#episode 3896

[bobbie-robron]

No, I’m never having kids. I’m just gonna have fun.

Before we begin, damn the A R M S 😍. Victoria is acting up (she’s bored according to Katie), spilling a drink on Robert which starts a rant about him never having kids (sure, Rob). In the pub, Robert is one of the gossiping about the Alan/Steph debacle that just went down.

14-Nov-2004

#classic ED #classic ED robert’s story #20041114 #episode 3896 #classic ED 2004 #200411 #more or less filler #one word A R M S 😍 #you used to love playing video game robert #sure rob no kids 👌 #katie sugden #robert sugden #karl davies #victoria sugden #syd woolfe #eric pollard #paddy kirk #emily dingle #viv hope

[ao3feed-merlinarthur]

by 0hHeyThereBigBadWolf

...can be lit with a bit of dragonfire.

Words: 3896, Chapters: 1/1, Language: English

Fandoms: Merlin (TV)

Rating: Teen And Up Audiences

Warnings: No Archive Warnings Apply

Categories: M/M

Characters: Leon (Merlin), Lancelot (Merlin), Gwaine (Merlin), Percival (Merlin), Elyan (Merlin)

Relationships: Merlin/Arthur Pendragon (Merlin), Knights & Merlin (Merlin)

Additional Tags: Dragon Merlin (Merlin), Wyverns, Episode: s04e01-02 The Darkest Hour, Friendship, Humor, Episode Fix-it, Dragonspeak, Pet Names, Shapeshifting, Do Not Re-Post To Another Site

#AO3 works tagged 'Merlin/Arthur Pendragon' #ao3feed #merthur #ao3feed merthur #ao3feed merlinarthur

[ao3feed-swanqueen]

Je ne suis pas en guerre contre toi (ça me change.)

read it on the AO3 at https://ift.tt/39yItGO

by AngelicaR2

[Challenge de mars 2020] : UA. 3x22. ""Je crois vraiment qu'il a le béguin pour toi." "C'est une blague ?" Neal l'avait toujours dit de toute façon, les voyages dans le temps, ça foutait systématiquement le bordel." Ou : Neal accompagne Emma quand elle repart dans le passé et ils atterrissent à une époque… particulière. Hookfire. SwanQueen.

Words: 3896, Chapters: 1/2, Language: Français

Fandoms: Once Upon a Time (TV)

Rating: Teen And Up Audiences

Warnings: No Archive Warnings Apply

Categories: F/F, M/M

Characters: Baelfire | Neal Cassidy, Captain Hook | Killian Jones, Emma Swan, Liam Jones (Once Upon a Time), Long John Silver, Prince Charming | David Nolan, Long John Silver's Crew (Once Upon A Time), Pirates - Character, Milah (Once Upon a Time)

Relationships: Baelfire | Neal Cassidy/Captain Hook | Killian Jones, Evil Queen | Regina Mills/Emma Swan, Baelfire | Neal Cassidy & Emma Swan, Captain Hook | Killian Jones & Liam Jones, Baelfire | Neal Cassidy & Milah

Additional Tags: Season 3 Finale, Episode: s03e22 There's No Place Like Home, Alternate Universe - Canon Divergence, Wicked Witch of the West | Zelena Dies, Baelfire | Neal Cassidy Lives, Time Travel, The Enchanted Forest (Once Upon a Time), Two-Handed Captain Hook | Killian Jones, Captain Hook | Killian Jones In Love, Crushes, Young Captain Hook | Killian Jones, Alternate Universe - Different First Meeting, Lies, Memory Loss, First Time, First Kiss, First Love, Reunions, Abandonment, Two Shot, Baelfire | Neal Cassidy & Emma Swan Friendship, Established Evil Queen | Regina Mills/Emma Swan, Emma Swan Plays Matchmaker, Bisexual Captain Hook | Killian Jones, Bisexual Baelfire | Neal Cassidy, Bisexual Emma Swan, Bisexual Evil Queen | Regina Mills, False Identity, Angst, Angst with a Happy Ending, Magic, Episode: s05e15 The Brothers Jones, Slash, Femslash, Paradox, Doctor Who References, Le Collectif NoName

read it on the AO3 at https://ift.tt/39yItGO

#IFTTT #ao3feed #fanfic #swan queen #ouat

[ao3feed-merlin]

The Darkest Hour

read it on the AO3 at https://ift.tt/2Silb2j

by 0hHeyThereBigBadWolf

...can be lit with a bit of dragonfire.

