3rd Grade Science Fair Projects

The 3rd grade may be the first time students are introduced to science fair projects. Children ask questions from a young age, but this is a great time to begin to apply the scientific method. Introduction to 3rd Grade Science Fair Projects 3rd grade is a great time to answer what happens if... or which is better...  questions. In general, elementary school students are exploring the world around them and learning how things work. The key to a great science fair  project at the 3rd-grade level is finding a topic that the student finds interesting. Usually, a teacher or parent is needed  to help plan the project and offer guidance with a report or poster. Some students may want to make models or perform demonstrations that illustrate scientific concepts. 3rd Grade Science Fair Project Ideas Here are some project ideas appropriate for 3rd grade: Do cut flowers last longer if you put them in warm water or in cold water? You can test how effectively flowers are drinking water by adding food coloring. Youll get the best results with white cut flowers, such as carnations. Do flowers drink warm water faster, slower, or at the same rate as cold water?Does the color of your clothing affect how hot or cold you feel when youre outside in the sunlight? Explain your results. This project is easiest if you compare solid colors, such as black and white t-shirts.Do all students in the class have the same size hands and feet as each other? Trace outlines of hands and feet and compare them. Do taller students have larger hands/feet or does height not seem to matter?How much does the temperature have to change for you to feel a difference? Does it matter whether its air or water? You can try this with your hand, a glass, a thermometer, and tap water of different temperatures.Are waterproof mascaras really waterproof? Put some mascara on a sh eet of paper and rinse it with water. What happens? Do 8-hour lipsticks really keep their color that long?Do clothes take the same length of time to dry if you add a dryer sheet or fabric softener to the load?Which melts faster: ice cream or ice milk? Can you figure out why this might happen? You can compare other frozen treats, such as frozen yogurt and sorbet.Do frozen candles burn at the same rate as candles that were stored at room temperature? Ideally, compare candles that are identical in every way except their starting temperature.Research what dryer sheets do. Can people tell the difference between a load of laundry that used dryer sheets and one that didnt use them? If one type of laundry was preferred over the other, what was the reason? Ideas might be scent, softness, and the amount of static.Do all types of bread grow the same types of mold? A related project would compare types of mold that grow on cheese or other food. Keep in mind mold grows quickly on bread, but migh t grow more slowly on other food. Use a magnifying glass to make it easier to tell the types of mold apart.Do raw eggs and hard-boiled eggs spin the same length of time/number of times?What type of liquid will rust a nail the quickest? You could try water, orange juice, milk, vinegar, peroxide, and other common household liquids.Does light affect how fast foods spoil?Can you tell from todays clouds what tomorrows weather will be? Tips for Success Choose a project that wont take too much time to complete. Performing an experiment or making a model often takes longer than one expects, and its better to have extra time than to run out at the last minute.Expect a 3rd-grade project to require adult supervision or help. This doesnt mean an adult should do the project for a child, but an older sibling, parent, guardian, or teacher can help guide the project, offer suggestions, and be supportive.Select an idea that uses materials you can actually find. Some project ideas might look great on paper, but be difficult to perform if the supplies are unavailable.

