Learn About the Applications of Fiberglass Composites

The use of fiberglass started during the Second World War. Polyester resin was invented in 1935. Its potential was recognized, but finding a suitable reinforcing material proved elusive – even palm fronds were tried. Then, glass fibers which had been invented in the early 1930’s by Russel Games Slaytor and used for glass wool home insulation, were successfully combined with the resin to make a durable composite. Although it was not the first modern composite material (Bakelite - cloth reinforced phenolic resin was the first), glass reinforced plastic (‘GRP’) quickly grew into a worldwide industry. By the early 1940s, fiberglass laminates were being produced. The first amateur use – the building of a small dinghy was in Ohio was in 1942. Early Wartime Use of Glass Fiber As a new technology, resin and glass production volumes were relatively low and as a composite, its engineering characteristics were not well understood. Nevertheless, its advantages over other materials, for specific uses, were apparent. Wartime metal supply difficulties focused on GRP as an alternative. Initial applications were to protect radar equipment (Radomes), and as ducting, for example, airplane engine nacelles. In 1945, the material was used for the aft fuselage skin of the US Vultee B-15 trainer. Its first use of fiberglass in main airframe construction was that of a Spitfire in England, though it never went into production. Modern Uses Almost 2 million tons a year of the unsaturated polyester resin (‘UPR’) component are produced worldwide, and its widespread use is based on a number of features besides its relatively low cost: low technology fabricationdurabilityhigh flexing tolerancemoderate/high strength/weight ratiocorrosion resistanceimpact resistance Aviation and Aerospace GRP is used extensively in aviation and aerospace though it is not widely used for primary airframe construction, as there are alternative materials which better suit the applications. Typical GRP applications are engine cowlings, luggage racks, instrument enclosures, bulkheads, ducting, storage bins and antenna enclosures. It is also widely used in ground-handling equipment. Automotive For those who love automobiles, the 1953 model Chevrolet Corvette was the first production car to have a fiberglass body. As a body material, GRP has never succeeded against metal for large production volumes. However, fiberglass has a big presence in the replacement body parts, custom and kit auto markets. Tooling costs are relatively low as compared with metal press assemblies and ideally, suit smaller markets. Boats and Marine Since that first dinghy in 1942, this is an area where fiberglass is supreme. Its properties are ideally suited to boat building. Although there were problems with water absorption, modern resins are more resilient, and the composites continue to dominate the marine industry. In fact, without GRP, boat ownership would never have reached the levels it has today, as other construction methods are simply too expensive for volume production and not amenable to automation. Electronics GRP is widely used for circuit board manufacture (PCBs) – there is probably one within six feet of you now. TVs, radios, computers, cellphones – GRP holds our electronic world together. Home Almost every home has GRP somewhere – whether in a bathtub or a shower tray. Other applications include furniture and spa tubs. Leisure How much GRP do you think there is in Disneyland? The cars on the rides, the towers, the castles – so much of it is based on fiberglass. Even your local fun park probably has water slides made from the composite. And then the health club – do you ever sit in a Jacuzzi? That’s probably GRP as well. Medical Because of its low porosity, non-staining, and hard wearing finish, GRP is ideally suited to medical applications, from instrument enclosures to X-ray beds (where X-ray transparency is important). Projects Most people who tackle DIY projects have used fiberglass at one time or another. It is readily available in hardware stores, easy to use (with a few health precautions to be taken), and can provide a really practical and professional looking finish. Wind Energy Building 100’ wind turbine blades is a major growth area for this versatile composite, and with wind energy a massive factor in the energy supply equation, its use is certain to continue to grow. Summary GRP is all around us, and its unique characteristics will ensure that it remains one of the most versatile and easy to use composites for many years to come.