Words: 3896, Chapters: 1/1, Language: English

Fandoms: Merlin (TV)

Rating: Teen And Up Audiences

Warnings: No Archive Warnings Apply

Categories: M/M

Characters: Leon (Merlin), Lancelot (Merlin), Gwaine (Merlin), Percival (Merlin), Elyan (Merlin)

Relationships: Merlin/Arthur Pendragon (Merlin), Knights & Merlin (Merlin)

Additional Tags: Dragon Merlin (Merlin), Wyverns, Episode: s04e01-02 The Darkest Hour, Friendship, Humor, Episode Fix-it, Dragonspeak, Pet Names, Shapeshifting, Do Not Re-Post To Another Site

read it on the AO3 at https://ift.tt/2Silb2j

#IFTTT #ao3feed #fanfic #merlin #bbc merlin

[divyasingh1]

Assignment week3

import statsmodels.api import statsmodels.formula.api as smf import seaborn import matplotlib.pyplot as plt

In [245]:

#S3BQ1A10A EVER USED OTHER DRUGS #S4AQ1 EVER HAD 2-WEEK PERIOD WHEN FELT SAD, BLUE, DEPRESSED, OR DOWN MOST OF TIME # S3AQ3C1 USUAL QUANTITY WHEN SMOKED CIGARETTES #TABLIFEDX NICOTINE DEPENDENCE - LIFETIME #S2BQ2E NUMBER OF EPISODES OF ALCOHOL DEPENDENCE df=file[['S3AQ3C1','TABLIFEDX','S3BQ1A10A','S4AQ1','S2BQ2E']] #rename columns df=df.rename(columns={'S2BQ2E':'alc_dip','S3AQ3C1':'cigarettes','TABLIFEDX':'nicotine_dipendence','S3BQ1A10A':'drugs','S4AQ1':'depression'}) df.head(10)

Out[245]:cigarettesnicotine_dipendencedrugsdepressionalc_dip

0NaN022NaN

1NaN022NaN

2NaN022NaN

3NaN022NaN

4NaN022NaN

5NaN0221.0

6NaN021NaN

7NaN022NaN

8NaN021NaN

9NaN0221.0

In [263]:

#eliminate Nan Values and type modifications df.cigarettes= df.cigarettes.replace(r'^\s*$', np.nan, regex=True) df.nicotine_dipendence= df.nicotine_dipendence.replace(r'^\s*$', np.nan, regex=True) df.drugs= df.drugs.replace(r'^\s*$', np.nan, regex=True) df.depression= df.depression.replace(r'^\s*$', np.nan, regex=True) df.alc_dip= df.alc_dip.replace(r'^\s*$', np.nan, regex=True) df = df[df['cigarettes'].notna()] df = df[df['nicotine_dipendence'].notna()] df = df[df['drugs'].notna()] df = df[df['depression'].notna()] df = df[df['alc_dip'].notna()] df['cigarettes']=df['cigarettes'].astype(int) df['nicotine_dipendence']=df['nicotine_dipendence'].astype(int) df['drugs']=df['drugs'].astype(int) df['depression']=df['depression'].astype(int) df['alc_dip']=df['alc_dip'].astype(int) #eliminate unknown rows df.drop(df.index[(df["cigarettes"] == 99)|(df["alc_dip"] == 99)|(df["drugs"] == 9)|(df["depression"] == 9)],axis=0,inplace=True)

In [264]:

#replace 2 with 0 df["drugs"].replace({2: 0}, inplace=True) df["depression"].replace({2: 0}, inplace=True)

In [265]:

#subtract the mean from the explanatory variable mean=df['alc_dip'].mean() df['alc_dip']=df['alc_dip']-mean

In [266]:

df['alc_dip']=df['alc_dip'].head(500) df['cigarettes']=df['cigarettes'].head(500) df['nicotine_dipendence']=df['nicotine_dipendence'].head(500) df['drugs']=df['drugs'].head(500) df['depression']=df['depression'].head(500)

In [267]:

#regression reg=smf.ols('cigarettes~drugs+depression+alc_dip+nicotine_dipendence',data=df).fit() print(reg.summary())

                           OLS Regression Results                             ============================================================================== Dep. Variable:             cigarettes   R-squared:                       0.042 Model:                            OLS   Adj. R-squared:                  0.037 Method:                 Least Squares   F-statistic:                     7.319 Date:                Wed, 17 Feb 2021   Prob (F-statistic):           8.26e-05 Time:                        11:37:07   Log-Likelihood:                -1935.8 No. Observations:                 500   AIC:                             3880. Df Residuals:                     496   BIC:                             3896. Df Model:                           3                                         Covariance Type:            nonrobust                                         =======================================================================================                          coef    std err          t      P>|t|      [0.025      0.975] --------------------------------------------------------------------------------------- Intercept              10.8613      0.626     17.359      0.000       9.632      12.091 drugs                  -5.0872      6.764     -0.752      0.452     -18.378       8.203 depression              1.7919      0.550      3.260      0.001       0.712       2.872 alc_dip                 2.1833      0.586      3.724      0.000       1.031       3.335 nicotine_dipendence     5.0633      1.109      4.564      0.000       2.883       7.243 ============================================================================== Omnibus:                      295.837   Durbin-Watson:                   1.877 Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3135.374 Skew:                           2.405   Prob(JB):                         0.00 Kurtosis:                      14.285   Cond. No.                     9.39e+15 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The smallest eigenvalue is 7.69e-30. This might indicate that there are strong multicollinearity problems or that the design matrix is singular.

