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A Theoretical Blueprint for Improving MLEs
by Dave Allen and Kevin Tenenbaum
Editor's Note: To give you a further taste of The Hardball Times Baseball Annual 2015, we are reprinting this article in its entirety. You can purchase the book here.
Bryce Harper's age impacted the way his MLEs were calculated. (via Matthew Straubmuller)
The following article is probably different from what you would normally read in these pages. We don't analyze data to find a specific result, but we take a theoretical approach to a concept that has been around for a long time. This article is simply a plan to improve upon the current construction of Major League Equivalencies (MLEs).
The baseball analytics community has come a long way since Bill James called MLEs one of the most important ideas he ever had, but the structure of MLEs has changed little since James introduced them. MLEs are one of the most valuable and fascinating areas of baseball analytics, and there is still much work to be done before we can be satisfied with them.
Tom Tango wrote an essay titled, "Issues with MLEs: Why I hate how they are used," in which he outlines five issues he has with MLEs. He concludes the article with this summary of the current state of the adjustments:
MLEs, as currently published, is a first step. We have many steps to go through before we can reduce the error range. We should not treat the currently-published MLEs as a final product."
We agree with Tango, and we feel that we can take a few steps towards improving the adjustments that we apply to minor league performance. While our list has some ideas in common with Tango's, we expand and take our own stance on these values and provide our own propositions as to how we plan to handle the problems that we hope people will take on in the future.
What Are MLEs?
MLEs are a translation of minor league statistics to major league ones. In other words, they approximate how well a player would have done if he had been in the major leagues, based on his performance in the minor leagues. It is important to note that MLEs are not projections. You can think of them as exchange rates that only convert minor league statistics into a major league context.
MLEs are typically multiplicative, which means that you simply multiply minor league performance by the MLE to get the adjusted value. For example, if the MLE from Triple-A to the majors for HRs is 0.8, and Player A hits 20 HR in Triple-A, we would have expected him to hit 0.8*20=16 homers in the majors if he had played at the top level over the same time period. MLEs make no claims about how Player A will perform in any other time period in the future.
Goal of MLEs
Any time you embark upon a project, it is a good idea to take a step back and lay out the model's goals. We feel MLEs should be the best possible tool for translating a player's performance from the minor leagues to the major leagues. These translations should be the best unbiased estimator of how a player would have performed in the major leagues given his minor league performance over the same time period for every player. Therefore, the adjustments need not be identical for all players at the same level, as variation in level may affect players differently.
These MLEs can be used for a variety of work in baseball, but we feel they are most useful in player forecasting. While remembering that an MLE is not a forecast unto itself, it is an essential part of a good forecasting system that uses minor league performance. These adjustments are essential because the forecaster needs to be able to use minor league statistics in the same way he or she uses major league statistics. Therefore, he or she can use MLEs to adjust minor league statistics so they correspond to the major league context. In other words, MLEs are important, and we hope to get the ball rolling towards improving forecast accuracy for young and inexperienced players.
Current Methodology
Below, we outline the general methodology used to generate MLEs.
First, restrict the sample of players to those who played at each of the two levels being compared over a short period (i.e. less than two years) but still have a decent sample size of plate appearances or total batters faced at both levels (i.e. > 50 plate appearances or total batters faced). Next, make a few other adjustments to ensure you are using the best sample of player possible. For instance, Brian Cartwright makes sure he only uses starting players that have at least 2.5 PA/G as a hitter. Then, take each of the performance samples and divide the major league performances by the minor league performances. The average of these ratios is the calculated MLE.
The methodology becomes slightly more complicated when we examine jumps of more than one level (i.e. Double-A to MLB). In this case, the MLE can be constructed with or without a method that we call chaining. With chaining, we would calculate an MLE for both Double-A to Triple-A and Triple-A to MLB and multiply, or chain, the two together to find the MLE. The other option would be to ignore chaining and simply calculate the MLE for players that played in both Double-A and MLB within the allotted amount of time. We will discuss the merits of each of these approaches in the next section.
Existing Issues
There are several issues with the above methodology we feel have been unaddressed for too long. Below, we outline six major issues with the current implementation of MLEs we believe hinder the accuracy of major forecasting systems.
1. Minor League Over-performance
When we look at the sample of players promoted from one level to another, we need to consider the thought process of the farm directors and general managers who decide when a player will be promoted. When making these decisions, they take several pieces of information into account, including roster construction, injuries, player attitude, scouting reports and player performance stats. Because these decision makers take a player's minor league performance into account, promoted players are more likely to have been over-performing than under-performing their true talent level while in the minor leagues.
Thus, we have a bias, as our MLEs will artificially be more extreme than they should be because the denominator (i.e. minor league performance) will overestimate players' talent levels.
2. Aging Effects
A second issue is that MLEs currently ignore player age, even though players will age between the middle of their minor league stint and the middle of their major league stint used to calculate the MLEs. This age difference poses a problem, as players of different ages will experience various magnitudes of aging effects on their performance.
For instance, consider the difference between Bryce Harper and Rick Ankiel. Harper was first called up to the major leagues at the age of 19, while Ankiel was first called up as a position player at 28 years of age. If the differences between the average age in the major league and minor league stints were identical, they still would have different aging effects because Harper is at a much steeper part of the aging curve than Ankiel. Therefore, an MLE constructed from many more Bryce Harpers than Rick Ankiels would be artificially closer to one because the MLB performance would be inflated by the players who benefited from aging a year or two. These influences can be fairly large, and they might not wash out in the sample. Therefore, it is important that MLE calculations account for this bias in their calculations.
3. Skill Profile
Another major issue with the current construction of MLEs is that league changes will affect each player differently. For instance, consider two position players with average strikeout percentages at Triple-A. These two players differ in that Player A has a high walk percentage, while Player B has a low walk percentage at the Triple-A level. Currently, we assume that a change in level will affect both these player identically on average.
However, this may be a naïve assumption, as Player A could be a player who has good patience, while Player B might have a problem making contact on his swings. Therefore, we would expect these players to experience a promotion to MLB differently. We would think that Player A would have a lower strikeout percentage than Player B at the major league level because Player B strikes out from ineptitude, while Player A strikes out through strategy. Thus, the MLE process would benefit from the examination of a player's other peripheral statistics instead of creating one value that applies to every player.
4. No Error Estimate
Historically, MLEs have been treated as simple estimates of league adjustments, and researchers and forecasters have ignored their uncertainty levels. This neglect is unacceptable for MLEs. Our adjustment for a transition from High-A to the majors is much more uncertain that the corresponding adjustment from Triple-A to the bigs. This poses a major problem when we combine league-adjusted statistics for different minor league levels and regress to the mean.
The major factor in how much we regress performance to the mean is the uncertainty around our true talent level estimates. In the majors, we can express this uncertainty in terms of the number of plate appearances in our sample. However, at the minor league level, we also must embed the uncertainty in our MLE estimate into the overall model uncertainty. Thus, we need to report our MLE estimates with a standard error in addition to the expected value of our MLEs. This will allow forecasters to essentially weight performance at higher levels more than lower levels based on the uncertainty of the MLE estimates, similar to how league-average performance is combined with player performance when we regress towards the mean.
5. A Better Look at Chaining
Currently, there are two major schools of thought on MLEs. Most systems use a method called chaining. Chaining is the practice of calculating league adjustments for change of one level at a time (i.e. Double-A to Triple-A and Triple-A to MLB) and multiplying the levels a player must go through to reach the majors. For example the MLE for Double-A would be the product of an adjustment from Double-A to Triple-A and an adjustment from Triple-A to the majors.
On the contrary, Brian Cartwright does not use chaining in his forecasting system, OLIVER. In an article he wrote for Baseball Prospectus in 2009, he shows that his MLEs perform better when they are not chained. While his results are intriguing, we believe that an inherent bias in the test sample means they require further testing.
All of the players that Cartwright uses for testing are good enough to reach MLB. Therefore, the direct method, which only incorporates players that played at both levels, will be overly optimistic in its adjustments because it only includes players who are promoted very quickly. This bias could create more accurate forecasts for players that do reach the majors, but it could be detrimental for those players a team is unsure of whether to promote or not. This bias presents an inherent problem, as the MLE could see an improvement in test results while actually being a worse estimate.
6. Experience at Level
Another bias current MLEs incorporate is the experience that a player has at a given level. There is evidence that minor league players perform better as they rack up experience at that level. This means there is a subsample of minor league performance data that is used in the calculation of MLEs that is inherently inflated above the players' true talent levels. Thus, our MLE estimates would benefit from removing the effect that experience at a level has on player performance in the minor leagues.
Solutions to Problems
In this section, we will go through each of the six issues we outlined above and propose a potential change to the current MLE model that we would like to see people use to craft MLEs in the future.
Promoted players are likely to be over performing their true talent levels (think Gregory Polanco when he was promoted to the Pirates). To work towards removing this selection bias, we propose regressing minor league stats towards the mean for MLEs. This will move the population average closer to the true talent level for the player group that we want to study (i.e. minor league players who could be promoted to the major leagues).
The big question here is what value we should regress these performance statistics toward. We can think of two possible choices:
Average of players within the league. This option does a great job of removing a portion of the overperforming player bias by shifting inflated statistics from overperforming players toward the league average. This change, therefore, does exactly what we want it to. However, the main reason to regress to the league average instead of the level mean would be that there are major differences in the environment of one league to the next at the same level. These differences in league mostly can be handled with park factors. Therefore, we would hope to find a larger sample for the prior.
Average of entire level. Therefore, we propose that minor league performance statistics be regressed to the level mean. Therefore, an individual player's Triple-A strikeout percentage performance would be regressed toward the average K% for all Triple-A players. This is the best answer we could come up with because it is the largest sample of available players that does not amplify the overperforming player bias.
Next, we will address the issue of aging. To incorporate the various magnitudes of aging effects on players in the major league and minor league samples, we propose all statistics are aged to the same year. This will essentially create age-neutral statistics for every player's performance at both the minor league and major league level.
We recommend performance be aged forward or backward to the peak age on the aging curve used. While it does not matter what age you choose, it does need to be consistent. The removal of aging bias from MLE calculations requires a different aging method than most researchers tend to use. Normally, people create aging curves using only major league performance. This method would not yield the optimal results, as you would be applying the aging effect to a sample of players that is different from the sample the curve was designed for.