La Dame and Cathy Ames Comparison Free Essays

In the poem â€Å"La Belle Dame Sans Merci† by John Keats and the story East Of Eden by John Steinbeck both authors similarly characterize women as merciless through the use of the literary technique of imagery. Both Steinbeck and Keats throughout their writings describe events and people in great detail. Both of the women are beautiful yet ‘wild’, put a man to sleep, and force someone to solitude. We will write a custom essay sample on La Dame and Cathy Ames Comparison or any similar topic only for you Order Now In the beginning of both the poem and story the authors give a very visual description of the women. They are both considered to appear on the outside as if they are â€Å"a faerys child† – beautiful. But when you look into their eyes a sense of being â€Å"wild† is within them. The wildness that the men see in their eyes foreshadows their merciless nature. The wildness alludes to and foreshadows the womens animalistic and heartless actions. In both storys the women seduce multiple men with their physical attractiveness in order to gain control of them and make the situation benefit them. The authors use imagery in their texts by explaining in detail the womens outstanding physical features in order to make the reader picture the women in the same way that the narrator does. Steinbeck and Keats effectivly project the images of the women into the minds of the reader. In the two pieces of literature both of the authors specify a scene using imagery in which the beautiful women make a man fall asleep in order to obtain what they want. In Steinbecks case it would be Cathy Ames overdosing Adam so she can sleep with Charles on the night of their wedding. While in Keats poem it is La Dame who slowly puts the unsuspecting knight to sleep so she can murder him. The women in these scenes commit awful acts but neither of them feel any remorse or conscience, which oes to show the women are truly merciless and have a â€Å"wild† nature. In Keats story he uses imagery in the knights vivid dream as a warning to show him all of La Dame’s past victims who had been lured in and killed before him. This shows that La Dame’s feeling of love are false and that she will continue to mercilessly hurt people. The authors uses of imagery in these scenes make the reader feel like they are in the story watching the horrible acts take place and make the reader lovingly hate the women for their cruel and clever actions. In both texts Steinbeck and Keats write a very visual scene about men leaving the eautiful women and cutting all ties to them because they realize that the women are fake, merciless and â€Å"wild†. In the poem written by Keats the knight wakes up as a lonely and â€Å"palely loitering† man because he realizes that if he continued to follow La Dame he would end up like all the other men who fell for her because she is â€Å"wild† and will never be tamed, she is merciless and all of her fealings were false. La Dame emotionally misconstrues the knight and leaves hime to live his life as a ‘dead’ person with his heart in a million pieces. In Steinbecks novel the whoremaster, Mr. Edwards, beats and then leaves Cathy to die because he feels that Cathy had constantly manipulated him and hurt him emotionally, physically and finically and he ever have to deal with her and be put through what he had been. Throughout each of the literary masterpieces both authors similarly characterize women as merciless through the use of the literary technique of imagery. Both of the women are beautiful yet ‘wild’, put a man to sleep, and force someone to solitude. Cathy Ames and La Dame were characterized as two despicipable women who inflicted pain and suffering upon others, to make up for the emotions that they cannot feel. How to cite La Dame and Cathy Ames Comparison, Papers