In [268]:

print( 'the model has coef=(1.82,2.48,4,29,2.08) and p-value(0,0.452,0,0) that suggests that there is not a strong evidence for drug and it could be a confounding variable. There is a strong collinearity which make me think about the capacity of the model so I continue with some other graphics')

the model has coef=(1.82,2.48,4,29,2.08) and p-value(0,0.452,0,0) that suggests that there is not a strong evidence for drug and it could be a confounding variable. There is a strong collinearity which make me think about the capacity of the model so I continue with some other graphics

In [269]:

## qqplot fig=statsmodels.graphics.gofplots.qqplot(reg.resid,line='r') print(' residuals do not follow normal distribution')

residuals do not follow normal distribution

In [270]:

#plot of residuals stdres=pd.DataFrame(reg.resid_pearson) fig2=plt.plot(stdres,'o',ls='None') l=plt.axhline(y=0,color='r')

In [271]:

#other regression diagnostic plot #figure=plt.figure(figsize(12,8)) #fig=statsmodels.graphics.regressionplots.plot_regress_exog(reg,'alc_dip')

In [272]:

print('the model has diffulties to intepret the response variable for the absence of normal distribution in errors.A deeper analysis is required to investigate the nature of the problem:one could be that the variable alc_dip assumes valuens between 1-98 but just values between 1 and 2 are realistic')

the model has diffulties to intepret the response variable for the absence of normal distribution in errors.A deeper analysis is required to investigate the nature of the problem:one could be that the variable alc_dip assumes valuens between 1-98 but just values between 1 and 2 are realistic

[srigadde]

Multiple Regression model

In [244]:

#import libraries import statsmodels.api import statsmodels.formula.api as smf import seaborn import matplotlib.pyplot as plt

In [245]:

#S3BQ1A10A EVER USED OTHER DRUGS #S4AQ1 EVER HAD 2-WEEK PERIOD WHEN FELT SAD, BLUE, DEPRESSED, OR DOWN MOST OF TIME # S3AQ3C1 USUAL QUANTITY WHEN SMOKED CIGARETTES #TABLIFEDX NICOTINE DEPENDENCE - LIFETIME #S2BQ2E NUMBER OF EPISODES OF ALCOHOL DEPENDENCE df=file[['S3AQ3C1','TABLIFEDX','S3BQ1A10A','S4AQ1','S2BQ2E']] #rename columns df=df.rename(columns={'S2BQ2E':'alc_dip','S3AQ3C1':'cigarettes','TABLIFEDX':'nicotine_dipendence','S3BQ1A10A':'drugs','S4AQ1':'depression'}) df.head(10)

Out[245]:cigarettesnicotine_dipendencedrugsdepressionalc_dip

0NaN022NaN

1NaN022NaN

2NaN022NaN

3NaN022NaN

4NaN022NaN

5NaN0221.0

6NaN021NaN

7NaN022NaN

8NaN021NaN

9NaN0221.0

In [263]:

#eliminate Nan Values and type modifications df.cigarettes= df.cigarettes.replace(r'^\s*$', np.nan, regex=True) df.nicotine_dipendence= df.nicotine_dipendence.replace(r'^\s*$', np.nan, regex=True) df.drugs= df.drugs.replace(r'^\s*$', np.nan, regex=True) df.depression= df.depression.replace(r'^\s*$', np.nan, regex=True) df.alc_dip= df.alc_dip.replace(r'^\s*$', np.nan, regex=True) df = df[df['cigarettes'].notna()] df = df[df['nicotine_dipendence'].notna()] df = df[df['drugs'].notna()] df = df[df['depression'].notna()] df = df[df['alc_dip'].notna()] df['cigarettes']=df['cigarettes'].astype(int) df['nicotine_dipendence']=df['nicotine_dipendence'].astype(int) df['drugs']=df['drugs'].astype(int) df['depression']=df['depression'].astype(int) df['alc_dip']=df['alc_dip'].astype(int) #eliminate unknown rows df.drop(df.index[(df["cigarettes"] == 99)|(df["alc_dip"] == 99)|(df["drugs"] == 9)|(df["depression"] == 9)],axis=0,inplace=True)

In [264]:

#replace 2 with 0 df["drugs"].replace({2: 0}, inplace=True) df["depression"].replace({2: 0}, inplace=True)

In [265]:

#subtract the mean from the explanatory variable mean=df['alc_dip'].mean() df['alc_dip']=df['alc_dip']-mean

In [266]:

df['alc_dip']=df['alc_dip'].head(500) df['cigarettes']=df['cigarettes'].head(500) df['nicotine_dipendence']=df['nicotine_dipendence'].head(500) df['drugs']=df['drugs'].head(500) df['depression']=df['depression'].head(500)

In [267]:

#regression reg=smf.ols('cigarettes~drugs+depression+alc_dip+nicotine_dipendence',data=df).fit() print(reg.summary())

                           OLS Regression Results                             ============================================================================== Dep. Variable:             cigarettes   R-squared:                       0.042 Model:                            OLS   Adj. R-squared:                  0.037 Method:                 Least Squares   F-statistic:                     7.319 Date:                Wed, 17 Feb 2021   Prob (F-statistic):           8.26e-05 Time:                        11:37:07   Log-Likelihood:                -1935.8 No. Observations:                 500   AIC:                             3880. Df Residuals:                     496   BIC:                             3896. Df Model:                           3                                         Covariance Type:            nonrobust                                         =======================================================================================                          coef    std err          t      P>|t|      [0.025      0.975] --------------------------------------------------------------------------------------- Intercept              10.8613      0.626     17.359      0.000       9.632      12.091 drugs                  -5.0872      6.764     -0.752      0.452     -18.378       8.203 depression              1.7919      0.550      3.260      0.001       0.712       2.872 alc_dip                 2.1833      0.586      3.724      0.000       1.031       3.335 nicotine_dipendence     5.0633      1.109      4.564      0.000       2.883       7.243 ============================================================================== Omnibus:                      295.837   Durbin-Watson:                   1.877 Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3135.374 Skew:                           2.405   Prob(JB):                         0.00 Kurtosis:                      14.285   Cond. No.                     9.39e+15 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The smallest eigenvalue is 7.69e-30. This might indicate that there are strong multicollinearity problems or that the design matrix is singular.

