What is the goal of China's 2011-2020 Anti-Poverty Program

Why do Li and Sicular (2014) believe China's minimum wage has not been effective in reducing inequality and poverty?

What has happened to life expectancy and education levels in China since reforms?

What does the Human Development index measure? How has China's average HDI changed in China since reforms? How does the HDI in China compare to the global average?

What does the Harrod-Domar model say about the relationship between investment and growth? What are the weaknesses of the Harrod-Domar model?

What is the "classical" progression of structural change in a developing economy?

How did the manipulation of prices during China's socialist era distort the pattern of structural change? What does the pattern look like when evaluated at market (2004) prices?

What is the largest sector in China today by share of GDP?

How does China's pattern of structural change differ from the "classical" progression? How is that related to globalization?

Why do some believe there is a housing bubble in China today?

How does the Chinese government explain the rapid expansion of the housing market?

Describe the four stages of the demographic transition.

What is the Total Fertility Rate? The Birth Rate?

In what two ways has China deviated from the standard demographic transition model?

How has China’s TFR compared to other Asian countries?

What was the One Child Policy? How has enforcement changed over time?

What were some major critiques of China’s fertility policy?

What defines a stable equilibrium in the marriage market?

Why would a gender imbalance intensify competition in the marriage market?

What positive economic effects do Wei and Zhang (2011) observe as a result of China's skewed sex ratio?

What is the dependency ratio? How and why will China's dependency ratio be changing in the next few years?

What "marginal" changes were made to reform urban labor markets in the initial phase of reforms?

Who were the xiagang?

What share of the population was employed in the state sector before 1978?

How did the average urban worker get a job during that time?

What is the Lewisian Turning Point (LTP)?

Use the appropriate diagram to explain what happens to wages as demand for industrial labor grows before the LTP and then after the LTP.

Is there strong evidence to support the existence of an LTP in China? What else might explain rising wages?

Why can the returns to education be used as evidence of efficient labor markets?

What is a Mincer equation?

How have rising returns to education affect educational achievement in China?

Why is migration an indication of healthy labor markets?

Why did labor reforms create a need to reform China's social security system?

What was the role of the agricultural collective in during China's Socialist Era?

According to Lin, how did the collectives create a “prisoners’ dilemma?” What effect did this have on agricultural productivity?

What is a dominant strategy? What is a Nash Equilibrium?

Why did China's TVEs grow so quickly?

What are the three "models" of TVEs we discussed in class? How would you characterize each one?

Why did the rapid growth of TVEs end?

What information asymmetry problem complicated TVE privatization?

What mechanism did the gov't design to solve this problem?

What is individual rationality? What is incentive compatibility?

What are non-performing loans? How are they related to SOE reform?

What is “Grasping the large, letting go the small?”

Why was it important to increase SOEs’ marginal retention rates?

What was the Company Law?

What is SASAC and why was it created?

What share of output is accounted for by SOEs today?

What are the four elements that must be changed to turn a socialist enterprise into a “capitalist” one?

What are the theoretical advantages of corporatization? How did it work in practice?

What is the difference between market-based and control-based models of corporate governance? Which model best fits governance in China?

What is was the Anti-Monopoly Law? Why do some believe it has not been enforced effectively?

What is "creative destruction"? Why might the continued prominence of SOEs in China limit the potential for "creative destruction"?

How does China rank globally in terms of exports and total trade?

What is China's total trade as a % of GDP?

Which country was China's biggest trading partner during the Socialist era?

What were the two elements of the “double airlock” system before reforms?

What are special economic zones? Where were they first set up?

What is export processing?

What are tariff vs. non-tariff barriers to trade? What role did they play in reforms?

When did China join the WTO? What reforms were necessary for it to do so?

Why is it important to look at imports as a measure of openness? How have imports in China been changing?

Which "mode" of foreign direct investment (FDI) is most common in China today?

What were the important findings of Hu and Jefferson's (2002) study of FDI in China?

How does China manage its exchange rate today? What type of system is this?

How would a country intervene in foreign exchange markets to defend a fixed exchange rate?

What is the "impossible trinity"? How is it related to China's exchange rate policy?

What is an environmental Kuznets curve?

What are scale, composition, and technique effects? How are they related to the Kuznets curve?

Where (broadly speaking) in China are the major air pollutants concentrated?

What did the World Bank estimate for the economic costs of China's pollution in 2007? Why might this be an underestimate?

What are the main causes of China's degraded water quality? How are these problems made worse by water shortages?

How much of China's surface water is unfit for direct human contact (Grade IV and above)?

What government body is responsible for environmental protection in China today?

What has undermined the effectiveness of China's system of discharge fees for pollution?

Why is China's arable land shrinking?

What is the pollution haven hypothesis?

Do Dean, Lovely, and Huang (2009) find evidence to support this hypothesis "on average?"

Why do Dean, Lovely, and Huang (2009) argue lower pollution taxes is not an effective strategy for attracting FDI?

This program will focus on utilizing functions, parameters, Python's math and random modules, and if-statements!

To Start: You should fork the provided REPL, run the program and observe the output, and then take a look through the code. You may not 100% understand the code, but taking a quick look through what's there will be helpful in the future.

You are going to systematically replace the TODO comments with code so that the program will work as intended.

