Korean diaspora Essay Example | Topics and Well Written Essays - 1000 words

Korean diaspora - Essay Example The north-eastern China started receiving Koreans from the thirteenth century itself. The Koreans in China are renowned for keeping their unique Korean traditions intact while assimilating the local languages and ideas. Many Korean-origin people in China, according to Piao, â€Å"do not know how to write or speak Korean, they have maintained their unique Korean customs. Such customs as not marrying while in formal mourning, women not binding their feet, and placing ceremonial food on a special table for the elderly remained until the 1940s† (48). There has also been a long tradition of Korean dissidents migrating to other countries, especially to China. During the period of Japanese colonialism in Korea, especially between 1910 and 1930, thousands of Koreans opposed to the regime have fled to China. The extensive migration of Korean peasants to Manchuria was even facilitated by the Japanese imperialists. Although oppressed by the ruling elites and by the conditions of misery, the Korean diaspora in China have rigorously upheld many aspects of their original nationality. By the establishment of different kinds of autonomous units after the establishment of the communist rule, the Koreans in China have not only been able to preserve their nationality but also to develop it significantly. The Chinese Communist party (CCP) too had played an important role in protecting the minority culture of the Koreans in China by organizing Korean cultural workers and Korean literary clubs. It was the direct result of CCP’s policy that â€Å"in areas that contained a concentration of one nationality, national autonomous regions should be established and the nationality’s language and writing system should be developed, along with the preservation of the nationality’s customs, traditions and religious beliefs†, argues Piao (75). Also, land reform policies initiated by the communist government in China have largely helped the Koreans diaspora to enhance their material development. Koreans in Japan In Japanese language, the immigrants from Korea are popularly called as sangokujin in a derogative fashion. The Koreans in Japan have always had a tensed relation with their old colonial masters and vice versa too. This tension still is expressed as â€Å"the continued ambiguity of the Korean community’s position between ‘troublesome’ new immigrants and Japanese nationals† as pointed by Chung (1). It has led to lower rates of naturalization of Koreans in Japan even after many decades of their arrival in Japan. It could also be argued, along the lines of Chung, that although the Koreans in Japan find it easy to be assimilated with the natives by fluently speaking Japanese and marrying with Japanese origins, â€Å"the law rate of naturalization suggests that a significant proportion of the Korean community has made a conscious decision to retain its Korean nationality† (1). As former, colonial subje cts, the Koreans have found it difficult to be integrated with the Japanese oppressive regime. It does not mean that the Korean diaspora in Japan is devoid of representation in the civil society. Although the Japanese state and society asserts the indigenous homogeneity of the Japanese people vis-a-vis the Korean immigrants, the Koreans have asserted themselves into the national scene through democratic participation and activism. Still,

The CIA created Osama bin Laden Research Paper Example | Topics and Well Written Essays - 1750 words - 1

The CIA created Osama bin Laden - Research Paper Example Accordingly, the following analysis will seek to engage with this very topic. As such, it would be necessary to delve deeply into storable roots of Al Qaeda and Osama bin Laden and determine whether or not a clear and determinant level of group exists with respect to the way in which this potential â€Å"asset† was handled. In accordance with the basic premise of investigative reporting, the discussion will be concentric upon those alleged and proven aspects of cooperation that exist between the Central Intelligence Agency and Osama bin Laden. Is the hope of this particular author that such a level of discussion will be beneficial in shedding further level of light on what can only be described as an increasingly murky and seemingly undecipherable relationship. As with many of the assets and informants that the Central Intelligence Agency seeks to develop around the globe, Osama bin Laden was of little importance to the agency prior to the Soviet invasion of Afghanistan in 1979. Recognizing that fellow Muslims were being oppressed by an atheist regime, Osama bin Laden, and indeed many others throughout the Islamic world, set out to provide moral, material, and direct support to the forces that were fighting against the Soviet occupation. As such, Osama bin Laden left what many individuals would describe as a comfortable life and began to utilize this fortune, time, and energy as a means of funneling money and material to the mujahedin. Although it is true that Osama bin Laden began to play a more active role as the 1980s progressed, it was this initial activity that placed him on the radar for Pakistani ISI, Saudi intelligence, and the Central Intelligence Agency. Further, as it was the United States’ direct and implicit goal to see the Soviet Union fail in its attempted invasion of Afghanistan, coordinating with anti-Soviet actions and supplying these individuals with the required material and

