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Rasmussen College Health Fair Presentation on Grief and Bereavement Presentation

Rasmussen College Health Fair Presentation on Grief and Bereavement Presentation.

The community has invited you to participate in a Health Fair/Presentation designed to enhance community knowledge about various health issues in the neighborhood. As a Registered Nurse in the community; you are to present on GRIEF AND BEREAVEMENT topic . You will need to choose a local organization that supports GRIEF AND BEREAVEMENT. You are to make a PowerPoint presentation.You will need to present on GRIEF AND BEREAVEMENT and the organization chosen for approximately 15-20 minutes. Preparation:Create a PowerPoint including the following information:How the registered nurse serves the community at the primary, secondary and tertiary levels of prevention related to GRIEF AND BEREAVEMENT. Brief description of the organization and how health promotion initiatives served by this organization are organized and financed (Example: How are the health promotion initiatives decided upon and where does the financing come from?) Identify quality improvement methods the organization utilizes to demonstrate they are improving health outcomes.Site your references using APA 7th edition format, including in-text citations and include full reference at the end of your presentation.Include any significant facts related to the topic and/or organization that may entice the learner’s interest.Presentation-Health Fair Rubric Exemplary Proficient Developing Unacceptable Comments Points CONTENT Accuracy and Depth of Content (Pertains to information presented on PowerPoint) Information presented is consistently accurate and current. Analysis, application, and synthesis of content is effective and relates to nursing practice. Presentation covers all required components clear and complete. Information presented is accurate. Analysis and application is demonstrated and offers depth to the presentation. Required components addressed, lacks detail/clarity. Gaps in knowledge in some of the information presented. Majority of the required components addressed, but not fully. Minimal content depth. Accuracy of information is questionable. Presentation does not address all of the required components. Use of Resources (Required minimum, 4) (Integration of resources means that it is clear where resources are integrated in the written material with in-text citations ) More than the required number of credible and current resources are used and deeply integrated into the presentation to support the content of the presentation. Required number of resources are used and cited to support the content of the presentation. Required resources integrated to demonstrate a connection between the source and the content. Required resources not used; lack of resources used to support the content. Does not meet presentation requirements. APA/Mechanics (Pertains to information presented on PowerPoint) Presentation has rare mechanical, grammatical, and APA errors. Presentation has occasional mechanical, grammatical, and APA errors. Additional editing would have been helpful. Presentation needs improvement and attention to improve mechanical, grammatical, and/or APA format. Presentation has significant mechanical, grammatical, and/or APA errors; risk for illegibility or plagiarism. Use of Communication/ Visual Aids Variety of aids used to enhance the presentation; creative selection of aids to deepen the audience’s understanding. Communication/visual aids professional and applicable to the topic. 1-2 aids used in the presentation to enhance the presentation and audience’s understanding. Aids used but did not stand out or enhance the delivery of the presentation. Aids used inappropriately. Font too small or too much information included. Unimportant information highlighted.
Rasmussen College Health Fair Presentation on Grief and Bereavement Presentation

Netflix and LinkedIn Online Services Discussion Questions.

PART1Online ServicesDescriptionHave you used Spotify Radio®, Netflix®, or Hulu®? Sites such as these provide access to content and also permit social engagement with other users.Spotify (Links to an external site.)Netflix (Links to an external site.)Hulu (Links to an external site.)There are so many different types of social networks. If you are launching a college career or entering the workforce, you do not want to miss LinkedIn and Twitter. Post popularity drives the posts on the Delicious Website.LinkedIn® (Links to an external site.)Twitter® (Links to an external site.)Online applications such as Spotify Radio and Hulu introduce a whole world of entertainment as the social networks mentioned, LinkedIn and Twitter provide new ways to connect. InstructionsFor this discussion activity, comment about your experience with any two of these online services. If you have not used any of these previously, then be sure to spend the time to at least get familiar with two. If you have used them all, then select another similar resource. How is their usability?What impact do they have upon society? Education? Communication? Business?What types of online services do you subscribe to for your own entertainment, education, or business interests?Be sure to use the guidelines below when completing this discussion activity.Your initial reply should be 2-3 paragraphs in length (about 200-250 words).Read your classmates’ replies and expand on the ideas of at least two of their posts.Feel free to challenge ideas if you disagree.Post your initial reply and responses in the rich content editor.Use the attachment tool when providing documentation that supports your reply in the rich content editor.PART2:ePortfolioAn Electronic Portfolio (ePortfolio) is a web-based method for you to save and showcase information about your educational career. It can be used for class assignments, resumes, etc. to show your skills and accomplishments to family, friends, faculty, staff, employers, and others.To begin, read about ePortfolios (Links to an external site.) in Canvas and view the following video:222 – ePortfolios (Vimeo – 6:32) (Links to an external site.)InstructionsThere are two parts to this activity. View each part to complete this assignment.Note: Once the course is complete, you may opt to retain or delete the ePortfolio. These ePortfolios can be very useful to help you demonstrate what you learned and what you have created.Part 1Create a presence on the Internet through the creation of a basic ePortfolio in Canvas. The Portfolio should be three pages at a minimum and include:A home page that contains:Your nameAn introductory paragraph about yourselfAn image, a photo, or something you likeA resume pageUse the resume content from Module 2.Save the file into PDF format (Links to an external site.) and attach the file or enter the text onto the page.A page that uses multimedia or images (embedded video/ images/ music playlist, etc.)You select the item(s)Refer to guides such as YouTube FAQ on embeds (Links to an external site.) and Canvas Portfolio embeds (Links to an external site.) as neededImages need to be uploaded one at a timePart 2When you complete this assignment, share your ePortfolio here and in the text box detail in a paragraph (about 100-150 words) what you found interesting and/or difficult about the process. Be sure to post the link to your portfolio in the text box.These pages will detail how to create and share your portfolio. Options for sharing for this activity include a public and private option.For the public share (Links to an external site.) option, you will create a public hyperlink and then merely copy and paste that link with the summary paragraph. For the private share (Links to an external site.) option, you will provide the instructor access via a code that you create and post with the summary paragraph.
Netflix and LinkedIn Online Services Discussion Questions