1. TODO 0 is in both main.py and coffee.py. Import the necessary functions from coffee.py into main.py. Then in coffee.py either import all of math and random right away, or import just the functions that you need as you write the code. DO NOT move the functions over to main.py. Use imports so that those functions can stay in coffee.py.
2. TODO 1 is inside the get_num_drinks() function. You should set the variable num_drinks to a random number from 2 and 12 (inclusive) to simulate the number of drinks the customer is ordering. This is not user input. This is a computer-generated pseudorandom number using Python's random module (Links to an external site.).
3. TODO 2 is below TODO 1. Print the random amount spent to the screen (see the first line in the sample outputs.)
4. TODO 3 is in the get_drink_price() function. Generate one of 4 random numbers to be used in TODO 4.
5. TODO 4 is below TODO 3. You need to write a series of if statements that use the random number from TODO 3 to select one of 4 random drink prices: $3.00, $3.75, $4.00, $4.50.
6. TODO 5 is below TODO 4. Print the random drink price to the screen (see the second line in the sample outputs.)
7. TODO 6 is in the bulk_discount() function. You should calculate the bulk discount as the square root of the total price. The total price is equal to the number of drinks * price of each drink. You will need to use Python's math module to make this calculation. Note that this function should return a decimal number for the discount. 25% = 0.25 discount
8. TODO 7 is below TODO 6. Conditionally print a message to the screen. If the user buys at least 6 drinks and each drink costs at least $4, print a message to the user about how nice they are to their friends.
9. TODO 8 is in the print_summary() function. Since the parameter discount is in the format 0.25 to be used in calculations, we need a new variable that contains 25 in order to print the receipt in a human-readable way.
10. TODO 9 is below TODO 7. Round this to only 2 decimal places show when printing a number to the screen. So instead of something like 23.45666666667 printing, we want 23.45 (or 23.46). Essentially, we want all references to money to print with 2 decimal places (though trailing zeros may be missing).
11. TODO 10. Bugs?! What? My code has bugs? Indeed it does. If you check out the receipt, the total after discount and savings are not correct. In print_summary, correct where the calculations are being made so that the summary reads properly.

Here are a couple sample runs of the program:

You're buying 5 drinks!
Each drink will cost $3.75.

****** CUSTOMER DISCOUNT SUMMARY ******
---------------------------------------
Total purchase amount:        $18.75
4.33% Discount:              -$0.81
---------------------------------------
TOTAL AFTER DISCOUNT          $17.94
---------------------------------------
You Saved                     $0.81
---------------------------------------

main.py:

# TODO 0: Import the functions from coffee.py into your main so that the errors go away in this file.

# DO NOT move the functions over into main.py. You should use imports so that the functions can stay in coffee.py

# Save the randomly generated number of drinks into the total_drinks variable

total_drinks = get_num_drinks()

# Save the randomly generated drink price into the total_drinks variable

cost_per_drink = get_drink_price()

# The purchase amount before discounts is the number of drinks * their price

overall_price = total_drinks * cost_per_drink

# Use purchase amount to calculate sale discount, save into variable

purchase_discount = bulk_discount(total_drinks, cost_per_drink)

# Pass the purchase amount and the discount to the print_summary function

print_receipt(overall_price, purchase_discount)

coffee.py:

# TODO 0: Import math and random, or just the functions you need from those modules

# get_num_drinks

# Parameters: none

# Return: should generate and return the number of drinks to order

# Side effect: should print the number of drinks

def get_num_drinks():

num_drinks = 0 # TODO 1: generate a random number between 2 and 12

print() # TODO 2: print how many drinks were ordered to the screen, in a full sentence.

return num_drinks

# get_drink_price

# Parameters: none

# Return: should choose and return one of four drink costs

# Side effect: should print the drink price

def get_drink_price():

drink_type = 0 # TODO 3: generate a random number representing the drink type

drink_price = 0

# TODO 4: write if-statements based on drink_type that will set drink_price to the appropriate value

print() # TODO 5: print the drink price to the screen, in a full sentence including $ and punctuation.

return drink_price

# bulk_discount

# Parameters: none

# Return: A discount equal to the square root of the

# total purchase amount, in decimal form.

# e.g. If the customer spend $25, this is a 5% discount, and should be returned as .05.

# Side effect: if the user buys at least six drinks, AND each drink costs at least $4, print "Wow, you must really like your friends!"

def bulk_discount(total_drinks, price_per_drink):

amount = 0.0 # TODO 6: calculate the square root of the purchased amount, using the math module. Remember to return the percentage as a decimal, e.g. .05 rather than 5.

# TODO 7: if the user buys at least six drinks, and each drink costs at least $4, print "Wow, your friends must really like you!"

return amount

# print_receipt

# Parameters: amount spent, discount

# Return: none

# Side effect: prints a summary of the customer's purchase and discount savings

def print_receipt(original_total, discount):

human_readable_discount = 0 # TODO 8: Generate the human-readable discount from the discount variable, e.g. .06 should be 6, to print a 6% discount

saved = original_total * discount

new_total = original_total - discount

# TODO 9: Fix the formatting of the summary below to print exactly 2 decimal places for each number

# TODO 10: Check the output and make sure that it makes sense. If not, it means there are bugs still in the code.

print("****** CUSTOMER DISCOUNT SUMMARY ******")

print("---------------------------------------")

print("Total purchase amount: \t\t $", original_total, sep="")

print(human_readable_discount, "% Discount: \t\t\t -$", saved, sep="")

print("---------------------------------------")

print("TOTAL AFTER DISCOUNT\t\t $", new_total, sep="")

print("---------------------------------------")

print("You Saved\t\t\t\t\t $", discount, sep="")

print("---------------------------------------")

please provide 3-5 questions with answers from the article below

Since the early days of Google, people throughout the company have ques- tioned the value of managers. That skepticism stems from a highly techno- cratic culture. As one software engineer, Eric Flatt, puts it, “We are a company built by engi- neers for engineers.” And most engineers, not just those at Google, want to spend their time designing and debugging, not communicating with bosses or supervising other workers’ progress. In their hearts they’ve long believed that management is more de- structive than beneficial, a distraction from “real work” and tangible, goal-directed tasks.

A few years into the company’s life, found- ers Larry Page and Sergey Brin actually wondered whether Google needed any managers at all. In 2002 they experimented with a completely flat organiza- tion, eliminating engineering managers in an effort to break down barriers to rapid idea development and to replicate the collegial environment they’d enjoyed in graduate school. That experiment lasted only a few months: They relented when too many people went directly to Page with questions about

expense reports, interpersonal conflicts, and other nitty-gritty issues. And as the company grew, the founders soon realized that managers contributed in many other, important ways—for instance, by com- municating strategy, helping employees prioritize projects, facilitating collaboration, supporting career development, and ensuring that processes and sys- tems aligned with company goals.

