Material Science HW 1 1. Using a Modulus of Elasticity versus Density materials selection chart, which materials would you choose to achieve the best performance for a bicycle frame, and why? Discuss the cost of different material options. 2. Bromine has two naturally occurring isotopes: 79Br, with an atomic weight of 78.918 amu, and 81Br, with an atomic weight of 80.916 amu. If the average atomic weight for Br is 79.903 amu, calculate the fraction-of-occurrences of these two isotopes. 3. The net potential energy EN between two adjacent ions is sometimes represented by the expression in which r is the interionic separation and C, D, and ρ are constants whose values depend on the specific material. (a) Derive an expression for the bonding energy E0 in terms of the equilibrium interionic separation r0 and the constants D and ρ using the following procedure: 1. Differentiate EN with respect to r and set the resulting expression equal to zero. 2. Solve for C in terms of D, ρ, and r0. 3. Determine the expression for E0 by substitution for C in the Equation above. (b) Derive another expression for E0 in terms of r0, C, and ρ using a procedure analogous to the one outlined in part (a). 4. The atomic radii of Li+ and O2- ions are 0.068 and 0.140 nm, respectively. (a) Calculate the force of attraction between these two ions at their equilibrium interionic separation (i.e., when the ions just touch one another). (b) What is the force of repulsion at this same separation distance?
UESTC4003 - Control Computer Lab Exercise 3 MATLAB for Control Engineering 1.0 Objectives The primary aim of this class is to use MATLAB to: • Represent state-space systems • Convert a system to state space form and other forms • Pole Placement for plants in phase-variable form. • Discrete-time transfer function analysis 2.0 State space representation of a transfer function (Exercise 3A) Consider the below system. 2.1 Find the state-space representation of the transfer function using manual techniques. 2.2 Define A,B, C and D matrices in MATLAB and create state-space object. 2.3 Convert the transfer function directly to state space (controller canonical form) using MATLAB. 2.4 Now convert the controller canonical form. to phase-variable form. and, compare with the manual calculations in part 2.1. (To obtain phase-variable form, perform Ap=inv(P)*A*P;Bp=inv(P)*B;Cp=C*P,Dp=D; assume phase variable form. matrices areAp, Bp, Cp and Dp and P=[0 0 1;0 1 0;1 0 0]). 2.5 Convert the controller canonical state space object (obtained in part 2.3) into parallel form. 2.6 Convert the transfer function to the observer canonical state-space representation. 2.7 Repeat part 2.6 using manual techniques and verify the answers. 3.0 State space representation to transfer function Consider the below state space representation of a system. where U(s) is the input and Y(s) is the output. 3.1 Define A,B, C and D matrices in MATLAB and create a state-space object in MATLAB. 3.2 Now convert state-space object into a transfer function. 3.3 Assume that it is desired to have closed-loop poles at −2 ± j4 and −10. Determine the corresponding state feedback-gain matrix for the design. 4.0 Discrete-time transfer function analysis (Exercise 3B) Consider the below control system which is used to regulate ventilation rate of an environment chamber by adjusting fan voltage. 4.1 Create the system shown above in MATLAB Simulink environment assuming aproportional controller (with a gain of 0.1) and a first order discrete-time transfer function of: Assume desired ventilation rate as 300 m3/hour. 4.2 Run the simulation and examine the results using the Scope in MATLAB Simulink (i.e. ventilation rate output and controller output). 4.3 Adjust the controller gain using trial and error. What is the effect on the steady state error and the speed of response? What happens if you keep increasing the value of the proportional controller gain to reduce the steady state error? 4.4 Modify your above simulation by replacing the proportional controller with an integral control. Note that the control algorithm is another Discrete Filter block with a discrete-time transfer function of: with an integral gain (kI) of 0.1. 4.5 Run the simulation and examine the results using the Scope block. Investigate different values of kI and find the maximum value of kI (approximately) that does not yield an overshoot. 4.6 How does this response compare with the equivalent proportional control case? The laboratory exercises will be assessed according to the below criteria: Assessment of Laboratory Exercises Laboratory exercises will be assessed according to the accuracy of the answers and designs provided. You will be required to include your results for the following parts: • Computer Lab 1 - Exercises 1A and 1B • Computer Lab 2 - Exercises 2A and 2B • Computer Lab 3 - Exercises 3A and 3B You will be assessed against the correctness of your answers: models, designs and results, and the correctness of your interpretation of these results. You should create a single word (pdf) file and copy your MATLAB answers, plots, and diagrams corresponding to the parts in each lab exercise mentioned earlier. Also, make sure to include annotations/discussions relating to your results where necessary. You must submit this report (as a word or pdf file) to Moodlevia the assessment link and failure to submit this will result in a mark of zero for computer-based tutorials. The deadline for the report submission is the 6th of December 2024 (Beijing time) and late submissions will not be accepted. References Gene F. Franklin, J. Da Powell, Abbas Emami-Naeini, Feedback Control of Dynamic Systems, 7th Edition. Katsuhiko Ogata, Modern Control Engineering, 5th Edition. Norman S. Nise, Control Systems Engineering, 7th Edition.
IU000146 2024/2025 TERM 2 Computational Inequalities: Detailed Unit Brief Task: Create adesign that attends to a computational inequality (e.g. problematises data surveillance, algorithmic bias) . The task consists of two parts, as described below. Part 1 A technical protype that illustrates a particular aspect of your design. For example: • How data is captured. • How data is processed and analysed. • How data is visually represented. Submission Requirements: • The prototype developed using JavaScript (unless otherwise agreed with the unit lead). • Submit your code file, with comments that explain the code functionality. At the start of the document, clearly mention how the technical prototype connects to the final Figma prototype. Part 2 A written assessment of your conceptual design that demonstrates critical engagement and incorporates the themes and discussions from the unit. Submissions Requirements: PDF that includes: • Project title: A concise and engaging title for your project. • Project description (max. 200 words) : Briefly explain your project, purpose, and how it addresses a computational inequality. • Figmadesign: Provide the link to an online hosted version of your final prototype. • Project Documentation (max. 5 pages): o include work-in-progress documentation, provide evidence of your design process, including drafts, sketches and other relevant materials. o include a brief reflective section on work-in-progress documentation, summarising the evolution of our project and key decisions on the design process. • Bibliography: A list of references that informed the project, including readings, frameworks, and class discussions.
