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Vancouver Rangers provide junior hockey entertainment for Clark County
Posted by Nick Daschel, Reporter
Date: Friday, October 7, 2016
in: Sports
VANCOUVER — The Portland Winterhawks have a loyal legion of fans from Clark County. The Vancouver Rangers are hoping to tap into the local loyalty for junior hockey and grow that fan base by giving them a home option.
The second-year Rangers kicked off their Western States Hockey League season earlier in October with a pair of home games at Mountain View Ice Arena on Mill Plain Blvd. in Vancouver. A Tier II junior hockey club with players from throughout North America and a few from other countries, Rangers' coach/co-owner Dave Daniello says his team plays about one level below the Winterhawks, with a clear mission.
"It's a pay-for-play league, if you will. Our job is to get them to college and develop them," Daniello said.
Joey Alexander, a forward from Wilmington, N.C., fights for the puck in a recent Vancouver Rangers game against Seattle. Photo courtesy of Vancouver Rangers.
Most of the Rangers, who range from ages 16 to 20, pay about $9,000, plus living expenses, to participate with the club during the seven-month season. They play a 52-game schedule, including 24 home games at Mountain View Ice Arena, and four games during a one-week showcase tournament in Las Vegas.
One distinct difference between the Rangers and Winterhawks is that players from the WSHL retain their NCAA college eligibility, since they are not being paid a stipend to play.
Daniello said five players from last year's inaugural Rangers squad went on to play at higher levels; two play in college, while three landed on professional clubs in Finland, Sweden and France.
"For a first year, I thought we did pretty well," Daniello said.
Daniello's stake in the Rangers is significant. He moved his family from Maine more than a year ago to start the Rangers junior hockey club. A long-time junior hockey coach who evolved into a player consultant, Daniello was presented an opportunity by what was then the club owners to operate the team.
Daniello is now one of four co-owners, along with Jerry Adamowicz of Vancouver, Jon Neill of Camas and a fourth partner from Texas. Daniello runs the day-to-day operations, as well as coaches the team.
Vancouver Rangers players celebrate following a goal against Seattle during the 2016-17 home opener. Photo courtesy of Vancouver Rangers.
The Western States Hockey League includes 28 teams, from Bellingham to El Paso, Tex, to as far east as Springfield, Mo. The Rangers play their games in the Northwest Division, against teams from Butte, Mont., Bellingham, Seattle, Medford, Ore., Lake Tahoe and McCall, Idaho. Daniello said the WSHL was interested in placing a team in Vancouver because it fit geographically in the middle of the Northwest Division.
Daniello says it costs between $200,000 and $300,000 to run a WSHL club each season. The team has a 55-passenger bus, travels approximately every other weekend for a pair of road games, and flies to the Vegas tournament in December, where last year more than 100 college coaches and pro scouts watched.
Some of the top players on Vancouver's roster are Paul Frys, a forward from Portland who had 70 points for the Rangers last season; Aaron Daniello, a goalkeeper and Dave's son; and Jacob Rushlow, a defenseman from Michigan.
Also currently on the Rangers' roster for an indefinite period is goalkeeper Ethan Middendorf, a Winterhawks' draft pick who was that team's third goalie. Daniello said the Winterhawks loaned Middendorf to the Rangers while on a long road trip in order to give him some playing time.
Tickets for Rangers home games are $10, and $5 for children 12 and under. Daniello says they have crowds ranging from 200 to 600 fans.
"The atmosphere is pretty electric. We have aluminum bleachers, and they stomp on the bleachers. It really gets loud in there," Daniello said.
Vancouver Rangers 2016-17 home schedule
Oct. 14 Whitefish Wolverines 7:00 p.m.
Oct. 16 Whitefish Wolverines 2 p.m.
Oct. 28 Southern Oregon Spartans 7 p.m.
Oct. 29 Southern Oregon Spartans 7:30 p.m.
Nov. 11 Tahoe Icemen 7 p.m.
Nov. 12 Tahoe Icemen 7:30 p.m.
Dec. 9 Bellingham Blazers 7 p.m.
Dec. 10 Bellingham Blazers 7:30 p.m.
Dec. 11 Bellingham Blazers 2 p.m.
Jan. 6 Butte Cobras 7 p.m.
Jan. 7 Butte Cobras 7:30 p.m.
Jan. 19 Seattle Totems 8 p.m.
Feb. 3 Tahoe Icemen 7 p.m.
Feb. 4 Tahoe Icemen 7:30 p.m.
March 3 Idaho Jr. Steelheads 7 p.m.
March 4 Idaho Jr. Steelheads 7:30 p.m.
We'd love to hear your comments!
Tags:Clark CountyVancouver
Nick Daschel, Reporter
Nick Daschel is a veteran journalist who has covered Clark County sports and news, as well as Oregon for nearly four decades. Nick, a graduate of Oregon State and lives in Vancouver, has worked as a reporter for many media outlets, including The Oregonian and The Columbian.
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Ron Daniel describes what earlier research on citation prediction can tell us about potentially valuable altmetrics and debates on whether there are areas in which new altmetrics might be discovered.
Many articles have been written about efforts to predict how many citations a research article will receive, based on indicators available before or shortly after publication. These efforts have widely varying results, with one effort predicting 14% of the variance in citation while another study ten years later reached over 92%. What was learned in that decade? What can this tell us about potentially valuable altmetrics, and are there areas in which new altmetrics might be discovered?
This special issue of Research Trends is about altmetrics – alternatives to the use of citation counts as the metric for assessing the impact of an article, a researcher or a journal. Citation counts do not tell the whole story (e.g. they don't value useful research software tools, useful advisory papers to young researchers, or research that can't be published for commercial or government security reasons). Having additional metrics to provide a more complete picture is a very welcome development. However, even in a future in which additional metrics are available to assess impact, citation counts will remain first among equals because of their intimate connection with the text of the article and the article's basis on prior work.
The current and continued importance of citation counts has led to the desire to predict how often an article will be cited in order to predict its future importance. Such predictions could be used to decide if an article should be published in one journal vs. another, to flag new research for scrutiny before citation counts have had time to accrue, to assess the development of a young researcher before many counts could have accrued, etc. Many articles have been written on this topic, but there has been very little consistency in their results. Four studies between 2002 and 2012 found that they could predict 14% 1), 20% (2), 60% (3) and 90% (4) of the variance in citation counts a few years after publication based on features available before or shortly after publication. A discrepancy this great requires some explanation!
This article has three goals. The first is to explain the discrepancy in the previous research results. The second is to evaluate the various indicators, a.k.a. features, which were used in the four articles. Features that are predictive of eventual citation counts might be particularly valuable altmetrics that serve as leading indicators of an article's merit. We need to be cautious when comparing results across the studies as they use different scientific domains, make predictions over different time periods, use different statistical methods, obtain results through different procedures, etc. For example, one study measured "newsworthiness" by having readers estimate it; another did so by searching news archives. Both found it to be a notable factor but not necessarily statistically significant. All of this means the results are only loosely comparable. However, we can look within each study to see which features did have significant effects and the relative magnitude of those effects. If the same feature is found to be significant, or not, across all studies then we are fairly safe in drawing conclusions about its utility. The third goal is to see if we can draw conclusions about potentially valuable altmetrics and areas where new altmetrics might be discovered.
From the time an article is first conceived, features begin to accrue that we can use to predict its future citation counts. This section looks at features available from the inception of the article up to the time where it has been accepted for publication in a journal. The features can be further subdivided into those that apply to the article itself, to the authors of the article, and to the journal which has accepted the article for publication. Those three categories are named as Content, Author, and Venue (4). What we will see is that even before an article is published, we have enough information to make fairly good predictions about its future rate of citations.
The earliest article we review (1), published in 2002, made the assumption that high quality research would be more heavily cited. They thought about what made high quality research and looked for corresponding features such as sample size, controls, blinding, etc. Sample size and the presence of a control group were found to have some effect, but not to the level of statistical significance. The other factors (blinded, randomized, prospective v. retrospective) were even weaker. The second article (2), published in 2007, also looked at study design factors and found them to have little effect. What they did find, however, was that large studies funded by industry, and with industry-favoring results, were statistically significant predictors of higher rates of citation in the future. These features are understandably important in the medical therapeutic space. Such studies are likely to show drugs and other therapies soon to be available. These factors don't seem likely to generalize to other domains.
Unlike the first study, which was confined to emergency medicine, the second study (2) considered the effect of the topic of the article. They found that cardiovascular and oncology articles were more likely to be cited than those on other topics such as anesthesiology, dermatology, endocrinology, gastroenterology, etc. Given the relative death rates of heart disease and cancer to the implications of the other specialties, this seems reasonable. Similarly, the third article (3), published in 2008, found that articles which provided therapeutic information were more cited, as were those which provided original research as opposed to review articles. That study also found that longer articles were cited fewer times, in a weak but statistically significant way. It also found that the more references an article contained, the more likely it was to be cited, although this effect was weak and not significant. The fourth article (4), published in 2012, found a weak effect that the more topics an article covered the higher the number of citations it received.
Table 1 lists the content-based features available before publication which were used in the four studies. Statistically significant values are highlighted. The key things to notice in this table are how few content-based features are significant, and how few of the features are used in multiple studies.
The effects of the author were not considered in the first study (1).The second study (2) only looked at whether the author byline indicated group authorship. This was found to be the most significant prediction feature in their study! This was a very important result. It indicated that article importance or quality was not easily measured by the presence or absence of some features we might call "good research practice". That realization led to significantly improved prediction accuracy in later work.
The last two papers (3, 4) looked at author-related features in more detail.
A strong result in (4) was the author's rank in citation counts. The citation counts for all the author's works were averaged, and the average counts were sorted to rank the authors. Figure 1, reproduced from (4), shows that being a very highly cited author is predictive of future citation counts. The rich get richer in other words. As can be seen however, this effect is limited and is only strong for authors in the top ranks of citation frequency.
Figure reproduced from Yan et al (2012) (4). We have sought permission for re-use of this figure.
Considerable attention has been paid to author-related factors in articles beyond the four we review here. (3) provides citations of articles that look at other effects such as nationality, gender, and alphabetic order of the author names.
Table 2 summarizes the effect of the author-based features available before publication. The key thing to notice is that the earliest study made no use of author information, while the latest and most accurate article tried many author-based features.
The only statistically significant variable found in the first study (1) was the impact factor of the journal in which the article was published. This was an early indication of the power of the venue in determining future citation counts. If we know the journal the article will be published in, we can make more confident predictions about its eventual citation count.