Google now has some layers but not as many as you might expect in an organization with more than 37,000 employees: just 5,000 managers, 1,000 direc- tors, and 100 vice presidents. It’s not uncommon to find engineering managers with 30 direct reports. Flatt says that’s by design, to prevent micromanag- ing. “There is only so much you can meddle when you have 30 people on your team, so you have to fo- cus on creating the best environment for engineers to make things happen,” he notes. Google gives its rank and file room to make decisions and innovate. Along with that freedom comes a greater respect for techni- cal expertise, skillful problem solving, and good ideas than for titles and formal authority. Given the overall indifference to pecking order, anyone making a case

for change at the company needs to provide compel- ling logic and rich supporting data. Seldom do em- ployees accept top-down directives without question.

Google downplays hierarchy and emphasizes the power of the individual in its recruitment efforts, as well, to achieve the right cultural fit. Using a rigor- ous, data-driven hiring process, the company goes to great lengths to attract young, ambitious self- starters and original thinkers. It screens candidates’ re?sume?s for markers that indicate potential to excel there—especially general cognitive ability. People who make that first cut are then carefully assessed for initiative, flexibility, collaborative spirit, evi- dence of being well-rounded, and other factors that make a candidate “Googley.”

So here’s the challenge Google faced: If your highly skilled, handpicked hires don’t value manage- ment, how can you run the place effectively? How do you turn doubters into believers, persuading them to spend time managing others? As it turns out, by applying the same analytical rigor and tools that you used to hire them in the first place—and that they set such store by in their own work. You use data to test your assumptions about management’s merits and then make your case.

Analyzing the Soft Stuff

To understand how Google set out to prove man- agers’ worth, let’s go back to 2006, when Page and Brin brought in Laszlo Bock to head up the human resources function—appropriately called people operations, or people ops. From the start, people ops managed performance reviews, which included annual 360-degree assessments. It also helped con- duct and interpret the Googlegeist employee survey on career development goals, perks, benefits, and company culture. A year later, with that foundation in place, Bock hired Prasad Setty from Capital One to lead a people analytics group. He challenged Setty to approach HR with the same empirical discipline Google applied to its business operations.

Setty took him at his word, recruiting several PhDs with serious research chops. This new team was committed to leading organizational change.

“I didn’t want our group to be simply a reporting house,” Setty recalls. “Organizations can get bogged down in all that data. Instead, I wanted us to be hypothesis-driven and help solve company prob- lems and questions with data.”

People analytics then pulled together a small team to tackle issues relating to employee well-being and productivity. In early 2009 it presented its ini- tial set of research questions to Setty. One question stood out, because it had come up again and again since the company’s founding: Do managers matter?

To find the answer, Google launched Project Oxygen, a multiyear research initiative. It has since grown into a comprehensive program that measures key management behaviors and cultivates them through communication and training. By November 2012, employees had widely adopted the program— and the company had shown statistically significant improvements in multiple areas of managerial effec- tiveness and performance.

Google is one of several companies that are ap- plying analytics in new ways. Until recently, organi- zations used data-driven decision making mainly in product development, marketing, and pricing. But these days, Google, Procter & Gamble, Harrah’s, and others take that same approach in addressing human resources needs. (See “Competing on Talent Analyt- ics,” by Thomas H. Davenport, Jeanne Harris, and Jeremy Shapiro, HBR October 2010.)

Unfortunately, scholars haven’t done enough to help these organizations understand and improve day-to-day management practice. Compared with leadership, managing remains understudied and undertaught—largely because it’s so difficult to describe, precisely and concretely, what managers actually do. We often say that they get things done through other people, yet we don’t usually spell out how in any detail. Project Oxygen, in contrast, was designed to offer granular, hands-on guidance. It didn’t just identify desirable management traits in the abstract; it pinpointed specific, measurable be- haviors that brought those traits to life.

That’s why Google employees let go of their skep- ticism and got with the program. Project Oxygen mir- rored their decision-making criteria, respected their need for rigorous analysis, and made it a priority to measure impact. Data-driven cultures, Google dis- covered, respond well to data-driven change.

Jim Young/Reuters

Screw the passengers.

That appears all too often to be the governing philosophy of the airline business.

Take the case of a United Airlines flight from Chicago to London last weekend. A technical problem forced the plane to abort its trans-Atlantic route and divert to Goose Bay in Canada. The 176 passengers were marooned there for more than 20 hours, sleeping in unheated military barracks at near-freezing temperatures.

“There was nobody from United Airlines to be seen anywhere,” one passenger told NBC News. “No United representative ever reached out to anybody, no phone calls, no human beings, no nothing. Nobody had any idea what was going on.”

It so happened that this came at the end of a week in which the world’s airline chiefs, junketing in Miami, celebrated their most lucrative year ever. They are projecting profits totaling $29.3 billion in 2015—almost double what they made in 2014.

And you must have noticed if you’re flying anywhere in the U.S. this summer that seat prices are not falling. Indeed, if the owners of those seats are suddenly feeling fat and happy, they are in no mood to pass on their swell feelings to you. It’s hard to imagine any other service industry being run like the airline business—but then there is no other business like the airline business.

So now we have a novel opportunity to see how airlines behave when, suddenly and much to their surprise, they find themselves with a business model that is working. If making a profit is a new experience for them, what effect will that have on their behavior?

First, let us consider why the numbers have been transformed.

There has been a steep change in the efficiency of jets. Beginning with the Boeing 787 Dreamliner, the combination of lighter but stronger composite materials in structures and a quantum leap in engine efficiency, using far less fuel, has slashed operating costs per airplane by as much as 30 percent.

In the last year, this windfall has been boosted by the large decline in oil prices.

However, these dual benefits are not being evenly spread either among airlines or continents. Airlines stuck with fleets of older airplanes are not getting these benefits. Fleet age has become far more decisive in deciding an airline’s profitability, particularly true in the U.S.

The three major U.S. legacy carriers—American, United, and Delta—failed to get in early to order the new generation of airplanes—the 787, the Airbus A350, revamped versions of the Boeing 777, the Airbus A320, and the Boeing 737—and allowed European, Middle Eastern, and Asian competitors to become first adopters and, thereby, reap the benefits of lower fuel costs.

