CS152 Lab Exercise 7: Working with Objects Before you get started you might want to view this helpful video provided for you by Prof. Maxwell in order to understand the difference between simulation (Euclidean) and visualization (Zelle Graphics window) coordinates: Simulation versus Visualization Coordinates Create a class to represent a Ball for a physics simulation L1. Create a new file named physics_objects.py L2. Create a Ball class in your physics_objects.py file Write a Ball class that will represent a ball in your simulation. Since this is a physics simulation, you will want to represent all of its relevant attributes such as mass, radius, position, velocity, and acceleration. L3. Write the init method of the Ball class Remember that we specify all the class attributes in the init method of the class. The init method should have at least two parameters: self and a GraphWin object which refers to the window object (e.g., win). You are free to add optional arguments such as position and radius, but these are not required. Inside the init method, create fields to hold each of the following pieces of information. 1. self.mass - give it an initial value of 1. 2. self.radius - give it an initial value of 1. 3. self.position - this will be a two-element list, representing the x and y location values. Give the x and y positions initial values of 0. 4. self.velocity - a two-element list, with initial values of 0. 5. self.acceleration - a two-element list, with initial values of 0. 6. self.win - a GraphWin object (the win parameter). Finally, two other attributes are needed in the init method. They both require a bit more explanation. These attributes are the “glue” that hold the simulation and the visualization coordinate systems together. Self.Scale In addition to the physical parameters, each Ball object needs to know how to draw itself to a visualization window. We want to be able to use different units for the physical parameters versus the visualization parameters. Aside: Have you ever made a model of something? For example, a model of a T. Rex dinosaur. Was it 12 feet tall and 40 feet in length? I doubt it! It had a scaling factor built into the model so that it could fit on your desk. In the case of our physics ball, we will add a scale field to the Ball class and give it an initial value of 10. The scale will transform. simulation coordinates to screen coordinates. Self.Vis This is the place in our code where the internal and external representations meet. We will use a Zelle graphics object to represent the physics ball on the screen. Create a field named vis and assign it a list with one element, which is a Zelle Graphics Circle object. A Circle object needs an anchor point for its center and a radius. For the anchor point x and y values use the Ball's position x-value multiplied by the scale field. The y position needs adjustment because the screen coordinates are different from the physics coordinates. In the physics coordinates a move in the positive y direction is up, but on the screen coordinates a move in the positive y direction is down (the upper left corner of the screen is at position (0,0)). This means that whenever we translate from physics coordinates to screen coordinates, we need to subtract the physics y position from the window's height. Therefore, the proper y coordinate for the Graphics Circle is win.get Height() minus the product of the Ball's position y value and the scale field. The final parameter for the Circle-creation function is the radius, which should be the Ball's radius value multiplied by the scale field. The entire statement might look like the following, depending on what you named your fields and how you imported the graphics module. self.vis = [ gr.Circle( gr.Point(self.pos[0]*self.scale, win.getHeight()-self.pos[1]*self.scale), self.radius * self.scale ) ] L4. Create a Draw method Create a draw method def draw(self): The method should loop over the self.vis list and have each item call its draw method just like we did in the lab prep exercise test2() function. L5. Create Getters and Setters Create getter and setter methods for each of the physical attributes of the Ball. For example, the get Mass method should return the value of the mass field of the object. The set Mass method should take in a new value as one of the parameters and assign it to the mass field of the object. When writing accessor methods, you want to avoid returning important encapsulated fields. For example, if the following get Position method returns a reference to the list being used by the object to store its position # a bad example def get Position(self): return self.position By returning a reference to the list, another program can now change the values stored in self.position. Instead, return a copy of the list. NOTE: DO NOT FOLLOW THE ABOVE EXAMPLE CODE IT’S AN EXAMPLE OF WHAT NOT TO DO. # a good example def get Position(self): return self.position[:] By returning a copy, another program can't unexpectedly edit the Ball's internal list. Use the following definitions for your getter/setter methods. You must follow these specifications or the test files will not work without modification. def get Position(self): # returns a 2-element tuple with the x, y position. def set Position(self, px, py): # px and py are the new x,y values def getVelocity(self): # returns a 2-element tuple with the x and y velocities. def setVelocity(self, vx, vy): # vx and vy are the new x and y velocities def getAcceleration(self): # returns a 2-element tuple with the x and y acceleration values. def setAcceleration(self, ax, ay): # ax and ay are new x and y accelerations. def get Mass(self): # Returns the mass of the object as a scalar value def set Mass(self, m): # m is the new mass of the object def get Radius(self): # Returns the radius of the Ball as a scalar value def set Radius(self, r): # (**Optional** You might implement this later when you are making your ball class fancier) r is the new radius of the Ball object. Note: This function will need to undraw the circle, create a new circle with the new radius, and draw it back into the window. setPosition and setRadius require a little bit of thought as they not only update the appropriate field, but also move or change the visualization. If a Circle is in screen space location A and you want it to be in screen space location B, then moving the object by the amount (B - A) does what you want to do. To calculate B-A, calculate the difference in simulation space and then multiply by the scale factor for x and by the negative scale factor for y. def set Position(self, px, py): # assign to x_old the current x position # assign to y_old the current y position # assign to the x coordinate in self.pos the new x coordinate # assign to the y coordinate in self.pos the new y coordinate # assign to dx the change in the x position times self.scale # assign to dy the change in the y position times -self.scale # for each item in the vis field of self # call the move method of the item, passing in dx and dy Once you have completed these steps, download the file testBall_1.py . The test function has code to test some of the getter/setter functions, but it is not complete. As you write get/set functions, add test code to the function to make sure your functions work properly. L6. Create an Update method Write an update method that implements Newtonian physics. Write a method, update that adjusts the internal position and velocity values based on current accelerations and forces. The method should use the equations of motion under uniform acceleration to update the velocity and position. The method also needs to move the visualization. The function will take in a time step, dt, that indicates how much time to model. The following is a step-by-step algorithm for updating the ball. def update(self, dt): # assign to x_old the current x position # assign to y_old the current y position # update the x position to be x_old + x_vel*dt + 0.5*x_acc * dt*dt # update the y position to be y_old + y_vel*dt + 0.5*y_acc * dt*dt # assign to dx the change in the x position times the scale factor (self.scale) # assign to dy the negative of the change in the y position times the scale factor (self.scale) # for each item in self.vis # call the move method of the graphics object with dx and dy as arguments.. # update the x velocity by adding the acceleration times dt to its old value # update the y velocity by adding the acceleration times dt to its old value To test the update method, uncomment the last section of the test Ball_1.py main function. It should implement Brownian motion of the ball like the explore.py program. L7. Write a test file for the Ball class Write a test file that will create a Ball in the center of the screen, give it a random initial velocity and an acceleration of (0, -20), and then loop until the ball leaves the screen, calling the ball's update function inside the loop. Note: Falling with 0 acceleration in the horizontal direction and -20 in the vertical direction, means that the ball is falling downward in the visualization window after being spawned. If the ball goes out of bounds (that is x component of position is less than 0 or the x component is greater than the width of the win object, or the y component of position is less than 0 or the y component of position is greater than the height of the win object), then the program should reposition the ball to the center of the screen and give it a new random velocity between -10 and 10. You can use fall.py as a template. Test your code and make sure you have a ball falling down, then re-spawning. Time to contemplate what we have done in this lab. Note that what you are seeing moving on the screen in test Ball and fall is no longer just a Zelle Circle object like the one in the explore.py program. We can’t move the ball any way we choose, but instead we have to follow the rules of physics that are defined in the equations of motion and implemented in our update method. One could say that the Zelle Graphics behaviors are wrapped into our physics ball class and are only indirectly accessible via the methods of the ball class. Food for thought:)
CS 152 Project 7: Object-Oriented Physics Simulation The focus of this project is to provide you with more experience writing classes. Now that you have a working Ball class, it's time to model a second physics object, a block, and then we will model collisions between balls and blocks. Tasks T1. Write a Block class Write a Block class that is similar to the Ball class, but uses a Zelle Rectangle to represent it in the window. The init method should have one required argument, which is the GraphWin object in which it is to be drawn. A Block does not need a radius field, but it does need fields for dx and dy that specify its horizontal and vertical size, respectively. Define the visualization rectangle so that the Block's position is in the middle of the rectangle. In addition to its init , the Block class needs to have the following methods. def draw (self) - draw the visualization objects into the window. de f und raw (self) - undraw the visualization objects. def getPosition (self) - return the position as a 2-element tuple. def setPosition (self, px, py) - update the Block's position. The function must also update the graphics object's position. def getVelocity (self) - return the Block's velocity as a 2-element tuple. def setVelocity (self, vx, vy) - update the Block's velocity. def getAcceleration (self) - return the Block's acceleration as a 2-element tuple. def setAcceleration (self, ax, ay) - update the Block's acceleration. def getWidth (self) - return the Block's width (dx) def setWidth (self) - (**optional**) changes the Block's width. This method will need to undraw the shapes in the vis list, create a new vis list, then draw the shapes. def getHeight (self) - return the Block's height (dy) def setHeight (self) - (**optional**) changes the Block's height. This method will need to undraw the shapes in the vis list, create a new vis list, then draw the shapes. def update (self, dt) - implement the equations of motion for the block. T2. Write a collision function Here is Prof. Maxwell talking about detecting collisions: Ball-block collisions Add the collision method to your Block class. def collision (self, ball): Write a function that returns True if the ball is intersecting the block and False otherwise. Use the Ball's get Position and get Radius functions to get that information. Then check if the ball is intersecting the block. Assume that the ball is a square for the purposes of detecting collisions. It will make the calculations easier. Simplifying assumptions are often used in this manner. Required Element 1: Explaining your collision function should be a key part of your report. You can use this test function to test the intersection code. When you run it, hit the spacebar to move the ball to a randomized location. T3. Add blocks to the fall program Add blocks to at least the bottom of the screen in your fall program from the lab. A block should disappear if it is hit by the ball. Required Element 2: Capture a short video of this program running. Submit it with your report. T4. Create an interactive demo Give the user some kind of control over how to launch a ball into the scene. Have the ball collide with blocks and do something. The blocks could change color, disappear, move, or do anything else you can code. Likewise, the ball could disappear or bounce, enabling it to hit other blocks. You can use the win.checkKey() function to see if the user has typed a key. If the user has typed the left, right, up, or down arrow keys, then checkKey will return 'Left', 'Right', 'Up', or 'Down' as the return value. The space bar returns the string 'space'. Required Element 3: Capture a short video of your interactive program and include it with your report. Required Element 4: Follow-up Questions 1. What is the purpose of having get and set functions for a class? Why not just access the object fields directly? 