ELEC3875 Bachelors Project Optical receiver Project Proposal Form 2022-23 Section 1: Project Description Briefly, describe the basic background area, aims and objectives of your project (approx 600 words): basic background area: With the advent of the information age and the increasing demand for high bandwidth and high speed data transmission, fiber optic communication has become the main means of transmitting large amounts of data, especially in long distance communication and high speed internet applications. Especially in LIDAR (Light Detection and Ranging System), optical receivers are used to detect the reflected back light signals and thus calculate the information such as distance, shape and speed etc. LIDAR is widely used in autonomous driving, UAV navigation and robot vision, which requires optical receivers with high sensitivity, fast response and high dynamic range performance. With the development of technologies such as 5G, Internet of Things (IoT), and satellite Internet, the need for high-performance optical receivers is becoming more pressing. These technologies require receivers to be miniaturized, intelligent, and able to operate at lower power consumption for portable and embedded applications, and these optical receivers, not only require devices that can operate under low light intensity conditions, but also require good anti-interference capabilities in complex environments. Aims and Objectives: Underwater communications can encounter significant difficulties such as increased signal attenuation, reduced penetration, massive absorption and significant interference. Penetration, massive absorption and significant interference, all of which lead to degradation of signal quality. Together, these problems shrink the communication range and complicate the system architecture, thus degrading the performance of RF technology. So, in this research , i would like to study an optical receiver for the reason that optical communication for underwater applications can provide more advantages such as high bandwidth, low attenuation small, less interference, better directionality, more security, and resistance to electromagnetic interference. Firstly, by reading through the various texts, I wanted to first find the most suitable optical communication for underwater use by using LEDs with different wavelengths of the same light color; then I also needed to find a suitable photodetector and compare the responsiveness, bandwidth, noise, and so on in order to support me in designing a better quality optical receiver. In addition to this, with the development of technology nowadays, is it possible to add artificial intelligence to the receiver, so that it can be measured autonomously at any time, so that the optical receiver is always in the best operating condition. Signal amplifier and filter design, I need to convert the detected photoelectric signals into electrical signals, in this process I need filters to eliminate the noise. Of course, in optical communication, receivers are generally required to demodulate the received signals, so it is also necessary to study how to improve the performance of optical receivers by optimizing the demodulation technique, including improving the anti-interference ability and reducing the bit error rate. The goal of this BEng project is to design and optimize a high-performance optical receiver that can meet the needs of application scenarios such as optical communication, sensing and detection. Through experiments and theoretical analysis, we investigate how to improve the sensitivity, bandwidth and anti-interference ability of the optical receiver. Optical receivers are made to perform. better in signal conversion and processing by exploring new materials, structures and algorithms. Section 2: Gantt Charts Your Gantt chart should have clear timescales and relate to your progress milestones: Section 3: Preliminary Resources Estimate Please supply as much information as you can (at this stage) about the resources you are likely to need. Will your project require the use of allocated bench space in a laboratory? Is your project likely to need the help of the Faculty Mechanical Workshop? (If ‘Yes’, describe briefly what this may involve) Is your project likely to need the help of the Faculty Electronics Workshop? (Typically for printed circuit boards) yes Will you need any special IT support? no Are you likely to need to purchase any special electronic components? Section 4: Societal factors: Before completing this section, please refer to the notes on " Societal impacts” part of the handbook. Sustainability: How will you consider Sustainability aspects in your project (if relevant)? Justify why if you think “Sustainability” is non-relevant to your project. Environmental Sustainability Environmentally friendly materials: When designing optical receivers, prioritize environmentally friendly materials, such as recyclable, low-toxicity or biodegradable materials, to reduce environmental impact. Modularized design: Design maintainable and upgradable optical receiver modules to avoid scrapping the entire device and reduce e-waste. Energy Sustainability High-performance design Reduce energy consumption: Optimize circuit and algorithm design to reduce the power consumption of optical receivers. Social Sustainability Affordability: Design affordable optical receivers so that more businesses or users can afford to use systems such as LIDAR, especially in areas with limited resources. Global sharing of technology: Promote open-source technology or standardized designs to facilitate global sharing of optical receiver technology and promote the overall development of society. Reduce social risks: Optimize the application of optical receivers in autonomous driving, drones and industrial control to enhance their reliability and reduce possible safety accidents. Ethics: Please select one of the options below. Please refer towww.leeds.ac.uk/ethics Is the proposed project is believed to raise any ethical issues? Yes/No If ‘Yes’, which one(s)? if “No” justify your answer? No Risk management: Identify, evaluate and risks (the effects of uncertainty) associated with your project and explain how you will mitigate their impacts. Since optical receiver design involves complex technologies such as high-precision components (e.g., photoelectric sensors and filtering circuits), signal processing algorithms, etc., it may result in a design solution that cannot be realized as expected or performs substandard. Impact: Project schedule is delayed. Receiver fails to achieve desired sensitivity or accuracy. Mitigation Measures: Conduct a technical feasibility study at the beginning of the project to verify the feasibility of key technologies. Use modular design to break down complex problems into manageable sub-problems. Prepare alternatives in advance, such as using off-the-shelf commercial photovoltaic components in lieu of hard to-achieve self-developed parts. Equality, diversity and inclusivity: How will you consider equality, diversity and inclusivity aspects in your project (if relevant)? Justify why if you think these are not applicable to your project. If the project involves presenting research, taking an inclusive approach can ensure that everyone has equal access to learning opportunities. I can use easy-to-understand language and format in project documentation and presentations. The use of optical receivers in areas such as surveillance, facial recognition or monitoring may raise ethical issues related to inclusion and equality (e.g., misuse of the technology). Avoidance in design may have an unequal impact on certain groups.
Exercise 1: SLR Analytics & Assessment This exercise is designed to give you practice running simple linear regression (SLR) models (econometric models with a single explanatory, or independent/RHS, variable). In the first SLR analysis, you’ll be looking at the relationship between the SAT's component ERW scores (Evidence-Based Reading and Writing) and overall SAT scores. In the subsequent SLR analyses you’ll be looking first at the relationship between weekly Spotify streams and iTunes sales in the US, and then at the relationship between annual %wins and runs scored (RS) and runs allowed (RA) in the Korean Baseball Organization. The data are as current as possible. Predicting SAT Scores In your first SLR analysis, you’ll be using SAT Evidence- Based Reading and Writing (ERW) scores to predict combined SAT scores. The data in this exercise are built from summary statistics for the 2024 SAT results, for college-bound students. Links to the data sources have been posted. 1. Login to the Exercise #1 Answers/Updates page and copy and paste your SAT dataset into Excel. You will be using this data to estimate the relationship between the 2024 SAT's Evidence-Based Reading and Writing scores (erw) and overall SAT scores (sat), with a sample of five observations. 2. Working in Excel: Let’s look at the relationship between erw and sat in your sample: a. Plot the data points using Excel's XY scatter chart … . putting erw on the horizontal (x) axis and sat (y) scores on the vertical axis. Estimated coefficients: β and β b. Use Excel‘s “add trendline” feature (right click on a data point in the scatterplot to see this option) to add a linear trendline to your XY scatter plot. Be sure to specify (under “Options”) that the equation and R2 be displayed along with the trendline. Record the trendline slope, intercept and R2 on the Answers/Updates page. i. Take a snapshot of your figure with the trendline results, save it as a jpg, png or pdf file and upload that file to the Exercise #1 Canvas Quiz Answers (it is not a Quiz, of course, but it is a convenient way to submit your snapshot). c. In class we derived the OLS formulas for the SLR intercept and slope estimates: Working towards these coefficients and continuing in Excel, compute the sample statistics used in these formulas (x , y , Sx , Sxx , Sy , Syy , Sxy and rxy) and record those on the Answers/Updates page. d. Use the sample statistics that you just derived to compute the SLR intercept and slope estimates, and record your slope and intercept coefficients on the Answers/Updates page. Do you get the same estimates of the slope and intercept that you saw with the trendline equation? Record your Yes/No answer on the Answers/Updates page. Use the SLR slope and intercept coefficients that you just derived to generate predicted SAT scores ( i = + xi ) and associated residuals (actuals – predicteds: i = yi − i ), for each observation. e. Compute the means and variances of the predicteds (the i ' s ) and the residuals (the i ' s ) and record those figures on the Answers/Updates page. i. You should have found that the mean of the predicteds is the mean of the actuals, y , and that the mean of the residuals is 0. Did you? Record your answer. Goodness-of-Fit: MSE, RMSE andR2 f. Working with the residuals that you computed, derive the SSR (Sum Squared Residuals) and calculate the MSE (Mean Squared Error): n − 2/SSR . Take the square root of this to calculate the RootMSE (RMSE). Record your answers on the Answers/Updates page. g. Use the variance of the actuals Syy to compute SST (Sum Squared Totals), and record your answer on the Answers/Updates page. i. Recall that since SST, the sum squared deviations of the actual values from their mean, is related to the variance of the y's: h. Use the calculated SSR and SST to calculate and verify that this agrees with the ratio of the variances of the predicteds and actuals: Record your answers on the Answers/Updates page. OK, let's move to Stata! 