text:
Arrow Season 6 Episode 23 "Life Sentence" set photos - Includes SPOILERS
THIS BLOG POST IS FOR "ARROW" SEASON 6 EPISODE 23 "Life Sentence" AND MAY CONTAIN POSSIBLE SPOILERS FOR THE EPISODE.
ALSO additional - Episode 22 spoiler as well.
A reminder I didn't think I had to post since its stated on the side of the page... but if you are going to post photos from this INCLUDE THE LINK TO THE POST with it...and don't use all of them. A maximum of FOUR (4) photos please.
As April winds down most TV shows film their season finales, and The CW superhero series Arrow was no different. I will post this BEFORE I get into describing what I saw (and in a rare departure for me... I will also comment on what I am aware of that I didnt see shot). I am going to state that I was simply going to post the photos, and let you, the audience try and decide the context. HOWEVER, after a crew member on set purposefully tried to prevent me from taking photos AFTER him & I had reached an agreement on when to shoot & when not to with an RCMP officer witnessing the entire conversation. My respect for a crew that I have found over the last two seasons to be more & more aggressive, bordering on abusive a couple times is done.
So as you will see in the shots below, Arrow filmed a scene at the "Star City Metro" Police Department at Burnaby City Hall. It had Stephen Amell as Oliver Queen, Jack Moore as William Clayton, Emily Bett Rickards as Felicity Smoak and Sydelle Noel as FBI Agent Samandra Watson, as well as a handful media, generic FBI & U.S. Marshal members. The scene played out with the FBI leading Oliver out in handcuffs, with awaiting media there. Oliver makes a brief statement, before being hauled away while William & Felicity follow behind. After that scene, which was Emilys last scene of the season, the crew moved to a different part of Burnaby City Hall to film more scenes.
This scene was interesting to me for a couple reasons, since I know in episode 22 Oliver "makes a deal" with the Feds.... so why the big dramatics in ep 23?
As I said, the crew were being uncooperative with me, even after an agreement had been reached, so there was very little for me to shoot. The 8 photos you see below, are really the best there was of the 21 photos I managed to take in total (those of you who have followed my blog for awhile know its rare I even have sets I post HERE under 21 photos.... so to only have take 21 total is a sign of how difficult this location was).
Later that night, they shot a couple more scenes. Nothing as juicy as that first one. But one was a pretty epic scene for the fact of who was in it. Stephen shot his last scene of the season, with an actor that I do believe is leaving the show permanently based on things I know now. He shot a scene with Paul Blackthorne where Quentin Lance was talking to Diaz while Oliver watched. Of course the suspicion has been for awhile that Quentin might be getting the big send off due to the fact the show has to cut little meat from the bones for the re-negotiations of the cast for season 7. I can NOT say for sure that he was killed off, but by Stephens cryptic tweet, as well as Pauls curious instagram post, and the fact Caity Lotz flew in to film 1 day of work in "hospital" setting for 2 scenes... things dont look good for the current Mayor.
Also, as a random side detail. I ran into 2 cast from the show at the movies the same night I shot that set. I congratulated them on a season 7, and asked 1 (I was assuming the other would be) of them if he was coming back, and he said "yes". That makes me believe NTA (New Team Arrow... the team name given to Curtis/Mister Terrific, Rene/Wid Dog & Dinah/Black Canary by the fandom for those of you not up to date on all of that.) will be back in season 7 in some capacity.
Arrow season 6 airs Thursday on The CW in the U.S. and CTV in Canada.
Arrow shoots a scene for season 6 (episode 13, maybe 12) with Katie Cassidy as Black Siren and Paul Blackthorne as Quentin Lance
THIS BLOG POST IS FOR "ARROW" SEASON 6 EPISODE 12 or 13 AND MAY CONTAIN POSSIBLE SPOILERS FOR THE EPISODE.
Last month I caught Arrow out and about shortly before they went on Holiday break in the downtown eastside/Gastown area of Vancouver.
Below are photos & video of the scene being shot.
The scene they were shooting on this day was pretty basic, with no dialogue happening. It simply had Team Arrow member Quentin Lance (played by Paul Blackthorne) walking along a street looking at his phone, being followed by earth 2 Laurel Lance, aka Black Siren (played by Katie Cassidy).
The scene played out with Quentin walking along, at 1 point he seems to hold his phone over his shoulder as if trying to see who is behind him, but whether or not he sees the doppleganger of his late daughter is unsure, as Laurel hastily runs for cover when she notices. Immediately after she hides, an SCPD cruiser goes by as well with its lights on.
Is Quentin trying to trap Laurel, or Laurel trying to trap Quentin? I guess we will need to wait a few more weeks to find out for certain. This also was important in that this was the first public shoot Katie Cassidy had done after the death of her own real life father two weeks earlier. While during a short break, I managed to get a couple autographs from Katie, and I passed along my condolences to her, and she thanked me for stating so.
Arrow retuens from the holiday break Thursday for the mid-season premiere of season 6 on The CW in the U.S. and CTV in Canada.
Arrow shoots scenes for episode 607 with Stephen Amell, Emily Bett Rickards, Paul Blackthorne, Juliana Harkavy and Jack Moore
THIS BLOG POST IS FOR "ARROW" SEASON 6 EPISODE 7 "THANKSGIVING" AND MAY CONTAIN POSSIBLE SPOILERS FOR THE EPISODE.
Arrow shot scenes for their 7th episode of season 6 in downtown Vancouver at the former Canada Post sorting facility. The structure, which closed as a postal outlet a year or so ago, has been primarily a secondary studio for a variety of television shows since then, including the likes of all the DC CW Arrowverse shows, and several others. While the structure is used mainly for interior scenes because of its large spacious design and ability to house all the equipment trucks a production needs, some productions, including on this day, Arrow, use its exterior.
On this particular day, Arrow used it as a Star City Police Headquarters (guess after the last one went boom in the season premiere last night they needed a new one). On hand for a Thanksgiving related food drive were Stephen Amell as Oliver Queen, Emily Bett Rickards as Felicity Smoak, Juliana Harkavy as Dinah Drake, Jack Moore as William Clayton and Paul Blackthorne as Quentin Lance... just a few days later, Paul did a charity swim with sharks to raise awareness for them.
A few scenes were shot there, including 1 where Felicity was talking to a mystery woman that seemed to be a reporter. While that was going on, there also seemed to be a scene going on with Oliver & Quentin down in the middle of the tented up food drive area.
A bit later a scene was shot with Felicity and Oliver arriving, meeting up, talking to William, before being interrupted by Quentin to join them elsewhere. Another scene after that saw Oliver, William and Felicity standing on a podium where Oliver made a very short speech before leaving.
They proceeded to shoot for a bit longer after that, but with a plethora of shots already of the scene, I opted to leave.
Arrow airs Thursdays for season 6 on The CW in the U.S. and CTV in Canada.
Arrow shoots a few scenes for the season 6 premiere with lots of cast in multiple locations in the Vancouver area
THIS BLOG POST CONTAINS PHOTOS AND POSSIBLE SPOILERS ABOUT THE SEASON 6 PREMIERE OF "ARROW". If you have not watched this episode yet, ad do not want to see or read any possible spoilers about it..... do not continue reading or viewing the photos below.
Arrow started filming season 6 of Arrow last month in a few locations. The two that I caught up to them at was in North Vancouver, in Princess Park that they were using as part of Lian Yu.
On set that day was everyone that we saw at the end of season 5 who were on the island actively trying to get out together. People I saw on set that day included Emily Bett Rickards, Willa Holland, David Ramsey, Echo Kellum, Juliana Harkavy, Manu Bennett and Anna Hopkins. I DID NOT see Katrina Law, but I did see a person dressed as Nyssa al Ghul there. I also never saw Katie Cassidy, Lexa Doig or Madison McLaughlin there.
All that said, who I saw, and didnt see does not mean 1 way or the other who did or didnt die in the episode, as much of what they filmed on this day was simply showing what everyone did in the final minutes before Adrian Chase threw a Donald Trump sized temper tantrum & blew up an entire island. I do know from things I do know about this episode and others so far, 1 person from the lists above is in a coma, and as of the time of writing this, nothing has changed with the character coming out of it, or being killed off yet. I suppose if/when they finally decide that will decide if/when the cast member playing the character decides on returning or not.
I managed to get some autographs that day from Anna Hopkins and Manu Bennett, despite crew being overly pushy about insisting they were "in a hurry" (despite the fact the minivan she said was in a hurry to leave sat there for another 4-5 minutes afterwards)
The area they filmed in was so well concealed by ravines and forest that it took quite a bit of effort to even find ANY shred of area that I could see the scene from. While I could hear pretty much everything that happened, my visibility was limited to two small cracks between some trees.... thus why the limited selection of photos I managed to get. Much of the scene had the cast running, so the only chance to really get a shot of them was after they were done running.
The best shots I got were of Emily Bett Rickards when she was finished shooting the entire scene, and wasnt in a rush to get back to her starting spot. In the pics, you can see Felicity is clearly covered in a variety of scratches.
After they changed the set up for the scene, I seemingly lost the spot I had as a crew member was then there stopping people from trying to see from there..... so i moved on for that day.
The only shot I know anyone got that day of Manu Bennett on set as Deathstroke was from twitter user @ArizonaJake
A few days later, I managed to catch up to Arrow shooting a scene on the Georgia viaduct. Tis is of course the same viaduct they used for that impressive Deathstroke army marching into Starling City back in season 2. On this day, they had several of the cast there shooting a variety of scenes on the bridge. While most of the shooting while I was there consisted of two actresses and their stunt doubles doing a fighting scene, others were in fact there, and did film at various times.... albeit, mostly after I left.
Katie Cassidy, Julia Harkavy, Stephen Amell, David Ramsey, Echo Kellum & Paul Blackthorne were all seen on set, along with most of their stunt doubles as well.
The majority of shooting took place with Harkavy, Cassidy & their stunt doubles. Below, at the bottom, is a quick video of 1 of the takes of the stunt team working thru the sequence.
A special bonus..... a rare guest photographer. Local photographer Lightbox Gallery allowed me to use a few of his photos from the day.. You will notice the difference almost immediately between my shots, done with a $500 camera, and his, done with a $3000-5000 camera. You can find other photos hes taken of different celebs on his Twitter and on his Instagram
While he did miss some of the stuff with Juliana and Katie out on set battling, he did get quite a few great shots, including a couple of Stephen, who I somehow never even noticed was on set at that time.
Arrow returns to The CW in the U.S. and on CTV in Canada on October 12 ,2017
You can also find my other behind the scenes Arrow blog posts going all the way back to season 1 HERE
You can find my Arrow Autographs gallery HERE
Arrow shoots scenes for Episode 414 "Code Of Silence" with Stephen Amell, Katie Cassidy, Emily Bett Rickards, Paul Blackthorne & guest stars
THIS POST MAY CONTAIN POSSIBLE SPOILERS FOR EPISODE 414 OF ARROW. If you do not want to see or read any potential spoilers prior to seeing the episode please leave now.
Arrow will be airing episode 414 "Code Of Silence" tomorrow.
The episode was directed by the shows stunt co-ordinator James Bamford, for his 2nd go at director this season.
Shortly after the show returned from their Winter break I caught them downtown Vancouver shooting scenes for this episode at the historic Vogue Theatre.
While the majority of the shooting was indoors throughout the day, there were a couple scenes shot out on the busy Granville Street on a cold January night.
Several fans waited in the dingy & dirty alley behind the theatre to see the cast. The only cast member who was willing to meet them that night was Stephen Amell. He took the time to meet with each person there and do photos with & sign autographs before heading back in to film more. While others were seen leaving & arriving, none of them stopped to meet the small crowd of fans.
The first scene shot had the big spoiler that I posted a few weeks ago... which of course everyone knows about now since its aired on TV. Emily Bett Rickards as Felicity Smoak in a wheelchair. She filmed scenes with Paul Blackthorne as Quentin Lance and Charlotte Ross as Donna Smoak in front of the Vogue, which was apparently the theatre for a "Star City Mayoral Debate" between newcomer Ruve Adams (aka - Ms. Damian Darhk) and Oliver Queen (aka - The Green Arrow)
After that scene wrapped, the crew took a while to set up for their next shoot, which took place on the awning of the Vogue.
Katie Cassidy and her stunt double dressed up as the Black Canary spent time up on the roof shooting scenes where they watch Janet Kidder as Ruve Adams leave in a towncar, and then after that watched as a supply truck drives off. It appears when shes watching them leave that shes speaking to someone (probably on 1 of the com devices Team Arrow uses often).
You can follow the stars of Arrow on their social media platforms
Arrow Twitter facebook
Stephen Amell Twitter facebook
Emily Bett Rickards Twitter facebook
Paul Blackthorne Twitter facebook
Katie Cassidy Twitter facebook
Charlotte Ross Twitter facebook
James Bamford Twitter
Arrow shoots the final scene for episode 318 with Colton Haynes and Paul Blackthorne in downtown Vancouver
Better late than never? Right? well, this year I have often fallen behind on getting blog posts up in time. Another example... this post.
Arrow shot scenes for episode 318 awhile ago in downtown Vancouver and did so with the blatant knowledge they were giving up a huge spoiler to those who witnessed it.
One of the things I LOVE about Arrow so far is their complete disregard for the public seeing their potential spoilers. While other productions in town treat insignificant filler scenes like they are filming the climactic scene in an Oscar Award winning movie and react to those scenes being posted publicly by onlookers by curling up into the fetal position in a corner and wetting themselves while crying, the writers and producers of Arrow seem to accept and, at times, revel in the fact the public comes to see their big scenes.
On this particular spoiler riddled scene, which aired in last weeks episode 318 "Public Enemy", Paul Blackthorne as Captain Quentin Lance was shooting a scene with Colton Haynes, but instead of being dressed as Roy Harper or Arsenal, he was dressed as.... The Arrow.
Of course, knowing now what the scene was for, it makes sense. But at the time, seeing this scene shot, everyone was pretty much standing around going "what the fuck is going on?".
i'll admit, not having of course seen the episodes that would lead up to this scene yet play out (it was filmed BEFORE the arc of Captain Lance hating Team Arrow again) I thought this was some type of "trick" scene that the captain was in on.... but nope, it certainly was not.
Arrow is off this week, but returns next Wednesday with a new episode on The CW in the U.S. and on CTV in Canada
You can of course see more of my Arrow blog posts of the past HERE.
Season 3 of ARROW has started filming - Pics of Stephen Amell, Katie Cassidy and Paul Blackthorne shooting scenes for ep 301
Arrow season 3 is under way in Vancouver, BC, Canada FINALLY!
The 1st public shoot (that anyone seems to have known of) was on Friday right in downtown Vancouver as the production took over Terry Fox Plaza at B.C. Place to film scenes that involved Oliver Queen walking along with Laurel Lance
The pair did a scene where they are walking from the stadium in the blazing heat we had here, eventually they stop and talk to a 3rd character, alive and seemingly well... Quentin Lance.
A bit later they did scenes where Laurel talks to Quentin in front of the media.
In these scenes, you see Quentin dressed up in a little different Police attire.... as he is now Captain Lance.
After those scenes, Paul Blackthorne wrapped his work on those scenes. Short time later Katie Cassidy also wrapped.
This left Stephen Amell there to film scenes that involved another cast member...which I will get to in my next blog post.
I managed to get autographs from all 3 of them at different times. This included a good 15-20 minute chat that Stephen had with those of us that were there. Glad to see he is still cool.
Arrow returns this fall on The CW in the U.S. and on CTV in Canada. for their 3rd season starting October 8, 2014.
You can follow Stephen Amell on Twitter or on Facebook (1 of the best celeb pages on facebook)
You can follow Paul Blackthorne on Twitter or on Facebook
You can follow Katie Cassidy on Twitter


