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Tag: Homework Solution

  • [Solved] Statistics Project

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    [>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics) Walkthrough recorded Spring 2025: [**https://arizona.box.com/s/ii2mhua71jusvwdwlnuhr63kzhuk09c8**](https://arizona.box.com/s/ii2mhua71jusvwdwlnuhr63kzhuk09c8) **Final Project** **For this project, we are going to p…

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    [>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics)

    Walkthrough recorded Spring 2025:

    [**https://arizona.box.com/s/ii2mhua71jusvwdwlnuhr63kzhuk09c8**](https://arizona.box.com/s/ii2mhua71jusvwdwlnuhr63kzhuk09c8)

    **Final Project**

    **For this project, we are going to pretend that you are doing an experiment on a topic of your choice. For demonstration purposes, I'm going to do an analysis on the topic of how much 30 people agree with me about COVID-19 vaccinations and how two different approaches (Approch A and Approch B) affect that agreement. I want you to think of a topic that you are interested and this final project will include an analysis of the data in light of these randomly selected results.**

    **Step 1:**

    Create your randomized data in Excel

    You can create random numbers that are in the range of data points you would expect from your measurements in your experiments. For example, if you had a Likert scale measure then you would create numbers between 0 and 10 or 1 and 5 etc.

    In a Word Document (or equivalent) Describe what your Experiment is about just like I did.

    in Excel use =rand() then create 3 column and 30 rows

    Insert a category row above this with labels that correspond to what your data is showing. EX My three columns are Agreement, Approach A and, Approach B.

    **Step 2:**

    Convert these columns into binomial (dichotomous) data. In my experiment, I'm interested in agreement so I will convert the percentage of the agreement into a dichotomous Agree or Disagree category.

    In excel, you can use this function

    =if(A2 > 0.5, "Agree","Disagree")

    And right corner drag across three columns and then down to the 30 participants.

    a.) Look at the pattern of Agreement and Disagreement and find the longest string of dichotomous variables you have discovered and calculate how likely that would be… Don't forget to multiply by the number of potential occurrences.

    EX:

    0.5^(largest string) so a string of 7 heads in a row is .5^7*90 …. ~0.78125 or ~78% should we be surprised to see this string of Heads in a row? Write a sentence or two about this result.

    b.) What is the likelihood of getting at least this many Agreements in this random sample? (I created three new columns and counted the agreements by modifying our if statement to read out 1 if agreement and 0 if disagreement and then count the number of agreements.

    EX = _If(A2>0.5,1,0))_

    BINOMDIST(num_successes, num_trials, prob_success, cumulative)

    EX:

    _=binomdist(42,90,.5,True)_

    For the probability of at least that many occurrences (Culmilitve percentage).

    _=binomdist(42,90,.5,False)_

    Probablity of getting exact percentage.

    Is the result you got probable or not? Write a sentence or two about the likelihood that you would get this result.

    If you were interviewing people for your study and you got this number of disagreements (or agreements) in a row should you be surprised?

    **Step 3:**

    Let's say that Approach A increases agreement by 10% and Approach B Decreases agreement 20%

    Note you can choose these effects for your own project but having an increase and decrease should be a good start.

    Change the data to reflect these calculations.

    in Excel

    _=B2*1.10_

    in new column and drag down

    _=C2*0.8_

    in another new column and drag down

    **Step 4:**

    Create a new column with the average of each of the three rows.

    In Excel you can use =average(highlight first row) in a new column then apply to the column by dragging the formula (bottom right corner of box when it turns into a black **+** and drag down)

    Now let's look at histograms for the averages vs. the random numbers

    a.) What should we expect to see? Which one is closer to a normal distribution? Write a paragraph explaining your findings.

    a. Insert Graph Histogram in Sheets for each column

    EX

    Please paste your histograms in the word file to demonstrate your work.

    **What do we expect to see in a distribution?**

    Most data will fall into a normal curve because this kind of data is usually measuring something that is a result of many different independent factors.

    Even though each factor by itself might have a non-normal distribution, i.e. for a single die the distribution is completely flat, the combination of multiple independent factors will more often than not create a normal distribution (bell curve).

    Does it have Skew?

    =skew(Highlight)

    in Excel

    Are there Floor or Ceiling effects?

    NOTE: For statistical analysis to work correctly the distribution has to be normal so translating the numbers into a different scale i.e. Log, Exponential etc. can turn a skew into an appropriate scale that shows a normal distribution.

    **Step 5:**

    Translation of scale to create a normal distribution

    ![Log data vs. normal data](https://www.sweetstudy.com/questions/statistics-project-20964155)

    It may seem strange but changing the scale doesn't affect the statistics but for the stats to work the data have to be on a normalized distribution.

    if we apply a logarithmic translation to a normal graph we should get non-normal distribution as a result but if you saw a logarithmic histogram (terrorist damage, earthquake energy etc.) you would be able to do this to reverse the process so we could do statistical analysis on the results.

    We can test this by creating another column that takes the natural log of our average column and then taking a look at the histogram to evaluate it's normality.

    _=ln(highlight first-row point then drag down)_

    and insert chart histogram of this column

    in new column in Sheets/Excel

    a.) Should we apply this transformation to our data before moving on? Why or why not? Take a couple of sentences to explain.

    b.) What is the Skew of each kind of data?