Invisible Labors by Lynn May Rivas Vision of American Values Paper Analysis.

In approximately 400 words, critically respond to the “Invisible Labors: Caring for the Independent Person” by Lynn May Rivas. Pay special attention to Rivas’ position in this argument—not just her point but also her possible biases. For example, when she speaks of “our American” values of independence vs. dependence and consumer vs. the consumed, who is the “we” of which she speaks? In other words, how does Rivas envision this “we” and “our America”? Further, who is Rivas’ audience? Does she in any way alienate a particular audience that you can imagine? Finally, consider the timeliness of the information. Is more historical context needed for this argument? Might the future also suggest new solutions that Rivas had not previously considered?I provided the text below
Invisible Labors by Lynn May Rivas Vision of American Values Paper Analysis

TAGUCHI’S DEFINITION OF QUALITY The old traditional definition of quality states quality is conformance to specifications. This definition was expanded by Joseph M. Juran (1904-) in 1974 and then by the American Society for Quality Control (ASQC) in 1983. Juran observed that “quality is fitness for use.” The ASQC defined quality as” the totality of features and characteristics of a product or service that bear on its ability to satisfy given needs.” Taguchi presented another definition of quality. His definition stressed the losses associated with a product..” It must be kept in mind here that “society” includes both the manufacturer and the customer. Loss associated with function variability includes, for example, energy and time (problem fixing), and money (replacement cost of parts). Losses associated with harmful side effects could be market shares for the manufacturer and/or the physical effects, such as of the drug thalidomide, for the consumer. TAGUCHI’S LOSS FUNCTION Taguchi’s quality philosophy strongly emphasizes losses or costs. W. H. Moore asserted that this is an “enlightened approach” that embodies “three important premises: for every product quality characteristic there is a target value which results in the smallest loss; deviations from target value always results in increased loss to society; [and] loss should be measured in monetary units (dollars, pesos, francs, etc.).” depicts Taguchi’s typically loss function. The figure also contrasts Taguchi’s function with the traditional view that states there are no losses if specifications are met. It can be seen that small deviations from the target value result in small losses. These losses, however, increase in a nonlinear fashion as deviations from the target value increase. Where L(Y) is the expected loss associated with the specific value of Y. Essentially, this equation states that the loss is proportional to the square of the deviation of the measured value, Y, from the target value, T. This implies that any deviation from the target (based on customers’ desires and needs) will diminish customer satisfaction. This is in contrast to the traditional definition of quality that states that quality is conformance to specifications. It should be recognized that the constant k can be determined if the value of L(Y) associated with some Y value are both known. Of course, under many circumstances a quadratic function is only an approximation. Since Taguchi’s loss function is presented in monetary terms, it provides a common language for all the departments or components within a company. Finally, the loss function can be used to define performance measures of a quality characteristic of a product or service. This property of Taguchi’s loss function will be taken up in the next section. But to anticipate the discussion of this property, Taguchi’s quadratic function can be converted to: This can be accomplished by assuming Y has some probability distribution with mean, a and variance o.2 This second mathematical expression states that average or expected loss is due either to process variation or to being off target (called “bias”), or both. TAGUCHI, ROBUST DESIGN, AND THE DESIGN OF EXPERIMENTS Taguchi asserted that the development of his methods of experimental design started in Japan about 1948. These methods were then refined over the next several decades. They were introduced in the United States around 1980. Although, Taguchi’s approach was built on traditional concepts of design of experiments (DOE), such as factorial and fractional factorial designs and orthogonal arrays, he created and promoted some new DOE techniques such as signal-to-noise ratios, robust designs, and parameter and tolerance designs. Some experts in the field have shown that some of these techniques, especially signal-to-noise ratios, are not optimal under certain conditions. Nonetheless, Taguchi’s ideas concerning robust design and the design of experiments will now be discussed. DOE is a body of statistical techniques for the effective and efficient collection of data for a number of purposes. Two significant ones are the investigation of research hypotheses and the accurate determination of the relative effects of the many different factors that influence the quality of a product or process. DOE can be employed in both the product design phase and production phase. A crucial component of quality is a product’s ability to perform its tasks under a variety of conditions. Furthermore, the operating environmental conditions are usually beyond the control of the product designers, and, therefore robust designs are essential. Robust designs are based on the use of DOE techniques for finding product parameter settings (e.g., temperature settings or drill speeds), which enable products to be resilient to changes and variations in working environments. . To achieve economical product quality design, Taguchi proposed three phases: system design, parameter design, and tolerance design. In the first phase, system design, design engineers use their practical experience, along with scientific and engineering principles, to create a viably functional design. To elaborate, system design uses current technology, processes, materials, and engineering methods to define