NoSQL Databases | Research Paper

NoSQL Databases | Research Paper In the world of enterprise computing, we have seen many changes in platforms, languages, processes, and architectures. But throughout the entire time one thing has remained unchanged relational databases. For almost as long as we have been in the software profession, relational databases have been the default choice for serious data storage, especially in the world of enterprise applications. There have been times when a database technology threatened to take a piece of the action, such as object databases in the 1990s, but these alternatives never got anywhere. In this research paper, a new challenger on the block was explored under the name of NoSQL. It came into existence because of there was a need to handle large volumes of data which forced a shift to building bigger hardware platforms through large number of commodity servers. The term NoSQL applies to a number of recent non-relational databases such as Cassandra, MongoDB, Neo4j, and Azure Table storage. NoSQL databases provided the advantage of building systems that were more performing, scaled much better, and were easier to program with. The paper considers that we are now in a world of Polyglot Persistence where different technologies are used by enterprises for the management of data. For this reason, architects should know what these technologies are and should be able to decide which ones to use for various purposes. It provides information to decide whether NoSQL databases can be seriously considered for future projects. The attempt is to provide enough background information on NoSQL databases on how they work and what advantages they will bring to the table. Table of Contents Introduction Literature Technical Aspects Document Oriented Merits Demerits Case Study MongoDB Key Value Merits Demerits Case Study Azure Table Storage Column Stores Merits Demerits Case Study Cassandra Graphs Merits Demerits Case Study Neo4j Conclusion References Introduction NoSQL is commonly interpreted as not only SQL. It is a class of database management systems and is does not adhere to the traditional RDBMS model. NoSQl databases handle a large variety of data including structured, unstructured or semi-structured data. NoSQL database systems are highly optimized for retrieval and append operations and offer less functionality other than record storage. The run time performance is reduced compared to full SQL systems but there is increased gain in scalability and performance for some data models [3]. NoSQL databases prove to be beneficial when a huge quantity of data is to be processed and a relational model does not satisfy the datas nature. What truly matters is the ability to store and retrieve huge amount of data, but not the relationships between them. This is especially useful for real-time or statistical analysis for growing amount of data. The NoSQL community is experiencing a rapid change. It is transitioning from the community-driven platform development to an application-driven market. Facebook, Digg and Twitter have been successful in using NoSQL and scaling up their web infrastructure. Many successful attempts have been made in developing NOSQL applications in the fields of image/signal processing, biotechnology, and defense. The traditional relational database systems vendors also assess the strategy of developing NoSQL solutions and integrating them in existing offers. Literature In recent years with expansion of cloud computing, problems of data-intensive services have become prominent. The cloud computing seems to be the future architecture to support large-scale and data intensive applications, although there are certain requirements of applications that cloud computing does not fulfill sufficiently [7]. For years, development of information systems has relied on vertical scaling, but this approach requires higher level of skills and it is not reliable in some cases. Database partitioning across multiple cheap machines added dynamically, horizontal scaling or scaling-out can ensure scalability in a more effective and cheaper way. Todays NoSQL databases designed for cheap hardware and using the shared-nothing architecture can be a better solution. The term NoSQL was coined by Carlo Strozzi in 1998 for his Open Source, Light Weight Database which had no SQL interface. Later, in 2009, Eric Evans, a Rackspace employee, reused the term for databases which are non-relational, distributed and do not conform to atomicity, consistency, isolation and durability. In the same year, no:sql(east) conference held in Atlanta, USA, NoSQL was discussed a lot. And eventually NoSQL saw an unprecedented growth [1]. Scalable and distributed data management has been the vision of the database research community for more than three decades. Many researches have been focused on designing scalable systems for both update intensive workloads as well as ad-hoc analysis workloads [5]. Initial designs include distributed databases for update intensive workloads, and parallel database systems for analytical workloads. Parallel databases grew to become large commercial systems, but distributed database systems were not very successful. Changes in the data access patterns of applications and the need to scale out to thousands of commodity machines