The Poverty Within The Caribbean - 1940 Words

This paper seeks to examine the extent to which theories on the culture of poverty, can be used to explain poverty within the Caribbean. Poverty is one of the most globally recognized social problems across the world with countries on all continents experiencing it whether they are classified as developed, developing or underdeveloped. It is characterized by a lack or deficiency of something considered vital to society. According to the World Bank (2000), poverty can be defined as â€Å"the economic condition in which people lack sufficient income to obtain certain minimal levels of health services, food, housing, clothing and education generally recognized as necessary to ensure an adequate standard of living. A global problem such as this leads to many theories being developed about it - what causes it and how it can be rectified, as well as the accompanying criticisms and oppositions. One of the most popular theories, if not the most popular, is the theory of the culture of poverty. This theory was introduced to the world by Oscar Lewis, an American anthropologist, as a result of his study of the ‘urban poor’ of Puerto Rico and Mexico. This theory postulates that persons in poverty essentially shared a general consensus that this will always be their place in life and therefore see no reason to try to change it. Lewis (1959) believed that this view led to them creating their own value systems since they were too uneducated and lazy to operate within those of the middle andShow MoreRelatedEnvironmental Damage Due to Development Has Affected Tourism in the Caribbean: Inevitable.830 Words   |  4 PagesTOPIC: Environmental Damage Due to Development Has Affected Tourism in the Caribbean: Inevitable. Audience: Faculty of built environment and tourism management students. 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Big Data Architecture, Goals and Challenges Free Essays

string(85) " other viable options that are available in the market, such as in-memory analytics\." Big Data Architecture, Goals and Challenges Coupons Jose Christianity Dakota State University Abstract Big Data inspired data analysis is matured from proof of concept projects to an influential tool for decision makers to make informed decisions. More and more organizations are utilizing their internally and externally available data with more complex analysis techniques to derive meaningful insights. This paper addresses some of the architectural goals and challenges for Big Data architecture in a typical organization. We will write a custom essay sample on Big Data Architecture, Goals and Challenges or any similar topic only for you Order Now Overview In this fast paced information age, there are many different sources on corporate outworks and internet is collecting massive amounts of data, but there is a significant difference in this data compared to the conventional data, much of this data is semi- structured or unstructured and not residing in conventional databases. â€Å"Big data† is essentially a huge data set that scales to multiple potables of capacity; it can be created, collected, collaborated, and stored in real-time or any other way. However, the challenge with big data is that it is not easily handled using traditional database management tools. It typically consists of unstructured data, which includes text, audio and video files, photographs and other data (Kavas, 2012). The aim of this paper is to examine the concepts associated with the big data architecture, as well as how to handle, process, and effectively utilize big data internally and externally to obtain meaningful and actionable insights. How Big Data is Different? Big data is the latest buzzword in the tech industry, but what exactly makes it different from traditional Bal or data analysis? According to MIT Sloan Management Review, big data is described as â€Å"data that is either too voluminous or too unstructured to be managed and analyzed through traditional meaner† (Davenport, Thomas, Berth, Bean, 2012). Big data is unlike conventional mathematical intelligence, where a simple sum of a known value yields a result, such as order sales becoming year-to-date sales. With big data, the value is discovered through a complex, refined modeling process as follows: make a hypothesis, create statistical models, validate, and then make a new hypothesis (Oracle, 2012). Additionally, data sources are another challenging and differentiating factor within big data analytics. Conventional, structured data sources like relational databases, spreadsheets, and yogis are further extended into social media applications (tweets, blobs, Faceable, linked posts, etc. ), web logs, sensors, RIFF tags, photos/videos, information-sensing mobile devices, geographical location information, and other documents. In addition to the unstructured data problem, there are other notable complexities for big data architecture. First, due to sheer volume, the present system cannot move raw data directly to a data warehouse. Whereas, processing systems such as Unprepared, can further refine information by moving it to data warehouse environment, where invitational and familiar Bal reporting, statistical, semantic, and correlation applications can effectively implemented. Traditional data flow in Business Intelligence Systems can depict like this, (Oracle. (2012). An Oracle white paper in enterprise architecture) Architectural Goals The preeminent goal of architecture big data solutions is to create reliable, scalable and capable infrastructure. At the same time, the analytics, algorithms, tools and user interfaces will need to facilitate interactions with users, specifically those in executive-level. Enterprise architecture should ensure that the business objectives remain clear throughout big data technology implementation. It is all about the effective utilization of big data, rather than big architecture. Traditional IT architecture is accustomed to having