and construct a new “system.” The system can be a new product or process, or an improved modification of an existing product or process. . EXAMPLES AND CONCLUSIONS As Thomas P. Ryan has stated, Taguchi at the very least, has focused “our attention on new objectives in achieving quality improvement. The statistical tools for accomplishing these objectives will likely continue to be developed.” Quality management “gurus,” such as W. Edwards Deming (1900-1993) and Kaoru Ishikawa (1915-), have stressed the importance of continuous quality improvement by concentrating on processes upstream. This is a fundamental break with the traditional practice of relying on inspection downstream. Taguchi emphasized the importance of DOE in improving the quality of the engineering design of products and processes. As previously mentioned, however,” his methods are frequently statistically inefficient and cumbersome.” Nonetheless, Taguchi’s design of experiments have been widely applied and theoretically refined and extended. Two application cases and one refinement example will now be discussed. Taguchi methods Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently also applied to, engineering, biotechnology, marketing and advertising. Professional statisticians have welcomed the goals and improvements brought about by Taguchi methods, particularly by Taguchi’s development of designs for studying variation, but have criticized the inefficiency of some of Taguchi’s proposals. Off-line quality control Taguchi’s rule for manufacturing Taguchi realized that the best opportunity to eliminate variation is during the design of a product and its manufacturing process. Consequently, he developed a strategy for quality engineering that can be used in both contexts. The process has three stages: System design Parameter design Tolerance design System design This is design at the conceptual level, involving creativity and innovation. Parameter design Once the concept is established, the nominal values of the various dimensions and design parameters need to be set, the detail design phase of conventional engineering. Taguchi’s radical insight was that the exact choice of values required is under-specified by the performance requirements of the system. In many circumstances, this allows the parameters to be chosen so as to minimize the effects on performance arising from variation in manufacture, environment and cumulative damage. This is sometimes called robustification. Tolerance design With a successfully completed parameter design, and an understanding of the effect that the various parameters have on performance, resources can be focused on reducing and controlling variation in the critical few dimensions Taguchi Method Design of Experiments The general steps involved in the Taguchi Method are as follows: 1. Define the process objective, or more specifically, a target value for a performance measure of the process. This may be a flow rate, temperature, etc. The target of a process may also be a minimum or maximum; for example, the goal may be to maximize the output flow rate. The deviation in the performance characteristic from the target value is used to define the loss function for the process. 2. Determine the design parameters affecting the process. Parameters are variables within the process that affect the performance measure such as temperatures, pressures, etc. that can be easily controlled. The number of levels that the parameters should be varied at must be specified. For example, a temperature might be varied to a low and high value of 40 C and 80 C. Increasing the number of levels to vary a parameter at increases the number of experiments to be conducted. 3. Create orthogonal arrays for the parameter design indicating the number of and conditions for each experiment. The selection of orthogonal arrays is based on the number of parameters and the levels of variation for each parameter, and will be expounded below. 4. Conduct the experiments indicated in the completed array to collect data on the effect on the performance measure. 5. Complete data analysis to determine the effect of the different parameters on the performance measure. A detailed description of the execution of these steps will be discussed next. Determining Parameter Design Orthogonal Array The effect of many different parameters on the performance characteristic in a condensed set of experiments can be examined by using the orthogonal array experimental design proposed by Taguchi. Once the parameters affecting a process that can be controlled have been determined, the levels at which these parameters should be varied must be determined. Determining what levels of a variable to test requires an in-depth understanding of the process, including the minimum, maximum, and current value of the parameter. If the difference between the minimum and maximum value of a parameter is large, the values being tested can be further apart or more values can be tested. If the range of a parameter is small, then less values can be tested or the values tested can be closer together. For example, if the temperature of a reactor jacket can be varied between 20 and 80 degrees C and it is known that the current operating jacket temperature is 50 degrees C, three levels might be chosen at 20, 50, and 80 degrees C. Also, the cost of conducting experiments must be considered when determining the number of levels of a parameter to include in the experimental design. In the previous example of jacket temperature, it would be cost prohibitive to do 60 levels at 1 degree intervals. Typically, the number of levels for all parameters in the experimental design is chosen to be the same to aid in the selection of the proper orthogonal array. Knowing the number of parameters and the number of levels, the proper orthogonal array can be selected. Using the array selector table shown below, the name of the appropriate array can be found by looking at the column and row corresponding to the number of parameters and number of levels. Once the name has been determined (the subscript represents the number of experiments that must be completed), the predefined array can be looked up. Links are