led to the birth of a new class of systems referred to as NoSQL databases which are now being widely adopted by various enterprises. Data processing has been viewed as a constant battle between parallelism and concurrency [4]. Database acts as a data store with an additional protective software layer which is constantly being bombarded by transactions. To handle all the transactions, databases have two choices at each stage in computation: parallelism, where two transactions are being processed at the same time; and concurrency, where a processor switches between the two transactions rapidly in the middle of the transaction. Parallelism is faster, but to avoid inconsistencies in the results of the transaction, coordinating software is required which is hard to operate in parallel as it involves frequent communication between the parallel threads of the two transactions. At a global level, it becomes a choice between distributed and scale-up single-system processing. In certain instances, relational databases designed for scale-up systems and structured data did not work well. For indexing and serving massive amounts of rich text, for semi-structured or unstructured data, and for streaming media, a relational database would require consistency between data copies in a distributed environment and will not be able to perform parallelism for the transactions. And so, to minimize costs and to maximize the parallelism of these types of transactions, we turned to NoSQL and other non-relational approaches. These efforts combined open-source software, large amounts of small servers and loose consistency constraints on the distributed transactions (eventual consistency). The basic idea was to minimize coordination by identifying types of transactions where it didnt matter if some users got old data rather than the latest data, or if some users got an answer while others didnt. Technical Aspects NoSQL is a non-relational database management system which is different from the traditional relational database management systems in significant ways. NoSQL systems are designed for distributed data stores which require large scale data storage, are schema-less and scale horizontally. Relational databases rely upon very structured rules to govern transactions. These rules are encoded in the ACID model which requires that the database must always preserve atomicity, consistency, isolation and durability in each database transaction. The NoSQL databases follow the BASE model which provides three loose guidelines: basic availability, soft state and eventual consistency. Two primary reasons to consider NoSQL are: handle data access with sizes and performance that demand a cluster; and to improve the productivity of application development by using a more convenient data interaction style [6]. The common characteristics of NoSQL are: Not using the relational model Running well on clusters Open-source Built for 21st century web estates Schema less Each NoSQL solution uses a different data model which can be put in four widely used categories in the NoSQL Ecosystem: key-value, document, column-family and graph. Of these the first three share a common characteristic of their data models called aggregate orientation. Next we briefly describe each of these data models. 3.1 Document Oriented The main concept of a document oriented database is the notion of a document [3]. The database stores and retrieves documents which encapsulate and encode data in some standard formats or encodings like XML, JSON, BSON, and so on. These documents are self-describing, hierarchical tree data structures and can offer different ways of organizing and grouping documents: Collections Tags Non-visible Metadata Directory Hierarchies Documents are addressed with a unique key which represents the document. Also, beyond a simple key-document lookup, the database offers an API or query language that allows retrieval of documents based on their content. img1.jpg Fig 1: Comparison of terminology between Oracle and MongoDB 3.1.1 Merits Intuitive data structure. Simple natural modeling of requests with flexible query functions [2]. Can act as a central data store for event storage, especially when the data captured by the events keeps changing. With no predefined schemas, they work well in content management systems or blogging platforms. Can store data for real-time analytics; since parts of the document can be updated, it is easy to store page views and new metrics can be added without schema changes. Provides flexible schema and ability to evolve data models without expensive database refactoring or data migration to E-commerce applications [6]. Demerits Higher hardware demands because of more dynamic DB queries in part without data preparation. Redundant storage of data (denormalization) in favor of higher performance [2]. Not suitable for atomic cross-document operations. Since the data is saved as an aggregate, if the design of an aggregate is constantly changing, aggregates have to be saved at the lowest level of granularity. In this case, document databases may not work [6]. .3.1.3 Case Study MongoDB MongoDB is an open-source document-oriented database system developed by 10gen. It stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. The language support includes Java, JavaScript, Python, PHP, Ruby and it also supports sharding via configurable data fields. Each MongoDB instance has multiple