 649, respectively; whislt the lowest days are Friday, Sunday, and Saturday, at 569, 608, and 619, respectively. - In means, the highest days are Monday, Wednesday, and Sunday at 742, 721, and 695, respectively; whilst the lowest days are Friday, Saturday, and Thursday at 616, 623, and 677, respectively. - There was a flippening in highest median intraday merits last week, between Wednesday and Monday, but the gap between them are small, only 2 merit points. So, in general, Monday has still been the highest day in terms of median intra-day merits over weeks. Calendar day is in GMT time.To take away all doubt: the first Merit was this one: 1516831941 1 2818066.msg28853325 35 877396 Use EpochConverter to convert 1516831941 (Unix Time) to GMT: Wednesday 24 January 2018 22:12:21. . tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f) by(dofw)
Summary for variables: merit
by categories of: dofw
dofw | N mean sd p50 p25 p75 min max
----------+--------------------------------------------------------------------------------
Sunday | 58.0 694.8 318.1 607.5 486.0 804.0 389.0 2463.0
Monday | 59.0 741.4 285.5 658.0 565.0 822.0 312.0 1862.0
Tuesday | 58.0 695.8 216.5 634.5 580.0 760.0 383.0 1326.0
Wednesday | 58.0 720.2 220.9 659.5 559.0 761.0 435.0 1268.0
Thursday | 58.0 676.2 214.7 646.5 514.0 774.0 347.0 1333.0
Friday | 58.0 615.9 217.2 569.0 487.0 698.0 348.0 1696.0
Saturday | 58.0 622.8 210.0 618.5 463.0 688.0 316.0 1409.0
----------+--------------------------------------------------------------------------------
Total | 407.0 681.2 245.9 626.0 522.0 764.0 312.0 2463.0
-------------------------------------------------------------------------------------------
Outliers displayed as red circles. Outliers non-displayed.
|
|
|
|
List of the top 50-highest day in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 13018 1 24jan2018 Wednesday 24 1 2018 2018w4 2018m1 |
2. | 6761 2 25jan2018 Thursday 25 1 2018 2018w4 2018m1 |
3. | 4493 3 26jan2018 Friday 26 1 2018 2018w4 2018m1 |
4. | 4192 7 30jan2018 Tuesday 30 1 2018 2018w5 2018m1 |
5. | 3799 6 29jan2018 Monday 29 1 2018 2018w5 2018m1 |
|-------------------------------------------------------------------------------|
6. | 3489 4 27jan2018 Saturday 27 1 2018 2018w4 2018m1 |
7. | 3188 5 28jan2018 Sunday 28 1 2018 2018w4 2018m1 |
8. | 2820 8 31jan2018 Wednesday 31 1 2018 2018w5 2018m1 |
9. | 2568 10 02feb2018 Friday 2 2 2018 2018w5 2018m2 |
10. | 2545 9 01feb2018 Thursday 1 2 2018 2018w5 2018m2 |
|-------------------------------------------------------------------------------|
11. | 2513 22 14feb2018 Wednesday 14 2 2018 2018w7 2018m2 |
12. | 2463 236 16sep2018 Sunday 16 9 2018 2018w37 2018m9 |
13. | 2308 14 06feb2018 Tuesday 6 2 2018 2018w6 2018m2 |
14. | 2167 12 04feb2018 Sunday 4 2 2018 2018w5 2018m2 |
15. | 2141 16 08feb2018 Thursday 8 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
16. | 2141 15 07feb2018 Wednesday 7 2 2018 2018w6 2018m2 |
17. | 2077 13 05feb2018 Monday 5 2 2018 2018w6 2018m2 |
18. | 1991 23 15feb2018 Thursday 15 2 2018 2018w7 2018m2 |
19. | 1867 11 03feb2018 Saturday 3 2 2018 2018w5 2018m2 |
20. | 1862 237 17sep2018 Monday 17 9 2018 2018w38 2018m9 |
|-------------------------------------------------------------------------------|
21. | 1747 18 10feb2018 Saturday 10 2 2018 2018w6 2018m2 |
22. | 1696 38 02mar2018 Friday 2 3 2018 2018w9 2018m3 |