2. Given a list of Zelle Graphics objects, write a for loop that would move each object in the list by (5, 10) in x and y. 3. The Zelle GraphWin class has functions getMouse() and checkMouse(). What is the difference between them? 4. What is the difference between simulation space and visualization space? Extensions Extensions are your opportunity to customize your project, learn something else of interest to you, and improve your grade. The following are some suggested extensions, but you are free to choose your own. Be sure to describe any extensions you complete in your report. ● Create more elaborate or additional setups/scenes/interactions. Include screen videos in your report/handin folder. ● Make other object classes for the simulation. Add other collision functions. A reasonable thing to do is to approximate an object with a box or a circle, even if it is not one of those shapes as it simplifies the calculations. ● Add the capability for balls to collide with one another. Keep it simple and get it working with just 2 balls first. ● Make the simulation more visually interesting. Add colors, or change the visualization of the ball or block class to something more complex (e.g. a smaller ball inside the larger ball. Write your project report Reports are not included in the compressed file! Please don’t make the graders hunt for your report. You can write your report in any word processor you like and submit a PDF document in the Google Classroom assignment folder. Or use a Google Document format. Review the Writeup Guidelines document in the Labs and Projects folder. Your intended audience for your report is your peers who are not taking CS classes. From week to week, you can assume your audience has read your prior reports. Your goal should be to explain to peers what you accomplished in the project and to give them a sense of how you did it. The following is a list and description of the mandatory sections you must include in your report. Do not include the descriptions in your report, but use them as a guide in writing your report. ● Abstract A summary of the project, in your own words. This should be no more than a few sentences. Give the reader context and identify the key purpose of the assignment. An abstract should define the project's key lecture concepts in your own words for a general, non-CS audience. It should also describe the program's context and output, highlighting a couple of important algorithmic and/or scientific details. Writing an effective abstract is an important skill. Consider the following questions while writing it. ○ Does it describe the CS concepts of the project (e.g. writing well-organized and efficient code)? ○ Does it describe the specific project application (e.g. generating data)? ○ Does it describe your solution and how it was developed (e.g. what code did you write)? ○ Does it describe the results or outputs (e.g. did your code work as expected and what did the results tell you)? ○ Is it concise? ○ Are all of the terms well-defined? ○ Does it read logically and in the proper order? ● Methods The method section should describe in clear sentences (without pasting any code) at least one example of your own computational thinking that helped you complete your project. This could involve illustrating how a key lecture concept was applied to creating an image, how you solved a challenging problem, or explaining an algorithmic feature that is essential to your program as well as why it is so essential. The explanation should be suitable for a general audience who does not know Python. ● Results Present your results in a clear manner using human-friendly images or graphs labeled with captions and interpreted for a general audience such as your peers not in the course. Explain, for a general, non-CS audience, what your output means and whether it makes sense. ● Reflection and Follow-up questions Draw connections between lecture concepts utilized in this project and real-world problems that interest you. How else could these concepts apply to our everyday lives? What are some specific things you had to learn or discover in order to complete the project? Look for a set of short answer questions in this section of the report template. ● Extensions (Required even if you did not do any) A description of any extensions you undertook, including text output or images demonstrating those extensions. If you added any modules, functions, or other design components, note their structure and the algorithms you used. ● References/Acknowledgements (Required even if there are none) Identify your collaborators, including TAs and professors. Include in that list anyone whose code you may have seen, such as those of friends who have taken the course in a previous semester. Cite any other sources, imported libraries, or tutorials you used to complete the project. Submitting your Project Report and Code Turn in your code by zipping the file and uploading it to Google Classroom. When submitting, double check the following. 1. Is your name at the top of each Python file? 2. Does every function have a docstring (‘’’ ‘’’) specifying what it does? 3. All your code is in your Project 07 folder? 4. Have you checked to make sure you have included all required elements and outputs in your project report? 5. If you have done an Extension, have you included this information in your report under the Extension heading? Even if you have not done any extensions, include a section in your report where you state this. 6. Have you acknowledged any help you may have received from classmates, your instructor, the TAs, or outside sources (internet, books, videos, etc.)? If you received no help at all, have you indicated that under the Sources heading of the report?
EEC 210 HW 8 1. An amplifier has a low-frequency forward gain of 5000 and its transfer function has three negative real poles with magnitudes 300 kHz, 2 MHz, and 25 MHz. a. Calculate the dominant-pole magnitude required to give unity-gain compen- sation of this amplifier with a 45° phase margin if the original amplifier poles remain fixed. What is the resulting bandwidth of the circuit with the feed- back applied? b. Repeat (a) for compensation in a feedback loop with a closed-loop gain of 20 dB and 45° phase margin. 2. The amplifier in the previous problem is to be compensated by reducing the magni- tude of the most dominant pole. a. Calculate the dominant-pole magnitude required for unity-gain compensation with 45° phase margin. Also, calculate the corresponding bandwidth of the circuit with the feedback applied. Assume that the remaining poles do not move. b. Repeat (a) for compensation in a feedback loop with a closed-loop gain of 20 dB and 45° phase margin. 3. For the CMOS operational amplifier shown below, calculate the open-loop voltage gain, unity-gain bandwidth, and slew rate. Assume that the gate of M9 is con- nected to the positive power supply and that the W/L of M9 has been chosen to cancel the right-half-plane zero. Also, assume that Xd = 0. 1 µm for all transistors operating in the active region, and use Table 2.4 at the end of this homework for other parameters. Compare your results with a SPICE simulation. 4. Assume the circuit below is used to generate the bias voltage to be applied to the gate of M9 in the op amp in the previous problem. Calculate the W/L of M9 required to move the right half-plane zero to infinity. Use L = 1 µm for all transis- tors. Let W8 = W10 = 150 µm, and W11 = W12 = 100 µm. Assume that Xd = 0. 1 µm for all transistors operating in the active region, and use Table 2.4 at the end of this homework for other parameters. 5. Consider the op amp in Problem 3 and the bias circuit in Problem 4. Assuming that the zero has been moved to infinity, determine the maximum load capacitance that can be attached directly to the output of the op amp and still maintain a phase margin of 45° . Neglect all higher order poles except any due to the load capaci- tance. Use the value of W/L obtained in Problem 4 for M9 with the bias circuit shown in that problem.
MANG3008 Strategic Management 2024-25 Module Handbook Module Aims Strategic management is central to the operation of a variety of businesses in different sectors and environments. In a competitive global environment, understanding strategic principles and tools for analysis are of great importance to managers, to assist them in enhancing firm performance. This module will provide you with an overview of issues relevant to strategic management. It aims to introduce concepts and techniques of strategic analysis, strategy formulation, strategic actions/choices and strategy implementation. Students are encouraged to analyse and think critically as well as apply concepts and tools. An intellectual application of these theories together with the self-development of your own ideas will be utilised to demonstrate transferability of skills via a series of practical case applications. Learning Outcomes A. Knowledge and understanding. Having successfully completed the module, you will be able to understand: thekeydimensionsofstrategicmanagementAnalysisEvaluationChoice diagnostic, practical and creative skills to analyse andevaluatea rangeofbusinesssolutionsntexts;A3. tifytheareasrequiringchange.B1. lemareas, underpinningrder to achievecriticalsuccess;B2. iousstakeholder groups;B3. assess the importance ofstructure, design, culture and workingenvironment toeffectivestrategic management;B5. .C1. yofsourcesin the publicdomain;C2. work collectively as an effective and efficient groupmember,including,whereappropriate,g others;
Mathematical Statistics Fall 2024 Midterm 1 1. [5pts] Suppose X is a random variable with mean µ and variance σ 2 ≠ 0. Which of the following variables always has mean 0 and variance 1? Circle none, one, or more. (a) Z = σ2/X − µ (b) Z = Xσ2 + µ (c) Z = σ/µ−X (d) Z = Xσ + µ (e) Z = σ2/X−µ 2. [5pts] Suppose X and Y are independent Bernoulli(0.5) random variables. Let Z1 = 2X −1, Z2 = 2Y − 1, Z3 = Z1Z2. Are Z1, Z2, and Z3 independent? Explain your answer. 3. Let X1, X2, X3, . . . be random variables with mean 0. Assume that Define Sn = Σn j=1 Xj . (a) [5pts] Compute Var [Sn] in terms of n. Your answer should not contain any series. (b) [5pts] Let an be any sequence of (non random) numbers with limn→∞|an| = ∞. For any > 0 use your answer to part (a) to upper-bound P(|Sn/an| > ). Does Sn/an converge to zero in probability? 4. The mean of a random integer chosen uniformly from {1, 2, 3, . . . , n} is (n + 1)/2 and the variance is (n 2 − 1)/12. Suppose the random variable X is uniformly chosen from the set {1, 2, . . . , N} where N is a Geometric(1/2) random variable. Recall that E [N] = 2 and Var [N] = 2. (a) Compute E [X]. (b) Compute Var [X].