databases, and each database can have multiple collections [2,6]. When a document is stored, we have to choose which database and collection this document belongs in. Consistency in MongoDB database is configured by using the replica sets and choosing to wait for the writes to be replicated to a given number of slaves. Transactions at the single-document level are atomic transactions a write either succeeds or fails. Transactions involving more than one operation are not possible, although there are few exceptions. MongoDB implements replication, providing high availability using replica sets. In a replica set, there are two or more nodes participating in an asynchronous master-slave replication. MongoDB has a query language which is expressed via JSON and has variety of constructs that can be combined to create a MongoDB query. With MongoDB, we can query the data inside the document without having to retrieve the whole document by its key and then introspect the document. Scaling in MongoDB is achieved through sharding. In sharding, the data is split by certain field, and then moved to different Mongo nodes. The data is dynamically moved between nodes to ensure that shards are always balanced. We can add more nodes to the cluster and increase the number of writable nodes, enabling horizontal scaling for writes [6, 9]. 3.2 Key-value A key-value store is a simple hash table, primarily used when all access to the database is via primary key. They allow schema-less storage of data to an application. The data could be stored in a data type of a programming language or an object. The following types exist: Hierarchical key-value store Eventually-consistent key-value store, hosted services, key-value chain in RAM, ordered key-value stores, multi value databases, tuple store and so on. Key-value stores are the simplest NoSQL data stores to use form an API perspective. The client can get or put the value for a key, or delete a key from the data store. The value is a blob that is just stored without knowing what is inside; it is the responsibility of the application to understand what is stored [3, 6]. 3.2.1 Merits Performance high and predictable. Simple data model. Clear separation of saving from application logic (because of lacking query language). Suitable for storing session information. User profiles, product profiles, preferences can be easily stored. Best suited for shopping cart data and other E-commerce applications. Can be scaled easily since they always use primary-key access. 3.2.2 Demerits Limited range of functions High development effort for more complex applications Not the best solution when relationships between different sets of data are required. Not suited for multi operation transactions. There is no way to inspect the value on the database side. Since operations are limited to one key at a time, there is no way to operate upon multiple keys at the same time. 3.2.3 Case Study Azure Table Storage For structured forms of storage, Windows Azure provides structured key-value pairs stored in entities known as Tables. The table storage uses a NoSQL model based on key-value pairs for querying structured data that is not in a typical database. A table is a bag of typed properties that represents an entity in the application domain. Data stored in Azure tables is partitioned horizontally and distributed across storage nodes for optimized access. Every table has a property called the Partition Key, which defines how data in the table is partitioned across storage nodes rows that have the same partition key are stored in a partition. In addition, tables can also define Row Keys which are unique within a partition and optimize access to a row within a partition. When present, the pair {partition key, row key} uniquely identifies a row in a table. The access to the Table service is through REST APIs [6]. 3.3 Column Store Column-family databases store data in column-families as rows that have many columns associated with a row key. These stores allow storing data with key mapped to values, and values grouped into multiple column families, each column family being a map of data. Column-families are groups of related data that is often accessed together. The column-family model is as a two-level aggregate structure. As with key-value stores, the first key is often described as a row identifier, picking up the aggregate of interest. The difference with column-family structures is that this row aggregate is itself formed of a map of more detailed values. These second-level values are referred to as columns. It allows accessing the row as a whole as well as operations also allow picking out a particular column [6]. 3.3.1 Merits Designed for performance. Native support for persistent views towards key-value store. Sharding: Distribution of data to various servers through hashing. More efficient than row-oriented systems during aggregation of a few columns from many rows. Column-family databases with their ability to store any data structures are great for storing event information. Allows storing blog entries with tags, categories, links, and trackbacks in different columns. Can be used to count and categorize visitors of a page in a web application to calculate analytics. Provides a functionality of expiring columns: columns which, after a given time, are deleted automatically. This can be useful in providing demo access to users or showing ad banners on a website for a specific time. 3.3.2 Demerits Limited query options for data High maintenance effort during changing of existing data because of updating all lists. Less efficient than all row-oriented systems during access to many columns of a row. Not suitable for systems that require ACID transactions for reads and writes. Not good for early prototypes or initial tech spikes as the schema change required is very expensive. 