The average age of the jets in the American fleet is 12.3 years; for United 13 years; and for Delta 17.2 years. It won’t be until at least 2020 that they can finally dump the oldest of their airplanes. (American has actually been delaying the delivery of some new jets that it ordered.)

Age doesn’t mean that an airplane is unsafe. Properly maintained 20-year-old jets are not in danger of falling apart. The frequency of flights determines retirement age more than years and the smaller single-aisle jets used on domestic routes age the fastest because they are making up to seven flights a day.

Age may not be dangerous but it sure registers with passengers when it contrasts with the comforts they encounter in the new generation of jets with their better cabin climate and quieter engines. So it’s not surprising that when airlines show up with all-new fleets as well as gracious cabin crews people start wondering, Why can’t it always be like this?

It’s also not surprising that the major American carriers are now trying to stop those airlines from coming to an airport near you.

When it comes to price and the domestic U.S. routes, not only are prices not coming down but there is persuasive evidence of price-fixing. The veteran investigative reporter James B. Stewart described this market as a classic oligopoly in a penetrating piece in The New York Times .

However, this is far from being a new phenomenon. These tactics began long before the final round of consolidation mergers when US Airways was swallowed by American Airlines in 2013. They have merely been continually refined to the point now when the airlines, suddenly enjoying profits, have responded not by lowering fares but by tightening control over the number of seats available and cutting back on flight frequency and destinations.

The reality is that the airlines don’t need to expose themselves to charges of collusion on fares and the operation of a hidden cartel that mutually governs capacity. That’s so 20th century.

These days their key tool is “yield management”—being able to precisely calculate how many seats should be available on any given route at any time of the day or night and adjusting the price hour-by-hour according to demand. This algorithm has become so refined and the market so controlled that each of the major airlines ends up looking at the same numbers on their computer screen. No human intervention is needed. In all but name it is a cartel—but one run entirely by unaccountable robots.

So?

We live in the world’s most vigorously capitalist marketplace. What’s wrong with airlines trying to make a decent profit, for once? And what is the point of them flying empty seats around the skies?

But I come back to my earlier point: How do these airline executives behave when, joy of joys, they find their balance sheets deeply in the black? Like a lot of other corporate minders they think a lot more about their shareholders than their customers. Short-termism rules. Wall Street responds to quarterly earnings, not patient long-term strategy.

A good example is Jet Blue. This airline was a rare example of a successful startup based on a maverick idea: super-chummy cabin staff and generously spaced seating. A new CEO (previously schooled by the stingy bean-counters at British Airways) is undermining that spirit by jamming more seats into the cabin and raising baggage charges, all at the behest of shareholders.

The problem is that the people running airlines in the U.S. have one part of their brain missing, the part that provides the service ethic. As well as fare-gouging they’re space gouging in the cabins. Even with the newest jets like the Dreamliner they are packing more seats into coach than the airplane designers (or nature) intended.

Q1. Read the above article and answer the questions that follow.

a. Why did the investigative reporter James B. Stewart describe US airlines as a classic Oligopoly?

b. What is the meaning of yield management as described in the above article?

c. Why did the writer accuse people running airlines of missing service ethics?

1.) In cell H9 create a VLOOKUP to indicate the description Expensive for pups priced $2,000 or more, Moderate for pups priced $1,000 or more but below $2,000, Good Dealfor pups priced $500 or more but less than $1,000 and Cheapfor all pups priced less than $500.  Begin the lookup table in cell E1.  Copy the VLOOKUP in cell H9 to cells H10:H202. 2.) In cell I9 create a Nested IF to duplicate the results of #1 above.  Copy the nested If in cell I9 to cells I10:I202. 3.) Sort the database by AKC Group and within AKC Group by Breed and within Breed by Color (all ascending order).  Create a Subtotal report showing the average price by AKC Group and the total number of pups available for each dog breed. Change to outline level 3.  Right-mouse click on the Sheet 1worksheet tab. Select “Move or Copy” and check mark “Create a copy” and click on (move to end).  Name the newly created worksheet tab, “Subtotal.”   Return to Sheet1and remove the subtotal report by clicking on the “Remove All” button in the Data/Subtotal menu. 4.) Use the “unique records only” feature of the Advanced Filter to indicate the unique varieties of dog colors and breeds.  Start the unique dog breeds in K1 and the unique dog colors in M1. Place the unique dog breed and dog color combinations starting in cell O1.  (You will have to do three separate Unique Advanced Filters.) 5.) Note that none of the filters in #4 require a criterion.  To demonstrate a Unique Filter witha Criteria output the unique breeds with a price of $2,500 or more.  Start your criteria in R1.  Start your output in R4. Output just the Breed names that meet the criteria.  (Check figure: 8) 6.) Insert a new worksheet.  On Sheet 3, create a one variable data table that indicates the total number of pups available for each breed and the average price for each breed.  (Copy/paste the Unique dog breeds extracted in #4.)  Place the northwest corner of the table in cell A5. Begin the required criteria range in cell A1.7.) Insert a new worksheet.  On Sheet 4, create a two variable data table that indicates the total number of pups available by dog breed and by dog color.  (Use the unique dog breeds & unique colors extracted in #4. Use the copy & paste special / transpose to copy vertical text as horizontal text.) Place the northwest corner of the table in cell A5.  Begin the required criteria range in cell A1. 8.) Turn off the display of zeroes in Sheet 4.  File ribbon / Excel Options/Advanced (scroll down to Display Options for this Worksheet: Sheet 4)  Remove the check mark in front of:   Show a zero in cells that have a zero value. 9.) Create a pivot table that indicates the total number of pups available by breed and the average price for each breed.  Place the northwest corner of the pivot table in cell E4 on Sheet 3. 10.) Create a pivot table that indicates the total number of pups available by dog breed and by dog color.  Place the pivot table as a new worksheet.  

