The Promise of the Dietary Supplement :: Research Science Diet Essays

The Promise of the Dietary Supplement Fat Busters, Fat Trappers, Fat Magnets and Fat Absorbers all share one ‘miraculous’ operator for all intents and purpose †chitosan. For what reason is this enhancement extremely popular in today’s weight reduction showcase? Will chitosan satisfy its touted potential? The dietary enhancement, chitosan is the handled, more water-dissolvable type of one of the most widely recognized substances in nature: chitin (KITE-in). The exoskeletons of arthropods (ants, shellfish, and so on.) and the dividers of numerous molds, yeast and parasites contain this structure square of nature. Chitosan was first found in 1811 by Henri Braconnot, executive of the greenhouse in Nancy, France. Bracannot saw that a specific substance (chitin) found in mushrooms didn't break up in sulfuric corrosive. In the course of the most recent 200 years, the investigation of chitosan has taken on a wide range of structures. A few different scientists keep on expanding on the first finding of Bracannot, finding new uses for chitin as they find various types of it in nature. Chitosan helps in the â€Å"reduction of triglycerides because of its capacity to tie dietary lipids, in this way lessening intestinal lipid absorption† (Koide, 1998). Interpretation? Fundamentally, chitin particles can lock on to substantial metals, amino acids and FAT. Chitin might have the option to ‘soak up’ fat in the digestive tract and flush it through the body before it very well may be retained. In the event that successful, this procedure should prompt weight decrease. Kill fat, consequently causing weight reduction Decrease cholesterol Give a protected and common health improvement plan with negligible/no reactions As per Sally Squires, in the March 28, 2000 issue of The Washington Post, a few components including chitosan ought to be considered before purchasing chitosan-based items: 1) The piece of chitin can shift contingent upon the seawater, the creature from which it is taken, and the season. There is no ‘pure form’ that can be depended on. 2) Following the Dietary Supplement Health and Nutrition Act in 1994, the FDA has a constrained capacity to control nutrients, minerals and other dietary enhancements (for example chitosan). It is, consequently, conceivable to stock shevles loaded with an item without giving related logical proof. 3) The Federal Trade Commission (FTC) won a $8.3 million judgment against an organization damaging government buyer insurance laws with bogus cases concerning chitin. 4) The FTC claim is the first of a few extra (non FTC) suits for bogus cases with respect to chitin and chitosan, including a claim documented by both Napa and Sonoma regions in California.

Reviewing Law on Sex with Minors for Social Media- myassignmenthelp

Question: Talk about theReviewing Law on Sex with Minors for Social Media. Answer: Presentation Sex with minors is a wrongdoing in practically all nations of the world. Furthermore, sharing obscene photographs of kids or sexting is additionally a wrongdoing. As of late, and with the progression in innovation, all the more significantly, online life, there are expanded instances of such offenses (Wittes, Poplin, Jurecic, Spera, 2016). Additionally, the judgment gave the convict has all the earmarks of being tolerant to the eyes of people in general. Along these lines, it is critical to take a gander at factors that one must place into contemplations in auditing the current laws. Extensively, the elements seem to include legal points of reference, considering the present law and making the vital improvement such that the law stays reasonable however not impacted by the open weight. Survey of the Law Right off the bat, Singapore as a state denies sex with kids younger than 16 years. The demonstration is a wrongdoing, and as of late, general society is at the cutting edge in pressurizing the administration to administer harder laws than the current ones. In particular, sex younger than 16 is illicit. Moreover, having intercourse with minors younger than 14 years is assault regardless of whether the youngster gives assent. The law contends that youngsters beneath that age are not fit for assent for sex. In addition, the law denies sharing of explicit materials that includes minors, in any stage, particularly, via web-based networking media (MOHAN, 2016)). These laws seem to sit not all that well with the general population, and they have a require their cancelation or rather an improvement to make them harder. Hence, what are factors that one should consider in assessing the laws? One of the most well-known methods of deciding if the law or judgment is simply and reasonable is by taking a gander at legal points of reference. These are past feelings to cases that seem comparable (Radics, 2013)). In an ongoing case that includes a military craftsmanship educator, Joshua Robinson, who carries out a four-year punishment for having intercourse with two 15 years of age kids seems merciful to general society. Also, the educator is in jail for sharing explicit photographs that include minors. Despite the fact that the open considers the to be as not reasonable, thinking about the greatness of the wrongdoing, legal points of reference are quite line with the decision (Chua, Chu, Yim, Chong, Teoh, 2014). The workplace of the lawyer general contends in reacting to the open objection, expresses that the judgment is reasonable and in accordance with the current law. The general population is of the supposition that the teacher should have been given harder discipline which incorporates canning, accordingly, squeezing the legal framework to survey the law. Another factor that should be placed into thought, most definitely is the various classes that exist about sex with minors. In doing the offense, different occurrences call for non-consistency in the death of judgment. Right off the bat, there is sex with minors that include viciousness. Another is what incorporate assault and furthermore one that delivers torment or harms the person in question (Terry, 2012). Notwithstanding that, there might be where the individual accused of a duty of ensuring the kid is the one submitting such an offense. Along these lines, in looking into the law on sex with the children, the different classes should be a factor to be considered trying to make laws that are simply and reasonable for the two gatherings. In conclusion, as laws serve people in general, it is imperative to remember the general population for looking into the laws on sex with minors as they structure some portion of those legitimately influenced by the very laws by and by. Notwithstanding that, Singapore pastor of equity likewise accepts that it is significant in remembering the general population for checking on laws on sex with youngsters (Vendius, 2015). In any case, it is not necessarily the case that the open need to impact the judicially in concocting laws on sex with minors yet they are not lawful specialists at all. End Along these lines, there are a few elements important to be placed into thought in auditing laws including sex with kids. In Singapore, the law is against an individual engaging in sexual relations with a youngster younger than 16. Notwithstanding that, the law is harder for those engaging in sexual relations with minors beneath the age of 14 is it considered as assault. In inspecting the law on sex with minors, it is essential to think about legal points of reference, the various classes under which the sex laws with kids falls and in conclusion, factor in broad daylight sees on the equivalent. The survey of the law is pivotal in light of the fact that people in general accepts that the law is excessively indulgent to the convicts. References Chua, J. R., Chu, C. M., Yim, G., Chong, D., Teoh, J. (2014). Execution of the Risk Need Responsivity structure over the adolescent equity offices in Singapore.Psychiatry, Psychology and Law,21(6), 877-889. MOHAN, S. C. (2016). Underage Commercial Sex and 9 Criminal Prosecutions in Singapore.Interpersonal Criminology: Revisiting Interpersonal Crimes and Victimization. Radics, G. B. (2013). Decolonizing Singapore's Sex Laws: Tracing Section 377A of Singapore's Penal Code. Terry, K. (2012).Sexual offenses and guilty parties: Theory, practice, and approach. Nelson Education. Vendius, T. T. (2015). Proactive Undercover Policing and Sexual Crimes against Children on the Internet.European Review of Organized Crime,2, 6-24. Wittes, B., Poplin, C., Jurecic, Q., Spera, C. (2016). Sextortion: Cybersecurity, youngsters, and remote sexual assault.Center for Technology at Brookings. https://www. brookings. edu/wp-content/transfers/2016/05/sextortion1-1. pdf. Accessed,16.