MLEs do not look at players who were in the majors for all years in the sample. Rather, they use data for players who have been promoted from the minor leagues to the major leagues recently. Therefore, we believe this information should be incorporated into any aging curve used to adjust performance in MLE calculations.
To solve the problem of level changes affecting each player differently, we propose that personalized MLEs be created for each player. Personalizing these adjustments would create a system in which every player is treated uniquely. For instance, a player who tends to strike out often would have a different MLE than a player who is able to make contact on all pitches. While we will not hypothesize on what these differences might look like, we can say that they should be treated differently when trying to predict their major league performances from their minor league performances.
We propose a solution to this issue that uses similarity scores to construct an MLE for each player. In this case, a similarity score would fall between zero and one. You can think of this as a weight. Therefore, we would create an MLE in the same way that we normally would with one small change. Rather than taking the straight average of the ratio of major league to minor league performance, the weighted average would be computed with weights proportional to the similarity scores.
Now, the most difficult part of this process actually would be to calculate the similarity scores. While it would be easy to use the simple similarity scores that Baseball-Reference publishes for all players, this would be an inappropriate measure of player similarity for MLE construction. Rather, we want to really key in on the variables that are important for successfully changing levels. To find these properties, it would be best to run a regression or other machine-learning technique to determine what variables have a lot of influence on how players adjust to new leagues. These factors would be different for each statistic we adjust and could range from biographical information like height to performance statistics like isolated power (ISO). We can calculate the difference between these values and then weight these differences to develop a similarity score to use in our weighted MLE and come up with a personalized MLE for each player to minimize our forecast errors.
Next, we address the question of how to measure the uncertainty surrounding our MLE estimates. To make any inferences about the MLE sampling distribution, we must generate an estimate of the actual distribution. To do so, we will use a technique called bootstrapping. To create the MLE sampling distribution, we repeatedly take the average of a random sample (with replacement) of players' ratios between their minor league and major league performance. Therefore, we have several averages of these resamples. We treat these averages as the resampling distribution of the MLE, thinking of it as representing how likely we are to draw each value of the MLE if we choose one at random.
While we could report this distribution, it makes it difficult to incorporate into the forecasting process without making any assumptions. We assume the resampling distribution is normally distributed, which is a good assumption because of the central limit theorem (which states that if we take enough independent samples from any probability distribution, the average of the samples will follow a bell curve with its average being the true mean). Therefore, we can take the standard error of the MLE estimate as the standard deviation of the resampling distribution. We then use this standard deviation to weight level-adjusted statistics by their true uncertainty in addition to understanding their uncertainty. Because of the normality assumption, knowing the expected value (i.e. the MLE) and the standard error gives us everything we need to recreate the MLE distribution.
Chaining presents an issue we have found very difficult to solve. The biased data makes it almost impossible to objectively compare the two methods (i.e., chaining or not). With that said, we believe it to be important that we can confidently say which method we prefer, or that either method works just fine. Therefore, we propose a Bayesian framework to test the utility of each method. Rather than testing the MLEs in a simple forecast and choosing the model with the smaller error, we suggest that the errors are compared while holding onto a prior belief that chaining is the better method. This Bayesian procedure will allow for a comparison of the accuracy of the two methods with the understanding that the test data are biased in favor of not chaining. This is admittedly an imperfect solution and an area for further brainstorming.
Incorporating experience at level will prove to be a difficult task going forward. To make this happen, we must quantify the effect of playing another game at the same level. While it would be easy to assume this phenomenon is a linear effect (i.e. the performance boost from game one to game two is identical to the boost from game 1,000 to game 1,001), we cannot do that because we expect there to be diminishing marginal returns for the effect. Therefore, you would gain a larger boost for games earlier in your career at that level than you would for later games.
What's needed is a slightly more complicated model that does a good job handling diminishing marginal returns. We believe the best model for this would be logarithmic. Therefore, we want to approximate the effect the log (games played at level) has on the player's performance. As always, we need to fit separate models for each statistic.
While we would like to solve this issue with a simple linear regression, there will be issues here because of a sampling bias. Players who play more games at a certain level are probably performing worse than players who play fewer games. To counteract this bias, we assume that, on average, an extra game will affect all players identically. We understand this may not be a good assumption, as we treat it as a flaw for current MLEs, but we do not have a better solution at this time. Therefore, if we take a subset of players who have played at least n games, we can run a linear regression that uses the log (games played at level) to predict player performance at a given level based on the number of games played at that level. Then, we can actually adjust the performance statistics that go into the MLE model for the effect that experience at that level would have on each player.
MLEs can be improved upon, and here we have a blueprint for doing so. Some of these changes may not help the process, but we believe they are all worth at least testing and seeing if they lead to any measurable improvements in forecast accuracy. Improving them will be a complicated process, but we believe that the framework here will reap benefits for those who have the time to act upon them.
Tom Tango, Tangotiger, "Issues with MLEs: Why I hate how they are used"
Brian Cartwright, Baseball Prospectus, "Prospectus Idol Entry: Brian Cartwright's Initial Entry"
Brian Cartwright, The Hardball Times, "Oliver, smarter than your average monkey"
Dave Allen is a visiting assistant professor of biology at Middlebury College. He studies forest succession and ecology. In his spare time—between his biology teaching and research—he still does some baseball analysis, but not as much as he would like. Kevin Tenenbaum is a senior mathematical sciences major and economics minor at Middlebury College. He has presented research at the SABR Analytics Conference, been published in the 2014 Hardball Times Baseball Annual, and interned for the Baltimore Orioles for the past two summers. He will join the Orioles' baseball operations department in a full-time position upon his graduation in May.
If the primary purpose is forecasting, why do you need MLEs anyway? If one is drilling down to the level of skill profile, why not simply do away with MLEs (at least mostly). You do need to have some of equivalency between the minor leagues and from there you can simply look at subsequent performance of players of same age and similar skill profile/performance for forecasting. I do not see that it is necessary to introduce the intermediate step of an MLE. How about calling it a "neutral triple A equivalency", but with the understanding that it is not simply… Read more »
Kevin Tenenbaum
Thanks for the comment Mike. I think that the question stems a lot from different definitions we may have for what an MLE is. When I think of an MLE, I do not see a slash line of translated statistics. Rather, I think of a quantification of the effect moving from league A to league B has on a player. In our case, league B happens to be the MLB, but it really doesn't matter in terms of forecasting, as long as you are consistent across players. You are correct in saying that you don't need MLEs in your forecast… Read more »
Thanks, Kevin. OK, I see the point. I doubt though that you will get a more accurate projection. What you gain in a larger sample size, you lose in the uncertainty of the minors to majors translation and the difference between minor league PAs and major league PAs. I should elaborate on the latter issue. The very fact that a player has had a significant number of major league PAs matters because of service time/option rules. So, let's say that you have two players with similar skill sets to Pompey. One has played in the minor leagues at age 21… Read more »
Brian Cartwright
Regarding the first half of the article – age is now an integral part of my Oliver MLEs. I adjust all player/team/seasons for ballpark and exact decimal age before making comparisons to calculate the league factors. I calculate two types of MLEs, with 'present' and 'peak' (age 27) values. Factors are calculated for and applied to the rates for each component stat, and are combined in a decision tree to get the final counting stats total. For example, a higher SO% means fewer balls in play, which results in fewer total home runs, even if the HR% hadn't changed. Once… Read more »
J. Cross
Good stuff. A lot to think about here. I think that once you start age-adjusting the stats (which I agree, of course that you should) you can start using data pairs for transitions that aren't in the same or even consecutive years. So, if you age adjusted a player's stats from high-A, you can compare them to his MLB stats three years later to get the high-A to MLB factor. When coming up with the factors, transitions with more time/years in between them would just receive less weight than transitions where the two sets are data are closer in time… Read more »
I'll look further into bootstrapping to see if it is something that I might be able to utilize.
Regarding chaining vs direct, I agree there are some biases in the process, but in the direct there is only one step where the biases may appear, in chaining it's present at every step and grows each level further away from MLB. As Jared noted, if the samples are properly adjusted for age there doesn't have to be a strict limit on number of years between samples.
Mike – I use three adjustments to the player's performance data in marking a projection
1) for age
2) for ball parks
3) for level of competition
What Kevin is describing in this article is how to best determine the level of competition in a given league, relative to MLB.
Even if one never calculates or publishes a single season 'MLE', the level of competition is a necessary factor in the computation of projections
This is a non-MLE specific question, but something that came up when I saw your point about only considering players with >50 PAs. Obviously this brings about the weirdness of someone with 51 PAs making the sample and someone with 50 not, even though one isn't any more predictive than the other. Is there a good reason not to include every possible sample, and also include a weight factor? Not linear, but something that followed the curve of significance closely, so that the difference between the factor for a player with 10 PAs and one with 50 is similar to… Read more »
You're correct, another method is to weight by either the smaller of the two samples being compared, or the harmonic mean of the two.
A more important issue, which Kevin mentioned, is that bench players suffer the 'pinch hit penalty'm which is about .030 wOBA IIRC. Guys may have played every day in Triple-A, then started once a week and did some PHing once promoted. Those are not apples to apples comparisons, so I use a PA/G cut off to try to get only players with regular playing time in each sample.
jim fetterolf
For Royals' minor leaguers, one of the main adjustments that needs to be made to projection is park factor. Both AA NW Arkansas and AAA 'Chasers play in band box parks more suitable to develop hitters for the AL East than the K in the central. We've seen that often enough with power bats like Hosmer and Moustakas over the years. For predictive value, Wilmington may be the best the Royals have, large pitchers' park facing pitchers usually more advanced for their professional experience than the hitters are. A hitter who does well in Wilmington is likely to do better… Read more »
Jim – We assumed that park factors were already controlled for at both the minor league and major league level. Sorry if we didn't mention that in the article.
Brian – Thanks for the comments. I am happy to see that you are using aging in your MLEs now. Sorry that we weren't aware of it. Can you talk a little bit more about how you use decision trees? I am not sure I follow. I am excited to see what changes you make in the future.
Great discussion here.
@Brian Is there an ETA for Oliver projections to be up on Fangraphs? Also, perhaps you could post an article with the top ~10 prospects your system is unusually high on for the long-term?