The second study only considered three of the top-line medical journals – JAMA, NEJM, and The Lancet. Nevertheless, they found a significant difference in citation rates between articles in those publications.
The third study did not use the impact factor, as it did not apply to all their sources for content. They discovered other measures that also reflected the article's venue. The strongest are the number of databases that index the journal, and the proportion of articles from the journal which are abstracted within two months by Abstracting and Indexing services and synoptic journals.
Table 3 summarizes the effect of the venue-based features available before publication. Note that no feature is used in more than one study. Curiously, impact factor was the only significant feature found in (1), but it is not used in the later studies. Perhaps the most surprising outcome summarized in this table is the strong effect due to the venues chosen by secondary publication sources like databases, A&I services, and synoptic journals. Given the concerns we all have about infoglut, it is both interesting to see the strength of this effect, and concerning that these effects do not seem to have been featured in any previous altmetric studies. More research in this direction seems justified.
By publication time, we know many facts about the Content, Author, and Venue. In the newly published phase of the article's lifecycle we shift our attention to early perceptions of the quality of the article, and to early indications of the use of the article.
The previous section showed that venues whose articles were frequently selected for abstraction tended to have more highly cited articles. For a single article, the number of times it is abstracted is also a statistically significant predictor (3) which is not available until shortly after publication. That study also showed that articles which were judged "clinically relevant" by the staff of a recommendation service were significantly more likely to have more citations in the future. These results are notable for the same reason as the venue results in the previous section – secondary publication sources have a predictive effect which is not being captured in current altmetrics.
Preprint access counts from arXive, etc.
Table 4 shows the effect of features available shortly after the article is published. The most noticeable aspect of this table is that very few post-publication features were used in the studies other than (3).
The fourth article (4) looked at temporal factors such as age of the article, as well as regression constants to control the growth and decay of citation rates over time. These results were not strong and other studies did not look at features for mature articles so a summary table is not provided. While none of the studies made significant use of features that become available later in the publication lifecycle, there is no shortage of possibilities. For example, we might look at a Page-Rank like scoring of the influence of the papers citing the particular paper of interest.
Nevertheless, the short story is clear. By the time an article is a few months old, we can make good predictions of its likelihood of future citations - especially for those articles which end up being highly cited. Lokker noted that for the papers with the highest citation counts at two years after publication, "Cited articles in the top half and top third were predicted with 83% and 61% sensitivity and 72% and 82% specificity" (3). In other words, only about 20% of the papers which ended up being highly cited were not predicted to be that way.
Despite low performance in early studies (14% in 2002), it has become clear over time that it is possible to make good predictions (92% in 2012) of the frequency of future citations. How was this advance achieved? Quite simply, the features being used in the later studies are very different from those used in the earliest ones. The early studies tried to use features around the content, but later work found those to be the weakest while features around the Author and Venue were the most predictive. If we set the power of the Author features to 1.0, the relative power of the Venue and Content features would be about .63 and .25, respectively. We cannot directly compare results across columns, and it is not safe to predict the accuracy any new study might achieve. All of the studies used different domains of literature, predictions over different time periods, different statistical measures, etc. Nevertheless, the pattern seems clear.
It is also interesting, and mildly reassuring, to see that the strongest of these measures operate, to some degree, in a manner independent of each other. Author and Venue are the two most predictive features. However, selecting an article for a journal is usually done in a peer review process that is blind to the identity of the author. Note that this also means these measures are not well-suited for an editorial board to choose articles, since the Venue would be constant and they could not look at the author's publication rank.
In a perfect world, the content of an article would determine its future citation count. We do not, however, have any easily-computed metric for the intrinsic quality and merit of an article. This is where Lokker's results about the importance of secondary sources such as the databases and synoptic journals are most interesting. We see that in the absence of reliable, easily-computed metrics, the subjective human-in-the-loop procedures of peer review, editorial boards, selection for secondary publications, and scientific reputation provide existing mechanisms which fill that void. This provides a potential area of altmetric research to obtain such measures in various fields and compare them with current altmetrics for a variety of purposes.
In this opening piece Mike Thelwall discusses the history of altmetrics and its value and potential uses.
In honour of Rare Disease Day, an international advocacy day to help raise awareness for rare diseases, Iris Kisjes investigated publication trends in the field using SciVal. In this piece she highlights some of the key institutions that are active in research on rare diseases.
28th February is Rare Disease Day, which is an international advocacy day to help raise awareness with the public about rare diseases, the challenges encountered by those affected, the importance of research to develop diagnostics and treatments, and the impact of these diseases on patients' lives.
Rare Disease Day was first observed in Europe in 2008. It was established by EURORDIS, the European Rare Disease Organization. In 2009, NORD partnered with EURORDIS in this initiative and sponsored Rare Disease Day in the United States. Since then, the concept has continued to expand beyond the US and Europe. In 2013, more than 70 countries participated.
Rare diseases collectively affect millions of people of all ages globally, of which approximately 18-25 million Americans. They are often serious and life altering; many are life threatening or fatal. In Europe a disease is considered rare when it affects no more than 5 individuals among 10,000 persons, whereas the US considers a disease to be rare when it affects less than 200,000 Americans. Since each of the roughly 7000 rare diseases affect only a relatively small population, it can be challenging to develop drugs and medical devices to prevent, diagnose, and treat these conditions. In general there is a lack of understanding of the underlying molecular mechanisms or even the cause or of many rare diseases. Hence, countries across the globe should share experiences and work together to help address these challenges successfully.
There are many challenges faced by these types of diseases, as they are often not well defined or characterized. Rarity also means that recruitment for trials is usually quite difficult, study populations are widely dispersed, and there are few expert centers for diagnosis, management and research. This is often accompanied by a lack of high-quality evidence available to guide treatment.
At Research Trends we were curious to learn more about the subject and to highlight some of the key institutions and authors that are contributing to the field of rare disease. To do this we examined the field by looking at publication trends using SciVal. To begin with we created a research area in SciVal based on the keyword search 'rare disease'. The keyword search searches through titles, abstracts and keywords of research papers within Scopus.
Setting up a research area in SciVal is fairly simple and can be completed in three easy steps. Figure 1 shows that for this search query a set of 12,818 publications were published between 1996 to present, where the US, Germany, Japan and France were the most prolific.
SciVal is a ready-to-use tool to analyze the world of research. It is based on Scopus data and primarily developed for research organizations to help establish, execute and evaluate their strategies within the context of their peers (through benchmarking) and collaborators (through collaboration networks). The solution also allows users to set up research areas to analyze contributors within the field and their corresponding publication and citation statistics.
The SciVal research area focuses its analysis on the last five years of publications for which we found the pool of research papers in this area was is fairly small. Over the course of 5 years (2009-2013) close to 5900 publications containing 'rare disease' were published around the globe, of which less than 20% originated from the US, see Table 1 (data date stamp 21 January 2014). One of the reasons why this set of research papers is small can be contributed to the simplicity of the search terms used. In the approach presented in this paper relevant articles were selected on the basis of the occurrence of the term 'rare disease' without including the names of the 7000 rare diseases themselves. We can therefore assume that papers related to many rare diseases would not be included in this set as not all papers related to specific diseases will include 'rare disease' in their abstract, keywords or title. In a follow-up study we could look into including search terms related to particular rare diseases to provide a more complete picture of this research field.
Despite this small sample set, we took a look at a number of institutions, and a number of authors that were the most prolific in this area. When looking at the three most prolific institutions around the globe they all originate from either France or the US, bar one from Germany. Hence, this article focuses on the two most prolific from both countries, namely Université Paris 5 and Harvard, see Table 1 and Table 2.
Using SciVal we took a closer look at the collaboration patterns of three individual intitutions, Harvard University, Université Paris 5 and University of Munich in 'rare disease' research. SciVal allows you to drill down from a worldwide, to a regional and right down to a country level view of the institutional collaborations.
As can be seen from Figure 2, Harvard University collaborated with 171 institutions worldwide on a total of 39 co-authored publications of its total of 61 publications, with the majority of the collaborations taking place with authors within the US and Europe. The average number of insitutions per paper are 4,38. You can also see here that out of the 61 articles 39 are co-authored with other North American research organizations. In fact, Harvard's top 20 collaborating institutions on 'rare disease' research are mainly from the US, only three were international cross-border collaborations, namely with Canada, UK and Switzerland. When one looks at the general collaboration trends exhibited by Harvard they seem inline with the trends exhibited within the area of 'rare disease' where the top collaborations are all national institutions with few international collaborating institutions from abroad featuring in the top 40.
You can see from Figure 3 that Université Paris 5 collaborated with 215 institutions worldwide on 62 co-authored publications out of its 77 total publications, resulting in an average of 3,48 institutions involved in each paper. From Figure 4 you can see that the majority of collaborating institutions are from France (72). In fact, there are only three non-French institutional collaborators amongst their top 20 rare disease collaborators, namely Canada, UK and Germany.
Here again in Figure 5 we find there are a large number of institutions that work on a number of papers, 73 institutions collaborated on 18 articles, with an average of 4 institutions per paper. The University of Munich enters into the most cross-boarder collaborations with half of its top 20 institutions coming from abroad, namely three US, two UK, two Dutch, one Canadian and one institute from the Czech Republic. It seems logical for collaboration to play a central role in 'rare disease' research, though it may be expected that cross-border collaborations would be most important to effectively propel the research forward, mainly due to the small patient numbers in each country.