In [268]:

print( 'the model has coef=(1.82,2.48,4,29,2.08) and p-value(0,0.452,0,0) that suggests that there is not a strong evidence for drug and it could be a confounding variable. There is a strong collinearity which make me think about the capacity of the model so I continue with some other graphics')

the model has coef=(1.82,2.48,4,29,2.08) and p-value(0,0.452,0,0) that suggests that there is not a strong evidence for drug and it could be a confounding variable. There is a strong collinearity which make me think about the capacity of the model so I continue with some other graphics

In [269]:

## qqplot fig=statsmodels.graphics.gofplots.qqplot(reg.resid,line='r') print(' residuals do not follow normal distribution')

residuals do not follow normal distribution

In [270]:

#plot of residuals stdres=pd.DataFrame(reg.resid_pearson) fig2=plt.plot(stdres,'o',ls='None') l=plt.axhline(y=0,color='r')

In [271]:

#other regression diagnostic plot #figure=plt.figure(figsize(12,8)) #fig=statsmodels.graphics.regressionplots.plot_regress_exog(reg,'alc_dip')

In [272]:

print('the model has diffulties to intepret the response variable for the absence of normal distribution in errors.A deeper analysis is required to investigate the nature of the problem:one could be that the variable alc_dip assumes valuens between 1-98 but just values between 1 and 2 are realistic')

the model has diffulties to intepret the response variable for the absence of normal distribution in errors.A deeper analysis is required to investigate the nature of the problem:one could be that the variable alc_dip assumes valuens between 1-98 but just values between 1 and 2 are realistic

[techsciresearch]

India Minimally Invasive Surgical Instruments Market to Register an Impressive CAGR through 2025 – TechSci Research

Growing technological advancements in the healthcare sector to drive India minimally invasive surgical instruments market

According to TechSci Research report, “India Minimally Invasive Surgical Instruments Market By Instrument (Endoscopes, Laparoscopy Access Instruments, Laparoscopy Handheld Instruments, Robotic Assisted Surgical Systems, Electrosurgery, Visualization and Monitoring System), By Type (Endoscopy, Laparoscopy, Robotics), By Application (Gynecology, Orthopedic Surgery, Respiratory, Oncology, Gastrointestinal, Urology, Cardiology, Others), By End-User (Hospitals, Surgical Centers, Others), By Region, Forecast & Opportunities, FY2026”, the India minimally invasive surgical instruments market is expected to witness impressive growth during the forecast period. The key factor behind the growth of India minimally invasive surgical instruments market is lower cost than in-patient and conventional open surgeries. Moreover, these kinds of surgeries involve minimal cuts or stitches, which enables patients to stay in hospital for a shorter period, thereby expected to drive the market growth over the coming years. Additionally, minimally invasive surgeries cause less damage to the tissues, which is anticipated to boost the growth of market in the years to come. In addition to this, improved patient quality of life coupled with reduction in healthcare costs and time are some other factors that are estimated to aid the growth of India minimally invasive surgical instruments market during the forecast period. However, surgeons need special training before they can perform minimally invasive surgeries. This type of surgery cannot be performed on patients who have had a previous “open” surgery in the upper or lower part of their belly, or patients with other medical problems. These factors might hamper the market growth in the year to come. Along with this, reimbursement challenges and uncertain regulatory framework in medical device market are some major factors that might act as major impediments to the growth of India minimally invasive surgical instruments through FY2026.

Browse XX market data Tables and XX Figures spread through XXX Pages and an in-depth TOC on " India Minimally Invasive Surgical Instruments Market "

https://www.techsciresearch.com/report/india-minimally-invasive-surgical-instruments-market/4940.html

The India minimally invasive surgical instruments market is segmented based on instrument, type, application, end-users and region. Based on end-user, the minimally invasive surgical devices market is segmented into hospitals, surgical centers and others. Out of these, the hospitals segment held the majority share in the market in terms of revenue until FY2019 and is further forecast to maintain its leading position during the next five years as well, which can be attributed to the fact that large number of minimally invasive surgical procedures are performed in hospitals owing to the presence of highly skilled healthcare professionals. Hospitals, whose reimbursement are being increasingly impacted by quality measures and episode-of-care payments prefer minimally invasive surgical devices because of their significant benefits. Based on instrument type, the market is segmented into endoscopes, laparoscopy access instruments, laparoscopy handheld instruments, robotic assisted surgical systems, electrosurgery, visualization and monitoring system. Among them, the handheld instruments segment dominated the market in FY2019 and is anticipated to hold its leading position over the coming years as well due to technological innovations and rapid adoption in minimally invasive surgeries. Additionally, these kinds of devices provide easier access during surgeries with instrument triangulation, thereby reducing the risk of potential mistakes.

