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3 years ago

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What are examples of Energy careers? Check all that apply. Carpenter Video Editor Electrical Power-Line Installer Printing Press

Operator Power Plant Operator Automotive Service Attendant

2 answers:

Rasek [7]3 years ago

7 0

Answer:

C,E,F

Explanation:

Right on edge 2021

dexar [7]3 years ago

6 0

Answer:

C, E, F

Explanation:

Correct on edge

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The elevation of the end of the steel beam supported by a concrete floor is adjusted by means of the steel wedges E and F. The b

Wewaii [24]

Answer:

a) P ≥ 22.164 Kips

b) Q = 5.4 Kips

Explanation:

GIven

W = 18 Kips

μ₁ = 0.30

μ₂ = 0.60

a) P = ?

We get F₁ and F₂ as follows:

F₁ = μ₁*W = 0.30*18 Kips = 5.4 Kips

F₂ = μ₂*Nef = 0.6*Nef

Then, we apply

∑Fy = 0 (+↑)

Nef*Cos 12º - F₂*Sin 12º = W

⇒ Nef*Cos 12º - (0.6*Nef)*Sin 12º = 18

⇒ Nef = 21.09 Kips

Wedge moves if

P ≥ F₁ + F₂*Cos 12º + Nef*Sin 12º

⇒ P ≥ 5.4 Kips + 0.6*21.09 Kips*Cos 12º + 21.09 Kips*Sin 12º

⇒ P ≥ 22.164 Kips

b) For the static equilibrium of base plate

Q = F₁ = 5.4 Kips

We can see the pic shown in order to understand the question.

7 0

3 years ago

Read 2 more answers

Fictional Corp is looking at solutions for their new CRM system for the sales department. The IT staff already has a fairly heav

Oksi-84 [34.3K]

Answer:

SaaS

Explanation:

Software as a service (SaaS) is also called software on demand, it involves a third party that centrally hosts the software and provides it to the end user.

All aspects of hosting is handled by the third party: application, data, runtime, middleware, operating system, server, virtualization, storage and networking are all handled by the provider.

This is an ideal software service for Fictional corp, as there will be no need to hire additional IT staff to maintain the new CRM software.

3 0

3 years ago

¿Qué es la masonería?

timofeeve [1]

Answer: freemasonry is Being a Mason is about a father helping his son make better decisions; a business leader striving to bring morality to the workplace; a thoughtful man learning to work through tough issues in his life.

Explanation:

3 0

3 years ago

A heat engine that rejects waste heat to a sink at 520 R has a thermal efficiency of 35 percent and a second- law efficiency of

xeze [42]

Answer:

The source temperature is 1248 R.

Explanation:

Second law efficiency of the engine is the ratio of actual efficiency to the maximum possible efficiency that is reversible efficiency.

Given:

Temperature of the heat sink is 520 R.

Second law efficiency is 60%.

Actual thermal efficiency is 35%.

Calculation:

Step1

Reversible efficiency is calculated as follows:

$\eta_{II}=\frac{\eta_{a}}{\eta_{rev}}$

$0.6=\frac{0.35}{\eta_{rev}}$

$\eta_{rev}=0.5834$

Step2

Source temperature is calculated as follows:

$\eta_{rev}=1-\frac{T_{L}}{T}$

$\eta_{rev}=1-\frac{520}{T}$

$0.5834=1-\frac{520}{T}$

T = 1248 R.

The heat engine is shown below:

Thus, the source temperature is 1248 R.

6 0

3 years ago

Let's model this housing price data! Before we can do this, however, we need to split the data into training and test sets. Reme

Lilit [14]

The program reads in a dataset into a pandas dataframe, and uses the train_test_split function in the sklearn library to split the data into <em>training and test sets</em>. The code goes thus :

import pandas as pd

<em>#import</em><em> </em><em>the</em><em> </em><em>pandas</em><em> </em><em>dataframe</em><em> </em><em>and</em><em> </em><em>alias</em><em> </em><em>it</em><em> </em><em>as</em><em> </em><em>pd</em>

from sklearn.model_selection import train_test_split

<em>#import</em><em> </em><em>the</em><em> </em><em>train_test_split</em><em> </em><em>function</em><em> </em>

housing_df = pd.read_csv('housing price.csv')

<em>#read</em><em> </em><em>in</em><em> </em><em>the</em><em> </em><em>housing</em><em> </em><em>data</em><em> </em>

features_df = df.iloc[:,1:]

<em>#seperate</em><em> </em><em>the</em><em> </em><em>features</em><em> </em><em>from</em><em> </em><em>the</em><em> </em><em>label</em><em> </em><em>;</em>

target_df = df.iloc[:,0]

<em>#put</em><em> </em><em>the</em><em> </em><em>label</em><em> </em><em>into</em><em> </em><em>a</em><em> </em><em>seperate</em><em> </em><em>dataframe</em><em> </em><em>as</em><em> </em><em>well</em><em>.</em><em> </em>

X_train, X_test, Y_train, Y_test = train_test_split(features_df, target_df, test_size = 0.1, random_state = 1)

<em>#uses</em><em> </em><em>tuple</em><em> </em><em>unpacking</em><em> </em><em>to</em><em> </em><em>randomly</em><em> </em><em>assign</em><em> </em><em>the</em><em> </em><em>data</em><em> </em><em>each</em><em> </em><em>of</em><em> </em><em>the</em><em> </em><em>4</em><em> </em><em>variables</em><em>.</em><em> </em>

<em>#</em><em>Test</em><em> </em><em>size</em><em> </em><em>is</em><em> </em><em>test</em><em> </em><em>percent</em><em> </em><em>of</em><em> </em><em>the</em><em> </em><em>entire</em><em> </em><em>dataset</em><em> </em>

Learn more :brainly.com/question/4257657?referrer=searchResults

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3 years ago