## Research “Burger King” details are below, which is my part that I’m working on for a group project:

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(Disregard above due date) Not accurate

Correct Due  date is

“tonight at midnight”

5 Key Points:

In preparation for my group project:

Research “Burger King” details are below, which is my part that I’m working on for a group project:

Find 5 Key points related to the assignment (i.e. examples and evaluation of website, ads, social media posts, articles, other)

Use at least 2 sources for your information and site them accordingly.

Please number the key points 1-5.

## Files Quiet Standing (QS) • Eyes open and Eyes Closed • FP1 = Force plate 1; FP2 = Force plate 2 • COP = Center of Pressure in x,y, and z • For = Force in x, y and z

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Files Quiet Standing (QS) • Eyes open and Eyes Closed • FP1 = Force plate 1; FP2 = Force plate 2 • COP = Center of Pressure in x,y, and z • For = Force in x, y and z

Task : 1. Graph sway (combine force plate data)

2. Analyze sway data– come up with some measures. How are the data sets different?

Here are some answers to questions that I have been getting about the homework due Tuesday.

what is the meaning of pre-Quiet Standing and post-Quiet Standing? For example, pre-QS closed = subject closed eye and step on the force plate, post-QS closed = subject close eyes after stand still on the platform  These are 4 different trials of collecting the same data.  One time the subject had their eyes closed another open and then after we did an intervention

– does unit of CopX and CopZ are in Meter(m) or Newton?  COP = center of pressure and is a location in Meters

-, As we need to merge two stabiligram, Are we simply take the middle point of FB1-COP and FB2-COP? For example, FB1-COP = A(-2,1) and FB2-COP B(3,2) => Merged COP = C(0.5, 1.5) or we need to consider Force vector( X,Y,Z). If we need to consider force vector apply at 2 foots, can you give me the equation? I had read the book and the article but they seem to not using two force plates.

Yes a simple way to merge the COP of the two force plates would be to just take the average.  I encourage you to think if this is the best solution and if so state your reason for that algorithm.

-Does sum of FB1-ForY and FB2-ForY equal to weight of the subject. Yes it should

• MENTIS_01_post_QS_closed_FPdata.xlsx
• MENTIS_01_pre_QS_closed_FPdata1.xlsx
• MENTIS_01_pre_QS_open_FPdata1.xlsx
• MENTIS_01_post_QS_open_FPdata.xlsx

## Why Do Gas Station Prices Constantly Change? Blame the Algorithm

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Why Do Gas Station Prices Constantly Change? Blame the Algorithm

Retailers are using artificial-intelligence software to set optimal prices, testing textbook theories of competition; antitrust officials worry such systems raise prices for consumers

The Knaap Tankstation gas station in Rotterdam, Netherlands, uses a2i Systems artificial-intelligence pricing software. Photo: Knaap Tankstation BV

By

Sam Schechner

Updated May 8, 2017 6:41 p.m. ET

ROTTERDAM, the Netherlands—One recent afternoon at a Shell-branded station on the outskirts of this Dutch city, the price of a gallon of unleaded gas started ticking higher, rising more than 3½ cents by closing time. A little later, a competing station 3 miles down the road raised its price about the same amount.

The two stations are among thousands of companies that use artificial-intelligence software to set prices. In doing so, they are testing a fundamental precept of the market economy.

In economics textbooks, open competition between companies selling a similar product, like gasoline, tends to push prices lower. These kinds of algorithms determine the optimal price sometimes dozens of times a day. As they get better at predicting what competitors are charging and what consumers are willing to pay, there are signs they sometimes end up boosting prices together.

Advances in A.I. are allowing retail and wholesale firms to move beyond “dynamic pricing” software, which has for years helped set prices for fast-moving goods, like airline tickets or flat-screen televisions. Older pricing software often used simple rules, such as always keeping prices lower than a competitor.

On the Same Track

Two competing gas stations in the Rotterdam area both using a2i Systems pricing software roughly mirrored each other’s price moves during a selected week.