provided to many of the predefined arrays given in the array selector table. These arrays were created using an algorithm Taguchi developed, and allows for each variable and setting to be tested equally. For example, if we have three parameters (voltage, temperature, pressure) and two levels (high, low), it can be seen the proper array is L4. Clicking on the link L4 to view the L4 array, it can be seen four different experiments are given in the array. The levels designated as 1, 2, 3 etc. should be replaced in the array with the actual level values to be varied and P1, P2, P3 should be replaced with the actual parameters (i.e. voltage, temperature, etc.) Array Selector Important Notes Regarding Selection Use of Orthogonal Arrays Note 1 The array selector assumes that each parameter has the same number of levels. Sometimes this is not the case. Generally, the highest value will be taken or the difference will be split. The following examples offer insight on choosing and properly using an orthogonal array. Examples 1 and 2 focus on array choice, while Example 3 will demonstrate how to use an orthogonal array in one of these situations. Example 1: # Parameter: A, B, C, D = 4 # Levels: 3, 3, 3, 2 = ~3 Array: L9 Example 2: # Parameter: A, B, C, D, E, F = 6 # Levels: 4, 5, 3, 2, 2, 2 = ~3 Array: modified L16 Example 3: A reactor’s behavior is dependent upon impeller model, mixer speed, the control algorithm employed, and the cooling water valve type. The possible values for each are as follows: Impeller model: A, B, or C Mixer speed: 300, 350, or 400 RPM Control algorithm: PID, PI, or P Valve type: butterfly or globe There are 4 parameters, and each one has 3 levels with the exception of valve type. The highest number of levels is 3, so we will use a value of 3 when choosing our orthogonal array. Using the array selector above, we find that the appropriate orthogonal array is L9: When we replace P1, P2, P3, and P4 with our parameters and begin filling in the parameter values, we find that the L9 array includes 3 levels for valve type, while our system only has 2. The appropriate strategy is to fill in the entries for P4=3 with 1 or 2 in a random, balanced way. For example: Here, the third value was chosen twice as butterfly and once as global. Note 2 If the array selected based on the number of parameters and levels includes more parameters than are used in the experimental design, ignore the additional parameter columns. For example, if a process has 8 parameters with 2 levels each, the L12 array should be selected according to the array selector. As can be seen below, the L12 Array has columns for 11 parameters (P1-P11). The right 3 columns should be ignored. Analyzing Experimental Data Once the experimental design has been determined and the trials have been carried out, the measured performance characteristic from each trial can be used to analyze the relative effect of the different parameters. To demonstrate the data analysis procedure, the following L9 array will be used, but the principles can be transferred to any type of array. In this array, it can be seen that any number of repeated observations (trials) may be used. Ti,j represents the different trials with i = experiment number and j = trial number. It should be noted that the Taguchi method allows for the use of a noise matrix including external factors affecting the process outcome rather than repeated trials, but this is outside of the scope of this article. To determine the effect each variable has on the output, the signal-to-noise ratio, or the SN number, needs to be calculated for each experiment conducted. The calculation of the SN for the first experiment in the array above is shown below for the case of a specific target value of the performance characteristic. In the equations below, yi is the mean value and si is the variance. yi is the value of the performance characteristic for a given experiment. {SN_{i}}=10logfrac{bar{y_{i}}^2}{{s_{i}}^2} Where bar y_{i}=frac {1}{N_{i}}sum_{u=1}^{N_{i}}y_{i,u} s_{i}^2=frac {1}{N_{i}-1}sum_{u=1}^{N_{i}}left ( y_{i,u}-bar y_{i} right ) i = Experiment;number u=Trial;number N_{i}=Number;of;trials;for;experiment;i For the case of minimizing the performance characteristic, the following definition of the SN ratio should be calculated: {SN_{i}}=-10logleft(sum_{u=1}^{N_{i}}frac{y_{u}^2}{N_{i}}right) For the case of maximizing the performance characteristic, the following definition of the SN ratio should be calculated: {SN_{i}}=-10logleft[frac{1}{N_{i}}sum_{u=1}^{N_{i}}frac{1}{y_{u}^2}right] After calculating the SN ratio for each experiment, the average SN value is calculated for each factor and level. This is done as shown below for Parameter 3 (P3) in the array: {SN_{color{red}P3,1}}=frac{(S_{N1} S_{N6} S_{N8})}{3},! {SN_{color{blue}P3,2}}=frac{(S_{N2} S_{N4} S_{N9})}{3},! {SN_{color{green}P3,3}}=frac{(S_{N3} S_{N5} S_{N7})}{3},! Once these SN ratio values are calculated for each factor and level, they are tabulated as shown below and the range R (R = high SN – low SN)of the SN for each parameter is calculated and entered into the table. The larger the R value for a parameter, the larger the effect the variable has on the process. This is because the same change in signal causes a larger effect on the output variable being measured. Problems Problem: You have just produced one thousand 55 gallon drums of sesame oil for sale to your distributors. However, just before you are to ship oil, one of your employees remembers that one of the oil barrels was temporarily used to store insecticide and is almost surely contaminated. Unfortunately, all of the barrels look the same. One barrel of sesame oil sells for $1000, while each assay for insecticide in food oil costs $1200 and takes 3 days. Tests for insectide are extremely expensive. What do you do? Solution: Extreme multiplexing. This is similar to using a Taguchi method but optimized for very sparse systems and specific cases. For example, instead of 1000 barrels, let us consider 8 barrels for now, one of which is contaminated. We could test each one, but that would be highly expensive. Another solution is to mix samples from each barrel and test the