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Shakespeare's most intense and terrifying tragedy, directed by Rufus Norris (The Threepenny Opera, London Road) will see Rory Kinnear (Young Marx, Othello) and Anne-Marie Duff (Oil, Suffragette) return to National Theatre to play Macbeth and Lady Macbeth. The ruined aftermath of a bloody civil war. Ruthlessly fighting to survive, the Macbeths are propelled towards the crown by forces of elemental darkness.
(3 hours and 30 min, PG-13) Contains scenes of a violent and bloody nature.


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Dąbrowa-Dołęgi – wieś w Polsce położona w województwie podlaskim, w powiecie wysokomazowieckim, w gminie Szepietowo.

W latach 1975–1998 miejscowość administracyjnie należała do województwa łomżyńskiego.

Wierni kościoła rzymskokatolickiego należą do parafii św. Stanisława Biskupa i Męczennika w Dąbrowie Wielkiej.

Historia
W XIX w. miejscowość tworzyła tzw. okolicę szlachecką Dąbrowa w powiecie mazowieckim, gmina Szepietowo, parafia Dąbrowa Wielka

W roku 1827 w skład okolicy wchodziły:
 Dąbrowa-Bybytki, 9 domów i 66 mieszkańców. Folwark Bybytki z przyległymi w Łazach i Nowejwsi o powierzchni 324 morgów
 Dąbrowa-Cherubiny, 17 domów i 95 mieszkańców
 Dąbrowa-Gogole, 14 domów i 71 mieszkańców
 Dąbrowa-Kity, 3 domy i 23 mieszkańców
 Dąbrowa-Łazy, 29 domów i 170 mieszkańców
 Dąbrowa-Michałki, 21 domów i 125 mieszkańców
 Dąbrowa-Moczydły, 24 domy i 149 mieszkańców
 Dąbrowa-Nowawieś
 Dąbrowa-Szatanki, 98 domów i 60 mieszkańców
 Dąbrowa-Tworki, 6 domów i 46 mieszkańców
 Dąbrowa-Wielka
 Dąbrowa-Dołęgi, 28 domów i 139 mieszkańców
 Dąbrowa-Rawki, 17 domów i 127 mieszkańców (obecnie nie istnieje)
 Dąbrowa-Zgniła, parafia Jabłonka, 31 domów i 150 mieszkańców (obecnie nie istnieje)

Współcześnie istnieją również:

 Dąbrowa-Kaski
 Dąbrowa-Wilki
 Dąbrowa-Zabłotne

W roku 1921 naliczono tu 18 budynków z przeznaczeniem mieszkalnym oraz 128 mieszkańców (68 mężczyzn i 60 kobiet). Narodowość polską podało 126 osób, a 2 białoruską.

Obiekty zabytkowe
 dom drewniany z lat 30. XX w.
 krzyż przydrożny, metalowy z cokołem kamiennym z roku 1894

Zobacz też
 Dąbrowa-Bybytki, Dąbrowa-Gogole, Dąbrowa-Kaski, Dąbrowa-Moczydły, Dąbrowa-Tworki, Dąbrowa-Wilki, Dąbrowa-Zabłotne, Dąbrowa-Łazy
 Dąbrowa
 Dąbrówka Kościelna

Przypisy

Szepietowo (gmina)

redpajama_set_name: RedPajamaWikipedia


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With interactive tables and fingertip ordering, Inamo has always been a fan of using technology in its restaurants.
The Checkit food safety management system means that the company is now using innovative mobile handsets that prompt, record and report food safety tasks, eliminating the need for paper food safety records for the dozens of critical daily checks that need to be carried out.
The technology has provided an additional assurance of high quality standards and complete compliance with food safety regulations and has meant that kitchen staff spend less time filling in forms and managers receive real-time insight into the essential routine tasks that have been carried out.
Inamo opened its first restaurant in Soho in 2008 and within two years it was named Time Out Restaurant of the Year. The founders, Noel Hunwick and Danny Potter, had been frustrated by slow service and lack of attention at restaurants and set about designing an interactive method of ordering.
In doing so, they created the world's first interactive dining table, using a unique projector system. This has since evolved and, as well as offering access to menus and the ability to order food and drinks instantly, diners can choose from a selection of virtual tablecloths, display their own photos, play games and even view a kitchen webcam.
Following its success, a further two restaurants have opened in Covent Garden and Camden. In total, the three restaurants can seat up to 250 diners at any one time and frequently serve over 1000 covers each day making it essential to ensure consistently high standards.
The Checkit system enables data to be recorded digitally via the handset and automatically uploaded to the Cloud, where it is stored securely. The data is converted into reports that are available to managers via an intuitive dashboard screen that's accessible on PCs or mobile devices.
Each Inamo restaurant has two Checkit handsets, one for the kitchen and one for front-of-house, which includes customised tasks such as cleaning and maintenance of the bar and toilets.
Checkit automated monitoring sensors were also recently installed in the kitchen to provide continuous temperature monitoring of fridges and freezers and increased efficiency.


The Lion King – quotable and relatable for any TCK. True Story: one of my TCK friends used the storyline in a job interview to convey her cultural context challenges and how she had to realize her upbringing shaped her to be a cultural navigator. (she got the job). I watched this film several times as a child and I still have the Simba stuffed animal on my shelf that I bartered for when I was 10 at a Korean street market. I sang the songs in school choirs (in Germany and in Japan). And two years ago, I saw the Broadway production in NYC (one of the best bday gifts ever btw). I have so many beautiful and strong memories from this story! And I was reminded of them when I captured this shot of one of the lions I interacted with at the Lion Reserve in South Africa.