Hourly change in price for unleaded Euro 95 gasoline;

three-hour moving average for hours that both stations were open*

1.5 U.S. cents a gallon

These new systems crunch mountains of historical and real-time data to predict how customers and competitors will react to any price change under different scenarios, giving them an almost superhuman insight into market dynamics. Programmed to meet a certain goal—such as boosting sales—the algorithms constantly update tactics after learning from experience.

Ulrik Blichfeldt, chief executive of Denmark-based a2i Systems A/S, whose technology powers the Rotterdam gas stations, said his software is focused primarily on modeling consumer behavior and leads to benefits for consumers as well as gas stations. The software learns when raising prices drives away customers and when it doesn’t, leading to lower prices at times when price-sensitive customers are likely to drive by, he said.

“This is not a matter of stealing more money from your customer. It’s about making margin on people who don’t care, and giving away margin to people who do care,” he said.

Driving the popularity of A.I. pricing is the pain rippling through most retail industries, long a low-margin business that’s now suffering from increased competition from online competitors.

“The problem we’re solving is that retailers are going through a bloodbath,” said Guru Hariharan, chief executive of Mountain View, Calif.-based Boomerang Commerce Inc., whose A.I.-enabled software is used by StaplesInc. and other companies.

Staples uses A.I.-enabled software to change prices on 30,000 products a day on its website. Photo: Richard B. Levine/ZUMA PRESS

The rise of A.I. pricing poses a challenge to antitrust law. Authorities in the EU and U.S. haven’t opened probes or accused retailers of impropriety for using A.I. to set prices. Antitrust experts say it could be difficult to prove illegal intent as is often required in collusion cases; so far, algorithmic-pricing prosecutions have involved allegations of humans explicitly designing machines to manipulate markets.

Officials say they are looking at whether they need new rules. The Organization for Economic Cooperation and Development said it plans to discuss in June at a round table how such software could make collusion easier “without any formal agreement or human interaction.”

“If professional poker players are having difficulty playing against an algorithm, imagine the difficulty a consumer might have,” said Maurice Stucke, a former antitrust attorney for the U.S. Justice Department and now a law professor at the University of Tennessee, who has written about the competition issues posed by A.I. “In all likelihood, consumers are going to end up paying a higher price.”

In one example of what can happen when prices are widely known, Germany required all gas stations to provide live fuel prices that it shared with consumer price-comparison apps. The effort appears to have boosted prices between 1.2 to 3.3 euro cents per liter, or about 5 to 13 U.S. cents per gallon, according to a discussion paper published in 2016 by the Düsseldorf Institute for Competition Economics.

Makers and users of A.I. pricing said humans remain in control and that retailers’ strategic goals vary widely, which should promote competition and lower prices.

“If you completely let the software rule, then I could see [collusion] happening,” said Faisal Masud, chief technology officer for Staples, which uses A.I.-enabled software to change prices on 30,000 products a day on its website. “But let’s be clear, whatever tools we use, the business logic remains human.”

Online retailers in the U.S., such as Amazon.com Inc. and its third-party sellers, were among the first to adopt dynamic pricing. Amazon.com declined to comment.

Since then, sectors with fast-moving goods, frequent price changes and thin margins—such as the grocery, electronics and gasoline markets—have been the quickest to adopt the latest algorithmic pricing, because they are the most keen for extra pennies of margin, analysts and executives say.

The pricing-software industry has grown in tandem with the amount of data available to—and generated by—retailers. Stores keep information on transactions, as well as information about store traffic, product location and buyer demographics. They also can buy access to databases that monitor competitors’ product assortments, availability and prices—both on the web and in stores.

A.I. is used to make sense of all that information. International Business Machines Corp. said its price-optimization business uses capabilities from its Watson cognitive-computing engine to advise retailers on pricing. Germany’s Blue Yonder GmbH, a price-optimization outfit that serves clients in the grocery, electronics and fashion industries, said it uses neural networks based on those its physicist founder built to analyze data from a particle collider.

