Answer Theory X techniques scientific management m.docx
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This document discusses different management techniques including Theory X techniques, scientific management, management development programs, and vestibule training. It suggests that management development programs are a solution to improve management skills and techniques. Management development programs can help train managers in modern approaches.
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Step 1: Load the Tox21 Dataset.
import numpy as np
np.random.seed(456)
import tensorflow as tf
tf.set_random_seed(456)
import matplotlib.pyplot as plt
import deepchem as dc
from sklearn.metrics import accuracy_score
_, (train, valid, test), _ = dc.molnet.load_tox21()
train_X, train_y, train_w = train.X, train.y, train.w
valid_X, valid_y, valid_w = valid.X, valid.y, valid.w
test_X, test_y, test_w = test.X, test.y, test.w
Step 2: Remove extra datasets.
# Remove extra tasks
train_y = train_y[:, 0]
valid_y = valid_y[:, 0]
test_y = test_y[:, 0]
train_w = train_w[:, 0]
valid_w = valid_w[:, 0]
test_w = test_w[:, 0]
Step 3: Define placeholders that accept minibatches of different sizes.
# Generate tensorflow graph
d = 1024
n_hidden = 50
learning_rate = .001
n_epochs = 10
batch_size = 100
with tf.name_scope("placeholders"):
x = tf.placeholder(tf.float32, (None, d))
y = tf.placeholder(tf.float32, (None,))
Step 4: Implement a hidden layer.
with tf.name_scope("hidden-layer"):
W = tf.Variable(tf.random_normal((d, n_hidden)))
b = tf.Variable(tf.random_normal((n_hidden,)))
x_hidden = tf.nn.relu(tf.matmul(x, W) + b)
Step 5: Complete the fully connected architecture.
with tf.name_scope("output"):
W = tf.Variable(tf.random_normal((n_hidden, 1)))
b = tf.Variable(tf.random_normal((1,)))
y_logit = tf.matmul(x_hidden, W) + b
# the sigmoid gives the class probability of 1
y_one_prob = tf.sigmoid(y_logit)
# Rounding P(y=1) will give the correct prediction.
y_pred = tf.round(y_one_prob)
with tf.name_scope("loss"):
# Compute the cross-entropy term for each datapoint
y_expand = tf.expand_dims(y, 1)
entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y_expand)
# Sum all contributions
l = tf.reduce_sum(entropy)
with tf.name_scope("optim"):
train_op = tf.train.AdamOptimizer(learning_rate).minimize(l)
with tf.name_scope("summaries"):
tf.summary.scalar("loss", l)
merged = tf.summary.merge_all()
Step 6: Add dropout to a hidden layer.
Step 7: Define a hidden layer with dropout.
Step 8: Implement mini-batching training.
train_writer = tf.summary.FileWriter('/tmp/fcnet-tox21',
tf.get_default_graph())
N = train_X.shape[0]
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 0
for epoch in range(n_epochs):
pos = 0
while pos N:
batch_X = train_X[pos:pos+batch_size]
batch_y = train_y[pos:pos+batch_size]
feed_dict = {x: batch_X, y: batch_y}
_, summary, loss = sess.run([train_op, merged, l], feed_dict=feed_dict)
print("epoch %d, step %d, loss: %f" % (epoch, step, loss))
train_writer.add_summary(summary, step)
step += 1
pos += batch_size
# Make Predictions
valid_y_pred = sess.run(y_pred, feed_dict={x: valid_X})
Step 9: Use TensorBoard to track model convergence.
include screenshots for the following:
1) a TensorBoard graph for the model, and
2) the loss curve.
.
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Natural birth techniques are various type such as/ water birth , alexender method, hypnosis, bradley method, lamaze method etcMore Related Content
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Match each description to the correct term. Describes how a system works such as who is involved, how data is entered. A. Business process diagram (BPD) source documents used, how data is processed and stored, what output is generated, and system controls. B. Narrative description A written step-by-step explanation of how a system works. C. Documentation A graphical description of data sources, data flows, transformation D. Program flowchart processes, data storage, and data destinations. E. Data flow Graphical description of the system which shows the flow of documents and F. Data source and destiantion information between departments and areas of responsibility. _. Document flowchart Graphical description of the system which shows relationship among input, H. Data flow diagram (DFD) processing and output. Graphical description of the system which shows the sequence of logical I. Context diagram operations a computer performs as it executes a program. 1. System flowchart People and organizations that send data to and receive data from the system; represented by boxes. Highest level DFD which provides a summary-level view of the system. A visual way to describe the different steps or activities in a business process. The movement of data among processes, stores, and destinations. Click Save and Submit to scrue and submit. Click Save All Answers to scne all answers.