applications within its own space and performs tasks without exposing internal data to the outside world. Big data on other hand, will consider any possible piece of information from any other application to be instated for analysis. This is aligned with big data’s overall philosophy: the more data, the better. Big Data Architecture Big data architecture is similar to any other architecture that originates or has a inundation from a reference architecture. Understanding the complex hierarchal structure of reference architecture provides a good background for understanding big data and how it complements existing analytics, 81, databases and other systems. Organizations usually start with a subset of existing reference architecture and carefully evaluate each and every component. Each component may require modifications or alternative solutions based on the particular data set or enterprise environment. Moreover, a successful big data architecture will include many open- source software components; however, this may present challenges for typical enterprise architecture, where specialized licensed software systems are typically used. To further examine big data’s overall architecture, it is important to note that the data being captured is unpredictable and continuously changing. Underlying architecture should be capable enough to handle this dynamic nature. Big data architecture is inefficient when it is not being integrated with existing enterprise data; the same way an analysis cannot be completed until big data correlates it with other structured and enterprise-De data. One of the primary obstacles observed in a Hoodoo adoption f enterprise is the lack of integration with an existing Bal echo-system. Presently, the traditional Bal and big data ecosystems are separate entities and both using different technologies and ecosystems. As a result, the integrated data analyses are not effective to a typical business user or executive. As you can see that how the data architecture mentioned in the traditional systems is different from big data. Big data architectures taking advantage of many inputs compared to traditional systems. (Oracle. (2012). An Oracle white paper in enterprise architecture) Architectural Cornerstones Source In big data systems, data can come from heterogeneous data sources. Typical data stores (SQL or Nouns) can give structured data. Any other enterprise or outside data coming through different application Apish can be semi-structured or unstructured. Storage The main organizational challenge in big data architecture is data storage: how and where the data can be stored. There is no one particular place for storage; a few options that currently available are HATS, Relation databases, Nouns databases, and In-memory databases. Processing Map-Reduce, the De facto standard in big data analysis for processing data, is one of any available options. Architecture should consider other viable options that are available in the market, such as in-memory analytics. You read "Big Data Architecture, Goals and Challenges" in category "Papers" Data Integration Big data generates a vast amount of data by combining both structured and unstructured data from variety of sources (either real-time or incremental loading). Likewise, big data architecture should be capable of integrating various applications within the big data infrastructure. Various Hoodoo tools (Scoop, Flume, etc. ) mitigates this problem, to some extent. Analysis Incorporating various analytical, algorithmic applications will effectively process this cast amount of data. Big data architecture should be capable to incorporate any type of analysis for business intelligence requirements. However, different types of analyses require varying types of data formats and requirements. Architectural Challenges Proliferation of Tools The market has bombarded with array of new tools designed to effectively and seamlessly organize big data. They include open source platforms such as Hoodoo. But most importantly, relational databases have also been transformed: New products have increased query performance by a factor of 1,000 and are capable of managing a wide variety of big data sources. Likewise, statistical analysis packages are also evolving to work with these new data platforms, data types, and algorithms. Cloud-friendly Architecture Although not yet broadly adopted in large corporations, cloud-based computing is well-suited to work with big data. This will break the existing IT policies, enterprise data will move from its existing premise to third-party elastic clouds. However, there are expected to be challenges, such as educating management about the consequences and realities associated with this type of data movement. Nonparametric Data Traditional systems only consider the data unique to its own system; public data never becomes a source for traditional analytics. This paradigm is changing, though. Many big data applications use external information that is not proprietary, such as social network modeling and sentiment analysis. Massive Storage Requirements Moreover, big data analytics are dependent on extensive storage capacity and processing power, requiring a flexible and scalable infrastructure that can be reconfigured for different needs. Even though Hoodoo-based systems work well with commodity hardware, there is huge investment involved on the part of management. Data Forms Traditional systems have typically enjoyed their intrinsic data within their own vicinity; meaning that all intrinsic data is moved in a specified format to data warehouse for further analysis. However, this will not be the case with big data. Each application and service data will stay in its associated format according to what the specific application requires, as opposed to the preferred format of the data analysis application. This will leave the data in its original format and allow data scientists to share existing data without unnecessarily replicating it. Privacy Without a