3. Import your data into Stata (use the FileImport command, or just copy and paste your data from Excel into Stata) and use Stata to run the regression of sat on erw (so: reg sat erw). Record your answers. a. You should get the same estimated intercept and slope coefficients, R2, MSE, and RMSE as above (with your work in Excel). Do you? Record your answer. b. Take a snapshot of your Stata regression results, save it as a whatever file and upload that file to the Exercise #1 Canvas Quiz Answers. 4. Continue working in Stata, and with the sat-erw regression: a. After running your SLR model, use Stata’s predict command to generate the predicted values (the i 's) and the residuals (the i = yi − i 's). Your Stata code might look something like: reg sat erw predict yhat predict uhat, residual b. Use Stata to calculate the sample correlation of the actuals (the sati ’s, the yi ' s ) with the explanatory variable (erw, the xi ' s ), rxy , and the sample correlation of the predicteds (the i ' s ) with the actuals, ry . Record your answer on the Answers/Updates page. i. You can do this with a single Stata correlation command: corr sat erw yhat ii. You should have found that those two correlations were the same. Did you? Record your answer on the Answers/Updates page. c. Square these sample correlations and verify that R2 = rxy(2) = r … so now you know why R2 is called R2 ! Record your verification on the Answers/Updates page. d. Finally, verify that the sample correlation of the predicteds (the i ' s ) and the residuals (the i ' s ) is zero: r = 0 . Record your verification on the Answers/Updates page. Meaningfulness (Economic Significance) 5. Continue working in Stata, and with the sat-erw regression: a. Beta Regression i. Use the , beta command to run a beta regression of sat on erw, and record the estimated intercept and slope coefficients on the Answers/Updates page. 1. Stata syntax: reg sat erw, beta ii. You should find that the estimated beta regression slope coefficient is also the correlation between sat and erw, which you generated in above. Do those two values in fact agree? Record your answer on the Answers/Updates page. iii. Would you say that this (the estimated slope coefficient) suggests a meaningful (non- trivial) relationship between sat and erw? Explain why you say what you say. Hint: Meaningfulness is very much in the eye of the beholder... it's a judgement call. But don’t ignore the issue!: You've spent a look of time looking at correlation coefficients in Stats and elsewhere, no doubt. Use that experience! b. Elasticity @ the means i. Using the sample statistics that you've already computed and the SLR estimated coefficients, predict the SAT score at the mean of erw scores (evaluate the SRF at the mean of erw). Record your answer on the Answers/Updates page. ii. Your answer should agree with the mean sat score, since = y − x , + x = y . Does it? Record your answer on the Answers/Updates page. 1. This reflects the fact that the SRF passes through the sample means. iii. Calculate the point elasticity of predicted SAT scores wrt (with respect to) changes in the erw score, evaluated at the means. Record your answer on the Answers/Updates page. (Recall that if the SRF is = + x , then = and elasticity = = . When this elasticity is evaluated at the means, it is x .) iv. Would you say that this estimated elasticity (at the means) suggests a meaningful (non-trivial) relationship? Explain why you say what you say. Record your answer on the Answers/Updates page. 1. Hint 1: As mentioned above, meaningfulness is very much in the eye of the beholder... it's a judgement call. But don’t ignore the issue! 2. Hint 2: Most would agree that elasticities larger than 0.4? 0.5? or so in magnitude are large and meaningful (economically significant), and that elasticities less than 0.1 or even 0.05, in magnitude are small, and not so meaningful or economically significant. 3. Hint 3: But where do you draw the line in-between? I have two answers: a. Do you have to draw a line? (See what the magnitudes are before you worry about this.), and b. Do you really have to draw a line? I tend to say < .1 is not so meaningful, above .3 is, and between 0.1 and 0.3, who knows? But that's just my opinion! 6. Skip for now: Return to your erw/sat Excel work. For each observation, compute the square of the x-distances from the mean and show that the OLS slope estimate is indeed a weighted average of the slopes of the lines joining each datapoint to the sample means point, where the weights are proportional to the square of the x-distances from the x-mean. You should have found that the OLS slope estimate is indeed a weighted average of the slopes of the lines joining each datapoint to the sample means point. Did you? Record your Answer. Take a snapshot of your Excel worksheet results and upload that to the Exercise #1 Canvas dropbox. a. Hint: This is discussed early in the OLS/SLR Analytics handout and slideshow … where I walk you through the calculation for a sample of four observations from the bodyfat dataset. For the remainder of this Exercise, you'll be repeating 2. (Excel) and 3. (Stata) above, working with different datasets. Warning: You’ll need to spend some time building these datasets. But that time will be time well spent! At some point we’ll review in class how to merge datasets in Excel and in Stata … which will be the most challenging part of constructing your final datasets. I have posted a handout with some tips to Canvas.
STATS 3DA3 Homework Assignment 3 Instruction • Due before 10:00 PM on Friday, February 28, 2025. • Upload a PDF copy of your solutions to Avenue to Learn. You do not need to rewrite the questions in your submission. • Late Submission Penalty: A 15% deduction per day will be applied to assignments submitted after the deadline. • Late Submission Limit: Assignments submitted more than 72 hours late will receive a grade of zero. • Grace Period for Accommodations: A 72-hour extension beyond the due date is granted for students with approved accommodations through SAS. • Your submission must follow the Assignment Standards listed below. Assignment Standards • Include a title page with your name and student number. Assignments without a title page will not be graded. • Use Quarto Jupyter Notebook for your work (strongly recommended). • Format your document with an 11-point font (Times or similar), 1.5 line spacing, and 1-inch margins on all sides. • Use a new page for the solution to each question (e.g., Question 1, Question 2, Question 3). – Clearly number all solutions and sub-parts. • Do not include screenshots in your submission; they will not be accepted. • Ensure your writing and referencing are appropriate for the undergraduate level. • You may discuss homework problems with other students, but you must prepare and submit your own written work. • The originality of submitted work will be checked using various tools, including publicly available internet tools. Assignment Policy on the Use of Generative AI • The use of Generative AI is not permitted in assignments, except for using GitHub Copilot as a coding assistant. – If GitHub Copilot is used, you must clearly indicate this in the code comments. • In alignment with McMaster academic integrity policy, it “shall be an offence knowingly to submit academic work for assessment that was purchased or acquired from another source”. This includes work created by generative AI tools. Also state in the policy is the following, “Contract Cheating is the act of”outsourcing of student work to third parties” with or without payment.” Using Generative AI tools is a form of contract cheating. Charges of academic dishonesty will be brought forward to the Office of Academic Integrity. Question: In this assignment, you will explore K-Nearest Neighbors (KNN) and Decision Tree classification algorithms. You will apply both techniques to a dataset from the UCI Machine Learning Reposi-tory, gaining hands-on experience in data retrieval, preprocessing, model building, and evaluation. This exercise is designed to strengthen your understanding of classification methods and their ap- plications in real-world scenarios. Dataset: The dataset for this assignment is the Wine Quality Database, which includes 12 input attributes to predict the wine quality. Your objective is to build classifiers that accurately predict the wine quality category based on these attributes. • Dataset Link: https://archive.ics.uci.edu/dataset/186/wine%2Bquality. 1) How many observations (rows) and features (variables) are present in the dataset? 2) What types of attributes are included in the dataset? Identify which attributes are numerical, categorical, or of other types. 3) Which variable serves as the response (target) if our goal is to build a classifier to predict the wine quality? 4) Are there any missing values in the dataset? If so, describe how you would handle them. 5) Display five rows from the original dataset, which includes both predictors and the response variable. Hint: You can access the predictors and response by using data.original in the fetched dataset. 6) Is any transformation necessary for the response variable? Apply the transformation if needed. Additionally, how balanced is the dataset in terms of the response variable? 7) Remove observations with quality scores of 3, 4, 8, and 9 from the original dataset. Use this filtered data to complete questions 8 through 19. Hint: Use isin([3, 4, 8, 9]) to identify the observations to drop. 8) After filtering, how many unique quality scores remain in the dataset? 9) Are there any potential outliers in the filtered dataset? Describe the method(s) you would use to identify them. Note: You do not need to handle the outliers, only describe how to detect them. 10) Separate the predictors and the response variable from the filtered dataset. 11) Are any data transformations necessary for the features before training a classification tree model? If so, explain the rationale and apply the transformation. 12) Split the dataset (filtered in Part (10) and transformed in Part (11)) into training (80%) and testing (20%) subsets. 13) Train a classification tree model using the training data and perform model selection through cross-validation (e.g., tuning tree depth). After identifying the best model based on validation performance, evaluate its final performance on the test data. Hint: Use the Gini index to grow the tree and classification accuracy for model selection. 14) Using the best classification tree model, identify the two most important features for predict- ing wine quality. 15) Write at least one statement summarizing the classification tree model’s performance and its implications in the context of the dataset and the problem. 16) Create copies of X_train and X_test from Part (12) and save them as X_train2 and X_test2. 17) Is any additional data transformation necessary for features before training a KNN classifier model? If so, write the rationale for the transformation and then apply the transformation to the features in X_train2 and X_test2. Hint: Explain why feature scaling may or may not be necessary for KNN and how it could affect model performance. 18) Using the training data (X_train2, y_train), train a K-Nearest Neighbors (KNN) classifier and perform model selection through cross-validation (e.g., tuning the neighborhood size). After selecting the best model based on validation performance, evaluate its final performance on the test data (X_test2, y_test). Note: 1) If any transformations were applied to X_train2 and X_test2 in Part 17, ensure those trans- formed datasets are used here. 2) Begin tuning the neighborhood size for cross-validation starting from 2. 19) Write at least one statement summarizing the KNN classifier model’s performance and its implications in the context of the dataset and the problem. 20) Write at least two statements that compare and contrast classification and KNN classifers performance and interpretation of the model on the test set. Grading scheme Codesandanswer[2]Codes[1]Codesandanswer[1]Codesandanswertodetectoutl Codesforcross-validation[1],rationaleforbestmodelselec[1],codesfortestperfomance[1]14.15.16.17.18.Codesandwriteananswer[1]1statement[1] Codesforcross-validation[1],rationaleforbestmodelselec[1],codesfortestperfomance[1]19.20.1statement[1]2statemenstocompareandcontrast[2]