text:
For Whom the Hell Tolls
Blending political agendas with Spiritual laws, concepts, and principals is a no-no. I personally experienced many confusing Christians weaving their political views and support with who Christ would endorse. The fact is Christ did not support any politics because He came to represent His Father's Kingdom. They are two distinctly different worlds with different rules and regulations. There is not one story in the Bible where Christ threw His hat into politics, yet with great aplomb people merge their own politics with the Kingdom of the Most High.
With that being said I have been an equal opportunity analyst of past Presidents who recklessly break Biblical law. Frankly, I am not sure why anyone who studies the teachings of Christ could become emotionally vested in any President. I have heard the arrogance of people claiming God chose Donald Trump as President. Actually, it was the Electoral College that elected President Trump and the Most High affords free will to the people to vote. After last weekends White Supremacist Protest in Charlottesville, Virginia, and the subsequent weaponized car murder America is at a tipping point.
President Trump is the most polarizing President I have seen since I have been aware of the political theater. I have heard some claim President Obama was polarizing, but that is not true. In the very beginning of his presidency he was the most celebrated President in America and the world. White Supremacist groups voiced their displeasure by placing nooses around the neck of a stuffed animal monkey and shouted racial slurs at the President and his family. However, this racially driven narrative was drowned out by his false promise of hope and change. In the end President Obama turned out to be just another politician and the rhetorical question became: How do you know he is lying? His lips are moving.
If I was to metaphorically encapsulate the relationship between President and the common citizen, the only difference would be this. President Bush, kicked in your door playing heavy metal music and assaults you on the kitchen floor, while President Obama, knocked on the door, ask if he could come in, lights a scented candle, turns on smooth jazz, and assaults you on the couch. Either way the American public is bent over a barrel without a kiss on the mouth because that is the nature of politics. All Presidents never live up to their promises while the Most High lives up to all of His! So mixing politics and Christ is silly if you think about it!
I say this leading up to the health and safety of President Trump. It is vital for social health of America the Secret Service double their efforts to protect President Trump. Assassination is another word for murder except driven by a political agenda. It would have grave consequences to the fabric of American society because it can become unraveled with the level of inflammatory open rhetoric not seen since prior to the Civil Rights Act. There is a segment of American society driven by low education, anger, hatred, and intolerance not just for Blacks, but all minorities in America. We have not seen the worst humanity has to offer as of yet, because the highly trained White Supremacist militias have not emerged.
The worst case scenario would be the assassination of President Trump and every White Supremacist hate group would erase the line between civilized society and total anarchy; murder sprees like Rosewood, Black Wall Street, and plantation violence would happen again. The only difference today is both sides are heavily armed which would mean intense fighting. The Governors of the effected States would declare martial law and the National Guard would be deployed. The death toll would be significant and like any reactionary movement it would spread to other cities. This would give the elite the opportunity to establish a new order birthed by chaos. David Duke, endorsed President Trump as many other hate based groups and continues to demand his loyalty.
The Federal Government has laws against hate crimes, yet these White Supremacist protest are fueled by hate speech. The mere fact Nazi flags were presented is an indictment against Ashkenazi Jewish people, yet it is allowed under Constitutional amendments. Hate speech is the drug for those driven by hate and President Trump emboldened hate groups throughout his campaign. How can a country be great with the theft of land and attempted genocide of Native Americans? The profiting of 400 years of free labor inhumane slavery of Black Americans? From spiritual principle if you are harming another in anyway that is defined as evil. The goal of White Supremacist is to make America evil again.
These very hate groups were scarred seeing a Black family in the White House for eight years. This means race hatred smoldered for eight years and the fire is slowly bursting forth. Hate group White males feel marginalized because of media saturation on minority issues. When in truth white males outnumber minorities over 200 to 1. They are still the faces of industry and hold the economic power in America. There is a disparity between Whites and Blacks (including other minorities) not just in wealth, but education as well. Why do they feel they are being left behind when they are leading statistically?
I cannot escape Biblical prophecy regarding the true identity of the Chosen People under a 400 year curse. It says they will be despised in all the Kingdoms and will never truly be accepted nor respected. As a people they will be hated until the curse timeline is complete. This curse cannot be over the ruling class majority! White Supremacist are threatened by an ethnic group that makes up less than 13% of the American population. How can such a small percentage be a threat to the majority? President Trump treaded water instead of immediately repudiating and naming the hate groups involved in the tiki lamp driven protest. President Trump, emboldened these hate groups with his ambiguous repudiation of these specific hate groups.
The whole premise of "Black Lives Matter" was that Black lives are being snuffed out at a higher frequency (during the arrest process) more than any other racial group. This was about the frequency and not the higher number of Whites killed during the arrest process because their frequency is lower than Blacks; they have more deaths simply because of their greater numbers. That was the logic in a nutshell, but I do not think it was a good name because the Black abortion rate is insanely high. It should be apparent ALL lives matter, but people love conflict and to argue. My greatest concern is an assassination attempt on President Trump. This heinous act would be like throwing gasoline on a fire and David Duke what become as big as Luke and Bo Duke.
If the elite decided to orchestrate this heinous act, it will lead to the erosion of Constitutional rights as result of the total anarchy inflicted on city infrastructure in the United State cities. If a White Supremacist zealot like James Alex Fields, Jr., decides to martyr himself and successfully assassinates President Trump more like minded White males will rise up. This will not be a civil war, but the unleashing of domestic terrorism. I believe at the root of all of this chaos are dark forces as described in Ephesians 6:12. This is truly a spiritual war and possession is real!
Popular culture television shows like, "The Simpsons" have an uncanny predictive capability and even produced an episode in 2004 where Donald Trump ran for President and won. Ominously, there was also an episode where Donald Trump was assassinated. I often wonder if the elite telegraph future events as means to side step the law of "reaping what you sow." Because these scenarios are wrapped up as entertainment there is no public outcry, but only acceptance. I would be remiss if I did not mention the viral discussion of 1893 book titled, "Baron Trump's Marvelous Underground Journey" by Ingersoll Lockwood. In this book the characters are: Baron Trump, Don, and a trip to Russia.
What are the chances of this? The character "Baron Trump" has a slight resemblance to President Trump's son Baron. I cannot escape thinking there is an occult relationship at play here. The follow up book is titled, "1900: Or; The Last President" and that is a disturbing connection in itself. If you have followed the research on this site you would be aware of the "Power of Three." You have seen this theme illustrated across cultures as three witches and three wishes, etc. Let's take a look at the pattern based on what I have shared.
THE POWER OF THREE:
1. The Simpsons predicted the election of Donald Trump and his assination
2. 19th century author pens, ""Baron Trump's Marvelous Underground Journey"
3. 19th century author pens, "1900: Or; The Last President"
Is the collective consciousness subconsciously creating reality with triggers inserted into our reality? Why wasn't this book shared before? President Trump, has been a public figure for decades and now this comes forward and it is a viral story that has made it to mainstream news outlets. The Universe must be like the large Haldron Collider where our thoughts create matter and bring spiritual things into the material world. This is just too uncanny not to have legs. President Trump is not a politician, he is a business man. He never held public office before and his whole Presidency so far is quite unorthodox.
What he has done is used rhetoric that appealed to White Supremacist and according to David Duke they are his constituents. This could be tens of thousands of voters and perhaps millions? I do not know the statistics on this, but if a hair on President Trumps head gets damaged mass protest will begin and it will not turn out well. The Scripture in the Bible instructs us to pray for our leaders and we should pray for the safety of President Trump. Regardless of politics we should be serving the Most High first and his laws and principles. I have uneasy feeling about "The Power of Three" regarding President Trump and I hope I am off base.
There is also the possibility all of this is a total distraction! Have you noticed the news coverage for Russia has died down? As soon as Americans and Guam feared for their own safety North Korea became the evil of the day. Next came the White Supremacist protest and the unfortunate death of THREE lives on that day. The Russian investigation of "follow the money" has taken a backseat to the latest news. Whatever the case have no fear because those who are spiritually protected by the Most High have eternal life insurance…don't leave home without it!
Barack ObamaObama

redpajama_set_name: RedPajamaCommonCrawl


text:
package org.intracer.wmua

import java.text.DecimalFormat
import java.util.Locale
import java.text.NumberFormat

import db.scalikejdbc.{Round, User}

case class ImageWithRating(
                            image: Image,
                            selection: Seq[Selection],
                            countFromDb: Int = 0,
                            rank: Option[Int] = None,
                            rank2: Option[Int] = None) extends Ordered[ImageWithRating] {

  val ownJuryRating = new OwnRating(selection.headOption.getOrElse(Selection(image.pageId, 0, 0, 0)))

  def unSelect(): Unit =
    ownJuryRating.unSelect()

  def select(): Unit =
    ownJuryRating.select()

  def isSelected: Boolean = ownJuryRating.isSelected

  def isRejected: Boolean = ownJuryRating.isRejected

  def isUnrated: Boolean = ownJuryRating.isUnrated

  def rate = ownJuryRating.rate

  def rate_=(rate: Int) {
    ownJuryRating.rate = rate
  }

  def rankStr = (
    for (r1 <- rank; r2 <- rank2)
      yield if (r1 != r2) s"$r1-$r2." else r1 + "."
    ).orElse(
    for (r1 <- rank)
      yield r1 + "."
  ).getOrElse("")

  def totalRate(round: Round): Double =
    if (selection.size == 1 && selection.head.juryId != 0)
      selection.head.rate
    else if (ratedJurors(round) == 0)
      0.0
    else
      rateSum.toDouble / ratedJurors(round)

  def rateSum = selection.foldLeft(0)((acc, s) => acc + Math.max(s.rate, 0))

  def jurors = selection.map(s => s.juryId).toSet

  def jurorRate(juror: User): Option[Int] = selection.find(_.juryId == juror.getId).map(_.rate)

  def jurorRateStr(juror: User): String = jurorRate(juror).fold("")(_.toString)

  def ratedJurors(round: Round): Long =
    if (round.isBinary) {
      round.numberOfAssignedJurors
    } else if (selection.headOption.exists(_.juryId == 0) && !round.optionalRate)
      countFromDb
    else if (selection.size == 1 && selection.headOption.exists(_.juryId != 0))
      1
    else if (round.optionalRate)
      round.numberOfJurorsForAverageRate
    else selection.count(_.rate > 0)

  def rateString(round: Round): String = {
    if (round.isBinary) {
      selection.count(_.rate > 0).toString
    } else {
      if (ratedJurors(round) == 0) "0"
      else s"${Formatter.fmt.format(totalRate(round))} ($rateSum / ${ratedJurors(round)})"
    }
  }

  def pageId = image.pageId

  def title = image.title

  def compare(that: ImageWithRating) = (this.pageId - that.pageId).signum
}

object ImageWithRating {

  def rankImages(orderedImages: Seq[ImageWithRating], round: Round): Seq[String] = {
    rank(orderedImages.map(_.rateSum))
  }

  def rank(orderedRates: Seq[Int]): Seq[String] = {

    val sizeByRate = orderedRates.groupBy(identity).mapValues(_.size)
    val startByRate = sizeByRate.keys.map { rate => rate -> (orderedRates.indexOf(rate) + 1) }.toMap

    orderedRates.map {
      rate =>
        val (start, size) = (startByRate(rate), sizeByRate(rate))
        if (size == 1)
          start.toString
        else
          start + "-" + (start + size - 1)
    }
  }
}

class Rating

class OneJuryRating(val selection: Selection) extends Rating {
  def isSelected: Boolean = selection.rate > 0

  def isRejected: Boolean = selection.rate < 0

  def isUnrated: Boolean = selection.rate == 0

  def rate = selection.rate
}

class OwnRating(selection: Selection) extends OneJuryRating(selection) {

  def unSelect() {
    selection.rate = -1
  }

  def select() {
    selection.rate = 1
  }

  def rate_=(rate: Int) {
    selection.rate = rate
  }

}

class TotalRating extends Rating

object Formatter {

  def getFormatter = {
    val df = NumberFormat.getNumberInstance(Locale.US).asInstanceOf[DecimalFormat]
    df.applyPattern("0.00")
    df
  }
  val fmt = getFormatter
}