    _=skew(highlight column)_

    in Sheets/Excel

    **Step 6:**

    Find the Mean, Median and Mode of your data. What do these measurements tell us about your data?

    =Average( _highlight data_) is the function for Mean

    Graph these means Please (Highlight means and insert bar chart)

    EXTRA CREDIT: Add SEM error bars.

    =Median( _highlight data_) is the function for Median

    =Mode( _highlight data_) is the function of Mode

    Note: for our data there likely isn't a mode but you can check your function on the dichotomous variables to see if it works.

    a.) When should you use medians vs. means? Which is best for Skewed distributions? Write a couple of sentences explaining why.

    b.) Getting modes for this type of data are inappropriate but we saw these in the histograms. If you were getting people to vote would you use plurality votes or rank-order voting write a couple of sentences as to why?

    _**Step 7:**_

    **Do a T-test between your data and another measurement of your choice and explain this difference/non-difference.**

    =ttest( _highlight data of first group, highlight data of second group, 2,1_)  is a function for a 2 tail paired t-test.

    or

    t.test(Data$ApprochA,Data$Agreement,paired=TRUE)

    t.test(Data$ApprochB,Data$Agreement,paired=TRUE)

    is there a significant difference between the People who Agree or Disagree?

    t.test(Data$Agreement~Data$DoAgree)

    a.) Does that result make sense? Explain in a couple of sentences.

    Would it be more appropriate to do an ANOVA?

    In R:

    one.way <- aov(Score ~ Group, data = Data)

    summary(one.way)

    In Sheets using a spreadsheet:

    [anova.xls](https://d2l.arizona.edu/content/enforced/1726113-218-2261-7W1BASV314101103201203/anova.xls?ou=1726113)

    Converting F to P-value. [https://www.socscistatistics.com/pvalues/fdistribution.aspx](https://www.socscistatistics.com/pvalues/fdistribution.aspx)

    b.) Explain why ANOVA is used vs. T-tests. What is an F ratio?

    **Step 8:**

    **Correlate the three first columns with each other**

    _In Excel_

    _=Correl(Highlight first group, highlight the second group)_

    a.) Which columns have the highest/lowest correlations? Is this result surprising (expected by random chance)?

    Extra Credit: Provide a Scatterplot with a trendline showing this relationship.

    **Step 9:**

    What do you do if your data has non-linear relationships?

    First, we have to create non-linear data.

    In a new column us the first column (EX Agreement) to apply a sin wave pattern.

    In excel:

    _=sin(row()/10)+A2_

    In a new column using the equation above to add a non-linear relationship to your data create Then drag down

    In R:

    _Data$Sin <- sin(seq(1,30,1)/10)_

    _Data$SinAndAgg <- Data$Sin+Data$Agreement_

    Check out the added pattern

    _plot(Data$SinAndAgg)_

    _lines(Data$Sin)_

    Now let's take that out again by taking the residuals

    Do a regression analysis to find out if other measurements could be used to predict your own. Explain the results.

    **Regression**

    As mentioned above correlations can be used to make predictions based on many different forms of data. If you are trying to predict something using correlated data it's called regression. For example, let's see if there is a relationship between Agreement and Agreement and Sin.

    _AggResid <- lm(SinAndAgg ~ Sin, data=Data)_

    _summary(AggResid)_

    a.) Was there a relationship between our combined factor Sin and Aggression and the Sin component?

    _Data$AggResid <- resid(AggResid)_

    _plot(Data$AggResid)_

    _abline(h=0)_

    b.) Let's look at what variance is leftover.

    How does this relate to the original Agreement data?

    plot(Data$AggResid~Data$Agreement)

    c.) Now let's see if there is a relationship between our combined factor Sin and Aggression and the Aggression component?

    _SinResid <- lm(SinAndAgg ~ Agreement, data=Data)_

    _summary(SinResid)_

    d.) Now, what is leftover?

    _Data$SinResid <- resid(SinResid)_

    _plot(Data$SinResid)_

    _abline(h=0)_

    e.) How does this relate to the original Sin data?

    _plot(Data$SinResid~Data$Sin)_

    **Step 10:**

    **Do an Exploratory Factor Analysis to see how our measures relate to each other.**

    It's not always clear what we should include in our models of what's going on. For example, we might ask the questions are both math and reading scores required in an analysis of our country data? Statisticians can help researchers narrow down what they think is important by doing an Exploratory Factor Analysis to see if we can combine/eliminate variables because they are redundant. We can often find that variables that were distinct factors are really related and can be combined into a single **Latent** variable (i.e. component 1, 2 of IQ etc.).

    or if you want to look at inflation data this has my data I've gathered. [https://docs.google.com/spreadsheets/d/1_zX6yhNj5SbrqvepUmMk06Xiu21j9WVoCXi0bka-1rk/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1_zX6yhNj5SbrqvepUmMk06Xiu21j9WVoCXi0bka-1rk/edit?usp=sharing)

    Upload data to R

    pc <-read.csv(file.choose())

    _princomp(pc, cor=TRUE)_

    _**AgreementsApprochRelated <-princomp(pc, cor=TRUE)**_

    _**summary(AgreementsApprochRelated)**_

    _**plot(AgreementsApprochRelated)**_

    **this is a kind of scree plot as describes here** [**https://en.wikipedia.org/wiki/Scree_plot#:~:text=In%20multivariate%20statistics%2C%20a%20scree,principal%20component%20analysis%20(PCA).**](https://en.wikipedia.org/wiki/Scree_plot#:~:text=In%20multivariate%20statistics%2C%20a%20scree,principal%20component%20analysis%20(PCA).)