A client has suffered damage to the anterior pituitary gland, reducing the ability to respond to increases in plasma osmolality. The nurse should monitor for what expected assessment findings? Select all that apply.

A middle-aged man with diabetes reports that he must strain to urinate, that his urine stream is weak and dribbling, and that his bladder never really empties.You know that all of his symptoms are likely caused by which diagnosis?

You know which clinical manifestations may be present with the diagnosis of acute nephritic syndrome? Select all that apply.

The nurse caring for a child with respiratory problems is concerned he may be developing respiratory failure. Upon assessment, which findings correlate to impending respiratory failure? Select all that apply.

You are reviewing a patient's laboratory results and notices the blood urea nitrogen (BUN):creatinine ratio is 16:1. This ratio most likely correlates to which factor in the client's medical history?

A client has stopped taking the prescribed anticholinergic medication for overactive bladder, due to the associated side effects of dry mouth, constipation, and reflux. Which other medication, injected directly into the bladder, is an alternative treatment for this condition?

A patient has recently been diagnosed with large cell carcinoma, and the nurse is preparing the client for diagnostic testing to identify distal metastases. Which sites should the nurse explain to the client as the primary focus for the investigation? Select all that apply.

A client, admitted after an automobile accident in which ther head and chest hit the steering wheel, has a combination of biomarker testing (SP-D, neutrophil chemotractor factor, and interleukin-8). The family asks, "Why are there so many blood tests?" The response by the nurse reveals these test results will confirm the client has developed which condition(s)? Select all that apply.

An older adult resident of a long term care facility with a recent history of repeated urinary tract infections and restlessness is suspected of having urinary retention. Which intervention by the care team is most appropriate to confirm urine retention?

A client with systemic lupus erythematosus (SLE) glomerulonephritis is experiencing a worsening of the disease and has progressed to the higher classes resulting in renal involvement. Which medication(s) will likely be prescribed by the health care provider to treat the deterioration of the renal function? Select all that apply.

A client has visited the health care provider reporting intermittent passing of blood-tinged urine over the last several weeks. Cytology confirms a diagnosis of invasive bladder cancer. Which statement by the provider is most accurate about treatment options?

The function 'make_enzyme_threads' has a memory bug. Fix this by simply re-ordering the lines in this function. It is simple fix but may take a while for you to find it. As a hint, you may want to pay attention to the addresses of the pointers that are passed to the individual enzymes

#include "enzyme.h"

int please_quit;
int use_yield;
int workperformed;

// The code executed by each enzyme.
void *run_enzyme(void *data) {
/* This function should :
1. Cast the void* pointer to thread_info_t* (defined in enzyme.h)*/

   thread_info_t *inp;

   inp = (thread_info_t *)(data);

/*2. Initialize the swapcount to zero*/

   int swapcount = 0;

/*3. Set the cancel type to PTHREAD_CANCEL_ASYNCHRONOUS (see pthread_setcanceltype)*/

   void *cancel_type = PTHREAD_CANCEL_ASYNCHRONOUS;

/*4. If the first letter of the string is a C then call pthread_cancel on this thread.
(see also, pthread_self)
Note, depeneding on your platform (and specifically for macOS) you have
to replace the call to pthread_cancel with
pthread_exit(PTHREAD_CANCELED) in order to make the cancel test
pass.*/

   if (s[0]) == 'C')

       pthread_cancel(pthread_self());

/*5. Create a while loop that only exits when please_quit is nonzero
6. Within this loop: if the first character of the string has an ascii
value greater than the second (info->string[0] > info->string[1]) then
- Set workperformed = 1
- Increment swapcount for this thread
- Swap the two characters around
7. If "use_yield" is nonzero then call pthread_yield at the end of the loop.
8. Return a pointer to the updated structure.
*/
       while (please_quit == 0) {

           if (s[0] >s[1]) {

               workperformed = 1;
               threadswapcount++;
               char c;
               c = s[0];
               s[0] = s[1];
               s[1] = c;

           }

           if (use_yield != 0)

               pthread_yield(pthread_self());

       }
while(0) {
pthread_yield();
};
return NULL;
}

// Make threads to sort string.
// Returns the number of threads created.
// There is a memory bug in this function.
int make_enzyme_threads(pthread_t * enzymes, char *string, void *(*fp)(void *)) {
int i, rv, len;
thread_info_t *info;
len = strlen(string);
info = (thread_info_t *)malloc(sizeof(thread_info_t));

for (i = 0; i < len - 1; i++) {
info->string = string + i;
rv = pthread_create(enzymes + i, NULL, fp, info);
if (rv) {
fprintf(stderr,"Could not create thread %d : %s\n", i, strerror(rv));
exit(1);
}
}
return len - 1;
}

// Join all threads at the end.
// Returns the total number of swaps.

int join_on_enzymes(pthread_t *threads, int n) {

   int i;
   int totalswapcount = 0;

   //initialize the cancelled count
   int cancelcount = 0; // just to make the code compile

                       // you will need to edit the code below

   for (i = 0; i<n; i++) {

       void *status;
       int rv = pthread_join(threads[i], &status);

       /*if join status is non zero, then cant join the threads
       Thus the condition is to check whether rv estimated
       Above has a non zero value*/
       if (rv != 0)
       {

           fprintf(stderr, "Can't join thread %d:%s.\n", i, strerror(rv));
           continue;
       }

       //if status is PTHREAD_CANCELED
       if ((void*)status == PTHREAD_CANCELED)
       {
           //increment the cancelled count
           cancelcount++;
           continue;
       }

       else if (status == NULL)
       {
           printf("Thread %d did not return anything\n", i);
       }

       else
       {
           printf("Thread %d exited normally: ", i);// Don't change this line

                                                   //need to cast the status with thread_info_t
                                                   //and then get the swap count as thread swap count
           int threadswapcount = (thread_info_t *)status->swapcount;

           // Hint - you will need to cast something.
           printf("%d swaps.\n", threadswapcount); // Don't change this line
           totalswapcount += threadswapcount;// Don't change this line
       }