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Mikhail's Portfolio- Mikhail Khodorkovsky was one of the infamous Russ.docx
Mikhail's Portfolio. Mikhail Khodorkovsky was one of the infamous Russian oligarchs, accumulating billions of dollars in wealth in the mid-1990s with the fall of the Soviet Union. But in 2003 he had been imprisoned by the Russian state for a decade. Upon his release from prison in 2013 he had taken up residence in Switzerland-with his money. In November 2014 Mikhail held a portfolio of USD203 million and CHF150 million in Swiss banks, in addition to accounts in Russia still holding RUB 1.1 billion. Using the exchange rate table , answer the following: a. What is the value of Mikhail's portfolio measured in Russian rubles? b. What is the value of Mikhail's portfolio measured in Swiss francs? c. What is the value of Mikhail's portfolio measured in U.S. dollars? d. Which currency demonstrated the greatest fluctuations in total value over the six dates? a. What is the value of Mikhail's portfolio measured in Russian rubles? Calculate the portfolio value as measured in rubles. (Round to the nearest ruble.) Data table Russo-Swiss Cross-2015 Switzerland has long served as a cornerstone of banking conservatism and a safe port in a world of currency storms. Swiss banks had long been used by investors from all over the world as financially secure institutions that would preserve the depositor's wealth with confidentiality. A large part of the security offered was the Swiss franc itself. Russian citizens of wealth had used Swiss banks heavily in recent years as a place where they could shelter their capital from politics, both inside Russia and the outside (EU and US) world. But beginning in the fall of 2014 the Russian ruble's value began to slide against the Swiss franc, threatening their wealth. (Click on the following icon in order to copy its contents into a spreadsheet.)
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Mike writes a letter detailing how his roommate cheated on his exam- H.docx
Mike writes a letter detailing how his roommate cheated on his exam. He didn't send it and no one else has seen it. However, he kept it in his desk just in case. Is this an example of defamation?
No, because it is not publicized
Yes, it is libel because it is written
Yes, it is slander because it is written
No, because it may be untrue
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Michael Jones recorded the following transactions during the month of.docx
Michael Jones recorded the following transactions during the month of April. Post these entries to the Cash account of the general ledger to determine the ending balance in cash. The beginning balance in cash on April 1 was $2 , 510 . (Post entries in the order of journal entries presented in the question.)
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microbiology List and explain the five characteristics of the adaptive.docx
microbiology
List and explain the five characteristics of the adaptive immune response. What occurs if the adaptive immune response mistakenly recognizes a protein made by the body as foreign instead of self. Why does lymph flow through lymph nodes before it is returned to the blood? Explain the relationship between plasma, interstitial fluid and lymph. Explain the differences between antigen presentation involving MHC I and MHC II. (There are three major differences.) Trace the path of antigen processing for exogenous pathogens and endogenous pathogens. List the five classes of antibody and explain the major differences between the classes.
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Nova lab The date ranges for each of the hominins in this puzzle are g.docx
Nova lab
The date ranges for each of the hominins in this puzzle are given below. Plot them on the timeline given. One has been done for you. Homo sapiens mya = millions of years ago; 0.5 mya = 500 , 000 years ago
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Marlin Company has the following projected costs for manufacturing and.docx
Marlin Company has the following projected costs for manufacturing and selling and administrative expenses: (Click the icon to view the projected costs.) January. If an input field is not used in the table leave the input field empty; do not enter a zero.) Data table All costs are paid in month incurred except: direct materials, which are paid in the month following the purchase; utilities, which are paid in the month after incurred; and property taxes, which are prepaid for the year on January 2. The Accounts Payable and Utilities Payable accounts have a zero balance on January 1.
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Note the Borel sets are countably generated 13-11- 2-9 Show that- if f.docx
Note the Borel sets are countably generated
13.11. 2.9 Show that, if f is measurable ( A ) , then there exists a countable subclass A f of A such that f is measurable ( A f ) .
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Not all experience the COVID-19 pandemic the same way- There are facto.docx
Not all experience the COVID-19 pandemic the same way. There are factors at play for who gets tested, treatment, and who is able to practice social distancing. A central problem is how to fairly distribute healthcare resources when adequate resources cannot be provided for all in need. Using either distributive justice and compensatory justice, explain how scarce resources should be distributed.