The overal results show a large number of institutions collaborting on one paper, and it seems that Munich in general is a more internationally focused research organization from the three universitites investigated, see Table 3 for the general collaboration trends for these universities.
By looking at the most prolific authors in the set, in addition to clicking though to the abstracts in Scopus from SciVal, we were able to get a better idea of the set of research papers we were looking at.
The most prolific authors in the dataset for 'rare disease' between 2009-2013 are: Dr. Domenica Taruscio, MD. Director at the National Centre for Rare Diseases, Instituto Superiore di Sanita, Rome, Italy; Dr. Steven Simoens, Katholieke Universiteit Leuven, Department of Pharmaceutical and Pharmacological Sciences, Leuven, Belgium; Dr. Stephen Groft, Director National Institutes of Health, Office of Rare Disease Research (see table 4).
Dr. Domenica Taruscio MD. focuses her research on setting systems in place that can, firstly, help train and inform clinicians to make the right diagnosis and secondly, improve the dissemination of information around symptomatic treatments. She has just spent the last 30 months on a feasibility study funded by the European Commission (DG Sanco) addressing regulatory, ethical and technical issues associated with the registration of rare disease patients and with the creation of an EU platform for the collection of data on rare disease patients and their communication among qualified users.
Dr. Steven Simoens, on the other hand, does not focus his research especially on rare disease themselves. He works within Pharmaceutical Sciences with a keen interest in pharmaco-economics and ethics which is where rare disease seem to come up in his research. His publication rate is very impressive with about 15 papers per year. 'Rare disease' research is a topic of concern for this field of interest as there are debates on the economic rationale behind society supporting any part of the rare disease value chain. There was a debate in the Netherlands, for example, in 2012 related to the cost of providing medication to patients with Pompe's disease. Medication for these patients cost between 400-700,000 Euros per patient per year. The economist Dr. Marc Pomp introduced the concept of the quality-adjusted life year (QALY), where the costs of medical treatment are placed in relation to the quality of life in those years for the patient. The level considered acceptable was set at 50,000 Euro per year, far below the cost of treating a Pompe patient, while stopping their treatment would would most likely cause them to die (1).
Each of these three individuals look at rare disease in very different ways, though all are in some way interested in the management of the research field, suggesting that our keyword search did in fact omit the research of 'rare disease' from the sample set. However, this also shows how much attention needs to be placed on attracting the public's attention for 'rare disease' and on building global awareness and a collective solidarity to support the population and their families affected by these rare and often severe diseases.
Which scientific stories are most shared on social media networks such as Twitter and Facebook? And do articles attracting social media attention also get the attention of scholars and the mass media? In this article, Dr. Andrew Plume and Mike Taylor provide some answers, but also urge for caution when characterizing altmetric indicators.
Which scientific stories are most shared on social media networks such as Twitter and Facebook?
Research on human health and social issues are often perceived as being the most shared scientific stories on social media networks such as Twitter and Facebook and – given their mainstream appeal – are often suggested to dominate the popular discussion around scholarly research online, but skeptics, such as David Calquhoun argue for their irrelevance: "Scientific works get tweeted about mostly because they have titles that contain buzzwords, not because they represent great science" (1).
So which is it to be? And do articles attracting social media attention also get the attention of scholars and the mass media? In this article, we seek to provide an approach to answering these questions.
With the rise of online scholarly publishing and the concomitant rise in the desire to create indicators of online attention to research articles and related outputs have come a number of providers of article-level data. A leading commercial provider of such data - collectively known as 'altmetrics' - is Altmetric.com, which tracks a variety of different indicators in four broad groups: Social Activity (e.g. Tweets and Facebook mentions), Mass Media (e.g. mentions on news sites such as BBC and CNN), Scholarly Commentary (e.g. mentions in scientific blogs), and Scholarly Activity (e.g. articles in reader libraries such as Mendeley). The overall collection and analysis of these references are brought together under the label "altmetrics".
In terms of the volume of online mentions of scholarly articles, Twitter and other social networks provide by far the largest number of data points. However, given Twitter's broad user base (the majority being non-academics) and limited information content (being restricted to 140 characters per tweet), other indicators may be more significant in terms of understanding scholarly usage (2). For example, Mendeley and CiteULike are examples of sharing and collaboration platforms used predominantly by researchers, while the mass media and scientific blogs tracked by Altmetric.com are written by professional science journalists or researchers themselves.
Data were collected from the Altmetric.com API over four months ending January 17th, 2014. On this date, the latest altmetric indicator data for all papers published in a selection of journals in 2013 with any online mentions captured by Altmetric.com were downloaded for analysis; in total, 13,793 articles with at least one altmetric indicator datapoint were included in this study. Please note, the actual Journals monitored are detailed in the raw dataset, which is published on Figshare.
The Altmetric.com data includes counts of online attention at article level from across a variety of different data sources. In order to simplify data analysis, we aggregated data counts into the four classes as defined above: Social Activity, Mass Media, Scholarly Commentary, and Scholarly Activity. For each class, articles were assigned to predefined percentile ranges (cohorts) based on the frequency of online mentions (see Table 1).
Table 1 - Cohorts of articles based on the frequency of online attention within each class.
For example, the 69 papers comprising the top 0.5% of social activity comprise 91,470 social actions, 445 mass media mentions, 540 scholarly comments and 1,571 scholarly actions, whereas the top 69 papers comprising the top 0.5% of mass media activity comprise 2,638 mass media mentions, 16,221 social actions, 779 scholarly comments and 4,856 scholarly actions.
Headline-grabbers: Which articles got most social media attention in 2013?
Of the 69 articles belonging to the 0.5% cohort in the Social Activity class (i.e. those articles most frequently mentioned in social media such as Twitter and Facebook, for example), just 8 of them are full-length articles reporting the results of original research. The remainder are typically editorial features or news items from leading weekly journals such as The Lancet, BMJ and Nature; see table 2 for the complete list. The original research articles cover topics in the popular consciousness including climate change, human health and diet, and online information and privacy: intuitively, the sort of articles one might expect to see attracting broad popular attention online. However, one article appears to have a less obvious popular slant (the Nature letter "Attractive photons in a quantum nonlinear medium") but closer examination shows that it describes a novel technique for forcing photons to interact in a quantum nonlinear medium which may have applications in quantum processing, where the ability to have photons 'see' each other could overcome present technological limitations.
The remaining 61 articles (almost exclusively news and editorial features about original research reported elsewhere) cover a variety of topics including several on topics close to the heart of the academy: research careers, science funding, the future of higher education and scholarly publishing. The preponderance of items in this group from Nature (primarily the Nature News and Nature News Feature sections of the publication) suggest that Social activity may be more likely to reflect attention to short journalistic versions of current research results rather than the original research articles themselves; a worthy follow-up to this study would be to track the variation in performance across altmetric indicator classes of an original research article and the current awareness 'news-worthy' version of the same research.
Table 2 - Full list of the 69 articles belonging to the 0.5% cohort in the Social Activity class including journal, article title, and DOI. Articles highlighted in orange are those representing full-length articles reporting the results of original research.
Social media attention: An indicator of scholarly impact or simply newsworthiness?
The articles which appear in the top 0.5% cohort in each of the four classes defined in this study are typically not the same ones: just 2 articles appear in all 4 lists. This suggests that the correlation between these 4 classes of altmetric indicators may not be very high. These two articles are both original research articles, one reporting the development of a method for creating human brain-like structures (called "cerebral organoids") in cell culture and using these to study the basis of brain development and disease (Nature article "Cerebral organoids model human brain development and microcephaly"); the other correlating online behaviour (in this case, Facebook 'likes') with personal information such as sexual orientation, ethnicity and political views, to create a model to predict such traits based solely on Facebook activity (PNAS article "Private traits and attributes are predictable from digital records of human behavior").
Further analysis of the overlap between the top 0.5% cohorts in each altmetric class is shown in Table 3: by far the greatest overlaps occur between the Mass media and Scholarly commentary classes, the lowest between Social activity and Mass media or Scholarly activity, and a moderate degree of overlap for the remaining pairwise combinations. Taken together, this suggests that - at least amongst this handful of articles receiving the most online attention – articles attracting a high degree of Social activity attract relatively little attention from the Mass media or from Scholarly activity and only a moderate degree of scholarly commentary. Conversely, there is a very high co-occurrence of articles receiving Mass media attention and Scholarly commentary. Taken together, these observations suggest that Social activity in particular is an indicator of a very different kind of online attention than the other three classes.
Table 3 - Co-occurrence counts of articles comprising the top 0.5% of articles in each class, where n varies between classes owing to tied rankings at the 0.5% cutoff between 69 and 76.
Figure 1 shows how this correlation varies across all percentile cohorts for articles with Social activity. Note that approximately 90% of social activity is constrained to 15% of articles, which is a significantly more skewed distribution than that of citations across articles within a journal (where some 90% of citations are to 50% of the articles; (3)). This implies a scarce attention economy in the Social activity spectrum, with many articles competing for a rare resource (reader attention). The only altmetric class with a distribution of attention across articles similar to that of citations across articles is Scholarly activity (which correlates very poorly with Social activity), where approximately 90% of Scholarly activity is represented by some 30-40% of articles (data not shown). The convergence of the curves in Figure 1 around the 15% cohort implies that at this point attention in all 4 classes is equally scarce, while in the cohorts above this point the only class showing a considerable degree of co-occurrence with Social activity is Scholarly commentary (also borne out by the Table 3 for the 0.5% cohort).
Figure 1 - Proportion of total activity per article across predefined percentile ranges (cohorts) for social activity.
It is clear from this exploratory work that altmetrics hold great promise as a source of data, indicators and insights about online attention, usage and impact of published research outputs. What is currently less certain is the underlying nature of what is being measured by current indicators represented within the four broad classes analysed here, and what can (and cannot) be read into them for the purposes of assigning credit or assessing research impact at the level of individual researchers, journals, institutions or countries.
What is strikingly clear from the qualitative analysis of the top 0.5% of papers for Social Activity is the lack of mentions of titles that have particularly titillating or eye-catching keywords: although most of the links are to summaries of research, rather than primary research articles themselves, they all contains serious scientific material.