Neural networks are a type of A.I. computer system inspired by the interconnected structure of the human brain. They are good at matching new information to old patterns in vast databases, which allows them to use real-time signals such as purchases to predict from experience how consumers and competitors will behave.

Algorithms can also figure out what products are usually purchased together, allowing them to optimize the price of a whole shopping cart. If customers tend to be sensitive to milk prices, but less so to cereal prices, the software might beat a competitor’s price on milk, and make up margin on cereal.

“They’re getting really smart,” said Nik Subramanian, chief technology officer of Brussels-based Kantify, who said its pricing software has figured out how to raise prices after it sees on a competitor’s website that it has run out of a certain product.

Algorithmic pricing works well in the retail gasoline market, because it is a high-volume commodity that is relatively uniform, leading station owners in competitive markets to squeeze every penny.

For years, price wars in cutthroat markets have followed a typical pattern. A retailer would cut prices to lure customers, then competitors would follow suit, each cutting a little more than the others, eventually pushing prices down close to the wholesale cost. Finally one seller would reach a breaking point and raise prices. Everyone would follow, and the cycle started all over.

Some economists say the price wars helped consumers with overall lower prices, but led to very thin margins for station owners.

Danish oil and energy company OK hired a2i Systems in 2011 because its network of gas stations was suffering from a decade-old price war. It changed what it charged as many as 10 times a day, enlisting a team of people to drive around the country and call in competitors’ prices, said Gert Johansen, the company’s head of concept development.

A2i Systems—the name means applied artificial intelligence—was started by Alireza Derakhshan and Frodi Hammer, both engineering graduates of the University of Southern Denmark, in Odense. Before focusing on fuel, they built other A.I. systems, including a game displayed on interactive playground floor tiles that adapted to the speed and skill level of the children running around on top.

For OK, a2i created thousands of neural networks—one for each fuel at each station—and trained them to compare live sales data to years of historical company data to predict how customers would react to price changes. Then it ran those predictions through algorithms built to pick the optimal prices and learn from their mistakes.

In a pilot study, OK split 30 stations into two sets, a control group and an a2i group. The group using the software averaged 5% higher margins, according to a paper Mr. Derakhshan presented last June at an A.I. conference in Seville, Spain.                                                                                                                                     (scroll down to continue reading…)

Scandinavian supermarket chain REMA 1000 says it will roll out price-optimization software made by Texas-based Revionics Inc. in coming months. Photo: Joseph Dean/Newscom/ZUMA Press

The new system could make complex decisions that weren’t simply based on a competitor’s prices, Mr. Derakhshan said in an interview.

One client called to complain the software was malfunctioning. A competitor across the street had slashed prices in a promotion, but the algorithm responded by raising prices. There wasn’t a bug. Instead, the software was monitoring the real-time data and saw an influx of customers, presumably because of the long wait across the street.

“It could tell that no matter how it increased prices, people kept coming in,” said Mr. Derakhshan.

On the outskirts of Rotterdam, Koen van der Knaap began running the system on his family-owned Shell station in recent months. Down the road, a station owned by Tamoil, a gasoline retailer owned by Libya’s Oilinvest Group, uses it too.

During a late-March week for which both Tamoil and Mr. van der Knaap provided hourly data, the costs for unleaded gas at the two stations—which vary in opening hours and services—bounced around independently much of the time, and generally declined, reflecting falling oil prices that week.

During some periods, however, the stations’ price changes paralleled each other, going up or down by more than 2 U.S. cents per gallon within a few hours of each other. Often, prices dropped early in the morning and increased toward the end of the day, implying that the A.I. software may have been identifying common market-demand signals through the local noise.

The station owners say their systems frequently lower prices to gain volume when there are customers to be won.

“It can be frustrating,” said Erwin Ralan, an electronics-store manager who was filling up at the Tamoil station that week. “Prices usually go up at the end of the day. But when you’re empty and you’re in a rush, there’s not much you can do.”