I was searching for a Property and found this listing (MLS® #8807881). Please send me more information regarding 507 Mountain Crest Dr, Wimberley, TX, 78676. Thank you!
I'd like to request a showing of 507 Mountain Crest Dr, Wimberley, TX, 78676 (MLS® #8807881). Thank you!


Bacterial wilt caused by Ralstonia solanacearum is an economically important soil borne disease that limits potato production worldwide. We tested the effectiveness of compost tea to suppress this disease on potato plants grown in soils artificially inoculated with a virulent isolate of R. solanacearum in a greenhouse experiment in Ethiopia. Soil drenches of nonaerated teas prepared from three types of matured compost sources (agricultural waste, vermicompost and solid municipal waste) were evaluated at three application times (seven days before inoculation, at time of inoculation and seven days after inoculation) in a randomized complete block design. The compost tea treatments attained variable levels of disease suppression. Based on the disease severity index, the agricultural waste compost tea applied at the time of inoculation resulted in the highest disease suppression at the conclusion of the experiment and also resulted in the least disease throughout the experiment (based on the area under the disease curve). Vermicompost tea had the least suppressive effect on symptom development and its application at time of inoculation resulted in the highest disease severity index ·of all treatments. Despite a higher final disease severity index, area under the disease curve values for the solid municipal waste compost tea at all application times were lower than the controls and most of the other treatments. This study suggests that some non-aerated compost teas may provide cheap and effective biological control, however the type of compost and timing of application has a substantial influence on effectiveness.


Discussion in 'Equipment and Gear Off Topic' started by Sockrny, Dec 22, 2015.
SD cards are now more popular than ever because of their usefulness. For one, they are very convenient to bring considering its size. You can use it to store your files from any of your gadgets such as your laptop, digital camera, and even your cellphone.
But sometimes, even the biggest of problems can come in small packages when all your files are accidentally wiped out from your card. When you've put important files inside your SD card and the card was accidentally formatted for some reason, you might need an extra hand to recover files from the SD card. For this, consider using Recovering Deleted Files recover files from SD card. It is a complete solution that lets you recover files from your SD card quickly and easily. It's also immensely useful in recovering lost data in other media like external drives and iPods so getting this software gives you good value as well as peace of mind.


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I have to admit, I didn't know much about Disney Infinity when my client called to ask me to make a cake for her son's birthday. Actually, I knew nothing. My kids really aren't into Disney anymore, so I though this was a new Disney movie I just hadn't heard of yet.
S, I googled it and the search kept coming up with all of the characters I already knew….Toy Story, Cars, even Nemo. What??? It took some research and my kids enlightening me to figure out it was a new video game!
Since my client wanted to use her son's game pieces on the top of the cake, I googled the game console and logo and cut them out by hand from red, black and gray fondant in a size to fit the 10″ key lime cake, iced with lime buttercream. I also added red and black stars of different sizes and a name plate and happy birthday sign with letters cut using my Wilton small letter cutter set. The icing and filling are lime buttercream (made from silky Swiss meringue buttercream flavored with fresh lime curd).
This was a fun cake to make and I now know all about Disney Infinity!
Happy birthday, Luca, hope it was a great one!


Partnerships, like any relationship, need support to be sustained. Once a partnership has been formed, maintaining the relationships is critical to guarantee that the partnership leads to fruitful results. Understanding each partners' priorities, interests, roles and responsibilities is important, as well as committing to the investment needed to for the partnership to be successful, is critical to effectively sustain good partnerships.


text:
\section{Introduction}
We study the problem of scheduling packets over links in a wireless network,
each link being a sender-receiver pair of wireless nodes.
Since wireless signals propagate in all directions, simultaneous
transmissions interfere with each other. This interference limits the
number of transmissions that can succeed simultaneously. A wireless
packet scheduling algorithm thus has to schedule packets efficiently,
with respect to the limits imposed by interference.

In this setting, consider the following two related problems. The first: Given a set of links, how quickly (i.e., using how few slots) can all of the links be scheduled, taking interference into account? The second: Given is a set of links, and packets arrive at the senders of each link according to some stochastic process, where they remain queued until successfully transmitted to the receiver. Can one ensure that queue sizes at the senders remain bounded, in expectation? The first question is an ``off-line'' algorithmic problem, whereas the second comes from a queueing theoretic perspective where the input is probabilistic.
In spite of their obvious commonalities, they are generally studied using quite disparate techniques. Our goal in this paper is to bridge a gap between these two related areas; specifically, to use recently developed algorithmic techniques to achieve results for the stochastic setting.