Altiscale

Altiscale INTRODUCTIONMartin: Hi. Today we are in Palo Alto in the Altiscale office. Hi, Raymie. Who are you and what do you do?Raymie: Hi. Yes, I’m Raymie Stata, the Founder and Chief Executive Officer of Altiscale. We do big data in the cloud using both Hadoop and Spark.Martin: What did you do before you started this company? And how did you come up with the idea for Altiscale?Raymie: Well, in many ways, the roots of Altiscale go all the way back to AltaVista. If you remember that search engine from the 1990’s. I was lucky enough to have a chance to work on that. I actually got out of college thinking that I would work in a research lab, and that research lab was at Digital Equipment Corporation where they created the AltaVista web search engine. So while I was there, I think pretty much everybody in that laboratory got pulled into the world of web search, and I was no exception. So I spent quite a few years working on AltaVista, getting experience in web search.And then after the dot co m crash in the early 2000’s, I actually started my first company which was Desktop Search Company, and we ran that for about four years and started to get some traction. Around 2004, that company was acquired by Yahoo. And about that time, actually slightly earlier, Yahoo had acquired a bunch of companies. They acquired Overture, and Overture, ironically enough, had just acquired AltaVista. So Yahoo had bought in all the old tech that I used to work with through Overture. They also bought the web search assets of FAST, which is a European search engine company, and a few other assets. So they were building up a search technology to go compete head to head with Google. And a little after those major acquisitions, they decided they wanted to have some Desktop Search as well, so they acquired my company.So that was 2004, and in the history of Hadoop, that was a significant year. That was the year that the MapReduce paper was published from Google. And for people in the search world, that was a really fascinating paper. I think for us, the basic technique that they were using, which was a cluster-wide search, was something that pretty much everybody in the search world had developed in a way. But the MapReduce paper took that basic technique and put it in a very elegant package where the mechanism of the MapReduce paradigm was very clearly separated from the application code. And so one could very easily develop and improve the framework code, the heart of the distributed system part, if you will, and pan it of the application code. And that very clean separation, I think, was missing in most of the search world.So it got our attention for sure at Yahoo and I think in other search companies at the time. But what was fascinating about that paper as well is it got the attention of, I think, a broader universe. At that time, Google was certainly in ascendance. People were beginning to think, “There’s some magic over there. They must know something we don’t kn ow.” And so that paper, I think, was seen as an insider’s look into the magic of Google. And so there was a lot of very broad fascination with that paper. So interestingly, other search engines, they were at that time a little bit more competitive in the world of search, so there were other search engines. I think other companies were interested in doing better than Google, so they all embarked on these very ambitious internal projects to do something better than Google.But there were a few of us at Yahoo who said, “Instead of trying to out-engineer Google, maybe what we can do is demystify this a little bit by basically taking that concept and implementing it in open source.” And I had known Doug Cutting prior to getting to Yahoo. We built some work with the Internet archive, and I got to know his work with the Nutch Foundation at the time. He had his own foundation. And so I was able to get Doug to come work at Yahoo first as a contractor and then ultimately as employee, a nd we decided, “Yes, we’re going to do the open source MapReduce.” And that complemented well work that was already in place in the Nutch search engine where they had also implemented this thing called the Google File System.So that was how Yahoo got involved with Hadoop. I think Yahoo over the years made massive investments in Hadoop that really made it the enterprise strength software that it is today. So I was actually just at the very beginning of my tenure at Yahoo that I was getting a project on the way. Through the years at Yahoo, I had a number of positions. So I started in the algorithmic web search team where I was the chief architect of that team. And then over the years, I was the chief architect of the search and advertising team, and then the chief architect of all of Yahoo, and ultimately became the CTO of Yahoo. And as I got broader and broader responsibilities at Yahoo, I helped drag Hadoop along with me to be used in broader and broader use cases across Yahoo . And as a result, by the time I left Yahoo, we had a very large central installation for Hadoop. We had 40,000 nodes of Hadoop over a thousand users being used for a very wide variety of use cases.When I started thinking about leaving Yahoo, since I had a lot of experience with Hadoop inside of Yahoo, I took a look to see, “Hey, what’s it like to use Hadoop outside of a company?” And not just Yahoo. I knew how folkswere using it at Facebook. Twitter wasn’t so big at the time, but eBay. And it was the same model, a large central cluster run by a very competent professional team. Outside of those larger Internet companies, what I saw was something very different, which was these small clusters, 20 nodes. Very often they were being supported, if at all, by an operations team. It was like a discretionary effort. They weren’t really full time. Usually there were other production activities that had more of a priority. So the users of these clusters were often stuck dealing wit h problems on their own.And one of the things that I didn’t fully appreciate until I left Yahoo and started looking outside Yahoo was just how much internal support our end users have for using Hadoop. So if something went wrong and they were not sure why, Hadoop likes to throw stack traces, for example, every time something goes wrong. At Yahoo, people just turn and say, “Hey, have you seen this before?” and there was a lot of people inside the company that had used it and had the experience for answering those questions. And ultimately, the central team that was operating Hadoop had seen pretty much everything that could ever go wrong and so could always answer a question for you.Again, on these small 20-node self-surved clusters, it’s a very different experience and people were spending days searching the web, desperately asking questions in these email forums to get some help in figuring out what’s going on. So in contrasting that experience of those larger Internet co mpanies where they had that professionally large scale Hadoop clusters to what I was saying in the industry, it became pretty clear that there was an opportunity to create a company that can offer Hadoop as a service the way you would experience it at Facebook or Twitter, in eBay or Yahoo. And that’s what we do at Altiscale.Martin: So Raymie, this means after you’ve left Yahoo, you’ve tried to validate your assumptions on whether there’s some kind of business need also because you have seen smaller companies which have been not very much focused on managing the cluster and so on. What was the next step then? So did you build some kind of MVP or did you just raise some money? What did you do?Raymie: By that point in time, I was a second time entrepreneur. There were investors who were interested in seeing what I was going to do next, so I had the benefit of having folks who were interested in backing me. So, I was able to raise a seed round fairly quickly. And we used that mo ney to start hiring because at the end of the day, getting the right talent is really the hardest part of the job and often causes the most delay, so that was very useful. And while we were doing that, even before I left Yahoo… In fact, my last title at Yahoo was entrepreneur in residence. I think I was Yahoo’s first and last EIR. And for various reasons, they gave me an opportunity to spend a few months starting to think about what my next steps were going to be. So I had the benefit of a couple of months of research upfront. And so I was able to validate that the issues that I saw indeed were pretty widespread.Martin: So once the investor wrote you a check, how did you go about acquiring your first employees? So how did you actually find them? Did you tap into a network? Did you post a job? Did you ask friends? What did you do?Raymie: By and large, it’s through networking, through people I knew directly. But there tends to be a transitive nature to that. So somebody I know i ntroduced me to who’s now our VP of Engineering, Ricardo Jenez. And even though we went to the same school, we both went to MIT, we didn’t quite overlap in time and we didn’t know each other at school but we got to know each other through a mutual friend. So sometimes it’s not quite that direct. I didn’t hire the friend but I hired the friend’s friend. So there’s levels of indirection there. And then once you hire… I hired Ricardo and our networks were slightly different. He spent some time at Google, for example, and was able to tap into the Google network a little bit more effectively than I could. So there is that spread of the corporate network as you bring people on. Your reach gets broader and broader.Martin: So Raymie, if you look back in time just by memory, the first 6 to 12 months, what was it really like? What type of obstacles did you really perceive in the day to day business where you say, “I think I’m not sure that this is the right business idea,â € or “There’re some kind of issue that I need to solve but actually I don’t know,” or something like that?Raymie: Yes. Let me think back. Again, getting that core initial team is always a big focus and at those stages where it’s literally three people and nothing, it’s a pretty big leap for people to sign up for that. And so that certainly took a fair bit of time. Altiscale is a bit unique as a technology company where a lot of technology companies, you’ve got some idea for some core IP and you hire a bunch of developers and they all go away, and you work on that IP for six months or so, and you really develop this relatively small compact piece of software that is really great in some dimension, and then you go out and you start to use it. And then you spend the next seven years putting layers of gunk around that beautiful center.At Altiscale, we inherited the Hadoop ecosystem, millions of lines of