Mathematical Statistics Fall 2024 Midterm 2 1. [20 pts] Let X1, . . . , Xn be IID, N(0, σ2) random variables. You may find it useful to recall that if X ∼ N(0, σ2), then Var(X2) = 2σ 4 . (a) [5pts] Show that the maximum likelihood estimator of θ = σ 2 is ˆσ 2 = n 1 ∑n j=1 Xj 2. (You may assume that any critical point is a global maximum, without checking second deriva-tives). (b) [5pts] Find the Fisher information, I(σ2). (c) [5 pts] σˆ 2 is an unbiased estimator of σ2 . What is the smallest possible variance among all possible unbiased estimators of σ2? (d) [5pts] Assume that n = 10. Find an exact 95% confidence interval for σ2 of the form. σ2 ∈ [b,∞) for some b. For partial credit you can find an asymptotic (as n → ∞) 95% confidence interval of the same form. instead (do not find both). 2. [10 pts] Suppose that Y is a uniform. random variable on (0,1) and Z is Bernoulli(1/θ). Y and Z are independent and X = Y Z. We are testing H0 ∶ θ = 2 versus H1 ∶ θ = 1. Consider the test that rejects H0 when X > 1/2. (a) [5 pts] What is the probability of type 1 error? (b) [5 pts] What is the probability of a type 2 error? 3. [10 pts] Suppose we ask 10 randomly chosen individuals what their favorite candy bar is among two candy bar options (A and B). The observed counts of reported favorites are given in the table below: Category A B Observed Count 4 6 We want to test whether these preferences are uniformly distributed across the categories. The null hypothesis H0 is that an individual prefers any one of the candy bars with equal probability (p0 = 1/2). Their alternative hypothesis is that some candy bars are preferred over others. Recall that the MLE of the true probability p that A is preferred is ˆp = 2/5. (a) [5 pts] Write the likelihood ratio test statistic Λ for this test and data. Use the numbers in the table, but an answer with fractions and powers is fine. (b) [5 pts] Determine a value c so that rejection when {Λ < c} results in a test with approximately 0.05 significance level.
7SSGN110 Environmental Data Analysis | Practical 2 | Introduction to R & data exploration 1. Introduction 1.1. About this practical This practical is focused on introducing you to the basics of R, RStudio and R Markdown. The aim of the practical is to advance your learning of new technical tools (R and RStudio) for data exploration, description and data visualisation. During the session we will be investigating the differences in characteristics in annual rainfall from four locations in the UK. The aim is to determine what impact longitude and altitude have on the amount and seasonal distribution of rainfall. The practical uses annual rainfall totals from four locations across northern England for the 50-year period from 1941 to 1990 (MetOffice, 1993). The attributes of the four sites that we will be investigating are given in Table 1 and plan/cross-sectional profiles of the four locations are in Figures 1 and 2. Table 1. Summary of the spatial characteristics of the four sites used in this practical SITE Denton Redmires Sheffield Kirk Bramwith Altitude (m) 93 305 131 7 Latitude 53.45 53.37 53.38 53.60 Longitude 2.21 1.57 1.47 1.07 Figure 1a. The spatial location of the four climate stations used in today’s practical. Note that this map was created using Digimap (2013), which allows access, customization, and annotation ordnance survey maps (e.g. for reports). Figure 1b. Altitude of the 4 study sites. One method for visualizing altitude vs. distance of the four climate stations, projected onto a ‘linear’ line, and relative distance along the line indicated as Eastings (km). 1.2. Practical structure The practical session comprises 4 parts: 1. Data familiarisation & producing descriptive statistics using MS Excel 2. Start R and First R Analysis – if you haven’t completed this already 3. Rainfall data exploration using R 4. Additional, optional exercise – air quality data exploration Associated with practical are 15 questions for you to answer. These are to test your understanding of key concepts in the practical. Answers to the questions & example script. will be posted on KEATS a few days after the practical session. 1.3. Required files & saving your data You can access the files for this practical, RainfallData.xlsx and RainfallData.csv via KEATS. You may remember that last week we put particular emphasis on establishing the correct place to save your data. Save your data to an appropriate working directory (folder) titled “EDA_Practical2”. 2. Data familiarization & descriptive statistics using Excel Before attempting any form. of analysis, it is important to know what our objective is, what data we have available to us and where this data was collected. Assuming most of you are more comfortable with Excel than R, initially we’ll do some brief data exploration in Excel. Hopefully, this way you’ll have some familiarisation with the data before introducing R. Calculate some summary statistics in Excel: 1. Download the worksheet ‘RainfallData.xlsx’ 2. In the worksheet containing your data click on View -> Freeze Panes -> Freeze Top Row. Now you will always be able to see the column labels if you scroll down the data in this view. 3. Scroll down to the bottom of the data. 4. Calculate the mean, median, maximum, minimum, standard deviation and range for the four locations. To do this enter the following formulas in different cells, selecting the appropriate data for ‘Data range’: • =AVERAGE(‘Data range’) • =MEDIAN(‘Data range’) • =MAX(‘Data range’) • =MIN(‘Data range’) • =STDEV(‘Data range’) Note: you’ll need to use some combination of these formulas to calculate the range. Using the values you have calculated answer the following questions: • Q1: Which site has the greatest inter-annual variation in rainfall? • Q2: Rank the locations in order of ‘wetness’ When did you last save your Excel Workbook? If you haven’t already you should get in the habit of saving your work (in Excel, Word, etc.) frequently. Do so now in Excel (.xslx) format. We’ll also save the data in Comma Separated Values (.csv) format. This can be done using Save As and selecting the appropriate file format. See online help or ask the GTAs if you need further assistance. This is good practice as csv format is generally what we will use with R (as we will now see). 3. Start R and First R Analysis The practical instructions assume you have read and followed the instructions in the StartR and First R Analysis activities online. If you have not yet worked through these activities STOP here and work through these activities before you start the rainfall data analysis. Once you have completed Start R and First R Analysis answer the following questions: • Q3: What command do you use to use the contents of an object? • Q4: What is wrong with the following line of code? TreeDiameters(mean) • Q5: What is the difference between the source and the console pane? 4. Rainfall data analysis using R 4.1. Getting started in R This document contains code. In places this code is annotated. You can do this useful by using the ‘#’ before any annotations you make. This is very useful for remembering what you have done and why! answer
Biology 200 Sample Midterm Exam Question 1. ( /5 marks) The following microscopy images (a) and (b) show two adjacent cells. The scale bar in both images represents 10µm. A. What type of microscopy technique was used to produce image (a)? How do you know? (2 marks) B. What type of microscopy technique was used to produce image (b)? How do you know? (2 marks) C. What is one benefit of using the technique in (b)? Be specific (1 mark). Question 2 ( /8 marks) For the following statements, indicate if they are true or false and provide a short rationale for your answer. (2 marks each) A. In order to remain fluid at a lower temperature, a cell membrane will be composed of phospholipids with a greater degree of unsaturation of their hydrocarbon tails. B. Only proteins can have a nuclear localization sequence. C. Core histone proteins have an abundance of negatively charged amino acids that form. ionic bonds with the DNA backbone. D. The hydrophilic pore within the interior of a single a-helix allows the passage of small molecules. Question 3 ( /7 marks) One of the primary characteristics of cancer cells is their ability to become mobile and move away from their site of origin. This characteristic (known as metastasis) allows cancer cells to develop into tumours at new sites in the body. There is a strong positive correlation between a cancer cell’s ability to move around and the fluidity of its membranes. Researchers at the University of Texas were trying to find ways to inhibit metastasis by changing the properties of the membranes of mammalian cancer cells. They tested a drug called Haloperidol, suggested to affect membrane fluidity as a potential inhibitor of metastasis. To this end they carried out a fluorescence recovery after photobleaching (FRAP) experiment using a fluorescent dye that binds to plasma membrane lipids in metastatic breast cancer cells. The results are shown in the graph above. A. Which treatment would be considered to be the experimental control? Explain your reasoning (1 mark) B. Describe the change in fluorescence recovery of cells in the presence and absence of Haloperidol and explain how it relates to the fluidity of the cell’s membrane in each case. (2 marks) C. Based on the data here, is Haloperidol a good candidate for inhibiting metastasis? Explain your logic. (2 marks) D. ABCA1 is a protein that has been shown to be involved in regulation of cholesterol levels. ABCA1 has also been shown to expressed in higher amounts in breast cancer cells that are undergoing metastasis. How might ABCA1 be promoting metastasis? Explain. (2 marks) Question 4 ( / 9 marks) The clcn5 gene encodes for a membrane protein that forms a chloride-proton channel in the kidney epithelium. Tanaka et.al. (2010) studied how the clcn5 gene is regulated by analyzing the transcription activity of different segments of DNA upstream of the clcn5 gene in the presence or absence of a transcription factor HNF-1a. Below is a schematic of the gene and DNA regions they analyzed: A. What is the control in this experiment? Explain why it is used. (1 mark) B. Describe what the data show for each DNA segment and explain based on this data how HNF-1a impacts the expression of the ccln5 gene. (4 marks) C. Consider if the first 3 base pairs at the 5’ end of segment 1 were mutated from TCT to AAA. Predict how this mutation could affect transcriptional activity of ccln5 in the above experiment. Justify your predicted experimental outcome. (2 marks) D. You are working in a lab and you decide to try and repeat the experiment from A and B. However your data do not agree with the published results (see your data below). Your supervisor points out that you were accidentally using cells that lack enzymes that modify core histone proteins by adding acetyl groups. Explain why this omission could explain your result. (2 marks) Question 5 ( / 8 marks) Wodrich et al. (2006) looked at the number of nuclear localization signals (NLS) and their locations within Adenovirus protein pVII. This is one of their experiments: ▪ Full length and deletion mutants of pVII were fused to GFP (green fluorescent protein) and injected into the cytoplasm of cells. ▪ Injected cells were viewed using fluorescence microscopy. a: Full length pVII (amino acids 1 to 198) b: Amino acids 1 to 81 (of primary sequence) c: Amino acids 82 to 198 (of primary sequence) d: Amino acids 82 to 114 (of primary sequence) e: Amino acids 115 to 169 (of primary sequence) f: Amino acids 170 to 198 (of primary sequence) A. Identify the cellular location of the fluorescence in each panel (a through f). (3 marks) B. What can you conclude from this experiment? Explain. (2 marks) C. Does this experiment tell you how many NLS there are in pVII? Explain your answer. (2 marks) Question 6 ( /9 marks) The CFTR protein is an integral membrane protein containing a single polypeptide chain that forms a channel that transports chloride ions across the plasma membrane. Cystic Fibrosis results from a mutation in this protein that causes it to mis-fold. Describe the importance of the amino acids found within CFTR’s primary sequence, and explain how the non-covalent interactions they make (with each other and with molecules in their environment) determine the folding of this structure, its location within the cell, and the function of this protein. Your answers should include aspects of how the properties of the amino acids and their non-covalent interactions with each other (intramolecular interactions) and intermolecular interactions with their environment, where these amino acids would be within the protein structure, and how these noncovalent interactions determine the overall function of the protein.