doubt, privacy is a big concern with big data. Consumers, for example, often want to know what data an organization collects. Big data is making it more challenging to have secrets and conceal information. Because of this, there are expected to be privacy concerns and conflicts with its users. Alternative Approaches Hybrid Big Data Architecture As explained earlier, traditional Bal tools and infrastructure will seamlessly integrate with the new set of tools and technologies brought by a Hoodoo ecosystem. It is expected that both systems can mutually work together. To further illustrate this incept, the detailed chart below provides an effective analysis (Arden, 2012): Relational Database, Data Warehouse Enterprises reporting of internal and external information for a broad cross section of stakeholders, both inside and beyond the firewall with extensive security, load balancing, dynamic workload management, and scalability to hundreds of terabytes. Hoodoo Capturing large amounts of data in native format (without schema) for storage and staging for analysis. Batch processing is primarily reserved for data transformations as well as the investigation of novel, internal and external (though mostly external) ATA via data scientists that are skilled in programming, analytical methods, and data management with sufficient domain expertise to accordingly communicate the findings. Hybrid System, SQL-Unprepared Deep data discovery and investigative analytics via data scientists and business users with SQL skills, integrating typical enterprise data with novel, multi-structured data from web logs, sensors, social networks, etc. (Arden, N. (2012). Big data analytics architecture) In-memory Analytics In-memory analytics, as its name suggests, performs all analysis in memory without enlisting much of its secondary memory, and is a relatively familiar concept. Procuring the advantages of RAM speed has been around for many years. Only recently; however, has this notion become a practical reality when the mainstream adoption of 64-bit architectures enabled a larger, more addressable memory space. Also noteworthy, were the rapid decline in memory prices. As a result, it is now very realistic to analyze extremely large data sets entirely in-memory. The Benefits of In-memory Analytics One of the best incentives for in-memory analytics are the dramatic performance improvements. Users are constantly querying and interacting with data in-memory, which is significantly faster than accessing data from disk. Therefore, achieving real- time business intelligence presents many challenges; one of the main hurdles to overcome is slow query performance due to limitations of traditional Bal infrastructure, and in-memory analytics has the capacity to mitigate these limitations. An additional incentive of in-memory analytics is that it is a cost effective alternative to data warehouses. SMB companies that lack the expertise and resources to build n appropriate data warehouse can take advantage of the in-memory approach, which provides a sustainable ability to analyze very large data sets (Yellowing, 2010). Conclusion Hoodoo Challenges Hoodoo may replace some of the analytic environment such as data integration and TTL in some cases, but Hoodoo does not replace relational databases. Hoodoo is a poor choice when the work can be done with SQL and through the capabilities of a relational database. But when there is no existing schema or mapping for the data source into the existing schema, as well as very large volumes of unstructured or MME-structured data, then Hoodoo is the obvious choice. Moreover, a hybrid, relational database system that offers all the advantages of a relational database, but is also able to process Unprepared requests would appear to be ideal. How to cite Big Data Architecture, Goals and Challenges, Papers

Free Trade Versus Government Regulated Trade

Question: Write a essay evaluating the role of governments in assuring that developing countries obtain a fair and adequate share of the benefits of international grade? Answer: Introduction: International trade is the share of capital, merchandise as well as services from corner to corner international borders and regions. Trading all the way through the globe provides customers as well as the nations the chances to be uncovered in terms of goods as well as services not accessible in their own nations. It has enormous economic, social and political importance not only for a particular country engaged in international trade but also for the world at large. Types of international trade: Trade for an economy can be either free that is market determined or through intervention from governments. In the international market place, the term free trade is commonly known as a policy with the help of which governments carry out both imports or exports operation without any restriction. It is characterized by absence of any trade distorting policies like taxes or quotas (Dent, 2010). On the other hand, in order to raise revenue and protect domestic industries of the country can impose some protectionist policies in the form of import substitution and export promotion. Importing similar goods poses challenges for domestic markets leading to loss in the domestic market share for those sectors. Governments can influence this trade from beginning to end with the help of tariffs along with quotas to manage the proportion of importation as well as their capability to contend with local companies (Graham, 1980). The government can also take certain export promotion measures like organizing trade fairs, giving pre-shipment and post shipment finance, subsidies, creating SEZs etc. Free trade versus government regulated trade: In order to evaluate the topic of this essay of evaluating the function of governments in ensuring a fair as well as ample share of the advantages of international trade, there is requiring critical assessment of untrammeled market commotion in against the government intervention. Analysis