Question please do this in MATLAB pls follow all requirements, you can try to do the extra credit but that part isnt necessary. thank you! Objectives: Learn how to write a convergent algorithm using Taylor series For Taylor Series, Read the Taylor documents included with this Lab. Study the series to see how it converges. Note that for these Trig functions Taylor converges quickly. The number of terms will vary based on the size of the angle and the size of epsilon. Requirements: Write the program using Dialog buttons and a while loop to continue until users chooses to stop program. 1. Use Dialog buttons to select the sine, cosine, or sinh. Ask the user to input k (global) in the script before you start the dialog buttons. In each case respectively: a. Have the user enter the angle. You can choose degrees or radians noting that radians are required as the parameter for the Matlab and your functions. b. call your trig function that you wrote using Taylor series (see item 4) c. call the standard trig function in Matlab. Output the answers to 4 significant digits for your function and the Matlab function to compare accuracy. Use the global k example to vary the accuracy. Find a k that lets your function be equivalent to the Matlab function. (We will use your function in a future lab.). Write the 3 functions using Taylor series for sine, cosine, and sinh using -k to control convergence. k must be a global variable. These can be embedded or external functions. Test these functions and create a table of values per the table below: (10PTS EXTRA CREDIT Instead of hand writing table Create an array for these answers and write them to an excel file.)

Question 41 2 pts Which XXX completes the code? class Players { public: void setName(string newName) { .. } void setAge(int newAge) { .. } void PrintDetails() { } string pName; int page; }; class SoccerPlayers: public players { public: void SetDetails(string newName) { ... } string Get League() { ... } private: string team; }; int main() { String leagueName; XXX newPlayers. SetName("Mel Allen"); newPlayers. SetAge(21); leagueName = newPlayers. Get League(); return ; } Players new Players; Players (Soccer Players) player Object; Soccer Players newPlayers; Players.Soccer Players new Players;

lululemon athletica, Inc., in 2020: Is the Recent Growth in Retail Stores, Revenues, and Profitability Sustainable? ASSIGNMENT QUESTIONS 1. How strong are the competitive forces confronting lululemon in the market for performance- based yoga and fitness apparel? Do a five-forces analysis to support your answer. 2. What does your strategic group map of the performance sports apparel industry look like? Is lululemon well positioned? Why or why not? 3. What do you see as the key success factors in the market for performance-based yoga and fitness apparel? 4. What does a SWOT analysis reveal about the overall attractiveness of lululemon’s situation? 5. What are the primary components of lululemon's value chain? 6. What are the key elements of lululemon's strategy? [If you have previously covered the Under Armour case, which features of lululemon's strategy stand out as being different from the strategy at Under Armour?] 7. Which one of the five generic competitive strategies discussed in Chapter 5 most closely approximates the competitive approach that lululemon is employing? 8. What do the data in case Exhibit 1 reveal about lululemon's financial and operating performance? You should use the financial ratios in Table 4.1 of Chapter 4 (pages 66-68) in performing calculations to determine which aspects of lululemon's financial performance might be characterized as impressive and those aspects you believe are in need of improvement. In addition to the ratios in Table 4.1, you should definitely calculate compound average growth rates (CAGR) for certain financial measures. The formula for calculating CAGR (in percentage terms) is as follows: CAGR % = [ending value + beginning value] 1 - 1 x 100 (where n = the number of year-to-year or period-to-period changes) 9. What 3-4 top priority issues do CEO Calvin McDonald and lululemon management need to address? 10. What recommendations would you make to lululemon CEO Calvin McDonald? At a minimum, your recommendations should cover what to do about each of the top priority issues identified in question 9.