DATA1002 / 1902 - Informatics: Data and Computation 2024 Sem2 Group Project Stage 1 Due: 11:59pm on Sunday at the end of week 6 Value: 5% of the unit Note: Get started your project ASAP. Discuss with your tutors and make use of Ed to ask questions, using the category “Group Report”, and the sub-category of “Stage 1” as needed. GROUPS This assignment is done in groups of 3 or 4. All students in a group must be attending the same lab session and will be reporting progress to your lab demonstrators. The project is handed in as a combined effort, but there is individual work prepared and incorporated into the whole report. THE PROJECT WORK FOR STAGE 1: Task Description Group/individual Details 1 Identify Topic Group 1. The group should define questions or issues that are not simply a factual matter, but instead looks at relationships where insights might be impactful forsome stakeholder groups. 2. We realise that you may not find data that completely resolves the issue you are targeting at, but all the data should be at least helpful to provide some insights. 2 Obtain Datasets and Metadata Individual 1. Each member needs to obtain a different dataset that can contribute to the group’s exploration of the topic. 2. You need to keep (and provide to us) a copy the data as you originally obtained it. 3. You need to state the relevant metadata, about this dataset, including a data dictionary (which indicates which attributes there are, and what each attribute means), and provenance (giving the whole chain, from the originalsource of the data, through any intermediate collections, up to the place where you got it from [and the date you obtained it]). It is preferred to use publicly available data (so we can check your work if we need to) but it is OK for you to work on privately-owned data so long as you have permission to use it, and permission to show them to the markers. If the group is hoping to score beyond average, the different datasets (from different members) should all come from different sources. 3 Ensure Data Quality Individual 1. Each member needs to work with his/her dataset to ensure high-quality data that can be analysed. 2. Each member can use Python or spreadsheet to transform. and clean the raw data. The details of this aspect vary a lot; it depends on the data you obtained. a. The data needed to be cleaned. b. If the data sources were carefully curated already, you would at least write a Python program that checks that the data is clean. At the end of this part of the work, you will have a dataset which should be high-quality. 4 Produce Summaries Individual Each member can use Python or spreadsheet to produce some very simple analysis of his/her cleaned dataset, that calculates some aggregate summaries of some of the attributes. 5 Integrate Datasets Group You need to bring your datasets to the group to create a single integrated/combined dataset. There is a data cleaning step involved here as well if there are duplicates, incomplete data, a need to group or aggregate cell contents etc. The effort ofthis can vary a lot, depending on the commonality between the datasets. It is fine if a member’s dataset is not integrated to the final dataset for future stage. Note: If the datasets have different kinds of information for different kinds of entities, itis hard to see how they can be usefully linked together to bear on a single topic. For example, it doesn’t make much sense to combine weather data from cities in NSW, with demographic data from a different state. 6 Write Report Group Working together as a group, you need to produce a report. The structure of the report is described in the Submission section below. There is a section of the report that will be written separately by each member, as well as a combined introduction that explains the topic or issue, and a combined discussion of the data integration activity. SUBMISSION FOR STAGE 1 1. There are three deliverables (links) for Stage 1 of the Project to be submitted to Canvas site. 2. All three deliverables should be submitted by one person, on behalf of the whole group. 3. The overall mark from this stage will appear under report submission in Canvas gradebook. Deliverable Description Report The report should have a front page, that gives the group name, and lists the members involved (giving their SID and unikey, not their name). The report has 3 main sections as follows: 1. Introduction: Topic / Question (maximum: 1 page - Group) A section that (i) describes the topic or question that you are interested to explore, (ii) defines some groups of stakeholders and says how they will be helped by understanding thistopic or addressing thistopic/issue, and (iii) includes a short discussion of the data you have obtained. 2. Dataset to work on (maximum: 4 pages per group member) In this section, there should be three subsections. a. A subsection that gives an in-depth description of the dataset, including clear statements of the relevant metadata. You need to clearly state the format and structure of the data. Please give a data dictionary listing the different attributes and their meaning. Also clearly identify the provenance: show the chain of transmission, from the original collector of the data to the place from which you obtained it; along with this, clearly show the rights or restrictionsfor use that are associated with the data. This subsection should give your thoughts on any strengths or limitations of the dataset, for the purpose of investigating the topic or question of interest. b. A subsection that describes how you ensured the data quality in the dataset. If there were any quality problems, describe what they were and how you cleaned the data; even if there were no problems, you need to describe what problems you have checked, and how you did the checks. If the cleaning was done with a spreadsheet, describe the steps clearly; if the cleaning or checking was done by Python code, then include the code in your report. c. A subsection that provides descriptive statistics of your dataset and explains how you did using Python code (show the code and also the output of the analysis). 3. Integration (maximum: 1 page - Group) Describe the decision-making process of data integration that produced a single dataset from the separate cleaned ones described above (e.g. Are datasets from each member used? Which parts of the dataset were integrated?). This section should give a clear account of the schema of the combined dataset. You need to include the Python code to carry out this integration. There is a restriction of number of pages for the report; write whatever is needed to show the reader that you have earned the marks, and don’t say more than that! Per-Member Datasets To submit your work, first create a single folder for the whole project. Inside this main folder, create separate subfolders for each group member, with each subfolder containing that member's work. Once organised, compress the main folder into a single file, such as a .zip or .tar.gz file. Finally, submit this compressed file through Canvas. The subfolderfor each membershould contain a. the raw dataset as it was obtained from the source b. any spreadsheet or Python code used for cleaning/checking c. the Python code to calculate some summaries, and d. the clean version of the dataset (if you have used Ed or some other browser-based approach to running your code, make sure you download a copy of the code to have a file you can include in yoursubmission). e. The clean version of the dataset should have a minimum of 10 features (attributes/variables) and a minimum of 100 records. You then compress the top folder (with all these subfolders and their contents), then submit the single compressed file. Integrated Dataset You will use this in later work. This should be submitted through the Canvas system, as a single file. Note that the integrated dataset needs to come with metadata which describes at least: • the provenance of the data, • any licence or other restrictions on use of the data, • description of all the changes you did between the original datasets and the final dataset; and • the meaning of each attribute, what format or units are used, etc • The clean version of the integrated dataset should have a minimum of 10 features (attributes/variables) and a minimum of 100 records. If the metadata is in a separate file from the integrated data, or if the data is divided among several files, then you need to put the multiple files into a folder and then compress the folder into one file for submission on Canvas. MARKING There are five components to be marked. Note that C1 & C5 are group marks, and all members will receive the same score, whereas C2, 3 & 4 are individual marks for the person who write the individual part of Section 2 of the report. Details of the marking rubrics can be found in the Canvas site. ID COMPONENT MAX POINT GROUP/IND DESCRIPTION C1 IDENTIFYING THE TOPIC 1 Group This component of assessment is based on the corresponding section of the report. C2 DATASETS AND METADATA 1 Ind This component is assessed based on the corresponding subsections of all the separate dataset sections of the report; the uploaded data and code may be checked by the marker as supporting evidence for claims made in the report C3 ENSURING DATA QUALITY 1 Ind This component is assessed based on the corresponding subsections of all the separate dataset sections of the report; the uploaded data and code may be checked by the marker as supporting evidence for claims made in the report. C4 PRODUCE SOME SIMPLE DATA SUMMARIES 1 Ind This component is assessed based on the corresponding subsections of all the separate dataset sections of the report; the uploaded data and code may be checked by the marker as supporting evidence for claims made in the report. C5 INTEGRATE THE DATASETS 1 Group This component is assessed based on the corresponding section of the report;the uploaded data and code may be checked by the marker as supporting evidence for claims made in the report.