code. And so when you’re at that first three going on six technical people and you’ve got to own millions of lines of code, that’s a unique challenge for a startup. And so another aspect of our early struggles was to figure out how are we going to make that work and where are we going to start, because it felt like the problem was so big. And there were some false starts. One thing we ultimately decided to do was to restrict how much of the Hadoop ecosystem we would start with, with the idea of growing over time, and then really focusing in on deployment automation and not on multi-tenancy. It was the first technical problem we were going to tackle was another way in which we started to reduce the problem and the scope to something that a small team could work on.BUSINESS MODEL OF ALTISCALEMartin: Raymie, let’s talk about the business model. So you briefly touched on your target customers basically, which is more, as I understood, the smaller companies, right?Raymie: No. We don’t think of them as smaller companies per se. We really think of companies more in terms of how much data they have versus how many people they have. So ultimately when people say a big company versus a small company, you can measure in revenue, but often revenue is pretty directly correlated to the number of people. That’s the more traditional metric of “Hey, how big is a company?” But for us as a big data service provider, what matters a lot more is how much data you have versus how many people you have. It turns out that some of the smaller companies have much more data than some of the largest companies.So from that perspective, you can look at the amount of data, if you will, that an organization is ready and willing to put into a data processing system like Hadoop. You can maybe call it small, medium and large, where small is, say, 10 terabytes or less. Medium is maybe we can call it 20 terabytes or 15 terabytes, so medium is when you get into many tens of terabytes to hundreds of terabytes, maybe to a petabyte or so, but it’s not multiple petabytes. And large is multiple hundreds of terabytes. And for us, we tend to target the ones in the middle, the folks that have more typically a hundred to a few hundred terabytes to a petabyte or two. It’s a good range. As you get smaller than that, I think the problems of Hadoop become a little bit more self-manageable. As you get bigger than that, additional complexities start to come in that we’ll get to tackle over time, but again as a startup, you need to be focused. So that’s how we measure the size of our customers. It turns out that it’s a good swath of companies and it covers some of the largest companies in the world and it covers some of the smallest ones. It’s all over the map in terms of how big the company itself is.Martin: Raymie, can you briefly describe what is the value proposition that you are delivering to those target customers?Raymie: Sure. Well, at those scales, when you start to get into a hundred plus terabytes of data, maintaining your big data infrastructure. In our case, that’s anchored in the Hadoop Distributed File System. The Hive Metastore actually is an important component and then Spark and YARN sitting on top of that. So it’s the core platform. As you get to that hundreds to many hundreds of terabytes of data, keeping that operating well and, in particular, keeping jobs running fast and running reliably, completing it successful. It turns out to become harder and harder as you scale up. And I think it’s just the nature of distributed systems. There’s this kind of exponential issue where as you add more and more pieces, it gets exponentially more difficult to keep it all running reliably.And so the value proposition of Altiscale is to keep your big data infrastructure running well as you grow, as you scale, allowing your customers to focus on what they’re doing with Hadoop and not get wrapped up in running it day to day or worrying about how they’re going to grow it over time, which itself is a significant issue.Martin: But still the customer is providing the jobs, or are you supporting and inviting the job? Or is it only that you are trying to have some kind of maintenance or monitoring on whether the jobs are running correctly or not?Raymie: That’s a good question. I think that what we do not typically do is actually write the jobs themselves. Either the customers do that or there’s lots of software services companies out there who will help you write big data applications. So we’re really focused on keeping those jobs running well, which includes, as you point out, monitoring the jobs and helping to deal with problems. However, I think one unique service that we can provide is that a lot of times you’ve written a job but it’s not quite right, it’s not scaling well, it has performance problems, and so I think one service that we do provide is that where there are those particularly problematic jobs, we’ve got enough experience where we can say, “Hey, it looks like you have a skew problem or a memory problem or this or that.” We can give advice to help people quickly get that job working. And again going back to those founding stories of Altiscale where people were spending weeks at a time wrestling with Hadoop over what turned out to be a relatively trivial issue. We could really save customers a lot of time that way. And if what they had to do is go out and find some external consultant to come in to look at that, it would just be ridiculously expensive. But the fact is we’re sitting there and we’re monitoring the jobs. There’s certain telltale signs. There’s certain problems, so we can very quickly and easily say, “Hey, here’s a problem that you might want to look at.”Martin: Raymie, are those problems identified by humans or are you using something like machine learning and then automatic recommendation engines on what to do if that problem occurs?Raymie: Yes. Well, that’s a great question because in many ways it incorporates, I think, a standard assumption that people make. You either use humans or you use algorithms.Martin: Or both?Raymie: Yes, but I think the “or both” is not an insignificant point. I think that having a blend of people and machines is really the secret of doing a lot of things well. Part of that is indeed how we help customers. There are some automatic alerts that look for certain patterns, but there’s also more manual things, and there’s a whole spectrum there. But even if you look at how we operate the clusters themselves, we’re going back to “Hey, what were some of the challenges upfront?” I think we decided not to go start way over on the side of too much automation but rather start in a more moderate point, really have the automation and the human operators work hand in hand, and based on actual real world experience, use that to drive the technology and also to use that to say, “Hey, what are people best at? And let’s use what they’re good a t,” whereas “Where can we use machines to really ultimately just pull labor out of the activity?” So getting a good man-machine symbiosis, as we like to call it, is really core to our approach to a lot of problems here at Altiscale.Martin: Raymie, imagine you are going to a potential client and ask him whether he would like to buy an Altiscale product. How do you sell this kind of product over competitor products like other big data platform providers?Raymie: Sure. Well, I think it depends on who the customer is and what are their existing experiences with Hadoop. In the early days, all of our customers were actually what I would call Hadoop veterans. And what I mean by that is they’re not only people who have used Hadoop before but as an organization, they actually had Hadoop in production fairly successfully and yet they shifted over to us, which is a little bit counterintuitive, but there’s a reason for that. The reason is that over time Hadoop doesn’t get easier. It actually gets harder because you do more and you do more and the technology, it’s hard to believe but it actually changes faster and faster over time. The rate of improvement in the Hadoop ecosystem is just stunning. But if you’re trying to operate Hadoop cluster, keeping pace with all that change is very challenging.So if a customer prospect is already fairly experienced with Hadoop, we talk the same language. It becomes a lot easier. You can go in and say, “Hey, are you having this problem? Are you having that problem? Is it a pain? When is your next upgrade?” A lot of times, actually it’s the upgrade thing which tips them over because they’ll be a year out of date and we could say, “Hey, the latest Spark has all that stuff. Can we use it? Oh, no. My internal users are very upset.” And we have a common language that we can use and common experiences, and the dialogue actually is a little bit easier.A variation on that team is a lot of folks have set up a Hadoop clu ster initially for the focus of supporting some kind of what you might call production pipeline, some kind of ETL process where data comes in, it gets processed, it goes some place. That cluster was originally set up to just do this very mechanical thing, and that software itself is not evolving very quickly, so the people in charge of the cluster view it as a maintenance problem. But then what happens is some data scientists start to dig up because the data that’s running through those pipelines is very interesting to them. And before you know it, you’ve got a bunch of data scientists who are trying to use this cluster and it’s not working because it wasn’t set up for their active use, it doesn’t have the tools that they want to use, and they find themselves frustrated that they don’t have the kind of data science environment that they really like. So there are two we can relate to the actual grounded experiences that they have. A lot of times they say, “Hey, why donâ €™t you get a separate data science environment do that at Altiscale and leave the production part?” That’s fine to have too, and that’s very successful for us.I think it has been more challenging for organizations that are new to Hadoop. They say, “Hey, I think we ought to do Hadoop.” And in context, they don’t know what they don’t know. They don’t know how complicated Hadoop is going to be. They think of it more as a traditional software application where “Yes, in the first year or two, it’s a big pain and its going to cost us a lot of money. But once you get over the hump, it becomes easier. It goes into maintenance mode and we can be done with it.” And educating those customers that it doesn’t look like that, it just actually gets worse and worse, becomes a challenge to us because they don’t have those experiences. They don’t have the vocabulary. Fortunately, since we had those earlier customers, we can use them