Syllabus Course Information Course Title: Macroeconomics Course Number: 202 Credit Hours: 3 Distribution of Contact Hours: LEC Credit Hrs = 3 Lec Hrs = 3 Cont Hrs = 3 Semester Reviewed: Spring 2025 Course Catalog Description ECON 202 - Macroeconomics 3 hrs (Sem I, II) A descriptive and analytical study of fundamental concepts of our national economy. It includes an analysis of the determination and fluctuations in national income and employment, monetary and fiscal policy, and international trade and finance. Economic analysis of monetary and fiscal policies is stressed. This course is a transferIN course. 3 lecture hours. Prerequisite(s): Students must qualify for ENGL 010 or ENGL 079, or higher. Course Designation This course is a: Lower Division ES Distance Ed, Major Course, UCC Course, transferIN Course Outcomes Upon completion of this course students will be able to: * Demonstrate critical thinking skills related to macroeconomic issues. * Recognize the connections between economics and their day-to-day lives. * Examine, through graphs and tables, relevant macroeconomic concepts and theories. * Recall the roles of households, businesses, and government in the economy. * Illustrate a comprehension of macroeconomic terminology. " Course Text and Materials Macroeconomics 9781260324808 Campbell R. McConnell, Sean Masaki Flynn, Dr., Stanley L. Brue McGraw-Hill Education 2018-01-01 3rd Brief edition. Course Content Upon successful completion of this course the student is expected to be able to: define economics and demonstrate a basic knowledge of economic principles identify the tools of the economist and demonstrate the ability to use those tools. demonstrate an understanding of the market system at macro level. demonstrate a basic understanding of the aggregate demand side of the market. demonstrate a basic understanding of the aggregate supply side of the market. demonstrate a basic knowledge of price elasticity and production costs exhibit a basic understanding GDP, national income, personal income, disposable income and its calculation using both expenditure and income method. exhibit a basic understanding of unemployment, its type, calculation of rate, limitations and the business cycle in general exhibit a basic understanding of inflation identify a basic understanding of the AD/AS model identify a basic understanding of fiscal and monetary policies exhibit a basic understanding of the international economy
COMP5048 Assignment 1A: Individual Work COMP5048 Visual Analytics 2025 S1 Assignment 1A Deadline: March 27 (Week 5) Thursday 11:59pm (pdf on Canvas) Construct good visualisations ofTWO of the following data to answer the given task. • You can use a suitable visualisation method chosen from the tools assigned to each category. • Create visualisations for each data according to the instructions given. • In your report, explain your justification for your selected visualisation and analysis methods, then evaluate and compare the pros and cons of your visualisations. Data: Choose one data from each category A, B: Category A: For this category, visualise the data using any tool from the following: D3, R A1: Wine Quality Data A2: Glass Classification Data For the selected data: • Visual Analysis 1: Compute a Parallel Coordinates of the data with a good ordering of the columns and justify your selection. • Visual Analysis 2: Compute an MDS ofthe data. Based on the MDS, select two columns and compute a Scatter Plot using the selected columns. Category B: For this category, visualise the data using any tool from the following: yEd, Tulip, NetworkX, Gephi B1: Board Game Reviews • Visual Analysis 1: 。 Compute a subgraph of the top 150 important board games using graph analysis. 。 Visualise the subgraph displaying the analysis result. • Visual Analysis 2: 。 Extract a subgraph based on your chosen topic of interest (e.g., specific category of boardgames). 。 Analyse and visualise the subgraph displaying the analysis result. B2: K-Pop Graph • Visual Analysis 1: 。 Compute a subgraph of the top 150 important artists and labels using graph analysis. 。 Visualise the subgraph displaying the analysis result. • Visual Analysis 2: 。 Extract a subgraph based on your chosen topic of interest (e.g., focus on certain labels or collaborators of a certain artist). 。 Analyse and visualise the subgraph displaying the analysis result. Submission: Minimum 8 page report Minimum 4 pages per data: • 1st page: Visual Analysis 1 • 2nd page: Discussion on Visual Analysis 1 • 3rd page: Visual Analysis 2 • 4th pages: Discussion of Visual Analysis 2 • Write your Discussion of each Visual Analysis with the following headings: 。 Design: tools and layouts with justification (design choice) 。 Evaluation: explain the findings you can see from each visualisation 。 Pros and Cons 。 Comparison • Acknowledge all your sources in References • You must acknowledge any usage of AI Appendix (not included in page count): • Provide code, if applicable, in Appendix • In Appendix, you can include one more visual analysis for each data: 。 Should be substantially different results using different techniques 。 Include description as above for each alternative visual analysis 。 Add comparison between different visualisations ofthe same data Marking Rubric: (6 marks per data; total 12 marks) • Quality of Visual Analysis 1: 3 marks (Visualisation: 1.5 mark, Discussion: 1.5 mark) • Quality of Visual Analysis 2: 3 marks (Visualisation: 1.5 mark, Discussion: 1.5 mark)
CEGE0037: Group Coursework Project 2025 Background Tomorrowville (see Figure 1) is a 5km2 virtual urban testbed that has been designed to represent typical physical and socio-economic aspects of evolving cities in the Global South. It is currently home to approximately 30,000 people, and is expected to double its population over the next 30-50 years. Tomorrowville is prone to multiple natural hazards, including flooding, debris flows, and earthquakes. The main interacting physical and social systems of Tomorrowville are recorded in a GIS spatial geodatabase and accompanying spreadsheets that consist of four layers: (i) Land- use layer; (ii) Buildings layer; (iii) Household layer; and (vi) Individual layer. The land-use layer provides information on the types of uses of land across Tomorrowville. The buildings layer includes a list of attributes required to compute the impact of natural hazards on each building across Tomorrowville (e.g., building height, occupancy, age of construction). The household layer represents the social connections of individuals who are members of the same household (e.g., their general income level, community facilities that members regularly attend), and can be used to capture their collective experience of a natural hazard event. The individual layer documents specific information on each person (e.g., gender, age, workplace location), which can be used to determine their general reliance on the built environment. The road network of Tomorrowville is stored in a further layer of the GIS, and a separate database provides important natural-hazard vulnerability information that can be directly mapped to the buildings layer. Figure 1: The Tomorrowville virtual urban testbed: (left panel) land uses within Tomorrowville; (right panel) buildings of Tomorrowville. Scope Your team represents CEGE Risk and Resilience Consultants Ltd. who have won a bid from the Tomorrowville Environment Agency (EA) to conduct a risk and resilience assessment for Tomorrowville, in the face of a 100-year flooding event. The purpose of the assessment is to help the EA and the wider Tomorrowville government to determine what risk-mitigation and resilience-enhancing policies would be appropriate to implement as the population of the urban area continues to grow. The first goal of your assessment is to estimate the (i) total amount and (ii) general characteristics (age, gender, income etc) of the Tomorrowville population that may be displaced as a result of the flood. You should make reasonable assumptions on the measurable causes of displacement (e.g., the amount of damage that would result in intolerable downtime, etc.), and you can ignore disruption to the transportation network. The second goal of your assessment is to determine Tomorrowville’s (a) workplace; (b) education; (c) healthcare; and (d) neighbourhood resilience to the flooding event. This resilience assessment should consider: (i) the downtime of relevant Tomorrowville facilities; and (ii) the general accessibility of these facilities for relevant individuals via the transportation network. You will need to propose an ad-hoc resilience metric to collectively capture these considerations, which should account for at least three temporal instances in the post-disaster recovery phase. You should make reasonable assumptions on the above considerations (e.g., damage-to-downtime/restoration mapping for various buildings), in addition to the following: 1. Assume that ≥25cm of flood depth renders a road segment inaccessible; 2. Assume that flood waters can only recede via permeation through the ground (i.e., evaporation of water is negligible); 3. Assume that buildings require full repair before they are deemed occupiable. Your findings from the above analysis should then be used to recommend targeted policies that the Tomorrowville government could implement to mitigate risk from, and enhance resilience to, future similar flooding events. At least one policy should be proposed for each goal. You will need to make a formal presentation on your assessment to hypothetical Tomorrowville EA representatives (i.e., Dr. Gemma Cremen, Mr. Kamal Achuthan, Mr. Roman Schotten, and Mr. Ali Atici). You should use the tools available here to help you carry out some calculations required for the assessment. Outputs of these tools may be further processed using any suitable software, e.g., Excel, Matlab or Python. You will also need to prepare a user guide to explain all of your calculations and assumptions (see Deliverables section below). Core Materials The following core materials - essential for the risk and resilience assessment to be carried out - have been uploaded to the coursework folder on Moodle: 1. TV_project.gdb, which includes the land-use and buildings layers of the Tomorrowville GIS geospatial database, the raster file of the relevant flooding scenario, and the road network; 2. TV_Population_Data.xlsx, which represents a dataset capturing information about individuals, their households, and their socio-economic attributes. 