Briefing Paper Marking Guide - Big Data

Introduction In this briefing paper, the ICT topic Big Data will be reviewed from literatures. Big data is the new IT buzzword that refers to the voluminous data processed in different business processes in different industries around us. There is explosive growth of the volume of structured and unstructured data in last decades. In the early days of implementation of information technology in different kind of organizations had implemented databases for working with data related to their business processes, but in last few years, emergence of social media and ecommerce have accelerated the growth of data outside organization and from individuals. For example, over social media like Facebook, people uploads and shares heavy volumes of images, texts, videos from different parts of the world at the same time their location details, machine details etc. are also being circulated. Analysis of these type of data reveals several interesting information about peoples lifestyles, choices etc. and busines ses are very interesting for these type of information. Working with these kind of data using typical database management software and information technologies is difficult. Big data and technologies have given rise of a new dimension in this case. It covers all technologies that helps to address the volume and complexities of these voluminous data. (Madden, 2012) In this briefing paper, it will discuss about the technology, current research and trends on big data in details. The Problem As, it has been already told that, processing of volumes of structured and unstructured data, using traditional database management systems or data processing systems were difficult. This was the problem that led to the invention of big data concept and related technologies. (Madden, 2012) There is common confusion around the term big data, that is, whether it refers to technology or volume of data. When vendors use the term big data they generally refers to the technologies like processes and tools that helps in working with volumes of data efficiently. So, the term encompasses any collection of complex and larger data set that is difficult to handle by typical database management or data processing applications. It also covers up the collection of tools to support processing of such data through different kind of operations like searching, curation, sharing, transfer, storage, visualization etc. (Zikopoulos, 2011) Bid Data: Characteristics There are some characteristics of a dataset that makes it a Big data data set. Those characteristics are, A. Volume Volume refers to the quantity of data, generated from a process or system. The potential and value of a dataset is directly proportional to its volume. This characteristic is the first criteria to classify a dataset as big data or not. Even this concept has been reflected on the term Big data itself. (Marz Warren, 2014) B. Velocity This characteristic is related to the rate of generation of data or how fast data is getting generated or processed to provide desired outcome. C. Variety Big data takes data from heterogeneous sources into consideration while processing the same. Variety in data sets helps to analyze it from different aspects and in deriving different outcomes. D. Veracity Veracity of data sets refers to the captured quality of those. Veracity of a dataset plays significant role in the accuracy of the outcomes from analysis of the data sets. (Zikopoulos, 2011) E. Variability Variability refers to the levels on inconsistencies present in the data and that show up any time during processing. This may be problematic for data analysts. F. Complexity Managing the processing of big data, is a complex process by itself. It becomes more complex when data comes from heterogeneous sources and larger in volume. These kind of data is needed to be interlinked, correlated and connected. Otherwise it is difficult to work on these data. Big Data: Technologies Computation power and storage for larger datasets are not a serious problem now a days. Advancement in electronics and digital technologies have made these solutions more efficient, easily available and cheaper. This has helped in emergence of big data. There is a paradigm shift from computer architecture to the mechanisms in data processing. There is a growing demand for data mining and analysis applications for big data. (Barlow, 2013) There are wide range of tools and technologies that supports the concept of big data and analysis, processing of the same. There are technologies like crowdsourcing, A/B testing, data fusion etc. along with machine learning, natural language processing, time series analysis, integration, simulation, genetic algorithms, signal processing, visualization etc. Tensors are the representative of multidimensional big data. Tensor based technologies and computation methods like multi- linear sub space learning helps in this case. Other than that there are database related technologies, parallel processing support, search based application, distributed file systems and databases, data mining, cloud computing etc. and Internet that supports big data revolution. There are big data analytics that processes the big data and helps in finding out different patters out of it. These patterns gives critical insights into data sets. Storage is an important issue for big data. A proposed solution is distributed and shared storage. Storage area network or SANs, Networked Area storage or NAS etc. come into these categories. However, big data practitioners are not quite interested in these solutions. There are RDBMS based storage solutions for big data that is capable of storing petabytes of data. (Madden, 2012) All these technologies supports big data in analysis of data from web, analysis of network monitoring logs, click stream analysis etc. There are data science applications like simulations for massive scale analysis of data, deployment of sensors etc. Parallel database systems like Vertica, Teradta, Greenplum etc. are powerful but expensive and hard to administer. There are lack of fault tolerance levels in case of longer queries. Hadoop is a popular big data technology accepted worldwide. (Roebuck, 2011) Big Data: Process There are number of phases in data processing in big data. Those are explained as, 1. Data Acquisition Big data takes data that are evolving from different industries and scientific researches, demographics, social media and ecommerce. However, all data is not equally important for a particular goal so after collecting data, it will be filtered. Data are collected from systems, social media and numerous sources. There can be operational or transactional data, structured and unstructured data. When it comes to big data them all types of data irrespective or format and type are collected. Later on these