    _**biplot(AgreementsApprochRelated)**_

    **are there any clusters/groups?**

    **loadings(AgreementsApprochRelated)**

    **gives a numerical table that shows you component loadings of each factor.**

    [**This link shows how to understand factor analysis with graphs.**](http://youtu.be/kAR93XfsrEs)

    **a.) Paste a bi-plot/loadings of your factor analysis and Tell us if you could combine any of the factors we are given? How many unique factors are in our dataset? What would you call them? i.e. how many groups do you see now and what did those measures have in common?**

  • [Solved] History

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    **files (1)** – ## DeepDiveModule8.docx **## DeepDiveModule8.docx** Deep Dive Conversation 8: Han Dynasty China and Imperial Rome (300 words) Hello! Remember, conversations are meant to help us think about, digest, discuss, and question the issues we are confronting and the materials we are engaging…

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    **files (1)**

    – ## DeepDiveModule8.docx

    **## DeepDiveModule8.docx**

    Deep Dive Conversation 8: Han Dynasty China and Imperial Rome (300 words)

    Hello! Remember, conversations are meant to help us think about, digest, discuss, and question the issues we are confronting and the materials we are engaging. Pick something from the Deep Dive options that looks interesting, read (or watch or listen to) it, and then tell us about it. Some tips:

    · Go beyond simple summaries of the item you selected (though your summary should be accurate)

    · Share what you found interesting, surprising, or significant about what you read (or viewed, or heard)

    · Connect the item back to what we read and/or viewed for this module ("Things everyone should read and review" from the module front page)

    Let's be sure that our conversations are informal, open, and engaging dialogues (and keep in mind you are more than welcome to use the video function to record yourself—note that it may take a few minutes to save). You can review the tips for conversations
    [here](https://uncfsu.instructure.com/courses/49327/pages/tips-for-deep-dive-conversations)—remember to check back through the module.

    _Deep Dive options_(remember, select
    **one** of these nine items to read, watch, or listen to):

    _Reading:_

    · J-STOR Daily:
    [The Roman Feast of DeathLinks to an external site.](https://daily.jstor.org/a-roman-feast-of-death/)

    ·
    _J-STOR Daily:_ [Holiday Supply Chain Issues of Ancient RomeLinks to an external site.](https://daily.jstor.org/holiday-supply-chain-issues-of-ancient-rome/)

    ·
    _J-STOR Daily:_ [Girls and Dolls in the Roman EmpireLinks to an external site.](https://daily.jstor.org/girls-and-dolls-in-the-roman-empire/)

    ·
    _J-STOR Daily:_ [In Han Dynasty China, Bisexuality was the NormLinks to an external site.](https://daily.jstor.org/in-han-dynasty-china-bisexuality-was-the-norm/)

    **## DeepDiveModule8.docx**

    Deep Dive Conversation 8: Han Dynasty China and Imperial Rome (300 words)

    Hello! Remember, conversations are meant to help us think about, digest, discuss, and question the issues we are confronting and the materials we are engaging. Pick something from the Deep Dive options that looks interesting, read (or watch or listen to) it, and then tell us about it. Some tips:

    · Go beyond simple summaries of the item you selected (though your summary should be accurate)

    · Share what you found interesting, surprising, or significant about what you read (or viewed, or heard)

    · Connect the item back to what we read and/or viewed for this module ("Things everyone should read and review" from the module front page)

    Let's be sure that our conversations are informal, open, and engaging dialogues (and keep in mind you are more than welcome to use the video function to record yourself—note that it may take a few minutes to save). You can review the tips for conversations
    [here](https://uncfsu.instructure.com/courses/49327/pages/tips-for-deep-dive-conversations)—remember to check back through the module.

    _Deep Dive options_(remember, select
    **one** of these nine items to read, watch, or listen to):

    _Reading:_

    · J-STOR Daily:
    [The Roman Feast of DeathLinks to an external site.](https://daily.jstor.org/a-roman-feast-of-death/)

    ·
    _J-STOR Daily:_ [Holiday Supply Chain Issues of Ancient RomeLinks to an external site.](https://daily.jstor.org/holiday-supply-chain-issues-of-ancient-rome/)

    ·
    _J-STOR Daily:_ [Girls and Dolls in the Roman EmpireLinks to an external site.](https://daily.jstor.org/girls-and-dolls-in-the-roman-empire/)

    ·
    _J-STOR Daily:_ [In Han Dynasty China, Bisexuality was the NormLinks to an external site.](https://daily.jstor.org/in-han-dynasty-china-bisexuality-was-the-norm/)

  • [Solved] Case study

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    What does it mean to have worsening joint pain?

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    What does it mean to have worsening joint pain?