To do this, a crucial first step is to choose the right interference model --
one that is faithful to physical reality yet is simple enough to be rigorously analyzable. In this paper, we adopt the SINR (Signal to Interference and Noise ratio) or \emph{physical} model of interference. Compared to the more traditional and widely studied graph based models, the SINR model has been found to be realistic, and is enjoying
increased attention and adoption~\cite{MaheshwariJD08,Moscibroda2006Protocol,moscibroda06b}. This model (precisely defined in Section \ref{sec:model}) is based on a realistic geometry of signal propagation (compared to unrealistic graph based interference models).

We are thus interested in algorithms that keep queue sizes bounded
when faced with stochastic packet arrivals over arbitrary periods of
time, assuming the SINR interference model.  A network in which this
goal is achieved is called \emph{stable}.  Stability has been an
widely-studied metric for analyzing the performance of scheduling
algorithms for wireless networks for quite some time.  In a seminal
work, Tassiulas and Ephremides \cite{TE92}, gave a characterization of
those stochastic processes for which stability is possible in
principle. The characterization is general enough to work for
virtually any interference model.  In light of this, the goal for the
algorithm designer is to produce an (simple and efficient) algorithm
that stabilizes networks for all (or a fair chunk) of these arrival
processes.  There is a long tradition of such work, e.g.,
\cite{bestInfocom08,DBLP:conf/sigmetrics/ModianoSZ06},
but they almost exclusively apply to graph-based interference models.

The Tassiulas-Ephremides characterization \emph{is} formulated as a computational problem, which, if solved, would stabilize a network under all potentially stabilizable arrival rates.
However, for the SINR model, this problem (known as the maximum
weighted \emph{capacity} problem) is NP-hard \cite{DBLP:conf/infocom/AndrewsD09}. Not much is known about the algorithmic complexity of this problem (see \cite{infocom12} about a recent centralized result for linear power and more discussion about its relation to network stability), and almost nothing about possible distributed implementations. Thus, alternative approaches need to be sought.

In this work, we develop efficient and distributed
scheduling algorithms for wireless network stability ---
by applying intuitions developed in recent
research on the SINR model (\cite{SODA11} and \cite{icalp11} contain many references), all of which provide approximation
algorithms to some relevant algorithmic questions.

One possible approach would be to apply algorithms for some of the
core optimization problems as black boxes. For example, there are
constant factor approximation algorithms for the capacity problem
\cite{HW09,SODA11,KesselheimSoda11}. These alone are not
sufficient, as there is no guarantee of fairness.  Still, they can be
easily turned into a $O(\log n)$-approximation for the weighted
capacity problem.  The problem with this approach, however,
is that these algorithms are centralized, with no effective
distributed algorithms in sight.  Distributed algorithms are
 of crucial importance in the current setting.  Our
approach is therefore more of a ``gray-box'' one -- while we adopt an
algorithm of \cite{KV10} as our basis, our analysis depends not on the
overall approximation factor, but on more subtle properties of that
algorithm.

Apart from being distributed, the main property a
scheduling algorithm should have is high efficiency.
``Efficiency'' has a specific technical meaning which we define in Section \ref{sec:model}.
Intuitively, it captures
how well the algorithm does compared to the Tassiulas-Ephremides characterization.

We achieve, depending on the algorithm chosen, efficiency ratios of
$\Omega(\frac{1}{\log^2 n})$, $\Omega(\frac{1}{\log n})$ and
$\Omega(\frac{1}{\log n (\log n + \log\log \Delta)})$, that are
comparable or better than existing work on this topic.  Our main
algorithm requires only a ``carrier sense'' primitive to make it
completely distributed.  This is in contrast to many distributed
algorithms in the literature (e.g.,
\cite{DBLP:conf/sigmetrics/ModianoSZ06,lqfmobihoc}) that are better
described as ``localized'' -- requiring an underlying infrastructure
for wireless nodes to communicate with nearby nodes.
This infrastructure, moreover, is usually not subject to the
interference constraints of the original network.  This is a rather
strong assumption, especially in light of the fact that in a wireless
network, one is presumably trying to establish such an infrastructure
in the first place.

The paper is organized as follows.  In Sections \ref{sec:model} and
\ref{sec:results} we present the system model, our results, and more
specific discussion on related work.  In Section \ref{sublin} we
describe a general algorithmic framework for wireless scheduling. We
then provide a specific instantiation of this framework that achieves
good throughput performance for a large class of power assignments in
general metric spaces, with implications for the power control case, where the power
can be selected by links separately. Finally, in Section \ref{sec:pc}, we
prove a more efficient, but centralized result for the power control case and present simulation results.

\section{Model and Preliminaries}
\label{sec:model}
\subsubsection*{The SINR Model}
The wireless network is modeled as a set $L$ of $n$ links, where
each link $l \in L$ represents a potential transmission from a sender
$s_l$ to a receiver $r_l$, both points in a metric space.
The distance between two points $x$ and $y$ is denoted $d(x,y)$.
The distance from
$l'$'s sender to $l$'s receiver is denoted $d_{l'l} = d(s_{l'}, r_{l})$.
The length of link $l$ is denoted
simply by $\ell = d(s_l, r_l)$.

The set may be associated with a \emph{power assignment}, which is an assignment of a transmission
power $P_l$ to be used by each link $l \in L$.
The signal received at point
$y$ from a sender at point $x$ with power $P$  is $P/d(x, y)^\alpha$ where the constant
$\alpha > 0$ is the \emph{path-loss exponent}.

We can now describe the  \emph{physical} or SINR-model of interference. In this model, a receiver $r_l$
successfully receives a message from the sender $s_l$ if and only if the
following condition holds:
\begin{equation}
 \frac{P_l/\ell^\alpha}{\sum_{l' \in S \setminus  \{l\}} P_{l'}/d_{l'l}^\alpha + N} \ge \beta \ ,
 \label{eq:sinr}
\end{equation}
where $N$ is the environmental noise, the constant $\beta \ge 1$ denotes the minimum
SINR (signal-to-interference-noise-ratio) required for a message to be successfully received,
and $S$ is the set of concurrently scheduled links in the same \emph{slot} (we assume that time is slotted.).
We say that $S$ is \emph{SINR-feasible} (or simply \emph{feasible}) if (\ref{eq:sinr}) is
satisfied for each link in $S$.

A power assignment $P$ is \emph{length-monotone} if $P_v \ge P_w$
whenever $\ell_v \ge \ell_w$ and \emph{sub-linear} if
$\frac{P_v}{\ell_v^{\alpha}} \le \frac{P_w}{\ell_w^{\alpha}}$ whenever
$\ell_v \ge \ell_w$. This class includes the most interesting and
practical power assignments, such that uniform power (all links use
the same power), linear power ($P_l = \ell^{\alpha}$, known to be
energy efficient in the presence of noise), and mean power ($P_l =
\ell^{\alpha/2}$, the assignment that produces maximum capacity in
this class). We will also consider the ``power control'' case, where
the power assignments are not predetermined, but have to be found out
by the algorithm, and can be arbitrary.

Let $\Delta = \frac{\ell_{\max}}{\ell_{\min}}$, where $\ell_{\max}$ and $\ell_{\min}$ are, respectively, the maximum and minimum lengths in $L$.

\begin{defn}
The \textbf{affectance} $a^P_{l'}(l)$ of link $l$ caused by another link $l'$,
with a given power assignment $P$,
is the interference of $l'$ on $l$ relative to the power
received, or
  \[ a^P_{l'}(l)
     = \min\left\{1, c_v \frac{P_{l'}}{P_l} \cdot
     \left(\frac{\ell}{d_{l' l}}\right)^\alpha\right\}\ ,
  \]
where $c_v = \beta/(1 - \beta N \ell^\alpha/P_l)$.
\end{defn}

The definition of affectance was introduced in \cite{GHWW09} and achieved the form
we are using in \cite{KV10}.
When clear from the context we drop the superscript $P$.
Also, let $a^P_l(l) = 0$.
Using the idea of affectance, Eqn.~\ref{eq:sinr} can be rewritten as
\[ a^P_S(l) \equiv \sum_{l' \in S}a^P_{l'}(l) \leq 1\ , \]
for all $l \in S$.

 A link can schedule at most one packet during a slot, in other words,
if a link has a queue, at most one packet from the queue can be scheduled during a single slot.

\subsubsection*{Stability of Stochastic Processes}

We assume that packets arrive at the sender of each link $l$ according to a stochastic
process with average arrival rate $m_l$.

We define stability as such.
\begin{defn}
An algorithm \textbf{stabilizes} a network for a particular arrival process if, under that
arrival process the average queue size at each link is bounded (ie, does not grow asymptotically with time).
\end{defn}
The \emph{throughput region} is then the set of all possible arrival rate
vectors such that there exists some scheduling policy that can stabilize the
network.
As proved in \cite{TE92}, the throughput region is characterized by
\[ \Lambda = \{\lambda : \lambda \preceq \phi, \text{for some } \phi
   \in Co(\Omega)\}\ , \]
here $\Omega$ is the set of vectors in $\mathbb{R}^n$ characterizing
all maximal feasible sets (i.e., each vector in $\Omega$ is a binary vector with
1's in indices corresnponding to links belonging to the relevant maximal feasible set).
$Co(\Omega)$ is the convex hull of $\Omega$; $\lambda$ and $\phi$ are vectors in $\mathbb{R}^n$, indicating arrival rates on links, and $\lambda \preceq \phi$ means that each entry of $\lambda$ is less than or equal to the corresponding entry in $\phi$.