redpajama_set_name: RedPajamaGithub


The differences between male and female work remain notable across regions and sectors. Women make up a smaller share of employment in all regions - amounting to a third of male employment in South Asia and the Middle East and North Africa. In general, women also work disproportionally in agriculture and in the informal sectors, both of which have lower levels of productivity than other sectors. Allowing these women to work more, and work in more productive areas, will reap huge economic dividends, and allow us to meet the challenge of the SDGs.
So how do we create jobs through growth? The key is in productivity - an elusive term. Sustained economic growth happens through higher levels of productivity, both within sectors and by shifting to sectors that generate higher value. Regions or countries that have managed to transform the structure of their economy towards industry and services have a higher gross national income per capita.
Low-productivity agriculture still dominates in Sub-Saharan Africa and South Asia . Raising agricultural productivity should facilitate greater structural transformation and a reallocation of resources and workers to other activities. Much of this agenda will need to focus on the service sector, in which more and more people are working. Many services are labor-intensive, but in many countries, their productivity remains low. Finding ways to make these jobs more productive is key to meeting the targets of SDG 8.
Many people around the world are not working to their full capacity. It might be that they are only working for a few hours a day, when they would prefer to be working full-time. Or that their skill levels are not equal to the job they have, such as graduates working as cleaners. But measuring the precise number is difficult, given that it is a question of preference. Unlike unemployment, under-employment is not binary; it is more nuanced. Even if it makes it hard to measure, it does not detract from the problem.
The need for full employment was recognized in Millennium Development Goals (MDGs). Much has been achieved. Unemployment in 2014 was, on average, 6 percent in low-income countries, 5 percent in lower middle-income countries, 6 percent in upper middle-income countries and 7 percent in high-income countries. But these numbers are skewed by the large number of people who are not actually in the work force. Globally the labor force participation rate stands at 69 percent, and is highest in low-income countries (79 percent). In these countries, people are generally too poor not to work and may be on subsistence or in informal low-productivity activities. For them, the real problem is not the lack of a job - it is the lack of productive work within that job.
The SDGs take on this jobs challenge. There is now recognition that poor-quality employment is as serious a problem as unemployment. This shift can be seen in how the jobs challenge has been articulated in the MDGs and now the SDGs.
At the heart of the twelve targets of SDG 8 is employment generation through expanded economic opportunities and growth. The private sector is called on to propel sustainable development and serve as a key engine of investment and growth. This will support the structural transformation that developing countries need by delivering better, more productive jobs.
Measuring progress towards the varied and complex targets associated with SDG 8 across all countries will be a challenge. While improvements continue to be made in establishing international standards on labor statistics, there are big differences in the systems and capacities for data collection and analysis. The size of the informal sector in many countries makes monitoring progress on this even more difficult.
There is a lot we do not know about the dynamic relationships between policies and employment, investment and jobs, and growth and jobs. In particular, we need an expanded data agenda that captures more of the demand side of labor markets, which will aid countries in developing strategic responses and policies to meet the targets under SDG 8. By doing this, we can help people find better jobs, make more money, provide greater opportunities for their families and escape the burden of under-employment.
Siv, many thanks for a really interesting post on an important topic. So much of the discussion is usually about sheer volume, but you have brought out the quality, gender and conceptual issues of the jobs debate much more clearly - very enlightening and I hope it is widely read.


text:
Maria David
Club: Serpentine Running Club
Runner number: 1687
Road Races X/C Multi-sport T & F
Date Series Time Graded Pace
07/04/2005 Assembly League Beckenham 2.8M 20:02 66.2% 7:05
29/05/2009 Last Friday 5k 22:13 69.0% 7:09
06/04/2002 Serpentine Handicap (97-04) 28:39 67.1% 7:01
01/06/2017 Assembly League Battersea 5k 29:12 55.4% 9:24
15/05/2005 Beverley 10k 44:25 68.1% 7:09
03/04/2005 Croydon 10k 44:44 67.6% 7:12
10/10/2004 Dulwich 10k 45:41 65.7% 7:21
29/05/2017 London 10,000 48:42 68.7% 7:50
02/01/2005 Tadworth 10M 1:27:15 56.6% 8:43
11/02/2018 Bramley 10M 1:31:55 60.3% 9:11
29/06/2003 Humber Bridge Half Marathon 1:45:15 62.5% 8:02
16/03/2003 Hastings Half Marathon 1:48:05 60.9% 8:15
06/02/2005 Watford Half Marathon 1:53:14 58.1% 8:38
05/03/2017 MK Festival of Running Half Marathon 1:56:41 62.1% 8:54
01/02/2004 Watford Half Marathon 2:16:50 48.1% 10:26
13/03/2005 East Hull 20M 2:50:15 60.8% 8:31
04/03/2018 Thames Riverside 20M 3:38:46 52.1% 10:56
14/04/2002 London Marathon 4:07:35 56.1% 9:26
14/04/2019 Paris Marathon 5:15:47 48.8% 12:02
03/11/2019 New York Marathon 5:23:18 47.6% 12:20
Cross Country, Trail and Fell Races Road Multi-sport T & F
Date Series Time Pace
20/11/2010 London Championship 6k (women) 27:22 7:20
03/01/2004 Middlesex XC Championships (women) 34:06 6:52
26/02/2005 North of the Thames XC Champs 6k (women) 36:20 9:45
19/02/2005 English National XC Championship (women) 39:29 7:56
29/01/2005 Southern Counties XC Championship (women) 43:12 8:41
11/01/2020 Metropolitan League 8k (women) 50:05 10:04
09/05/2015 Bollington Festival 3 Peaks Fell Race 1:16:14 13:38
27/10/2018 Beachy Head Marathon 6:47:27 15:32
Multi-sport and Other Races Road X/C T & F
Date Series Time
31/10/2004 Jekyll & Hyde Park Hallowe'en Duathlon 1:33:00
14/05/2017 Crystal Palace Triathlon 1:49:30
18/05/2003 Swindon Duathlon 2:31:15
25/04/2004 Ashbourne Duathlon 2:51:48
06/04/2003 Powerman 3:55:51
13/11/2004 Ballbuster Duathlon 4:05:38
Track and Field Meetings Road X/C Multi-sport
No meetings found.


text:
04.27.2016 Travel and Tour World
Cornell Finds Revinate Surveys Delivers Impressive TripAdvisor Results
Revinate, the software as a service company that helps hotels reinvent the guest experience, today announces the initial results of a study conducted by Chris Anderson, an associate professor at the Cornell School of Hotel Administration. The study compared the average performance of 80 hotels across five global hotel brands, with properties in the U.S., Europe, and Asia Pacific, for 12 months previous to and following the launch of Revinate Surveys.
The study found that as a result of using the Revinate Surveys solution with integrated TripAdvisor review collection, hotels realized impressive gains in TripAdvisor market ranking, leading to increased occupancy and potential revenue growth.
58% of properties increased their popularity ranking on TripAdvisor, while another 14% maintained their ranking
Additionally, 54% of the properties who did not increase their ranking still increased their rating scores relative to their competitive set
Overall, properties experienced a 3x average increase in review volume
Hotels that were behind their competitors in the number of reviews before launch had more than double the average number of reviews of their competitive sets after launch
Average rating scores increased by 2%
Properties saw a 3% average increase in positive reviews
"User reviews have become a critical aspect of the travel research process, as evidenced by TripAdvisor having over 350 million unique monthly visitors," says Anderson. "In this report we show via a simple series of before-and-after tests with Revinate that the encouragement of reviews via post-stay surveys not only increases the number of reviews posted to TripAdvisor, but also the hotel's actual review scores ."
For hotels in the study, occupancy also increased by 1%. "This goes to show that integrated publishing with Revinate Surveys drives significant TripAdvisor benefits and even increased occupancy. While hotels may not immediately see performance impact, they have a huge opportunity to evaluate whether they can increase ADR or capture additional on-property spend, resulting in improved RevPAR and profitability," says Marc Heyneker, CEO of Revinate.
"In combination with striving to deliver an exceptional guest experience, Revinate Surveys has been game changing for TFE Hotels," says Tammy Marshall, Chief Operating Officer. We're so pleased with how much we've improved on TripAdvisor, not to mention the incremental revenue we've gained as a result of increased visibility on the world's largest travel site."
As a TripAdvisor Platinum Partner, Revinate has also been recognized by TripAdvisor for best-in-class performance with respect to data integration, mobile-friendliness, and review conversion. Through this integration partnership, hotels access the "best of both worlds" by capturing private feedback to improve operations and guest satisfaction, while also driving a large volume of new reviews to stimulate demand on TripAdvisor. Revinate Surveys enables hotels to easily manage all of their guest feedback in one dashboard.
© 2020 Tenaya Capital Inc. All rights reserved.a FINE site


SOURCE: Where can I find the Opera Browser app to download back on my Hisense Smart TV besides google? Or how can I reinstall Opera browser app back on my Hisense Smart TV?
Is Hisense Smart TV android ? Is it possible to download Google Play on Hisense Smart TV?
As long as it is an Android based operating system installed the TV from the factory. It must be a "Smart" TV to have it.