to provide some degree of testimony, an d that helps. Over time, we’ve been working on other ways to help educate people who are new to Hadoop that they should really think twice before they get too deep into running it themselves.Martin: So thinking about the Hadoop ecosystem, what are from your perspective the biggest misconceptions or mistakes the end users or companies are doing when they are interacting with Hadoop? You briefly touched on one.Raymie: Yes. Obviously for us, we think mistake number one is run it yourself. Let the experts do that for you. It saves you a lot of time and effort. I think that putting that one aside, I mentioned before that the rate of innovation in and around Hadoop is quite high, and I do think that another mistake people make is that they think they need to have the very, very latest, and a lot of times the latest new project that gets announced is really not ready to be used in a production way. So I think that’s actually another fairly common mistake.I think another mistake that pe ople have is they don’t understand very well what I would call the performance characteristics of various components. They understand the functional characteristics. When I put this query in, I’m supposed to get those results out. But they don’t understand the performance characteristics depending on how much data is in the table, for example. Is there a skew in the data? There’s a lot of factors that will determine what kind of performance you’re going to get. And I think there’s this unrealistic expectation that it’s just going to be fast for everything, and they don’t engineer their applications around a realistic expectation for what can be done. I think HBase actually is a particularly challenging technology in that regard. It’s great in many dimensions but I think people tend to think that it can do anything and it can’t and then get themselves in trouble. So understanding what are the performance characteristics and the failure characteristics to some degr ee and making sure that you engineer your application around those instead of making unrealistic expectations is another lesson for first time Hadoop users.ADVICE TO ENTREPRENEURS FROM RAYMIE STATA In Palo Alto (CA), we meet Founder CEO of Altiscale, Raymie Stata. Raymie talks about his story how he came up with the idea and founded Altiscale, how the current business model works, as well as he provides some advice for young entrepreneurs.INTRODUCTIONMartin: Hi. Today we are in Palo Alto in the Altiscale office. Hi, Raymie. Who are you and what do you do?Raymie: Hi. Yes, I’m Raymie Stata, the Founder and Chief Executive Officer of Altiscale. We do big data in the cloud using both Hadoop and Spark.Martin: What did you do before you started this company? And how did you come up with the idea for Altiscale?Raymie: Well, in many ways, the roots of Altiscale go all the way back to AltaVista. If you remember that search engine from the 1990’s. I was lucky enough to have a chance to work on that. I actually got out of college thinking that I would work in a research lab, and that research lab was at Digital Equipment Corporation where they created the AltaVista web search engine. So while I was there, I think pretty much everybody in that laboratory got pulled into the world of web search, and I was no exception. So I spent quite a few years working on AltaVista, getting experience in web search.And then after the dot com crash in the early 2000’s, I actually started my first company which was Desktop Search Company, and we ran that for about four years and started to get some traction. Around 2004, that company was acquired by Yahoo. And about that time, actually slightly earlier, Yahoo had acquired a bunch of companies. They acquired Overture, and Overture, ironically enough, had just acquired AltaVista. So Yahoo had bought in all the old tech that I used to work with through Overture. They also bought the web search assets of FAST, which is a European search engine company, and a few other assets. So they were building up a search technology to go compete head to head with Google. And a little after those major acquisitions, they decided they wanted to have some Desktop Search as well, so they acquired my company.So that was 2004, and in the history of Hadoop, that was a significant year. That was the year that the MapReduce paper was published from Google. And for people in the search world, that was a really fascinating paper. I think for us, the basic technique that they were using, which was a cluster-wide search, was something that pretty much everybody in the search world had developed in a way. But the MapReduce paper took that basic technique and put it in a very elegant package where the mechanism of the MapReduce paradigm was very clearly separated from the application code. And so one could very easily develop and improve the framework code, the heart of the distributed system part, if you will, and pan it of the application code. And that very clean separation, I think, was missing in most of the search world.So it got our attention for sure at Yahoo and I think in other search companies at the time. But what was fasc inating about that paper as well is it got the attention of, I think, a broader universe. At that time, Google was certainly in ascendance. People were beginning to think, “There’s some magic over there. They must know something we don’t know.” And so that paper, I think, was seen as an insider’s look into the magic of Google. And so there was a lot of very broad fascination with that paper. So interestingly, other search engines, they were at that time a little bit more competitive in the world of search, so there were other search engines. I think other companies were interested in doing better than Google, so they all embarked on these very ambitious internal projects to do something better than Google.But there were a few of us at Yahoo who said, “Instead of trying to out-engineer Google, maybe what we can do is demystify this a little bit by basically taking that concept and implementing it in open source.” And I had known Doug Cutting prior to getting to Yahoo. W e built some work with the Internet archive, and I got to know his work with the Nutch Foundation at the time. He had his own foundation. And so I was able to get Doug to come work at Yahoo first as a contractor and then ultimately as employee, and we decided, “Yes, we’re going to do the open source MapReduce.” And that complemented well work that was already in place in the Nutch search engine where they had also implemented this thing called the Google File System.So that was how Yahoo got involved with Hadoop. I think Yahoo over the years made massive investments in Hadoop that really made it the enterprise strength software that it is today. So I was actually just at the very beginning of my tenure at Yahoo that I was getting a project on the way. Through the years at Yahoo, I had a number of positions. So I started in the algorithmic web search team where I was the chief architect of that team. And then over the years, I was the chief architect of the search and advertisi ng team, and then the chief architect of all of Yahoo, and ultimately became the CTO of Yahoo. And as I got broader and broader responsibilities at Yahoo, I helped drag Hadoop along with me to be used in broader and broader use cases across Yahoo. And as a result, by the time I left Yahoo, we had a very large central installation for Hadoop. We had 40,000 nodes of Hadoop over a thousand users being used for a very wide variety of use cases.When I started thinking about leaving Yahoo, since I had a lot of experience with Hadoop inside of Yahoo, I took a look to see, “Hey, what’s it like to use Hadoop outside of a company?” And not just Yahoo. I knew how folkswere using it at Facebook. Twitter wasn’t so big at the time, but eBay. And it was the same model, a large central cluster run by a very competent professional team. Outside of those larger Internet companies, what I saw was something very different, which was these small clusters, 20 nodes. Very often they were being sup ported, if at all, by an operations team. It was like a discretionary effort. They weren’t really full time. Usually there were other production activities that had more of a priority. So the users of these clusters were often stuck dealing with problems on their own.And one of the things that I didn’t fully appreciate until I left Yahoo and started looking outside Yahoo was just how much internal support our end users have for using Hadoop. So if something went wrong and they were not sure why, Hadoop likes to throw stack traces, for example, every time something goes wrong. At Yahoo, people just turn and say, “Hey, have you seen this before?” and there was a lot of people inside the company that had used it and had the experience for answering those questions. And ultimately, the central team that was operating Hadoop had seen pretty much everything that could ever go wrong and so could always answer a question for you.Again, on these small 20-node self-surved clusters, it ’s a very different experience and people were spending days searching the web, desperately asking questions in these email forums to get some help in figuring out what’s going on. So in contrasting that experience of those larger Internet companies where they had that professionally large scale Hadoop clusters to what I was saying in the industry, it became pretty clear that there was an opportunity to create a company that can offer Hadoop as a service the way you would experience it at Facebook or Twitter, in eBay or Yahoo. And that’s what we do at Altiscale.Martin: So Raymie, this means after you’ve left Yahoo, you’ve tried to validate your assumptions on whether there’s some kind of business need also because you have seen smaller companies which have been not very much focused on managing the cluster and so on. What was the next step then? So did you build some kind of MVP or did you just raise some money? What did you do?Raymie: By that point in time, I was a seco nd time entrepreneur. There were investors who were interested in seeing what I was going to do next, so I had the benefit of having folks who were interested in backing me. So, I was able to raise a seed round fairly quickly. And we used that money to start hiring because at the end of the day, getting the right talent is really the hardest part of the job and often causes the most delay, so that was very useful. And while we were doing that, even before I left Yahoo… In fact, my last title at Yahoo was entrepreneur in residence. I think I was Yahoo’s first and last EIR. And for various reasons, they gave me an opportunity to spend a few months starting to think about what my next steps