mixtures. Mix barrels 1,2,3,4 —> Sample A Mix barrels 1,2,5,6 —> Sample B Mix barrels 1,3,5,7 —> Sample C We claim that from testing only these three mixtures, we can determine which of the 8 barrels was contaminated. Let us consider some possible results of these tests. We will use the following label scheme: /-, /-, /- in order of A, B, C. Thus, ,-, indicates A and C showed contamination but not B. Possible Result 1: -,-,- The only barrel not mixed in was #8, so it is contaminated. Possible Result 2: ,-,- Barrel #4 appears in A, but not in B and C. Since only A returned positive, barrel #4 was contaminated. Possible Result 3: -, ,- Barrel #6 appears in B, but not in A and C. Since only B returned positive, barrel #6 was contaminated. We can see that we have 23 = 8 possible results, each of which corresponds to a particular barrel being contaminated. We leave the rest of the cases for the reader to figure out. Solution with 1,000 barrels: Mix samples from each barrel and test mixtures. Each mixture will consist of samples from a unique combination of 500 barrels. Experiments required = log2 (1000) =~10. Solution with 1,000,000 barrels: Experiments required = log2(1000000)=~20. Thus, by using extreme multiplexing, we can greatly reduce the # of experiments needed, since the # of experiments scales with log2(# of barrels) instead of # of barrels. Worked out Example A microprocessor company is having difficulty with its current yields. Silicon processors are made on a large die, cut into pieces, and each one is tested to match specifications. The company has requested that you run experiments to increase processor yield. The factors that affect processor yields are temperature, pressure, doping amount, and deposition rate. a) Question: Determine the Taguchi experimental design orthogonal array. The operating conditions for each parameter and level are list A: Temperature A1 = 100ºC A2 = 150ºC (current) A3 = 200ºC B: Pressure B1 = 2 psi B2 = 5 psi (current) B3 = 8 psi C: Doping Amount C1 = 4% C2 = 6% (current) C3 = 8% D: Deposition Rate D1 = 0.1 mg/s D2 = 0.2 mg/s (current) D3 = 0.3 mg/s a) Solution: The L9 orthogonal array should be used. The filled in orthogonal array should look like this: This setup allows the testing of all four variables without having to run 81 [=34=(3 Temperatures)(3 Pressures)(3 Doping Amounts)(3 Deposition rates)] separate trials. b) Question: Conducting three trials for each experiment, the data below was collected. Compute the SN ratio for each experiment for the target value case, create a response chart, and determine the parameters that have the highest and lowest effect on the processor yield. b) Solution: Shown below is the calculation and tabulation of the SN ratio. {S_{m1}}=frac{(87.3 82.3 70.7)^{2}}{3}=19248.0,! {S_{T1}}=87.3^2 82.3^2 70.7^2=19393.1,! {S_{e1}}={S_{T1}}-{S_{m1}}=19393.1-19248.0=145.0,! {V_{e1}}=frac{S_{e1}}{N-1}=frac{145.1}{2}=72.5,! {SN_{1}}=10 log frac{(1/N)(S_{m1}-V_{e1})}{V_{e1}}=10 log frac{(1/3)(19248.0-145.1)}{145.1}=19.5,! Shown below is the response table. This table was created by calculating an average SN value for each factor. A sample calculation is shown for Factor B (pressure): {SN_{color{red}B1}}=frac{(19.5 17.6 22.2)}{3}=19.8,! {SN_{color{blue}B2}}=frac{(21.4 14.3 24.0)}{3}=19.9,! {SN_{color{green}B3}}=frac{(19.3 29.2 20.4)}{3}=23.0,! The effect of this factor is then calculated by determining the range: Delta = Max – Min = 23.0-19.8=3.2,! It can be seen that deposition rate has the largest effect on the processor yield and that temperature has the smallest effect on the processor yield. Extreme Example: Sesame Seed Suffering Problem: You have just produced one thousand 55 gallon drums of sesame oil for sale to your distributors. However, just before you are to ship oil, one of your employees remembers that one of the oil barrels was temporarily used to store insecticide and is almost surely contaminated. Unfortunately, all of the barrels look the same. One barrel of sesame oil sells for $1000, while each assay for insecticide in food oil costs $1200 and takes 3 days. Tests for insectide are extremely expensive. What do you do? Solution: Extreme multiplexing. This is similar to using a Taguchi method but optimized for very sparse systems and specific cases. For example, instead of 1000 barrels, let us consider 8 barrels for now, one of which are contaminated. We could test each one, but that would be highly expensive. Another solution is to mix samples from each barrel and test the mixtures. Mix barrels 1,2,3,4 —> Sample A Mix barrels 1,2,5,6 —> Sample B Mix barrels 1,3,5,7 —> Sample C We claim that from testing only these three mixtures, we can determine which of the 8 barrels was contaminated. Let us consider some possible results of these tests. We will use the following label scheme: /-, /-, /- in order of A, B, C. Thus, ,-, indicates A and C showed contamination but not B. Possible Result 1: -,-,- The only barrel not mixed in was #8, so it is contaminated. Possible Result 2: ,-,- Barrel #4 appears in A, but not in B and C. Since only A returned positive, barrel #4 was contaminated. Possible Result 3: -, ,- Barrel #6 appears in B, but not in A and C. Since only B returned positive, barrel #6 was contaminated. We can see that we have 23 = 8 possible results, each of which corresponds to a particular barrel being contaminated. We leave the rest of the cases for the reader to figure out. Solution with 1,000 barrels: Mix samples from each barrel and test mixtures. Each mixture will consist of samples from a unique combination of 500 barrels. Experiments required = log2(1000)=~10. Solution with 1,000,000 barrels: Experiments required = log2(1000000)=~20. Thus, by using extreme multiplexing, we can greatly reduce the # of experiments needed, since the # of experiments scales with log2(# of barrels) instead of # of barrels. Other Methods of Experimental Design Two other methods for determining experimental design are factorial design and random design. For scenarios with a small number of parameters and levels (1-3) and where each variable contributes significantly, factorial design can work well to determine the specific interactions between