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With companies often spending up to 50 % of income on externally supplied materials and services, procurement requires a strategic approach.


text:
Fighting The Bite
Through a community-led effort in Odisha Tata Trusts is minimising the deathly damage malaria can inflict and helping eliminate the scourge in the longer run
April 2020 | 1500 words | 5-minute read
Lachendra Sakadaka has come to learn much about malaria — and much more about the death and devastation it can cause.
A 29-year-old tribal from Turukupa village in Odisha's Rayagada district, Mr Sakadaka has lost two of his five children to the disease. Time was when he thought that was the destiny of tribal communities like his, cut off from the modern world and its amenities.
Traditional healers were the first, and frequently the only, recourse for Mr Sakadaka and his fellow villagers when malaria struck, and they were no match for a killer disease that took no prisoners.
Mr Sakadaka may have suffered the same end when he went down with malaria a few months back. But he did not and helping him beat back the infection was a comprehensive malaria-control programme run by Tata Trusts in South Odisha.
A malaria awareness camp being conducted in Bandapipli village in the Kandhamal district of south Odisha
The endeavour in Odisha is the most substantial of the four malaria-control initiatives undertaken by the Trusts — the other three are in Bilaspur in Chhattisgarh, Gadchiroli in Maharashtra and Gudalur in Tamil Nadu — for reasons that range from leadership and presence in the community to strong collaborations with the state government and civil society.
Five-year plan
The current phase of the Trusts' intervention, called 'taking Odisha towards elimination of malaria', aims to rid South Odisha of deaths from the disease in five years and its incidence by 40%. There's more to the effort than that.
The Trusts have been supporting livelihood programmes in South Odisha since 2006 but these were not enough in a region where maternal and infant mortality and malnutrition existed alongside other development challenges.
"Without a comprehensive development approach that includes health and education, social and economic progress is unattainable in the Odisha hinterland," says Jayeeta Chowdhury, the former lead for the malaria control and elimination programme. "We make this possible through smart partnerships."
The health programme in South Odisha was initiated in 2009. The emphasis was on malaria because it was not just a biomedical problem but a socioeconomic issue, claiming the maximum lives in a place where health systems were either absent or in a derelict state.
In 2010, the South Odisha Malaria Control Programme — involving the Trusts and a network of partners — was rolled out. It started with a survey that laid bare the stark reality of the region: malaria and malnutrition were rampant.
The first phase of the programme, in 2014 and 2015, set the tone for its success, then and later. A community-based approach was adopted, with the focus on screening and treating malaria-positive patients living in high-transmission areas through camps. Important in this context was understanding the link between malaria and malnutrition, the treatment of affected children and, not least, selecting and working with the right partners.
This effort led to a decline of 60-80% in falciparum malaria in the target villages. It wasn't enough. The Trusts needed a fresh perspective and what followed was a collaborative strategy with the state government as partner.
In 2015, the Trusts changed tack and took a plunge into direct implementing. A year later it cemented a partnership with the Government of Odisha. This enabled a scaling up of the programme and sowed the seeds for its sustainability. Rather than replicating the government's role, it allowed the Trusts to add value to the programme.
Tula Majhi from Kamarpadar village in Kalahandi district survived a malaria attack when she was pregnant
South Odisha in the crosshairs
South Odisha being a malaria-endemic zone was the reason the Trusts made it the setting for their direct intervention programme. The magnitude of the problem and, most significantly, the cost extracted by the disease in terms of physical suffering and economic loss, were the prime motivators for the Trusts to remain invested in the initiative.
Odisha accounts for 40% of malaria cases in India, a country that is burdened with about 4% of global malaria cases (according to a report by the World Health Organization). India has set 2027 as the deadline to eliminate malaria and it is on the road to making that happen.
Consistent and continual efforts have made India a beacon of hope in the global battle against the disease. According to the World Malaria Report 2018, India was the only one among 11 countries to register a decline in incidence — 24% between 2016 and 2017 — among those most affected by the disease. And the country's success is largely due to a substantial decrease (about 80%) in malaria cases in Odisha.
The decrease has been striking in the remote and heavily-forested hilly regions where Mr Sakadaka and others from tribal communities live. Mostly landless or marginal farmers, these communities subsist by banking on forest produce. A malarial attack is an emergency for them in more ways than one.
"When I contracted malaria a couple of years ago, it cost me Rs. 1,500 to get treated at a private clinic," says Basanti Lundruka, a 20-year-old from Turukupa. "That was a huge amount of money for me."
Beyond the obvious health and monetary benefits, there was the realisation that every other social development programme in the region would be undermined if malaria was not tackled on a priority basis.
The Trusts joined hands with the Government of Odisha in March 2016 and the agenda was cemented: malaria control and prevention, along with research and technological innovations that would improve the quality of lives of people in the southern districts of Odisha.
The state government committed itself to bearing much of the cost of the initiative: for rapid diagnostics kits, anti-malarial drugs, insecticides and volunteer wages. The Trusts were tasked with rolling out the programme in remote tribal villages where services were slender.
Five district subdivisions in South Odisha — Muniguda, Bissamcuttack, Thuamul Rampur, Lanjigarh and Kotagarh — were chosen as the focus areas. The rough and tough terrain made it difficult for the project to get running in quick time, and there were other challenges as well.
Difficulty to access
"The villages where we work are dominated by tribal communities and they are not easy to access," says Subhasis Panda, senior general manager, Livolink Foundation, the Trusts' partner in the project. Add to that a lack of knowledge about health.
Reaching the scattered tribal hamlets can take up to three hours on foot and that's the only way to go. Inundated rivers and streams during the monsoon months, the dangers posed by wild animals and reptiles and finding trained people were problems confronted and overcome.
A pillar of the programme has been village health volunteers (VHVs), youth in the 20-35 age bracket chosen from within the community. The VHVs have been trained and certified under the National Vector Borne Disease Control Programme (NVBDCP), an umbrella initiative launched by the central government.
VHPs are the frontline workers, the boots on the ground, in the fight against malaria in Odisha. They are deployed in villages where there are no regular health workers. They are the first respondents in any fever episode in the village. They ensure early tests, prompt treatment and follow up for treatment compliance.
For Kumuwadi Dei, a septuagenarian from Turibhejiguda in Kalahandi district, the VHV in her village, Srimati Majhi, was a life saviour. "She gave me the medicine, ensured I took it on time and even prepared food for me," says Ms Dei.
The VHVs have, additionally, improved the general health of the populace and the Odisha government has approved a proposal to allow the volunteers to provide medicines for other ailments at what are known as 'fever treatment depots'. "It shows the competence of the VHVs and the trust that they have generated," says Mr Panda.
Multi-pronged strategy
The Trusts have adopted a multipronged strategy to make the programme potent. Twice-a-year screenings of the residents of malaria-endemic villages has helped reduce parasite density. These mass screenings have helped pinpoint asymptomatic patients as well as identify cases of malnutrition, which heightens the malaria risk.
The involvement of the community at various stages of the intervention has been a vital factor in its success. Through village meetings and communication material, the tribal communities have been educated about the disease and armed with the knowhow to deal with it.
Technology has been a cornerstone of the programme. Rapid-diagnosis kits (similar to pregnancy-testing kits) were supplied by government and the Trusts deployed a smartphone-integrated microscope for diagnosis and detection. The results emerging from these efforts have been heartening.
The concerted endeavour in South Odisha has brought desperately needed respite to the tribal communities of the spread (since March 2016, when the programme began, there have been no recorded deaths due to malaria). Progress had been significant but there's much ground left to be covered.
More investments in surveillance systems and continuous reporting, placing trained human resources in remote geographies, innovating with vector-control methods, and diagnostics and drugs for better treatment and care — all of these are on the agenda to improve the outcomes emerging from the programme. And the Trusts are ready as well.
—Samod Sarngan
Community Tata Trusts Health
Covid-19 Relief In Pictures, Part One
Tata companies organised immediate measures to support Covid-19 relief across India and the UK.
Transforming Education
Tata Trusts has emerged a key stakeholder in India's education ecosystem.
Digital Goes Rustic
The internet saathi programme has benefitted over 25 million people in India's villages.


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This girl has been wanting to try out a totally crazy new hairstyle. Step inside this salon and see what you can do in this makeover game. we are confident you are loss to get the first-rate age ever and that is significant representing us, later each that is reason we are hither, to achieve the first-rate we buoy in method to create confident you get a overnice age and with he equitable characters each the pastimes buoy be the first-rate ones. So approach and try away this super pretty game in which your mission is actually crystallize, on the other hand not extremely simple, you get the opportunity to create a new acquaintance , a beauteous blonde who desires you to be her hairdresser. Trim her hair, give it some highlights and make it look totally fabulous. This is a pretty realistic game and we are more than sure that you are going to do a perfect job, just be creative and use the instructions the game gives you. Good luck!