On the basis of this preliminary study, we urge caution in characterizing all altmetric indicators in a similar way, as it is likely that different indicators may measure different types of online attention from different types of readers. This finding is similar to that reported by Priem, Piwowar and Hemminger in 2012 (4). We also suggest that careful delineation of document types (as long used for citation-based indicators) must be applied to correctly evaluate (for example) the relative social activity attracted by a news or editorial item versus an original research article; these values are likely to be the inverse of their usual relationship in citation terms. In short, in the excitement and promise of this burgeoning new field of Informetrics, we must be sure to ask ourselves: what is it that we are measuring, and why?
This paper would not be possible without the kind support of Euan Adie at Altmetric.com in providing access to these data for research purposes.
(3) Seglen, P.O. (1992) The skewness of science. Journal of the American Society for Information Science, 43 (9) pp. 628–638.
Dr. Andrew Plume and Dr. Judith Kamalski demonstrate how download data can be used in research assessment to offer a different perspective on national research impact, and to give a unique view of knowledge exchange between authors and readers in the academic and corporate sectors.
For this issue Dr. Gali Halevi interviewed Dr. Daphne Getz, to hear more about the way research evaluation is carried out in Israel, and in particular on the recent report on Israeli scientific output published by the Samuel Neaman Institute, for which she was the lead investigator.
In 2013, the Samuel Neaman Institute published a report covering the Israeli scientific output from 1990 to 2011, identifying the country's leading scientific disciplines and comparing them to countries around the world. With its unique geographical location and demographic composition, Israel presents an interesting case of scientific capabilities and output as well as collaborative trends. For this issue, we interviewed Daphne Getz, the lead investigator of this report.
Dr. Daphne Getz is the head of the CESTIP (Center of Excellence in Science, Technology and Innovation Policies), and has been a senior research fellow at the Samuel Neaman Institute (SNI) since 1996. Dr. Getz is a specialist in R&D policy, technology and innovation, policies on new and emerging technologies, and relationships between academia, industry and government, among others. She has represented the academia and the Technion (Israel Institute of Technology) in the MAGNET R&D Consortia and also represents Israeli academia in several EU and UN projects. She has a D.Sc. from the Technion in Physical Chemistry and has served in several positions related to R&D management in the industry. Over the years, Dr. Getz has initiated numerous projects, including Israeli indicators for Science, Technology and Innovation, evaluation of R&D programs, and the evaluation of Israeli R&D outputs using Bibliometrics.
Could you briefly describe SNI (Samuel Neaman Institute), its core activities and role in informing science policy in Israel?
Samuel Neaman Institute (SNI) is an Israeli organization established in 1978 at the Technion (the Israel Institute of Technology). Its main objective is to conduct independent multi-disciplinary research and provide insights into Israel's Science, Technology & Innovation (STI), education, economy and industry as well as infrastructure and social development for policy makers. The institute has a key role in outlining Israel's national policies in science, technology and higher education and serves decision makers through its research projects and surveys. The institute operates within the framework of a budget funded by Mr. Samuel Neaman and external research grants from the Ministry of Science, Technology and Space, the Office of the Chief Scientist in the Ministry of Economy, the Ministry for Environmental Protection, the European Commission's Seventh Framework Programme grants, and more. SNI employees are highly professional analysts chosen because of their level of expertise in different disciplines. Each year, the institute conducts many projects and publishes numerous reports covering a variety of topics related to Israel's technological, economic and social capabilities.
What types of evaluation programs does SNI develop and conduct?
In some cases, SNI is requested to evaluate specific scientific programs, for example, the Scientific Infrastructure Program of the Ministry of Science and Technology, which was launched in 1995 in an attempt to bridge the gap between basic and applied research. SNI was called to methodologically evaluate how and to what extent this program benefitted the Israeli economy and society. In addition, the institute set out to study the effectiveness of the program, its actual successes and failures, and to help decision makers set priorities in R&D policies and investments.
The OCS supports several scientific programs aimed to support technology transfer between academia and research institutions and the industry. SNI was called to evaluate some of these programs and analyze their effectiveness, success and future development to ensure well-constructed processes for technology transfer to industry.
From time to time, SNI is called upon to evaluate specific institutions within academia. In such cases SNI uses quantitative and qualitative methodologies to evaluate their performance in terms of output, influence and contribution to science, economy and society.
Since 2003, the institute uses advanced bibliometric methodologies and conducts in-depth studies on the quality and quantity of Israeli research outputs (especially relating to scientific publications and patent analysis). Specific fields such as Nanoscience and Nanotechnology, Aerospace Engineering, Energy, Environment, and Stem Cells are analyzed and benchmarked against the rest of the world.
What data does the institute collect and analyze in order to produce reports on Israel's STI capabilities?
SNI uses a variety of data sources in order to conduct its research and produce its reports, including intellectual property (such as patents and trademarks), human resources and demographics, as well as infrastructure and economic indicators. In addition, SNI established a Bibliometric department, which focuses on analyzing publication data such as number of journal articles, number of citations, conferences etc., as well as scientific collaborations with the international community.
Which indicators did the institute develop in order to be able to benchmark Israel's STI?
SNI developed and maintains a large and diverse database of indicators relating to the monitoring and evaluation of R&D activities, scientific capabilities and technological infrastructures and to the funding of such activities in Israel. This database has become the most reliable and trusted source for STI evaluation in the country. In 2013, SNI published the fourth edition of "Indices of Science, Technology and Innovation in Israel: An International Comparison". It contains key data on Israel's Science and Technology input and output and covers more than a decade of international comparisons, as well as many other indices, including position indicators. In the framework of patent research, SNI developed the "distinct invention" indicator. This indicator is based on patent family data and is aimed at neutralizing double counting of identical patent applications (inventions) as a result of their filing in numerous patent offices around the world.
According to the latest report, Israel's leading scientific disciplines are Space Science, Material Sciences, Molecular Biology & Genetics, and Biology & Biochemistry. Leading sub-disciplines are Cell & Tissue Engineering, Biomaterials, Biophysics, Biochemistry & Molecular Biology, Biomedical Engineering, Composite Materials, and Nanotechnology. A significant growth by quantity was seen in disciplines such as Economics and Social Sciences.
Some of the leading trends found, based on both quantitative and qualitative measures, are Tissue Engineering, Physics (Particles & Fields), Astronomy & Astrophysics, Cell Biology, and Biochemistry & Molecular Biology. In some of the sub-disciplines within these areas of research, Israel has a leading global role.
Overall, of Israel's scientific publications in 2011, 46% was the result of international collaboration (40% in 2007). The main countries with which Israeli scientists collaborate are the USA, Germany and France. In addition to these, we found a significant growth in collaborations with South East Asian countries such as Singapore. An analysis of USPTO patent data relating to the 1999-2008 time period revealed that 83% of the cooperation in inventive activity was conducted with American inventors (highly influenced by the scope of US multinational firms' activities in Israel), 10% with inventors from EU-27 countries (mainly Germany, France and the UK) and 7% with inventors from the rest of the world.
An appropriate distribution of funding is always a challenge for decision makers. In our report we demonstrated that although highly funded disciplines such as Neuroscience did perform well, other - less funded - areas such as Space Science and Cell & Tissue Engineering showed significant growth and development. This enabled us to highlight areas that will need policy and funding attention in the coming years.
SNI produces numerous studies on Israel's STI; could you please mention one or two of such studies (e.g. environmental conservation, energy) and their main results?
One of the research reports we produced in 2013 was "Science & Technology Education in Israel", which aimed to provide indicators to inform strategy makers in education, and to help prepare them for a possible shortage in Science and Technology teachers in high schools. A unique report titled "Success stories" features 78 success stories that depict ultra-orthodox individuals in Israel, both men and women, who have successfully integrated into the world of academic education, employment and the military. Another "hot" topic is Energy; we have an ongoing project named "Energy Master Plan", responsible for evaluating the environmental impacts of the different potential energy scenarios as well defining environmental indicators to the energy market. The Energy Forum Meetings aim to provide a platform where professionals can discuss specific energy related topics. At the same time, the forum allows multilateral discussions encouraging projects in the fields of renewable energy and energy conservation. The forum meetings serve as a platform for defining professional, applicable positions, to be used by relevant decision makers. Other reports and findings can be found on our website: http://www.neaman.org.il/Publications.
Given the variable delays and uncertain linkages between R&D inputs and outputs (and ultimately, economic development), how do you draw conclusions (if indeed you do) on the impact of STI activities on the Israeli economy?
The question of causation or causality between R&D inputs and economic outputs is a well-known and researched problem in the R&D economic literature. The main criticism is that a large number of models dealing with the relationship between technological change and economic growth probe the linkage directly by simply looking at the inputs (e.g. scientific publications, patents) and outputs (e.g. firm sales, GDP), without analyzing or understanding the process binding them.
In the process of our work in SNI, we place great emphasis on qualitative methodologies (interviews, surveys and unstructured questionnaires using open-ended questions) that to our best knowledge are better suited to understanding and probing the mechanism (the "black box") linking scientific inputs and economic performance.
A number of quantitative studies dealing with the relationship between R&D investments and economic growth were conducted in SNI (see "R&D Outputs in Israel – A Comparative Analysis of PCT Applications and Distinct Israeli Inventions"; "Investments in Higher Education and the Economic Performance of OECD Countries: Israel in an International Perspective"). In both of these studies we addressed the question of causality by developing a two-stage model of scientific and technological innovation. In this model R&D investments generate scientific and technological outputs (e.g. patents) and these technological outputs turn back into inputs which explain economic performance. In the process of this work much emphasis was placed on the quality of the R&D indicators. For example, we extracted patent application data by priority date (which is the earliest filing date of the patent application anywhere in the world), as opposed to application or grant date, in order to more accurately represent the time of invention. Concurrently, the use of temporal bias (time lag) between R&D inputs and economic outputs is actually essential to correctly represent the real-world relationship and sequence between stimulus and response.
Currently, the institute's investigators are working on several reports focusing on technology transfer and collaboration between industry and academia, international scientific collaborations, and energy sources.