23. | 1608 25 17feb2018 Saturday 17 2 2018 2018w7 2018m2 |
24. | 1579 21 13feb2018 Tuesday 13 2 2018 2018w7 2018m2 |
25. | 1448 17 09feb2018 Friday 9 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
26. | 1442 19 11feb2018 Sunday 11 2 2018 2018w6 2018m2 |
27. | 1411 24 16feb2018 Friday 16 2 2018 2018w7 2018m2 |
28. | 1409 32 24feb2018 Saturday 24 2 2018 2018w8 2018m2 |
29. | 1403 27 19feb2018 Monday 19 2 2018 2018w8 2018m2 |
30. | 1382 34 26feb2018 Monday 26 2 2018 2018w9 2018m2 |
|-------------------------------------------------------------------------------|
31. | 1354 48 12mar2018 Monday 12 3 2018 2018w11 2018m3 |
32. | 1333 37 01mar2018 Thursday 1 3 2018 2018w9 2018m3 |
33. | 1331 20 12feb2018 Monday 12 2 2018 2018w7 2018m2 |
34. | 1326 35 27feb2018 Tuesday 27 2 2018 2018w9 2018m2 |
35. | 1322 56 20mar2018 Tuesday 20 3 2018 2018w12 2018m3 |
|-------------------------------------------------------------------------------|
36. | 1294 238 18sep2018 Tuesday 18 9 2018 2018w38 2018m9 |
37. | 1289 26 18feb2018 Sunday 18 2 2018 2018w7 2018m2 |
38. | 1279 30 22feb2018 Thursday 22 2 2018 2018w8 2018m2 |
39. | 1268 239 19sep2018 Wednesday 19 9 2018 2018w38 2018m9 |
40. | 1266 29 21feb2018 Wednesday 21 2 2018 2018w8 2018m2 |
|-------------------------------------------------------------------------------|
41. | 1249 428 27mar2019 Wednesday 27 3 2019 2019w13 2019m3 |
42. | 1245 41 05mar2018 Monday 5 3 2018 2018w10 2018m3 |
43. | 1233 68 01apr2018 Sunday 1 4 2018 2018w13 2018m4 |
44. | 1227 57 21mar2018 Wednesday 21 3 2018 2018w12 2018m3 |
45. | 1186 33 25feb2018 Sunday 25 2 2018 2018w8 2018m2 |
|-------------------------------------------------------------------------------|
46. | 1169 28 20feb2018 Tuesday 20 2 2018 2018w8 2018m2 |
47. | 1161 351 09jan2019 Wednesday 9 1 2019 2019w2 2019m1 |
48. | 1159 50 14mar2018 Wednesday 14 3 2018 2018w11 2018m3 |
49. | 1146 69 02apr2018 Monday 2 4 2018 2018w14 2018m4 |
50. | 1138 153 25jun2018 Monday 25 6 2018 2018w26 2018m6 |
|-------------------------------------------------------------------------------|
List of the top 50-lowest days in terms of intra-day merits:
. list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 312 335 24dec2018 Monday 24 12 2018 2018w52 2018m12 |
2. | 316 333 22dec2018 Saturday 22 12 2018 2018w51 2018m12 |
3. | 325 340 29dec2018 Saturday 29 12 2018 2018w52 2018m12 |
4. | 347 338 27dec2018 Thursday 27 12 2018 2018w52 2018m12 |
5. | 347 298 17nov2018 Saturday 17 11 2018 2018w46 2018m11 |
|-------------------------------------------------------------------------------|
6. | 348 304 23nov2018 Friday 23 11 2018 2018w47 2018m11 |
7. | 370 122 25may2018 Friday 25 5 2018 2018w21 2018m5 |
8. | 376 191 02aug2018 Thursday 2 8 2018 2018w31 2018m8 |
9. | 376 342 31dec2018 Monday 31 12 2018 2018w52 2018m12 |
10. | 377 326 15dec2018 Saturday 15 12 2018 2018w50 2018m12 |
|-------------------------------------------------------------------------------|
11. | 379 220 31aug2018 Friday 31 8 2018 2018w35 2018m8 |
12. | 383 217 28aug2018 Tuesday 28 8 2018 2018w35 2018m8 |
13. | 385 214 25aug2018 Saturday 25 8 2018 2018w34 2018m8 |
14. | 386 339 28dec2018 Friday 28 12 2018 2018w52 2018m12 |
15. | 389 341 30dec2018 Sunday 30 12 2018 2018w52 2018m12 |
|-------------------------------------------------------------------------------|