  • [Solved] VII

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    see attached **files (2)** – ## HLS3307VII.docx – ## UnitVII.pdf **## HLS3307VII.docx** There are numerous artificial intelligence applications used in homeland security for defense, intelligence, diplomacy, surveillance, cybersecurity, and other areas in both the public and private sector. For this…

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    Assignment Prompt & Instructions

    see attached

    **files (2)**

    – ## HLS3307VII.docx

    – ## UnitVII.pdf

    **## HLS3307VII.docx**

    There are numerous artificial intelligence applications used in homeland security for defense, intelligence, diplomacy, surveillance, cybersecurity, and other areas in both the public and private sector. For this assignment, you will create a PowerPoint presentation which examines current and emerging artificial intelligence (AI) and machine learning technologies being utilized in homeland security. Such technologies include but are not limited to:

    1. metamaterial electronically scanning array (MESA) radar,

    2. the global travel assessment system (GTAS),

    3. APEX Screening at Speed

    Your PowerPoint presentation must present at least three different types of artificial intelligence or machine learning technologies as follows:

    1. Two of the AI/machine learning technologies you present should be in use today by first responders, local/state/federal law enforcement agencies, and/or private sector entities in their homeland security efforts.

    2. Present one AI or machine learning technology that is in development or testing and expected to be used in future homeland security efforts.

    For each of the three AI/machine learning technologies you present, you must:

    1. Summarize their purpose and how they work and are (or can be) used in homeland security operations.

    2. Explore how they assist in public and private sector homeland security efforts. Be sure to address specifically how they have advanced (or will advance) homeland security capabilities and effectiveness.

    Your PowerPoint should conclude with a discussion of terrorist use of AI technology as a threat to homeland security.

    Your PowerPoint presentation must be 8-10 slides, not including the title and reference slides. You must use the speaker notes function in PowerPoint to augment each content slide with a minimum of 75 words. The speaker notes should enhance or provide amplifying information on the slide content and not repeat it.

    You must use a minimum of three sources, other than images, to support your presentation. Sources must be from academic or reliable industry resources. Al sources used must have proper citations and references in APA style.

    **## UnitVII.pdf**

    HLS 3306, Homeland Security Technology 1

    Course Learning Outcomes for Unit VII

    Upon completion of this unit, students should be able to:

    4. Assess terrorist technological threats to homeland security.

    5. Describe the future of homeland security technologies.

    6. Investigate the use of artificial intelligence (AI) in homeland security.
    6.1 Summarize artificial intelligence and machine learning technologies.
    6.2 Discuss how AI and machine learning technologies have advanced homeland security

    capabilities and effectiveness.
    6.3 Convey emerging and future artificial intelligence technologies.

    Required Unit Resources

    In order to access the following resources, click the links below.

    Khan, A., Imam, I., & Azam, A. (2021). Role of artificial intelligence in defence strategy: Implications for global

    and national security. Strategic Studies, 41(1), 19.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=tsh&AN=150468892&site=ehost-live&scope=site

    Verhelst, H. M., Stannat, A. W., & Mecacci, G. (2020). Machine learning against terrorism: How big data

    collection and analysis influences the privacy-security dilemma. Science & Engineering Ethics, 26(6),
    2975–2984.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=asn&AN=147734319&site=ehost-live&scope=site

    Unit Lesson

    The lesson for this unit is presented in the following PowerPoint presentation and consists of both slide
    content and additional information provided in the audio narration. Click the speaker icon if it is shown on a
    slide to hear the additional lesson audio content.

    Unit VII Lesson PowerPoint Presentation

    You may also access this presentation with a transcript of the audio portions in printable form in the Unit VII
    PowerPoint PDF.

    A transcript for the audio portion of each slide can be viewed in the speaker notes section of each individual
    slide.

    Suggested Unit Resources

    In order to access the following resources, click the links below.

    The following article examines the role and impact of artificial intelligence and machine learning on the
    intelligence community both now and in the future.

    UNIT VII STUDY GUIDE

    Artificial Intelligence (AI) in
    Homeland Security

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935906_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935906_1)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1)

    HLS 3306, Homeland Security Technology 2

    UNIT x STUDY GUIDE

    Title

    Brantly, A. F. (2018). When everything becomes intelligence: Machine learning and the connected world.
    Intelligence & National Security, 33(4), 562–573.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=tsh&AN=129927820&site=ehost-live&scope=site

    The following Department of Homeland Security (DHS) webpage outlines the DHS artificial intelligence and
    machine learning technologies being created by the Science and Technology Directorate.

    U.S. Department of Homeland Security. (2020, September 9). Feature article: S&T’s transportation security

    laboratory evaluates artificial intelligence and machine learning technologies.
    https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-
    artificial-intelligence

    The following document outlines DHS’s artificial intelligence strategy.

    U.S. Department of Homeland Security. (2020, December 3). U.S. Department of Homeland Security artificial

    intelligence strategy. https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=129927820&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=129927820&site=ehost-live&scope=site)

    [https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence](https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence)

    [https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence](https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence)

    [https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf](https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf)

    [https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf](https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf)

    – Course Learning Outcomes for Unit VII
    – Required Unit Resources
    – Unit Lesson
    – Suggested Unit Resources

    **## UnitVII.pdf**

    HLS 3306, Homeland Security Technology 1

    Course Learning Outcomes for Unit VII

    Upon completion of this unit, students should be able to:

    4. Assess terrorist technological threats to homeland security.

    5. Describe the future of homeland security technologies.

    6. Investigate the use of artificial intelligence (AI) in homeland security.
    6.1 Summarize artificial intelligence and machine learning technologies.
    6.2 Discuss how AI and machine learning technologies have advanced homeland security

    capabilities and effectiveness.
    6.3 Convey emerging and future artificial intelligence technologies.

    Required Unit Resources

    In order to access the following resources, click the links below.