Diet, exercise and other healthy habits can help control symptoms of heart failure and improve your overall health and well-being. You can reverse some forms of heart failure by reducing salt and fluids from your body. If you have a chronic form, we help you establish healthy habits of diet and exercise so that you can live a full life.
Medications help reduce the work of your heart and may help your heart beat stronger.
It's important to fill your prescriptions regularly without missing a dose. Even if you feel better, don't stop taking medicine without talking to your healthcare provider.
This free program provides follow-up care and support for patients recently discharged from the hospital.
Certified nursing assistants who specialize in cardiac care visit you Monday through Saturday for the first five days after discharge and return two or three days a week throughout your first month at home.
After the first month, your at-home care team sets up visits on an as-needed basis.
To be eligible for this program, you must live within 60 miles of Charlottesville.


text:
Farewell St. Paul University Catholic Center
On Sunday, January 3, 2016, St. Paul University Catholic Center celebrated its last mass in its current building. Originally conceived in 1906 and dedicated in 1910, a new building is planned to open in 2017 at the existing location (723 State Street on Library Mall).
Learn more about this iconic building and its history on campus:
Found in the Archives! St. Paul University Catholic Center
The UW-Madison Archives houses 11 linear feet of material documenting the history of St. Paul's, plus photographs and three scrapbooks of clippings (Accession #2011/329).
For more information about this building or UW campus history in general, contact uwarchiv@library.wisc.edu. On, Wisconsin!