3. Description_of_TV_Population_Data.pdf, which has accompanying metadata for the dataset of (2). Supporting Materials The following documents are included in the coursework folder on Moodle, and should be used for making reasonable assumptions as part of the assessment: 1. A Simulation-Based Framework for Earthquake Risk-Informed and People- Centered Decision Making on Future Urban Planning 2. A State-of-the-Art Decision Support Environment for Risk-Sensitive and Pro-Poor Urban Planning and Design in Tomorrow’s Cities 3. Implication of building inventory accuracy on physical and socio-economic resilience metrics for informed decision-making in natural hazards You are also expected to conduct your own independent research to identify other sources of information relevant to your assessment that may be missing from the above documents. Deliverables Your team is expected to deliver the following during class time (11am to 1pm) on 24th March 2024: 1. (70% of the total marks) An in-person presentation ten minutes in length (followed by two minutes of questioning by hypothetical EA representatives), covering (with equal importance): a. the answers that address both goals of the assessment; b. your overall (high-level) approach to finding these answers; c. the policies you recommend on the basis of a; d. critical evaluation of your analysis, including limitations; and e. recommendations for further investigations that you believe should be conducted by the EA to obtain more accuracy across various elements of your assessment. The mark scheme for the presentation is as follows: Criteria Marks Available (%) Visual presentation (well designed, appropriately referenced, presented in a logical sequence) 10 Professional delivery (good eye contact, audible voice, good language skills, equivalent participation of all group members) 15 Content (technical terms are well defined, presentation contains accurate information, all items listed in the project document are covered) 60 Time (presentation length is within the time limits) 5 Questions (thorough and correct response to questions) 10 Your team is also expected to deliver the following, to be uploaded in a single zip file (titled as GroupNumber.zip) to the coursework submission folder on the Moodle page before the project deadline (17:00, 28th March 2025): 2. A zip file containing all of the documentation (e.g., spreadsheets, scripts) related to your calculations. Please ensure that these are sufficiently detailed to support your work and include short (one sentence) explanatory comments for each calculation. Note that this will not be formally assessed, but may be used to deduct marks if insufficient evidence is provided to support the content of the presentation 3. (30% of the total marks) A written user guide for your work of no more than 1200 words and no more than 4 sides of A4 (including title references), covering: a. Methodology for determining the answers for both goals b. Any sources or references from which values, methods, metrics etc. were taken c. An explanation of any assumptions or estimations Note: this is not a report, and does not need any introduction or conclusions etc. 4. An attribution statement of max 1 side A4 stating the percentage of total work conducted on each of the following elements by each team member (i.e. such that the contributions of all team members sum to 100% for each element): a. Developing the methodology for goal one b. Calculations for goal one c. Policy analysis for goal one d. Developing the methodology for goal two e. Calculations for goal two f. Policy analysis for goal two g. Critical evaluation of results, including recommendations h. Preparation & delivery of presentation i. Preparation of user guide 5. A copy of your presentation slides Note: you DO NOT need to include a general introduction to Tomorrowville in either your presentation or user guide, and instead should focus only on any analysis you have conducted within your team. The mark scheme for the written deliverables is as follows: Criteria Marks Available (%) Completeness (all goals of the assessment are addressed, at least one policy is proposed for each goal, assumptions and methodology are clear from the user guide, sufficient references are included in the user guide) 25 Accuracy (calculations of displacement and resilience are correct, appropriate policies are recommended) 40 All assumptions are well justified, using appropriate references 25 Presentation is in a professional format; figures/tables are clear and consistent; user guide and accompanying code are clearly and thoughtfully formatted 10
CIVL 250 Hydraulics I Winter 2025 Laboratory Session Students are required to attend their scheduled laboratory session at the Queen’s University Coastal Engineering Laboratory (QCEL), located on West Campus (refer to the map posted on onQ for directions). Please note the following important guidelines: • Steel toed safety shoes are mandatory to enter the lab. • Student attendance is mandatory – lab reports submitted by students who did not attend their laboratory session will not be graded. Note: if there are any questions regarding the lab, please contact the lab Teaching Assistant: Name: Paramon Koutorjevski Email:[email protected] The purpose of this laboratory session is three-fold: 1. Examination of hydraulic circuit components: gain firsthand experience by observing the various components ofa hydraulic circuit. 2. Exploration of hydraulic jump phenomenon: acquire insights into the hydraulic jump phenomenon by manipulating the flume’s headgate and tailgate positions, and adjusting the flow rate through the valve. 3. Familiarization with hydraulic measurement: develop familiarity with measuring hydraulic quantities such as velocity, flow rate, and flow depth. During the laboratory session, a comprehensive description of the hydraulic circuit components will be provided. To enhance understanding of the hydraulic jump phenomenon, students are tasked with inducing the largest possible hydraulic jump. While in the lab, record measurements of the flume dimensions and, additionally, take the following key measurements: 1. Flow depth (h): measure in cross-sections both upstream and downstream of the hydraulic jump. 2. Flow velocity at 0.4h: measure at a point 0.4 times the flow depth (h) from the flume bottom, located in the midst of the flow cross-section downstream of the hydraulic jump. 3. Length measurements: record the length of the flow reach upstream of the hydraulic jump, the length of the hydraulic jump itself, and the length of the flow reach downstream of the hydraulic jump. Subsequent to the laboratory session at QCEL, address the following: a. Sketch of hydraulic circuit: illustrate the experimental channel configuration, i.e., the hydraulic circuit components, incorporating elements such as the sump, pump(s), piping system, constant head tank, flow valve, entrance and exit chambers, diffuser, flow straighteners, headgate, tailgate, flume, mechanical jack, electrical panel, etc. b. Sketch of the hydraulic jump: provide a visual representation clearly indicating the values of flow depth upstream and downstream of the hydraulic jump, along with other pertinent lengths. c. Flow rate calculation: calculate the flow rate in the flume during your experimental session. Deliverable: One report per lab group. Submit a short (no more than 4 pages in total; no appendices) lab report in the form of a PDF containing the following sections: • Cover page: include the title of the report, the name of the author(s), course, department, and university details. • Introduction: state the purpose and scope of the report. • Methodology: briefly describe the procedure to produce a hydraulic jump. Include details about the instruments used. The sketch of the hydraulic circuit must be included in this section. • Results and discussion: present the findings derived from the laboratory session. Include the sketch of the hydraulic jump alongside measured quantities, including flow depths, flow velocity, lengths of flow reaches, and flow rate. Interpret the results and discuss their significance. • Conclusions: reflect on whether the objectives of the laboratory session were successfully accomplished. Share insights gained from the laboratory session. Being precise and concise is essential for engineers: therefore, any pages beyond the allotted limit will not be considered. Marking scheme: This lab is worth 10% on your final course grade. This percentage will be distributed as follows: • 2.5% will be given for attending the laboratory session and actively participate during the session. • 7.5% corresponds to the lab report. Due date: • Friday, March 14th, 23:59, for students attending the lab on March 7th. • Friday, March 21st, 23:59, for students attending the lab on March 14th.