Department of Electronic and Information Engineering EIE2111 Lab 6: Arrays and Vectors Introduction This laboratory exercise is designed to give you hand-on experience in Arrays and Vectors. Questions 1. A small airline has just purchased a computer for its new automated reservation system. You have been asked to program the new system. You write a program to assign seats on each flight of the airline (capacity: 6 seats). Your program should display the following menu —Please type ‘0’ for reservation, type ‘1’ for checking and type ‘2’ for exit. If the person types 0, your program should prompt the user to input his/her name, the type of seats (first class or economy class) and the number of seats. Use a one-dimensional array of objects to represent the seating chart of the plane. A class should be provided for creating objects in the array. The class should include two parameters: the passenger’s name (a string) and an integer to indicate whether a seat is occupied or not. Initialize all the elements (objects) of the array to indicate that all seats are empty. When a seat is assigned, set the corresponding integer to 1 to indicate that the seat is no longer available and also the passenger’s name to reserve such seat. Your program should, of course, never assign a seat that has already been assigned. When the first class section is full, your program should ask the person if it is acceptable to be placed in the economy section (and vice versa). If yes, then make the appropriate seat assignment. If no, then print the message "Next flight leaves in 3 hours." The following is an example to create an array of objects: class abc { }… int main() { abc arrayOfObjects [10]; … } You may have a problem if you use both cin and getline to read the input. If you use cin first and then getline, you need to put a dummy getline between them to remove the newline character from cin. 2. Write a program that simulates the rolling of two dices. The program should use rand to roll the first dice and should use rand again to roll the second dice. The sum of the two values should then be calculated. [Note: Each dice can show an integer value between 1 and 6, so the sum of the two values will vary from 2 to 12, with 7 being the most frequent sum and 2 and 12 being the least frequent sums.] Note that there are 36 possible combinations of the two dices. Your program should roll the two dices 3,600 times. Use a one-dimensional array to store the frequencies of the sums of the two dices. Print the results in a tabular format. Also, determine if the totals are reasonable (i.e., there are six ways to roll a 7, so approximately one-sixth of all the rolls should be 7). 3. Combine the above two programs into one. The following is the sample output: Instructions a. You are required to submit your C++ programs (the whole projects created in Microsoft Visual Studio 2019) in Question 3 to Blackboard. Zip all of them into a single file. b. The deadline of the submission: Check the course information. c. It is not required to create any header files for the above exercises. It is fine if all program codes are in the main program.
Utility System Design 2024–2025 Coursework Release: 11/11/2024 Submission: 16/12/2024 Coursework value: 30% Introduction The purpose of the coursework is to provide you with an opportunity of demonstrating the application of knowledge acquired in the course to a larger scale industrially related problem. You will make use of knowledge gained in lectures, problem solving, and computer-based practicals (including available Guides), and apply it to produce a feasible, validated utility system. You have a certain period of time to produce the coursework. You will also be expected to apply your own background skills as Chemical Engineers. You will be expected to produce an appropriately structured, written, and presented report, with a limited number of pages. You are provided with some guidance in relation to the structure of the report to be produced, but part of the assessment is to evaluate your skills in reporting the work that you have performed, its validity, and relevance. The coursework is group based – with groups comprising three members. You are required to choose the members of the group and register the group and members in Blackboard. Marks are awarded for group contributions and for individual contributions. For group contributions, all members of the group are awarded the same mark. Individual contributions are to be labelled with the member making the contribution. Overall Requirements You are first required to build and simulate the existing operating conditions of a Utility System, as illustrated in Figure 1, using the STAR v153 software (group task). The second task is to optimise the Utility System, with the objective of minimising the operating costs (group task). You are then required to compare the optimal Utility System with the original Utility System. Finally, the third task (individual task) is to modify and simulate the original Utility System (not the optimal Utility System) in order to evaluate improvements in heat/power recovery in parts of the existing utility system and so make more effective use of fuel or existing equipment. Each member of the Group will be required to undertake one of the three modification tasks (different task each member). After you have implemented the changes, you are required then to compare these overall changes on the utility system to the original Utility System. Figure 1: Flowsheet of the Utility System Legend: B = Boiler GT = Gas Turbine HRSG = Heat Recovery Steam Generator ST = Steam Turbine L = Letdown station P = Process (Steam User) SG = Steam Generator De-A = Deaerator Utility System Data There are some missing flowrate values in the Utility System in Figure 1 which you need to determine before you start inputting the data to STAR. Site Configuration data should be left as default values. Other relevant information is provided in the tables below. Current flowrate data are required for simulation purposes while minimum and maximum flowrate data are needed for optimisation purposes. Some values, for example turbomachinery power, can only be determined by the actual simulation of the utility system. Table 1: Fuel data Fuel LHV Price Natural Gas (NG) 36,000 kJ Nm–3 0.2 $ Nm–3 Fuel Oil 40,000 kJ kg–1 0.6 $ kg–1 Table 2: Boiler data Boiler Fuel Current Flowrate (kg s–1) Minimum Flowrate (kg s–1) Maximum Flowrate (kg s–1) Efficiency (%) Blowdown (%) Boiler 1 NG 80 0 100 90 2 Boiler 2 NG 90 0 110 85 3 Boiler 3 Fuel Oil 120 0 120 75 5 Table 3: Gas Turbine data Type Fuel Shaftwork (MW) Casing Losses (%) GT Industrial-based correlation NG 110 0 Table 4: Heat Recovery Steam Generator data Fuel Current Flowrate (kg s–1) Minimum Flowrate (kg s–1) Maximum Flowrate (kg s–1) Efficiency (%) Blowdown (%) HRSG NG To be determined Same as current Same as current 73 10 Table 5: Steam Turbine general data ST Configuration Shaftwork model Isentropic Efficiency (%) Mechanical Efficiency (%) Power Correction Factor ST1 Generator Constant isentropic efficiency 85 97 1 ST2 85 ST3 80 ST4 75 Table 6: Steam Turbine flowrate data Current Flowrate (kg s–1) Minimum Flowrate (kg s–1) Maximum Flowrate (kg s–1) ST1 Inlet 180 0 200 Outlet HP 50 0 200 Outlet MP 130 0 200 ST2 Inlet 100 0 120 Outlet 100 0 120 ST3 Inlet 70 0 100 Outlet LP 30 0 100 Outlet Cond 40 0 100 ST4 Inlet 80 0 100 Outlet 80 0 100 General Guidance It is worthwhile to have looked at the released Practicals and familiarized yourself with the STAR software before attempting this task. There may be items required that are not mentioned in the Guides. Use the Help system to assist in these cases. As the STAR software is frequently updated, some of the screenshots may not correctly reflect what you see on screen. However, the information you require for simulation and optimisation is clearly provided. Creating the initial model and simulation of the Utility System can be time consuming. Work as a group to discuss and overcome these obstacles. The software will indicate frequently, when you select simulation, that values have not been provided or there are not enough degrees of freedom to perform the task. This means that so many values are fixed it is impossible for the software to calculate appropriate values for those that are missing. Also note that you may have too many degrees of freedom, e.g., there are too many unknowns and the software cannot calculate the missing values. Consequently, it is suggested that you build up the Utility System flowsheet step-by-step, and simulate each step before progressing to the next. For example, input the steam headers, link them by letdown stations, add a steam generator and then simulate. Then add a process load and simulate again. This way it is easier to resolve smaller problems. When you have modelled and simulated the Utility System, you then need to optimise it. Within the Steam Network Environment in STAR there are Optimisation options. You should optimise the system by minimising the operating costs. When you have simulated and optimised the original Utility System, then individual member tasks should be performed using the original Utility System as the starting point of the modifications. Individual Member 1 Task A senior utility engineer is proposing to increase the overall steam generators’ efficiency. All steam boilers and the HRSG undergo a blowdown where heat (in the form of saturated water) can be recovered to raise additional steam. Use this strategy to reduce the overall fuel consumption by modifying the utility system. Model and simulate the modified system and then compare with original Utility System (simulated). Individual Member 2 Task The utility system manager is looking at exploiting the potential of the GT/HRSG system. There is an opportunity to implement supplementary firing and to increase the temperature of the exhaust gases entering the HRSG to 850°C, thus raising additional steam. Use this strategy to reduce the overall fuel consumption by modifying the utility system. Model and simulate the modified system and then compare with original Utility System (simulated). Individual Member 3 Task Planned maintenances will reduce HP steam use by 20%, MP steam use by 30% and LP steam by 45%. The technical director of the utility system believes that this an excellent opportunity to shut down the low-efficient fuel oil-fired boiler, however the load on the NG- fired boilers could be increased if necessary. Use this strategy to reduce the overall fuel consumption by modifying the utility system. Model and simulate the modified system and then compare with original Utility System (simulated). Marking Scheme and Report Submission It is up to you to decide the exact format of your report and what should be included to support the work you have done. A marking scheme and marking sheet will be made available for reference to help you with this. However, the report should include the following sections: • Introduction, Aims and Objectives (Group - maximum 5 marks) • Methodology (Group - maximum 5 marks) • Original simulation results and main features of the utility system (Group - maximum 30 marks) • Optimisation and features of the optimised system (Group - maximum 15 marks) • Member 1 Task modification and simulation (Individual - maximum 30 marks) • Member 2 Task modification and simulation (Individual - maximum 30 marks) • Member 3 Task modification and simulation (Individual - maximum 30 marks) • Conclusions (Group - maximum 5 marks) Marks are also awarded for the presentation of the report (Group - maximum 10 marks). Total is 100 marks maximum per group member. As the space is limited, present the information that you think is relevant and reflects the work that has been done and the points that you are attempting to make to an audience. Not all information can be presented as space is not sufficient. Part of the skill is deciding what is relevant and how to express it. Do not be verbose. You will need to include graphics from STAR (the simulated and optimised flowsheet). You may also want to produce a schematic of the type shown in Figure 1. The most relevant results (again up to you to decide) should be presented in your own Tables. These should include information about the steam headers, fuels, boilers, gas turbines, heat recovery steam generators, steam turbines, vents, letdown stations, etc. The report should be completed in MS Word, and should not contain more than 24 pages (plus an additional page for the front cover). Pages above this limit will be penalized by a reduction of 5% per additional page in your overall mark. Late submission of the coursework will also result in a penalty of a reduction in your overall mark. The later the submission the larger the reduction! The reduction is set by the University’s policy. If you have any mitigating circumstances that would result in a late submission, follow the appropriate policy. Note as this is Group based work there are no extensions for DASS students. In addition, a pdf version of the report should be produced and it should be uploaded to Blackboard. There will be an area in the Utility System Design Blackboard space for this and appropriate instructions a week prior to the submission date. The submitted report in Blackboard will be checked by TurnitinUK for plagiarism and collusion. Be warned!