Final Exam Operations Analytics: Simulation SCOT 500M, Spring 2025 Individual Final Exam: Due Friday, February 28, 11 p.m. On Canvas A few guidelines for the exam: • This assignment is to be done entirely individually. • You may discuss it only with the professor or the TA. • There are 2 different cases included in the exam. 1 requires Arena, and 1 uses Crystal Ball (or @Risk or Python). Please make sure you submit all 2 cases. Some cases are easier than others. • You do not need to develop a comprehensive report for each case. It is best to copy the questions and submit answers clearly for each question. Please make sure to answer each part clearly. • Include printouts or screen shots of any models you develop and simulation analyses. (Ido not need to see all trials, though) • To facilitate grading, please adhere to the following: o Please submit each exam separately, on Canvas o Include your Student ID on each case Discrete Event Simulation using Arena Case I: Creative Analysis of a Service System (20 points) Make sure to run Arena in Batch Mode. It is much faster that way. Background: in this part you are expected to show your creativity and understanding of queuing processes, and use discrete event simulation to develop and analyze a relatively simple system. This is an open-ended case. You are not being asked to model a specific system. You need to come up with the example to analyze, and analyze the system you describe using Arena. You may come up with the idea from any source or example you like, but are not allowed to discuss this with anyone else. Part of your grade (4 points) on this assignment is based on your creativity. Creativity is measured on 3 dimensions: 1. A more creative scenario will earn more credit for creativity. Please note that creativity does not require more components in the Arena model. You can be creative with few modules. 2. Creative metrics for evaluating the scenario will also earn credit for creativity 3. Creative solutions for the problem you propose is also valued Assignment: there are many ways to improve a system that involves queueing systems. Frequently, these systems have customers waiting for service. We discussed quite a few in class during the semester. You are tasked with evaluating one (new) system and evaluating performance of the system under various scenarios. 1. Describe the scenario you propose to analyze. Clarify your creativity in this example. 2. Develop a model in Arena that reflects your scenario. There are no limits on how you choose to model the system. The model must have at least one of each of the following components (you may have more of each component): o Arrivals o Decision node or nodes o Process o Assign o A schedule o Optional components are not required, but are worth 1 point each A variable A Record module 3. Explain metrics used to evaluate the system. These could be the default metrics from Arena, or new metrics. You should discuss at least 4 metrics. 4. You may (but are NOT required to) develop a creative metric. This can be a combination of current metrics from Arena, or something else you measure in the simulation. 5. Run the model for the base case, with at least 100 trials. Discuss key results from the Arena analysis. Make sure to highlight the metrics you proposed earlier. 6. Suggest a recommendation for improving performance of the system. This recommendation must involve a policy class related to a key parameter or input of the model. Explain the rationale for using this policy class. The purpose of this policy class is to identify the relationship between one aspect of the model and various performance metrics you suggested earlier. (As an example, the policy class might be to change the arrival rate. The levels of the policy are the different arrival rates. This is probably a bad example, because arrival rates are not under your control.) 7. Analyze this policy class using the Arena PAN tool. The policy class must have at least 10 levels for the policy parameter. (This is called a Control in PAN.) 8. Develop a table to clearly identify the relationship between different levels of the policy class and the metrics proposed earlier. 9. Present at least 3 trade-off curves between important metrics of the model. 10. Make a recommendation to management. Provide arguments justifying the recommendation. Your write-up should clearly explain the scenario being analyzed, metrics, recommendation, and key conclusions. Highlight aspects of the analysis that you think are creative. Operations Analytics: Simulation Monte Carlo Simulation using Crystal Ball / @Risk / Python Case II: Project Management with Correlated Activities (5 points) Consider the following rather simple project. • Activities A and B start immediately. The time for each activity follows a Normal distribution with mean 50 days, and standard deviation 10 days. o The completion time for activities A and B is correlated, and the correlation parameter is P. We will investigate how varying the correlation between these activities affects completion time of the project. • Activity C begins only after both Activities A and B are completed. The time to complete Activity C follows a Uniform. distribution between 20 and 40 days. Time for Activity C is not correlated with the other activities. • Formally, we have: o Time-C ~ U[20 , 40] o Corr(Time-A , Time-B) = P • Develop the analysis in Crystal Ball (or @Risk or Python) • Evaluate completion time for the project, as a function of different levels of correlation between times of Activities A and B. Remember that correlation can be between -1 and +1. Include a printout of the completion times. • If the objective is shortest completion time, what’s the best form of correlation between activities? Explain this result.
MTHM506 - Statistical Data Modelling Individual assessment sheet Marks achieved in this assignment will contribute towards 50% of the final module mark. You should attempt all questions on this sheet. Note that the questions are organised in the order we covered the topics, and not in order of difficulty. Therefore it is advised that you read through the questions first, and start working on those that you feel more comfortable with. Deadline: Noon (12pm), on 28th February 2025 You should submit one pdf via ELE containing your solutions - it should be written up using word processing software (e.g. LaTeX, R Markdown, or Word). Solutions are expected to be concise, well structured and well presented. Commented R code (e.g. ‘model
GEOL0030 Seismology II Coursework 1: Set Week 3, to be submitted Wednesday Week 7 via Moodle Please note that marks are awarded not just for getting the correct answer, but also for the quality of the answer, i.e. for the explanation and for the way it is set out. All necessary proofs and auxiliary calculations must be included in your answers. Use appropriate values for any required parameters. Answer ALL questions. All questions carry equal marks. 1. Boxcar functions are often used to describe the time history of earthquakes. (a) Calculate analytically the Fourier transform. of a boxcar function: f(t) = 1 , for –T < t < T, = 0 , otherwise [4%] (b) Using Matlab calculate the Fourier transform. of a discrete version of the boxcar function f(t) considered in question 1(a) for a reasonable value of T of your choice. Plot the real and imaginary parts of the solution. [4%] (c) Using Matlab, build plots comparing the solutions obtained on 1(b) with the analytical solutions obtained in 1(a). [2%] (d) Using Matlab, compute and plot the convolution between two boxcar functions with T=3 s and T=20s. Plot its spectrum and discuss its characteristics. [5%] 2. Consider Love waves in a medium that comprises one layer of thickness h of material with shear wave velocity b1 underlain by a half-space of material with a higher velocity b2. Love waves can be treated as the constructive interference between SH waves incident on the interface between the layer and the half-space, with an incidence angle larger than the critical angle. (a) Write the mathematical expression of the Love wave displacement in the layer as the sum of an upgoing and a downgoing plane wave. [2%] (b) In the half-space we only need to consider one upgoing plane wave. Write the corresponding mathematical expression. [2%] (c) Since seismic surface waves travel along the Earth’s surface, their energy is trapped near the surface and hence their displacement must decay as z →∞ . Derive the relationship between apparent velocity and shear velocity imposed by this radiation condition. [3%] (d) Write the mathematical expressions describing the appropriate boundary conditions at the free surface and at the interface between the layer and the half-space. [3%] (e) Using the boundary conditions from 2(d) along with the expressions in 1(a)-1(c) derive the relationships between the amplitude coefficients of the displacement in the layer and in the half-space. These relationships should involve the shear modulus in the layers, the wavenumber and the thickness of the layer. [4%] (f) Using the results obtained in 2(e) to obtain the Love wave dispersion relation. [2%] (g) Using reasonable values for the various parameters involved in the dispersion relation in 2(f) to model the Earth’s crust, determine the fundamental mode values of ω that satisfy the Love wave dispersion relation at values of phase velocity of 3.8, 4.0, 4.2 and 4.4 km/s. Sketch the c(T) dispersion curve. [4%] (h) Determine the values of ω that satisfy the Love wave dispersion relation for the first higher mode. Add sketches of the corresponding dispersion curve to the diagram built in (g). [3%] (i) Discuss the differences between the fundamental and higher mode Love wave dispersion curves plotted in (g) and (h) in the context of their sensitivity to shear wave velocity. [3%] 3. (a) Assume the following expressions for the phase velocity as a function of wavelength (l): and where c0, c1, a, b and λ0 are positive constants. Sketch the corresponding dispersion curve. [6%] (b) Give the expressions of the group velocity as a function of wavelength and roughly sketch the group velocity curve. [10%] Table 1. Measured wave periods of various spheroidal and toroidal modes. 4. Using the relation between modes and traveling waves and the information in Table 1, answer the following questions: (a) Since the mode 0T2 samples the mantle quite uniformly, assume that the phase velocity appropriate for this mode is the average mantle shear wave velocity in PREM and find the wave period that you would expect. Discuss how your result compares to the actual wave period in Table 1. [5%] (b) Determine the phase velocity for three modes with similar periods: 4T67, 10T40 and 13T7. Interpret the differences obtained. [5%] (c) Determine the phase velocities and wavelengths of waves corresponding to the modes 0S3, 0S30 and 0S130. Interpret the trend of the velocities. Which of these modes would you expect to be most affected by lateral heterogeneity in the Earth, and why? [5%] 5. Explain the main principles of the following methods used to calculate theoretical seismograms: (a) The reflectivity method; (b) The normal mode summation method; (c) The finite differences method. Discuss and compare the strengths and limitations of these methods for applications involving imaging the Earth’s deep interior. [18%] 6. Define mathematically the resolution of an inverse problem and discuss its physical meaning, with examples. [10%]