2. Consider the mechanical system shown in Figure. We assume that the system is linear. The external force u(t) is the input to the system, and the displacement y(t) of the mass is the output. The displacement y(t) is measured from the equilibrium position in the absence of the external force. This system is a single-input, single-output system. From the diagram, the system equation is This system is of second order. This means that the system involves two integrators. Let us define state variables ı(t) and x2(t) as u(t) x1(t)- y(t) x2(t)-t) M-5 kg, b-4Ns/m, k-5 N/m, u(t)-step function a) Obtain state space representation of the mechanical system (both theoretically and MATLAB) y0) b) Convert the state space to transfer function in Matlab c) Obtain block diagram of the mechanical system (both theoretically, MATLAB and Simulink)

please answer the top and bottom questionas I'm almost out of credit. Thank you! = Irradiance profiles at an open ocean station were found to fit a value for KPAR = 0.025 m-1 in the near surface water column. Using this information, calculate the euphotic depth and the 1st optical depth at this location. Comment on whether at this location it is likely to be case 1 or case 2 waters? For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac). Β Ι Ο Ꭶ Paragraph Arial 10pt Ix OG Q 6 ते = = = ... Ki AA X2 X, ABC > [ † {;} © Ra Ky + P O WORDS POWERED BY TINY QUESTION 3 points Save Answe List 3 reactive oxygen species typically photochemically produced in seawater.

Are the following systems (i) Linear? (ii) Causal? (iii) Time-invariant? Consider x to be the input signal and justify your answers, specifying key conditions/assumptions as needed. (a) y[n] = x[n] +1 (b) y[n] = x[-n] (c) y[n] = z[n + 1] – x[n]

I need this exercise to be implemented in MATLAB. (NotSimulink) ܕ EXAMPLE 2.1. This example illustrates the ability of the adaptive linear combi- ner, trained with the LMS algorithm, to estimate the parameters of a linear model. The input data consist of 1,000 zero-mean Gaussian random vectors with three components, that is, re R3xl; and the bias is set to zero, or B = 0. The variances of the components of r are 5, 1, and 0.5, respectively. The assumed linear model is given by b = [1,0.8, -1]. To generate the target values (desired outputs), the 1,000 input vectors are used to form a matrix X = (x ? ... $1,000), and the desired outputs are computed according to d=b'x. The covariance matrix of the vector input signals can be estimated as 1.000 C Irx= XXT 1,000 1,000 h=1 [30]. Using the LMS algorithm in Table 2.1, with a value of Ho = 0.9/Amax = 0.1936, where Imax is the largest eigenvalue of the covariance matrix Cy, and t = 200 (the search time constant), the input vectors along with the associated desired output values are presented to the linear combiner. The criter- ion used to terminate the learning process involved monitoring the square root of the MSE values every time step k. The learning process was terminated when VJ = leik) < 10-8, where elk) = dík) – W" (k)(k). The initial values of the synaptic weight vector were selected as zero-mean Gaussian random numbers with a variance of 0.25, Winitial = (-0.3043, -0.8195,0.3855)". The LMS learning process was terminated after only 204 iterations (training epochs). In other words, after the first 204 input vectors along with the associated desired output values were presented, the network converged. The final synaptic weight matrix was given as wrinat = [1.000000, 0.800000, -1.0000001", which is exactly the assumed linear model b (out to six decimal places). In fact, the L2-norm of the difference between the linear model band the final weight vector is | 5-Wfinal ||2= 1.505404 x 10-7. Figure 2.17 shows the progress of the learning rate parameter as it is adjusted according to the search-then-converge schedule. As we can see from the plot, at the beginning of training u does not change much; then toward the end of training it becomes much smaller. Figure 2.18 shows the root mean square (RMS) value of the performance measure, that is, VJ, as training of the network progresses. This exercise is similar to problems in system identification of estimating a parameter vector associated with a dynamic model of a system given only input/output data from the system, that is, parametric system identification [31]. W finai $) X():11C1, 5000) )- d=6x X(2,:) = randa (1, 1000 X(2, ) d=$3x3 3 SX1000) X(3,:) = sqrt(0.5) Condn (11000) : {{x1000) scalar coux-x***/1000 randa (N) normally distrand. numbers I rand (0) NXN uniform dist the interval (0.0, 1.0)