redpajama_set_name: RedPajamaCommonCrawl


text:
//
//  ShoppingCarController.h
//  cycj-shop
//
//  Created by 李杰 on 2016/12/22.
//  Copyright © 2016年 李杰. All rights reserved.
//

/**
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#import <UIKit/UIKit.h>

@interface ShoppingCartController : UIViewController

@end

redpajama_set_name: RedPajamaGithub


text:
Democratic Russia has little cause for optimism
In this interview originally published in Freedom in 2008, Rob Ray talks to Vadim, a Russian anarchist, about the state of the country in that year.
Rob Ray: What is the economic situation in Russia at the moment?
Vadim: The economic situation is very different now compared with the crash of the 1990s, when industrial production declined drastically. Of course, growth hasn't come as a result of neoliberal shock therapy but rather follows from rising international oil prices. The industries which are developing now are in oil and gas production, building (many of the workers in the sector are from the other republics of the ex-'Soviet Union', who often have no rights or even legal status) and in metal production (mainly for export).
Some traditional industries (such as textiles and machine-building) are in decline. But it is necessary to take into account that most existing productive capacity and the whole infrastructure is very old and can be left to ruin in the near future. The other big problem is the very uneven situation in different regions. In such cities as Moscow, St.Petersburg or Nizhni Novgorod there is major development of services, in the banking industry etc. But many regions are in decline.
According to the state the general sum of backpay owed was 4.435 billion Rb. (about $171m) in May last year. Unofficially, it is higher, in particular for migrant workers (often they simply aren't paid at all). Generally, wages in Russia remain very low. Officially, it is $400 a month on average (in Moscow it's around $1,000), but the real situation is very different. In 2005, only 18% of population obtained more than $430 (12000 Rb.), 24% from $250 (7000 Rb.) to $430 (12000 Rb.) and 18% $180–250 (5–7,000 Rb.). In many regions and industries the situation is much worse. While workers in private services obtain good wages, in agriculture, textiles and in public service the wages are rarely greater than 5,000 Rb.
What is unemployment like?
It is not easy. For market reforms like those in Russia, an unemployment level of 20 or 30 % was expected. Actually it is (according to Western data) 7–8%. Growth is restrained due to low wages and non-payment of wages.
But the situation is not uniform here either. Unemployment in the south of Russia is on average three times more than in regions such as Moscow, St.Petersburg or Wolga, and twice much as in Siberia and the Far East. Youth unemployment is very high and people with further education also have difficulty finding work: 28% (14% in Moscow) and 11% (32% in Moscow) respectively.
But only a minority of unemployed register themselves with the state service when looking for work because it is ineffective. In the first three months, the unemployed obtain 75% of their monthly wage, in the next four months 60% and in another five months 45%. After one year, the dole is very low: from $27 to $111 a month in 2006 (it depends on the region). This is of course insufficient for life. So there are many migrations to big cities as Moscow by people independently looking for work (mainly non-skilled).
Furthermore, a lot of employment is only part-time or casual, in particular in the Far East and Wolga regions (here and there about 20% of workers); in Moscow between 10 and 20%. This sector represents around 5-6 million people.
What is the situation in education, healthcare and utility (water, power) provision?
All these sectors are in a deep crisis. In spite of a good financial situation for the country ($406.6 billion in gold reserves, of which the government's found $117 billion for stabilization, and foreign debt amounts to $113 billion), the ruling clique continues to destroy free public services (such as education and health care) and to increase the prices for provision of water, power and other housing and communal services.
At the same time, active privatisation occurs. So the possibilities for finding free places in education, the quality of public healthcare service, drugs at reduced prices etc are falling. In education, the principles and mechanics of selection are being introduced, the number of paid student places are growing. There are many private schools and universities.
The house-and-communal-services-reform bill has brought through the privatisation of utility provision. Unfortunately, there is very little resistance against these moves. The student movement as such is absent. The main target of protests for people in the city is rather the problem of commercial re-planning and the building of elite housing.
What is trade unionism like?
All trade unions in the modern Russia are bureaucratic and anti-worker structures. The biggest organisation is the Federation of Independent Trade Unions of Russia (in Russian: FNPR) which is a new name for the old 'Soviet' official trade unions. The leaders of FNPR claim they have 28 million members in their unions but the majority don't do anything and only pay dues.
It`s important that FNPR privatised the big property of old 'Soviet' unions, such as sanatoriums, rest homes, tourist bases, hotels, sports establishments, stadiums, homes of culture etc for its own benefit. So the FNPR is on the one hand a giant bureaucratic apparatus of paid functionaries, and on the other it's a social infrastructure with less expensive rest and health possibilities in sanatoriums, presents for children, for people in difficult situations etc. It is often the case that there are also leading managers or members of the administration of businesses in the unions of FNPR. So it is no possible to say that the FNPR is a normal trade union in Western sense!
The unions of FNPR try normally to agree with business leaders peacefully and regard the strike as an extreme action. At the end of the 1990s, teachers and health workers (most public sector workers are in the FNPR unions) and the miners of FNPR struck, but the leadership took control: they prevented the generalization of struggle and suppressed all initiatives from below. The FNPR supported the Labour Law in 2001 which helped give business more powers to dismiss their workers and promoted precarization.
Apart from the FNPR, there are many different trade unions and federations which name themselves "free" and stress their "independency" from a "Soviet" heritage. They don`t have leading managers in top positions . But they are also vertical, bureaucratic structures, mostly with paid functionaries and they declare their fidelity to social partnership between the workers, bosses and authorities.
There are examples of self-organised workers' resisting too - from individual sabotage action to little spontaneous strikes or "Italian" strikes. But self-organised collective actions are still rare.
What are the most powerful political strands at the moment?
The ruling political current around Putin is nationalist, strongly centralist and authoritarian in internal politics and neoliberal in economic politics. The general political objective is to reestablish Russia as a great power. The regime rests on the support of some parts of big business and on structures in the secret service and military.
The Putin group utilized aggressive Russian nationalism and spread anti-Caucasian hysteria (including the recommencement of the colonial war in Chechnya) to increase their power.
Of course, the spreading of these spirits stimulated very much the growth of pure fascist political tendencies. Gangs of boneheads terrorize migrant workers, foreign students and even children of parents coming from different republics of the ex "Soviet Union". There are violent attacks and murders. Moreover, there were cases of big spontaneous pogroms in some cities (Kondopoga, Stavropol) against non-Russian populations.
The ruling group is too nationalistic, of course. But they are afraid of a split in Russia and try to show themselves as more "moderate" nationalism. They want to strengthen the centralisation of power by limiting State federalism, by seizing regional administration from above and revising their relationship with regional elites on more favourable terms.
As for the state's economical and social politics, it is oligarchic and neoliberal. The ruling group reallocates property, taking control of some companies and driving back economic competitors.
This isn`t a real broadening of state control but the strengthening of existing economic forces coupled with the state power. The social measures of government are openly in favor of the rich. So Russia is one of very few countries where income tax isn't progressive: all must pay a flat rate of 13% of income – both multi-millionaires and ordinary workers!
Are there any causes for optimism?
Optimism? I don`t think that the actual situation generates much optimism because the level of self-organised resistance is very low now. But we are pessimists in our understanding of situation, optimists in our actions. Resistance is for us not only a question of survival but also one of human dignity.
The younger generations don`t have so many illusions about private market capitalism as existed at the beginning of the 1990s. They are mainly passive now, but we hope they will overcome the social shock of that decade and will begin to react and resist. Especially when we take into consideration that economic growth in Russia, linked with the oil prices, isn`t stable and the majority of people don`t have chances in this system.
Rob Ray
Jan 26 2009 23:06