Creative Assignment Guidelines Introduction and Goal: Communicating science information to a general audience is essential for people to gain interest in science and combat misinformation. Scientific jargon is a barrier for people outside of the field and scientists constantly need to think of ways to convey concepts without jargon. This assignment wants to you explore your creative side and help you learn how to communicate science to broad audiences, as it has been shown that learning to communicate concepts to a broad audience can enhance learning (https://pubmed.ncbi.nlm.nih.gov/23471252/). You are to create a visual/interactive way to convey a cell biology topic to a general audience. It can take the form. of a short comic strip (e.g. 4-6 panels), a couple well-thought out memes or GIFs, an informative social media post (e.g. Instagram carousel of 4-6 images), a short TikTok/Instagram reel (1-2 minutes), etc. We will allow image, PDF, GIF and video formats for submission. Submissions can be hand-drawn or created digitally (e.g. canva) if submitting images. If you have an idea that is not covered here, please discuss with the instructor before going ahead with it. Formatting Guidelines: APA 7th edition, you must reference any resources you use to create your assignment. You should have 1 primary research reference at minimum. What topic should I choose? Anything related to cell biology! You are able to pick any topic area that is interesting to you, as long as your submission is unique. You can refer to topics covered in our BIOC10 class or anything you found interesting from BIOB10, as a starting point. You could also use any cell biology topics recently featured in the media as a starting point as well. Potential topics: https://www.nature.com/subjects/cell-biology Additional example topics not in the screenshot: - Stem cells - Cancer - Extracellular matrix - Cell junctions Examples of suggested formats: https://www.asbmb.org/asbmb-today/opinions/062321/building-a-scientific-community-one-meme-at-a-time https://sites.wp.odu.edu/dowdy-psyc304/2020/11/22/cell-bio-meme/ https://theawkwardyeti.com/?s=cell+biology http://www.beatricebiologist.com/?s=cell https://www.amoebasisters.com/gifs.html https://www.amoebasisters.com/parameciumparlorcomics/category/cells @rciscience on Instagram @sciencewhizliz on Instagram @science.sam on Instagram or TikTok @doctor.brein on Instagram or TikTok @julia.ravey.science on Instagram or TikTok @sketchingscience on Instagram @science.uncovered on Instagram or TikTok @unambiguousscience on Instagram @scienceupfirst on Instagram @raventhesciencemaven on Instagram or TikTok
MAS6041 2023–24 1 THE DISSERTATION PROCESS 1.1 THE DISSERTATION The dissertation constitutes one-third of the entire MSc programme, i.e. 60 credits and so you should expect to spend between 400 and 600 hours working on it. Doing so will require careful planning to make sure that you use your time effectively and produce the best dissertation you can. The dissertation is also your opportunity to demonstrate how independent you have become as a statistician. While each project comes with a supervisor, they are intended for guidance and support, not to give you a recipe for how to complete your research. The final submission will be very much your piece of work and hopefully something you can look back on with pride and recognise your intellectual contribution. It is up to you to take responsibility for what you produce. This document is intended to make some points about the process of working on and writing up an MSc dissertation for module MAS6041. Advice relating to supervision, the Dissertation Support Programme (DSP) and Dissertation Support Worker (DSW) will be given in a separate document on Blackboard. The latter two are relevant only for full-time students, part-time students will get all of their dissertation support from the Supervisor. 1.2 THE DISSERTATION PROJECT ALLOCATION AVAILABLE TOPICS In Semester 2 Week 1, you will be given a list of 10–12 research topics. These cover broad research areas that can accommodate several research questions through selection and combination of specific data sets, models and methods; each combination constitutes a dissertation project. Each student will provide a list of 4 topics, ordered by preference by 12:00 Tuesday 27 February 2024. These will be used in the allocation process by the MSc academic staff, who will strive to give each student their first or second choice—although some exceptions may be unavoidable since supervision work must be spread fairly evenly between staff . Allocations will be announced by Week 8. After their topic has been allocated, each student must look for and select at least one project —i.e. a dataset, model and method combination within the topic. Full- time students will initially meet with their supervisor in groups (determined by the allocated topic) to discuss their selection and begin the process of establishing their unique dissertation project. STUDENT DISSERTATION PROPOSALS Most students choose a dissertation topic from the list provided. Students are only likely to propose their own project if their daily job involves them in data analysis or if they have a strong interest in a particular topic derived from previous professional experience. To be considered, the project must have a substantial statistical/probabilistic component of suitable MSc level. All students considering proposing their own project must submit a project proposal. This is a rather detailed description of the research to be conducted and the relevant personal background, comprising: • Project Title • Project description, including a chapter by chapter plan of the contents (350 words) • Dataset(s) to be used and agreement of continued access to the data until completion of the dissertation (200 words) • Work plan, including a Gantt chart and a contingency plan in case of change of status (200 words) • Description of academic/professional background relevant to the project (300 words) The proposal must be submitted using the template on Blackboard by the end of Week 4 (17:00 1 March 2024). There you will find a file with some examples of previously accepted student-proposed projects—bear in mind these are examples and do not conform to the template. STUDENTS WITH ALLOCATED PROJECTS Students who have a project allocated from previous years, but have made little or no progress, may consider switching to this model if they wish. You would need either to submit a list with your preferred topics or to produce a project proposal as described above. 1.3 DEADLINES Fully understanding the goals of your project and then setting up a consistent and effective work-flow are your first goals and should be completed as soon as possible after the exams, in your first year of study. Alongside this you will need to undertake careful project planning and record keeping. For full-time students, we provide a recommended work schedule as part of the DSP (see separate document), with deadlines for various activities. Full-time students must meet all of the deadlines in the DSP schedule unless a prior arrangement has been made with the DSP lead (Prof. Caitlin Buck). 1.4 THE DISSERTATION DEADLINE For full-time students the deadline for submitting your dissertation is Friday 20 Septem- ber 2024. Extensions to the time limit for submission of the dissertation are only given for exceptional reasons (i.e. certificatedmedical grounds or substantial extenuating circumstances). For full-time students, non-submission of a dissertation by the September deadline entails failure of the module (but re-submission may be allowed twelve months later). For those doing the course part-time, the year the dissertation is due for submission is determined in the first instance by the end-date of your registration, in some cases this can be changed to accommodate taking the course over a longer time, but this should be done well in advance of the September deadline, by formal request to the Course Director. 1.5 DISSERTATION REQUIREMENTS DISSERTATION PREPARATION You are required to produce your dissertation using either the LATEXor Bookdown template provided on Blackboard. You must not change any aspects of the file that will alter the geometry of the pages or the size of the font. Dissertations that do not conform precisely to the templates will, as a minimum, be penalised under Presentation during the examination process and, in extreme cases, may not be examined at all (although resubmission a year later may be permitted). DISSERTATION LENGTH Clear writing is usually concise, to the point, and avoids unnecessary repetition. It is thus a requirement that the main matter of your dissertation be not more than 70 pages, including all figures and tables. Those working on theoretical topics, with little need for figures and tables in their dissertation, should aim for considerably fewer than 70 pages (typically 30–50 pages). If you do submit something longer, examiners will read only the first 70 pages (or 50 pages of more theoretical material). Note too that, regardless of length, any material in appendices will only be inspected cursorily by examiners. You should use appendices judiciously, in the way outlined in the next section. GENERAL ADVICE A supervisor cannot say in advance that a dissertation will pass; the examiners determine this. However we do expect the following standards from any good dissertation. Elements It is important that the dissertation shows how the time devoted to the project was spent. It is fine to include ideas that did not work, or did not deliver what was expected. Research is often about trying new things and not everything always works out as initially hoped. You should not describe absolutely everything you did. A dissertation is not simply a diary of your work. You will need to be concise and selective in what to include so that it is interesting to the reader. The write-up should be areflection on the analyses done, paying most attention to the parts that are helpful for understanding. That said, you must be absolutely sure to avoid all possible risk of accusations of plagiarism or collusion (see below). If your dissertation involves developing your own code (ie writing routines/functions than are novel), then they should be included in the main text, either as pseudo-code or the code itself, with appropriate annotations and descriptions. Routine function calls need not be included in the main text (lm, glm, ar, bayesreg, stan, etc), but rather