16. | 394 345 03jan2019 Thursday 3 1 2019 2019w1 2019m1 |
17. | 395 228 08sep2018 Saturday 8 9 2018 2018w36 2018m9 |
18. | 397 320 09dec2018 Sunday 9 12 2018 2018w49 2018m12 |
19. | 399 262 12oct2018 Friday 12 10 2018 2018w41 2018m10 |
20. | 402 329 18dec2018 Tuesday 18 12 2018 2018w51 2018m12 |
|-------------------------------------------------------------------------------|
21. | 405 287 06nov2018 Tuesday 6 11 2018 2018w45 2018m11 |
22. | 412 403 02mar2019 Saturday 2 3 2019 2019w9 2019m3 |
23. | 412 222 02sep2018 Sunday 2 9 2018 2018w35 2018m9 |
24. | 415 109 12may2018 Saturday 12 5 2018 2018w19 2018m5 |
25. | 415 278 28oct2018 Sunday 28 10 2018 2018w43 2018m10 |
|-------------------------------------------------------------------------------|
26. | 418 186 28jul2018 Saturday 28 7 2018 2018w30 2018m7 |
27. | 420 187 29jul2018 Sunday 29 7 2018 2018w30 2018m7 |
28. | 421 192 03aug2018 Friday 3 8 2018 2018w31 2018m8 |
29. | 422 140 12jun2018 Tuesday 12 6 2018 2018w24 2018m6 |
30. | 424 276 26oct2018 Friday 26 10 2018 2018w43 2018m10 |
|-------------------------------------------------------------------------------|
31. | 424 313 02dec2018 Sunday 2 12 2018 2018w48 2018m12 |
32. | 426 277 27oct2018 Saturday 27 10 2018 2018w43 2018m10 |
33. | 428 418 17mar2019 Sunday 17 3 2019 2019w11 2019m3 |
34. | 430 284 03nov2018 Saturday 3 11 2018 2018w44 2018m11 |
35. | 430 264 14oct2018 Sunday 14 10 2018 2018w41 2018m10 |
|-------------------------------------------------------------------------------|
36. | 432 208 19aug2018 Sunday 19 8 2018 2018w33 2018m8 |
37. | 432 221 01sep2018 Saturday 1 9 2018 2018w35 2018m9 |
38. | 433 282 01nov2018 Thursday 1 11 2018 2018w44 2018m11 |
39. | 435 154 26jun2018 Tuesday 26 6 2018 2018w26 2018m6 |
40. | 435 190 01aug2018 Wednesday 1 8 2018 2018w31 2018m8 |
|-------------------------------------------------------------------------------|
41. | 444 182 24jul2018 Tuesday 24 7 2018 2018w30 2018m7 |
42. | 445 143 15jun2018 Friday 15 6 2018 2018w24 2018m6 |
43. | 450 373 31jan2019 Thursday 31 1 2019 2019w5 2019m1 |
44. | 451 206 17aug2018 Friday 17 8 2018 2018w33 2018m8 |
45. | 454 283 02nov2018 Friday 2 11 2018 2018w44 2018m11 |
|-------------------------------------------------------------------------------|
46. | 455 167 09jul2018 Monday 9 7 2018 2018w28 2018m7 |
47. | 455 229 09sep2018 Sunday 9 9 2018 2018w36 2018m9 |
48. | 457 216 27aug2018 Monday 27 8 2018 2018w35 2018m8 |
49. | 458 324 13dec2018 Thursday 13 12 2018 2018w50 2018m12 |
50. | 458 227 07sep2018 Friday 7 9 2018 2018w36 2018m9 |
|-------------------------------------------------------------------------------|
During the period from 24/1/2018 to 01/4/2019, the minimum and maximum of intra-day merits are 312 and 13018 , on 24/12/2018 and 24/1/2018, respectively. |
|
|
|
Time-series plots:Full dataset:Truncated dataset:
Basic statistics:Full dataset:. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 431.0 773.5 496.6 641.0 529.0 797.0 312.0 4493.0
----------------------------------------------------------------------------------------------
Applied formulas in previous weeks, potential outliers are days have intra-day merits beyond 127 or 1199 . di 797-529
268
. di 268*1.5
402
. di 797+402
1199
. di 529-402
127
There are 42 outliers in full dataset, in total. Those days are: . count if (merit >= 1199 | merit <= 127) & merit != .