In the best case, one would like stabilize all of $\Lambda$. If that is not possible, the hope is to achieve a high \emph{efficiency ratio}:

\begin{defn}
The \textbf{efficiency ratio} $\gamma$ of a scheduling algorithm
is $\gamma = \sup\{\gamma: \text{all networks are}$ stabilized for all
$\lambda \in \gamma \Lambda \}$.
The algorithm is then \textbf{$\gamma$-efficient}.
\end{defn}
We assume that the arrival processes are independent accross time (and links).
For a certain efficiency $\gamma$, for all permissible arrival rate vectors $\lambda$, $ \lambda \preceq \sum_{i} m_i M_i$ where each $M_i$ is a maximal feasible set, and $m_i$ are weights such that $\sum_i m_i  = \gamma$. Let the
expected arrival rate on a link $l$ be $m_l$, it can be easily seen that
\begin{equation}
\label{linktofs}
m_l = \sum_{i: l \in M_i} m_i \leq \gamma \ .
\end{equation}

\section{Results and related work}
\label{sec:results}
Our main results are:

\begin{theorem}
\label{mainth1}
For all given networks with links on metric spaces, and all sub-linear, length-monotone
power assignments, there exists a $\Omega(\frac{1}{\log^2 n})$-efficient distributed algorithm.
\end{theorem}

\begin{theorem}
\label{mainth2}
For all given networks with links on the Euclidean plane,
there exists a $\Omega\left(\frac{1}{\log n (\log n + \log \log \Delta)}\right)$-efficient distributed power control algorithm.  A
centralized algorithm exists that achieves
$\Omega\left(\frac{1}{\log n}\right)$ efficiency.
\end{theorem}

We are aware of two earlier papers on stability in the SINR model.
In \cite{lqfmobihoc}, the authors study the Longest Queue First algorithm (a classical algorithm that can be seen as a natural extension of maximal weighted matching). They show that LQF is not stable, but a variant works well.
A ``localized'' implementation is provided, i.e., it is shown that the algorithm can be implemented in a distributed manner if links can communicate with
other ``neighboring'' links arbitrarily. The achieved efficiency ratio in
$\Omega(\frac1{\Delta^{\alpha}})$ (the dependence on $\Delta$ is not explicitly mentioned, but can be seen to be necessary). Our recent paper \cite{ciss12} is a companion of the current work,
where an extremely simple and completely distributed algorithm achieving $\Omega(\frac1{\Delta^{\alpha}})$ efficiency is introduced.
In comparison, the results in the current work involve efficiency that is logarithmically dependent on $n$ (and, in one case, doubly logarithmic in $\Delta$).
Dependence on $n$ and $\Delta$ are theoretically not comparable, and either could be preferable in practice. The distributed algorithm in this paper has to assume
a carrier-sense primitive, which is not assumed in \cite{ciss12}. However, it does not need to have a special communication infrastructure with neighboring links.

The body of work on wireless network stability in \emph{other} models is too vast to survey properly. In terms of efficiency ratio,
a range of results have been derived
in a variety of models. Naturally one seeks efficiency of $1$ \cite{DBLP:conf/sigmetrics/ModianoSZ06} whenever
possible, but results for efficiency ratios of $1$ under certain conditions \cite{secordorder}, or $\frac{1}{6}$ \cite{bestInfocom08} can be found in the literature.
Ratios in terms of certain network characteristics are known as well \cite{bestInfocom08,lqfmobihoc}.
For the SINR model, which is being studied only very recently, an efficiency ratio of a
constant that is independent of network parameters is not known.

Technically, we depend heavily on \cite{KV10} that provides a $O(\log^2 n)$ approximation algorithm for the scheduling problem. The algorithm and technical aspects of this work used here will be introduced in the following section as needed.

We are aware of a very recent unpublished work of Kesselheim \cite{dynamickesselheim} achieving results in the SINR model very similar to the present paper.

\section{Main Algorithm}
\label{sublin}

The basic algorithmic framework used is listed as \alg{General} below. For simplicity,
we treat it as a centralized procedure first and discuss distributed implementations later.

\begin{algorithm}
\caption{General($\theta$, $\calA$)}

\begin{algorithmic}[1]
     \STATE The algorithm maintains a FIFO queue $\mathcal{S}$ of sets, such that each $S\in \mathcal{S}$ is feasible.
     \STATE At the beginning, $\mathcal{S} \leftarrow \emptyset$.
     \FOR{time $t \leftarrow{} 1, 2, \ldots $}
       \IF{$\mathcal{S}$ is non-empty}
         \STATE Schedule the first $S \in \mathcal{S}$
         \STATE $\mathcal{S}\leftarrow{}\mathcal{S} - S$
       \ENDIF
       \IF{$t{}\bmod{}\theta = 1$}
	\STATE Let $L = $ new packet arrivals in the time period $[t - \theta, t - 1]$
	\STATE $q = t / \theta$
	\STATE Use algorithm $\calA$ to find 	   a schedule $\mathcal{R}_q = \cup_j R'_j$ for $L$
	\STATE Append $\mathcal{R}_q$ (in any order) to $\mathcal{S}$
	\ENDIF
       \ENDFOR
\end{algorithmic}
\label{alg1fig}
\end{algorithm}

The algorithm takes two parameters. One is $\theta$, a number that defines the ``period''
of the algorithm. The second parameter is  an algorithm $\calA$ which can
solve the \emph{scheduling problem}, used
as a black box by \alg{General} to compute schedules. The \emph{scheduling problem} is the optimization
problem where given a set $L$ of links, one seeks to partition $L$ into minimum number of sets such
that each of these sets is feasible (i.e., can be transmitted in one slot). Since the problem is NP-hard,
we will work with approximation algorithms. Depending on the result we seek, we will set $\theta$ and $\calA$
accordingly.

\alg{General} can be alternatively described in the following way. The algorithm divides
the time slots into consecutive
periods of length $\theta$ each. Let us denote these periods as $C_1, C_2 \ldots$ etc.
At the beginning of period $C_q$, the algorithm
computes a schedule $\mathcal{R}_{q-1}$ of the links produced in $C_{q-1}$. It does so using
$\calA$. \alg{General} then adds these computed feasible sets to the set $\mathcal{S}$.
Now during each slot of $C_q$, the algorithm schedules
the first set from $\mathcal{S}$ (which is implemented as a FIFO queue).
It does this until $C_q$ ends, in which case it moves on to the next period, or
until  $\mathcal{S}$ is empty, in which case it waits until the end of $C_{q}$. Note that there is
nothing to schedule during $C_1$, we just wait during this time.

Let $Q^t_l$ be the queue length at link $l$ at time $t$.
First, note that $Q^t_l \leq S^t$ for all $l$, where $S^t =
|\mathcal{S}|$ at time $t$ ($\mathcal{S}$ is as in the algorithm $\alg{General}$). This is the
number of slots we require  to schedule all links outstanding at time $t$. Obviously, one cannot schedule all links
in time less than the size of the longest queue (since copies of the same link cannot be scheduled together).
Thus a bound on $S^t$ immediately gives us a bound on $Q^t_l$ (for all $l$). Consequently, from now on we will focus on
bounding $S^t$. Also note that it suffices to bound $S^t$ on period boundaries, i.e., at times $t$ such that $t \bmod \theta = 1$. This is because, in expectation, the queue lengths cannot grow by much during the course of a period.
Let $\mainC$ be a large enough constant.

For simplicity, we will assume that the arrival distributions at every link $l$ is a Bernoulli random variable with mean $m_l$.
To prove Theorem \ref{mainth1}, we set $\theta = 10 \mainC^2 \log^2 n$.

\subsection{The scheduling algorithm}
We also select as $\calA$
the scheduling algorithm described in \cite{KV10}. It is known \cite{icalp11} that
this algorithm achieves a O$(\log n)$-approximation factor to the scheduling problem. We are, however, interested in a slightly different performance measure of the algorithm in \cite{KV10}. For a link set $R$,
define the \emph{maximum average affectance}
$\avgA(R) = \max_{Q \subseteq R} \frac{1}{|Q|}\sum_{l \in Q}\sum_{l' \in Q} a_{l}(l')$. It is known that:

\begin{theorem}{\cite{KV10}}
  The algorithm $\calA$ has expected running time of at most $\mainC \log n \cdot \avgA(R)$ on a link set $R$.
  \label{algeff}
\end{theorem}

We now turn our attention to proving the stability of the algorithm, i.e., Thm.~\ref{mainth1}.
Given the efficiency claimed in Thm.~\ref{mainth1}, it sufficient to deal with stochastic processes satisfying
\begin{equation}
\sum_{M_i} m_i \leq \frac1{5 \rho^2 \log^2 n} \ .
\label{loadbound1}
\end{equation}

\begin{lemma}
\label{schednumberbound1}
$\Ex(|\mathcal{R}_q|) \leq O(\log^2 n) < \theta$  for all $q$.
\end{lemma}
\begin{proof}
Define $a^{t+}(l)$ to be the outgoing affectance from a link $l$ to longer links appearing in slot $t$. Formally, if $X_l(t)$ is the Bernoulli random variable denoting the number of packets that arrived at the sender of link $l$ during slot $t$, then
\begin{equation}
a^{t+}(l) \equiv \sum_{l' \in L, l' \geq l} a_{l}(l') X_{l'}(t) \ .
\label{aplusdef}
\end{equation}
Let $A^+(l)$ be the sum of $a^{t+}(l)$ over all $\theta$ slots in the period, or,
\[ A^+(l) = \sum_{t = 1}^{\theta} a^{t+}(l) \ . \]
Now, we claim,
\begin{claim}
$\Ex(A^+(l)) \leq \frac{\theta}{\effC \log n}$.
\label{expectedA}
\end{claim}
\begin{proof}
In \cite{KV10}, it is shown that for any feasible set $M_i$
\begin{equation}
\label{afftohig}
\sum_{l' \in M_i, l' \geq l} a_l(l') \leq \mainC \log n \ .
\end{equation}

Thus, for any time slot $t$,
\begin{align*}
\Ex(a^{t+}(l)) & \overset{1}{=} \sum_{l' \in L, l' \geq l} a_{l}(l') \Ex(X_{l'}(t)) \overset{2}{=} \sum_{l' \geq l} a_l(l') m_{l'} \overset{3}{\le} \sum_{l' \geq l} a_l(l') \sum_{i: l' \in M_i} m_i \\
& \overset{4}{=} \sum_{i} m_i \sum_{l' \in M_i, l' \geq l} a_l(l')
 \overset{5}{\le} \mainC \log n \sum_i m_i \leq \frac{1}{\effC \log n} \ ,
\end{align*}
 with explanations of numbered (in)equalities:
\begin{enumerate}
\item By definition of $a^{t+}(l)$ (Eqn.~\ref{aplusdef})
\item $\Ex(X_{l'}(t)) = m_{l'}$ by definition, since they both express the expected number of packets arriving in each time slot on $l'$.
\item By Eqn.~\ref{linktofs}.
\item Rearrangement.
\item By Eqn. \ref{afftohig}.
\item By Eqn.~\ref{loadbound1}.
\end{enumerate}
\end{proof}

Now, by the Chernoff-Hoeffding inequality (see, for example, Eqn.~1.8, Thm.~1.1 of \cite{dubhashi}):
\[ \Pro(A^+(l) \geq r) \leq \frac{1}{2^{r}}\ , \]
for all $r \geq \frac{2 \theta}{\effC \log n} = 4\rho\log n$. Defining $A^+_{\max} = \max_l A^+(l)$, and union bounding we get,

\begin{equation}
\Pro(A^+_{\max} \geq r) \leq \frac{n}{2^{r}} \ .
\end{equation}

\noindent We analogously define
$a^{t-}(l) \equiv \sum_{l' \in L, l' \geq l} a_{l'}(l) X_{l'}(t)$ to be the incoming affectance from longer links. We similarly define $A^-_{max}$, and obtain that
$\Pro(A^-_{\max} \geq r) \leq \frac{n}{2^{r}}$,
which depends on the bound
\begin{equation}
\label{afffromhig}
\sum_{l' \in M_i, l' \geq l} a_{l'}(l) \leq \mainC \ ,
\end{equation}
also proven in \cite{KV10}. (Note that the bound here is
tighter than Eqn.~\ref{afftohig}).