ECON30001 Problem Set 1 Semester 1 2024 Question 1. When considering a possible Raphael painting, the National Gallery lists five possible attributions that could be made: • By Raphael (B): almost certainly painted by the master himself. • Attributed to Raphael (A): possibly painted by the master, some doubt. • by the Studio of Raphael (S): painted by a pupil, probably under the master’s di-rection. • a Follower of Raphael (F): painted by someone at the time, influenced by Raphael. • an Imitator of Raphael (I): painted by someone highly influenced by Raphael, per- haps at a much later date. John is an art collector, looking to find Raphael paintings. He can place an Imitator, but cannot distinguish between other attributions. Peter and Robert have some expertise in art identification. Peter can place any painting into either “By or Attributed or Studio” or “Follower or Imitator” . Robert can place any painting into either “By or Attributed”, “Studio or Follower”, or “Imitator” . (a) Write down each person’s information structure. Sketch them in a state-space diagram. (b) Does Robert have better information than John? (c) Does Peter have better information than John? (d) Does asymmetric information exist between Peter and Robert? (e) Suppose that John really wants a “By Raphael” . Since Peter cannot accurately identify this attribution, is it worth listening to him at all? Question 2. There is a virus, disease Y, that is a serious concern for human health. Fortu- nately, a very accurate test for disease Y has been developed. The test is 99.9% accurate. Someone with disease Y is correctly diagnosed with probability 0.999, and someone not infected is correctly diagnosed with probability 0.999. It has been estimated that 50,000 people in the population of 100 million are infected. Suppose that Bob is selected at random and tested. He receives the diagnosis that he is infected. What is the probability that Bob is infected? Question 3. Consider two expected utility representations U = Eu and V = Ev . Prove that U and V are ordinally equivalent if u and v are cardinally equivalent. Question 4. In the lecture, we used the axioms of mixture monotonicity, reduction, sub- stitution, and continuity. There are many other axiomatisations in the literature. A well- known axiom is the following: (The Sure Thing Principle) For all lotteries p1 ,… , pJ, q1 ,…, qK, r and s we have: where α1 , …, αJ and β1 ,… , βK are such that the compound lotteries above are well-defined. (a) Represent the lotteries above diagrammatically and explain the intuition of the sure- thing principle. (b) Substitute the expected utility functional form to show that the sure-thing principle is a necessary condition for expected utility maximisation. Question 5. Consider the following choices: and, This pair of choices was first proposed by Nobel Laureate Maurice Allais in the early 1950s, shortly after the publication of von Neumann and Morgenstern’s work. Allais’ prediction, later confirmed by a large body of work in experimental economics, was that the modal pattern of preferences would be:2 A ≻ B and A' ≺ B' . (a) Show, by substituting the expected utility functional form, that the modal pattern of preferences are incompatible with expected utility maximisation. (b) Show that it is the sure thing principle in particular that is violated in Allais’ example (see Question 4).
DATA1002 / 1902 - Informatics: Data and Computation 2024 Sem2 Project Stage 1 (Notes) Due: 11:59pm on Sunday at the end of week 6 Value: 5% of the unit TASK 1: IDENTIFY TOPIC: For example, it is not a good to ask just “which country has the highest level of wealth?” but it is a good choice of question to ask “what influences the level of wealth in a community?” . You might look at datasets that relate to the economy, climate, education, type of government etc. TASK 2: OBTAIN DATASETS AND METADATA: While you can choose datasets as you wish, there are some extra requirements if you aim for higher marks, not just Pass level. • If the group is hoping to score above Pass level, • The different datasets (from the different members) should all “have independent origin” . • Note that this refers to the origin or primary source of the datasets; it is ok for the data to have been obtained from the same data-providing website, as long as the origins are different. • For example, data.gov.au offers the possibility to download many datasets, similarly, the various competitions at kaggle.com often have datasets from quite different origins. • You also should ensure that each dataset has “ medium volume of data”, so that automation of processing becomes crucial. • For defining volume, we will consider the number of “values”: for the most common case, rectangular data e.g., CSV. The contents of a field for an item would be a value. A dataset is considered as medium volume if it contains at least 1,000 values. If we visualise the data loaded into a spreadsheet with 100 rows and 10 columns, it will have 1,000 values (excluding the column headers). • Finally, it isn’t graded in this stage, but to score well in later stages, your integrated dataset needs to contain both the following: • at least one attribute whose value is either a count or a measurement (a number in terms of some unit), and • at least one attribute whose value is a string (or numeric identifier where the values are not meaningful as numbers, like the student id). TASK 3: ENSURE DATA QUALITY: For example, the work needed may be removing instances that have corrupted or missing values or filling in those missing values in some sensible way; you may be correcting obvious spelling mistakes or bringing different date formats to a common standard; maybe you need to remove duplicate rows, or deal with inconsistent information (e.g., two different values for population of the same country). TASK 4: PRODUCE SOME DATA SUMMARIES: For example, you might calculate (and show the code in your report) the highest value of wealth among all the countries, or the number of countries in the Asia region. This is not intended to be a detailed exploration of the data (that will come in Stage Two), but it is simply a demonstration that the dataset is now in a form. where you can work with it, and that you have the required skills in Python coding. TASK 5: INTEGRATE THE DATA SETS: For example, we might want to integrate climate data from different locations. It is easiest when the datasets share a structure (for example, they may represent climate data in different states, all with the same schema such as “city, date, maxtemp, mintemp, rainfall (mm)”), so that integration is nothing more than combining the rows one after another (perhaps adding an extra attribute to distinguish which dataset each row came from). However, if the datasets do not share a structure, then there is a lot of decisions needed to find a common structure into which all can be placed (and how to deal with eg missing values etc). Another case is where the datasets contain different attributes for the same entities; for example, one might have climate data for cities on dates, and another dataset has transport data for the same cities and dates. In that case, the integration can simply make a longer row for each entity, with all the different attributes from the datasets (eg city and date, followed by climate attributes and then by transport attributes). However, care is needed, if the entities are described with different formats etc, so that transformation is needed to allow them to be matched up across data sets.
7SSGN110 – Environmental Data Analysis Module Syllabus [SEM1, 2024‒25] Assignment Details Coursework Assignment #1 (optional): This assignment comprises 0% of the TOTAL MARK for this module, it is purely formative (optional) The WORD LIMIT is 500 words. The submission deadline is as indicated on your KEATS module page under the section Assessment and Feedback. This assignment will be marked using the Coursework criteria detailed in the Geography Student Handbook. Write a report to compare daily water levels in the River Frome, Dorset, to rainfall amounts in the upstream catchment, over the period May 2003 – April 2005. You should use data and outcomes of EDA Practical 1 along with data downloaded from the UK National River Flow Archive (NRFA) website. Use Excel to analyse and present the data. Put your work into an appropriate context by introducing peer-review and other types of literature, appropriately formatted, to illustrate your points. Remember to follow the guidelines for proper formatting of the coursework. In addition to the data analyses in Practical 1 of EDA you should analyse daily rainfall data for the River Frome, Dorset. Moggridge and Goodson (2005) collected their data at Frampton, upstream of gauging station 44004 ‘Frome at Dorchester’. Information about stations and the data collected at them are available online from the NRFA website which is managed by the Centre for Ecology and Hydrology (CEH). The NRFA website URL is: http://nrfa.ceh.ac.uk You should download daily rainfall data and plot a time series of rainfall for the period that matches that of the data collected by Moggridge and Goodson (2005). You will then be able to compare rainfall to water level. Your coursework should include three figures, each with a caption so that it can be interpreted independently of the main text (if complete, these captions should account for around 200 words of the word limit). Your three figures should comprise: · A scatter plot depicting the calibration for the PT voltage outputs · A time series of daily water level in the river Frome at Frampton for the data collected by Moggridge and Goodson (2005) · A time series of daily rainfall in the catchment of the river Frome upstream of Dorchester Remember that your figure captions should indicate the source of the data they present. Structure your report using numbered sub-headings. A suggested structure for your report is: 1. Introduction: Provide a brief overview of the River Frome, Dorset, and its hydrology. 2. Data: Briefly describe the data examined in the report. 3. Methods: Briefly describe how pressure transducer voltage outputs were converted to water level readings. The scatter plot would likely be best presented in this section. 