data are filtered and compressed before processing. The most challenging part of data acquisition is, filtering out the unnecessary data. It must be done in a way so that useful information dont get discarded. Data science deals with numerous issues that helps to define different filters to ensure, accuracy and relevancy of collected data. (Marz Warren, 2014) For streaming data from online sources, it is not always possible to store and process those data to filter those later on. Rather it needs an on the fly approach to work on such streamlines of data from web. There are online data analytics applications and systems that helps in filtering and collected data from online streaming data. Next big challenge in to create metadata from acquired data. This is not easy. Meta data should give details about the sources and structure of data. There are metadata acquisition systems that can automatically record metadata without any human intervention. However, there are lots of things to do with metadata after recording those correctly. There is a pipeline for analysis of big data. Metadata is required in every stage of the pipeline. Thus acquisition of data refers to the collection of technologies, tools and processes of collecting data, filtering it and recording metadata of data at the same time without storing and processing data every time. 2. Cleaning and Extraction of information Data analysis needs some level of uniformity of data. Thus, after acquiring data, it is needed to be cleaned and ready for processing. Data analysis will require data in correct formant otherwise the results of the analysis will not be accurate and effective. It needs an information extraction process that will bring out the required information from the piles of data from heterogeneous sources. Then it should present the extracted data in a structured form. The process is technically challenging. For example, there are data like images and videos. Extracting information from these formats of data and presenting the same in structured format are really hard. A common misconception is, big data always provides truth. This is not the case all the time. The truthfulness of big data and analysis depends on these extraction steps. It depends on how effectively truth is getting extracted from raw data. There are different constraints on valid data and error models that are well recognized. However, till now there are many domain of big data where these constraints are still not available. 3. Integration, Aggregation and Representation of Data It has been already discussed that data comes from different heterogeneous sources. Those are no structured and in right format even. It is not possible to acquire and clean data then store the same in data repositories. There are processes like integration, aggregation of those data and then representing those in the right format to sore and process in future. Data analysis is a complex process. For large scale data analysis it is needed to have effective analysis and the process should be automated. In data analysis process, different semantics and data structures are needed to be expressed in correct formats that are readable by computers and can be resolved by robots. Data integration is important and there are additional works for making the data error free using automated system. There are different alternative solutions for storing data other than databases. Each of these alternatives have its own advantages and disadvantages. Designing database or correct storage solution is needed to be done very carefully. There are many decision making tools to provide assistance in designing databases. 4. Processing of Query, data modeling and analysis of data Making query in traditional databases and processing of query in big data, are fundamentally different. Big data contain volumes of dynamic, interrelated, heterogeneous data. These forms larger networks of interrelated data. There are higher level of data redundancy. These redundancies can be explored through validation, crosschecking etc. There are inherent clusters and these clusters reveals relationships among collections of data. (Roebuck, 2011) Data mining is a related topic here. It required, cleaned, integrated, trustworthy, easily accessible and effective data that can help in declarative query through data mining interfaces and computing environments. Big data supports provisions of interactive data analysis in real time applications. Scaling of complex queries is also supported. However, there is a problem with analysis of big data. That is lack of co- ordination in the systems that stores data, support SQL queries and analytics for performing non-SQL data processing, for example statistical analysis, data mining etc. 5. Interpretation Obtaining only results from analysis is not enough. It needs to explain or provide enough explanatory details about those results so that someone can interpret the results from analysis. There are visualizations used in this case. (Marz Warren, 2014) Challenges in Big data There are number of challenges in big data. Some of those are already explained in related contexts. Still, most prevailing challenges are, Heterogeneous sources and nature of data. Incompleteness in data. Problem with effective cleaning and extraction of data. Scale and volume of data. Timeliness of data. Privacy of data. (ene Polonetsky, 2013) Human collaboration needed in certain phases and lack of it in some cases. Lack of suitable and effective system architecture for big data only. Conclusion In this briefing paper, there is a discussion on an emerging topic in ICT, called big data. After the introduction, there is the problem statement that had given rise to the concept of big data. In the sub sequent sections there are discussions on different characteristics, technology etc. related to big data, finally a detailed description of processes in processing of big data. In the end there is a summary of challenges in big data. References Barlow, M., 2013. Real-Time Big Data Analytics: Emerging Architecture. s.l.:O'Reilly Media, Inc.. Boyd, D. Crawford, K., 2011. Six Provocations for Big Data, s.l.: SSRN. ene, O. Polonetsky, J., 2013. Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property , XI(5). Leskovec, J., Rajaraman, A. Ullman, J. D., 2014. Mining of Massive Datasets. s.l.:Cambridge University Press. Madden, S., 2012. From Databases to Big Data. IEEE Computer Society, 16(3), pp. 4-6. Marz, N. Warren, J., 2014. Big Data: Principles and Best Practices of Scalable Realtime Data Systems. s.l.:Manning Publications Company. Roebuck, K., 2011. Storing and Managing Big Data - NoSQL, Hadoop and More: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors. s.l.:Emereo Pty Limited. Zikopoulos, P., 2011. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. s.l.:McGraw Hill Professional.