3.3.3 Case Study Cassandra A column is the basic unit of storage in Cassandra. A Cassandra column consists of a name-value pair where the name behaves as the key. Each of these key-value pairs is a single column and is stored with a timestamp value which is used to expire data, resolve write conflicts, deal with stale data, and other things. A row is a collection of columns attached or linked to a key; a collection of similar rows makes a column family. Each column family can be compared to a container of rows in an RDBMS table where the key identifies the row and the row consists on multiple columns. The difference is that various rows do not need to have the same columns, and columns can be added to any row at any time without having to add it to other rows. By design Cassandra is highly available, since there is no master in the cluster and every node is a peer in the cluster. A write operation in Cassandra is considered successful once its written to the commit log and an in-memory structure known as memtable. While a node is down, the data that was supposed to be stored by that node is handed off to other nodes. As the node comes back online, the changes made to the data are handed back to the node. This technique, known as hinted handoff, for faster restore of failed nodes. In Cassandra, a write is atomic at the row level, which means inserting or updating columns for a given row key will be treated as a single write and will either succeed or fail. Cassandra has a query language that supports SQL-like commands, known as Cassandra Query Language (CQL) [2, 6]. We can use the CQL commands to create a column family. Scaling in Cassandra is done by adding more nodes. As no single node is a master, when we add nodes to the cluster we are improving the capacity of the cluster to support more writes and reads. This allows for maximum uptime as the cluster keeps serving requests from the clients while new nodes are being added to the cluster. 3.4 Graph Graph databases allow storing entities and relationships between these entities. Entities are also known as nodes, which have properties. Relations are known as edges that can have properties. Edges have directional significance; nodes are organized by relationships which allow finding interesting patterns between the nodes. The organization of the graph lets the data to be stored once and then interpreted in different ways based on relationships. Relationships are first-class citizens in graph databases; most of the value of graph databases is derived from the relationships. Relationships dont only have a type, a start node, and an end node, but can have properties of their own. Using these properties on the relationships, we can add intelligence to the relationship for example, since when did they become friends, what is the distance between the nodes, or what aspects are shared between the nodes. These properties on the relationships can be used to query the graph [2, 6]. 3.4.1 Merits Very compact modeling of networked data. High performance efficiency. Can be deployed and used very effectively in social networking. Excellent choice for routing, dispatch and location-based services. As nodes and relationships are created in the system, they can be used to make recommendation engines. They can be used to search for patterns in relationships to detect fraud in transactions. 3.4.2 Demerits Not appropriate when an update is required on all or a subset of entities. Some databases may be unable to handle lots of data, especially in global graph operations (those involving the whole graph). Sharding is difficult as graph databases are not aggregate-oriented. 3.4.3 Case Study Neo4j Neo4j is an open-source graph database, implemented in Java. It is described as an embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs rather than in table. Neo4j is ACID compliant and easily embedded in individual applications. In Neo4J, a graph is created by making two nodes and then establishing a relationship. Graph databases ensure consistency through transactions. They do not allow dangling relationships: The start node and end node always have to exist, and nodes can only be deleted if they dont have any relationships attached to them. Neo4J achieves high availability by providing for replicated slaves. Neo4j is supported by query languages such as Gremlin (Groovy based traversing language) and Cypher (declarative graph query language) [6]. There are three ways to scale graph databases: Adding enough RAM to the server so that the working set of nodes and relationships is held entirely in memory. Improve the read scaling of the database by adding more slaves with read-only access to the data, with all the writes going to the master. Sharding the data from the application side using domain-specific knowledge. Conclusions NoSQL databases are still evolving and more number of enterprises is switching to move from the traditional relational database technology to non-relational databases. But given