BUSM208 Strategic Marketing 2024/2025 Module Level Learning Outcomes to be assessed No Module Learning Outcome Description 1 A1 Examine and evaluate marketing strategies and apply them to specific businesses contexts 2 A4 Examine key issues associated with the implementation of marketing strategies and marketing campaigns 3 B3 Apply marketing knowledge to analyse, deconstruct and solve strategic marketing problems 4 B5 Be able to develop and present a market-led strategy of sustainable competitive advantage. 5 B6 Demonstrate an understanding of the practicalities and limitations of marketing strategy implementation 6 C1 Develop and further strengthen the ability to think creatively and reflect critically 7 C4 Demonstrate market research and sensing skills - developed through web search exercises, independent study and interaction with peers Assurance of Learning (selected modules only): contribution to Programme Level Learning Outcomes No Programme Learning Outcome Description 1 LO 1.4 Advance qualitative and quantitative research skills 2 LO 1.6 Engaging with notions of business innovation, entrepreneurial behaviour and enterprise development, and the management and exploitation of intellectual capital 3 LO 2.4 Apply conceptual and practical strategic interventions to the global marketplace 4 LO 2.7 To creatively, and in instrumental terms improve business and management practice, including within an international context. 5 LO 2.8 Establish criteria, using appropriate decision-making techniques including identifying, formulating and solving business problems 6 LO 3.6 Development of lifelong learning skills, including engendering an enthusiasm for business and continuing personal and professional development Assessment instructions for students (as per QMPlus ‘Assessment Information’ tab) 1. The module learning outcomes being assessed See above. 2. Instructions and guidance Assessment: Individual Report (100%), 3000 words excluding appendices (4000 words including appendices) Submission date: 16th April 2025, Wednesday, 15:00 pm. Part 1 (85%) The length of the report should be no more than 2500 words excluding references and appendices. Marks may be deducted if you overshoot the word limit. The stated word counts may be exceeded by a maximum of 5%. Please pay attention to writing and referencing style. The preparation of this individual report and the exchange of experiences in the classrooms or group meetings are major learning aspects of the Markstrat simulation. This assessment requires you to prepare a report which may be considered as briefing to the new CMO which will take over the management of the firm who you have been handling during the simulation. The report should demonstrate your understanding and application of the conceptual frameworks and topics discussed in this module, and how they have been applied in the MarkStrat simulation. The report should include the following elements (Review) analysis of your performance (Key Decisions) main strategies pursued (Adjustments to Strategy) main adjustments made to changes in the environment (Learnings) key points learned through past successes and failures (Recommendations) for the future The coherence of report structure and clarity in the overall presentation of the arguments, as well as the appropriate use of evidence and cases to support your arguments, are essential. Part 2 (15%) Individual reflection on your MarkStrat learning experience during this module (500 words max). 1) What have you learned comparing before and after your MarkStrat simulation experience especially when it comes to practical applications of marketing strategy concepts? 2) In retrospect, what would you have done differently to improve your learning experience 3. Assessment rubric with weighted criteria Part 1: Simulation Score15% Part 1: Main analysis 60% Part 1: Structure & use of evidence, graphs and appendices 10% Part 2: Individual reflection 15% 4. Assurance of Learning measures: performance thresholds for assessment criteria “significantly exceeds expectations” [outstanding/excellent] at equivalent of 70+, “exceeds expectations” [good] at equivalent of 60-69, “meets expectations” [average] by achieving equivalent of 50-59; “does not meet expectations” [poor/outright fail] at equivalent of 49 or less
Syllabus Course Information Course Title: Earth Science Laboratory Course Number: 100L Credit Hours: 1 Distribution of Contact Hours: LAB Credit Hrs = 1 Lab Hrs = 2 Cont Hrs = 2 Semester Reviewed: Spring 2025 Course Catalog Description GEOS 100L - Earth Science Laboratory 1 hr (Sem I, II) Laboratory activities to accompany GEOS 1 00. This course is a transferIN course. 2 laboratory hours. Prerequisite(s): A grade of C or better or concurrent enrollment in GEOS 1 00. Course Designation This course is a: Lower Division ES Distance Ed, Major Course, UCC Course, transferIN Course Outcomes Upon completion of this course students will be able to: * Understand the composition and formation of Earth structures and materials. * Understand how the scientific method relates to earth science. * Apply critical and ethical thinking skills to earth science issues. * Understand the connectivity between the four principal components of the Earth system and humankind. * Apply methods utilizing quality research resources by which the Earth and universe are studied and their importance. Course Text and Materials Escience Lab Kit SKU KIT3360-02 Physical Geology Old Post Book Store Redemption Code Escience Course Content Understand the connectivity between the four principal components of the Earth system. Understand how the scientific method is used in various fields of science. Understand how minerals and rocks are identified, classified, and useful to humankind. Understand plate tectonics theory including comprehension of different plate boundaries, associated geologic landforms and events, and evidence for moving plates. Comprehend earthquake causes, energy, hazards, and how earthquake waves are used to determine the structure of the Earth. Demonstrate knowledge of the different types of weathering including soil forming processes and mass wasting. Understand the different components of the water cycle including various stream and groundwater processes and resulting landforms and resources. Demonstrate knowledge of the origin of oceans including investigation of oceanic landforms, ocean water chemistry, characteristics, and movement. Understand the structure and characteristics of Earth’s atmosphere including comprehension of atmospheric pressure, temperature, moisture, interaction with energy from the Sun and Earth, and relevance to the biosphere and climate. Demonstrate knowledge of various meteorological phenomenon such as lifting mechanisms, clouds, and precipitation. Comprehend the general characteristics and movement of bodies in our solar system. Understand the origin of the universe and the events leading to the formation of stars, galaxies, planets, and other celestial phenomenon.
Math 6B Worksheet 7 Winter 2025 Due Monday, Feb 24, at 11:59pm. 1. Evaluate the line integral where (a) C is the circle x2 + y2 = 1. (b) C is the circle (x — 1)2 + (y — 1)2 = 1. 2. Evaluate the work done by the force field F(x, y) = ⟨x, x2 + 3y2⟩ on an object moving along the straight line segments (0, 0) → (4, 0) → (2, 4) → (0, 0), which is a triangle. 3. Let R(x1, x2, x3) = 2x1i + x1x2 2 j + x1x2x3 k and S be the surface boundary of the solid bounded by x12 + x22 = 1, x12 + x22 = 4, x3 = 0, and x3 = 4. Evaluate the flux of R out of S. 4. Evaluate (x12 + x22)dS, where S is that section of the paraboloid x3 = 2(x12 + x22) between x3 = 0 and x3 = 4, along with the disc x12 + x22 ≤ 2, x3 = 4, oriented outwards. 5. Let R(x1, x2, x3) = x1 2 i + x2 2 j + x3 2 k, and C be the boundary of the circle x12 + x22 = 4 with x3 = 4 and CCW orientation when viewed from the origin. Evaluate the circulation R · dr. 6. Let F(x1, x2, x3) = (2x1 + x2) i + (2x2 — x1)j, and C be the helix C(t) = ⟨cost, sint, t⟩, t ∈ [0, 3π], along with the long segment from (—1, 0, 3π) to (1, 0, 0). Evaluate the circulation F · dr.
FIT2014 Assignment 1 1st Semester 2025 Linux tools, logic, regular expressions, induction DUE: 11:55pm, Friday 28 March 2025 (Week 4) Start work on this assignment early. Bring questions to Consultation and/or the Ed Forum. Instructions • Generative AI tools must not be used for any part of this Assignment. You must not use generative artificial intelligence (AI) to generate any materials or content in relation to this Assignment (or any other assessment in this unit). For example, no GPT, DeepSeek, Copilot, Claude, Cohere, Gemini/Bard, etc. • The work you submit for this Assignment must be your own individual work. • To start work, download the workbench asgn1 .zip from Moodle. Create a new Ed Workspace and upload this file, letting Ed automatically extract it. Edit the student-id file to contain your name and student ID. Refer to Lab 0 for a reminder on how to do these tasks. • The workbench provides names for all solution files. These will be empty, needing replacement. Do not add or remove files from the workbench. • Solutions to written questions must be submitted as PDF documents. You can create a PDF file by scanning your legible (use a pen, write carefully, etc.) hand-written solutions, or by directly typing up your solutions on a computer. If you type your solutions, be sure to create a PDF file. There will be a penalty if you submit any other file format (such as a Word document). Refer to Lab 0 for a reminder on how to upload your PDF to the Ed workspace and replace the placeholder that was supplied with the workbench. • Every PDF file submitted must also contain your name and student ID at the start. • When you have finished your work, download the Ed workspace as a zip file by clicking on “Download All” in the file manager panel. You must submit this zip file to Moodle by the deadline given above. • To aid the marking process, you must adhere to all naming conventions that appear in the assignment materials, including files, directories, code, and mathematics. Not doing so will cause your submission to incur a one-day late-penalty (in addition to any other late-penalties you might have). Be sure to check your work carefully. Your submission must include: • the file student-id, edited to contain your name and student ID • a one-line text file, prob1.txt, with your solution to Problem 1; • an awk script, prob2.awk, for Problem 2; • a PDF file prob3.pdf with your solution to Problem 3; • an awk script, prob4.awk, for Problem 4; • a file prob5.pdf with your solution to Problem 5. Initially, the asgn1 directory contains empty files (or dummy files) with the required filenames. These must each be replaced by the files you write, as described above. Before submission, check that each of these empty files is, indeed, replaced by your own file, and that the student-id file is edited as required. Introduction to the Assignment In Lab 0, you met the stream editor sed, which detects and replaces certain types of patterns in text, processing one line at a time. These patterns are actually specified by regular expressions. In this assignment, you will use awk which does some similar things and a lot more. It is a simple programming language that is widely used in Unix/Linux systems and also uses regular expressions. In Problems 1–4, you will construct an awk program to construct, for any directed graph, a logical expression that describes the conditions under which the directed graph has a normal 2-regular subgraph. Finally, Problem 5 is about applying induction to a problem about structure and satisfiability of some Boolean expressions in Conjunctive Normal Form (CNF). Introduction to awk An awk program takes an input file and processes it line-by-line. In an awk program, each line has the form. /pattern / { action } where the pattern is a regular expression (or certain other special patterns) and the action is an instruction that specifies what to do with any line in the input file that contains a match for the pattern. The action (and the {. . . } around it) can be omitted, in which case any line that matches the pattern is printed. Once you have written your program, it does not need to be compiled. It can be executed di- rectly, by using the awk command in Linux: $ awk -f programName inputFileName Your program is then executed on an input file in the following way. // Initially, we’re at the start of the input file, and haven’t read any of it yet. If the program has a line with the special pattern BEGIN, then do the action specified for this pattern. Main loop, going through the input file: { inputLine := next line of input file Go to the start of the program. Inner loop, going through the program: { programLine := next line of program (but ignore any BEGIN and END lines) if inputLine contains a string that matches the pattern in programLine, then if there is an action specified in the programLine, then { do this action } else just print inputLine // it goes to standard output } } If the program has a line with the special pattern END, then do the action specified for this pattern. Any output is sent to standard output. You should read about the basics of awk, including • the way it represents regular expressions, • the variables $1, $2, etc., and NF, • the function printf( ··· ), • if statements • for loops. For these, you will only need simple loops like for (i = 1; i
COS30045 Data Visualisation Semester 1 2025 PART A: Unit Summary Unit Code(s) Unit Title Duration Total Contact Hours Requisites: Pre-requisites mmingNoneNoneNoneNone12.5HawthornBlendedProjectAssignmentLaboratory andDesignExercises
PUBH7600 Introduction to Epidemiology Semester 1, 2025 ASSESSMENT 1 Due Date: Monday 31 March, 2 pm (QLD time) Instructions: Complete the following short answer and calculation-based tasks (Q1 to 4, including all the sub-questions). • This assessment is based on the learning objectives and concepts from Modules 1-3 and the required readings. The data presented in this assessment is from both real and hypothetical sources. • The value of the marks of each question is shown alongside the corresponding question. There are 50 marks in total, and this assignment will contribute 25% towards the overall assessment for this subject. • For calculations, please show your formulae and full working for calculations (not simply the final answer), as you will receive part marks for applying the correct formula, even if you do not arrive at the correct answer. • When performing calculations, please do NOT round numbers until the final answer is reached to avoid compounding errors due to early rounding. Please round all final answers to 2 decimal points. E.g., if your calculated final answer is 12.345, please round your answer to 12.35. Your assignment should be typed as a Microsoft Word document, with adequate space left between questions. Be as succinct as possible in your answers and use the numbers of marks and suggested lengths of response for a question as a guide to how much detail is required. Question 1 (17 marks) Background Immigration present unique health challenges as changes to dietary patterns can significantly impact cardiovascular outcomes among migrant populations. Researchers conducted a study investigating the relationship between dietary acculturation and hypertension among first-generation immigrants in an urban Australian community. The study aimed to understand how the adoption of Australian dietary patterns influenced cardiovascular health outcomes among immigrants who had lived in Australia for less than 5 years at baseline. The study enrolled 3500 adults aged 40-45 years and followed then from January 2014 to December 2023. At the enrolment, all participants underwent comprehensive health assessment, which identified 421 individuals with existing hypertension. During the study period, the investigators faced several challenges in maintaining the study population. By the end of the study: • 180 participants had relocated to different cities and were lost to follow-up despite multiple contact attempts • 95 participants had died • 249 new cases of hypertension were diagnosed. (a)What is the study design used in this study? Justify with your own words [2-3 sentences, 1 mark] (b)What was the prevalence of hypertension at the beginning? [show formula and working; 2 marks] (c)What was the prevalence of hypertension at the end of the study? [show formula and working; 2 marks] (d) What was the cumulative incidence of hypertension for this study? [show formula and working; 2 marks] (e) What is the person–years at risk of hypertension? [show formula and working; 2 marks] (f) What was the incidence rate of hypertension for this study period? [show formula and working; 2 marks] (g) Explain the difference in calculation of cumulative incidence and incidence rate. [2-3 sentences, 2 marks] (h) Consider the aim of study and population, which disease measure would help the investigator most and why? Include your calculated results in your explanation. [1 paragraph; 4 marks] Question 2 [Total 8 marks] Background In early March 2024, a Municipal Public Health Centre in Western Japan received an unusual increase in reports of oedema, headache, fatigue, nausea, palpitations, and/or dizziness from several clinics. The initial investigation revealed that within a two-week period, 15 previously healthy adults had been hospitalised with severe acute kidney injury (AKI) of unknown origin. Preliminary patient interviews suggested a possible connection to dietary supplements marketed for weight loss. These interviews revealed that many affected individuals had purchased supplements through online platforms or drug stores in the preceding months. As reports continued to emerge the health centre established an illness investigation team. The team conducted a survey with 150 individuals who had purchased supplements in the past month, collecting information about their supplement consumption patterns and whether they had experienced AKI (Table 1). Table 1: Data collected by investigators exploring link between dietary supplements and acute kidney injury ets262Red Rice yeast capsule2825L-Carnitine supplement273Age groupWhite, non-HispanicBlack, non-HispanicHispanicMaternalmortality rate(MMR)Number oflive birthsMaternalmortality rate(MMR)ExpecteddeathsMaternalmortalityrate (MMR)ExpecteddeathsYoungerthan 2510.8324,64031.3101.69.530.825–3917.91,449,36549.2713.116.9244.940 andolder83.966,770174.5116.570.747.2Total191,840,77549.5931.216.2322.9COPDNonCOPDTotalDailyuseofbiomassfuelforcooking150640790Gas cooking10018521952total25024922742COPDNonCOPDTotalDaily smoking120802922Nosmoking13016901820total25024922742
ELEC230 - Lab session 4 In this lab session we will install the packages need for Asssignment 3. The true purpose of lab session 4 is to catch up with previous sessions, so if you haven’t completed labs 2 and 3 this is your chance to catch up. This lab sheet is short in comparison and could easily be done once assignment 3 is release. However, if you want to have an early play with Gazebo, this sheet will allow you to. Install Gazebo In a terminal run the following command: $ sudo apt install gazebo11 $ cd $ gedit .bashrc Add to the last line of the .bashrc file: $ source /usr/share/gazebo-11/setup.bash Save and exit the .bashrc $ source .bashrc This will install gazebo, to test it has installed correctly, simply type into the terminal $ gazebo Have a look at some of the worlds that come with gazebo, e.g. run $ gazebo /usr/share/gazebo-11/worlds/willowgarage.world To see the office where ROS was designed. Play around with your mouse and keyboard, so you get comfortable with controlling the world. Install TurtleBot We will start by installing the packages we need to get turtlebot working $ sudo apt update $ sudo apt-get install ros-foxy-turtlebot3* Now type into a terminal $ export TURTLEBOT3_MODEL=burger We need to do this every time we open a new terminal to tell ROS what model of robot we want to use. You can also add it to your .bashrc script. to avoid having to do this. Next, run the following command, it will open up gazebo and load the turtlebot3_world world fileand put our robot model in it. $ ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py In a new terminal type the following command $ export TURTLEBOT3_MODEL=burger $ ros2 run turtlebot3_teleop teleop_keyboard Keeping this terminal open you should be able to make your robot navigate around the world. In assignment 3 we will be instructing the robot to do this automatically by itself!