Humanities Final Flashcard

Humanities Final

The US Federal Minimum Wage

The US Federal Minimum Wage What is the current U.S. federal minimum wage? The answer to that question can be trickier than you might think.While the current US federal minimum wage was last set at $7.25 per hour on July 24, 2009, your age, type of employment, even where you live can alter the legal minimum hourly wage your employer is required to pay. What is the  Federal Minimum Wage Law? The federal minimum wage is established by and regulated under the Fair Labor Standards Act of 1938 (the FLSA). In its final form, the act applied to industries whose combined employment represented only about one-fifth of the US labor force. In these industries, it banned oppressive child labor and set the minimum hourly wage at 25 cents, and the maximum workweek at 44 hours. Who Must Pay the Federal Minimum Wage? Today, the minimum wage law (the FLSA) applies to employees of enterprises that do at least $500,000 in business a year. It also applies to employees of smaller firms if the employees are engaged in interstate commerce or in the production of goods for commerce, such as employees who work in transportation or communications or who regularly use the mails or telephones for interstate communications. It also applies to employees of federal, state or local government agencies, hospitals and schools, and it generally applies to domestic workers. Details of the Federal Minimum Wage The following details apply only to the the federal minimum wage, your state may have its own minimum wage rates and laws. In cases where state minimum wage rates differ with the federal rate, the higher minimum wage rate always applies.Current Federal Minimum Wage: $7.25 per hour (as of July 24, 2009) may vary under the following conditions: Younger Workers: If you are under 20 years of age, you may be paid as little as $4.25 per hour during your first 90 consecutive calendar days of employment. Students, Apprentices and the Disabled: Certain full-time students, student learners, apprentices, and workers with disabilities may be paid less than the minimum wage under special certificates issued by the US Department of Labor. Workers Who Earn Tips: Employers who allow workers to keep tips must pay a cash minimum wage of at least $2.13 per hour IF they claim a tip credit against their federal minimum wage obligation of $7.25 per hour. In other words, if your tips plus cash wages do not equal at least $7.25 per hour, your employer must make up the difference. Overtime Pay: Federal law requires payment of at least 1-and-1/2 times your regular rate of pay for all hours worked over 40 in a workweek. Child Labor: An employee must be at least 16 years old to work in most non-farm jobs and at least 18 to work in non-farm jobs declared hazardous by the Secretary of Labor.Persons 14 and 15 years old are allowed to work before or after school in some non-manufacturing, non-mining and non-hazardous jobs IF: They work no more than - 3 hours on a school day or 18 hours in a school week; 8 hours on a non-school day or 40 hours in a non-school week. Work may not begin before 7 a.m. or end after 7 p.m., except from June 1 through Labor Day, when evening hours are extended to 9 p.m. Different rules apply in agricultural employment. Other Special Exemptions: Under the Fair Labor Standards Act of 1938 (FLSA), executive, administrative, professional, and outside sales employees are exempted from the minimum wage and overtime requirements of the FLSA, provided they meet certain tests regarding job duties and responsibilities and are compensated on a salary basis. Minimum Wages in the States By law, states are allowed to establish their own minimum wages and regulations. However, anytime the state minimum wage differs from the federal minimum wage, the higher rate applies. For specifics and updates on the minimum wages and regulations in all 50 states and the District of Columbia, see: Minimum Wage Laws in the States from the U.S. Department of Labor. Enforcement of the Federal Minimum Wage Law The Wage and Hour Division of the U.S. Department of Labor administers and enforces the Fair Labor Standards Act and, thus, the minimum wage with respect to private employment, State and local government employment, and Federal employees of the Library of Congress, U.S. Postal Service, Postal Rate Commission, and the Tennessee Valley Authority. The FLSA is enforced by the U.S. Office of Personnel Management for employees of other Executive Branch agencies, and by the U.S. Congress for covered employees of the Legislative Branch.Special rules apply to state and local government employment involving fire protection and law enforcement activities, volunteer services, and compensatory time off instead of cash overtime pay.For information on enforcement of state minimum wages and other state labor laws, see: State Labor Offices/State Laws, from the US Department of Labor. To Report Suspected Violations Suspected violations are abuses of federal or state minimum wage laws should be reported directly to District Office of the U.S. Wage and Hour Division nearest you. For addresses and phone numbers, see: Wage and Hour Division District Office LocationsFederal law prohibits discriminating against or discharging workers who file a complaint or participate in any proceedings under the Fair Labor Standards Act.