were going to be. So I had the benefit of a couple of months of research upfront. And so I was able to validate that the issues that I saw indeed were pretty widespread.Martin: So once the investor wrote you a check, how did you go about acquiring your first employees? So how did you actually fi nd them? Did you tap into a network? Did you post a job? Did you ask friends? What did you do?Raymie: By and large, it’s through networking, through people I knew directly. But there tends to be a transitive nature to that. So somebody I know introduced me to who’s now our VP of Engineering, Ricardo Jenez. And even though we went to the same school, we both went to MIT, we didn’t quite overlap in time and we didn’t know each other at school but we got to know each other through a mutual friend. So sometimes it’s not quite that direct. I didn’t hire the friend but I hired the friend’s friend. So there’s levels of indirection there. And then once you hire… I hired Ricardo and our networks were slightly different. He spent some time at Google, for example, and was able to tap into the Google network a little bit more effectively than I could. So there is that spread of the corporate network as you bring people on. Your reach gets broader and broader.Martin: So Raymie, if you look back in time just by memory, the first 6 to 12 months, what was it really like? What type of obstacles did you really perceive in the day to day business where you say, “I think I’m not sure that this is the right business idea,” or “There’re some kind of issue that I need to solve but actually I don’t know,” or something like that?Raymie: Yes. Let me think back. Again, getting that core initial team is always a big focus and at those stages where it’s literally three people and nothing, it’s a pretty big leap for people to sign up for that. And so that certainly took a fair bit of time. Altiscale is a bit unique as a technology company where a lot of technology companies, you’ve got some idea for some core IP and you hire a bunch of developers and they all go away, and you work on that IP for six months or so, and you really develop this relatively small compact piece of software that is really great in some dimension, and then you go out and you st art to use it. And then you spend the next seven years putting layers of gunk around that beautiful center.At Altiscale, we inherited the Hadoop ecosystem, millions of lines of code. And so when you’re at that first three going on six technical people and you’ve got to own millions of lines of code, that’s a unique challenge for a startup. And so another aspect of our early struggles was to figure out how are we going to make that work and where are we going to start, because it felt like the problem was so big. And there were some false starts. One thing we ultimately decided to do was to restrict how much of the Hadoop ecosystem we would start with, with the idea of growing over time, and then really focusing in on deployment automation and not on multi-tenancy. It was the first technical problem we were going to tackle was another way in which we started to reduce the problem and the scope to something that a small team could work on.BUSINESS MODEL OF ALTISCALEMartin: Raymi e, let’s talk about the business model. So you briefly touched on your target customers basically, which is more, as I understood, the smaller companies, right?Raymie: No. We don’t think of them as smaller companies per se. We really think of companies more in terms of how much data they have versus how many people they have. So ultimately when people say a big company versus a small company, you can measure in revenue, but often revenue is pretty directly correlated to the number of people. That’s the more traditional metric of “Hey, how big is a company?” But for us as a big data service provider, what matters a lot more is how much data you have versus how many people you have. It turns out that some of the smaller companies have much more data than some of the largest companies.So from that perspective, you can look at the amount of data, if you will, that an organization is ready and willing to put into a data processing system like Hadoop. You can maybe call it small , medium and large, where small is, say, 10 terabytes or less. Medium is maybe we can call it 20 terabytes or 15 terabytes, so medium is when you get into many tens of terabytes to hundreds of terabytes, maybe to a petabyte or so, but it’s not multiple petabytes. And large is multiple hundreds of terabytes. And for us, we tend to target the ones in the middle, the folks that have more typically a hundred to a few hundred terabytes to a petabyte or two. It’s a good range. As you get smaller than that, I think the problems of Hadoop become a little bit more self-manageable. As you get bigger than that, additional complexities start to come in that we’ll get to tackle over time, but again as a startup, you need to be focused. So that’s how we measure the size of our customers. It turns out that it’s a good swath of companies and it covers some of the largest companies in the world and it covers some of the smallest ones. It’s all over the map in terms of how big the company itself is.Martin: Raymie, can you briefly describe what is the value proposition that you are delivering to those target customers?Raymie: Sure. Well, at those scales, when you start to get into a hundred plus terabytes of data, maintaining your big data infrastructure. In our case, that’s anchored in the Hadoop Distributed File System. The Hive Metastore actually is an important component and then Spark and YARN sitting on top of that. So it’s the core platform. As you get to that hundreds to many hundreds of terabytes of data, keeping that operating well and, in particular, keeping jobs running fast and running reliably, completing it successful. It turns out to become harder and harder as you scale up. And I think it’s just the nature of distributed systems. There’s this kind of exponential issue where as you add more and more pieces, it gets exponentially more difficult to keep it all running reliably.And so the value proposition of Altiscale is to keep your big data in frastructure running well as you grow, as you scale, allowing your customers to focus on what they’re doing with Hadoop and not get wrapped up in running it day to day or worrying about how they’re going to grow it over time, which itself is a significant issue.Martin: But still the customer is providing the jobs, or are you supporting and inviting the job? Or is it only that you are trying to have some kind of maintenance or monitoring on whether the jobs are running correctly or not?Raymie: That’s a good question. I think that what we do not typically do is actually write the jobs themselves. Either the customers do that or there’s lots of software services companies out there who will help you write big data applications. So we’re really focused on keeping those jobs running well, which includes, as you point out, monitoring the jobs and helping to deal with problems. However, I think one unique service that we can provide is that a lot of times you’ve written a job b ut it’s not quite right, it’s not scaling well, it has performance problems, and so I think one service that we do provide is that where there are those particularly problematic jobs, we’ve got enough experience where we can say, “Hey, it looks like you have a skew problem or a memory problem or this or that.” We can give advice to help people quickly get that job working. And again going back to those founding stories of Altiscale where people were spending weeks at a time wrestling with Hadoop over what turned out to be a relatively trivial issue. We could really save customers a lot of time that way. And if what they had to do is go out and find some external consultant to come in to look at that, it would just be ridiculously expensive. But the fact is we’re sitting there and we’re monitoring the jobs. There’s certain telltale signs. There’s certain problems, so we can very quickly and easily say, “Hey, here’s a problem that you might want to look at.”Mar tin: Raymie, are those problems identified by humans or are you using something like machine learning and then automatic recommendation engines on what to do if that problem occurs?Raymie: Yes. Well, that’s a great question because in many ways it incorporates, I think, a standard assumption that people make. You either use humans or you use algorithms.Martin: Or both?Raymie: Yes, but I think the “or both” is not an insignificant point. I think that having a blend of people and machines is really the secret of doing a lot of things well. Part of that is indeed how we help customers. There are some automatic alerts that look for certain patterns, but there’s also more manual things, and there’s a whole spectrum there. But even if you look at how we operate the clusters themselves, we’re going back to “Hey, what were some of the challenges upfront?” I think we decided not to go start way over on the side of too much automation but rather start in a more moderate point, really have the automation and the human operators work hand in hand, and based on actual real world experience, use that to drive the technology and also to use that to say, “Hey, what are people best at? And let’s use what they’re good at,” whereas “Where can we use machines to really ultimately just pull labor out of the activity?” So getting a good man-machine symbiosis, as we like to call it, is really core to our approach to a lot of problems here at Altiscale.Martin: Raymie, imagine you are going to a potential client and ask him whether he would like to buy an Altiscale product. How do you sell this kind of product over competitor products like other big data platform providers?Raymie: Sure. Well, I think it depends on who the customer is and what are their existing experiences with Hadoop. In the early days, all of our customers were actually what I would call Hadoop veterans. And what I mean by that is they’re not only people who have used Hadoop before but as an organization, they actually had Hadoop in production fairly successfully and yet they shifted over to us, which is a little bit counterintuitive, but there’s a reason for that. The reason is that over time Hadoop doesn’t get easier. It actually gets harder because you do more and you do more and the technology, it’s hard to believe but it actually changes faster and faster over time. The rate of improvement in the Hadoop ecosystem is just stunning. But if you’re trying to operate Hadoop cluster, keeping pace with all that change is very challenging.So if a customer prospect is already fairly experienced with Hadoop, we talk the same language. It becomes a lot easier. You can go in and