42
. list id merit date if (merit >= 1199 | merit <= 127) & merit != .
+-------------------------+
| id merit date |
|-------------------------|
1. | 3 4493 26jan2018 |
2. | 4 3489 27jan2018 |
3. | 5 3188 28jan2018 |
4. | 6 3799 29jan2018 |
5. | 7 4192 30jan2018 |
|-------------------------|
6. | 8 2820 31jan2018 |
7. | 9 2545 01feb2018 |
8. | 10 2568 02feb2018 |
9. | 11 1867 03feb2018 |
10. | 12 2167 04feb2018 |
|-------------------------|
11. | 13 2077 05feb2018 |
12. | 14 2308 06feb2018 |
13. | 15 2141 07feb2018 |
14. | 16 2141 08feb2018 |
15. | 17 1448 09feb2018 |
|-------------------------|
16. | 18 1747 10feb2018 |
17. | 19 1442 11feb2018 |
18. | 20 1331 12feb2018 |
19. | 21 1579 13feb2018 |
20. | 22 2513 14feb2018 |
|-------------------------|
21. | 23 1991 15feb2018 |
22. | 24 1411 16feb2018 |
23. | 25 1608 17feb2018 |
24. | 26 1289 18feb2018 |
25. | 27 1403 19feb2018 |
|-------------------------|
27. | 29 1266 21feb2018 |
28. | 30 1279 22feb2018 |
30. | 32 1409 24feb2018 |
32. | 34 1382 26feb2018 |
33. | 35 1326 27feb2018 |
|-------------------------|
35. | 37 1333 01mar2018 |
36. | 38 1696 02mar2018 |
39. | 41 1245 05mar2018 |
46. | 48 1354 12mar2018 |
54. | 56 1322 20mar2018 |
|-------------------------|
55. | 57 1227 21mar2018 |
66. | 68 1233 01apr2018 |
234. | 236 2463 16sep2018 |
235. | 237 1862 17sep2018 |
236. | 238 1294 18sep2018 |
|-------------------------|
237. | 239 1268 19sep2018 |
426. | 428 1249 27mar2019 |
+-------------------------+
Only one of them occured in 2019, on 27/3/2019, at 1249 merits circulated in total. Truncated dataset:. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 407.0 681.2 245.9 626.0 522.0 764.0 312.0 2463.0
----------------------------------------------------------------------------------------------
Applied same formulas I used in earlier analyses, potential outliers are days have intra-day merits beyond 159 or 1127. . di 764-522
242
. di 242*1.5
363
. di 764+363
1127
. di 522-363
159
There are 26 outliers in total, only three of them occured in 2019, on 09/1/2019, 14/01/2019, and 27/3/2019, at 1161, 1127, and 1249, respectively. . count if (merit >= 1127 | merit <= 159) & merit != .
26
. list id merit date if (merit >= 1127 | merit <= 159) & merit != .