Q11a. Would the majority of your jurisdiction's citizens be more likely to choose higher taxes to avoid service cuts to police services or would they choose cuts to police services to avoid higher taxes?
Would the majority of your jurisdiction's citizens be more likely to choose higher taxes to avoid service cuts to police services or would they choose cuts to police services to avoid higher taxes?


As you know, here at Ecard Mint we like to highlight the genuinely useful from the not so useful when it comes to online marketing information available on the web. As you probably know, there are plenty of articles, blogs, free tips and tools out there for you to browse, but having to decipher the good from the bad can be overwhelming, to say the least. This week we've found the six marketing metrics, courtesy of Hubspot, which definitely falls under the 'genuinely useful'. Enjoy!
This useful marketing guide, brought to you by Hubspot, offers a marketing cheat sheet of metrics and a guide that looks at the most critical measures of marketing. These range from six marketing metrics that prove the value of your marketing by detailing formulas that enable you to calculate metrics along with explanations to interpret your own marketing success. Through use of scenario, Hubspot offers scenarios that demonstrate why these metrics are important. By following the online guide, you can not only interpret which aspects of your marketing strategy need more focus and which should be avoided, resulting in a more effective online strategy.
This entry was posted in Marketing advice, New corporate ecards website, Social media marketing on October 21, 2013 by Ecard Mint.


DOES YOUR BOW SIGHT NEED THIRD-AXIS ADJUSTABILITY?
If you're a bowhunter and you've done some reading up, you've probably heard the phrase "third-axis adjustment." It's a term for one of several axes on which a bow sight can be rotated and adjusted. Bow sights should be plumb with the bow so that the shooter can be better be assured that the bow is not canted, because inadvertently canting the bow can ruin accuracy at long range. But before getting to the third axis, we should briefly discuss the first and second.
To understand what is called the first axis, picture an imaginary line running through the bow sight from left to right that would allow the bottom of the sight to swing toward or away from the archer like a mud flap on a truck. This axis should be perfectly perpendicular with the bow's riser before the next two axes can be properly leveled. It can be aligned using a bow square, a regular carpenter's square or even a door frame that you know to be plumb. Or, of course, you can take your bow into an archery pro shop and let them do it – for a small fee.
For the second axis, imagine a line running from the target, through the bow sight, to the archer's eye. All of the sight pins should line up vertically on this plane. (In other words, they should form a vertical line that's parallel to the bow's riser.) It's important to get this second axis perfectly plumb with the bow (and level with the ground) so that the archer can use the sight's bubble level while shooting and know that if it is level, then the pins are in line, and so the arrow will strike on the same vertical plane no matter the sight pin chosen. It's very important for precision shooting at longer distances.
The third axis, however, is not important for the average bowhunter who doesn't shoot much past 40 yards and/or at extreme upward and downward angles. Sitting 15 feet up in a treestand and shooting something at 35 yards doesn't constitute an extreme angle or need for precision third-axis adjustment. It's more regarded by western hunters who may shoot at targets 60 yards distant and 40 yards down (or up) a cliff face. Extreme target archery tournaments, like the Total Archery Challenge, make a point of setting up these difficult shots. So, if you're not one of these guys who must shoot at such crazy angles, you probably shouldn't fret over your third-axis adjustment. But if you are an extreme target shooter, a mountain hunter – or just a perfectionist – you can enhance your long-range accuracy by making sure it's perfectly adjusted. It's complicated to explain by mere words, but here goes.
Imagine a plane running vertically down though the bow sight that would allow the bow sight to rotate like a revolving door. If that door (the sight) is slightly pulled slightly open or pushed back (in relation to the bow's riser) and then angled up or down (as if taking an extreme uphill or downhill shot), the relationship between the bow sight and the ground can change, causing it to become out of level with the ground, and therefore causing your arrow to be off.
But it's not as simple as using a square to make sure the sight's third axis is plumb with the riser while it's in a vise; third axis is best evaluated while at full draw. Torque placed on the bow by the shooter's hand and the cable guard can affect the third axis. While there are specialized tools designed to check the third axis, you can check it at home, if you know what you are doing. To check, draw a perfectly vertical scribe line at the bottom of a wall. Place a bubble level on the bow. Then, while facing the wall, draw the bow, and while aiming it down at a 45-degree angle, make sure the bow is level by viewing the bubble level. Next, make sure the sight pins perfectly line up on the scribe line. If they do not, you'll need to make adjustments to the third axis.
Some newer and more expensive bow sights have third-axis adjustments, but most hunting sights do not. For these, a small shim such as a strip of duct tape or a length of business card placed between the sight's mounting bracket and the riser can often tilt the sight's third axis enough to do the trick. But if you are serious about perfection, you are probably best served by buying a sight with an integral third axis hinge that can be easily adjusted.
Fact is, most hunters – especially eastern whitetail hunters – can hunt their whole lives and never worry a third-axis adjustment. But if you're a bowhunter who might shoot a mule deer from a cliff face or plan to bowhunt sheep – or maybe you're just an archery geek – be sure to check your third axis.


text:
using BreweryDB.Models;

namespace BreweryDB.Interfaces
{
    public interface IStyle
    {
        string Id { get; set; }
        string CategoryId { get; set; }
        ICategory Category { get; set; }
        string Name { get; set; }
        string ShortName { get; set; }
        string Description { get; set; }
        double IbuMin { get; set; }
        double IbuMax { get; set; }
        double AbvMin { get; set; }
        double AbvMax { get; set; }
        double SrmMin { get; set; }
        double SrmMax { get; set; }
        double OgMin { get; set; }
        double FgMin { get; set; }
        double FgMax { get; set; }
        string CreateDate { get; set; }
        string UpdateDate { get; set; }
    }
}