say, “Hey, are you having this problem? Are you having that problem? Is it a pain? When is your next upgrade?” A lot of times, actually it’s the upgrade thing which tips them over because they’ll be a year out of date and we could say, “Hey, the latest Spark has all that stuff. Can we use it? Oh, no. My internal users are very upset.” And we have a common language that we can use and common experiences, and the dialogue actually is a little bit easier.A variation on that team is a lot of folks have set up a Hadoop cluster initially for the focus of supporting some kind of what you might call production pipeline, some kind of ETL process where data comes in, it gets processed, it goes some place. That cluster was originally set up to just do this very mechanical thing, and that software itself is not evolving very quickly, so the people in charge of the cluster view it as a maintenance problem. But then what happens is some data scientists start to dig up because the data that’s running through those pipelines is very interesting to them. And before you know it, you’ve got a bunch of data scientists who are trying to use this cluster and it’s not working because it wasn’t set up for their active use, it doesn’t have the tools that they want to u se, and they find themselves frustrated that they don’t have the kind of data science environment that they really like. So there are two we can relate to the actual grounded experiences that they have. A lot of times they say, “Hey, why don’t you get a separate data science environment do that at Altiscale and leave the production part?” That’s fine to have too, and that’s very successful for us.I think it has been more challenging for organizations that are new to Hadoop. They say, “Hey, I think we ought to do Hadoop.” And in context, they don’t know what they don’t know. They don’t know how complicated Hadoop is going to be. They think of it more as a traditional software application where “Yes, in the first year or two, it’s a big pain and its going to cost us a lot of money. But once you get over the hump, it becomes easier. It goes into maintenance mode and we can be done with it.” And educating those customers that it doesn’t look like that, it j ust actually gets worse and worse, becomes a challenge to us because they don’t have those experiences. They don’t have the vocabulary. Fortunately, since we had those earlier customers, we can use them to provide some degree of testimony, and that helps. Over time, we’ve been working on other ways to help educate people who are new to Hadoop that they should really think twice before they get too deep into running it themselves.Martin: So thinking about the Hadoop ecosystem, what are from your perspective the biggest misconceptions or mistakes the end users or companies are doing when they are interacting with Hadoop? You briefly touched on one.Raymie: Yes. Obviously for us, we think mistake number one is run it yourself. Let the experts do that for you. It saves you a lot of time and effort. I think that putting that one aside, I mentioned before that the rate of innovation in and around Hadoop is quite high, and I do think that another mistake people make is that they think they need to have the very, very latest, and a lot of times the latest new project that gets announced is really not ready to be used in a production way. So I think that’s actually another fairly common mistake.I think another mistake that people have is they don’t understand very well what I would call the performance characteristics of various components. They understand the functional characteristics. When I put this query in, I’m supposed to get those results out. But they don’t understand the performance characteristics depending on how much data is in the table, for example. Is there a skew in the data? There’s a lot of factors that will determine what kind of performance you’re going to get. And I think there’s this unrealistic expectation that it’s just going to be fast for everything, and they don’t engineer their applications around a realistic expectation for what can be done. I think HBase actually is a particularly challenging technology in that rega rd. It’s great in many dimensions but I think people tend to think that it can do anything and it can’t and then get themselves in trouble. So understanding what are the performance characteristics and the failure characteristics to some degree and making sure that you engineer your application around those instead of making unrealistic expectations is another lesson for first time Hadoop users.ADVICE TO ENTREPRENEURS FROM RAYMIE STATAMartin: So Raymie, over the last two companies that you started, what have been the major learnings that you got and that you can share with other people interested in becoming an entrepreneur?Raymie: Well, I think one thing that you’ll hear over and over again and in some sense you can never do enough of it is that the more you can talk to and engage potential users of your product, the better off you are. And there’s no point in diminishing returns there. And so I would say that that would go to the top of the list because I think people alwa ys think they’ve done enough of that, and myself is no exception to that, and yet if you push yourself a little bit harder, you find out, “Wow, this is even better.” So I would say never be satisfied with the amount of customer input you get.I think on a related note, what I’ve noticed, in fact, if you compare, I’ve also consulted with another, I’ve been involved with startups in various capacities going back for many years actually, and one of the things that I’ve observed is I think the rate at which you can start to actually engage a customer in some type of activity, even as a paid customer, but if you put that aside, like “Hey, give this a try. I’m getting it to try this with folks” way, way earlier than you think is at all realistic.I think there’s a tendency to feel like, “Oh, you have to get to a certain point,” especially a few more engineering during the company. You have to get the thing to a certain point, and if you engage too early, it’s goi ng to be wasteful to the customer or to you. And I think that what time has shown, especially the last 10 to 15 years, is that in fact you can engage with folks by actually giving them software way earlier than the typical engineer thinks is possible and benefit from doing that. So I think that not only talking to a large number of customers early on but here you have to be a little bit mindful. You can’t do too manyof these, but engaging with one or two almost from the very beginning is really, really critical as well.Martin: When did you start engaging with customers?Raymie: When did we start engaging customers? Let’s see. I’d have to go back. I don’t know what the timing was. I do think it was probably six to nine months into starting. It’s pretty early but maybe it could have been even faster. But it was a nice little tiny startup itself. It was actually a hedge fund. And it was interesting because they had not that much data. It was less than a terabyte. But they were expecting that was a sample of a much larger data set they were going to get, which would have measured in a few tens of terabytes. Still not huge. And they were expecting that to come any minute now. So they were a little bit in a panic. As a hedge fund, it was the perfect customer for us because they were not at all interested in running Hadoop. Any smart people they had, they weren’t figuring out traits, not figuring out Hadoop, which was actually not the case in many other companies where if you’ve got smart people. In some sense smart people get bored in a lot of companies, and so you throw them on Hadoop to keep them challenged. And so that can sometimes be a challenge for us because we’re saying, “Hey, I know that’s interesting, but let us take that away from you. Find something else to do.” At hedge funds, that’s not an issue, so it was a great first customer for us.Another thing that was great about it is that there’s alleged tens of terabytes a day that wa s going to show up any minute now, it took a couple of months for that data to show up, so we had time to get seasoned and to get ready for that larger data set.Martin: What other type of advice can you provide, Raymie?Raymie: As I indicated, on the one hand, I was lucky to have a little bit of a track record and therefore the ability to raise money upfront, but I think that’s a mixed blessing. As soon as you raise money, there’s a clock that starts to tick and people want to say, “Okay, how many customers do you have?” And I think a more modern, quite honestly, kind of approach is for two to five people to really take a year to somehow figure out how to make that work for themselves personally from a financial perspective but to really take a longer period of time to find that idea to do the kinds of things that I was talking about, which is to talk to a number of people and to maybe even engage in a couple. So I think all things being equal, in this regard I’d suggest do ing something a little different from what I did, which is to be a little bit more patient in the beginning phases.One thing that I am happy that I did, it might seem a little bit mechanical, but in both cases, I took time to educate myself on some of, you might call it, the formalities of running a company in terms of getting the IT people work in place, getting a documentation system in place, having your records in good shape. For example, if you go down the path I just recommended, which is you spend a year doing stuff and you find pay dirt, like here is this thing and you find an investor that wants to go in, they’re going to put you through a due diligence process. And if you had all of your funds come in and hack on the weekends and there’s no clear ownership of the code and there’s no records or anything like that, you could find yourself either spending a lot of time trying to put all that back into place or taking a lot of risk, where essentially you say, “Hey, I p romise that if my friend who came hack for that weekend comes and sues the company after we’ve become a billion-dollar company, I will own that and I will take all of that risk,” which is not a good thing to do. So I think while I would recommend not necessarily being in a big hurry to raise a lot of money in an early company without seasoning your idea, I would say getting the formalities of the company in place early on and being very clean with respect to your records is important.Martin: Raymie, thank you so much for sharing the knowledge.Raymie: Sure. My pleasure.Martin: And if you are a company with lots of data but actually you don’t want to bother managing them yourself, check out Altiscale.Raymie: Thank you.