+-------------------------+
| id merit date |
|-------------------------|
1. | 27 1403 19feb2018 |
2. | 28 1169 20feb2018 |
3. | 29 1266 21feb2018 |
4. | 30 1279 22feb2018 |
6. | 32 1409 24feb2018 |
|-------------------------|
7. | 33 1186 25feb2018 |
8. | 34 1382 26feb2018 |
9. | 35 1326 27feb2018 |
11. | 37 1333 01mar2018 |
12. | 38 1696 02mar2018 |
|-------------------------|
15. | 41 1245 05mar2018 |
22. | 48 1354 12mar2018 |
24. | 50 1159 14mar2018 |
25. | 51 1130 15mar2018 |
30. | 56 1322 20mar2018 |
|-------------------------|
31. | 57 1227 21mar2018 |
42. | 68 1233 01apr2018 |
43. | 69 1146 02apr2018 |
127. | 153 1138 25jun2018 |
210. | 236 2463 16sep2018 |
|-------------------------|
211. | 237 1862 17sep2018 |
212. | 238 1294 18sep2018 |
213. | 239 1268 19sep2018 |
325. | 351 1161 09jan2019 |
330. | 356 1127 14jan2019 |
|-------------------------|
402. | 428 1249 27mar2019 |
+-------------------------+
|
|
|
|
It is unnecessary to know exactly they exchanged merits and money for those merits. Simply looking at post history, if the post history shows one user always post trash posts and another plus point is those merited posts are shitty ones too. If the phenomeno occur from time to time, or one or two posts received 20 or 50 merits with one-line posts, trash contents, it is enough to say about merit abusements. How can you report to moderator of the Merit is being sold outside the forum? I have seen this kind of offers as well in Facebook groups that is related in cryptocurrencies. Its hard to catch them because if its being sold outside the forum, you can't tell what username is being used by the seller unless someone is willing to go undercover and play as a buyer.
However, admin (theymos) even don't think people should tag merit abusers with red (negative trust) because admin think that they will run out of sMerits soon. It is obviously that the opinion of admin applied for small abusements only. But I think if there would be a penalty to merit seller, merit buyer should be treated the same way too.
Yes, reputable users or user who have ability to earn significant amount of merits won't sell their sMerits for such $15 earnings because they can earn much more without such risk of merit abusements. most of those who get them in large amounts are members with a good reputation which they can not risk.
|
|
|
|
|
With the cost of $15 per merit, it means that a Member need to spend $1350 for more 90 merits to become a Full Member.
The cost, in my opinion, is to expensive. Additionally, how long the buyers (of Full Member, for example) can get their money back?
Years, maybe, and there are some other risks such as being discovered as merit abusers, and got red trust then don't have chance to join good campaign.
Moreover, even they buy 90 merits to be promoted to Full Members, if their post histories contain mostly shitshows, they won't have chance to get acceptance, too. Because managers do not only care about merits, they also care more about post quality.
|
|
|
|
To be honest, no crypto currency could ever survive long term if it doesn't have a solid community and I think DOGE clearly demonstrate this strength which is rarely seen among other crypto projects.  It seems that what Dogecoin need to survive is its community, it does not need to have developers behind. Over years, without or very limited updated of Source codes, Dogecoin has still get strong support from its community, and the coin has gradually but solidly built up its very good use cases around so many aspects, on global scale. I have never seen any coin like Dogecoin, surived and grown well nearly without developers, without source code's updates. |
|
|
|