India Medtronic Pvt. Ltd., Johnson & Johnson Private Limited, Stryker India Pvt. Ltd, B.Braun Medical India Pvt.Ltd., Abbott India Limited, Smith & Nephew Healthcare Pvt Ltd, Zimmer Biomet India, Boston Scientific India Pvt Ltd, Becton Dickinson Private Limited, Conmed Linvatec India Pvt Ltd are some of the leading players operating in India minimally invasive surgical instruments market.

Download Sample Report @ https://www.techsciresearch.com/sample-report.aspx?cid=4940

Customers can also request for 10% free customization on this report.

“India minimally invasive surgical instruments market is forecast to register high growth until FY2026 as the key market players are trying to capture the untapped markets by introducing innovative products such as surgical robots, etc. Moreover, rising medical tourism in the country is expected to bolster the market growth in the coming years. Furthermore, rising geriatric population and road crashes are further anticipated to positively influence the market growth through FY2026.”, said Mr. Karan Chechi, Research Director with TechSci Research, a research-based India management consulting firm.

“India Minimally Invasive Surgical Instruments Market By Instrument (Endoscopes, Laparoscopy Access Instruments, Laparoscopy Handheld Instruments, Robotic Assisted Surgical Systems, Electrosurgery, Visualization and Monitoring System), By Type (Endoscopy, Laparoscopy, Robotics), By Application (Gynecology, Orthopedic Surgery, Respiratory, Oncology, Gastrointestinal, Urology, Cardiology, Others), By End-User (Hospitals, Surgical Centers, Others), By Region, Forecast & Opportunities, FY2026” has evaluated the future growth potential of India minimally invasive surgical instruments market and provides statistics & information on market size, structure and future market growth. The report intends to provide cutting-edge market intelligence and help decision makers take sound investment decisions. Besides, the report also identifies and analyzes the emerging trends along with essential drivers, challenges and opportunities in India minimally invasive surgical instruments market.

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 Contact

Mr. Ken Mathews

708 Third Avenue,

Manhattan, NY,

New York – 10017

Tel: [phone]

Email: [email protected]

#India Minimally Invasive Surgical Instruments Market #Minimally Invasive Surgical Instruments Market #Minimally Invasive Surgical Instruments Market Analysis #Minimally Invasive Surgical Instruments Market Size #India Minimally Invasive Surgical Instruments Market Growth #India Minimally Invasive Surgical Instruments Market Share #India Minimally Invasive Surgical Instruments Market Forecast #Minimally Invasive Surgical Instruments Market Trends

[ao3feed-voltronlegendarydefender]

Hope's Story

read it on the AO3 at http://ift.tt/2vIXplS

by LadyLienDa

When Voltron answered a distress signal from the Olkari, they didn't expect to find a human girl living amongst them. Who is this girl? Where did she come from?

This fic was intended to give a bit more backstory on Hope, my original fan character from my Purpose series. If you haven't read that series, I suggest you do so before reading this one.

Words: 3896, Chapters: 1/1, Language: English

Fandoms: Voltron: Legendary Defender

Rating: General Audiences

Warnings: No Archive Warnings Apply

Categories: Gen

Characters: Keith (Voltron), Original Characters, Voltron Team

Additional Tags: Original Female Character - Freeform, Backstory, olkarion, Olkari, Season 2 episode 4, Team as Family, New Teammate

read it on the AO3 at http://ift.tt/2vIXplS

#ao3feed #fanfic #voltron #voltron: legendary defender #ao3feed-voltronlegendarydefender

[ao3feed-saphael]

Le Pouvoir De La Musique Et Du Cœur

read it on the AO3 at http://ift.tt/2tGIWGp

by Kaaaaarooooo

Après avoir trahi le clan, Simon est dévasté et décide de tout faire pour être pardonné. En tant que musicien dans l’âme, une idée lui vint à l'esprit ... Raphaël et le clan vont-ils lui pardonner?

Words: 3896, Chapters: 1/1, Language: Français

Fandoms: Shadowhunters (TV)

Rating: General Audiences

Warnings: No Archive Warnings Apply

Categories: M/M

Characters: Simon Lewis, Raphael Santiago, Magnus Bane, Lily Chen, New York Vampire Clan Member(s) (Shadowhunter Chronicles)

Relationships: Simon Lewis/Raphael Santiago, Magnus Bane & Simon Lewis

Additional Tags: Malec mention only, No season 2, FIx It, Episode 1x13, Fluff, Magnus Bane & Simon Lewis Friendship

read it on the AO3 at http://ift.tt/2tGIWGp

#saphael #simon x raphael #raphael x simon

[ao3feed-victuuri]

Free Skate

read it on the AO3 at http://ift.tt/2lTB24Y

by Karin Mazaki (KarinMazaki)

I originally posted this story on another site and wrote it after the ninth episode. Please forgive as it takes off from what was promised in that episode and goes from there. As Yuuri and Viktor's relationship moves forward they are happy in love but not everyone is happy for them. They are public figures and as such represent their countries. Certain problems crop up concerning Viktor's family and the Russian Government. How will this affect their relationship?