It is not hard to verify that that
$\avgA \leq A^+_{\max} + A^-_{\max}$. Thus,
\begin{align*}
\Ex(\avgA) & \leq \Ex(A^+_{\max}) + \Ex(A^-_{\max}) \leq 2 \Ex(A^+_{\max}) \\
& \leq \frac{2 \theta}{\effC \log n} + \sum_{i = 1}^{\infty} 2^i \frac{2 \theta}{\effC \log n} \frac{n}{2^{2^i \frac{2 \theta}{\effC \log n}}} \\
& \leq  \frac{2 \theta}{\effC \log n}  + \frac{2 \theta}{\effC \log n}
 = \frac{4 \theta}{\effC \log n} \ .
\end{align*}

Now, by Theorem \ref{algeff}, $\Ex_{\calA}(|\mathcal{R}_q|) \leq \mainC \log n \cdot \avgA$, where $\Ex_{\calA}$ denotes expectation over the random bits of the algorithm (which is randomized).  Noting that the random bits of the algorithm are independent of the arrival process, we can use the bound on $\Ex(\avgA)$
to claim that

\[ \Ex(|\mathcal{R}_q|) \leq \mainC \log n \cdot \frac{4 \theta}{\effC \log n} < \theta \ .\]
\end{proof}

Now we can prove Thm.~\ref{mainth1}.
Note that by
Lemma \ref{schednumberbound1}, the expected scheduling cost required for packets produced
during a single period ($\Ex(|R_q|)$) is strictly smaller than the scheduling capacity of a single period ($\theta$).
With this observation, we can reduce our system to a very basic queueing system:

\begin{itemize}
\item A single server, an infinite queue, and slotted time. The time slots in this system correspond to the periods of the original system.
\item At the beginning of each slot a fixed number $s$ of packets are served and leave the system ($s$ corresponds to $\theta$).
\item At the end of every slot, a random number of new packets arrive. This is a random variable $A$ on the  non-negative
integers, and $\Ex(A) = a$ ($a$ corresponds to $\Ex(|\mathcal{R}_q|)$).
\end{itemize}
Now, if $a < m$, the corresponding countable Markov chain has a stationary distribution, and if $a$ is square integrable,
the expected queue length will be finite (see \cite{Asmussen}, for example). The condition $a < m$ is easily seen to be true
(since $\Ex(|\mathcal{R}_q|) < \theta$ by Lemma \ref{schednumberbound1}). Square integrability of
$|\mathcal{R}_q|$ follows from the fact that $\mathcal{R}_q$ admits a large deviation bound (this
is implicit in the proof of Lemma \ref{schednumberbound1}). Of course the ``queue length'' of this system
corresponds to $S^t$, the number of slots the outstanding packets would require to be scheduled.
Thus we have proven  $\Ex(S^t)$ to be bounded, and as observed before, this is enough to complete the proof of the theorem.

\subsection{Implications for the power control problem}

It was shown
in \cite{DBLP:conf/esa/Halldorsson09,SODA11} that
\emph{mean power} (where $P_l$ is set to $\ell^{\alpha/2}$) achieves a
good approximation to the power control problem. Since
Thm.~\ref{mainth1} covers the mean power assignment, this gives us a
 distributed algorithm for the power control problem. To
achieve the bound claimed in Thm.~\ref{mainth2} for the distributed
algorithm, the main ingredient is the following bound (analogous to
Eqn.~\ref{afftohig}).

\begin{lemma}[\cite{DBLP:conf/esa/Halldorsson09,SODA11}]
If M is a feasible set with respect to \emph{any} power assignment,
\begin{equation}
\sum_{l' \in M, l' \geq l} a_{l'}(l) \leq c_3 (\log n + \log \log \Delta) \ ,
\end{equation}
where the affectance $a_{l'}(l)$ is measured using mean power.
\end{lemma}
With this bound (and a similar analog of Eqn.~\ref{afffromhig}) in hand, the proof technique of Thm.~\ref{mainth2}
can be duplicated to achieve the bound claimed in Thm.~\ref{mainth2} for distributed algorithms.

\subsection{Distributed implementation}
\label{sec:distr}

We now demonstrate how to implement \alg{General} in a distributed fashion.
Interestingly, we require very few additional assumptions to make this work.
The basic tool is the
algorithm in \cite{KV10}, listed as \alg{Distr-SingleLink}.

\begin{algorithm}
\caption{Distr-SingleLink}
\label{alg3}
\begin{algorithmic}[1]
     \STATE $k \leftarrow 0$
     \WHILE{transmission not successful}
       \STATE $q =  \frac{1}{4 \cdot 2^k}$
       \FOR{$\frac{8 \ln n}{q}$ slots}
          \STATE transmit with i.i.d. probability $q$
       \ENDFOR
       \STATE $k \leftarrow k + 1$
     \ENDWHILE
\end{algorithmic}
\label{alg13ig}
\end{algorithm}

The first thing to note here is that \alg{Distr-SingleLink} itself is completely distributed.
In other words, \alg{General}, if applied to the links
produced during a single period, could be implemented in a distributed manner straightaway.
The challenge is that our bounds assume that the algorithm works in a FIFO
manner, thus \alg{Distr-SingleLink} for a packet produced in $C_q$ should not start
executing until all packets produced up to $C_{q-1}$ have been successfully scheduled.

To implement this, we assume that each sender in the system maintains a few counters.
The first counter $cur$ keeps track of the current period, and a second counter $s$ tracks the current
period being \emph{scheduled} (naturally $s \leq cur$).
For each outstanding packet $p$ in the queue, the sender also maintains the period
in which it was generated ($g_p$).
The counter $cur$ is easily maintained, by incrementing it once every $\theta$ slots.
The third counter is equally simple, when a packet $p$ arrives, $g_p$ is assigned
the current value of $cur$. We will describe how $s$ is maintained below, but note that
given $s$, the algorithm can now be easily implemented in a distributed fashion. For each
packet $p$, the sender waits until $s = g_p$, and then runs \alg{Distr-SingleLink} for $p$ until
the link successfully transmits.

Maintaining $s$ is slightly more, but not too, involved.
It is here that we need to make an additional assumption, which is that nodes (senders) can
sense the channel to determine transmission activity.
This is a not uncommon assumption (see, e.g., \cite{ScheidelerRS08}), based on the Clear Channel Assessment capability in the 802.11 wireless standard.

Let us divide the time slots into consecutive pairs.  The first time
slot is used for normal transmission (i.e., executing \alg{General}
and \alg{Distr-SingleLink}).  The second slot is used for
signaling. Senders that are transmitting currently (i.e., senders that
have at least one non-transmitted packet $p$ for which $g_p = s$) use
the signaling slot to simply signal that they have still not
completed.  Thus when all links from period $s$ succeed, a silent
signaling slot appears.  All senders register this event by sniffing
the channel, and increment $s$.

From a practical point of view, wasting every other slot for
signaling, as well as assuming some sort of ``perfect'' carrier sensing
capability, is problematic.
Simulation studies presented are done
with practically reasonable approximations to these
assumptions (indeed, these heuristics seem to help the algorithm).

\section{Extensions and simulations}
\label{sec:pc}
In the power control version of the problem, selecting (an arbitrary) power for
each link is a part of the problem.
Feasible sets are now those for which there exists an (unknown) power
assignment that allows for simultaneous transmissions of all the links.

Using a centralized algorithm of \cite{KesselheimSoda11}, a $O(\frac1{\log n})$ throughput is achievable.
We omit details.

\begin{figure}
\begin{center}
\includegraphics[width=0.49\textwidth]{DistrSingleLink_v1}
\includegraphics[width=0.49\textwidth]{FuzzyMaxQueue}
\caption{The maximum queue lengths for the distributed algorithm Distr-SingleLink.   The problem instances are based on random topology with $n = 200$, $\ell_{\min} = 1$, $\ell_{\max} = 20$, $\alpha = 2.5$ and $\beta = 1$.} \label{fig:maxqueue}
\end{center}
\end{figure}

We implemented \alg{General} (using \alg{Distr-SingleLink}) on a random topology. As Fig.~\ref{fig:maxqueue} shows, efficiency ratios upto 0.4 is achieved, which is quite good. Details are omitted due to space restrictions.

\bibliographystyle{plain}

redpajama_set_name: RedPajamaArXiv


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Liberia

 Panta (distrikt), Bong County,
 Panta (klan), Bong County,

Robotskapade Liberiaförgreningar

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text:
Soon May the Wellerman Come, ook wel bekend als Wellerman, is een oud zeemanslied. In het lied wordt regelmatig verwezen naar de "wellermen", bevoorradingsschepen van de Engelse gebroeders Weller die naar Nieuw-Zeeland waren geëmigreerd. In 2020 en 2021 werden versies van de Britse volksmuziekgroep The Longest Johns en de Schotse muzikant Nathan Evans virale hits op de sociale-mediasite TikTok, wat leidde tot een "rage op sociale media" rond shanty's.