text:
Rohit Sharma Didn't Travel To Australia After IPL Because His Father Was Suffering From COVID-19
by CA Editor
Rohit Sharma (Credits: Twitter)
Questions are being raised on Rohit Sharma's commitment as he is looking likely to miss at least two Tests in the upcoming series against Australia. The opening batsman is currently at the National Cricket Academy (NCA) for his rehab after suffering a hamstring injury in IPL 2020.
That injury had initially kept him out of all the three squads for the tour of Australia. But after he returned to action for Mumbai Indians, he was included in the Test squad. However, his participation in the Test series is in doubt as he is reportedly not close to being fully fit and the mandatory quarantine rules in Australia.
Rohit Sharma. Credit: BCCI
A 14-day quarantine is currently mandated in Australia due to Covid-19. The first Test of the four-match series starts on December 17, but India are also slated to play a three-day warm-up match on December 11 before that. For Rohit Sharma to be out of quarantine by December 10, he would have to be in Australia by November 26.
However, he is very much unlikely to get the permission to leave for Australia soon. And the opener is now facing the heat for not traveling directly to Australia from UAE. Team India had left for Australia right after the IPL came to an end earlier this month.
Also Read: Who Asked Rohit Sharma To Head To The NCA, Asks BCCI As Opener Faces Heat For Not Traveling To Australia From UAE
Why did Rohit Sharma return to India after IPL 2020?
While Team India left for Australia from the UAE, Rohit Sharma returned to India with the Mumbai Indians camp. He is facing the heat for the same, as many are suggesting that traveling with the team would have made him available for selection for the Test series right from the start.
Rohit Sharma. (Credit: BCCI Twitter)
Fingers are being pointed at Rohit for his commitment with many even suggesting that the star batsman was not willing to play Test cricket. The reason, however, is completely different. According to journalist Boria Majumdar, Rohit Sharma had returned to India because his father was suffering from COVID-19.
"The reason Rohit didn't travel with the team [to Australia], came back to Mumbai with Mumbai Indians is because his father had COVID. That is the reality.
"After that, if he didn't want to play red-ball series, there was no reason for him to travel to the NCA. He could have easily stayed back with Ritika and family. So there is absolutely no reason to say that Rohit didn't want to play the red-ball series," Boria was quoted as saying by Sports Today.
Also Read: Sourav Ganguly Picks Wriddhiman Saha And Rishabh Pant As The Two Best Wicketkeepers In India Currently
Also Read: New Zealand vs West Indies: Colin de Grandhomme And Ajaz Patel Injured Ahead Of The Test Series
CA Editor
T Natarajan Brought Us Back In The Game: Washington Sundar
IPL 2021: Franchises Told To Complete Retention Process By January 20
Pakistan Announce Squad For South Africa Tests, Include 9 Uncapped Players


text:
Is a Reuben Irish food?
July 24, 2021 July 24, 2021 Charlotte SmithMiscellaneous
Is a Reuben Irish food? The Reuben sandwich — that famous combination of corned beef, sauerkraut, Swiss cheese, rye bread and Thousand Island dressing — wasn't invented in Ireland, nor by an Irish cook, according
What is Reuben made of?
What is the difference between a Reuben and a Rachel?
Why is it called Reuben sandwich?
What is a traditional Irish dish?
Is Russian dressing the same as Thousand Island?
Is a Reuben made with pastrami or corned beef?
What's on a California Reuben?
Are Reubens Irish or German?
What should you not wear in Ireland?
What is the most famous Irish dish?
Is Russian dressing the same as Catalina?
The Reuben sandwich — that famous combination of corned beef, sauerkraut, Swiss cheese, rye bread and Thousand Island dressing — wasn't invented in Ireland, nor by an Irish cook, according to the most prevalent accounts (one credits a hotel in Omaha, Nebraska; another a deli in New York City).
The reuben is truly a classic…and for good reason. The usual components are: corned beef, Swiss, sauerkraut, and Russian dressing—always on rye bread—and it's typically pressed or grilled.
They are both grilled sandwiches made with rye bread but the other ingredients are switched up: A Reuben has corned beef, swiss cheese, sauerkraut, and Russian dressing. A Rachel sandwich has sliced turkey, swiss cheese, coleslaw, and Russian or Thousand Island dressing.
The sandwich was invented by William Hamerly, a New York accountant and bachelor cook. He named it for Arnold Reuben, founder of Reuben's New York Restaurant. Hamberly named the sandwich for Reuben, not because he founded the famous Reuben Restaurant, but because he admired his charitable works.
Representative dishes include Irish stew, bacon and cabbage, boxty, soda bread (predominantly in Ulster), coddle, and colcannon. Modern Irish food still uses traditional ingredients, but they are now being cooked by chefs with world influences and are presented in a more modern and artistic style.
Difference Between Russian Dressing and Thousand Island Dressing. The main difference between the two is the use of pickles in Thousand Island dressing, giving it more sweetness and texture. Russian Dressing is known for a more piquant or spicy flavor from chili sauce and prepared horseradish.
If you want to get technical, a Reuben sandwich made with pastrami is called a "Rachel". A classic Reuben is always made with corned beef! Both meats come from the same cut of beef (brisket) and are brined for up to a week, but afterwards corned beef is boiled while pastrami is seasoned with a dry spice mix and smoked.
carrots 2, finely shredded.
white cabbage ¼ small, finely shredded.
red onion 1/2 small, finely sliced.
white wine vinegar.
mayonnaise.
large white bloomer 4 slices.
sliced smoked turkey 150g.
emmenthal cheese 4 slices.
Reuben egg rolls Typically served with Thousand Island dressing (instead of Russian dressing) as an appetizer or snack, they originated at Mader's, a German restaurant in Milwaukee, Wisconsin, where chef Dennis Wegner created them for a summer festival circa 1990.
The most important thing when deciding what to wear in Ireland is to always look presentable and avoid anything too revealing, like crop tops or short shorts. You will be cold and look inappropriate. In general, day-to-day Ireland style is casual but always polished.
Don't leave Ireland without trying…
Soda bread. Every family in Ireland has its own recipe for soda bread, hand-written on flour-crusted note paper and wedged in among the cookery books.
Irish stew.
Colcannon and champ.
Boxty.
Boiled bacon and cabbage.
Smoked salmon.
Black and white pudding.
The only major difference here is that sweet Catalina sauce is usually thinner than Russian dressing. This is because it contains oil instead of mayonnaise. That whiteness just isn't the same. As explained, sweet Catalina sauce comes with a tangy and creamy oil-and-vinegar base.
How do you say good with computers on a resume?
Where can I post my resume?


Lemon verbena is enhanced by being steeped with dried lime blossoms.
1. Pour water over lemon verbena, lime leaves, lime blossoms, and honey. Let cool, and then steep overnight in the refrigerator. Strain, Serve over ice with garnish if desired.


text:
The High Five: How Many Oscars Will 'Dallas Buyers Club' Earn At Sunday's Academy Awards?
by Eric Aasen 28 Feb 2014
Five stories that have North Texas talking: A big weekend for a Dallas-themed movie; Greg Abbott's personal connection to the Texas gay marriage case; Allen ISD is closing its palatial stadium due to cracks; and more.
Matthew McConaughey earned an Oscar nomination for actor in a leading role for his portrayal of Ron Woodroof in "Dallas Buyers Club."
(Credit: Anne Marie Fox / Focus Features)
How will "Dallas Buyers Club" do at the Oscars on Sunday? The film has earned six Oscar nods, including a nomination for best film. Matthew McConaughey is up for a nomination for actor in a leading role, while Jared Leto is in the running for actor in a supporting role. The film also earned nominations for film editing, original screenplay and makeup and hairstyling. The film portrays a Dallas electrician and hustler, Ron Woodroof, an HIV positive man in the 1980s who helps AIDS patients get the medication they need. "Dallas Buyers Club" was a big winner at the Golden Globes. McConaughey plays Woodroof, who decides to fight the death sentence the disease promised to those who contracted it in the 1980s. McConaughey dropped about 40 pounds for the role. The public radio show "The Takeaway" explored the movie this week in its "Real People/Best Pictures" series – the show interviewed William Waybourn was the president of the Dallas Gay Alliance in the 1980s. Woodroof was a volunteer there, and also received services through the center's clinic. Waybourn discussed Woodroof and the movie. On Thursday, KERA's "Think" discussed all things Oscar. NPR recently reported on the process of making the movie – it was a long, complicated process. Last fall, NPR reported on Woodroof's days in Dallas.
What does Texas Attorney General Greg Abbott have in common with Mark Phariss, one of the plaintiffs who sued the state to challenge its gay marriage ban? They were law school classmates. They knew each other. Phariss tells KERA they were good friends. And now the attorney general and Phariss, an attorney who lives in Plano with his long-time partner, are on opposite sides of Texas' brewing battle over gay marriage. Phariss told KERA that during law school at Vanderbilt, he discussed politics over dinner with Abbott and his wife. Phariss is a Democrat, Abbott a Republican. Abbott said in a statement: "Cecilia and I were, indeed, friends with Mark Phariss. We remember Mark from our law school days and his early days as a lawyer in San Antonio. … There are good, well-meaning people on both sides of this issue. This shows that Americans can in fact debate substantial issues without being disagreeable."
One in three North Texans can't weather a financial storm that lasts 90 days. The problem's known as asset poverty, and it doesn't discriminate. A job loss, health emergency, even legal trouble is enough to plunge a third of our friends and neighbors into financial distress. KERA's series One Crisis Away is following four North Texas families on the financial edge. On Thursday night, KERA hosted a free public forum that explored asset poverty. We livestreamed the event — we've posted the video here. The discussion will air as a special on KERA-13 in March.
The Allen school district is closing its $60 million football stadium after extensive cracks were found in the building's concrete. School officials said Thursday that Eagle Stadium in Collin County would stay closed until at least June and possibly into the upcoming school year. Eagle Stadium opened two years ago amid heavy fanfare and national attention for its price tag and splashy facilities. It seats 18,000 and features a 38-foot wide high-definition video screen. Cracks were found in the concrete of the stadium concourse.
Leta Andrews, the 76-year-old coach of the Lady Pirate basketball team at Granbury High School, is retiring at the end of the school year. Her career spans 51 years. Andrews scored 1,416 victories. She took 14 teams to the state tournament. Andrews became the winningest high school basketball coach ever on Dec. 7, 2010, Granbury ISD says. Learn more about Andrews. WFAA recently profiled her. "As I look back at my career, I've been very blessed that I had the opportunity to teach girls here in Granbury to love basketball the way I loved basketball in Granbury," Andrews told WFAA.
VOLUNTEER: Heard Natural Science Museum & Wildlife Sanctuary
@ Heard Natural Science Museum & Wildlife Sanctuary
Volunteers are a vital part of the Heard's success. The Heard is currently s...
Museums & Attractions: Nature & Science
Texas Video Showdown
@ Musea
This is a unique, online, ongoing, musical challenge, video event. What is ...
Music: Indie
THROUGH 1SEP 2017
Hidden Treasures: Celebrating 75 Years
@ Fort Worth Museum of Science and History
It's an exhibit unlike any other filled with iconic artifacts from a teach...
Museums & Attractions: History


text:
Prima Divizie Daneză sau Viasat Divisionen este cea de-a doua competiție daneză în sistemul fotbalistic național.