BUSM214 Sustainability Marketing, Ethics & CSR 2024/2025 Module Level Learning Outcomes to be assessed (Reference to Module Proposal Form) No Module Learning Outcome Description 1 A2 Understand sustainable consumer behaviours across different consumption settings. 2 A3 Discuss the role of marketing in creating, communicating, and delivering value for business, society, and the planet. 3 B2 Evaluate sustainability practices and opportunities. 4 B3 Analyse the challenges facing both organizations and individuals while applying sustainability marketing. 5 B4 Integrate sustainability into organizations' marketing strategies using strategic tools and techniques. 6 C1 Propose an original idea and develop it into a research proposal to solve/answer sustainability-related problems/questions. Assurance of Learning (selected modules only): contribution to Programme Level Learning Outcomes (see programme rubric map) No Programme Learning Outcome Description 1 LO 1.1. Evaluates the breadth and depth of the debates in the relevant field 2 LO 2.1 Selects credible sources of data 3 LO 2.3 Evaluates the quality of data 4 LO 2.4 Supports argument with relevant data 5 LO 2.5 Assesses strength of arguments in academic literature and debates in a relevant field 6 LO 2.5 Compares and contrasts theoretical perspectives 7 LO 2.9 Acknowledges the social context of business practice 8 LO 2.10 Recommends solutions that could be applied in practice 9 LO 2.1 Selects credible sources of data 10 LO 3.2 Expresses arguments coherently through writing 11 LO 3.4 Displays good structure, formatting, style. and presentation of writing Assessment instructions for students (as per QMPlus ‘Assessment Information’ tab) 1. The module learning outcomes being assessed See above. 2. Instructions and guidance Students are required to work on (1) a group presentation (20%) and (2) an individual essay (80%). The coursework will start with teams doing a sustainability analysis for their selected case study. This will become the benchmark from which each student will develop a sustainability marketing strategy for that specific case. (1) Group Presentation (20%) Context: In the current era of climate crisis, every company must thoroughly assess the opportunities and risks posed by climate change. Imagine that you and your team are assigned to a group-based consultancy task to analyse the impact of a specific company. The analysis will include a thorough understanding of sustainable performance, challenges, and opportunities. Your consultancy task lasts for 3 weeks (Weeks 3-5). You and your team will be “competing” with other teams working on the same case. Each group will be “presenting” the outputs during their seminar (See Presentation Instructions on QM+ for details). (2) Individual Essay (80%) Context Based on the group presentation you prepared and delivered. Your client gave you the task of formulating the company’s sustainability marketing strategy (SMS). Following the guidelines, frameworks, and tools discussed in class, you proposed SMS is expected to capitalize on one of the sustainability challenges/directions identified in the group presentation. The outcome is an actionable, and evidence-based SMS for the selected case. A high-quality outcome is critical for the future of your client’s competitive and sustainable position. Your essay should include: Executive Summary • Outline the key sustainability issue facing your firm (e.g., opportunity, or problem). • Summarize the key outcomes of your work: cover the most relevant aspects of your proposed SMS. o Word range (ideally, 200 words). Tip – Be concise and consider your busy boss/client who is looking for a succinct/interesting story. Steve Jobs, for example, famously engaged in this tactic with employees in the elevator at Apple (He would ask you “what you were working on”). Section 1: Sustainability Direction, and Objectives • Elaborate on the firm’s sustainability approach and direction given its internal resources and external market opportunities. • Develop strategic focus and sustainability marketing objectives o Word range (ideally, 500 words). Section 2: Proposed Sustainable Marketing Strategy • Select a sustainable consumer segment, assess its attractiveness, and build an empathy map. • Craft your value proposition and positioning statement. • Decide on the key sustainability marketing tactics. o Word range (ideally, 1500 words) Note – You must justify the proposed SMS and make evidence-based choices/decisions. You need to use the tools/frameworks discussed in class (e.g., 5Cs, and 4Vs models). Section 3: Implementation Approach, and KPIs • Identify the implementation challenges and approach. • Propose the right combination of environmental, economic, and social KPIs o Word range (ideally 600 words). Conclusion • Craft a reflective account detailing the specific tools/skills you've gained while working on this project, such as a particular framework or tool, and explore how you envision applying this knowledge in practical business scenarios. o Word range (ideally 200 words) References Notes and common questions Word limit • 3000 words; + and − 10%. • Please include a word count on the front page. • References are not included in the word count. • Any work submitted over the word limit will be marked in full but penalised by 5 marks. • Students can report the outcomes of their work using tables, figures, or paragraph formats (all are accepted, and included in word count). References • Students must use any of the academic reference styles (e.g., APA). • We expect in-text citations and a full reference list at the end. • Failure to reference or to reference correctly will result in a severe loss of marks. • If you take a direct quote from, e.g., an article, use quotation marks and add page numbers. Plagiarism • Make sure to use your own words and write your summaries. • Copy & paste from any source is NOT accepted. • Read more about plagiarism and how to avoid it (e.g., under Assessment Tab on QMPlus) 3. Assessment rubric with weighted criteria • Executive Summary: 5% • Section 1: 20% • Section 2: 40% • Section 3: 15% • Conclusion: 5% • Overall structure/writing: 15% 4. Assurance of Learning measures: performance thresholds for assessment criteria For PG: “significantly exceeds expectations” [outstanding/excellent] at equivalent of 70+, “exceeds expectations” [good] at equivalent of 60-69, “meets expectations” [average] by achieving equivalent of 50-59; “does not meet expectations” [poor/outright fail] at equivalent of 49 or less
AcF 633 - Python Programming for Data Analysis Final Individual Project 20th March 2025 noon/12pm to 10th April 2025 noon/12pm (UK time) This assignment contains one question worth 100 marks and constitutes 55% of the total marks for this course. You are required to submit to Moodle a SINGLE .zip folder containing a SINGLE Jupyter Notebook .ipynb file OR Python script .py file, together with any supporting .csv files (e.g. input data files. However, do NOT include the ‘GOOG 202001.csv.gz’ data file as it is large and may slow down the upload and sub- mission) AND a signed coursework coversheet. The name of this folder should be your student ID or library card number (e.g. 12345678.zip, where 12345678 is your student ID). In your main script, either Jupyter Notebook .ipynb file or Python .py file, you do not have to retype the question for each task. However, you must clearly label which task (e.g. 1.1, 1.2, etc) your subsequent code is related to, either by using a markdown cell (for .ipynb file) or by using the comments (e.g. #1 .1 or ‘‘‘1 .1’’’ for .py file). Provide only ONE answer to each task. If you have more than one method to answer a task, choose one that you think is best and most efficient. If multiple answers are provided for a task, only the first answer will be marked. Your submission .zip folder MUST be submitted electronically via Moodle by the 10th April 2025 noon/12pm (UK time). Email submissions will NOT be considered. If you have any issues with uploading and submitting your work to Moodle, please email Carole Holroyd at [email protected] BEFORE the deadline for assistance with your submission. This assignment is AI Assessment AMBER (i.e. Generative AI tools can be used in an assistive role). Please refer to the University position page: University position on Artificial Intelligence for more details about AI Assessment RAG categories. If you use AI to assist your work, you are required to submit an AI appendix. The following penalties will be applied to all coursework that is submitted after the specified submission date: Up to 3 days late - deduction of 10 marks Beyond 3 days late - no marks awarded Good Luck! Question 1: Task 1: High-frequency Finance (Σ = 35 marks) The data file ‘GOOG 202001.csv.gz’ contains the tick-by-tick transaction data for stock GOOG in January 2020, with the following information: Fields Definitions DATE TIME M SYM ROOT EX SIZE PRICE NBO NBB NBOqty NBBqty BuySell Date of transaction Time of transaction (seconds since mid-night) Security symbol root Exchange where the transaction was executed Transaction size Transaction price Ask price (National Best Offer) Bid price (National Best Bid) Ask size Bid size Buy/Sell indicator (1 for buys, -1 for sells) Import the data file into Python and perform the following tasks: 1.1: Write code to perform the filtering steps below in the following order: (15 marks) F1: Remove entries with either transaction price, transaction size, ask price, ask size, bid price or bid size ≤ 0 F2: Remove entries with bid-ask spread (i.e. ask price - bid price) ≤ 0 F3: Aggregate entries that are (a) executed at the same date time (i.e. same ‘DATE’ and ‘TIME M’), (b) executed on the same exchange, and (c) of the same buy/sell indicator, into a single transaction with the median transaction price, median ask price, median bid price, sum transaction size, sum ask size and sum bid size. F4: Remove entries for which the bid-ask spread is more that 50 times the median bid-ask spread on each day F5: Remove entries with the transaction price that is either above the ask price plus the bid-ask spread, or below the bid price minus the bid-ask spread Create a data frame. called summary of the following format that shows the number and proportion of entries removed by each of the above filtering steps. The proportions (in %) are calculated as the number of entries removed divided by the original number of entries (before any filtering). F1 F2 F3 F4 F5 Number Proportion Here, F1, F2, F3, F4 and F5 are the columns corresponding to the above 5 filtering rules, and Number and Proportion are the row indices of the data frame. 