Words: 3896, Chapters: 1/1, Language: English

Fandoms: Yuri!!! on Ice (Anime)

Rating: Teen And Up Audiences

Warnings: No Archive Warnings Apply

Categories: M/M

Characters: Katsuki Yuuri, Viktor Nikiforov

Relationships: Yuuri Katsuki/Viktor Nikiforov

Additional Tags: Romantic Fluff, Drama, Current Events, Sports, Ice Skating

read it on the AO3 at http://ift.tt/2lTB24Y

#ao3feed #fanfic #victuuri #victor nikiforov #katsuki yuuri #yuri!!! on ice #yuri on ice #yoi

[ao3feed-damerey]

Jedikiller

read it on the AO3 at http://ift.tt/2gm2GHp

by lannisterslioness

PEACE has returned to the galaxy after a long and hard fought battle against The First Order. Many were lost along the way, but many were gained as well, including the return of Luke Skywalker and redemption of Ben Solo. Those strong with the Force are being trained in the ways of the Gray Jedi and ushered into the galaxy under the wisdom of Luke himself, along with his apprentice - and daughter - Rey - while the galaxy's future has never looked brighter.

However, when other Force users around the galaxy begin to go missing and are found either dead or disconnected from the Force, it is undeniable that a new darkness is growing.

Despite wanting to charge into action again, things have changed for Rey; including the fact of that she is about to be a mother and is married to the newest Senator in the Republic - Poe Dameron. Knowing the galaxy and new order of Jedi need her, Rey will risk everything to end the darkness threatening her family once and for all. But just how much is Rey willing to lose in order to end the return of the Sith?

Words: 3896, Chapters: 1/1, Language: English

Fandoms: Star Wars - All Media Types, Star Wars Sequel Trilogy, Star Wars Episode VII: The Force Awakens (2015)

Rating: Teen And Up Audiences

Warnings: Major Character Death

Categories: F/M

Characters: Poe Dameron, Rey (Star Wars), Ben Solo, Luke Skywalker, Finn (Star Wars), Rose Tico, BB-8 (Star Wars), C-3PO (Star Wars), R2-D2 (Star Wars), Original Jedi Character(s), Original Sith Character(s), Original Characters

Relationships: Poe Dameron/Rey

Additional Tags: Post-First Order, Senator Poe Dameron, Gray Jedi, Jedi Rey, Sith

read it on the AO3 at http://ift.tt/2gm2GHp

#IFTTT #ao3feed #fanfic #damerey #star wars #tfa #poe dameron #rey

[vfstreaming]

Vfhd Fr 1 Rue Sesame Saison 31 Streaming Vostfr 2000 Gratuit

Vfhd Fr 1 Rue Sesame Saison 31 Streaming Vostfr 2000 Gratuit

Cliquez ici pour le streaming >>> https://is.gd/KSsSne

Regarder Gratuit >>>https://is.gd/KSsSne

[Vfhd@fr] » 1 Rue Sesame » Saison 31

Prochain épisode : Saison 50 Épisode 31

Épisode 31 (2020-06-13)

Saison 50 Épisode 31

Regardez le nouvel épisode

1 Rue Sesame Saison 31 (Tous les épisodes)

3240

Titre: 1 Rue Sesame

Date de première diffusion: 1969-11-10

Dernière date de diffusion: 2020-06-06

Nombre de saisons: 50

Nombre d’épisodes: 2899

Pays d’origine: US

Langue originale: en

Runtime: 54 minutes 60 minutes

Production: Sesame Workshop / Children’s Television Workshop /

Genres: ComédieKids

1 Rue Sesame

Season 31

Vue d’ensemble::