4. Results & Analysis: Compare the two time-series you created, to identify similarities, differences and any possible relationships between water level and rainfall. Discuss what you find with reference to the hydrology of the River Frome, Dorset and the likely processes that produced the observed trends. Background to the field data collected by Moggridge and Goodson (2005) can be found in: · Gurnell et al. (2006), available from http://dx.doi.org/10.1002/rra.929; Moggridge and Goodson (2005) collected their data at site B in this study · Cotton et al. (2006), available from http://doi.org/10.1016/j.geomorph.2006.01.010; One of the sites in this study was also near Dorchester. You can, of course, cite other sources that you think are useful. Note that we have not given you proper citations for Gurnell et al. (2006) and Cotton et al. (2006) as you will need to cite these properly yourself in the reference list at the end of your report. You will also need to include a proper reference for the NRFA data you use. You can use the same reference for Moggridge and Goodson (2005) as provided below. This coursework is your first on this module (formative, for feedback only) and likely also your first at King’s. Its purpose is for you to demonstrate your understanding of the fundamentals of data analysis, manipulation and presentation, and how we expect you to present your written coursework in the Dept. of Geography at King’s. You should take care to present and format your report as indicated in the Basic Skills materials you have received. References Cited Kabacoff, R.I. (2012) Quick R [Online] Available at: http://www.statmethods.net/graphs/index.html [Accessed 17 September 2015]. Moggridge, H. and Goodson, J. (2005) River Frome Electronic Data. Collected as part of NERC research grant NER/T/S/2001/00930 (LOCAR programme). Coursework Assignment #2: This assignment comprises 100% of the TOTAL MARK for this module The WORD LIMIT is 2500 words The submission deadline is as indicated on your KEATS module page under the section Assessment and Feedback. This assignment will be marked using the Coursework criteria detailed in the Geography Student Handbook. Perform. quantitative analyses on a dataset of your choice from the broad environmental sciences (aquatic, atmosphere, terrestrial, marine, urban, etc.). See below for guidance on finding an appropriate dataset for analysis. Use two fundamentally different kinds of advanced quantitative analyses, at least one of which should have been covered in the module (i.e. at least one of: inferential statistics, correlation/regression, time-series analysis, data reduction). The two types of analysis should be comprehensive and utilise the full range of opportunities provided by the data. Descriptive statistics (including tables of stats, histograms, boxplots, etc.) are considered preliminary exploration/description and does NOT count as one of the advanced quantitative analyses. Visual presentation of your data or time-series plotting does NOT count as quantitative analysis. A simple KS-test that can be achieved with one button in SPSS or one line-command in R does NOT qualify as comprehensive. Write a report that demonstrates your skills in analysing these data, based on simple research questions or objectives. Your report should include the following numbered sub-sections: 1. Introduction: State and explain the type of data or environmental variable(s) you have analysed and what the main goal of this analysis is. Provide some context in terms of relevant published research 2. Data and Methods: 1. Describe how the data were collected, or where they come from 2. Describe the spatial and/or temporal characteristics of the data (resolution, precision, organisation or structure of the dataset) 3. Outline the quantitative methods used to analyse the data - explain why these methods are appropriate given the characteristics of the data. Include only brief reference to the software used to perform. the analyses. Methods that have been presented in lectures do not need to be explained, except details or parameters that are unique to this study 3. Results: Present results from your quantitative data analyses using tables and figures as appropriate; see further guidance on figure and tables below. 4. Analysis and Discussion: 1. Interpret the results with reference to the introductory context 2. Discuss possible further study and/or management implications 5. References: see guidance below Cover sheet The front page of your report should include a filled out cover sheet (provided on KEATS). On the cover sheet you have to provide some short details on the analyses that you included in your report, as well as the details on the data that you used in your report (note that this does not replace the description of data and analysis in the report itself). Data You will need to find an environmental data set for either at least one variable that changes through time (minimum 400 values per variable) or several variables such that the total number of data values is at least 400 (with minimum 4 variables and minimum 20 records per variable; e.g. a data matrix of 8 variables each with 50 records = 400 data values). Examples of some excellent sources of environmental data are: CDIAC (2015), CEH (2015), Dryad (2015), ECN (2015), KCL (2015), LTER (2015), NBN (2015), NCDC (2015), NGDC (2015), USGS (2015). The following specific requirements also apply: 1. FIGURES and TABLES. Use a MINIMUM of 4 and a MAXIMUM of 10 figures and/or tables. Note that one figure can have multiple parts to it (use peer-reviewed journals as examples, labelling each part A, B, C, with ONE figure caption for the entire figure). Remember tables and figures are not limited to use only in the results section. 2. REFERENCES. Your coursework should include 15 or more references from peer-reviewed journal articles (and will most likely include many more than 15). Remember to follow the guidelines for proper formatting of the coursework and as indicated in the ‘Basic Skills' material you received at the start of term. These will aid you to make your coursework look much more professional in terms of tables, figures, etc. References Cited CEH (2017) The Centre for Ecology & Hydrology (Data Holdings) [Online] Available at: https://www.ceh.ac.uk/data [Accessed 13 September 2017]. Includes large set of data holdings hosted by CEH on ecology and hydrology in the UK. Dryad (2017) The Dryad Digital Repository [Online] Available at: http://datadryad.org/ [Accessed 13 September 2017]. Freely re-useable data files associated with published article from across the sciences and medicine. ECN (2017) The Environmental Change Network Data Centre [Online] Available at: http://data.ecn.ac.uk/ [Accessed 13 September 2017]. Manages data for five UK integrated environmental monitoring networks. ESS-DIVE (2017) Environmental System Science Data Infrastructure for a Virtual Ecosystem. [Online] Available at: http://ess- dive.lbl.gov/ [Accessed 13 September 2017]. ESS-DIVE is designed to provide long-term stewardship and use of data from observational, experimental, and modeling activities from research across a range of scientific disciplines including hydrogeology, geophysics, biogeochemistry, climate, and ecology. KCL (2017) The London Air Quality Network. [Online]. Available at: http://www.londonair.org.uk/ [Accessed 13 September 2017]. Access to wide range of pollutant measurements, by specific London area, for (in some cases) back to 1993, and depending on measurement, with 15' resolution. See ‘Download Data'. LTER (2017) The US Long Term Ecological Research Network Data Portal. [Online] Available at: http://portal.lternet.edu [Accessed 13 September 2017]. Contains over 6000 metadata entries for ecological datasets contributed by 27 past and present LTER sites. NBN (2017) National Biodiversity Network Atlas [Online] Available at: https://data.nbn.org.uk/ [Accessed 13 September 2017]. Data gateway for NBN UK biodiversity data (>100 million species records) NCDC (2017) USA National Climatic Data Center, Climate Data Online. [Online]. Available at: https://www.ncdc.noaa.gov/cdo-web/ [Accessed 13 September 2017]. World's largest archive of weather data. If you have problems downloading any of this data, you may need to use a geography master's room computer, as these IP addresses should be ‘recognized' as academic ones. NGDC (2017) USA National Geophysical Data Center Home Page. [Online]. Available at: http://www.ngdc.noaa.gov/ [Accessed 13 September 2017]. Links to large amounts of data, including palaeodata, pertinent to physical geographers. USGS (United States Geological Survey) (2017) USGS Water Resources. [Online] Available at: http://water.usgs.gov/ [Accessed 13 September 2017]. Extensive selection of water related data.
ACS323 Assignment Intelligent Systems Academic Year: 2024/2025 Module: ACS323 Title: Intelligent Systems Date Set: Friday 06/12/2024 at 15:00 Date for Submission: Monday 23/12/2024 at 23:59 Task: Produce written solutions to all the assignment questions. Where numerical answers are required, show your full procedure, including details of the fuzzy rules in terms of grid- tables, fuzzy membership functions, scaling factors, plots and diagrams. THERE IS NO LIMIT TO THE NUMBER OF PAGES YOU WISH TO SUBMIT. Notes: • This work is worth 40% of your overall mark for this module • PARTS A & B carry equal weighting of 50 Marks each • You are reminded that the submitted work must be personal. PLEASE READ ALL OTHER ANSCILLARY MATERIAL CAREFULLY THAT SUPPORTS THIS ASSIGNMENT SHEET TO ENSURE THAT YOU ARE SUBMITTING ALL WORK MATERIAL IN THE REQUIRED WAY: “guide notes for acs323 assignment 2024 2025.pdf” “instructions on how to provide acs323 assignment files 2024 2025.pdf” PART A- FUZZY DECISION-MAKING Consider the process relating to muscle relaxation, as seen in your Laboratory Session PART A, represented by the following Equations [PLEASE NOTE THE NEW VALUE RELATING TO THE PREDOMINANT TIME-CONSTANT- CHANGE IT IN YOUR MATLAB-SIMULINK MODEL]: where Y is the overall output of the system (muscle relaxation), U is the input to the system (amount of drug infused). The main objective is to design a closed-loop control strategy which should maintain a steady level of muscle relaxation (output ‘Y’) by manipulating the level of drug infused (input ‘U’), given a reference target of muscle relaxation (‘Ref’)- see Figure A1- Use a reference target of 0.8 all throughout and a simulation time of 300 minutes: a) Write a two-page survey (a critic) about three (3) applications of fuzzy logic based technologies (choose one or several applications areas: control, prediction, classification, fault monitoring, etc.) for real-world problems which you would have identified from the open literature (include full details of all references hence used- these can be research papers, general articles, and/or weblinks). [8 MARKS] b) Design a Fuzzy PI-type controller, as shown in Figure A1, via a fuzzy rule-base which should include 25 fuzzy rules with Gaussian Membership Functions. [The candidate is expected to use: 1. the fuzzy 3D surface to tune the rules; 2. their knowledge of tuning the PID tuning factors, all in order to obtain the best possible outcome for the output response in terms of minimum overshoot, fast rise-time and fast settling-time; include simulations with disturbances]. [13 MARKS] c) Transform the fuzzy rule-base designed in Part A)b) into a Fuzzy PD-type controller which should also include 