Update:Converted intra-day merits for days in 2019. . list id merit date day month2 year week month dofw if year == 2019
+------------------------------------------------------------------------------+
| id merit date day month2 year week month dofw |
|------------------------------------------------------------------------------|
343. | 343 603 01jan2019 1 1 2019 2019w1 2019m1 Tuesday |
344. | 344 526 02jan2019 2 1 2019 2019w1 2019m1 Wednesday |
345. | 345 394 03jan2019 3 1 2019 2019w1 2019m1 Thursday |
346. | 346 1082 04jan2019 4 1 2019 2019w1 2019m1 Friday |
347. | 347 835 05jan2019 5 1 2019 2019w1 2019m1 Saturday |
|------------------------------------------------------------------------------|
348. | 348 783 06jan2019 6 1 2019 2019w1 2019m1 Sunday |
349. | 349 570 07jan2019 7 1 2019 2019w1 2019m1 Monday |
350. | 350 782 08jan2019 8 1 2019 2019w2 2019m1 Tuesday |
351. | 351 1161 09jan2019 9 1 2019 2019w2 2019m1 Wednesday |
352. | 352 987 10jan2019 10 1 2019 2019w2 2019m1 Thursday |
|------------------------------------------------------------------------------|
353. | 353 878 11jan2019 11 1 2019 2019w2 2019m1 Friday |
354. | 354 711 12jan2019 12 1 2019 2019w2 2019m1 Saturday |
355. | 355 978 13jan2019 13 1 2019 2019w2 2019m1 Sunday |
356. | 356 1127 14jan2019 14 1 2019 2019w2 2019m1 Monday |
357. | 357 813 15jan2019 15 1 2019 2019w3 2019m1 Tuesday |
|------------------------------------------------------------------------------|
358. | 358 880 16jan2019 16 1 2019 2019w3 2019m1 Wednesday |
359. | 359 1018 17jan2019 17 1 2019 2019w3 2019m1 Thursday |
360. | 360 611 18jan2019 18 1 2019 2019w3 2019m1 Friday |
361. | 361 643 19jan2019 19 1 2019 2019w3 2019m1 Saturday |
362. | 362 658 20jan2019 20 1 2019 2019w3 2019m1 Sunday |
|------------------------------------------------------------------------------|
363. | 363 683 21jan2019 21 1 2019 2019w3 2019m1 Monday |
364. | 364 618 22jan2019 22 1 2019 2019w4 2019m1 Tuesday |
365. | 365 735 23jan2019 23 1 2019 2019w4 2019m1 Wednesday |
366. | 366 715 24jan2019 24 1 2019 2019w4 2019m1 Thursday |
367. | 367 615 25jan2019 25 1 2019 2019w4 2019m1 Friday |
|------------------------------------------------------------------------------|
368. | 368 587 26jan2019 26 1 2019 2019w4 2019m1 Saturday |
369. | 369 655 27jan2019 27 1 2019 2019w4 2019m1 Sunday |
370. | 370 734 28jan2019 28 1 2019 2019w4 2019m1 Monday |
371. | 371 612 29jan2019 29 1 2019 2019w5 2019m1 Tuesday |
372. | 372 510 30jan2019 30 1 2019 2019w5 2019m1 Wednesday |
|------------------------------------------------------------------------------|
373. | 373 450 31jan2019 31 1 2019 2019w5 2019m1 Thursday |
374. | 374 595 01feb2019 1 2 2019 2019w5 2019m2 Friday |
375. | 375 940 02feb2019 2 2 2019 2019w5 2019m2 Saturday |
376. | 376 571 03feb2019 3 2 2019 2019w5 2019m2 Sunday |
377. | 377 796 04feb2019 4 2 2019 2019w5 2019m2 Monday |
|------------------------------------------------------------------------------|
378. | 378 776 05feb2019 5 2 2019 2019w6 2019m2 Tuesday |
379. | 379 559 06feb2019 6 2 2019 2019w6 2019m2 Wednesday |
380. | 380 548 07feb2019 7 2 2019 2019w6 2019m2 Thursday |
381. | 381 611 08feb2019 8 2 2019 2019w6 2019m2 Friday |
382. | 382 623 09feb2019 9 2 2019 2019w6 2019m2 Saturday |
|------------------------------------------------------------------------------|
383. | 383 559 10feb2019 10 2 2019 2019w6 2019m2 Sunday |
384. | 384 642 11feb2019 11 2 2019 2019w6 2019m2 Monday |
385. | 385 585 12feb2019 12 2 2019 2019w7 2019m2 Tuesday |
386. | 386 671 13feb2019 13 2 2019 2019w7 2019m2 Wednesday |
387. | 387 649 14feb2019 14 2 2019 2019w7 2019m2 Thursday |
|------------------------------------------------------------------------------|