variables. However, factorial design gets increasingly complex with an increase in the number of variables. For large systems with many variables (50 ) where there are few interactions between variables, random design can be used. Random design assigns each variable a state based on a uniform sample (ex: 3 states = 0.33 probability) for the selected number of experiments. When used properly (in a large system), random design usually produces an experimental design that is desired. However, random design works poorly for systems with a small number of variables. To obtain a even better understanding of these three different methods, it’s good to get a visual of these three methods. It will illustrate the degree of efficiency for each experimental design depending on the number of variables and the number of states for each variable. The following will have the three experimental designs for the same scenario. Scenario. You have a CSTR that has four(4) variables and each variable has three or two states. You are to design an experiment to systematically test the effect of each of the variables in the current CSTR. Experimental Design #1: Factorial Design By looking at the # variables and # states, there should be a total of 54 experiments because (3impellers)(3speeds)(3controllers)(2valves)=54. Here’s a list of these 54 experiments: Experimental Design #2: Taguchi Method Since you know the # of states and variables, you can refer to the table above in this wiki and obtain the correct Taguchi array. It turns out to be a L9 array. With the actual variables and states, the L9 array should look like the following: Experimental Design #3: Random Design Since we do not know the number of signal recoveries we want and we don’t know the probabilities of each state to happen, it will be difficult to construct a random design table. It will mostly be used for extreme large experiments. Refer to the link below to help you obtain a better grasp on the random design concept. Dr. Genichi Taguchi Dr. Taguchi built on the work of Plackett and Burman by combining statistics and engineering to achieve rapid improvements in product designs and manufacturing processes. His efforts led to a subset of screening experiments commonly referred to the Taguchi Techniques or the Taguchi Method®. Major Premises of Taguchi Techniques Focus on the robustness of the product. Make the product correctly in spite of variation in materials and processes. Design the product to be insensitive to the common cause variation that exists in the process. Quantify the effects of deviation using the Quality Loss Function The Quality Loss Function, L(y), provides both a conceptual and a quantifiable means to demonstrate the impact of deviation from target. Noise Factors Taguchi calls common cause variation the “noise.” Noise factors are classified into three categories: Outer Noise, Inner Noise, and Between Product Noise. Taguchi’s approach is not to eliminate or ignore the noise factors; Taguchi techniques aim to reduce the effect or impact of the noise on the product quality. Quality Loss Function The Loss Function can help put the cost of deviation from target into perspective. The loss represents a summation of rework, repair, warranty cost plus customer dissatisfaction, bad reputation, and eventual loss of market share for the manufacturer. Signal to Noise Ratio Taguchi’s emphasis on minimizing deviation from target led him to develop measures of the process output that incorporate both the location of the output as well as the variation. These measures are called signal to noise ratios. The signal to noise ratio provides a measure of the impact of noise factors on performance. The larger the S/N, the more robust the product is against noise. Calculation of the S/N ratio depends on the experimental objective: Derivation of Taguchi Matrices Taguchi matrices are derived from classical Full Factorial arrays. As with Plackett-Burman designs, Taguchi designs are based on the assumption that interactions are not likely to be significant. Taguchi designs have been developed to study factors at two-levels, three-levels, four-levels, and even with mixed levels. The levels in Taguchi matrices have historically been reported as Level 1 and Level 2 for two-level experiments. These levels are no different than the Low (-) Level and the High ( ) Level used in Full Factorial designs and by Plackett and Burman. For more than two levels, experimenters typically use Level 1, Level 2, Level 3, etc. for Taguchi designs. Types of Taguchi Designs A series of Taguchi designs for studying factors at two-levels are available. Two-level designs include the L4, L8, and L16 matrices. The L4 design studies up to 3 factors. The most popular Taguchi designs are the L8 and L16 that study up to 7 and 15 factors respectively. The L4, L8, and L16 designs are geometric designs based on the 22, 23, and 24 Full Factorial matrices respectively. They are based on the Full Factorials so that interactions can be studied if desired. Non-geometric Taguchi designs include the L12, L20, and L24 designs that can study up to 11, 19, and 23 factors respectively. There are other two-level Taguchi Matrices, both geometric and non-geometric, designed to study even more factors, but it is rare that larger numbers of factors can be studied in a practical, feasible, or cost-effective manner. Analysis of Interactions While Taguchi views interactions as noise factors and most likely not significant, he does offer techniques to evaluate the impact of two-way interactions on responses. Taguchi provides two techniques to explore interactions in a screening experiment. The linear graph is a graphical tool that facilitates the assignment of factors and their interactions to the experimental matrix. Some experimenters find the interaction tables developed from the linear graphs to be easier to use. Three-Level Matrices * Taguchi screening designs for three levels exist. o The L9 looks at 4 factors at 3 levels. o An L27 can be used to study up to 13 factors at 3 levels and an L81 can evaluate up to 40 factors at 3 levels. * Taguchi designs for 4 levels and 5 levels are available. Matrices with Outer Arrays