their limitations, they will never completely replace the relational databases. The future of NoSQL is in the usage of various database tools in application-oriented way and their broader adoption in specialized projects involving large unstructured distributed data with high requirements on scaling. On the other hand, an adoption of NoSQL data stores will hardly compete with relational databases that represent reliability and matured technology. NoSQL databases leave a lot work on the application designer. The application design is an important part of the non-relational databases which enable the database designers to provide certain functionalities to the users. Hence a good understanding of the architecture for NoSQL systems is required. The need of the hour is to take advantage of the new trends emerging in the world of databases the non-relational databases. An effective solution would be to combine the power of different database technologies to meet the requirements and maximize the performance.

English Language Learners: Families and Schools Essay examples -- ELL

Diverse cultures within the United States are rapidly developing and growing and the educational sector is the number one target to ensure that English –learners are receiving adequate education. Within the educational sector there are administrators and teachers who are involved in students lives on a daily basis to ensure that education is equal. In order to achieve the vital objective of equality, socio-cultural influences on ELL students, bilingualism and home language use, parental and community resources, and partnerships between families and schools all have to be considered to provide an opportunity for equal education. The American society has a vast influence on students who are English learners. In this case it is prominent for educators to provide the best knowledge, creative strategies for learning and classroom management skills that are reliable to give these students the best education. Becoming more perceptive and analytic observers as educators enables teachers to detect aspects of children's everyday learning experience from home that could be adapted for use in school (Leighton, Hightower, Wrigley, 1995). In order to understand the most important aspect about ELL students it is significant that the teacher become knowledgeable about the students’ cultural background. One way to accomplish this task is to become familiar with a student’s background by reading multicultural literature on the students’ culture, tradition, religion, and beliefs. Engage with the parents and family members to get the most important information about the student to know how the student lea rns. Once the educator has learned the students’ cultural background it will be easier to instruct the student and for student to learn. An ELL... ...education and students to flourish academically. References Academic Writing Tips. Org. (2011). ELL families and schools. Retrieved April 5, 2012 from, http://academicwritingtips.org/component/k2/item/640-ell-families-and-schools.html?tmpl=component&print=1 Cummins (1994). Knowledge, power, and identity in teaching English as a second language: Educating second language children. Cambridge, England: Cambridge University Press. Leighton, M. S., Hightower, A. M., & Wrigley, P. (1995). Funds of knowledge for teaching [Electronic version]. In Model strategies in bilingual education: Professional development. Washington, DC: U.S. Department of Education. Retrieved April 5, from http://www.ed.gov/pubs/ModStrat/pt3i.html Robertson, K. (2007). Bilingual family night for ELL families. Retrieved April 5, 2012 from, http://www.colorincolorado.org/article/18800/

Positive and Negative Effects of the Industrial Revolution

The Industrial Revolution was a change in the mid-18th century from small scale, domestic production of goods to machine-based, mass production of goods. It is usually thought of as having mostly or only positive impacts on Europe. Although the revolution did have many positive impacts, it had its fair share of negative impacts as well. Some of the positive outcomes included the overall increase in production and value of goods, improved efficiency of how these goods were made, and the development of new power sources. The Industrial Revolution also caused a great increase in population and urbanization.This increase resulted in several negative impacts. Some included unsafe working and living conditions, child labor, and lack of many public services. Clearly, the Industrial Revolution had a huge impact on European society with both positive and negative effects. The Industrial Revolution had many positive effects. Overall, the increase in quality, quantity, and efficiency of goods w ere the main positive impacts of the Industrial Revolution. However, it all started in the agricultural industry. Due to numerous inventions and improvements in the agricultural methods, many of the people who worked the lands on manors had to move to the cities.This caused a growth in the number of cities as well as a growth of the population living in the cities- urbanization. This was one positive effect of the revolution. Inventions in the textile industry also were developed. The first was the flying shuttle which greatly sped up the weaving process. This invention led to a chain of new inventions that continually increased the speed and efficiency of production