    Khan, A., Imam, I., & Azam, A. (2021). Role of artificial intelligence in defence strategy: Implications for global

    and national security. Strategic Studies, 41(1), 19.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=tsh&AN=150468892&site=ehost-live&scope=site

    Verhelst, H. M., Stannat, A. W., & Mecacci, G. (2020). Machine learning against terrorism: How big data

    collection and analysis influences the privacy-security dilemma. Science & Engineering Ethics, 26(6),
    2975–2984.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=asn&AN=147734319&site=ehost-live&scope=site

    Unit Lesson

    The lesson for this unit is presented in the following PowerPoint presentation and consists of both slide
    content and additional information provided in the audio narration. Click the speaker icon if it is shown on a
    slide to hear the additional lesson audio content.

    Unit VII Lesson PowerPoint Presentation

    You may also access this presentation with a transcript of the audio portions in printable form in the Unit VII
    PowerPoint PDF.

    A transcript for the audio portion of each slide can be viewed in the speaker notes section of each individual
    slide.

    Suggested Unit Resources

    In order to access the following resources, click the links below.

    The following article examines the role and impact of artificial intelligence and machine learning on the
    intelligence community both now and in the future.

    UNIT VII STUDY GUIDE

    Artificial Intelligence (AI) in
    Homeland Security

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935906_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935906_1)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1)

    HLS 3306, Homeland Security Technology 2

    UNIT x STUDY GUIDE

    Title

    Brantly, A. F. (2018). When everything becomes intelligence: Machine learning and the connected world.
    Intelligence & National Security, 33(4), 562–573.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=tsh&AN=129927820&site=ehost-live&scope=site

    The following Department of Homeland Security (DHS) webpage outlines the DHS artificial intelligence and
    machine learning technologies being created by the Science and Technology Directorate.

    U.S. Department of Homeland Security. (2020, September 9). Feature article: S&T’s transportation security

    laboratory evaluates artificial intelligence and machine learning technologies.
    https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-
    artificial-intelligence

    The following document outlines DHS’s artificial intelligence strategy.

    U.S. Department of Homeland Security. (2020, December 3). U.S. Department of Homeland Security artificial

    intelligence strategy. https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=129927820&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=129927820&site=ehost-live&scope=site)

    [https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence](https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence)

    [https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence](https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence)

    [https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf](https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf)

    [https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf](https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf)

    – Course Learning Outcomes for Unit VII
    – Required Unit Resources
    – Unit Lesson
    – Suggested Unit Resources

    **## HLS3307VII.docx**

    There are numerous artificial intelligence applications used in homeland security for defense, intelligence, diplomacy, surveillance, cybersecurity, and other areas in both the public and private sector. For this assignment, you will create a PowerPoint presentation which examines current and emerging artificial intelligence (AI) and machine learning technologies being utilized in homeland security. Such technologies include but are not limited to:

    1. metamaterial electronically scanning array (MESA) radar,

    2. the global travel assessment system (GTAS),

    3. APEX Screening at Speed

    Your PowerPoint presentation must present at least three different types of artificial intelligence or machine learning technologies as follows:

    1. Two of the AI/machine learning technologies you present should be in use today by first responders, local/state/federal law enforcement agencies, and/or private sector entities in their homeland security efforts.

    2. Present one AI or machine learning technology that is in development or testing and expected to be used in future homeland security efforts.

    For each of the three AI/machine learning technologies you present, you must:

    1. Summarize their purpose and how they work and are (or can be) used in homeland security operations.

    2. Explore how they assist in public and private sector homeland security efforts. Be sure to address specifically how they have advanced (or will advance) homeland security capabilities and effectiveness.

    Your PowerPoint should conclude with a discussion of terrorist use of AI technology as a threat to homeland security.

    Your PowerPoint presentation must be 8-10 slides, not including the title and reference slides. You must use the speaker notes function in PowerPoint to augment each content slide with a minimum of 75 words. The speaker notes should enhance or provide amplifying information on the slide content and not repeat it.

    You must use a minimum of three sources, other than images, to support your presentation. Sources must be from academic or reliable industry resources. Al sources used must have proper citations and references in APA style.

    **## UnitVII.pdf**

    HLS 3306, Homeland Security Technology 1

    Course Learning Outcomes for Unit VII

    Upon completion of this unit, students should be able to:

    4. Assess terrorist technological threats to homeland security.

    5. Describe the future of homeland security technologies.

    6. Investigate the use of artificial intelligence (AI) in homeland security.
    6.1 Summarize artificial intelligence and machine learning technologies.
    6.2 Discuss how AI and machine learning technologies have advanced homeland security

    capabilities and effectiveness.
    6.3 Convey emerging and future artificial intelligence technologies.

    Required Unit Resources

    In order to access the following resources, click the links below.

    Khan, A., Imam, I., & Azam, A. (2021). Role of artificial intelligence in defence strategy: Implications for global

    and national security. Strategic Studies, 41(1), 19.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=tsh&AN=150468892&site=ehost-live&scope=site

    Verhelst, H. M., Stannat, A. W., & Mecacci, G. (2020). Machine learning against terrorism: How big data

    collection and analysis influences the privacy-security dilemma. Science & Engineering Ethics, 26(6),
    2975–2984.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=asn&AN=147734319&site=ehost-live&scope=site

    Unit Lesson

    The lesson for this unit is presented in the following PowerPoint presentation and consists of both slide
    content and additional information provided in the audio narration. Click the speaker icon if it is shown on a
    slide to hear the additional lesson audio content.