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Noncoding RNA Is RNA that is not transcribed Includes some protein com.docx
Noncoding RNA Is RNA that is not transcribed Includes some protein components Includes rRNA and tRNA Is not translated A and b C and D
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Marilyn was supposed to pay $1-460 to Bernice on March 2- Some time la.docx
Marilyn was supposed to pay $1,460 to Bernice on March 2. Some time later Marilyn paid Bernice an equivalent payment of $1,552.84, allowing for a time value of money of 3.9% compounded monthly. When did Marilyn make the payment?
Marylin made the payment ________ months later.
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Nine (9) years ago Bruce invested $1-950- Today- the investment is wor.docx
Nine (9) years ago Bruce invested $1 , 950 . Today, the investment is worth $4 , 700 . It interest is compounded annually, what annual rate of return did Bruce earn on his investment? Do not round intermediate calculations. Round your answer to one decimal place. %
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Next we'll look at the fern sporophyte- This stage probably looks more.docx
Next we'll look at the fern sporophyte. This stage probably looks more like a "plant" to you than any organism we've observed thus far. The leaf of the fern is the site of photosynthesis, but the underside of the leaf is also the site of spore production in structures known as sori (singular: sorus). Each sorus contains multiple sporangia, organs that each produce many spores. The spores are then dispersed away from the parent plant through the air, often with the help of spring-like structures that help to catapult the spores. Based on your observations of the moss and fern sporophytes, which type of plant do you think is able to disperse their spores a greater distance? Explain your answer. How might the development of vascular tissue relate to this difference in spore dispersal capability?
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Net Assets without Donor Restrictions - Board Designated NET ASSETS WI.docx
Net Assets without Donor Restrictions - Board Designated NET ASSETS WITH DONOR RESTRICTIONS Net Assets with Donor Restrictions closing entry HAVEN HOSPITAL STATEMENT OF OPERATIONS for the year ended June 302020 Revenues without Donor Restrictions: Net Assets Released_Erom Restrictions: Expiration of Time Restrictions Satisfaction of Program Restrictions Total Net Assets Released From Restrictions Total Operating Revenues Expenses: Salaries and Benefits Supplies Utilities Insurance Depreciation Interest Total Operating Expenses Operating Loss Other Income Unrestricted Gifts and Bequests Income on Endowment Investments Income on Assets Whose Use is Limited Net Realized and Unrealized Gains on Investments - Undesignated Net Realized and Unrealized Gains on Investments - Board Designated Total Other Income Excess of Revenues over Expenses Net Assets Released From Restrictions: Satisfaction of Property Plant and Equipment Restrictions Increase in Net Assets without Donor Restrictions HAVEN HOSPITAL STATEMENT OF CHANGES IN NET ASSETS for the year ended June 302020 Net Assets without Donor Restrictions: Excess of Revenues over Expenses Net Assets Released From Restrictions: Satisfaction of Property Plant and Equipment Restrictions Increase in Net Assets without Donor Restrictions Net Assets with Donor Restrictions: Contribution Revenue Net Realized and Unrealized Gains on Investments Other Investment Income Net Assets Released From Restrictions Increase (Decrease) in Net Assets with Donor Restrictions Increase in Net Assets Net Assets, beginning of year Net Assets, end of year HAVEN HOSPITAL STATEMENT OF FINANCIAL POSITION AS OF JUNE 30, 2020 ASSETS CURRENTASSETS Cash and Cash Equivalents $ Patient Accounts Receivable (Net of Allowances for Contractual Adjustments and Implicit Price Concessions) Contributions Receivable Supplies Total Current Assets NONCURRENT ASSETS Investments - Assets Limited as to Use Long-Term Investments Property, Plant, and Equipment Less: Accumulated Depreciation Total Assets LIABILITIES AND NET ASSETS CURRENT LIABILITIES Accounts Payable $ Long-Term Debt: Current Installment Total Current Liabilities Long-term Liabilities Total Liabilities Net Assets without Donor Restrictions: Board Designated Other Undesignated Total without Donor Restrictions Net Assets with Donor Restrictions Total Net Assets Total Liabilities and Net Assets HAVEN HOSPITAL STATEMENT OF CASH FLOWS for the year ended June 302020 Cash Flows From Operating Activities: Change in Net Assets Adjustments to Reconcile Change in Net Assets to Net Cash Provided by Operating Activities: Depreciation Decrease in Patient Receivable (Net of Allowance) Increase in Supplies Decrease in Contributions Receivable (Net of Allowance) Increase in Accounts Payable Gains on Long-Term Investments Contributions Restricted to Investment in Property, Plant and Equipment Contributions Restricted to Long-Term Investment in Endowments Investment Income Restricted to Endowment Cash Flo.