and quantity. Eventually domestic production of goods evolved into larger machines run in factories, mass production, and the need for larger power sources.This change from domestic production of goods to factory-based production was another positive effect (Docs 1a and 1b). The large-scale production of goods, first in the textile industry, caused a decrease in the price of these goods. This in turn caused a better economy. Therefore, as the goods became cheaper and the economy became better, the demand and production increased. These two impacts, the improved economy and decrease in price of goods were also positive. Because the machines mass producing goods in factories were so large, a new, stronger power source became a necessity.This resulted in the invention of the water frame, which then led to the steam engine and the internal combustion engine (used mostly in vehicles and other modes of transportation). This development of new power sources was yet another positive impact of the revolution. Finally, as urbanization and industrialization caused unsafe living and working conditions, a series of reforms were made to improve these conditions. Reform groups such as the Labor Unions advocated for improvements in the laborers’ conditions in which they were subjected to work and live. The Factory Act of 1833 was among several laws passed prohibiting child labor (Doc 2).The Sadler Commission, a government sponsored organization, sent inspectors to the factories to enforce the new laws that improved the laborers’ lives at work. Clearly, the Industrial Revolution had numerous positive impacts on Europe. Several negative impacts of the Industrial Revolution also developed despite its numerous positive effects. Many of the negative impacts actually came as a result of urbanization and industrialization. According to document 3, the number of large cites just about doubled between 1801 and 1851. The primary negative impacts of the Industrial Revolution included the working and living conditions of the workers.Because of their low social status and lack of money and land, the laborers were forced to work for people of higher social status, or the bourgeoisie. As previously mentioned, the workers, or the proletariat, were forced off of their land as their manual agri cultural labor was replaced by faster, more efficient machines. With almost no money, they moved to the cities and set to work right away. Unfortunately, their poverty earned them horrible working and living conditions. They received very little pay given the number of hours they put in- they were usually overworked for up to 16 hours a day.The machines they worked around were very large and dangerous, and a worker could get fired if he or she was injured by a machine. This lack of worker’s compensation, or job security, was one negative impact of the revolution. Another major negative effect of the revolution was the worker’s dangerous working conditions. Even the few hours the workers spent at home were pretty unsafe. Due to lack of plumbing and garbage disposal, the streets of the slums often exposed the inhabitants to many diseases because they were â€Å"filthy and strewn with animals and vegetable refuse† (Doc 5).The families of the proletariat were also o ften overcrowded- many times multiple families were crammed into one small living space. The unsanitary and overcrowded living conditions of the workers provided yet another negative effect of the Industrial Revolution. The extent of how unsanitary their living conditions were is shown in this quote: â€Å"†¦ the annual loss of life from filth and bad ventilation are greater than the loss from death or wounds in any wars in which the country has been engaged in modern times† (Doc 6).This proves that not only were the working conditions of the proletariat dangerous, but the unsanitary conditions in which they lived also provided an unsafe environment that caused the deaths of many. Finally, one dominant, negative issue that resulted from the Industrial Revolution was child labor. Children were often used in the factories and coal mines because of their size. In the textile factories, they were small enough to dart under the machines and try to fix tangled threads with the ir quick, nimble fingers.However, this work was quite dangerous because they could lose their fingers if they were not fast enough; if they got injured in this way, or any way really, they could then lose their job. Fortunately, child labor was an issue soon solved due to the Labor Acts of 1833, 1842 (Mines Act), 1845, and 1874. Undoubtedly, the Industrial Revolution had a considerable number of negative effects. The Industrial Revolution was certainly one movement that had a huge impact on Europe. As with many things, it had its pros and cons. Some pros comprised of the increase of production, quality, and quantity of goods.This increase also led to another positive effect: an improved economy due to lowered prices of goods. Some cons included the unsafe working conditions workers suffered through as well as the unsanitary, overcrowded living conditions they had to deal with. Also, early on, lack of worker’s compensation, or job security, and public services such as plumbing and proper garbage disposal did not make matters any better. The Industrial Revolution may have had its ups and downs, but nonetheless, completely changed the face of Europe by the time of its end.