1.2: Using the cleaned data from Task 1.1, write code to compute Realized Volatil- ity (RV), Bipower Variation (BV) and Truncated Realized Volatility (TRV) mea- sures (defined in the lectures) for each trading day in the sample using different sampling frequencies including 1 second (1s), 2s, 3s, 4s, 5s, 10s, 15s, 20s, 30s, 40s, 50s, 1 minute (1min), 2min, 3min, 4min, 5min, 6min, 7min, 8min, 9min, 10min, 15min, 20min and 30min. The required outputs are 3 data frames RVdf, BVdf and TRVdf (for Realized Volatility, Bipower Variation and Truncated Realized Volatil- ity respectively), each having columns being the above sampling frequencies and row index being the unique dates in the sample. (10 marks) 1.3: Use results in Task 1.2, write code to produce a 1-by-3 subplot figure that shows the ‘volatility signature plot’ for RV, BV and TRV. Scale (i.e. multiply) the RVs, BVs and TRVs by 104 when making the plots. Your figure should look similar to the following. (5 marks) 1.4: Using a 5min sampling frequency and a 5% significance level, write code to conduct a jump test to test whether or not there are jumps in the prices of GOOG on each date in the sample. Your jump test should be based on the test statistic where Jt = max(RVt−BVt , 0) is an estimate of the jump variation on day t. If there are no jumps on day t, zt ∼ N(0, 1) (see the lecture slides on high-frequency finance for more details). Store the output in a data frame called jumpdf that has row indices being the unique dates in the sample and columns including ‘RV’, ‘BV’, ‘J’, ‘jump’, which respectively capture the RV, BV, jump variation, and whether there are jumps (i.e. ‘jump’ is ‘Yes’) or not (i.e. ‘jump’ is ‘No’) on each date. (5 marks) Task 2: Return-Volatility Modelling (Σ = 20 marks) Refer back to the csv data file ‘SP100-Feb2023.csv’ that lists the constituents of the S&P100 index as of February 2023 that was investigated in the Group Project. Import the data file into Python. Using your student ID or library card number (e.g. 12345678) as a random seed, draw a random sample of 2 stocks (i.e. tickers) from the S&P100 index excluding stocks ABBV, AVGO, CHTR, DOW, GM, KHC, META, PYPL and TSLA. Import daily Adjusted Close (Adj Close) prices for both stocks between 01/01/2010 and 31/12/2024 from Yahoo Finance. Compute the log daily returns (in %) for both stocks and drop days with NaN returns. Perform the following tasks. 2.1: Using data between 01/01/2010 and 31/12/2021 as in-sample data, write code to find the best-fitted AR(m)-GJR-GARCH(p, o, q) model with Student’s t errors for the log returns of each stock that minimizes AIC, with m, q ∈ {0, 1, 2, 3}, p ∈ {1, 2, 3} and 1 ≤ o ≤ p. Print the best-fitted AR(m)-GJR-GARCH(p, o, q) output for each stock and a statement similar to the following for your stock sample. Best-fitted AR(m)-GJR-GARCH(p,o,q) model for GILD: AR(3)-GJR-GARCH(1,1,2) - AIC = 11310 .9499 Best-fitted AR(m)-GJR-GARCH(p,o,q) model for GOOG: AR(3)-GJR-GARCH(1,1,1) - AIC = 10495 .7030 (7 marks) 2.2: Use the best-fitted AR(m)-GJR-GARCH(p, o, q) model in Task 2.1 to test for the presence of ‘leverage effects’ (i.e. asymmetric responses of the conditional variance to the positive and negative shocks) in the return series of each stock. Draw and print your test conclusion using a 5% significance level. (5 marks) 2.3: Write code to plot a 2-by-5 subplot figure that includes the following diagnos- tics for the best-fitted AR(m)-GJR-GARCH(p, o, q) model found in Task 2.1: Row 1: (i) Time series plot of the standardized residuals, (ii) histogram of the standardized residuals, fitted with a kernel density estimate and the density of a fitted Student t distribution, (iii) ACF of the standardized residuals, (iv) ACF of the squared standardized residuals, and (v) time series of the fitted conditional volatility. Row 2: The same subplots for the second stock. Your figure should look similar to the following for your sample of stocks. Com- ment on what you observe from the plots. (8 marks) Task 3: Return-Volatility Forecasting (Σ = 25 marks) 3.1: Using the remaining data from 01/01/2022 to 31/12/2024 as out-of-sample data, write code to produce one-step analytic forecasts, together with 95% confidence interval, for the returns and conditional volatility of each stock us- ing the respective best-fitted AR(m)-GJR-GARCH(p, o, q) model found in Task 2.1. Also produce the one-step return forecasts and 95% CI and the conditional volatility for a competing model AR(1)-GARCH(1,1) with Student’s t errors. For each stock and each model, the forecast output is a data frame with 4 columns f, fl and fu, and volf corresponding to the one-step return forecasts, 95% CI lower bounds and upper bounds for return forecasts, and one-step conditional volatility forecasts. (7 marks) 3.2: Use results in Task 2.3, write code to plot a 3-by-2 subplot figure showing: Row 1: the one-step return forecasts against the true values during the out-of-sample period for both stocks in your sample, plus the 95% confidence interval of the return forecasts for (i) best-fitted AR(m)-GJR-GARCH(p, o, q) model found in Task 2.1 and (ii) the competing AR(1)-GARCH(1,1) model; and (iii) the one-step conditional volatility forecasts produced by the two competing models. Row 2: The same subplots for the second stock. 3.3: Denote by et+h|t = yt+h − ybt+h|t the h-step forecast error at time t, which is the difference between the observed value yt+h and an h-step forecast yb t+h|t produced by a forecast model. Four popular metrics to quantify the accuracy of the forecasts in an out-of-sample period with T ′ observations are: The closer the above measures are to zero, the more accurate the forecasts. Now, write code to compute the four above forecast accuracy measures for one-step return forecasts produced by the best-fitted AR(m)-GJR-GARCH(p, o, q) model found in Task 2.1 and the competing AR(1)-GARCH(1,1) model for each stock in your sample. For each stock, produce a data frame containing the forecast accuracy measures of a similar format to the following, with columns being the names of the above four accuracy measures and index being the names of the competing models under consideration: MAE MSE MAPE MASE AR(3)-GJR-GARCH(1,1,2) AR(1)-GARCH(1,1) Print a statement similar to the following for your stock sample: For GILD: Measures that AR(3)-GJR-GARCH(1,1,2) model produces smaller than AR(1)-GARCH(1,1) model: Measures that AR(1)-GARCH(1,1) model produces smaller than AR(3)-GJR-GARCH(1,1,2) model: MAE, MSE, MAPE, MASE (7 marks) 3.4: Using a 5% significance level, conduct the Diebold-Mariano test for each stock in your sample to test if the one-step return forecasts produced by the best-fitted AR(m)-GJR-GARCH(p, o, q) model found in Task 2.1 and the competing AR(1)- GARCH(1,1) model are equally accurate based on the four accuracy measures in Task 3.3. For each stock, produce a data frame containing the forecast accuracy measures of a similar format to the following: MAE MSE MAPE MASE AR(3)-GJR-GARCH(1,1,2) AR(1)-GARCH(1,1) DMm pvalue where ‘DMm’ is the Harvey, Leybourne & Newbold (1997) modified Diebold- Mariano test statistic (defined in the lecture), and ‘pvalue’ is the p-value associ- ated with the DMm statistic. Draw and print conclusions whether the best- fitted AR(m)-GJR-GARCH(p, o, q) model produces equally accurate, signifi- cantly less accurate or significantly more accurate one-step return forecasts than the competing AR(1)-GARCH(1,1) model based on each accuracy measure for your stock sample. Your printed conclusions should look similar to the following: For GILD: Model AR(3)-GJR-GARCH(1,1,2) produces significantly less accurate one-step return forecasts than model AR(1)-GARCH(1,1) based on MAE . Model AR(3)-GJR-GARCH(1,1,2) produces equally accurate one-step returns forecasts as model AR(1)-GARCH(1,1) based on MSE . Model AR(3)-GJR-GARCH(1,1,2) produces significantly less accurate one-step return forecasts than model AR(1)-GARCH(1,1) based on MAPE . Model AR(3)-GJR-GARCH(1,1,2) produces significantly less accurate one-step return forecasts than model AR(1)-GARCH(1,1) based on MASE . (6 marks) Task 4: (Σ = 20 marks) These marks will go to programs that are well structured, intuitive to use (i.e. provide sufficient comments for me to follow and are straightforward for me to run your code), generalisable (i.e. they can be applied to different sets of stocks (2 or more)) and elegant (i.e. code is neat and shows some degree of efficiency).