    Unit VII Lesson PowerPoint Presentation

    You may also access this presentation with a transcript of the audio portions in printable form in the Unit VII
    PowerPoint PDF.

    A transcript for the audio portion of each slide can be viewed in the speaker notes section of each individual
    slide.

    Suggested Unit Resources

    In order to access the following resources, click the links below.

    The following article examines the role and impact of artificial intelligence and machine learning on the
    intelligence community both now and in the future.

    UNIT VII STUDY GUIDE

    Artificial Intelligence (AI) in
    Homeland Security

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935906_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935906_1)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1)

    HLS 3306, Homeland Security Technology 2

    UNIT x STUDY GUIDE

    Title

    Brantly, A. F. (2018). When everything becomes intelligence: Machine learning and the connected world.
    Intelligence & National Security, 33(4), 562–573.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=tsh&AN=129927820&site=ehost-live&scope=site

    The following Department of Homeland Security (DHS) webpage outlines the DHS artificial intelligence and
    machine learning technologies being created by the Science and Technology Directorate.

    U.S. Department of Homeland Security. (2020, September 9). Feature article: S&T’s transportation security

    laboratory evaluates artificial intelligence and machine learning technologies.
    https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-
    artificial-intelligence

    The following document outlines DHS’s artificial intelligence strategy.

    U.S. Department of Homeland Security. (2020, December 3). U.S. Department of Homeland Security artificial

    intelligence strategy. https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=129927820&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=129927820&site=ehost-live&scope=site)

    [https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence](https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence)

    [https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence](https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence)

    [https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf](https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf)

    [https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf](https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf)

    – Course Learning Outcomes for Unit VII
    – Required Unit Resources
    – Unit Lesson
    – Suggested Unit Resources

    **## HLS3307VII.docx**

    There are numerous artificial intelligence applications used in homeland security for defense, intelligence, diplomacy, surveillance, cybersecurity, and other areas in both the public and private sector. For this assignment, you will create a PowerPoint presentation which examines current and emerging artificial intelligence (AI) and machine learning technologies being utilized in homeland security. Such technologies include but are not limited to:

    1. metamaterial electronically scanning array (MESA) radar,

    2. the global travel assessment system (GTAS),

    3. APEX Screening at Speed

    Your PowerPoint presentation must present at least three different types of artificial intelligence or machine learning technologies as follows:

    1. Two of the AI/machine learning technologies you present should be in use today by first responders, local/state/federal law enforcement agencies, and/or private sector entities in their homeland security efforts.

    2. Present one AI or machine learning technology that is in development or testing and expected to be used in future homeland security efforts.

    For each of the three AI/machine learning technologies you present, you must:

    1. Summarize their purpose and how they work and are (or can be) used in homeland security operations.

    2. Explore how they assist in public and private sector homeland security efforts. Be sure to address specifically how they have advanced (or will advance) homeland security capabilities and effectiveness.

    Your PowerPoint should conclude with a discussion of terrorist use of AI technology as a threat to homeland security.

    Your PowerPoint presentation must be 8-10 slides, not including the title and reference slides. You must use the speaker notes function in PowerPoint to augment each content slide with a minimum of 75 words. The speaker notes should enhance or provide amplifying information on the slide content and not repeat it.

    You must use a minimum of three sources, other than images, to support your presentation. Sources must be from academic or reliable industry resources. Al sources used must have proper citations and references in APA style.

    **## UnitVII.pdf**

    HLS 3306, Homeland Security Technology 1

    Course Learning Outcomes for Unit VII

    Upon completion of this unit, students should be able to:

    4. Assess terrorist technological threats to homeland security.

    5. Describe the future of homeland security technologies.

    6. Investigate the use of artificial intelligence (AI) in homeland security.
    6.1 Summarize artificial intelligence and machine learning technologies.
    6.2 Discuss how AI and machine learning technologies have advanced homeland security

    capabilities and effectiveness.
    6.3 Convey emerging and future artificial intelligence technologies.

    Required Unit Resources

    In order to access the following resources, click the links below.

    Khan, A., Imam, I., & Azam, A. (2021). Role of artificial intelligence in defence strategy: Implications for global

    and national security. Strategic Studies, 41(1), 19.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=tsh&AN=150468892&site=ehost-live&scope=site

    Verhelst, H. M., Stannat, A. W., & Mecacci, G. (2020). Machine learning against terrorism: How big data

    collection and analysis influences the privacy-security dilemma. Science & Engineering Ethics, 26(6),
    2975–2984.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=asn&AN=147734319&site=ehost-live&scope=site

    Unit Lesson

    The lesson for this unit is presented in the following PowerPoint presentation and consists of both slide
    content and additional information provided in the audio narration. Click the speaker icon if it is shown on a
    slide to hear the additional lesson audio content.

    Unit VII Lesson PowerPoint Presentation

    You may also access this presentation with a transcript of the audio portions in printable form in the Unit VII
    PowerPoint PDF.

    A transcript for the audio portion of each slide can be viewed in the speaker notes section of each individual
    slide.

    Suggested Unit Resources

    In order to access the following resources, click the links below.

    The following article examines the role and impact of artificial intelligence and machine learning on the
    intelligence community both now and in the future.