   }
   return totalswapcount;
}

/* Wait until the string is in order. Note, we need the workperformed flag just
* in case a thread is in the middle of swapping characters so that the string
* temporarily is in order because the swap is not complete.
*/
void wait_till_done(char *string, int n) {
int i;
while (1) {
pthread_yield();
workperformed = 0;
for (i = 0; i < n; i++)
if (string[i] > string[i + 1]) {
workperformed = 1;
}
if (workperformed == 0) {
break;
}
}
}

void * sleeper_func(void *p) {
sleep( (int) p);
// Actually this may return before p seconds because of signals.
// See man sleep for more information
printf("sleeper func woke up - exiting the program\n");
exit(1);
}

int smp2_main(int argc, char **argv) {
pthread_t enzymes[MAX];
int n, totalswap;
char string[MAX];

if (argc <= 1) {
fprintf(stderr,"Usage: %s <word>\n",argv[0]);
exit(1);
}

// Why is this necessary? Why cant we give argv[1] directly to the thread
// functions?
strncpy(string,argv[1],MAX);

please_quit = 0;
use_yield = 1;

printf("Creating threads...\n");
n = make_enzyme_threads(enzymes, string, run_enzyme);
printf("Done creating %d threads.\n",n);

pthread_t sleeperid;
pthread_create(&sleeperid, NULL, sleeper_func, (void*)5);

wait_till_done(string, n);
please_quit = 1;
printf("Joining threads...\n");
totalswap = join_on_enzymes(enzymes, n);
printf("Total: %d swaps\n", totalswap);
printf("Sorted string: %s\n", string);

exit(0);
}

C Language - The function 'make_enzyme_threads' has a memory bug. Fix this by simply re-ordering the lines in this function. It is simple fix but may take a while for you to find it. As a hint, you may want to pay attention to the addresses of the pointers that are passed to the individual enzymes.

//enzyme.h contents

//Note I could not use tag openers in the includes.

#define _GNU_SOURCE
#include (pthread.h)
#include (stdio.h)
#include (sys/types.h)
#include (string.h)
#include (stdlib.h)
#include (unistd.h)

#define MAX 100

typedef struct {
char *string; // keep testing and swap contents if necessary so that string[0] < string[1]
int swapcount; // used later
} thread_info_t;

extern int please_quit;
extern int workperformed;

void *run_enzyme(void *data);
int make_enzyme_threads(pthread_t * enzymes_tid, char *string, void *(*fp)(void *));
int join_on_enzymes(pthread_t *threads_tid, int n);

int smp2_main(int, char**);

// XXX macOS compatibility
#ifdef __APPLE__
#define pthread_yield() sched_yield()
#endif

//enzyme.c contents

#include "enzyme.h"

int please_quit;
int use_yield;
int workperformed;

// The code executed by each enzyme.
void *run_enzyme(void *data) {
//This function should :
//1. Cast the void* pointer to thread_info_t* (defined in enzyme.h)
//2. Initialize the swapcount to zero
  //3. Set the cancel type to PTHREAD_CANCEL_ASYNCHRONOUS (see pthread_setcanceltype)
/*4. If the first letter of the string is a C then call pthread_cancel on this thread.
(see also, pthread_self)
Note, depeneding on your platform (and specifically for macOS) you have
to replace the call to pthread_cancel with
pthread_exit(PTHREAD_CANCELED) in order to make the cancel test
pass.*/
//5. Create a while loop that only exits when please_quit is nonzero
/*6. Within this loop: if the first character of the string has an ascii
value greater than the second (info->string[0] > info->string[1]) then
- Set workperformed = 1
- Increment swapcount for this thread
- Swap the two characters around*/
//7. If "use_yield" is nonzero then call pthread_yield at the end of the loop.
//8. Return a pointer to the updated structure.

while(0) {
pthread_yield();
};
return NULL;
}

// Make threads to sort string.
// Returns the number of threads created.
// There is a memory bug in this function.
int make_enzyme_threads(pthread_t * enzymes, char *string, void *(*fp)(void *)) {
int i, rv, len;
thread_info_t *info;
len = strlen(string);
info = (thread_info_t *)malloc(sizeof(thread_info_t));

for (i = 0; i < len - 1; i++) {
info->string = string + i;
rv = pthread_create(enzymes + i, NULL, fp, info);
if (rv) {
fprintf(stderr,"Could not create thread %d : %s\n", i, strerror(rv));
exit(1);
}
}
return len - 1;
}

// Join all threads at the end.
// Returns the total number of swaps.
int join_on_enzymes(pthread_t *threads, int n) {
int i;
int totalswapcount = 0;

// Just to make the code compile. You will need to replace every usage of
// this variable in the code below. When you are done, placeholder can be
// deleted.

int holder = 0;

for(i = 0; i < n; i++) {
void *status;
int rv = pthread_join(threads[i], &status);

if (holder) {
fprintf(stderr,"Can't join thread %d:%s.\n",i,strerror(rv));
continue;
}

if ((void*)holder == PTHREAD_CANCELED) {
continue;
} else if (status == NULL) {
printf("Thread %d did not return anything\n",i);
} else {
printf("Thread %d exited normally: ",i);// Don't change this line
int threadswapcount = holder;
// Hint - you will need to cast something.
printf("%d swaps.\n",threadswapcount); // Don't change this line
totalswapcount += threadswapcount;// Don't change this line
}
}
return totalswapcount;
}