described, detailing in particular the choice of parameters. There is no need to include or describe the use of code for routine summaries (eg plotting, summary tables) If the main objective of your dissertation is developing code (eg building a new package), then it is advisable to make it available electronically, detailing examples of its use in the main text. In any case, it would make sense to have examples of the computational implementation in the appendix, eg a worked example with annotated code, for reference. Writing style. You should write for another student on the MSc who knows nothing of the project, treating sketchily what would be common knowledge to you both, but with care material that would benewordifficult to such a person. All dissertations should have a proper introduction and include a clear description of the problems that will be considered and the aims and objectives of the study. The material should be properly ordered and organised. This involves dividing it into suitable chapters and sections, using satisfactory cross-referencing (ideally, including page numbers, provided they are right), and putting in appendices material that obscures the flow or is included only for the record. Communication should be precise and effective. All specialised terms and mathematical notation must be defined at first used and then be applied consistently. Aim for clarity of writing and exposition; try to avoid patterned accounts of similar analyses. Pay attention to: spelling, the right choice of words, numbering pages, constructing sentences properly, laying pages out well, producing titled and labelled graphs and tables that are intelligible and that add to the discussion, and soon. Make sure the references and citations are handled properly, using BibTeX, as illustrated in dissertation templates. Other aspects, including insight There are a variety of other aspects that contribute to the quality of adissertation. These are hard to describe exactly but are suggested by the list: difficulty of ideas used; novelty of techniques; originality; independent thinking; individual expression; critical writing (as opposed to regurgitation); appreciation of the meaning, context and significance of work. 1.6 PLAGIARISM AND COLLUSION You must make clear the extent of any work in your dissertation that is not your own. Such work could be, for example, a program or library written by somebody else, or an account of theory in a particular book which you draw on extensively. If you are in any doubt about whether an explanation is needed then it almost certainly is. You will already know that plagiarism and collusion are very serious offences (see the guidance here). In the context of an MSc dissertation any significant plagiarism or collusion would undoubtedly lead to the degree not being awarded. Unless agreed in writing with your supervisor at the start of your project, and acknowl- edged formally in your dissertation report, all use of generative AI in the dissertation research and writing process is forbidden. If we detect use of such tools, we will assume that you are seeking to gain an unfair advantage over other students on the dissertation module and you will be subject to the University’s disciplinary processes. 1.7 IF THINGS GO WRONG If there are significant difficulties with supervision these can be discussed, in confidence, with the Course Director, Dr Kostas Triantafyllopoulos, or with the Dissertation Module Leader, Dr Miguel Juarez. Students should also make sure that their supervisor is informed of any special circumstances that should betaken into account in grading the dissertation. 2 SUPERVISION FOR PART-TIME STUDENTS Due to the varying nature and needs of the projects it is not possible to give a widely applicable description of the supervision process. In addition, supervisors and support workers need to take time away from teaching for holidays, conferences and other research activities (especially in the summer) and so careful planning and flexibility will be needed by all concerned. Part-time students should try to meet in-person with their supervisor if they are in Sheffield anyway (e.g. for exam weeks), and should expect around 10 hours of dedicated support from their supervisor in total. You should use some of these 10 hours of supervisor support to get feedback on drafts of the dissertation. We recommend that you seek feedback on one chapter of your writing. Do not however expect your supervisor to read and comment on the entire dissertation. The following is usually a good approach. • Ask your supervisor to read one draft chapter (but not the introduction) and return formal feedback on the style, content and layout. This chapter must be sent to the supervisor by a pre-agreed date well before the September submission deadline. You should not expect them to correct basic linguistic issues, but they will highlight if there are serious concerns in this regard. • Based on this feedback, you may choose to redraft and then use further support time allotted to you to resubmit to you supervisor for further comments. You should not expect your supervisor to read more than two drafts of a single chapter, however. • Make sure that you leave sufficient time for your supervisor to read your draft chapter. Providing that you pre-agree a submission date, you should normally expect them to provide you with written feedback within two weeks from sending it to them — reading and giving detailed feedback is a time consuming process. 2.1 SCHEDULING SUPERVISOR TIME It is crucial to realise that your supervisor may not be able to work on the sametime scale as you and they may be away for periods of the year (especially the summer). You cannot expect your supervisor to fit in supervision of an entire dissertation in the few weeks before the submission date. You must therefore carefully plan your work so as not to leave everything to the last minute.
COSC 2P13 – Assignment 1 – Is this all an excuse to print ‘handsome Squidward’? Note: the most common problem for this style of assigned task is not reading. Consider a small makerspace: several people needing to share a comparatively small number of stations and tools, while trying to maximize their output. Through the power of politely taking turns, they can create all sorts of wonderful things! But the catch is that, eventhough some projects in the real world can be built as separate components and slapped together at the end, these projects need to be done rigidly in sequence, since everyone needs to know who’s actually going to be using what. Stations: And what might that “what” be, you ask? Well, we have several tools and stations: • FDM printer o (What you’d normally associate with the term “3D printer”) • Resin printer o Because sometimes you realize you haven’t had arash in ages! • Soldering iron o Please don’t pick it up by the hot part … • Toaster oven o Equally good for solder reflowing and tiny pizzas! • Lathe o Because who needs their hair attached to their head? • Mill o Of the general variety • Airbrush o Great for when your lungs aren’t purple enough! To confirm: any of the above maybe used infinitely, but only by up to one person/job at a time. Naturally, all hobbies need to turn into a ‘side hustle’, so most ‘makers’ will end up trying to produce an inordinate number of a given piece. After all, why make one super-special chessboard just for you, when you can produce a thousand that are almost as good as a six-dollar one from Walmart, with terrible margins and nearly no profit? And speaking of which, what are the projects/knickknacks they might be completing? Projects: • Figurines for Mazes and Monsters 1. First print the figures on the resin printer 2. Then paint each using the airbrush 3. Start amoral panic about DND and make a movie about it • Motor Controllers with Custom PCBs 1. Use the soldering iron to tin the through-hole components 2. Run the board through the mill to cut out the traces 3. Brush on some flux 4. Spread on the solder paste 5. Throw it into the toaster oven to flow the solder 6. Run it through the mill again to clear out any bridges 7. Use the soldering iron to touchup any weak joints 8. Plug in the motors • Chess Set 1. Print out the white pieces using the resin printer 2. Turn the white rooks on the lathe 3. Print out the black pieces using the resin printer 4. Turn the black rooks on the lathe 5. Use the FDM printer to produce the board 6. Box all the parts up together • Toaster pastry 1. Pop a pastry into the toaster oven 2. Consume half the pastry 3. Regret not letting the pastry cool 4. Resume consuming the pastry 5. Wonder if it’s smart to use the same oven for both pastries and lead solder • Cup holder 1. Throw the filament into the toaster oven to dry it 2. Produce the basic holder on the FDM printer 3. Use pure acetone in the airbrush station to smooth the layer lines 4. Set it aside to dry for a bit • SAK scales 1. Grab some aluminum stock 2. Throw it onto the mill to carve out the scales 3. Debur with a file if necessary 4. Wash 5. Throw into the toaster oven to dry completely 6. Anodize • Flashlight 1. Throw some round stock into the lathe to hollow it out 2. Drill a couple holes 3. Use the soldering iron to connect some batteries, an LED, and a switch Since you’re managing the makerspace, you aren’t actually worried about the makers themselves (so no need to worry about e.g. names), but rather just the knickknacks that they might be producing, and how many. Similarly you’ll notice the steps include a few things that don’t directly rely on the defined stations; those will never act as bottlenecks. Remember, this is first and foremost a concurrency task, and should be designed as such. (That means the primary concern is simply with ensuring none of the designated stations is used by more than one task simultaneously) There are two parts to this assignment: a coding task, and diagram portion. Coding The minimal requirements of your program are: • Present the user with a menu, wherein s/he may decide: ◦ Which knickknacks to make (with up to seven knickknacks, this means up to 7 threads) ◦ The number of each knickknack to prepare (specifically a separate number for each knickknack) ◦ How often should progress be reported? (Explained further below) • Start a separate thread for each knickknack ◦ Each type should be implemented into its own thread, since it has its own lock requirements ◦ Each knickknack tries to report upon completing one (subject to above/below) ◦ Each knickknack type always reports when completed its iterations ▪ Obviously there's no reporting if none are being made at all • The program then reports on the total number of knickknacks produced • If this isn't obvious, both increasing the tally and output are also a'resource' (Of course, in this context, a ‘resource’ and a ‘station’ are interchangeable) Additional notes: Write your threads immensely clearly, and comment it. • Comment usefully. Don't add nonsense like identifying that a loop “loops”, or that .lock() “locks” • Also,proper indentation, please? Obviously, you'll have several .java files for this. Yes, you need to acquire locks for each resource, and it must be clear that you're following the ‘recipe ’. (For my own implementation, I actually included comments for when the 'extra steps'took place, just to keep track of where I was in the ‘recipe’, eventhough it didn't affect the output) For the 'progress'part, you'll probably want to make at least a million of each knickknack, which