    UNIT VII STUDY GUIDE

    Artificial Intelligence (AI) in
    Homeland Security

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=150468892&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site)

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=147734319&site=ehost-live&scope=site)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935906_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935906_1)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1)

    [https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1](https://online.columbiasouthern.edu/bbcswebdav/xid-143935903_1)

    HLS 3306, Homeland Security Technology 2

    UNIT x STUDY GUIDE

    Title

    Brantly, A. F. (2018). When everything becomes intelligence: Machine learning and the connected world.
    Intelligence & National Security, 33(4), 562–573.
    https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?dire
    ct=true&db=tsh&AN=129927820&site=ehost-live&scope=site

    The following Department of Homeland Security (DHS) webpage outlines the DHS artificial intelligence and
    machine learning technologies being created by the Science and Technology Directorate.

    U.S. Department of Homeland Security. (2020, September 9). Feature article: S&T’s transportation security

    laboratory evaluates artificial intelligence and machine learning technologies.
    https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-
    artificial-intelligence

    The following document outlines DHS’s artificial intelligence strategy.

    U.S. Department of Homeland Security. (2020, December 3). U.S. Department of Homeland Security artificial

    intelligence strategy. https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf

    [https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=129927820&site=ehost-live&scope=site](https://libraryresources.columbiasouthern.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=tsh&AN=129927820&site=ehost-live&scope=site)

    [https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence](https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence)

    [https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence](https://www.dhs.gov/science-and-technology/news/2020/09/09/feature-article-st-tsl-evaluates-artificial-intelligence)

    [https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf](https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf)

    [https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf](https://www.dhs.gov/sites/default/files/publications/dhs_ai_strategy.pdf)

    – Course Learning Outcomes for Unit VII
    – Required Unit Resources
    – Unit Lesson
    – Suggested Unit Resources

  • [Solved] Discussion replies

    ✓ Verified Academic Solution Available

    Executive Solution Overview

    **175 words each** Post 1 I've read a lot about the dangers of spear-phishing, especially how it uses psychological tricks (CISA, 2021). Unlike generic phishing emails, spear-phishing targets specific people to steal sensitive info or money. Attackers often gather public information to make their em…

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    Assignment Prompt & Instructions

    **175 words each**

    Post 1

    I've read a lot about the dangers of spear-phishing, especially how it uses psychological tricks (CISA, 2021). Unlike generic phishing emails, spear-phishing targets specific people to steal sensitive info or money. Attackers often gather public information to make their emails appear convincing, playing on emotions such as trust or fear ("Counterintelligence tips," n.d.).

    I can spot this kind of threat by paying close attention to the interaction instead of reacting on impulse. For instance, if I receive a desperate-sounding email that appears to come from my university's financial aid office and demands quick action, the fake urgency is a major warning sign (CISA, 2021). I make sure to check the sender's email address carefully for minor misspellings, such as swapping letters in the domain name. I also hover over links to verify the website URL before clicking (CISA, 2021).

    While I can't stop cybercriminals from targeting my inbox, I can do things to lower my risk. My main tactic is out-of-band verification: if I receive an email requesting sensitive information, I confirm the request by calling the sender at a trusted, saved phone number ("Counterintelligence tips," n.d.). I also use Multi-Factor Authentication (MFA) on all my important accounts, so stolen passwords alone can't give an attacker access (CISA, 2021). In the end, staying aware of new social engineering tricks helps reduce the damage these threats can cause.

    **References**

    _Avoiding social engineering and phishing attacks | cisa_. America’s Cyber Defense Agency . (2021, February  1). https://www.cisa.gov/news-events/news/avoiding-social-engineering-and-phishing-attacks

    Counterintelligence tips. (n.d.). https://www.dni.gov/files/NCSC/documents/campaign/Counterintelligence_Tips_Spearphishing.pdf

    Post 2

    I love that this is the topic this week because I dealt with a cyber threat at work today. I work as a case manager in behavioral health care so the content on my work computer is highly sensitive information and any leak of that information would be a breach of HIPAA. Today when looking up a website, that was supposed to be 'arrest.org', which shows you any arrest in any U.S. state, I accidently typed arrest.com, which immediately sent me a virus pop us. Not even 5 minutes later, I start receiving pop up after pop up that kept trying to get me to "get rid of the virus." Every time I cleared one away, 5 more came up. Luckily, the guys from IT were still in the office and able to help me. The person I was working with in IT said that viruses like this have the ability to go into permissions and allow themselves to send popups without the user ever giving permission.

    Luckily, pop up scams are pretty easy to identify, in this example, they were bright, unable to be closed, said "PROTECT AGAINST VIRUSES" and "YOU HAVE A WEB VIRUS" with a big CLICK ME button, and overall rather annoying. Though not all are like that. Some can look like they come from your software protection company, in emails, or just on the sides of what ever website you may be using. Unfortunately, you can't always tell which choices are the wrong ones till it's too late, like typing in the web address and it automatically taking you to the very obviously sketchy page.

    The bright side, at least that I found, is that threats like these can be easier to avoid. They're goal is to create a sense of panic, to make you think you have been hacked immediately, when you haven't. They want you to panic bad enough to click them so the actual virus can be placed. In order to address and prevent pop up viruses like the one I dealt with today, the Federal Trade Commission suggests using security software, recognizing common threats like phishing emails or fake security warnings, and avoid risks like using unfamiliar websites and downloading free stuff or content from emails you don't recognize (FTC, 2025).