/* Wait until the string is in order. Note, we need the workperformed flag just
* in case a thread is in the middle of swapping characters so that the string
* temporarily is in order because the swap is not complete.
*/
void wait_till_done(char *string, int n) {
int i;
while (1) {
pthread_yield();
workperformed = 0;
for (i = 0; i < n; i++)
if (string[i] > string[i + 1]) {
workperformed = 1;
}
if (workperformed == 0) {
break;
}
}
}

void * sleeper_func(void *p) {
sleep( (int) p);
// Actually this may return before p seconds because of signals.
// See man sleep for more information
printf("sleeper func woke up - exiting the program\n");
exit(1);
}

int smp2_main(int argc, char **argv) {
pthread_t enzymes[MAX];
int n, totalswap;
char string[MAX];

if (argc <= 1) {
fprintf(stderr,"Usage: %s \n",argv[0]);
exit(1);
}

// Why is this necessary? Why cant we give argv[1] directly to the thread
// functions?
strncpy(string,argv[1],MAX);

please_quit = 0;
use_yield = 1;

printf("Creating threads...\n");
n = make_enzyme_threads(enzymes, string, run_enzyme);
printf("Done creating %d threads.\n",n);

pthread_t sleeperid;
pthread_create(&sleeperid, NULL, sleeper_func, (void*)5);

wait_till_done(string, n);
please_quit = 1;
printf("Joining threads...\n");
totalswap = join_on_enzymes(enzymes, n);
printf("Total: %d swaps\n", totalswap);
printf("Sorted string: %s\n", string);

exit(0);
}

CASE STUDY

The winter was somewhat mild in some parts of the United States in February 2018. Although Spring Practice for football had not officially begun at Mid-Atlantic University, the players were expected to work out on their own informally and stay in top physical condition. An extremely close-knit group of athletes, mutually dedicated to the goal of winning the national championship in the Fall. When upper respiratory infections began circulating among the players, the team physician decided to send specimens to the State Health Department where virologists examined 23 specimens and obtained a positive test for influenza virus for 15 of them. They also confirmed that most of the virus isolates contained the influenza strain A/Victoria, a common type of influenza that occurs every winter. Unfortunately, they could not identify three virus isolates and were uncertain about four others. These seven specimens were forwarded to a federal laboratory where scientists specialize in infectious diseases.

Two months prior to the outbreak at the university, health officials in Vietnam had ordered the destruction of 12 million chickens after evidence surfaced that a "bird flu" strain of virus resulted in 20 confirmed influenza cases, 12 of which proved to be fatal. Health authorities in Hong Kong confirmed that the virus involved human-to-human transmission. Shortly before the campus outbreak at Mid-Atlantic University, local health officials temporarily closed a large meat distribution company in the area that supplied food to the school because its poultry was suspected of being infected with an influenza strain.

While the specimens were on their way to the federal laboratory, more players reported feeling ill and on February 15, an offensive tight-end named Dave Murray died in the university infirmary complaining of flu-like symptoms, his position coach had told him two days beforehand to take a week off and not return to workouts until he felt 100 percent healthy.The same day that he died, an opinion piece in a national daily newspaper by a prominent virologist noted that pandemics occur approximately every 30 years or so. The last one had swept the globe four decades earlier. Over the course of the next few days, State Health Department laboratory experts were unable to identify the strain of two additional influenza-positive specimens, one of which was obtained from the player's corpse. These specimens also were forwarded to federal scientists who discovered that the three previously unidentified isolates, along with the two new ones represented a different virus type. Even more serious was the possibility that the isolates might be closely related to an avian influenza virus believed to have swept the world in a pandemic in 1918, killing an estimated 50 million persons around the world and approximately one-half million victims in the United States. Hong Kong health authorities feared that the virus uncovered there was the same as the strain implicated in that earlier pandemic.

The many deaths that occurred then were due to an accompanying bacterial pneumonia prior to the advent of modern antibiotics.

Human-to-human spread of avian influenza had not been seen in the U.S, in at least 50 years and Dr. Lionel Traister, head of the federal infectious disease agency, contacted officials from other federal agencies and major State health departments around the country and invited them to an emergency meeting at his offices on March 1. Those scientists present who possessed a sense of public health history noted that the 1918 pandemic began relatively mildly in the spring and then returned with a vengeance in the fall, accounting for the vast majority of deaths. As a group, they agreed that the university outbreak could be a harbinger of more lethal and widespread disease on the not-too-distant horizon. No precise estimate of the extent of the risk ever was voiced, however, such as indicating that there might be a 25 percent chance the nation may be headed for a serious outbreak. All that could be stated was that there was a possibility of a pandemic. A question that loomed rather large in their minds was what to do about going public with these concerns. A delicate balance existed between sounding a warning to public health officials around the country and inciting a panic. The media frenzy associated with the outbreak of other influenzas in recent years provided a cautionary lesson. They decided to wait for the results of another round of tests, which in the next three days should confirm whether the virus uncovered at the university was avian influenza. Their worst fears soon were realized. The virus was avian influenza. Another emergency meeting was called on March 7 with the invitation list extended to virologists from private clinics, universities, and pharmaceutical companies. Meanwhile, the search for a further outbreak of the virus among the football team and among the rest of the student body had not yielded any new active cases nor had it spread beyond the campus to the community nearby. Outside the U.S., the World Health Organization had not reported any outbreaks of avian flu nor were any new cases reported in Hong Kong. Had the avian flu returned to its source among poultry or was it spreading in humans sub-clinically, waiting for an explosive eruption during the next flu season?

Although no new cases had been reported, the discussion at the meeting centered around: the logistics of vaccine production and distribution, field testing and licensing vaccines, and how to go about conducting a national immunization campaign. The general consensus was that even if all the vaccine needed could be produced by the beginning of the next flu season, which could occur in September or October, it still might take another 10 weeks to immunize the entire population of the U.S. Even then, it would take an additional two weeks after vaccination for protective immunity to be conferred. Stockpiling vaccine and waiting until an outbreak of flu occurred was not viewed as a workable option since infection produced disease much faster than a needle stick provided immunity. Only one person at this meeting, Dr. Virginia Bell who was director of a State health department on the West Coast, addressed the issue of stockpiling vaccine until clearer signals emerged that would warrant the start of a mass immunization program. Her basic concern was that caution should be exercised when considering the possibility of injecting any foreign substance into the bodies of more than 300 million Americans. She wanted to know at what point do preparations to immunize the entire population stop and the plan is changed to stockpile the vaccine instead. Bell's comments were made dispassionately and seemed to have little or no impact on the group. She did not argue her case any further.