Liste d’épisodes

Episode 1

Episode 3851 2000-01-03

Episode 2

Episode 3852 2000-01-04

Episode 3

Episode 3853 2000-01-05

Episode 4

Episode 3854 2000-01-06

Episode 5

Episode 3855 2000-01-07

Episode 6

Episode 3856 2000-01-10

Episode 7

Episode 3857 2000-01-11

Episode 8

Episode 3858 2000-01-12

Episode 9

Episode 3859 2000-01-13

Episode 10

Episode 3860 2000-01-14

Episode 11

Episode 3861 2000-01-17

Episode 12

Episode 3862 2000-01-18

Episode 13

Episode 3863 2000-01-19

Episode 14

Episode 3864 2000-01-20

Episode 15

Episode 3865 2000-01-21

Episode 16

Episode 3866 2000-01-24

Episode 17

Episode 3867 2000-01-25

Episode 18

Episode 3868 2000-01-26

Episode 19

Episode 3869 2000-01-27

Episode 20

Episode 3870 2000-01-28

Episode 21

Episode 3871 2000-01-31

Episode 22

Episode 3872 2000-02-01

Episode 23

Episode 3873 2000-02-02

Episode 24

Episode 3874 2000-02-03

Episode 25

Episode 3875 2000-02-04

Episode 26

Episode 3876 2000-02-07

Episode 27

Episode 3877 2000-02-08

Episode 28

Episode 3878 2000-02-09

Episode 29

Episode 3879 2000-02-10

Episode 30

Episode 3880 2000-02-11

Episode 31

Episode 3881 2000-02-14

Episode 32

Episode 3882 2000-02-15

Episode 33

Episode 3883 2000-02-16

Episode 34

Episode 3884 2000-02-17

Episode 35

Episode 3885 2000-02-18

Episode 36

Episode 3886 2000-02-21

Episode 37

Episode 3887 2000-02-22

Episode 38

Episode 3888 2000-02-23

Episode 39

Episode 3889 2000-02-24

Episode 40

Episode 3890 2000-02-25

Episode 41

Episode 3891 2000-02-28

Episode 42

Episode 3892 2000-02-29

Episode 43

Episode 3893 2000-03-01

Episode 44

Episode 3894 2000-03-02

Episode 45

Episode 3895 2000-03-03

Episode 46

Episode 3896 2000-03-06

Episode 47

Episode 3897 2000-03-07

Episode 48

Episode 3898 2000-03-08

Episode 49

Episode 3899 2000-03-09

Episode 50

Episode 3900 2000-03-10

Episode 51

Episode 3901 2000-04-24

Episode 52

Episode 3902 2000-04-25

Episode 53

Episode 3903 2000-04-26

Episode 54

Episode 3904 2000-04-27

Episode 55

Episode 3905 2000-04-28

Episode 56

Episode 3906 2000-05-01

Episode 57

Episode 3907 2000-05-02

Episode 58

Episode 3908 2000-05-03

Episode 59

Episode 3909 2000-05-04

Episode 60

Episode 3910 2000-05-05

Episode 61

Episode 3911 2000-05-08

Episode 62

Episode 3912 2000-05-09

Episode 63

Episode 3913 2000-05-10

Episode 64

Episode 3914 2000-05-11

Episode 65

Episode 3915 2000-05-12

Partagez cette émission avec vos amis

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1 Rue Sesame Saison 31

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[dramalooks-blog]

D’ Originals 8 May 2017 Full Episode HD Replay

D’ Originals 8 May 2017 Full Episode HD Replay

Watch Online D’ Originals 8 May 2017 Full Episode HD Replay in High Quality Video:

View On WordPress

#D’ Originals #D’ Originals 8 May 2017 #D’ Originals 8 May 2017 Dailymotion Video #D’ Originals 8 May 2017 Episode D’ Originals 8 May 2017 Pinoy Replay #D’ Originals 8 May 2017 Full Episode #D’ Originals 8 May 2017 Full Episode HD #D’ Originals 8 May 2017 Full Episode Replay #D’ Originals 8 May 2017 HD #D’ Originals 8 May 2017 HD Replay #D’ Originals 8 May 2017 Watch Online #D’ Originals 8 May 2017 Youtube #D’ Originals full episode download #D’ Originals pinoy hd replay

[ao3feed-yurionice]

Free Skate

read it on the AO3 at http://ift.tt/2lTB24Y

by Karin Mazaki (KarinMazaki)

I originally posted this story on another site and wrote it after the ninth episode. Please forgive as it takes off from what was promised in that episode and goes from there. As Yuuri and Viktor's relationship moves forward they are happy in love but not everyone is happy for them. They are public figures and as such represent their countries. Certain problems crop up concerning Viktor's family and the Russian Government. How will this affect their relationship?

Words: 3896, Chapters: 1/1, Language: English

Fandoms: Yuri!!! on Ice (Anime)

Rating: Teen And Up Audiences

Warnings: No Archive Warnings Apply

Categories: M/M

Characters: Katsuki Yuuri, Viktor Nikiforov

Relationships: Yuuri Katsuki/Viktor Nikiforov

Additional Tags: Romantic Fluff, Drama, Current Events, Sports, Ice Skating

read it on the AO3 at http://ift.tt/2lTB24Y

#IFTTT #ao3feed #fanfic #yuri on ice

[bobbie-robron]

Classic ED schedule - week 2 (2024)

Daz does his best to get help for Alan but is seen as the boy who cried wolf unless provided proof - the pills! Jimmy is insensitive about the death of Sadie’s horse. Scott is in the doghouse for a character reference as well as not being allowed to look after Jean. Steph flees with Alan to the quarry where the coppers finally catch up to her (and later charge her with attempted murder) and Alan is taken to hospital. Sadie offers Tom and Charity her wedding planner expertise along with roping Katie in to help. The coppers look into Shelly’s disappearance. Jimmy’s infatuation with Chloe continues. Steph is free to leave while Alan stays with Betty after release from hospital. Bob and Scott swap adobes. Sadie confides in Zoe about her planning to seduce (toy boy) Robert and it works! Danny and Donna break up.

UK START TIME 1:25PM

08-Jan: 10-Nov-2004** (3893), 11-Nov-2004 (3894)

UK START TIME 1:40PM

09-Jan: 12-Nov-2004** (3895), 14-Nov-2004** (3896)

10-Jan: 15-Nov-2004** (3897), 16-Nov-2004** (3898)

11-Jan: 17-Nov-2004** (3899), 18-Nov-2004** (3900)

12-Jan: 19-Nov-2004** Sadie seduces Robert (3901), 21-Nov-2004** (3902)

**Robert appears in the episode

NEXT WEEK (may change due to potential preemptions): Steph is charged with Shelly’s murder. Scott receives his sentence. Syd leaves the village as Chas doesn’t love him back. Terry and Louise participate in the Dales Dash. Rodney and Paul find common ground until a drunken slur destroys it. Sadie is ready to have a baby. Jack agrees to let Katie stay with them.