ostensibly means calling a method to report 'another pastry baked', etc. millions of times. That'd be silly. So let the user also choose an X, for reporting 'every X knickknacks'. • e.g. 5,000,000 of each knickknack (35 million total), but only report onevery millionth knickknack None of the steps absolutely need to hang onto stations indefinitely, but it might be reasonable for some to hold onto them abit longer than others. For the most part, this will only really affect the next section. Use Java with IntelliJ. To be clear, use only Java and IntelliJ. (e.g. don’t try writing it in something else, dumping in additional nonsense or leaving something out, and then wondering why you received a zero for not having a complete project) JDK 11 is preferred, but in particular the marker may or may not have above JDK 13. You do not need to add any external libraries (including no extra .jar’s outside of possibly BasicIO per below). Please adhere to coding conventions covered in the course. Remember: these sorts of assignments are designed to reinforce very specific concepts; you’re not evaluated based on whether or not the outcome happens to align with what’s desired (nor do you get free marks just because you ‘submitted files’). You are permitted to use that BasicIO library from first-year, if you wish. You certainly aren't required to. e.g. Scanner for input and System.out.println are more than adequate. Don't bother with javafx/swing/awt, for the sake of simpler grading. Make sure to include sample output. Again, please don’tbother with any random libraries. Beyond the basic threading/mutual exclusion and IO for Java, you shouldn’t require anything else. (Please remember you’ve been given sample on the Brightspace page) Diagram Being able to visualize things is important. Let’s try out Draw.IO! There's a version you can install, but you can also just use the website:https://app.diagrams.net/ For some knickknacks (however many you need to do this), show: • Sequences in which you could request/release locks for the knickknacks, such that: ◦ A deadlock is possible ◦ Include a schedule of interleaving those steps that creates a deadlock ◦ A resource allocation graph proving the deadlock • Sequences in which you could request/release locks for the knickknacks, such that: ◦ Avoiding a deadlock is possible ◦ A schedule of interleaving those steps that does not create a deadlock ◦ A resource allocation graph proving the absence of deadlock To confirm: whatever sequences you choose, they must be supported by the original ‘recipes ’! • This isn't as restrictive as it initially sounds As a tip: if you actually do this part after the coding portion, you might need to force yourself to ‘unlearn’ how the steps work. In particular, if you code it correctly there obviously won’t be any possibility of deadlocks, but only because you did it correctly. The first part above (for triggering the deadlock) is to emphasize why that ‘doing it correctly’ part actually matters. Additional requirements: • Using draw.io is mandatory • These are sometimes a nuisance to grade, so include all files (the .drawio/xml file, and also a .pdf) ◦ You might find it safer to additionally include a .png copy ◦ No, you can't hand-draw it. No, you can't screenshot. Both lead to irritation and huge files Submission Include everything you used to make everything. Bundle everything into a .zip file. • Not a .rar. Not a .7z. Not a .tar.gz • Ignore this step, and you will receive a zero ◦ No, seriously ▪ You remember the part about this thing being worth 20% of your final grade, right? • I'm not kidding; this is entirely within your control ◦ If you think this is getting excessive, this disclaimer gets a bit bigger each term ▪ (For a reason) Submit the .zip through Brightspace. Remember it can get a bit laggy at times, so please don't leave yourself 40 seconds to submit. • Or 2 minutes ▪ Or 4 minutes (again, these notices keep getting longer and longer…). Seriously, just submit it?
Group Project 2/2024INX246 ADVANCED COMPUTER PROGRAMMING FOR MODERN MANAGEMENTProject Overview Group project: 3 - 4 members per group Develop a website with database and a group report Project submission and presentation (on-site):– Submit project and report in mango before giving a presentation – Demo 5 mins and Q&A 5 mins– Sec 001 002 003 March 3rd, 2025, at 13:00-15:00– Sec 701 702 March 6th, 2025, at 13:00-15:00Website Requirements Develop an interactive website using HTML CSS PHP and other related technologies Integrate website with a backend database to handle basic CRUD (Create, Retrieve,Update, Delete) functionality Use sessions to manage user login and track user levels (admin, member, general user) Use CSS for styling and layout the website for responsiveness Develop creative features or apply other innovative techniques Submit all code files, database scripts, images and other related files in mango beforegiving a presentationReport Requirements Introduce your website for ex. introduction, objective, user levels, keyfunctions, innovative features and technologies, output screenshots, etc. Create flowcharts of your system separated by user levels, for ex. admin,member, general user. List member names and student IDs and explain the duties of each member Submit the report in mango in pdf format before giving a presentationAssessment Rubric (20%)Criteria Excellent (5) Good (4) Satisfactory (3) Inadequate (2) Poor (1)Database Design 30+ fields with well organized tables20-29 fields with clear structure but have minor issues 10-19 fields withbasic structure but have some issues1 – 9 fields with poor table designincomplete database design with no tableFunctionality & Logic(CRUD)All CRUD operations are fully functional, efficient, and optimized.CRUD operations work, but with minor performance or logic issues.Basic CRUD functionality, with some performance or logic issues.Some CRUD operations fail, or business logic is incomplete.CRUD operations are missing or almost failedSession Management (User Level)Proper session handlingUsing session but may have minor issues.Basic session handling with some issuesMissing session managementor with major issues.No session managementUI/UX Design (CSS)Highly responsive with well-structured CSS.Clear design and CSS applied but with minor issuesBasic CSS implementation with some issuesPoor CSS applied, responsive design incompleteVery poor design or with No CSS Innovation and Report Creative features or techniques AND submit reportUse of modern techniques, but some advanced features missing. AND submit reportUses only basic techniques, no interactivity ANDsubmit reportLimited innovation; AND submit report.No innovative features OR No reportAssessment Note During Q&A, if a group member is unable to answer a random question from the teacher, their score will be deducted by 3 points. For example, if the group gets 18 points, the members who answer correctly will get18 points but the members who are unable to answer correctly will receive only 15 points.
Final Exam 600.464/664 Artificial Intelligence Spring 2019 Basic Search 10 points 1. (5 points) Number the nodes (1, 2, ...) in the order they are explored in depth-first search. Process children left to right. 2. (5 points) Number the nodes (1, 2, ...) in the order they are explored in iterative deepening search. Enter multiple numbers, if a node is visited more than once. Process children left to right. Informed Search 10 points Consider the search space below, where S is the start node and G satisfies the goal test. Arcs are labeled with the cost of traversing them and the heuristic cost to the goal is reported inside nodes (so lower scores are better). 3. (10 points) For A* search, indicate which goal state is reached at what cost and list, in order, all the states popped of of the OPEN list. You use a search graph to show your work. Do not expand paths that revisit states at higher cost. Note: When all else is equal, nodes should be removed from OPEN in alphabetical order. Path to goal (cost): States popped of of OPEN: First Order Logic 20 points 4. (6 points) Convert the following sentences into first-order predicate calculus logic: If a student gets a good grade in an exam in a class, they are happy. If a student takes a exam for a class, studied for the exam in the class, and is smart, they get a good grade. If a student takes a class, they take the exam. Jane takes the class Artificial Intelligence. Jane studied for the exam in Artificial Intelligence. Jane is smart. 5. (6 points) Convert all rules to Conjunctive Normal Form (CNF). You do not need to restate rules that are already in CNF. 6. (8 points) Carry out a resolution proof of the statement Jane is happy. Hint: start with : Happy(Jane). Probabilistic Reasoning 15 points If students know the material, they answer a yes/no question correctly p(cjk) = 99% of the time. If students do not know the material, they get it right p(cj:k) = 50% of the time. When a students answer a question correctly, you want to know the probability p(kjc) that they know the material. 7. (5 points) Give an explanation why this is not su cient information to compute the probability p(kjc)? 8. (5 points) Assume that half of the students know the material. Compute the probability p(kjc). 9. (5 points) Instead, assume that on average 80% of the students answer the question correctly. Compute the probability p(kjc). Machine Learning 10 points Consider the following plot of data points (circles and squares). 10. (5 points) Build a linear classifier (x; y) → shape. You do not have to get the optimal parameter values for the model but they should be reasonable. 11. (5 points) Draw a decision tree for this data. Will it classify all examples correctly? Reinforcement Learning 20 points Consider the non-deterministic reinforcement environment drawn below. States are represented by circles, and actions by squares. The Probability of a transitions is indicated on the arc from actions to states. Immediate rewards are indicated above and below states. Once the agent reaches the end state the current episode ends. 12. (15 points) The current policy is to always take action X. (a) What paths could be taken? (b) What is each path’s probability? (c) What is each path’s reward? (d) What is the utility of each state? 13. (5 points) Assuming that under the current policy state b has higher utility than state a. (a) How will a greedy agent that has access to the transition probabilities change the policy? (b) Is the updated policy guaranteed to be the best policy? Natural Language Processing 15 points 14. (5 points) Co-reference resolution. (a) Consider the following sentences. The boy kicks the ball. He scores a goal. To which noun (boy, ball or goal) does the pronoun he refer to? How can a computer identify the right noun? (b) Consider the following sentences. Jane went to the store to eat a pizza. It was very tasty. To which noun (Jane, store or pizza) does the pronoun it refer to? How can a computer identify the right noun? 15. (10 points) N-Gram Language models. You are given the following text (tokenized, lowercased) as training data (with as beginning-of-sentence marker and as end-of-sentence marker). the sun is shining . the girl is going to the park with the boy . the boy gives the girl a flower . she is happy . the boy is happy , too . You use this data to train a bigram language model with maximum likelihood estimation. What score will it assign to the following sentence? the girl is happy .