Synopsis

De tekst van het lied beschrijft een vangschip genaamd "Billy of Tea" en zijn jacht op een echte walvis.
Het lied beschrijft hoe de bemanning van het schip hoopt op een "wellerman" die hun voorraden luxeartikelen komt brengen, met het refrein: "Spoedig moge de wellerman komen, om ons suiker en thee en rum te brengen." Een wellerman was een werknemer van de gebroeders Weller. In de walvisvaart in het 19e-eeuwse Nieuw-Zeeland bezaten de gebroeders Weller schepen die proviand verkochten en naar walvisvaartboten brachten. Volgens de vermelding van het lied op de website van de New Zealand Folk Song, kregen de arbeiders van deze baai-walvisjachtstations (de walvisjagers) geen loon, maar werden ze uitbetaald in slops (kant-en-klare kleding), sterke drank en tabak.
Het refrein gaat verder met de bemanning die hun hoop zingt dat "op een dag als het tonguin''' voorbij is, we ons verlof zullen nemen en gaan". "Tonguing" verwijst in deze context naar de praktijk van het snijden van reepjes walvisblubber om er olie van te maken.
De volgende verzen beschrijven de vastberadenheid van de kapitein om de walvis in kwestie binnen te halen, zelfs als de tijd verstrijkt en meerdere walvisboten in de strijd verloren gaan.
In het laatste couplet beschrijft de verteller hoe de Billy o' Tea nog steeds verwikkeld is in een voortdurende strijd met de walvis, waarbij de wellerman voorraden komt brengen om de kapitein en de bemanning te versterken.

 Nathan Evans-versie

De versie van de Schotse muzikant Nathan Evans verhoogde de bekendheid van het nummer Wellerman, wat leidde tot een toename van de belangstelling voor zeemansliederen en een veelvoud aan remixes en nieuwe versies. Evans' versie werd geprezen om zijn "authentieke gevoel van stoïcijnse verdraagzaamheid" dat jonge mensen in lockdown aanspreekt. Het nummer geproduceerd door Nathan Evans bereikte een nummer 2 positie in de UK Singles Chart en nummer 1 in de verkoopslijst. In februari 2021 zongen Evans, 220 Kid en Billen Ted het lied voor het CBBC-televisieprogramma Blue Peter''.  Een nieuwe versie van "Wellerman" met de Duitse folkband Santiano werd op 19 februari 2021 als single uitgebracht.

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When do you legitimately get to call yourself a Data Scientist?
Oh, just what the [expletive deleted] is that?
Dude, seriously, did you like, just make that up?
Responses to this very-legitimate, potentially disarming question, will need to be saved for another time – though I'm sure a quick Google search will reveal a just-what-the-[expletive deleted]-is-Data-Scientist elevator pitch.
To return to the question intended for this post however, let's focus for a moment on how a best-selling author 'became' a writer.
I was recently listening to best-selling author Jeff Goins being interviewed by podcast host Srini Rao on an episode of the Unmistakable Creative. Although the entire episode (and the podcast in general, frankly) is well worth the listen, my purpose here is to extract the discussion relating to Goins' own process of becoming a writer. In this episode of the podcast, Goins recalls the moment when he believed he was a writer. He then set about behaving as a writer – essentially, the hard work of showing up every single day just to write. Goins continues by explaining how based upon his belief ("I am writer") and his behavior (i.e., the practice of writing on a daily basis), he ultimately realized his belief through his actions (behavior) and became a writer. With five, best selling books to his credit, plus a high-traffic-blog property, and I'm sure much more, it's difficult now to dispute Goins' claim of being a writer.
I suppose, then, that by direct application of Goins' algorithm, you can start the process merely by believing you're a Data Scientist. Of course, I think we all know that that'll only get you so far, and probably not even to a first interview. More likely, I think that most would agree that we need to have some Data Science chops before we would even entertain such an affirmation – especially in public.
Become – Whereas I don't have best sellers or even a high-traffic blog site to draw upon, I have been able to assemble a variety of relevant artifacts into a Portfolio. Included in the Portfolio are peer-reviewed articles that have appeared in published journals with respectable impact factors. This, for a Data Scientist, is arguably a most-stringent validation of an original contribution to the field. However, chapters in books, presentations at academic and industry events, and so on, also serve as valuable demonstrations of having become a Data Scientist. Though it doesn't apply to me (yet?), the contribution of code would also serve as a resounding example – with frameworks such as Apache Hadoop, Apache Spark, PyTorch, and TensorFlow serving as canonical and compelling examples.
Behave – Not since the time I was a graduate student have I been able to show up every day. However, recognizing the importance of deliberate practice, there have been extended periods during which I have shown up every day (even if only for 15 minutes) to advance some Data Science project. In my own case, this was most often the consequence of holding down a full-time job at the same time – though in some cases, as is evident in the Portfolio, I have been able to work on such projects as a part of my job. Such win-win propositions can be especially advantageous for the aspiring Data Scientist and the organization s/he represents.
Believe – Perhaps the most important outcome of engaging in the deliberate act of putting together my Data Science Portfolio, is that I'm already in a much more informed position, and able to make a serious 'gut check' on whether or not I can legitimately declare myself a Data Scientist right here and right now.
The seemingly self-indulgent pursuit of developing my own Data Science Portfolio, an engagement of active self-curation, has (quite honestly) both surprised and delighted me; I clearly have been directly involved in the production of a number of artifacts that can be used to legitimately represent myself as 'active' in the area of Data Science. The part-time nature of this pursuit, especially since the completion of grad school (though with a few notable exceptions), has produced a number of outcomes that can be diplomatically described as works (still) in progress … and in some cases, that is unfortunate.
I am a demonstrably engaged and passionate student of Data Science – an aspiring Data Scientist, per se … one who's actively working on becoming, behaving and ultimately believing he's a Data Scientist.
Based on my biases, that's what I currently feel owing to the very nature of Data Science itself.
Just published on the Bright blog is my follow-up post regarding my poster presentation (poster, video) at the 2015 Rice Oil and Gas HPC Workshop. In addition to summarizing the disruptive potential for Apache Spark in energy exploration and other industries, this new post also captures my shift in emphasis from Apache Hadoop to Spark. Because the scientific details of my investigation are more-than-a-little OT for the Bright blog, I thought I'd share them here.
RTM has a storied history of being performance-challenged. Although the method was originally conceived by geophysicists in the 1980s, it was almost two decades before it became computationally tractable. Considered table stakes in terms of seismic processing by today's standards, algorithms research for RTM remains highly topical – not just at Rice, York and other universities, but also at the multinational corporations whose very livelihood depends upon the effective and efficient processing of seismic-reflection data. And of particular note are the consistent gains being made since the introduction of GPU programmability via CUDA, as innovative algorithms for RTM can exploit this platform for double-digit speedups.
RTM modelling is inherently compute intensive. In RTM, propagating seismic waves are modeled using the three-dimensional wave equation. This classic equation of mathematical physics needs to be applied twice. First in the forward problem, assumptions are made about the characteristics of the seismic source as well as variations in subsurface velocity, so that seismic waves can be propagated forward in time from their point of origin into the subsurface (i.e., an area of geological interest from a petroleum exploration perspective); this results in the forward or source wavefields in the upper-branch of the diagram below. Using seismic traces recorded at arrays of geophones (receivers sensitive to various types of seismic waves) as well as an assumed subsurface-velocity model, these observations are reversed-in-time (hence the name RTM), and then backwards propagated using the same 3D wave equation; this results in the receivers' wavefields in the lower-branch of the diagram below. It is standard practice to make use of the Finite Difference Method (FDM) to numerically propagate all wavefields in space and time. In order to ensure meaningful results (stable and non-dispersive from the perspective of numerical analysis) from application of FDM to the 3D wave equation, however, both time and 3D space need to be discretized into small steps and grid intervals, respectively. Because the wave equation is a Partial Differential Equation in time and space, the FDM estimates future values using approximations for all derivatives. And in practice, it has been determined that RTM requires high-order approximations for all spatial derivatives if reliable results are to be optimally obtained. In short, there are valid reasons why the RTM modeling kernel is inherently and unavoidably compute intensive.
RTM data exceeds memory capacity. From the earliest days of computational tractability around the late 1990s, standard practice was to write the forward/source wavefields to disk. Then, in a subsequent step, cross-correlate this stored data of forward wavefields with the receivers' wavefields. Using cross-correlation as the basis for an imaging condition, coherence (in the time-series analysis sense) between the two wavefields is interpreted as being of geological interest – i.e., the identification in space and time of geological reflectors like (steeply dipping) interfaces between different sedimentary lithologies, folds, faults, salt domes as well as reservoirs of even more complex geometrical structure. Although the method consistently delivered the 'truest images' of the subsurface, it was literally being crushed by its own success, as multiple-TB data volumes are typical for the forward wavefields. The need to write the forward wavefields to disk, and then re-read them piecemeal from disk during cross-correlation with the receivers' wavefields, results in disk I/O emerging as the significant bottleneck.
Not surprisingly then, researchers like Liu et al. have programmed GPUs using CUDA for maximum performance impact when it comes to implementing RTM's modeling kernel. However they, along with a number of other researchers, have introduced novel algorithms to address the challenge of disk I/O. As you might anticipate, the novel aspect of their algorithms is in how they make use of the memory hierarchy presented by hybrid-architecture systems based on CPUs and accelerators. (Although CUDA 6 introduced a kernel module to allow for shared memory between CPUs and GPUs in the first quarter of 2014, I am unaware of the resulting contiguous memory being exploited in the case of RTM.) Programming GPUs via CUDA is not for the feint of coding heart. However, the double-digit performance gains achieved using this platform have served only to validate an ongoing investment.
Inspired by the in-memory applications of GPUs, and informed about the meteoric rise of interest in Apache Spark, the inevitable (and refactored) question for the Rice workshop became: "RTM using Spark? Is there a case for migration?" In other words, rather than work with HDFS and YARN in a Hadoop context, might Spark have more to offer to RTM?
Forward wavefields could reside in memory and be rendered available without the need for disk I/O during the application of the imaging condition – i.e., as forward and receivers' wavefields are cross-correlated. This is illustrated in a modified version of RTM's computational workflow above. You should be skeptical about the multiple-TBs of data involved here – as you're unlikely to have a single system with such memory capacity in isolation. This is where the Distributed aspect of RDDs factors in. In a fashion that mimics Hadoop's use of distributed, yet distinct disks to provide the abstraction of a contiguous file system, RDDs do the same only with memory. Because RDDs are inherently Resilient, they are intended for clustered environments where various types of failures (e.g., a kernel panic followed by a system crash) are inevitable and can be tolerated. Even more enticing in this use case involving RTM wavefields, the ability to functionally transform datasets using Spark's built-in capability for partitioning RDDs means that more sophisticated algorithms for imaging RTM's two wavefields can be crafted – i.e., algorithms that exploit topological awareness of the wavefields' locality in memory. In confronting the second challenge identified above by Liu et al., an early win for in-memory RTM via RDDs would certainly demonstrate the value of the approach.
Gathers of seismic data could reside in memory, and be optimally partitioned using Spark for wavefield calculations. Once acquired, reflection-seismic data is written to an industry-standard format (SEG Y rev 1) established by the Society of Exploration Geophysicists (SEG). Gathers are collections of data for pairs of sources and receivers that have depth (typically) in common. (This is referred to as a Common Depth Point or CDP gather by the industry.) RTM is systematically applied to each gather. Although this has not been a point of focus from an algorithms-research perspective, even in the innovative cases involving GPUs, the in-memory possibilities afforded by Spark may be cause for reconsideration. In fact Professor Huang and his students, in the Department of Computer Science at Prairie View A&M University (PVAMU) in the Houston area, have already applied Spark to SEG Y rev 1 format seismic data. In a poster presented at the Rice workshop, not only did Prof. Huang demonstrate the feasibility of introducing RDDs via Spark, he indicated how this use is crucial to a cloud-based platform for seismic analytics currently under development at PVAMU.
Apply alternate imaging conditions. For each (CDP) gather, coherence between RTM's two wavefields comprises the basis for establishing the presence of subsurface reflectors of geological origin. Using cross-correlation, artifacts introduced by complex reflector geometries, for example, are de-emphasized as the gather is migrated as-a-whole. Whereas it represents the canonical imaging condition envisaged by the originators of RTM in the 1980s, cross-correlation is by no means the only mechanism for establishing coherence between wavefields. Because Spark includes support for machine learning (MLlib), graph analytics (GraphX) and even statistics (SparkR), alternate possibilities for rapidly establishing imaging conditions have never been more accessible to the petroleum industry. Spark's analytics upside for imaging conditions is much more about introducing new capabilities than computational performance. For example, parameter studies based upon varying gathers and/or velocity models might serve to reduce the levels of uncertainty inherently present in inverse problems that seek to image the subsurface in areas of potential interest for the exploitation of petroleum resources. Using Spark'ified Genetic Algorithms (e.g., derivative of Spark-complimentary ones already written in Scala), for example, criteria could be established for evaluating the imaging conditions resulting from parameter studies – i.e., naturally selecting the most-appropriate velocity model.
Alternate implementation of the modeling kernel. Is it possible to Spark'ify the RTM modeling kernel? In other words, make programmatic use of Spark to propagate wavefields via the FDM implementation of the 3D wave equation. And even if this is possible, does it make sense? Clearly, this is the most speculative of the suggestions here. Though most speculative, in asking more questions than it presently answers, also the most intriguing. How so? At its core, speculation of this kind speaks to the generality of RDDs as a paradigm for parallel computing that reaches well beyond just RTM using FDM, and consequently of Spark as a representative implementation. Without speculating further at this time, I'll take the 5th, and close conservatively here with: Further research is required.
Real-time streaming. Spark includes support for streamed data. Whereas streaming seismic data upon acquisition in real time through an RTM workflow appears problematical even to blue-skying me at this point, the notion might find application in related contexts. For example, perhaps a stream-based implementation involving Spark might aid in ensuring the quality of seismic data in near real time as it is acquired, or be used to assess the resolution adequacy in an area of heightened interest within a broader campaign.
RTM has a past, present and future of being inherently performance-challenged. This means that algorithms research remains topical. Noteworthy gains are being made through the use of GPU programmability involving CUDA.
Using some 'novel exploitation' of HDFS and YARN, Hadoop might afford some performance-related benefits – especially if diskless HDFS is employed. Performance aside, the analytics upside for Hadoop is arguably comparable to that of Spark, even though there would be a need to make use of a number of separate and distinct applications in the Hadoop case.
Spark is much easier to integrate with an existing HPC IT infrastructure – mostly because Spark is quite flexible when it comes to file systems. Anecdotal evidence suggests that this is a key consideration for organizations involved in petroleum exploration, as they have incumbent storage solutions in which they have made significant and repeated investments. Spark has eclipsed Hadoop in many respects, and the risk of adoption can be mitigated on many fronts.
From in-memory data distributed in a fault-tolerant fashion across a cluster, to analytics-based imaging conditions, to refactored modeling kernels, to possibilities involving data streaming, Spark introduces a number of possibilities that are already demanding the attention of those involved in processing seismic data.
In making use of Spark in the RTM context, there is the potential for significant depth and breadth. Of course, the application of Spark beyond RTM serves only to deepen and broaden the possibilities. Spark is based on sound research in computer science. It has developed into what it is today on the heels of collaboration. That same spirit of collaboration is now required to determine how and when Spark will be applied in the exploration for petroleum, in other areas of the geosciences, as well as in other industries – possibilities for which have been enumerated elsewhere.
Shameless plug: Interested in taking Spark for a test drive? With Bright Cluster Manager for Apache Hadoop all you need is a minimal amount of hardware on the ground or in the cloud. Starting with bare metal, Bright provides you with the entire system stack from Linux through HDFS (or alternative) all the way up to Spark. In other words, you can have your test environment for Spark in minutes, and get cracking on possibilities for Spark-enabling RTM or almost any other application.