STAT 3515Q: Design of Experiments — Spring 2025 Homework 3 Due date: March 21 (Friday), 11:59 p.m. ● This homework covers Chapter 4 and part of Chapter 5. ● The datasets are provided in the Excel file. ● The computational questions may be completed using SAS or R. However, for exam purposes, you should also understand how to solve them manually. ● Even if software is used for calculation, you still need to clearly write down the formulas used by the software and reasons for any conclusion. ● For model adequacy checking, you need to check the following plots (see Section 4.1.2, Figures 4.4-4.6): normal Q-Q plots; plot of residuals vs. fitted values; plots of residuals vs. each predictor (including residuals vs. treatments, residuals vs. blocking factor) 1. The effect of three different lubricating oils on fuel economy in diesel truck engines is being studied. Fuel economy is measured using brake-specific fuel consumption after the engine has been running for 15 minutes. Five different truck engines are available for the study, and the experimenters conduct the following RCBD. Truck Oil 1 2 3 4 5 1 0.500 0.634 0.487 0.329 0.512 2 0.535 0.675 0.520 0.435 0.540 3 0.513 0.595 0.488 0.400 0.510 (a) Discuss why RCBD is needed for this experiment and describe how the randomization is conducted. What is the difference between RCBD and the completely randomized design? (b) Set up the appropriate hypotheses. Use mathematical notation, and explain the symbols that you are using. (c) Show the formula for the test statistic and compute its value. (d) What is distribution of the test statistic under the null hypothesis? (e) Using α = 0.05, what is your conclusion? (f) Use the Tukey’s method with α = 0.05 to make comparisons among the three lubricating oils to determine specifically which oils differ in brake-specific fuel consumption. (g) Conduct model adequacy checking. 2. The effect of five different ingredients (A, B, C, D, E) on the reaction time of a chemical process is being studied. Each batch of new material is only large enough to permit five runs to be made. Furthermore, each run requires approximately 1.5 hours, so only five runs can be made in one day. The experimenter decides to run the experiment as a Latin square so that day and batch effects may be systematically controlled. She obtains the data that follow. Day Batch 1 2 3 4 5 1 A=8 B=7 D=1 C=7 E=3 2 C=11 E=2 A=7 D=3 B=8 3 B=4 A=9 C=10 E=1 D=5 4 D=6 C=8 E=6 B=6 A=10 5 E=4 D=2 B=3 A=8 C=8 (a) Discuss why a Latin square is needed for this experiment. What are the features of a Latin square? (b) Set up the appropriate hypotheses. Use mathematical notation, and explain the symbols that you are using. (c) Show the formula for the test statistic and compute its value. (d) What is distribution of the test statistic under the null hypothesis? (e) Using α = 0.05, what is your conclusion? (f) Conduct model adequacy checking. 3. An industrial engineer is investigating the effect of four assembly methods (A, B, C, D) on the assembly time for a color television component. Four operators are selected for the study. Furthermore, the engineer knows that each assembly method produces such fatigue that the time required for the last assembly may be greater than the time required for the first, regardless of the method. That is, a trend develops in the required assembly time. Hence, a third factor, the order of assembly, is introduced. Moreover, the engineer suspects that the workplaces used by the four operators may represent an additional source of variation, so a fourth factor, workplace (α,β,γ,δ) is further introduced. This yields the Graeco-Latin square that follows. Operator Order of Assembly 1 2 3 4 1 Cβ = 11 Bγ = 10 Dδ = 14 Aα = 8 2 Bα = 8 Cδ = 12 Aγ = 10 Dβ = 12 3 Aδ = 9 Dα = 11 Bβ = 7 Cγ = 15 4 Dγ = 9 Aβ = 8 Cα = 18 Bδ = 6 (a) Discuss why a Graeco-Latin square is needed for this experiment. What are the features of a Graeco-Latin square? (b) Set up the appropriate hypotheses. Use mathematical notation, and explain the symbols that you are using. (c) Show the formula for the test statistic and compute its value. (d) What is distribution of the test statistic under the null hypothesis? (e) Using α = 0.05, what is your conclusion? (f) Conduct model adequacy checking. 4. An engineer suspects that the surface finish of a metal part is influenced by the feed rate and the depth of cut. He selects three feed rates and four depths of cut. He then conducts a factorial experiment and obtains the following data: Feed Rate (in/min) Depth of Cut (in) 0.15 0.18 0.2 0.25 0.2 74 79 82 99 64 68 88 104 60 73 92 96 0.25 92 98 99 104 86 104 108 110 88 88 95 99 0.3 99 104 108 114 98 99 110 111 102 95 99 107 (a) Briefly describe how to conduct the randomization for this design. (b) Specify the statistical model and the corresponding assumptions (including constraints). Then set up the appropriate hypotheses. Use mathematical notation, and explain the symbols that you are using. (c) Show the formula for the test statistics and compute their values. (d) What are distributions of the test statistics under the null hypothesis? (e) Using α = 0.05, what is your conclusion? (f) Obtain parameter estimates for the fitted model. (g) Use the Tukey’s method with α = 0.05 to make comparisons among different feed rates and draw conclusions. (h) Use the Tukey’s method with α = 0.05 to make comparisons among different depths of cut and draw conclusions. (i) Do we need to perform slicing? If so, conduct the analysis and draw conclusions. (j) Conduct model adequacy checking.
Lab 1 – INT 302: Image Processing Start Date: 2025-03-11 Deadline: 2025-03-25 15% of the final marks Late Submission Policy: 5% of the total marks available for the assessment shall be deducted from the assessment mark for each working day after the submission date, up to a maximum of five working days. Objectives: 1- Introducing the image processing capabilities of Matlab and its Image Processing Toolbox. 2- Learn to read and display different images. 3- Learn basic image processing steps. 4- Master different image enhancement techniques Download: Download the files of Lab1_material.zip from the Learning Mall, unzip the file into a folder Lab1_material, which contains “lenna512color.bmp”and “lenna512.bmp”. Tasks: 1. Task1 (15 marks) Download image “lenna512color.bmp” . Use the functions imread to load the image into Matlab, and conduct the following questions, please specify the intermediate process of how you conduct the questions: (1) Display the image and decouple the image into RGB components. Write the transformation code by yourself. Please plot both original image and the decoupled components. Please describe your observation about the plotted images. (5’) (2) Change the color space into CMY and show the images of CMY components. Write the transformation code by yourself. Please plot both original image and the decoupled components. Please describe your observation about the plotted images. (5’) (3) Change the original image into gray level and show the gray image. Write the transformation code by yourself. Please describe your observation about the plotted images. (5’) 2. Task 2 (35 marks) In this task, we use the monochrome image Lenna (i.e., “lenna512.bmp”) to do the following sub tasks, and let’s call the original image Lenna as I0. (1) Write a function to measure the Peak Signal to Noise Ratio (PSNR) between two gray images in dB. For the peak value use 255. (5’) where mse is the mean square error, and it is evaluated as: (2) I0 -> down-sampling to I1 with 1/2 size of I0 (both horizontally and vertically) using nearest neighbor interpolation (implement it by yourself). Display it and compare to the original image. Explain your findings in the report; (10’) (3) I1-> up-sampling to I1’ with the same size of I0 using (a)nearest neighbor interpolation; (b) bilinear interpolation; (c) bicubic interpolation. Display it and compare to the original image. Explain your finding in the report. (10’) (4) Calculate the PSNR between the original image I0 and the up-sampled images, i.e., nearest, bilinear, and bicubic, respectively. Compare the results of different interpolation methods. Explain your finding in the report. (10’) (Note: for the bilinear and bicubic interpolation, you use the matlab function directly). Image nearest bilinear bicubic PSNR (dB) 3. Task 3 (12 marks) In this task, we use the monochrome image Lenna (i.e., lenna512.bmp) to do the following sub tasks. You need to conduct power-law transform on the input image, where power-law transform. is s = cr-, please plot the transformed images with c=1, Y=0.04, 0.4, 5, 25. Please write one function to generate this image instead of calling matlab function directly and explain your finding in the report. (12’) 4. Task 4 (38 marks) 1) Add AWGN noise to the image Lenna (i.e., lenna512.bmp) and display the noisy image. Name it as im_SP. Please write one function to generate this image instead of calling matlab function directly (you can use Matlab function to generate uniform random numbers, e.g., rand()). (the μ and σ of the gaussian, please set as the following: treat your ID number as a number set, compute the μ and σ for the gaussian. e.g., ID=1234567, μ =(1+2+3+4+5+6+7)/7=4, and the corresponding σ . please write down the computing process in your report, without this process, 5-mark deduction will be conducted.) and set k in the ppt slides as 0.1, 0.5, 0.7, 1. Plot the images with different k in your report and write your observation and analysis from the plotted images. (10’) 2) Apply the median filter with a 3X3 window and a 5X5 window on the image im_SP (you can use Matlab function medfilt2). Display and evaluate the PSNR of the obtained images. For each window size, comment on how effectively the noise is reduced while sharp edges and features in the image are preserved. (8’) 3) Implement the averaging filter 3X3 to filter the image im_SP by yourself (you are not allowed to use Imfilter and fspecial directly). Compute the PSNR and display the filtered image. You can use zero padding for the boundary pixels. (8’) 4) As you experimented with the averaging and median algorithms, what different “performance” did you notice? Which one is better for removing salt & pepper noise? and why? (12’) Lab Report Write a short report which should contain a concise description of your results and observations. Include listings of the Matlab scripts that you have written. Describe each of the images that you were asked to display. Answer each question completely: - Do not attach the code at the end of the report, just put the useful code under each question. - The results maybe contain some figures, please add some index and caption of each figure. Report format: Single column; Font size: #12; Page number: no more than 15; Submission before 2025-03-25. - Electrical version to LM with a rar (ZIP) of all files • Rar file name: INT302-Lab1-Name-studentID.rar/zip • One file with same file name of Rar/zip File: Report ( with studentID, name, Lab title on the homepage) • One folder: codes and other materials. (I can run it directly) Marking scheme 80%-100% Essentially complete and correct work. 60%-79% Shows understanding, but contains a small number of errors or gaps. 40%-59% Clear evidence of a serious attempt at the work, showing some understanding, but with important gaps. 20%-39% Scrappy work, bare evidence of understanding or significant work omitted.