MECH4350 Indoor Air Quality in Buildings Department of Mechanical and Aerospace Engineering Home work 1 22 Feb 2025 Regulations · This quiz is consisted of 2 pages. Students shall complete all questions to get full score · The completion time for this quiz is 45 minutes · All answers shall be written on the provided space of the same question sheet Questions 1. What is the suggested fresh air supply rate for a mechanically ventilated indoor environment with light smoking and without smoking by ASHRAE 62 standard? 2. Please illustrate in a graphical manner on what would be the best arrangement for natural ventilation in a rectangular room in terms of their opening positions and explain why. 3. What is the presumption of applying air exchange rate? Please provide the definition of air exchange rate for a premises. 4. Please discuss the differences between SBS and BRI
7026SSL - Strategic Portfolio and Programme Management - 2223JANMAY Faculty of Business and Law Introduction: A portfolio lifecycle main purpose is to Understand, Categorise, and Prioritise projects and change initiatives to deliver outcomes and strategic objectives (Beringer et al., 2012). This report will categorise the initiatives and provide a detailed NPV Analysis in consideration with the Risk Appetite and prioritise the initiatives depending on the strategic objectives. Finally, provide Recommendations on the best initiatives for University of Edinglow (UoE). Portfolio Management & Gap Analysis: A portfolio is a continuous process of illustrating of the vision and strategic objectives of an organisation, in which change initiatives are assessed and decided to align with strategic intent (Dezhkam et al., 2019). UoE main vision is to achieve their Strategic Objectives (SO) of innovations and quality education. Therefore, it requires the application of portfolio management by utilising the portfolio lifecycle. Figure 1: Portfolio Cycle Source: (Dezhkam et al., 2019). Categorise: According to Elbok & Berrado, (2018) The main purpose of this stage is to classify change projects depending on which SO they support based on the overall benefit and characteristics. Furthermore, the Gap Analysis which is identified by Gausemeier et al. (2018) as a technique that illustrates the connection between projects and SO’s depending on the direct impact of the projects on the achievement of goals. Table 1: Gap Analysis Maintaining their standards and achieving student satisfaction. Increase the number of European Union and UK students. Upgrade its real estate by adding new teaching blocks and associated IT infrastructure. Reduce its administrativ costs by a total of 15% over the next three years. 5G Campus ✓✓✓ ✓✓ ✓✓ ✓✓ - New student records system - - ✓✓✓ ✓✓ ✓✓✓ Campus renewable energy - ✓ ✓✓✓ ✓ - - Online degree courses ✓✓✓ ✓✓ ✓✓✓ - Bursary scheme Source: (Gausemeier et al., 2018) Table 2: Gap Analysis Legend Criteria High Align Medium Align Low Align - Not Aligned UoE identified 4 strategic objectives that can be summarised as: 1. Increase the number of European Union and UK students. 3. Reduce its administrative costs by a total of 15% over the next three years.
Java Lab 5 Fall 2022 Due: Thursday, September 15, 10:10 AM EDT In this lab, you will practice with loops, arrays, the String class, calling methods, and console input and output. Problem statement: You will write the code needed to complete Palindromer, which takes phrases entered by the user and checks whether they are palindromes – strings that read the same in both directions. Note that real palindromes are supposed to be words or phrases that actually mean something; for this program, you'll simply check the characters, not the sense (or lack thereof) in English of the phrases. Create a project named Lab5. Download the files Palindromer.java and testPalindrome.java into the src directory. The main method is already coded for you – do not change it. You will provide the code for the other methods of class Palindromer. The program first prompts the user to enter an integer no larger than 10. It will then prompt the user for that many phrases. The program then tests if the phrases are palindromes by first removing any non-letter characters, converting the characters to upper case for conformity, then testing the cleaned-up phrases. It will display only the phrases that are palindromes and keep count of how many of the phrases are palindromes, displaying that count at the end. Figure 1 shows a typical run of the program, with the user entering two phrases – the first is a palindrome and the second one is not. Figure 2 shows a run where the user enters three phrases; although the first one reads the same both ways, it uses only non-letters so does not count as a palindrome; the second contains punctuation which does not prevent it from being a palindrome; the third is a nonsense string but still a palindrome. Figure 3 shows a longer palindrome, attributed to Peter Hilton [Wikipedia]. Solution Design: The program has one class, Palindromer, with a main method and five other methods as shown in the class diagram. The main method prompts the user for the number of palindromes to be entered. After an error check, it calls inputPalindromes( ) to enter that many phrases into Palindromer's internal String array. It then calls displayPalindromes( ), which should examine each cleaned-up phrase to see if it's a palindrome; it does this using the helper methods cleanString( ) – this method is coded for you – andisPalindrome( ); it displays the phrases, one by one, and whether or not they are palindromes, and counts the number of phrases that are palindromes. Finally, the main method displays that count. See Palindromer.java for comments about the specifications. inggetPcount():int
FNCE 5321: Finanical Risk Modeling II Spring 2025 Group Project 1 Instructions: You can work in groups of up to 5 students. Please email your project report to the TA by the deadline (one report per group), and indicate clearly the members of your group on the first page of your submitted report. The report should include your solutions/answers as well as the codes. Question 1 For Question 1, use the data in Q1Data.csv, which contains daily closing prices on a stock market index for the period from July 2nd 1962 to December 30th 2016. (A) Calcuate daily log returns Rt+1 ≡ ln(St+1) − ln(St), where St+1 is the closing price on day t + 1, St is the closing price on day t, and ln(·) is the natural logarithm. Plot the closing prices and returns over time. (B) Calculate and report the mean and standard deviation of daily log returns. (C) Calculate the first through 100th lag autocorrelations of daily log returns. Plot the autocorrelations against the lag order. (D) Calculate the first through 100th lag autocorrelations of squared daily log returns. Plot the autocorrelations against the lag order. Do squared daily log returns have more or less autocorrelations compared with daily log returns? (E) Suppose you own a portfolio which is 100% invested in this market index. Estimate the 1-day, 1% VaRs on each day assuming daily log returns are normally distributed and updating volatility estimates with the RiskMetrics model, i.e. where Rt is the daily log return at day t, and σt+1 is the standard deviation of Rt+1. Plot the estimated VaRs. (Hint: Use the standard deviation from Part (B) as σ1 to start the iteration.) (F) Estimate the 1-day, 1% VaRs on each day using Historical Simulation with a 250-day moving estimation window. Plot the estimated VaRs. (G) What are the major differences between the two VaRs estimated in Part (E) and Part (F)? (H) Estimate the 1-day, 5% VaRs on each day using Historical Simulation with a 250-day moving estimation window. Are the 5% VaRs smaller or larger than the 1% VaRs estimated in (F)? Explain why with intuition. (I) Formally test whether the daily closing prices and the daily log returns are stationary using the augmented Dickey-Fuller tests. Question 2 (A) Suppoese Xt follows an AR(1) model: Xt = 0.9Xt−1 + ∈t , where σ∈ = 0.5. Simulate an episode of Xt with 1 million observations (Hint: Use the R command arima.sim). Calculate the first through 10th lag autocorrelations of the simulated series. Compare the estimated autocorrelations with the theoretical counterparts (Hint: the theoretical autocorrelations for AR(1) is ρτ = φ τ 1 , in which τ is the lag.) (B) Suppose Zt = Wt + Yt . Both Wt and Yt follow an AR(1) model: Wt = 0.5Wt−1 + ∈w,t (1) Yt = 0.5Yt−1 + ∈y,t (2) in which σ∈w,t = σ∈y,t = 0.5. Is an AR(1) model appropriate to describe Zt? Question 3 For Question 3, use the data in Shillerdata.csv, which contains the monthly closing price, dividends, earnings, and the PE ratio for S&P 500. (A) Formally test whether the dividend series is stationary. (B) Calculate the PD ratio on S&P 500 as follows: Both PD and PE are valuation ratios. What is the unconditional correlation between the two series? Formally test whether the PD ratio is stationary. (C) Redefine the PD ratio in logs: PD = log(Price) − log(Dividends) Formally test whether the PD ratio is stationary.