Following the Chain of Command

Lionel Traister was regarded as a tough, highly competent, and occasionally wily career bureaucrat. Not only was he committed to advancing public health measures, he would never be accused of failing to advance himself when opportunity knocked. The appearance of the avian flu virus seemed to be one of those rare occasions when he could be at center stage to demonstrate to a wide audience both his own and his agency's capabilities. Strict caps were imposed on spending for that fiscal year because of a serious budget deficit. Recognizing that a national immunization program would require a supplemental appropriation, coupled with the fact that the federal bureaucracy was so slow moving, he knew that quick, decisive action on his part was imperative. The key was to frame the situation in urgent terms that persons higher-up in the administrative chain would find difficult to ignore. Consequently, he prepared a document that combined a sense of extreme urgency with a set of propositions that would be difficult to counter. On March 11, he contacted his superior, Dr. Myles Borash who was the Assistant Secretary of Health, to let him know that the memorandum was on its way to him. Traister followed procedures by addressing it to Department Secretary Wilma Kester from Assistant Secretary Borash. In the chain of command, Traister was one of six agency heads reporting to Borash. As Assistant Secretary, even though Borash was a highly regarded physician, he was at the low end of the political appointments chain. Essentially, his job was to assume responsibility for official health policy within the Administration. The facts presented in the memorandum were clear and understandable.

The second option proposed a minimal response, with the federal role limited to: advising vaccine manufacturers, providing a stimulus to State/local health departments to take action, and educating the public. Reasons favoring this choice included high visibility, less responsibility for the eventual outcome if it proved to be unfavorable, and reduced federal spending. Arguments against this approach were that drug companies might not produce enough vaccine and significant portions of the population such as the poor and the aged might never be immunized. The third option called for total federal intervention. The main argument favoring this course of action was that widespread availability and distribution of vaccine would be assured. Opposing arguments were the high cost of such a campaign and the fact that the American people would not be favorably disposed to a program that left out the private sector.

The last option involved a combined approach that would have the advantage of using both public and private sectors. The federal government could purchase the vaccine needed, have it tested for safety and efficacy by federal agencies, and distribute it through health departments at all levels of government as well as in hospitals, clinics, and physician offices. This choice would provide a good vehicle for having all facets of the health care system work together cooperatively to assure that every American would have an opportunity to be immunized. The memorandum concluded with a recommendation to pursue the fourth option.

On March 13, the memorandum was discussed at the weekly meeting of the Secretary. Recognizing the likelihood that there might not be a pandemic in the making, there still was a great amount of attention focused on what happened earlier in the 20^th century. Kester indicated that she wanted to meet with Borash and Traister in her office the following morning along with the directors of federal agencies involved in licensing vaccines and overseeing research on viruses. At this session one day later, Traister recommended that the federal government undertake action as recommended in the fourth option. When Kester asked what the probability of a pandemic is, the answer from Traister was "unknown." Nobody at the meeting was willing to assign a probability, but Traister said that it is greater than zero. When asked if it was possible to produce enough vaccine and have it administered, the response was in the affirmative, but with the caveat that time was of the essence and that a decision would have to be made quickly.

Hearing no dissent and determining that every person in the room was in accord with this recommendation, Secretary Kester decided that it was appropriate to bring this matter to the attention of White House staff. Her reasons were: the government's top scientists favored a course of action, a probability of a pandemic greater than zero had to be assumed, and that there would be no credible way after a pandemic struck of telling the public that the government had not prepared to meet the threat because the probability was low and the costs of an immunization program outweighed the benefits. She also had enough political acumen to realize that even if she rejected the memorandum, it still might be leaked to the media.

Kester wrote a memorandum that same day to the head of the federal budget office, indicating that a request for a supplemental appropriation of $1 billion would be forthcoming. She stated that: "There is evidence that there will be a major flu epidemic this coming fall. The indication is that we will see a return of a virus that killed 500,000 Americans in 1918. The projections are that this virus will kill as many as one-and-one-half million persons in this country. The drug industry must be advised now in order to have enough vaccine produced for a mass immunization program. A decision will have to be made in the next week or so."

Reactions at the State Level

Hiram Waters was only one of two individuals who raised questions about a national immunization program during the widely televised Congressional hearings. His closest friend was one of 60,000 fatalities around the nation during the 1957 Asian flu epidemic. He was a staunch advocate of pediatric immunization programs, but did not believe that there was sufficient evidence to warrant what was being proposed. He indicated that his State would accept the vaccine gladly, but none of it would be distributed to local health departments, private physician offices, and hospitals until new cases of the flu began to emerge.  

Bayside was the largest city in his State. It possessed three academic health centers and because of changes in the health care delivery system occasioned by the growth of managed care, these entities were in fierce competition with one another. In medical circles, an oft-repeated question was whether all three could survive. The State already had a huge surplus of physicians, more per capita than any other State in the Union, and a large oversupply of hospital beds.

Coastal Health Center was the most aggressive of the three health centers. Not only had its executives negotiated a contract with the largest managed care company in the State, they were raiding the Atlantic Health Center, the smallest of the three entities as measured by the size of medical staff and the number of inpatient beds. Atlantic, despite its smaller size was more of a boutique operation with a worldwide reputation for providing care of the highest quality.  

Richard Medvecky, CEO of Coastal, viewed Atlantic as a potential acquisition through merger.

He knew that if he ever made such a move, his chief competitor, Bayside Health Center, would counter it immediately. Administrators at Coastal and Bayside knew that an initial move by either side would trigger a bidding war, one in which both could end up being losers. Medvecky conceived of another way of achieving dominance in this highly charged health care environment. He began making offers to Atlantic physicians who were the chiefs of Neurology, Radiology, and Surgery to switch to Coastal. The offer consisted of: doubling their salaries, providing ten-year contracts with guaranteed hefty annual pay raises, and furnishing perquisites such as additional compensation to cover the costs of their children's college education. These offers were accepted.

Based on a case study on the outbreak of a disease along lines of avian influenza that cuts across national boundaries. Adopt the perspective of one of the actors in the situation (e.g., legislators, governmental agency directors, professional association director, vaccine manufacturers) regarding that person’s role in the episode as it unfolds and provide a critique of the performance of the other actors in dealing with the problem adequately.