Why Does Mint Make Your Mouth Feel Cold

Youre chewing mint gum or sucking on a peppermint candy and draw in a breath of air and no matter how warm it is, the air feels icy cold. Why does this happen? Its a trick mint and the chemical called menthol play on your brain that convinces your taste receptors they are exposed to cold. How Mint Tricks Your Mouth Sensory neurons in your skin and mouth contain a protein called  transient receptor potential cation channel subfamily M member 8  (TRPM8). TRPM8 is an ion channel, meaning it regulates the flow of ions between cellular membranes much as an aquatic channel regulates transit between bodies of water. Cold temperatures permit Na and Ca2 ions to cross the channel and enter the nerve cell, changing its electric potential and causing the neuron to fire a signal to your brain which it interprets as a sensation of cold. Mint contains an organic compound called menthol that binds to TRPM8, making the ion channel  open as if the receptor was exposed to cold and signaling this information to your brain. In fact, menthol sensitizes the neurons to the effect that doesnt wear off as soon as you spit out mint toothpaste or stop chewing a breath mint. If you take a sip of cold water right afterward, the cool temperature will feel especially cold. Other chemicals affect temperature receptors, too. For example, capsaicin in hot peppers causes a sensation of heat. What do you think would happen if you combined the heat of peppers with the cold of mint?

Employee Relations An Overview - Free Essay Example

Sample details Pages: 7 Words: 2088 Downloads: 7 Date added: 2017/06/26 Category Statistics Essay Type Narrative essay Did you like this example? Employee Relations Assignment Task 1 Employee relations are concerned with gaining peopleà ¢Ã¢â€š ¬Ã¢â€ž ¢s commitment to the achievement of the organizationà ¢Ã¢â€š ¬Ã¢â€ž ¢s business goals and objectives in a number of different situations. These include: Public, private and not for profit organisations (the so called voluntary sector) Unionised and non-unionised organisations Primary, manufacturing and service- sector Organisations Large organisations (including multinational companies) and small and medium-sized enterprises (SMEs) It is all about ensuring that the organizational change is accepted. (Gennard Hayward 2005) Employee Relations means the work related relationship between the employee and the employer to be on good terms which will result to contribute in an organizations productivity and the motivation level of the employees. Employee relations aim is to eliminate problems and issues related to work which an employee is unable to get solution to on its own. Unitarism- A managerialist stance which assumes that everyone in an organization is a member of a team with a common purpose. The unitarist view is implicit in American models of HRM. It embodies a central concern of HRM, that an organizations people, whether managers or lower-level employees, should share the same objectives and work together harmoniously. From this perspective, conflicting objectives are seen as negative and dysfunctional. (Alan Price, 2007) Unitarism- This means that the managers of a company tries to motivate its employees by making their objectives into the employees and expect them to follow all the orders by them, working together with mutual goals for example providing incentives to them for per piece they produce and recognizing them for the work they are doing for the business. Plularism- It is the existence of more than one ruling principle. The pluralist approach to industrial relations accepts to conflict as inevitable but containable through various institutional arrangements. Work organisations are microcosms of society. (Singh Kumar, 2011) Plularism- This is when the employeeà ¢Ã¢â€š ¬Ã¢â€ž ¢s in an organization elect their group leader and are expected to be left free for their own decision making. The managers and the employee have two different views which results into conflicts in the organization. Task 2 Trade Union- Employees generally share many of the same interests, such as improving their pay, having a pleasant environment in which to work, being treated fairly by their employer, being given proper training, working in a safe environment. Forming a trade union is a way of helping employees to achieve improvements in these different aspects of their employment- a trade union is a type of pressure group. (Borrington Stimpson, 2006) Trade Unions are group of workers who join together to ensure that their interests of workers are not harmed because of the organization, they help in improving the working environment and conditions of their members. The different types of trade unions are- Don’t waste time! Our writers will create an original "Employee Relations: An Overview" essay for you Create order General Union- This union is for semi-skilled and unskilled workers from various occupations in different industries. For example- Drivers, Cleaners etc Industrial Union- This union represents all the different workers from the same industry. Example: The National Union of Miners (N.U.M) representing all the workers at different stages. Craft Union- They represent skilled workers from same or different work industries and this union is comparatively small and limited in number. White-Collar Unions- They represent professional skilled workers from different industries. Example: Teachers, Scientist, Office Workers History Of Trade Unions Compared to the year 1979 the British system has had a vast change by intervening in the legislations formed by the labour market in order to co-operate with the enterpreneurs and maintain a healthy competition. Between 1979 and 1997 these reformation of regulations had taken place which are still in practice by the new Labour Governments 2010. à ¢Ã¢â€š ¬Ã‹Å"à ¢Ã¢â€š ¬Ã¢â€ž ¢During the year 1901 a compay called Taff Vale Railway sued the Amalgamated Society of Railway Servants for losses during a strike. As a result of the case the union was fined  £23,000. Up until this time it was assumed that unions could not be sued for acts carried out by their members. This court ruling exposed trade unions to being sued every time it was involved in an industrial dispute. After the 1906 General Election the Liberal Government passed the 1906 Trades Disputes Act which removed trade union liability for damage by strike action.à ¢Ã¢â€š ¬Ã¢â€ž ¢Ãƒ ¢Ã¢â€š ¬ â„ ¢ Simkin, 1973- 2013 Because of this trade dispute act the voluntarist system came into practice which was recognized and approved by the employer and the unions which meant that the government could not intervene directly in handling the conflicts of employee relations due to the trade union immunity legislation. On the other hand, in order to make the economy situations better the government initiated industrial relations reform from the year 1970à ¢Ã¢â€š ¬Ã¢â€ž ¢s to reverse the economic decline and most of these reforms were constructed by the Thatcher Government from the year 1979 to 1990, which further resulted the government transition from voluntarist to neo-liberal. The broad shifts in economic policy- During the year 1945 to 1979 there was a concentrated distribution of government income and they generated jobs for all During the year 1979 to 1997 the government on controlling inflation and focused on making the labour market more flexible During 1997 to the present condition, the government are still continuing to monitor and control on the inflation in their economy and recovering from the global crisis and reducing the deficit. Broad shifts in governmentà ¢Ã¢â€š ¬Ã¢â€ž ¢s public sector policy Through the election of the Thatcher Government who came into power following the neo-liberal forms there was a minor shift in the public sector policy which had resulted into limiting the public expenditure and its size. During the year 1979-1997 privitisation had occurred in the public sector which reduced its size from 30% to 22% leading to the britishers nearly employing one quarter of its total population. In 1997-2010 governments brought a few changes to the industrial relation laws earlier introduced between 1979-1997 but did not change it completely, they had declared a minimum wage requirement in 1999. Britain is known to have the longest history in unionism and the first country to industrialise. It is during the 19th century when skilled craft workers had formed the first union and later all other different classes of labour formed their own unions such as semi-skilled, unskilled and female manua l workers. These uses have started taking different forms from the late 19th century. During World War II the white collar unionsied workers were in public sector, but after 1960 the private sector white collar workers unionised themselves too. The British Union after World War II- During the year 1948, the government put up a wage freeze in an attempt to reduce the deficit in the balance of payment and the union congress had agreed to it though they knew that there will be a strong opposition because there was an increase in the community