Echipele sezonului 2017–2018

Campioane

Note
 ^ a b c d e f (Danish) "Historien om Danmarksmesterskabet i fodbold". Danish Football Association. http://www.dbu.dk/page.aspx?id=690 . Retrieved 2007-02-22.
 ^ "Danmarksturneringen 1945 – 46". Peders fodboldstatistik. http://home6.inet.tele.dk/pin/Danmarksturneringen/1945-46.htm . Retrieved 2007-02-22.
 ^ (Danish) "Køges første DM-titel blev til på "Ungarsk Rapsodi"". Køge Boldklub. http://www.koge-boldklub.dk/dmtriumfer/dm54.html . Retrieved 2007-02-22.
 ^ "Danmarksturneringen 1956 – 57". Peders fodboldstatistik. http://home6.inet.tele.dk/pin/Danmarksturneringen/1956-57.htm. Retrieved 2007-02-22.
 ^ "Danmarksturneringen 1958". Peders fodboldstatistik. http://home6.inet.tele.dk/pin/Danmarksturneringen/1958.htm. Retrieved 2007-02-22.
 ^ "Danmarksturneringen 1975". Peders fodboldstatistik. http://home6.inet.tele.dk/pin/Danmarksturneringen/1975.htm. Retrieved 2007-02-22.
 ^ "Danmarksturneringen 1986". Peders fodboldstatistik. http://home6.inet.tele.dk/pin/Danmarksturneringen/1986.htm. Retrieved 2007-02-22.

Ligi naționale de fotbal din Europa


text:
<?php

namespace NAD\AddressingBundle\Entity;

use Symfony\Component\Validator\Constraints as Assert;
use Gedmo\Mapping\Annotation as Gedmo;
use Doctrine\ORM\Mapping as ORM;
use JMS\Serializer\Annotation as JMS;

/**
 * City
 *
 * @author Mohammadreza Razzaghi <razzaghi229@gmail.com>
 *
 * @ORM\HasLifecycleCallbacks()
 * @ORM\Entity(repositoryClass="NAD\AddressingBundle\Entity\CityRepository")
 * @ORM\Table(name="nad_addressing_city")
 */
class City
{
    /**
     * @var integer
     * @ORM\Id
     * @ORM\Column(type="integer")
     * @ORM\GeneratedValue(strategy="AUTO")
     * @JMS\Type("integer")
     */
    private $id;

    /**
     * @var string
     * @ORM\Column(type="string", length=255)
     * @Assert\Type(type="string")
     * @JMS\Type("string")
     */
    private $name;

    /**
     * @var Region
     * @ORM\ManyToOne(targetEntity="NAD\AddressingBundle\Entity\Region")
     * @ORM\JoinColumn(name="region_id", referencedColumnName="id")
     * @JMS\Type("NAD\AddressingBundle\Entity\Region")
     */
    private $region;

    /**
     * @var Country
     * @ORM\ManyToOne(targetEntity="NAD\AddressingBundle\Entity\Country")
     * @ORM\JoinColumn(name="country_id", referencedColumnName="id")
     * @JMS\Type("NAD\AddressingBundle\Entity\Country")
     */
    private $country;

    /**
     * @var \DateTime
     * @ORM\Column(type="datetime")
     * @Gedmo\Timestampable(on="create")
     * @JMS\Type("DateTime")
     */
    private $createdAt;

    /**
     * @var \DateTime
     * @ORM\Column(type="datetime")
     * @Gedmo\Timestampable(on="update")
     * @JMS\Type("DateTime")
     */
    private $updatedAt;

    public function __toString()
    {
        return $this->getName();
    }

    /**
     * Get id
     *
     * @return integer
     */
    public function getId()
    {
        return $this->id;
    }

    /**
     * Set name
     *
     * @param  string $name
     * @return City
     */
    public function setName($name)
    {
        $this->name = $name;

        return $this;
    }

    /**
     * Get name
     *
     * @return string
     */
    public function getName()
    {
        return $this->name;
    }

    /**
     * Get createdAt
     *
     * @return \DateTime
     */
    public function getCreatedAt()
    {
        return $this->createdAt;
    }

    /**
     * Get updatedAt
     *
     * @return \DateTime
     */
    public function getUpdatedAt()
    {
        return $this->updatedAt;
    }

    /**
     * Set country
     *
     * @param  Country $country
     * @return City
     */
    public function setCountry(Country $country = null)
    {
        $this->country = $country;

        return $this;
    }

    /**
     * Get country
     *
     * @return Country
     */
    public function getCountry()
    {
        return $this->country;
    }

    /**
     * Set region
     *
     * @param  Region $region
     * @return City
     */
    public function setRegion(Region $region = null)
    {
        $this->region = $region;

        return $this;
    }

    /**
     * Get region
     *
     * @return Region
     */
    public function getRegion()
    {
        return $this->region;
    }
}


text:
SunEdison prepares to sell TerraForm Global
The bankrupt renewable energy developer appears to be fishing for bidders for TerraForm Global, as many investors seem to prefer buying pieces instead of the whole SunEdison empire.
July 20, 2016 Christian Roselund
The Amanecer solar project was the largest in Latin America when built at 101 MW.
SunEdison has requested that TerraForm Global take steps to facilitate its own sale, as revealed in a presentation by the "yieldco" to investors. TerraForm Global is the company which holds many of SunEdison's wind and solar projects outside the United States.
In this presentation, TerraForm Global notes that it is in "active discussions" with SunEdison for a "jointly-supported sales process", but the company has made no decision at present to support any bidder, structure or transaction.
The news that SunEdison is looking for a buyer for TerraForm Global comes less than a month after CEO Ahmad Chatila was replaced by John S. Dubel, who was hired from outside SunEdison to restructure the company.
It also comes as Bloomberg reports that SunEdison has not found a new buyer for the entire business, three months after filing for chapter 11 bankruptcy protection. Citing two anonymous sources, Bloomberg says that the company has received over 100 indications of interest for specific assets, including a controlling stake in its initial yieldco, TerraForm Power.
TerraForm Global holds a fleet of 917 MW of wind and solar assets, including 307 MW of wind in Brazil and 299 MW of wind and solar in India, with its other assets spread across the developing world in Asia, Africa and South America.
The company has not filed results since its last form 10-Q in November 2015, however a statement of preliminary Q1 results shows revenue of $47-52 million and a net loss of $0-8 million. However, despite $41 million in acquisition fees, $76 million in bond repurchase and debt service and $41 million in SunEdison reimbursement for senior notes, the company closed the quarter with $787 million in unrestricted cash.
However, the company also notes that it is "highly dependent" on SunEdison, and that the "SunEdison bankruptcy poses substantial risks to our business, operations and financial condition".
Pingback: Marcacci Communications
Pingback: TerraForm Power reveals preliminary 2015, Q1 2016 results – pv magazine USA
Pingback: BREAKING: NRG applies to acquire 2.1 GW of SunEdison solar and wind projects – pv magazine USA


Sweating is an essential body function to keep the body cool, but in some people, the sweat glands become overactive resulting in constantly annoying and embarrassing wet underarm circles, or sweaty hands. Hyperhidrosis is a medical condition in which a person sweats excessively and they sweat even when the body does not need cooling.
Areas affected by hyperhidrosis include the underarms (axillary), soles of the feet, palms of the hands, face and the scalp. Palmar hyperhidrosis or sweaty palms is the most common form of hyperhidrosis, causing excessive sweating of the hands. It is thought that hyperhidrosis is a result of over activity of the sympathetic nervous system.
Instead of camouflaging your underarm stains by wearing black or dark clothing or constantly applying more antiperspirant, which contains aluminum, there is a simple and affordable treatment – Botox or Dysport. Yes, the same injectables you love to relax your brow wrinkles and crow's feet, can be injected to calm down the sweat glands, resulting in four to five months of relief at a time. Patients have reported they've received relief for six months or more; however, we tell patients the estimated duration based on the information from the manufacturers.
Botox is FDA approved for use in the axillary area. Dr. Jochen also has injected it in the soles and palms with good success. For axillary, the typical dosage is about 50 units of Botox per underarm. Sometimes insurance companies will pay for this treatment, depending on your insurance, and in some cases if you can show that other treatments have failed, such as medications, which have unwanted side effects and topical prescription antiperspirants, which are not a great solution for other areas of the body.
If you are tired of dealing with excessive sweating caused by hyperhidrosis, give us a call to schedule a consultation to see if Botox or Dysport might be a good option to help improve your quality of life.