Travel the World - Letter to a Friend - Assignment

Travel the World - Letter to a Friend - - Assignment Example nding money and time traveling to the City was, therefore, to experience the culture and art of London, to learn the various kinds of art within the city that may have influenced Albert in his career. In his painting, Albert depicts ambient photographic-like scenery of a lake amidst rocky mountain cliffs with grazing deer on the shore in the foreground. The cheap means of travelling to London from Huston Texas was through the Turkish airlines that cost $610 to and fro. The great London atmosphere also extends its hotels with varying affordable rates. Within the city of London, I am staying at the Central Park Hotel, a three star hotel located at the heart of the city that costs  £43 per night (FareCompare.com). London is the capital city of the England in Europe popularly known for its monarchy system under the rule of a queen. There are nearly over 300 languages spoken in the city, however, its main culture mainly ties around the arts, museums and various festivals. In particular, as for architecture, London prides itself of a number of landmarks and cultural iconic objects that are globally associated with the city such as the Big Ben, as well as the Buckingham Palace. The city is also popular for the fast foods vendors that specialize in making the burgers, pies and pizza that forms one of the main popular foods among most city dwellers and workers. In terms of entertainment, the city is best recognized for its particular preference for rock music dating from the 1960s and is the home for various globally renowned rock bands and musicians. The city is also home for some of the globally recognized teams in the game of soccer. Like other major cities across Europe, London has an oceanic t emperate climate with the four climatic seasons like most of Europe (The Guardian). Albert made his iconic photograph-like painting using oil-based paints and soft brush to make the portrait on canvas measuring approximately six and ten feet in size. Albert gracefully employs

How are convergence and interactivity defined in new media studies Essay

How are convergence and interactivity defined in new media studies Using examples, critically evaluate these concepts in relation to those of remediation and intra-activity - Essay Example product of convergence between â€Å"old† and â€Å"new† media through â€Å"remediation,† which they examined in their book, Remediation: Understanding New Media. This essay aims to study how convergence and interactivity are defined in new media studies. Through several examples and definitions, it critically evaluates these concepts in relation to remediation and intra-activity. According to new media scholars, convergence is defined through technological convergence, the lens of consumer agency (Lister et al., 2009: 48; Suchman, 2007), cultural/system/corporate convergences (Jenkins, 2008; Murdoch, 2000), and remediation (Bolter and Grusin, 2000), while interactivity has been defined as a cause, enabler, and result of convergence (Murdoch, 2000; Manovich, 2001); however, the â€Å"myth of interactivity† (Manovich, 2001: 74) and the process of inter-activity (Barad, 2007) criticise the intuitive and interactive notions of new media interactivity and con vergence (Hay and Couldry, 2011). Before convergence is further understood, the meaning of new media must be explored first because it shapes the philosophical conceptualisation of convergence. One of the common definitions of new media is the interaction between old and modern media, especially computers, mobile information and telecommunication devices, and the Internet. New media is more complex and varied than the use of current web and mobile technology interfaces, nonetheless. In the article, â€Å"How Should We Read New Media and New Technologies?† Gà ¶kà §ek (2011) cautioned people in seeing new media as a single and homogenous object, when it is composed of a â€Å"...collection of objects which should be analysed economically, socially, culturally, politically, philosophically, theoretically and technologically† (71). He resisted separating new media from its social context, as well as bundling it into a simplistic view of networked and interactive modern technological systems. Manovich (20 01), in The Language