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Iran ranks seventh in the world for kiwi production, with over 3200 MT produced in 2012. Kiwifruit can be stored for over 6 months under appropriate conditions, but fruit softening and fruit rots (Botrytis cinerea) can cause severe losses during cold storage, transit, distribution and retail. Kiwifruit is a climacteric fruit very sensitive even at very low concentrations of ethylene (5-10 ppb). In fact, the capacity of Botrytis cinerea to produce ethylene has been linked to the pathogenesis of the mould. As a matter of fact, it is well know in the industry that ethylene removal is a must in kiwifruit storage either alone or in combination with other postharvest technologies. In this article, we discuss different aspects related to the application and benefits of the technology of ethylene removal by chemisorption in kiwifruit.
Kiwifruit [Actinidia deliciosa] is a member of the family Actinidiaceae originating from south-east Asia. It was introduced from China into New Zealand in 1904 and, from there, its cultivation rapidly expanded to other continents. It is a relatively recent commercially-grown fruit, with significant quantities being produced only after the 1980's. Iran ranks seventh in the world for kiwi production, with over 3200 MT produced in 2012 (FAO, 2012).
Kiwifruit can be stored for over 6 months under appropriate conditions (Mitchell et al., 1990; Feng et al., 2006). However, fruit softening and fruit rots can cause severe losses during cold storage, transit, distribution and retail. The most important cause of decay in kiwifruit after harvesting is grey mould caused by Botrytis cinerea Micheli ex. Pers (Spadaro et al., 2010).
Kiwifruit is a climacteric fruit. Ethylene has been known for many years to initiate the ripening of climacteric fruits whether the ethylene is derived endogenously or from exogenous sources. Kiwifruit produces very low amounts of ethylene when stored at the right conditions of maturity, temperature and atmosphere (< 0.1 ml/kg*h), but it is very sensitive to the presence of ethylene even at very low concentrations. As little as 5-10 ppb of ethylene will induce fruit softening (Wills et al., 2001). Ethylene exposure is also involved in the development of a number of physiological disorders such as hard-core, white-inclusions and pericarp translucency.
It is also well-know that decay-causing infections may not develop until the tissue ripens or ages because the fruit becomes less resistant to the invading pathogen. Ethylene is not only produced by higher plants. Other microorganisms such as fungi or bacteria are able to produce ethylene during pathogenesis. The capacity of Botrytis cinerea to produce ethylene has been reported (Qadir et al., 1996). According to these authors, the fact that high ethylene production occurs with such small amounts of mycelia suggests a possible role for fungal produced ethylene in Botrytis cinerea pathogenesis of sensitive fruit such as kiwifruit.
Niklis et al. (1997) reported the following evidence: in a commercial kiwifruit coldstore where a number of Botrytis-rotted fruit are distributed within the stored fruits, besides the fruits that will be infected around these nests, the infected kiwifruit will produce enough ethylene to cause early ripening of the healthy fruit in the same storage room. Botrytis infestation in several storage buns or boxes may induce severe economic losses of the total stored fruit in one big storage room.
According to Wills et al. (2001), intervention to limit the ethylene action should be warranted for the most sensitive species such as kiwifruit.
In a coldstore, the fruit is releases ethylene steadily. To maintain ethylene levels below the established threshold, the media must be capable of eliminating ethylene gas faster than the fruit emission ratio. This is especially important in kiwi storage, because of the high sensitivity of the fruit at very low levels of the gas. The media must also have an ethylene absorption capacity high enough to protect the fruit during the whole period of conservation (Wills et al., 2004).
Aside from the technical properties of the media, the equipment design needs to be adequate for the system to be effective. In coldstores, the system's ventilation should force the air through the media at a flow rate equivalent to the volume of the cold store so that the air is completely renewed at least once every hour. For fruit transport and distribution, other systems such as filters installed in the rerurn grid or individual sachets placed in each fruit box can be used. The media never comes into contact with the fruit, leaves no residue, and is compatible with organic produce.
Tables 1 and 2 show the efficacy of the commercial media Bi-OnÒ (Bioconservacion SA, Spain) in extending the life of kiwifruit (Oyarzun et al., 2004). According to the authors, the Bi-OnÒ sachets had an important effect on the ripening delay of kiwi fruits during the cold storage at 0ºC (softening and ºBrix content). Bi-OnÒ is an extruded media made of natural clays and potassium permanganate with an ethylene absorption capacity of 4.5 ml C2H4/kg (as tested following the method B-3150-14 – registered at the Intellectual Property Register as Scientific Work, Spain-). Bi-OnÒ media is also being used successfully in Iranian commercial colds stores since more than 10 years.
It is generally accepted that ethylene removal is still an essential intervention in kiwifruit storage to maximize postharvest life and effectively reduce losses. Ethylene removal by chemisorption is a technology that can be easily applied in the cold storage, transit, distribution and retail. It is also effective when used in combination with other technologies such as Controlled Atmospheres, MAP packaging and 1-MCP.
Effective chemisorption systems must be capable of eliminating ethylene gas faster than the fruit emission ratio in order to maintain ethylene under the safe levels of 5-10 ppb during the whole period.
Figure 1.Storage of Hayward Kiwifruit: Influence of 1-MCP and ethylene absorption (*).
Table1. Evolution of pulp hardness (lbs) in kiwi fruit stored for 7 weeks at 0ºC in common perforated plastic bags (*) (1).
Table 2. Evolution of Soluble Solids (Brixº) in kiwi fruit stored for 7 weeks at 0ºC in common perforated plastic bags (*) (1).