of free trade: Free trade as described in the Theory of Comparative Advantage by David Ricardo, (Ricardo, 1857) explores that there is requiring incorporation of economic welfare activities, if the particular nation evidenced a lower opportunity cost. Also, removal of trade barriers like tariffs, leads to lower price for the imported goods increasing their demand. So, producers will get more incentive to produce. As the scales of production increase, investment and income multiplier effect works to expand the economy. Also, firms exporting goods grows. However there are several disadvantages of free trade also, like unbalanced development of the economy of only those sectors in which the country has a comparative advantage. Also cut throat competition leads to dumping in which exporters to retain foreign market share sell their products at very cheap rates. Analysis of government regulated trade: According to Carbaugh, the most prevalent concept that free trade is the best is challenged by the idea of industrial policy, which states that the domestic economy as a whole can be made better off by strategically implementing trade restrictions (Carbaugh, 1980). According to the views of Protectionists, it can be said that novel or newly established organizations irrespective of the industries must be secluded to provide them chances to enhance their activities as well as compete internationally through measures like export finance, subsidies etc. On the other hand, there are critics that opined that some of these newly established organizations irrespective of the industries do not have opportunity to grow up because of the industry giant. At the same time regulations followed by the industry also makes sure that exploration of the economy is the best way to maintain the balance in trade activities. Therefore, it is become obvious that while a nation conduits all its capital into a small number of industries, the economy as whole evidence the major due to such dependency. Role of government in developing countries: Developing countries mainly focus on the export of primary goods like agricultural goods, raw materials and fuel. According to economists, for achieving self sustaining growth and to be self reliant, a country should focus on taking advantages on its international trade. But at the same time, dependence on exports of primary goods makes a country unstable because prices of goods in international markets tend to be indecisive. Elasticity of the demand for primary products are usually low, so changes in price of these products do not bring out expected amount of changes in the quantity demanded. So, government regulation in primary markets of developing countries may not be of much help (Carbaugh, 1980). But in certain cases, government regulation may be preferred to free trade. Carbaugh cited a classic example of industrial policy actions in Japan, during its early years of development, when the Japanese government protected and provided generous subsidy to launch and sustain what have now become its key industries namely, telecommunications equipments, semiconductors, pharmaceuticals and software. Without government regulation, the private sectors, or more specific way, the growers as well as small and medium size organization will face major issue in terms of production. These particular scenarios arise in Senegal, while it engaged in free trade with EU. Senegal in 1990 produced about 73,000 tons of tomatoes. In free trade with EU, in 1996, its production came down to 20,000 because of availability of cheaper tomatoes from EU. Here, government could have regulated by increasing import duties. Conclusion: Because of the questionable benefits from both aspects, the free trade policy still considered as the most debatable aspect. However, from the above analysis it can be said that poorer nations in against their trading partners, undergo major issues mainly because of the unfairness as well as expensive nature of a free trade agreement. Free trade is not fair trade (Fridell, 2010). By engaging in free trade, nations will enforce their private sector to involve in race with public sectors of stronger economies. The regulation is certainly equal but the capacities of the countries are not. The above mentioned case example of Senegal related Free Trade comes to explores how nations that do not have an equivalent aggressive advantage are predictable to engage in recreation alongside a stronger player and, in the process, fail miserably. But this debate is never ending. Economist Milton Friedman is against the concept that globalization would marginalize developing and poorer countries. Instead, he stresses on the fact that a third world country will also benefit from free trade. Increased world free trade will spread technology and knowledge from the so called rich countries worldwide, so that it will be beneficial for these new entrants as well as their natives (Friedman, 1980). Thus, the desirable trade policy for an economy is an optimum mix of free trade (for weaker sectors) and government regulation (for sectors which have comparative advantage). References: Carbaugh, R. (1980).International economics. Cambridge, Mass.: Winthrop Publishers. Dent, C. (2010). Freer Trade, More Regulation? Commercial Regulatory Provisions in Asia-Pacific Free Trade Agreements. Competition Change, 14(1), 48-79. doi:10.1179/102452910x12587274068114 Fridell, G. (2010). Fair Trade, Free Trade and the State. New Political Economy, 15(3), 457-470. doi:10.1080/13563460903288213 Friedman, M., Friedman, R. (1980).Free to choose. New York: Harcourt Brace Jovanovich. Goldman, R., Rubin, S., Graham, T. (1984). Environment and Trade: The Relation of International Trade and Environmental Policy.The American Journal Of International Law,78(2), 513. doi:10.2307/2202305 Lumina, C. (2010). Free trade or just trade? The World Trade Organisation, human rights and development (Part 1). Law, Democracy Development, 12(2). doi:10.4314/ldd.v12i2.52892