BAFI1029 Derivatives and Risk Management Assessment 2 - Task 2 - Individual Trading Session #2 (15%) Report and Excel Due Date: Week 10 - Friday, 21st March 2025 by 23:59 (Singapore Time) Assessment Task This is an individual task. In this assessment, students are required to use the Trading Simulator tool from CME Group to trade on future products to hedge risk and/or take advantage of speculation. In the #2 trading session, you will focus on Equity Index future products. The goal of this individual assignment is to gain a better understanding of the future market and risk management process, by testing and refining your trading strategies. Below are the steps you need to follow to accomplish the task: ● Attend your local class in Week 8. Use the GME Group account created in Week 5. ● Your instructor will use 1.5-2 hours to go through the task, elaborate the basic specs of Energy future products, explain the trading rules and demonstrate how to trade. You can start trading after instructor’s demonstration. ● Your trading aims to hedge your risk exposure to the stock market risk as well as generating short-term profit. For student whose student number end with odd number, assume you hold a S&P 500 index fund worth $2,000,000 on the trading session day. For student whose student number end with even number, assume you hold a Nasdaq 100 index fund worth $2,000,000 on the trading session day. You aim to use Equity index future products to hedge your price risk. ● You also have $100,000 USD cash on hand at the beginning of your trading. You must use at minimum 50% of your account balance to hedge your stock price risk. Meanwhile, you are allowed to have up to 50% of your account balance to speculating/arbitraging, and the speculation/arbitrage products are not limited to Equity Index futures (e.g., other future products, you can even use Crypto futures to earn short-term profit, but also mind the potential loss). ● Please reset your game from GME Group simulator before your Task 2. Then you can trade any time after your instructor’s demonstration, till any date before 21st March 2025. You can trade as many times as you want, as long as you can justify your trading philosophy. You can do some trials at the beginning of the trading period to get familiar with the platform. When you decide to officially start to implement your strategy, please do not reset the game before your last trading date. ● You can take both long and short positions in the future contracts. Your orders might be rejected by the system because of margin shortage or market close. When your account balance drops to near zero, you are basically out of the game. Note: Please remind to leave some time to consolidate your trading record and report. For example, someone wants to stop trading on 15th March 2025 and prepare the excel and report between 16th-21st March, some others want to trade till 20th March 2025 and prepare the report and excel in several hours or one day. Both are good, as long as you are confident about the quality of your submission. ● Please use the excel template to record your trading and balance on a daily basis, or whenever you make a trade. It is not necessary to flatten (close out) all your open positions. It is a good practice to keep a record on your daily account balance, profit and loss as well as open positions, to facilitate consolidating your report. Please do note that the template is just a basic version provided by the teaching team, you will need to modify it to satisfy your needs. ● Based on your trading history, profit/loss from your futures account, and the income/cost from your physical asset, you need to form a report to summarize your trading exercise. Note: Since the contracts can’t be bought in fraction, a tiny variation from the specified budget is acceptable. You can choose to hold some Cash if you believe the investment opportunity is not good enough, but also need to justify this decision in your report. CME Institute Trading Simulator Trading Simulator replicates live futures markets by leveraging real market data. A constant stream of new prices informs your strategies for CME Group’s top products across all 6 asset classes, including Bitcoin and Micro E-mini futures. The Access to the simulator is free, all you need is a CME Group Login account. Please create an account before trading. Your report must include the following sections: 1. Trading objectives: Give an overview of your trading objectives. 2. Summarize your hedging strategy Provide a summary on how you use Equity Index future products to hedge your stock market price risk. The content should include but not limited to: • How much percentage do you hedge your portfolio? And why? • What strategy you employed to hedge (e.g., delivery month, contract price, contract amount, long or short, etc)? • What is the performance of your hedging by the end of your last trading date? And how the spot price change for your fund holding? 3. Summarize your speculation trading Provide a summary on how you use future contracts to speculate/arbitrage during your trading period. The content should include but not limited to: • Do you think the 50% limits allocated on speculation is too high? And why? Do you feel speculation is risky from your trading exercise? • How the speculation performed and explain your profit/loss? • How your speculation strategy differs from last time (Trading session 1)? Total=15 marks Submission • The report should follow a structured format, starting with an executive summary and followed by sub-sections addressing all questions/tasks. Essential components of the report include page numbering, sections numbering, main body, executive summary, reference list, introduction and conclusion. • The report should be no longer than 1000 words (-/+ 15%), excluding executive summary, references and appendix. The student can have up to 2-page appendix. Citation and reference must be provided where necessary. The Excel file contains your workings to support the reported analysis. • Please prepare the report and a spreadsheet to record your trading activities for Assessment Task 2, submit them along with those for Assessment 2 Task 1. • All submissions must be made electronically on Canvas, accompanied by acover sheet, through Canvas => Assignments => “Assessment 2: Report and spreadsheet submission”. • The submission must be using 1 or 1.5 spacing and 12-point Times New Roman font. • Students must ensure their reports are free from academic issues like copying, plagiarism, sharing work, collusion, and collaboration with other groups, maintaining a similarity rate below 30%. Academic misconduct can result in course failure, permanent academic records, and graduation delays due to the investigation time by the COBL Integrity office. • Students are required to keep back-ups of all submitted work just in case any are lost. • The instructor of your enrolled trading session will mark your assessment. Note: ● This instruction includes suggestions on items to include in the report, more information for parts you think are important may be included as you feel necessary, keeping in mind the word limit. ● The teaching team is not supposed to comment on your calculation workings or identify where your calculation mistakes. The teaching team will provide guidance to make sure that you are on the right track. However, it is still your responsibility to investigate your work and identify the errors.
Algorithms and Data Structures (ADS2) Assessed Exercise 2 This exercise is for submission using Moodle and counts for 10% of the total assessment mark for this course. This exercise is worth a total of 20 points. The deadline for submission is Monday 24 March 2025 at 4:30pm. Exercise This exercise has two parts. In the first part, you are asked to implement in Java an ADT, and define an efficient algorithm to solve a practical problem in the second part. Submission Submit the Java sources of your implementations and a short (maximum 3 pages) report briefly describing what you have done in each part of the exercise. Your report should include clear instructions on how to run your code. Part 1 The Dynamic Set is an abstract data type (ADT) that can store distinct elements, without any particular order. There are five main operations in the ADT: • ADD(S,x): add element x to S, if it is not present already • REMOVE(S,x): remove element x from S, if it is present • IS-ELEMENT(S,x): check whether element x is in set S • SET-EMPTY(S): check whether set S has no elements • SET-SIZE(S): return the number of elements of set S Additionally, the Dynamic Set ADT defines the following set-theoretical operations: • UNION(S,T): return the union of sets S and T • INTERSECTION (S,T): return the intersection of sets S and T • DIFFERENCE(S,T): returns the difference of sets S and T • SUBSET(S,T): check whether set S is a subset of set T a) Implement in Java the Dynamic Set ADT defined above using a binary search tree. Explain in the report your implementation, noting the running time of each operation. You can use a self-balancing binary tree but no extra marks will be awarded. Also, you are not allowed to rely on Java library classes in your implementation. [8] b) A naïve definition of UNION(S,T) for a BST-based implementation of the Dynamic Set ADT consists in taking all elements of BST S one by one, and inserting them into BST T. Describe in the report an implementation with a better running time. [2] Part 2 TheMin-priority Queue is an abstract data type (ADT) for maintaining a collection of elements, each with an associated value called a key. The ADT supports the following operations: • INSERT(Q,x): insert the element x into the queue Q. • MIN(Q): returns the element of Q with the smallest key. • EXTRACT-MIN (Q): removes and returns the element of Q with the smallest key. a) Implement an efficient algorithm in Java to solve the following problem: You are given n ropes of different lengths (expressed as integers), and you are asked to connect them to form a single rope with the minimum cost. The cost of connecting two ropes is equal to the sum of their lengths. Given a sequence of rope lengths, the expected outputs are a sequence of rope connection operations and the total cost. Use your implementations of the Min-priority Queue ADT in your solution. [7] b) Give a brief description of your implementation, explaining why a priority queue is needed for an efficient algorithm. [2] c) What is the output for this instance of the problem 4,8,3,1,6,9,12,7,2? [1]
Introduction to Operations Management (IDS 532) Assignment 3: Queuing and Quality Possible points: 20 Note: The data for Q1 and Q2 can be found in the Excel file titled “A3Data.xlsx”. 1. Electronic Circuit Manufacturing (10 points) Resistors for electronic circuits are manufactured on a high-speed automated machine. The machine is setup to produce a large run of resistors of 1,000 ohms each. To setup the machine and create a control chart to be used throughout the run, 15 samples were taken with four resistors in each sample. The complete list of samples and their measured values are given in the "resistor" sheet in the Assignment 3 Excel file. Develop an x-chart and plot the values. From the chart, what comments can you make about the process? (Use three-sigma control limits) 2. State and Local Police (10 points) The state and local police departments are trying to analyze crime rates so they can shift their patrols from decreasing-rate areas to areas where rates are increasing. The city and county have been geographically segmented into areas containing 5,000 residences. The police recognize that not all crimes and offenses are reported: People do not want to become involved, consider the offenses too minor to report, are too embarrassed to make a police report, or do not take the time, among other reasons. Every month, because of this, the police contact a random sample of 1,000 of the 5,000 residences by phone for data on crimes. (Respondents are guaranteed anonymity.) The sheet named “police” in the Assignment 3 Excel file contains crime data collected for one area over the past 15 months. Construct a p-chart for 95% confidence (1.96 sigma or approx. 2 sigma) and plot each of the months. What comments can you make regarding the crime rate in the last three months?