End

## Briefly describe the organization and the budget document you selected.

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Submit a 6 page double-spaced paper in APA format (not including the cover and reference pages) in which you do the following:

Briefly describe the organization and the budget document you selected.

Identify the specific type of budget document you selected.

Evaluate the budget document using GFOA criteria.

Explain whether or not the budget document serves the constituents and other stakeholders of the organization.

Use from the library at least two scholarly sources that were published within the past five years to support your paper. Be sure to follow APA guidelines when citing your sources.

## Unit V Essay Write an es say describing how knowledge of water

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Unit V Essay

Write an es

say describing how knowledge of water

only systems may influence the incident commander’s incident action

plan.

Your response must be at least one page in length, double spaced, and 12

point Times New Roman font

.

All sources

used, including the textbook,

must be referenced; paraphrased and quoted material must have accompanying APA

citations.

## 1. What is the need for mental health services with incarcerated persons? Has this changed over the years? If so, what was the change, and how and why has the change occurred? Explain your response.

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1.  What is the need for mental health services with incarcerated persons? Has this changed over the years? If so, what was the change, and how and why has the change occurred? Explain your response.

2.

Differentiating between inmates whose behavior is      determined by serious mental illness versus criminal intent has been a      long-standing correctional practice. Discuss the benefits and problems      with using this approach.

3.    An inmate who engages in disruptive behavior      (including, but not limited to, SIB) agrees to a BTP, but is known to have      a psychotic disorder. How might this affect your plan?

## Assignment 1: LASA # 2—Capital Budgeting Techniques

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Assignment 1: LASA # 2—Capital Budgeting Techniques

As a financial consultant, you have contracted with Wheel Industries to evaluate their procedures involving the evaluation of long term investment opportunities.  You have agreed to provide a detailed report illustrating the use of several techniques for evaluating capital projects including the weighted average cost of capital to the firm, the anticipated cash flows for the projects, and the methods used for project selection.  In addition, you have been asked to evaluate two projects, incorporating risk into the calculations.

You have also agreed to provide an 8-10 page report, in good form, with detailed explanation of your methodology, findings, and recommendations.

Company Information

Wheel Industries is considering a three-year expansion project, Project A.  The project requires an initial investment of \$1.5 million. The project will use the straight-line depreciation method. The project has no salvage value. It is estimated that the project will generate additional revenues of \$1.2 million per year before tax and has additional annual costs of \$600,000.  The Marginal Tax rate is 35%.

Required:

1. Wheel has just paid a dividend of \$2.50 per share. The dividends are expected to grow at a constant rate of six percent per year forever. If the stock is currently selling for \$50 per share with a 10% flotation cost, what is the cost of new equity for the firm? What are the advantages and disadvantages of using this type of financing for the firm?
2. The firm is considering using debt in its capital structure. If the market rate of 5% is appropriate for debt of this kind, what is the after tax cost of debt for the company? What are the advantages and disadvantages of using this type of financing for the firm?
3. The firm has decided on a capital structure consisting of 30% debt and 70% new common stock. Calculate the WACC and explain how it is used in the capital budgeting process.
4. Calculate the after tax cash flows for the project for each year. Explain the methods used in your calculations.
5. If the discount rate were 6 percent calculate the NPV of the project. Is this an economically acceptable project to undertake? Why or why not?
6. Now calculate the IRR for the project. Is this an acceptable project? Why or why not? Is there a conflict between your answer to part C? Explain why or why not?

Wheel has two other possible investment opportunities, which are mutually exclusive, and independent of Investment A above.  Both investments will cost \$120,000 and have a life of 6 years. The after tax cash flows are expected to be the same over the six year life for both projects, and the probabilities for each year’s after tax cash flow is given in the table below.