25 fuzzy rules with Gaussian Membership Functions. [The candidate is expected to use: 1. the fuzzy 3D surface to tune the rules; 2. their knowledge of tuning the PID tuning factors, all in order to obtain the best possible outcome for the output response in terms of minimum steady-state error, minimum overshoot, fast rise-time and fast settling-time; include simulations with disturbances]. Figure A1- Fuzzy-PI Control of Muscle Relaxation [12 MARKS] d) Using the closed-loop data from Part A)c) derive an ANFIS based controller for the process, which is described by Equations (A. 1) and (A.2), to achieve a similar control performance as that in c). What would be the advantages of such a new system? [12 MARKS] e) Compare the controllers in Parts A)b), A)c), and A)d) in terms of: flexibility in the structure (controller type) and performance (accuracy). For the latter you can rely on one or more performance indices, e.g. Mean Absolute Error (MAE), Mean Square Error (MSE), and Root-Mean Square Error (RMSE). [5 MARKS] PART B- FUZZY PREDICTIVE MODELLING On Black-Board, you will find one (1) file named “acs323assignmentdata.mat” (in the folder “ Module Assignment”) relating to industrial data. This data set should reflect a system with four (4) inputs (Input 1, … , Input 4) and one (1) output. The minimum and maximum values for all inputs and outputs in the provided data can be found using the “min” and “max” MATLAB commands. Upload this file onto your local drive which you will subsequently use to carry-out tasks Parts B)a)-B)e) in MATLAB. Once you have uploaded this file in MATLAB and double-clicked on it, it will collapse into two (2) files: one file, “acs323assignmentdata”, contains the actual quantitative data, and the second file, “explanation”, provides details on the names for each of the five (5) data features. a) Use the ANFIS tool in MATLAB to obtain a fuzzy TSK-type model, with 3 membership functions for each input. The fitness of the model should be assessed with the use of a quantitative index (or indices) such as the RMSE, MSE or MAE to establish the validity of the model. You can use one performance index or more than one all throughout. [The candidate will partition the data accordingly, select the most appropriate type of fuzzy MFs, output function and the number of learning epochs which will lead to the best outcome in the least-square sense]. [18 MARKS] b) Using the model derived in Part B)a) find the values of the output for the following input vectors: Input 1 = 0.15; Input 2 = 0.22; Input 3 = 1; Input 4 = 0.011; Input 1 = 0.06; Input 2 = 0.28; Input 3 = 0.4; Input 4 = 0.012; [4 MARKS] c) Extend the fuzzy modelling exercise conducted in Part B)a) to include 4 membership functions for each input, then 5 membership functions for each input. Here also, the fitness of the models should be assessed with the use of a quantitative index (or indices) such as the RMSE, MSE or MAE to establish the validity of the models. [The candidate will partition the data accordingly, select the most appropriate type of fuzzy MFs, output function and the number of learning epochs which will lead to the best outcome]. [18 MARKS] d) Using the models derived in Part B)c) find the values of the output for the following input vectors: Input 1 = 0.15; Input 2 = 0.22; Input 3 = 1; Input 4 = 0.011; Input 1 = 0.06; Input 2 = 0.28; Input 3 = 0.4; Input 4 = 0.012; [4 MARKS] e) Compare the models derived in Parts B)a) and B)c) and draw your own conclusions with respect to model accuracy and generalisation properties as far as: 1. Data partitioning between training and testing; 2. The number of fuzzy MFs, are concerned. [6 MARKS]
Systems and Networks COMPSCI 4043 Monday 13th December 2021 1. (a) Express the following in 16-bit two’s complement representation, giving your answers in hexadecimal. Show your working. i. 2056 ii. -2056 [4] (b) Write a segment of Sigma 16 code (not a complete program) to swap the contents of registers R1 and R2, using R3 as a temporary holding register. Supposing none of the registers R3 to R15 are free. How would you now swap R1 and R2 in the most efficient manner? What is the additional overhead in memory cycles? [6] (c) A Sigma16 programmer mistakenly puts a single DATA statement initialisinga variable, x, to $FF02, at the beginning of the assembly language instead of the end. What will happen when the program runs? [3] (d) A Sigma16 system has two arrays, X and Y, of seven 16-bit signed numbers (each) in memory. Write an assembly language program to overwrite array X so that X[i] is formed from the larger of the ith elements of X and Y, i.e. xi = max(xi, yi) [7] For reference, here is part of the instruction set of the Sigma16 CPU. lea Rd, x[Ra] Rd:= x +Ra load Rd, x[Ra] Rd:= mem[x +Ra] store Rd, x[Ra] mem[x +Ra]:=Rd add Rd,Ra,Rb Rd:= Ra+Rb sub Rd,Ra,Rb Rd:= Ra-Rb mul Rd,Ra,Rb Rd:= Ra*Rb div Rd,Ra,Rb Rd:= Ra/Rb, R15:=Ra mod Rb and Rd,Ra,Rb Rd:= Ra AND Rb inv Rd,Ra,Rb Rd:= NOT Ra or Rd,Ra,Rb Rd:= Ra OR Rb xor Rd,Ra,Rb Rd:= Ra XOR Rb cmplt Rd,Ra,Rb Rd:= RaRb shiftl Rd,Ra,Rb Rd:=Ra logic shifted left Rb places shiftr Rd,Ra,Rb Rd:=Ra logic shifted right Rb places jumpf Rd, x[Ra] If Rd=0 then PC:=x+Ra jumpt Rd, x[Ra] If Rd0 then PC:=x+Ra jal Rd, x[Ra] Rd:= pc, pc: =x +Ra trap Rd,Ra,Rb PC:= interrupt handler jump x[Ra] PC:= x +Ra 2 (a) List 3 similarities and 3 differences between an interrupt and a reset. [6] (b) The following Sigma 16 code is intended to take a 10-element array of two’scomplement numbers (only first element is shown) and replace all the elements with their cubes (X[i] is replaced by X[i]*X[i]*X[i]). However, although the code will assemble, it contains several errors. i. Draw up a register use table for the program (suitable for inclusion as comment). ii. Identify the errors and explain how you would correct them. iii. Write out the corrected program. LEA R1,1[R0] ;Set R1 to constant 1 LOAD R2,0[R0] ;i:=0 LOAD R3,n[R0] ;Set R3 to n FORLOOP CMPLT R14,R3,R2 ;Is i
Assignment 2: MIPS Multicycle Processor Assignment Outline l Assignment 2 accounts for 15% of your overall mark in the EEE339 module. This assignment is to test your understanding of the behavioural specification of the MIPS-Lite multi-cycle processor design presented in the lectures. l A sample Verilog code is available at Appendix A. l You need to write the machine code of your MIPS instructions into the desired locations of the memory in the following Verilog code. l To ensure that it is your own work that is being assessed, Appendix B indicates the specific tasks for each student. l The assignment is to be undertaken in a series of steps which you should complete in order. 1. Sketch the ASM chart of the state machine. [ 10 Marks] 2. Write a programme to: [50 Marks in total and 10 Marks each] a. Load the data stored in locations X and Y of the data memory into registers P and Q. b. Add register P with constant 6 and store the result in the register R. c. If the result in registers Q and R is equal to each other, then branch to instruction a. d. Store the result in the register R into the memory location 24. e. Jump to instruction a. 3. Simulate your program. To test instructions d and e, you need to change the data in the data memory at some stage. [ 10 Marks] 4. Add code to support R-type instructions such as AND, OR, sub, slt, and NOR, and simulate the instruction specified for each student in the attached table. [20 Marks] 5. Add some extensions to cope with the exceptions when an opcode not supported by this MIPS-lite processor is detected or PC goes into an address for data memory. [ 10 Marks] Reports Your report should include the following contents: 1. The ASM chart for the state machine. 2. The MIPS code and the corresponding machine code. 3. Simulation waveforms for the opcode, state, PC, IR, ALUOut and relevant registers (P, Q, R) must be demonstrated and annotated. You should clearly indicate why the simulations show that the operation is correct or incorrect. 4. Description of the modifications made to implement the additional instruction or extensions. Highlight the changes made in the code. 5. Lab demonstration for your work is not required. 6. The submission deadline is at 11:59 pm on Wednesday 11th December 2024 (Week 13). A single pdf file should be submitted to the Learning Mall.
Assignment Remit Programme Title BSc Accounting & Finance, BSc Money Banking & Finance Module Title Management Accounting Module Code 07 33177 Assignment Title The role of management accounting in addressing organisational challenges Level I Weighting 15% Hand Out Date 17/10/2024 Deadline Date & Time 09/12/2024 12pm Feedback Post Date 10/01/2025 Assignment Format Other Assignment Length 1 page Submission Format Online Team Module Learning Outcomes: This assignment is designed to assess the following module learning outcomes. However, your submission will be marked using the Grading Criteria given in the section on the next page. LO2. Discuss how management accounting and the role of management accountants is changing LO3. Analyse the nature and role of management accounting in different organisational contexts LO4. Assess the behavioural issues which have to be considered by management accountants; LO5. Evaluate the usefulness of management accounting techniques and reports in specific contemporary situations. Please note that these learning outcomes will be covered by addressing the requirement. You should not attempt to address them directly. Assignment: The primary role of management accounting is usually seen as assisting managers in fulfilling the goals of their organization. Management accounting provides a range of tools and techniques to assist in this role. You are required to: Choose a tool or technique addressed in the module. Identify and evaluate how this tool or technique is used by organisations. Your evaluation should be supported with evidence. Summarise your findings in a single page poster. Your investigation should go beyond the content of the Management Accounting module (although content from the module can be included) and therefore credit will be given for the use of relevant academic and professional literature that effectively supports your poster. All posters should contain a reference list. Your poster should be aimed at managers in an organisation who are not accountants but have completed a short “Introduction to accounting” course and so are aware of the basics of management accounting. Your group will be required to submit a progress report by 12pm on Monday 25th November. Further details of this can be found on Canvas. Grading Criteria / Marking Rubric Your submission will be graded according to the following criteria: 1. Poster design 2. Poster content 3. Use of evidence 4. Referencing 5. Submission of progress report See the marking rubric at the end of the remit for more information on how your work will be marked and graded.