Beatboxing

Introduction As I was surfing through the internet, a viral video had caught my attention. At the time, it had roughly one million views and thus sparked my interest, so I watched it. In this video, a fairly large man began to move his lips and multiple sounds came out. He starts out slowly by setting a bass foundation and eventually adds a rhythmic part to the foundation. To the rhythm and foundation, he then adds a melody. Within the first 15 seconds, I recognize that he is indeed playing Bille Jean by Michael Jackson and it caught 100 percent of my attention. This man made me wonder how a human could possibly play 3 different parts of a song and how was he able to accomplish this feat. Now what was he doing in this video exactly? This man was beatboxing. Beatboxing is the art of producing drum beats, rhythm, and musical sounds using one’s mouth, lips, tongue, voice, nasal passage and throat.[i] Thus this video had caused me to further examine beatboxing and try to answer the question: how is this man able to play multiple different parts, when the only source is him? To attempt to even answer this question, this paper will first give a brief history on beatboxing to give the reader a basic understanding on the concept of beatboxing before exploring how this type of music interacts with the brain. The video clip will then be analyzed to form a conclusion based on the analysis. The Link: http://www.youtube.com/watch?v=ayzoj7YB7IA History of Beatboxing Prehistory of Beatboxing The root of beatboxing is vocal percussion and it has been a part of human history for hundreds of years and can be traced back to Africa. As part of African ritualistic music, vocal percussion patterns such as, “hup, hup, hup, hup” and “Ch Ka Ch Ch” were used to help performers become induced into a trance like state, in addition to using clapping and stamping to maintain rhythm. Then during the 17th Century, when African slaves were taken to plantations, African music was blended with European folk and brass band music becoming jazz and blues. These black slaves were generally poor and usually couldn’t afford musical instruments and so improvised with their bodies and voices to create music. “Claps and clicks became the drums, and low hums because the double bass; the two back bones of blues and jazz music. One would hum, one would clap, stick and hit things as the drums, and one would sing. This would eventually evolve into imitating many sounds, such as the ‘shhchh’ of a soft snare and the ‘tssa’ of the hi-hat being played with brushes. Blues groups found a way to make their music with nothing but their voices…Immediately, this form of vocal percussion became a staple of urban culture, that is, culture of the street.”[ii] Old Skool: The Beginning of Beatboxing Beatboxing, like graffiti, seems to have begun as an urban art form. It appears with the beginning of hip-hop, which gets its start from DJs spinning records, while MCs are rapping. MCs could also be seen rapping over drum machine (also known as the beat box) beats. Since these drum machines couldn’t have been purchased in the ghettos (aka poor urban cities), people began trying to imitate these drum machines with their mouths and thus became human beatboxers. New School: Beatboxing As We Know It Now During the 1990s, a new type of beatboxer appeared that developed new sounds and techniques. A great example of this is a beatboxer from 1999 called Rahzel, who used a method called auditory illusion to make listeners believe that he is indeed singing and beatboxing at the same time. The beatboxing song that Rahzel first revealed this new sound and technique with was ‘If Your Mother Only Knew’ which was reconfigured from Aaliyah’s 1997 song ‘If Your Girl Knew’. How Does Beatboxing Work in The Brain? Auditory Continuity Illusion Audio continuity affects whether a frequency component is thought of as being continuous in time or if a frequency component contains gaps. Our brains can perceive a song as being continuous, even if it is not. Auditory continuity works by filling in these missing gaps with a different sound, which our brain then “fills in” the missing portions of the song, even if they aren’t there. Our brain is thus producing a perception of a sound that is not truly there because it thinks that the two sounds are taking place at the same time. This is how Rahzel is able to make one believe that he is singing and beatboxing at the same time in the ‘If Your Mother Only Knew’ song. What listener does really hear is this pattern: B[iff] your Pff[mother] B B[on] B[ly] B[knew] B[knew] B B B Pff B = Classic Kick Pff = Classic Snare although the brain actually interprets this pattern as 2 different streams. A link to this song is: Grouping by Pitch Proximity