    I got very lucky that I was still at work when this happened and that I work with a team of people who could help.

    FTC. (2025). Malware: How To Protect Against, Detect, and Remove It. Consumer Advice. Federal Trade Commission. https://consumer.ftc.gov/articles/malware-how-protect-against-detect-and-remove-it

  • [Solved] Week 1

    ✓ Verified Academic Solution Available

    Executive Solution Overview

    1. Answer all questions/criteria with explanations and detail. – Identify a clinical problem for which an NP could advocate for an evidence-based change that is client-focused. Avoid topics that are related to full practice authority, staffing, or burnout. The problem should be centered around clien…

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    Assignment Prompt & Instructions

    1. Answer all questions/criteria with explanations and detail.
    – Identify a clinical problem for which an NP could advocate for an evidence-based change that is client-focused. Avoid topics that are related to full practice authority, staffing, or burnout. The problem should be centered around clients and the care nurse practitioners provide for the population.
    – Describe the scope and impact of the problem.
    – Discuss the role of the NP in addressing the problem.
    – Explain why the problem is of interest to you.
    – This week, you will identify a client-focused clinical problem for which a nurse practitioner (NP) could advocate for an evidence-based practice (EBP) change.

  • [Solved] Perception

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    Read/review the following resources for this activity: – Textbook: Chapters 1, 2, and 5 (optional 3 and 4) – Lesson – [The Communication ProcessLinks to an external site.](https://chamberlain.primo.exlibrisgroup.com/permalink/01CUCON_INST/f6kb8f/cdi_proquest_miscellaneous_2376171201) [25:35] – Min…

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    Read/review the following resources for this activity:

    – Textbook: Chapters 1, 2, and 5 (optional 3 and 4)
    – Lesson
    – [The Communication ProcessLinks to an external site.](https://chamberlain.primo.exlibrisgroup.com/permalink/01CUCON_INST/f6kb8f/cdi_proquest_miscellaneous_2376171201) [25:35]
    – Minimum of 1 outside scholarly source (in addition to the textbook/lesson)

    Perception of messages is not as straightforward as we need it to be, nor as easy. Additionally, in our changing society, we are learning how to interact with people who culturally have different beliefs, values, and attitudes. For the initial post, address the following questions. Use the provided headings, and, under each heading, answer the question in a full paragraph. Please use your textbook and an outside scholarly source in your post, and make sure you cite both sources using APA formatting.

    – **Scenario**
    – Think of a time when you interacted with someone from a different culture. Using the communication model, how did your conversation evolve? Was there a miscommunication? working in a nursing home as an activity assistant.
    – **Perception**
    – How can our understanding of perception help us become more effective communicators?
    – **Change**
    – What could you have done differently?
    – **References**
    – Place your two APA references in this section.

    – [\
    \
    ASSISSMENTGUIDELINESTWO.docx](https://www.sweetstudy.com/files/assissmentguidelinestwo-docx-10510827 "ASSISSMENTGUIDELINESTWO.docx")

  • [Solved] due asap

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    In-depth study guidance and analytical solution covering due asap.

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    Full assignment prompt and step-by-step guidance are included in the downloadable solution file.

  • [Solved] Map concept

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    Marian is a 43-year-old Asian female who presents to your outpatient clinic with complaints of fever and productive cough. – Denies tobacco and drug use – Negative medical history – Family history positive for cardiovascular disease in her father – Reports a fever (max temp 101 F), fatigue, generali…

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    Marian is a 43-year-old Asian female who presents to your outpatient clinic with complaints of fever and productive cough.

    – Denies tobacco and drug use
    – Negative medical history
    – Family history positive for cardiovascular disease in her father

    – Reports a fever (max temp 101 F), fatigue, generalized muscle aches, and productive cough of green sputum x 5 days
    – Starting today, Marian has developed shortness of breath with minimal activity.

    Review of systems:

    – VS: Temp 101.1 F, HR 94 bpm, RR 32 breaths/min, BP 92/48 mm Hg, SaO2 91% on room air
    – **General**: Appears stated age; speech clear
    – **Skin:** warm to touch; pallor present
    – **HEENT:** Nasal congestion present
    – **Respiratory:** crackles (rales) auscultated bilaterlly in lower lobes, positive use of accessory muscles at rest, tachypnea, dyspnea with exertion
    – **Cardiovascular:** S1 and S2 auscultated, no murmurs present. Denies chest pain.
    – **Gastrointestinal:** bowel sounds auscultated in all quadrants; abdomen soft and nontender
    – **Genitourinary:** Denies dyruria
    – **Musculoskeletal:** No edema of extremeties. Strength 5/5 in bilateral upper and lower extremities.
    – **Neurological:** alert and oriented x 4; PERRLA

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  • [Solved] History – American history Historical assignment

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    [>American history homework help](https://www.sweetstudy.com/fields/history-american-history) – [\ \ Screenshot_4-3-2026_75847_dallascollege.brightspace.com.jpeg](https://www.sweetstudy.com/files/screenshot-4-3-2026-75847-dallascollege-brightspace-com-jpeg-10510689 "Screenshot_4-3-2026_75847_da…

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    [>American history homework help](https://www.sweetstudy.com/fields/history-american-history)

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