#classic ED #classic ED coming up #week 2 2024

[vfstreaming]

Vfhd Fr 1 Rue Sesame Saison 31 Streaming Vostfr 2000 Gratuit

Vfhd Fr 1 Rue Sesame Saison 31 Streaming Vostfr 2000 Gratuit

Cliquez ici pour le streaming >>> https://is.gd/S4iffD

Regarder Gratuit >>>https://is.gd/S4iffD

[Vfhd@fr] » 1 Rue Sesame » Saison 31

Prochain épisode : Saison 50 Épisode 31

Épisode 31 (2020-06-13)

Saison 50 Épisode 31

Regardez le nouvel épisode

1 Rue Sesame Saison 31 (Tous les épisodes)

3240

Titre: 1 Rue Sesame

Date de première diffusion: 1969-11-10

Dernière date de diffusion: 2020-06-06

Nombre de saisons: 50

Nombre d’épisodes: 2899

Pays d’origine: US

Langue originale: en

Runtime: 54 minutes 60 minutes

Production: Sesame Workshop / Children’s Television Workshop /

Genres: ComédieKids

1 Rue Sesame

Season 31

Vue d’ensemble::

Liste d’épisodes

Episode 1

Episode 3851 2000-01-03

Episode 2

Episode 3852 2000-01-04

Episode 3

Episode 3853 2000-01-05

Episode 4

Episode 3854 2000-01-06

Episode 5

Episode 3855 2000-01-07

Episode 6

Episode 3856 2000-01-10

Episode 7

Episode 3857 2000-01-11

Episode 8

Episode 3858 2000-01-12

Episode 9

Episode 3859 2000-01-13

Episode 10

Episode 3860 2000-01-14

Episode 11

Episode 3861 2000-01-17

Episode 12

Episode 3862 2000-01-18

Episode 13

Episode 3863 2000-01-19

Episode 14

Episode 3864 2000-01-20

Episode 15

Episode 3865 2000-01-21

Episode 16

Episode 3866 2000-01-24

Episode 17

Episode 3867 2000-01-25

Episode 18

Episode 3868 2000-01-26

Episode 19

Episode 3869 2000-01-27

Episode 20

Episode 3870 2000-01-28

Episode 21

Episode 3871 2000-01-31

Episode 22

Episode 3872 2000-02-01

Episode 23

Episode 3873 2000-02-02

Episode 24

Episode 3874 2000-02-03

Episode 25

Episode 3875 2000-02-04

Episode 26

Episode 3876 2000-02-07

Episode 27

Episode 3877 2000-02-08

Episode 28

Episode 3878 2000-02-09

Episode 29

Episode 3879 2000-02-10

Episode 30

Episode 3880 2000-02-11

Episode 31

Episode 3881 2000-02-14

Episode 32

Episode 3882 2000-02-15

Episode 33

Episode 3883 2000-02-16

Episode 34

Episode 3884 2000-02-17

Episode 35

Episode 3885 2000-02-18

Episode 36

Episode 3886 2000-02-21

Episode 37

Episode 3887 2000-02-22

Episode 38

Episode 3888 2000-02-23

Episode 39

Episode 3889 2000-02-24

Episode 40

Episode 3890 2000-02-25

Episode 41

Episode 3891 2000-02-28

Episode 42

Episode 3892 2000-02-29

Episode 43

Episode 3893 2000-03-01

Episode 44

Episode 3894 2000-03-02

Episode 45

Episode 3895 2000-03-03

Episode 46

Episode 3896 2000-03-06

Episode 47

Episode 3897 2000-03-07

Episode 48

Episode 3898 2000-03-08

Episode 49

Episode 3899 2000-03-09

Episode 50

Episode 3900 2000-03-10

Episode 51

Episode 3901 2000-04-24

Episode 52

Episode 3902 2000-04-25

Episode 53

Episode 3903 2000-04-26

Episode 54

Episode 3904 2000-04-27

Episode 55

Episode 3905 2000-04-28

Episode 56

Episode 3906 2000-05-01

Episode 57

Episode 3907 2000-05-02

Episode 58

Episode 3908 2000-05-03

Episode 59

Episode 3909 2000-05-04

Episode 60

Episode 3910 2000-05-05

Episode 61

Episode 3911 2000-05-08

Episode 62

Episode 3912 2000-05-09

Episode 63

Episode 3913 2000-05-10

Episode 64

Episode 3914 2000-05-11

Episode 65

Episode 3915 2000-05-12

Partagez cette émission avec vos amis

×

1 Rue Sesame Saison 31

Inscription gratuite

Pour regarder cet épisode en ligne, vous devez créer un compte GRATUIT. L’inscription est facile et rapide. L’accès prend moins de 1 minute

Créer mon compte Sécurisé vérifié

Formats disponible