membership of the union due to the war, it was between the year 1948 to 1968 the trade union membership became 10.2 million from 9.3million perhaps due to TUC supporting this wage freeze decision by the government. During the late 1960à ¢Ã¢â€š ¬Ã¢â€ž ¢s the union membership started to increase, the people who were not members of the trade union were impressed by their powers which had improved the working environment and wages of their members which lead to an overall increase of 12.6 million in 1970s. With its membership peaking at 12.6 million in the 1970s the membership had reduced by 5 million leading to only 7.6million memebers in 1979, it is because of the rules enacted by the Conservative government which was opposed by the labour unions they created policies and legislation which involved banning the tactics such as secondary picketing which had been used successfully by the miners industry in the year l972 and l974. à ¢Ã¢â€š ¬Ã‹Å"à ¢Ã¢â€š ¬Ã¢â€ž ¢The Conservatives eased unions out of many institutions that were based on tripartism (an earlier form of social partnership). The most important were the various Industrial Training Boards, which were generally abolished. The most symbolic move was the downgrading and eventual termination (in 1992) of the National Economic Development Council, where six TUC leaders had met leading employers and government ministers monthly since 1962. The TUC also lost its monopoly on nominating trade unionists to public bodies (such as employment tribunals).à ¢Ã¢â€š ¬Ã¢â€ž ¢Ãƒ ¢Ã¢â€š ¬Ã¢â€ž ¢ (unionhistory.info) With the falling trade union membership there was also a decline in the strikes from a total of 3906 strikes with loss of 11 million working days it fell to 116 strikes with only 15700 loss of working days. It was during the year 1997-2010 the government did not change their practice of legislations and continued the trends by encouraging private se ctor into the involvement of public sector. In 2008 there were only 193 unions from a total of 1348, due to the recession between 2008-2009 there was a 7% decline in the membership of the Great Britain employees between 2008 and 2011 which is not a high percentage as employees would want to protect their interest in the recession period. In 2011 the total number of employees including male and female who are the members of trade union is about 6,396 and in 2012 its 6,455 which shows us that the trade unions are still at power to an extent to provide security to the welfare of their memebers following all the rules and regulations which are both the government and unions have agreed to. Task 3 The three main players in Employee Relations- The Government The governments obligation is to maintain price fluctuations and a surplus on the balance of payment. They monitor the organizational activities, pass laws for them and issue policies to protect the right of workers and consumers in a country, and to maintain an overall balance in the economical activities to create further jobs for the citizens in the country. Employee They play a significant role in employee relations, they are people who are hired by employers and are paid in the form of wages for the tasks and duties delegated to them, if they feel that their interests are not protected or if their demands are not fulfilled they approach to the trade unions who bring pressure to the employers in an organization. Trade Unions are group of workers who join together to ensure that their interests of workers are not harmed because of the organization, they help in improving the working environment and conditions of their members through collective bargaining and other different methods in order to protect the right of the workers employed in an organization. Employers An employer is a person who employs workers in an organization and pays them wages or salaries and delegates duties and responsibilities to them. He expects that the workers should follow his orders and makes all the important decisions in an organization. Employers have more power and authority over their workers however if the workers are members of a trade union then there can be certain disputes. An employerà ¢Ã¢â€š ¬Ã¢â€ž ¢s aim is for the growth and establishment of the organization in a profitable way, this may lead to a conflict if workers demand for high wages regularly. If they feel like they are not fairly treated and they do not feel safe in their jobs, then there can be certain consequences which can damage the interest of the business for example poor production, absenteeism and strikes. Reference List Task 1 John Gennard and Geoffery Hayward (2005). Employee Relations (CIPD revision guide), London: Chartered Institute of Personnel and Development (ebook) Available at- https://books.google.ae/books?id=qmbQWLGGVTgCprintsec=frontcoverdq=Employee+Relations+(CIPD+revision+guidehl=ensa=Xei=nFn-Uob_CcLS0QXLqoH4Bwved=0CDEQ6AEwAA#v=onepageq=Employee%20Relations%20(CIPD%20revision%20guidef=false Price. A (2007). Human Resource Management 3rd ed. UK: Thomson Learning (ebook) Available at- https://books.google.ae/books?id=Gdp4FcmqXqwCprintsec=frontcover#v=onepageqf=false P.N. Singh Neeraj Kumar(2011). Employee Relation Management. India: Dorling Kindersley, licences of Pearson Education South Asia (ebook) Available at- https://books.google.ae/books?id=uP3m2X3OJR8Cprintsec=frontcoverdq=employee+relation+managementhl=ensa=Xei=VVX-UsKIAoHO0AXohoGQBQved=0CDEQ6AEwAA#v=onepageq=employee%20relation%20managementf=false Accessed on 14th Feb 2014 Task 2 Definition of Trade Union Karen Borrington and Peter Stimpson (2006). Business Studies 3rd ed. London: Hodder Murray 1906 Trades Disputes Act which removed trade union liab ility for damage by strike action.à ¢Ã¢â€š ¬Ã¢â€ž ¢Ãƒ ¢Ã¢â€š ¬Ã¢â€ž ¢ Simkin, 2013 https://www.spartacus.schoolnet.co.uk/Ldisputes.htm?menu=TU  ©JohnSimkin, September 1997 June 2013 Accesssed on Feb 16th https://www.unionhistory.info/timeline/1960_2000_6.php Dave Lyddon, Centre for Industrial Relations  © London Metropolitan University Accesed on feb 17th https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/204169/bis-13-p77-trade-union-membership-2012.pdf  © Crown copyright 2013 Page | 1

Aids Is The Disease Caused By Human Immunodeficiency Virus...

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Leonardo Da Vinci Argumentative Essay Example For Students

Leonardo Da Vinci Argumentative Essay It was the period of the renaissance when Leonardo da Vinci was born on April 15, 1452. Leonardo was born a farmhouse in Anchiano, which is 2 miles away from Vinci. The family of Leonardo lived in this area since the 13th century. The father of Leonardo da Vinci, Ser Piero, was 25 years old; he was a public notary when Leonardo was born. The mother of Leonardo was called Catarina. Her first name is all what is known today. The Baptismal chapel in Vinci is where Leonardo was christened. Leonardo was christened from the name Piero da Bartholomew to the name Lionardo not Leonardo. The chapel is inside the church of Vinci. The church beside the castle of Vinci are formed the skyline of his town. Leonardo lived in Anchiano for five years until he settled to Vinci. Vinci is a small town, placed at the foot of Monte Albano. From this time he was member of his fathers family, but he was never considered born to his mother and father. In Vinci Leonardo went to school. It was told that teacher s of Leonardo da Vinci were despaired about all the questions and doubts of Leonardo. Leonardo learned at school to read write and mathematics. He also learned geometry and Latin. Later Leonardo tried to improve his knowledge in Latin, because he thought that he didnt learn enough Latin in school. This may be the reason why Leonardo did his notes in Italian. Leonardo lived in Vinci until 1466. With the age of 14 Leonardo moved to Florence where he began to work in the workshop of Verrocchio. Verrocchio was at this time the most gifted artist in Florence. He was a sculptor, painter, goldsmith, bronze caster and more. Verrocchio had a lot of influence on Leonardo. Verrocchio was fascinated by the drawings of Leonardo so he gave him a place in his workshop. Leonardo worked at the workshop of Verrocchio with some other famous artists like Botticelli. Leonardo started working with mixing of colors and then he painted simple parts of paintings. There are no works of Leonardo known between 1466 and 1472, but Leonardo taught himself to paint in oils at this time. In June 1472 Leonardo was listed in the red book of painters from Florence. With the membership in the painters guild of Florence ended the apprenticeship of Leonardo. Leonardo didnt leave the workshop of Verrocchio at the end of his apprenticeship. The first known work of Leonardo da V inci is a pen and ink drawing of the Arnovalley. Leonardo drew it on August 5, 1473. It shows the style of Leonardo, because he drew the landscape in a way that it could be real. Nobody else before did it in this way. In 1476 Leonardo and Verrocchio created the painting Baptism of Christ. The conclusion is that Leonardo already made mechanical studies at this early time.