text:
Blog December 15, 2021
How the COVID-19 Pandemic Influenced the Practice of Pathology
Kim Futrell, MT(ASCP), MSHI
The COVID-19 pandemic shook up the healthcare system, and many of the changes that were implemented may stick around and improve healthcare delivery. The pandemic forced pathologists to revisit telehealth and digital pathology tools that allow remote access.
The Pandemic as a Catalyst for Digital Pathology
The pandemic served as a catalyst for pathologists to adopt digital workflows and embrace the advantages of these tools. As a result, the value of digital pathology and remote pathology services is gaining wider acceptance.
Many pathologists needed a way to work from home due to physical distancing guidelines. Digital pathology platforms allow pathologists to view, analyze, share, and annotate digital slides from their home offices, eliminating delays in diagnosis and treatment. This allows pathologists to work from home and maintain quality patient care standards while minimizing the risk of COVID-19 exposure and spread.
The pandemic also moved certain legislation forward more rapidly. The Centers for Medicare and Medicaid Services (CMS) issued a waiver that allowed pathologists to work remotely. The FDA relaxed some of its guidelines regarding the approval and validation of digital pathology–related technology to allow pathologists to work from home. A study at The Ohio State University found that following the CMS waiver, 60% of pathologists reported using digital pathology for remote primary diagnosis and 85.7% stated that they would continue to use these tools if restrictions were permanently lifted.
AI on the Horizon
Digital pathology enhanced with artificial intelligence (AI) methodologies is gaining acceptance. There are opportunities to use AI for diagnostic workups, particularly in guiding personalized cancer treatments. For pathologists to benefit from AI, they need digitized slides as a starting point. Currently, there are several pathology-oriented AI tools (clinical decision-support algorithms) available to assist pathologists in reviewing cases.
AI, which refers to computers that can "think" and learn, is quickly gaining traction across healthcare. Machine learning (ML) is one type of AI that involves computers with the ability to develop models based on information received without any further programming or interventions. Specific to pathology, ML can aid in the discovery of patterns within images that can be used to make predictions. For example, ML algorithms are currently being used to help pathologists assess H&E stains used in the prognosis of breast cancer and lung carcinoma.2
AI tools can enhance digital imaging by providing faster diagnoses and decreasing labor costs. These tools can augment the workflow, aiding clinical decision support and enhancing care delivery. In the future, AI tools are expected to progress from supporting automated case review to computer-assisted diagnosis.2
More Successful AI Examples in Pathology
ML and deep learning algorithms can aid pathologists' decision-making by allowing them to review large amounts of detailed data with computer-level accuracy. The Ohio State University Comprehensive Cancer Center uses digital images as part of their routine diagnostic workflow. They have determined that due to increased productivity and error reduction, the digital pathology system will pay for itself in five years.1 AI tools and digitized images are being used for repetitive tasks like identifying metastases in lymph nodes, grading cancers, and developing biomarker detection tools to help determine which patients will benefit from a particular drug.1
The FDA recently approved an AI software—Paige Prostate—to assist in prostate biopsy review. As an adjunct to pathologist review of digitized prostate biopsy images and to aid in defining the best initial diagnosis, Paige Prostate identifies areas of interest on the images that contain the highest likelihood of cancer for further review.3
While these are exciting endeavors, there are still hurdles ahead before AI technologies are widely used. Implementing digital imaging and AI tools so they function well in daily practice has several challenges that will need to be addressed. For example, to gain pathologist buy-in, AI tools need to be easy to use, be trustworthy, and demonstrate a financial gain.
1. Lujan, G.M., Savage, J., Shana'ah, A., et. al. Digital Pathology Initiatives and Experience of a Large Academic Institution During the Coronavirus Disease 2019 (COVID-19) Pandemic. Arch Pathol Lab Med. May 2021, 145 (9): 1051–1061.
2. Zarella, M.D., Bowman, D., Aeffner, F., et al. A practical guide to whole slide imaging: A white paper from the digital pathology association. Archives of Pathology & Laboratory Medicine. 2019; 143 (2): 222–234. https://doi.org/10.5858/arpa.2018-0343-RA
3. Ridley, E.L. FDA clears 1st prostate pathology AI software application. LabPulse.com. September 2021. Accessed at: www.labpulse.com/index.aspx?sec=nws&sub=rad&pag=dis&itemid=802302


After Wismec RX2 20700 kit, Reuleaux series have a new member which is more powerful- Reuleaux RX2 21700 kit. It is compatible with 21700 battery as well as 18650 battery. Equipped with dual 21700 batteries, RX2 21700 is capable of firing up to 230W. And the GNOME atomizer is featuring the retractable top filling solution and convenient bottom airflow control. It can hold 2ml/4ml liquid and apply with WM coils for intense flavor.


Introducing the 2016 Toyota RAV4. With just over 20,000 miles on the odometer, this 4 door sport utility vehicle prioritizes comfort, safety and convenience. Under the hood you'll find a 4 cylinder engine with more than 170 horsepower, providing a smooth and predictable driving experience. It's equipped with tons of terrific amenities, but it won't break your budget. Like all wheel drive, a rear window wiper, a tachometer, an outside temperature display, front bucket seats, cruise control, an overhead console, and 1-touch window functionality. Premium sound drives 6 speakers, providing you and your passengers a sensational audio experience. Toyota also prioritized safety and security by including: head curtain airbags, front side impact airbags, traction control, brake assist, ignition disabling, and 4 wheel disc brakes with ABS. Electronic stability control stands out as a technologically savvy innovation, keeping you better connected to the road. This vehicle has achieved Certified Pre-Owned status, by passing Toyota's comprehensive certification process. We pride ourselves in the quality that we offer on all of our vehicles. Please don't hesitate to give us a call.


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San Lorenzo, auch als San Lorenzo del Pailón bekannt, ist eine Kleinstadt und die einzige Parroquia urbana ("städtisches Kirchspiel") im Kanton San Lorenzo der ecuadorianischen Provinz Esmeraldas. Die Parroquia besitzt eine Fläche von 251,9 km². Die Einwohnerzahl lag im Jahr 2010 bei 25.096. Davon wohnten 23.265 Einwohner in der Stadt San Lorenzo. Wie in vielen Gemeinden der Provinz bilden Afroecuadorianer die Mehrheit der Bevölkerung.

Lage
Die Parroquia San Lorenzo liegt an der Pazifikküste Nordwest-Ecuadors. Das Gebiet erstreckt sich über das küstennahe Tiefland und umfasst auch die Insel Isla Tatabrero. Im Norden reicht das Areal bis an die Mündung des Grenzflusses Río Mataje in den Pazifik. Die Stadt San Lorenzo befindet sich knapp 100 km ostnordöstlich der Provinzhauptstadt Esmeraldas.

Die Parroquia San Lorenzo grenzt im äußersten Norden an Kolumbien, im Nordosten an die Parroquia Mataje, im Osten und im Südosten an die Parroquias Tululbí, Calderón und Carondelet, im Südwesten an die Parroquias Concepción und Tambillo sowie im Nordwesten an die Parroquias Pampanal de Bolívar (Kanton Eloy Alfaro) und Ancón de Sardinas.

Geschichte
Die Stadt geht auf eine Gründung von Pedro Vicente Maldonado im Jahr 1743 zurück. Im Jahr 1941 wurde die Parroquia Teil des neu geschaffenen Kantons Eloy Alfaro. Am 22. März 1978 wurde der Kanton San Lorenzo gegründet und San Lorenzo wurde eine Parroquia urbana und Sitz der Kantonsverwaltung.

Wirtschaft und Verkehr
San Lorenzo ist eine Hafenstadt an der Pazifikküste. Die Stadt ist Endpunkt der Fernstraße E10, die von Esmeraldas entlang der Küste führt. Die Fernstraße E15 von Ibarra nach Mataje an der kolumbianischen Grenze verläuft 10 km weiter östlich.

Ökologie
Die Küstenregion der Parroquia befindet sich im Schutzgebiet Reserva Ecológica Manglares Cayapas Mataje.

Persönlichkeiten
 Enner Valencia (* 1989), Fußballspieler
 Beder Caicedo (* 1992), Fußballspieler
 Juan Caicedo (* 1992), Diskuswerfer
 Félix Torres (* 1997), Fußballspieler
 Jackson Porozo (* 2000), Fußballspieler

Weblinks
 Bevölkerungsstatistik bei www.citypopulation.de

Parroquia in der Provinz Esmeraldas
Ort in Ecuador
Ort in Südamerika

redpajama_set_name: RedPajamaWikipedia


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Q: How to reuse a for variable in django I'm trying to display me df in a table to my web app (without using .tohtml because I need a dynamic table).
It seems that I can't use the key/column variable from my loop :

            {% for header in BDD_Data %}
             {{header}}
            {% endfor %}

    {%  for key in BDD_Data_size %}

            {% for column in BDD_Data %}
             {{BDD_Data[column][key]}}
            {% endfor %}

    {% endfor %}

My error:

I think I've any problems with my data because if I write {{column}} / {{key}} instead of {{BDD_Data[column][key]}} it displays all the values from my dataframe.

A: The trick was to use mydataframe.values :

            {% for header in BDD_Data %}
             {{header}}
            {% endfor %}

    {%  for value in BDD_Data.values %}

            {% for cell in value %}
             {{cell}}
            {% endfor %}

    {% endfor %}

redpajama_set_name: RedPajamaStackExchange