text:
What Manga Should You Read?
If you might be an enthusiast, finding a purpose to download anime videos at the cheapest price would surely be appealing for. With the emergence of new sites are usually making them available to viewers at affordable rates, it is no wonder which fans are wooed to their sites. Along with many choices, there is a classic dilemma of which to consider.
Card Captors is of a girl called Sakura, she opened an e-book one day and then a whole couple of cards was released and flew around town and matters. Now she has to get rid of it. But https://www.mangatrend.net has to contend having a douche-bag love interest. Yay! Plus I think she's ten so that's where we get the cute brought on by.
I believe most on the problems they encounter "in the industry" come from your industry itself. First off, comic companies very often will publish only for those are generally already within the industry. Comic publishers usually target their marketing towards those who are already here and creators tend generate product simply for those of which are already well-versed in economy. Very rarely does the known about bringing new readers in really ever pop up, which is insane. Travellers to move be like Hollywood only creating films for those who work in film and television. As wacky as it sounds, this seems to be the mentality with the industry most importantly.
The Japanese Manga Trend publication industry is huge and has recently expanded overall. This worldwide expansion has greatly influenced artists to draw manga cartoons. These popular cartoons have launched a huge promote for manga comic strips.
There at this moment paid normally where membership for unlimited access to anime video downloads be less expensive than $30. Why outlets trouble of downloading free when lots of people so safely and conveniently for so little money? Share additional look at some characteristics in a good web site.
Cosplay actually is a street fashion at first, along with the development, it is now a very fashionable art for make use of them. There are a lot of people of which are so fond of cosplay, they have their own cosplay herd. Cosplay is so popular due to the fact that anime and manga in order to in humans' life for a long time plus the anime fans and manga lovers simply want to replay some along with favourite plots of land.
Join the city where perfect share your opinions, post your threads, comments, pm's, wallpaper, AMV'S, manga chapters, anime series, and much more. Join now and lets create family portrait.

redpajama_set_name: RedPajamaCommonCrawl


UK: Abusive Islamonazi propagandist gets kid-glove treatment while meek Christian evangelist gets nicked……. – The Tundra Tabloids…….
The comparison here tells a sad story for our country.
Previous Post Previous Post Delingpole: Has Tommy Robinson Exposed the BBC's Dangerous, Far-Left Bias?
Next Post Next Post Finland: Leftist cartoonist's poster character Muhammed Al-Zomal or 'Mämmilän Mukku' for upcoming cartoon festival censored by race detectives…….


At Element we proudly offer Eco-Friendly Crates upon request by any customer. By utilizing reusable Eco-Friendly Crates we have dramatically reduced the waste that may be produced by using non-reusable materials. Our Eco-Friendly Moving Crates are produced from recycled materials because we understand that we must reduce our reliance on plastic products. Rubber and other oil based products are becoming more difficult to produce from scratch, and there is no need to create something new and wasteful, when we can reuse and recycle.
Call us today to speak with one of our Certified Relocation Specialists about our Eco-Friendly Moving Crates. At Element, we strive to be part of the solution, never part of the problem.


Carfentrazone-ethyl resistance in an Amaranthus tuberculatus population is not mediated by amino acid alterations in the PPO2 protein.
Home » Topics » Nutrition » Research » Carfentrazone-ethyl resistance in an Amaranthus tuberculatus population is not mediated by amino acid alterations in the PPO2 protein.
Summary of "Carfentrazone-ethyl resistance in an Amaranthus tuberculatus population is not mediated by amino acid alterations in the PPO2 protein."
To date, the only known mechanism conferring protoporphyrinogen IX oxidase (PPO)-inhibitor resistance in waterhemp (Amaranthus tuberculatus) is a glycine deletion in PPO2 (ΔG210), which results in cross-resistance to foliar PPO-inhibiting herbicides. However, a metabolism-based, HPPD-inhibitor resistant waterhemp population from Illinois (named SIR) was suspected of having a non-target site resistance (NTSR) mechanism due to its resistance to carfentrazone-ethyl (CE) but sensitivity to diphenylethers (DPEs). In greenhouse experiments, SIR sustained less injury than two PPO inhibitor-sensitive populations (WCS and SEN) after applying a field-use rate of CE, and after initial rapid necrosis, regrowth of SIR plants was comparable to a known PPO inhibitor-resistant population (ACR) possessing the ΔG210 mutation. Dose-response analysis determined 50% growth reduction rates in CE-resistant (SIR and ACR) and sensitive (SEN) waterhemp populations, which showed SIR was 30-fold resistant compared to SEN and two-fold more resistant than ACR. Deduced amino acid sequences derived from SIR PPX2 partial cDNAs did not contain the ΔG210 mutation found in ACR or other target-site mutations that confer PPO-inhibitor resistance previously reported in Palmer amaranth (Amaranthus palmeri). Although several SIR cDNAs contained amino acid substitutions, none were uniform among samples. Additionally, SIR plants treated with malathion and CE showed a significant reduction in biomass accumulation compared to CE alone. These results indicate robust CE resistance in SIR is not mediated by amino acid changes in the PPO2 protein, but instead resistance may be conferred through a NTSR mechanism such as enhanced herbicide metabolism.
Interspecific and Intraspecific Transference of Metabolism-Based Mesotrione Resistance in Dioecious Weedy Amaranthus.
Reduced absorption and impaired translocation endows glyphosate resistance in Amaranthus palmeri harvested in GR soybean from Argentina.
Confirmation of herbicide resistance mutations Trp574Leu, ΔG210, and EPSPS gene amplification and control of multiple herbicide-resistant Palmer amaranth (Amaranthus palmeri) with chlorimuron-ethyl, fomesafen, and glyphosate.
A Mild and Efficient Preparation of Phytosteryl Amino Acid Ester Hydrochlorides and Their Emulsifying Properties.


text:
Les Stethacanthidae forment une famille éteinte d'holocéphales. Ils ont vécu du Dévonien supérieur jusqu'au Carbonifère.

Description
On les reconnaît très bien grâce aux « enclumes » que les mâles portent sur leur dos.

Liste des genres
Selon  :
 genre Akmonistion
 genre Bethacanthus
 genre Cobelodus
 genre Danaea
 genre Gutturensis
 genre Orestiacanthus
 genre Petrodus
 Physonemus chesterensis
 Physonemus parvulus
 genre Stethacanthulus
 genre Stethacanthus
 genre Symmorium

Notes et références

Liens externes

Symmoriida
Famille de poissons préhistoriques (nom scientifique)
Chondrichthyen du Paléozoïque
Poisson du Carbonifère
Poisson du Dévonien
Taxon fossile décrit en 1970


Millionaire Society Membership Review - Is Millionaire Society Membership a Scam or Not ?
Hello everyone, Milan Chymcak here. So I finally bought access to Millionaire Society membership (it wasn't so expensive at all – it costs 4 dollars). And before I will try to give you my honest Millionaire Society review, I have to tell you what you can expect from this membership and how this works (if this works).
This is monthly basis membership that will help you (even if you are newbie) to create your first online income stream. In this membership you will find very useful information (even if you are experienced internet marketer). I will try to be in this review 100% honest with you, guys. So there is my honest Millionaire Society review ..
This Millionaire Society is created by Mack Michaels (very popular internet marketer, just google him) that will provide you knowledge that you need. Basically all his techniques and methods are step by step, so it's really hard to fail.
As I said, this Millionaire Society is based on monthly subscription, so he promises that he will add every month 20 hours (and more) of video training. I member just 3 days, so let's see how it goes. When I start to watch all his videos, my personal opinion is that some his videos are very boring (even if you will learn new stuff), because his voice is so monotonous. Anyway you will learn a lot from his videos. He will teach you in first month how to get your first dollar online with his method.
So what all you will get after register ?
Well there are over 40 videos (I didn't count them), 7 special training programs, free support (directly from Mack) and also you will get access to his softwares and automated tools (personally sorry, I didn't tested them yet, because I am on laptop right now). And as I mentioned at the start of this review, you can sign up just for 4 dollars. So you can try it almost for free.
So what you can really expect from this Millionaire Society ?
As I mentioned there are a lot of training videos and methods. Then you will also get access to their special products, blueprints (you will normally have to pay for it on places like warriorforum). But one of best thing that you will get from this membership is – mentoring. Nothing will learn you so well as mentoring and honest support.
As I also said this methods are step by step – you will get to do-list and once you will follow your instructions, you can't fail at this moment. So you will learn how to create website and sell it for thousands within a few months – it's called domain flipping. It's really easy to follow all instructions.
Honestly if you are totally newbie, then you will be overloaded with information. Because once you will enter Millionaire Society membership, there are a lot of methods and so on. So you have to focus on just one – this is my advice that will help you with your success.
Honestly I would definitely recommend this Millionaire Society course to anyone that want to start successful online business. But honestly this course costs 4 dollars for first month, but you have to expect that you will have to also pay for hosting, domain and so on. This is necessary, but if you will follow all instructions, I think you will get your investment really quickly back. Don't forget also that they have 60 days money back guarantee.
And if you purchase this Millionaire Society membership through us (link is below) I will give you our bonuses for free (50 dollars value) that will help you with your online business. Anyway I hope that you found this Millionaire Society review useful and helpful.
Is Millionaire Society Membership a Scam ?