Investment B                                                      Investment C

Probability      After Tax                               Probability      After Tax
Cash Flow                                                             cash flow

0.25                      \$20,000                                       0.30               \$22,000

0.50                        32,000                                        0.50                   40,000

0.25                        40,000                                        0.20                    50,000

1. What is the expected value of each project’s annual after tax cash flow? Justify your answers and identify any conflicts between the IRR and the NPV and explain why these conflicts may occur.
2. Assuming that the appropriate discount rate for projects of this risk level is 8%, what is the risk-adjusted NPV for each project? Which project, if either, should be selected? Justify your conclusions.

Assignment 1 Grading Criteria Maximum Points

Correctly calculated the cost of new equity and explained the calculations, as well as the advantages and disadvantages of using this type of financing for the firm. (CO4) 20

Correctly calculated the cost of new debt and explained the calculations, as well as the advantages and disadvantages of using this type of financing for the firm. (CO4) 20

Correctly calculated the weighted average cost of capital and explained how and why it is used in the capital budgeting process. (CO4) 20 Correctly calculated the annual cash flows for the projects and explained the methods used in the calculations. (CO1)  44 Evaluated the projects using the NPV method and came to the correct conclusions based on the decision rules for the NPV. (CO2)44 Evaluated the projects using the IRR method and came to the correct conclusion based on the decision rules for the IRR. Identified any conflicts between the IRR and the NPV and explained why these conflicts may occur. (CO 3) 44

Correctly introduced risk into the evaluation by using the expected values as the cash flows and evaluated these cash flows using risk adjusted discounted rates. (CO 5) 44

Written in a clear, concise, and organized manner; demonstrated ethical scholarship in accurate representation and attribution of sources; displayed accurate spelling, grammar, and punctuation.64

Total:300

## Resources: University of Phoenix Material: Individual Case Studies, University of Phoenix Material: Behavior Plan Template, and the “Effective Treatment for Addicted Criminal Justice Clients” article located on the National Criminal Justice Reference Service website.

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Resources: University of Phoenix Material: Individual Case Studies, University of Phoenix Material: Behavior Plan Template, and the “Effective Treatment for Addicted Criminal Justice Clients” article located on the National Criminal Justice Reference Service website.

Use the “Effective Treatment for Addicted Criminal Justice Clients” article as a sample for how to write a behavior plan. The “Case Study” section near the end of the article provides a good example of a behavior plan.

Choose one behavioral theory from your course textbook.

Choose one of the following case studies from the University of Phoenix Material: Individual Case Studies:

• Case One: Violet
• Case Two: Max
• Case Three: Onur

Complete the University of Phoenix Material: Behavior Plan Template based on your selected behavioral theory for your selected case-study patient—Violet, Max, or Onur.

Incorporate one peer-reviewed research study as justification for the theory you used in your plan.

Format your plan consistent with APA guidelines.

## Resources: University of Phoenix Material: Individual Case Studies, University of Phoenix Material: Behavior Plan Template, and the “Effective Treatment for Addicted Criminal Justice Clients” article located on the National Criminal Justice Reference Service website.

Click here to order this assignment @Essaybay.net. 100% Original.Written from scratch by professional writers.

Resources: University of Phoenix Material: Individual Case Studies, University of Phoenix Material: Behavior Plan Template, and the “Effective Treatment for Addicted Criminal Justice Clients” article located on the National Criminal Justice Reference Service website.

Use the “Effective Treatment for Addicted Criminal Justice Clients” article as a sample for how to write a behavior plan. The “Case Study” section near the end of the article provides a good example of a behavior plan.

Choose one behavioral theory from your course textbook.

Choose one of the following case studies from the University of Phoenix Material: Individual Case Studies:

• Case One: Violet
• Case Two: Max
• Case Three: Onur

Complete the University of Phoenix Material: Behavior Plan Template based on your selected behavioral theory for your selected case-study patient—Violet, Max, or Onur.

Incorporate one peer-reviewed research study as justification for the theory you used in your plan.

Format your plan consistent with APA guidelines.