Need help Window Admin 2 10- Which DNS servers are at the highest leve.docx
Need help Window Admin 2
10. Which DNS servers are at the highest level of the DNS namespace? a. root name servers b. .com name servers c. cachedns name servers d. forwarding name servers 11. True or False: In the DNS design, the stub zone contains only SOA and NS resource records, plus host resource records that identify the authoritative servers for a zone. Short Answer Ouestions (1 point each) -- Use Any Sources (Online or Print media) 12. What are the characteristics of a strong password? 13. List the steps to find out what domain function level your server currently has? 14. What is called the main or unified tool (part of AD ) that is used for managing numerous aspects of a 2019 server?
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Need help filling out the missing sections of this code- the sections.docx
Need help filling out the missing sections of this code. the sections missing are step 6, 7, and 9.
Step 1: Load the Tox21 Dataset.
import numpy as np
np.random.seed(456)
import tensorflow as tf
tf.set_random_seed(456)
import matplotlib.pyplot as plt
import deepchem as dc
from sklearn.metrics import accuracy_score
_, (train, valid, test), _ = dc.molnet.load_tox21()
train_X, train_y, train_w = train.X, train.y, train.w
valid_X, valid_y, valid_w = valid.X, valid.y, valid.w
test_X, test_y, test_w = test.X, test.y, test.w
Step 2: Remove extra datasets.
# Remove extra tasks
train_y = train_y[:, 0]
valid_y = valid_y[:, 0]
test_y = test_y[:, 0]
train_w = train_w[:, 0]
valid_w = valid_w[:, 0]
test_w = test_w[:, 0]
Step 3: Define placeholders that accept minibatches of different sizes.
# Generate tensorflow graph
d = 1024
n_hidden = 50
learning_rate = .001
n_epochs = 10
batch_size = 100
with tf.name_scope("placeholders"):
x = tf.placeholder(tf.float32, (None, d))
y = tf.placeholder(tf.float32, (None,))
Step 4: Implement a hidden layer.
with tf.name_scope("hidden-layer"):
W = tf.Variable(tf.random_normal((d, n_hidden)))
b = tf.Variable(tf.random_normal((n_hidden,)))
x_hidden = tf.nn.relu(tf.matmul(x, W) + b)
Step 5: Complete the fully connected architecture.
with tf.name_scope("output"):
W = tf.Variable(tf.random_normal((n_hidden, 1)))
b = tf.Variable(tf.random_normal((1,)))
y_logit = tf.matmul(x_hidden, W) + b
# the sigmoid gives the class probability of 1
y_one_prob = tf.sigmoid(y_logit)
# Rounding P(y=1) will give the correct prediction.
y_pred = tf.round(y_one_prob)
with tf.name_scope("loss"):
# Compute the cross-entropy term for each datapoint
y_expand = tf.expand_dims(y, 1)
entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y_expand)
# Sum all contributions
l = tf.reduce_sum(entropy)
with tf.name_scope("optim"):
train_op = tf.train.AdamOptimizer(learning_rate).minimize(l)
with tf.name_scope("summaries"):
tf.summary.scalar("loss", l)
merged = tf.summary.merge_all()
Step 6: Add dropout to a hidden layer.
Step 7: Define a hidden layer with dropout.
Step 8: Implement mini-batching training.
train_writer = tf.summary.FileWriter('/tmp/fcnet-tox21',
tf.get_default_graph())
N = train_X.shape[0]
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 0
for epoch in range(n_epochs):
pos = 0
while pos N:
batch_X = train_X[pos:pos+batch_size]
batch_y = train_y[pos:pos+batch_size]
feed_dict = {x: batch_X, y: batch_y}
_, summary, loss = sess.run([train_op, merged, l], feed_dict=feed_dict)
print("epoch %d, step %d, loss: %f" % (epoch, step, loss))
train_writer.add_summary(summary, step)
step += 1
pos += batch_size
# Make Predictions
valid_y_pred = sess.run(y_pred, feed_dict={x: valid_X})
Step 9: Use TensorBoard to track model convergence.
include screenshots for the following:
1) a TensorBoard graph for the model, and
2) the loss curve.
.
Name the organism (parasite) seen under the microscope- Plospodiun tak.docx
Name the organism (parasite) seen under the microscope. Plospodiun takiparum Trypanosoma cruzi Saccharornyces cerevisiae Entamoeba histolybica What disease does this organism cause? Malaria Dysentery African Sleeping Sickness Chagas Disease Endospore stain: Which method of staining was used to make this slide? Kinyoun method Ziehl-Neelsen method Schaeffer-Fulton method Domer method Gram Stain: What is the result for Picture B? Gram Negative Gram Positive Question 11 What staining procedure was used to make this slide?
.
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