388. | 388 607 15feb2019 15 2 2019 2019w7 2019m2 Friday |
389. | 389 523 16feb2019 16 2 2019 2019w7 2019m2 Saturday |
390. | 390 607 17feb2019 17 2 2019 2019w7 2019m2 Sunday |
391. | 391 565 18feb2019 18 2 2019 2019w7 2019m2 Monday |
392. | 392 637 19feb2019 19 2 2019 2019w8 2019m2 Tuesday |
|------------------------------------------------------------------------------|
393. | 393 696 20feb2019 20 2 2019 2019w8 2019m2 Wednesday |
394. | 394 504 21feb2019 21 2 2019 2019w8 2019m2 Thursday |
395. | 395 509 22feb2019 22 2 2019 2019w8 2019m2 Friday |
396. | 396 657 23feb2019 23 2 2019 2019w8 2019m2 Saturday |
397. | 397 608 24feb2019 24 2 2019 2019w8 2019m2 Sunday |
|------------------------------------------------------------------------------|
398. | 398 896 25feb2019 25 2 2019 2019w8 2019m2 Monday |
399. | 399 736 26feb2019 26 2 2019 2019w9 2019m2 Tuesday |
400. | 400 553 27feb2019 27 2 2019 2019w9 2019m2 Wednesday |
401. | 401 707 28feb2019 28 2 2019 2019w9 2019m2 Thursday |
402. | 402 508 01mar2019 1 3 2019 2019w9 2019m3 Friday |
|------------------------------------------------------------------------------|
403. | 403 412 02mar2019 2 3 2019 2019w9 2019m3 Saturday |
404. | 404 1001 03mar2019 3 3 2019 2019w9 2019m3 Sunday |
405. | 405 708 04mar2019 4 3 2019 2019w9 2019m3 Monday |
406. | 406 677 05mar2019 5 3 2019 2019w10 2019m3 Tuesday |
407. | 407 787 06mar2019 6 3 2019 2019w10 2019m3 Wednesday |
|------------------------------------------------------------------------------|
408. | 408 711 07mar2019 7 3 2019 2019w10 2019m3 Thursday |
409. | 409 712 08mar2019 8 3 2019 2019w10 2019m3 Friday |
410. | 410 723 09mar2019 9 3 2019 2019w10 2019m3 Saturday |
411. | 411 656 10mar2019 10 3 2019 2019w10 2019m3 Sunday |
412. | 412 635 11mar2019 11 3 2019 2019w10 2019m3 Monday |
|------------------------------------------------------------------------------|
413. | 413 680 12mar2019 12 3 2019 2019w11 2019m3 Tuesday |
414. | 414 687 13mar2019 13 3 2019 2019w11 2019m3 Wednesday |
415. | 415 804 14mar2019 14 3 2019 2019w11 2019m3 Thursday |
416. | 416 580 15mar2019 15 3 2019 2019w11 2019m3 Friday |
417. | 417 482 16mar2019 16 3 2019 2019w11 2019m3 Saturday |
|------------------------------------------------------------------------------|
418. | 418 428 17mar2019 17 3 2019 2019w11 2019m3 Sunday |
419. | 419 657 18mar2019 18 3 2019 2019w11 2019m3 Monday |
420. | 420 758 19mar2019 19 3 2019 2019w12 2019m3 Tuesday |
421. | 421 651 20mar2019 20 3 2019 2019w12 2019m3 Wednesday |
422. | 422 720 21mar2019 21 3 2019 2019w12 2019m3 Thursday |
|------------------------------------------------------------------------------|
423. | 423 674 22mar2019 22 3 2019 2019w12 2019m3 Friday |
424. | 424 625 23mar2019 23 3 2019 2019w12 2019m3 Saturday |
425. | 425 594 24mar2019 24 3 2019 2019w12 2019m3 Sunday |
426. | 426 576 25mar2019 25 3 2019 2019w12 2019m3 Monday |
427. | 427 726 26mar2019 26 3 2019 2019w13 2019m3 Tuesday |
|------------------------------------------------------------------------------|
428. | 428 1249 27mar2019 27 3 2019 2019w13 2019m3 Wednesday |
429. | 429 927 28mar2019 28 3 2019 2019w13 2019m3 Thursday |
430. | 430 725 29mar2019 29 3 2019 2019w13 2019m3 Friday |
431. | 431 655 30mar2019 30 3 2019 2019w13 2019m3 Saturday |
432. | 432 851 31mar2019 31 3 2019 2019w13 2019m3 Sunday |
|------------------------------------------------------------------------------|
433. | 433 987 01apr2019 1 4 2019 2019w13 2019m4 Monday |
434. | 434 700 02apr2019 2 4 2019 2019w14 2019m4 Tuesday |
435. | 435 616 03apr2019 3 4 2019 2019w14 2019m4 Wednesday |
+------------------------------------------------------------------------------+
For days in 2018, please get it there
|
|
|
|
|
Show Posts