Classification Essay Students Essay

Being a student in today’s society holds an immense amount of pressure to do one thing: graduate. Thirteen years of school prepare students for the next major steps of their lives. Every student faces many struggles and frustrations before graduation day arrives. One may be able to surmise a few details about a student before they put pencil to paper or even speak. One can also make assumptions about a student based on his or her seating position. Motivation, determination, and concentration will establish a student’s amount of success. The three categories of high school students are underachievers, average students, and overachievers. The first category of students is the underachievers, or the back row. These students lack the characteristics needed to be successful. They are also the ones who think a success is showing up to class. Oftentimes, they will be failing classes and not even bother to hand in assignments. If they do pass their classes, it is with hardly passing grades and little knowledge gained. This typical student can be seen coming late to class with papers spewing out of his unorganized notebook. Work will not be completed on time, if finished at all. He will not participate in class discussions, or he may sleep throughout the period. In some cases, the only thing these students need is a little extra help. Procrastination, poor attendance, and laziness are few of the common characteristics of an underachiever. Underachievers revel in disrupting class and being a general nuisance to all involved. Homework transforms into spitballs and paper airplanes, and there are a multiple excuses prepared for why the assignment is not finished. They do anything possible to get out of class, and can be often found wandering halls, in the bathroom, or in the comfortable chairs in the office. A step above the underachievers are those students that do just enough in order to succeed, the average achievers, middle row. These students are often capable of achieving academic success, but lack motivation. Many athletes fall into this category, and only because they need to be eligible to participate in their respective sport. Choosing not to make any extra effort, they receive average grades and maintain average attendance. They may not stand out in class, but their work is always completed. These students view extra credit opportunities as a waste of time. These mediocre scholars are also the ones who believe that Wikipedia is among the greatest inventions of all time, while turnitin. com is not. Students who contain these characteristics make up the most common category. The final group of students are the overachievers. They sit prim and proper in the front row raising their eager hands. These workaholics are the ones whom teachers adore and whom fellow students despise. They often exceed the expectations of any teachers. They constantly work hard and are active in class. They take notes verbatim of what the teacher says, excel in group discussions, and much to the demise of the other students, raise the academic bar to an unattainable level. Although they are not often the greatest athletes, or the best at communicating with the opposite gender, they separate themselves with their cerebral work ethic. These scholars find reading entertaining, and would much rather solve a Rubik’s cube than run a mile. One of them will be inevitably become Valedictorian, and will give a speech at the podium come graduation day. When analyzing these students, one could be looking at future doctors, engineers, and various activists. Being average is not necessarily a bad thing, and being an overachiever is not necessarily a good thing either. The student with 4. 0 GPA in high school may not go on to become the neuroscientist everyone thought she would, while the student that took three gym classes may become a famous athlete. Of course, there is leeway for fluctuation in this formula of judgement. Ultimately, it is up to each individual to decide which type of student he or she will become: part of the back row, part of the middle row, or part of the front row.