reflection paper

online dissertation writing reflection paper. I need an explanation for this History question to help me study.

The paper should be a 2-3 page paper, double spaced. 12 font times new roman
There is no prompt for the paper. It is meant to get your reaction/response to the reading.Despite the fact that you are required to give a short summary of the paper (including the name of the author) and what the main points propounded in the article, the paper is NOT supposed to be a summary of the article.After giving the summary (meaning providing the context) you are required to give you reaction to that article. Was it interesting? completely new material to you/ or dd you find that the author did not do justice to the subject.? Does this article make you think of the subject in a different way?
The paper should have some kind of a thesis and needs to be an academic paper, so please make sure that you have topic sentences and a consclusion.
You are free to use any type of citation, hoever, please ensure that you are consistent throughtout this paper.
reflection paper

Gender Studies homework help

Gender Studies homework help. Theories play a vitally important role in guiding research and organizing and making sense of research findings. In spite of the great importance of theory-building and theory testing within your field of specialization, there is no generally accepted conception of what a theory is. Because your dissertation must contribute to theory, you must have a clear understanding of the variety of conceptions of theory, types of theories, and ways of contributing to theory and be able to justify how, exactly, your study contributes to theory.Part 1Using Gelso (2006), Harlow (2009),ÿWacker (1998), and five additional peer-reviewed articles from your specialization, discuss scholarly views on the nature and types of theory. Compare and contrast at least three views of what constitutes a theory, including the view you will use in Part 3 of this question. Be sure to distinguish theory from related concepts, such as hypothesis, paradigm, model, and concept.Part 2Using Ellis & Levy (2008), Harlow, E. (2009), and five additional peer-reviewed articles, review the scholarly literature on the relationship between theory and research and the ways research (quantitative and qualitative) can contribute to theory. Discuss at least three ways research can contribute to theory.Part 3Pick a theory (in one of the views of what constitutes a theory that you identified in Part 1) of current interest directly related to the topic area of your dissertation. A theory is currently of interest if there are articles published on it in the past five years. Using at least 10 published, peer-reviewed research articles:1. Explain how the theory adds or may add to our understanding of your field and/or research topic.2. Discuss and analyze the literature on two areas of controversy or unanswered questions related to the theory.The structure of your paper should be as follows:Title pageBody (10-15 pages, no more or less; APA Style; use appropriate headings for organization of the paper)References (APA Style)Learning Outcomes:1. Compose a theoretically sound and conceptually rich essay that demonstrates knowledge of fundamental subject areas of a Learner’s academic discipline and specialization.Gender Studies homework help

NRS 410V Grand Canyon University Nursing Process Approach to Care Cancer Question

NRS 410V Grand Canyon University Nursing Process Approach to Care Cancer Question.

Write a paper (1,750-2,000 words) on cancer and approach to care based on the utilization of the nursing process. Include the following in your paper:Describe the diagnosis and staging of cancer.Describe at least three complications of cancer, the side effects of treatment, and methods to lessen physical and psychological effects.Discuss what factors contribute to the yearly incidence and mortality rates of various cancers in Americans.Explain how the American Cancer Society (ACS) might provide education and support. What ACS services would you recommend and why?Explain how the nursing process is utilized to provide safe and effective care for cancer patients across the life span. Your explanation should include each of the five phases and demonstrate the delivery of holistic and patient-focused care.Discuss how undergraduate education in liberal arts and science studies contributes to the foundation of nursing knowledge and prepares nurses to work with patients utilizing the nursing process. Consider mathematics, social and physical sciences, and science studies as an interdisciplinary research area.You are required to cite to a minimum of four sources to complete this assignment. Sources must be published within the last 5 years and appropriate for the assignment criteria and relevant to nursing practice.Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.Benchmark InformationThis benchmark assignment assesses the following programmatic competencies:RN-BSN2.1: Incorporate liberal arts and science studies into nursing knowledge.3.1 Utilize the nursing process to provide safe and effective care for patients across the life span.
NRS 410V Grand Canyon University Nursing Process Approach to Care Cancer Question