Each student will write a short research paper for a peer-reviewed research paper that pertains to the week’s assigned reading. This will be a detailed summary of the research paper and what you gained from the research. Each week, you will find an article/peer-reviewed research paper that pertains to the week’s assignment.If you have a difficult time, Google Scholar is a wonderful location to find these types of articles:

https://scholar.google.com/

Once you find the article, you will simply read it and then write a review of it. Think of it as an article review where you submit a short overview of the article.

Your paper should meet the following requirements:

• Be approximately 2-3 pages in length, not including the required cover page and reference page.

• Follow APA6 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.

• Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources.

• Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.

Write 16 pages thesis on the topic raping within dating. Rickert, Psy.D., and C.M. Weinmann, Ph.D. Traditionally, it was believed that women are raped by some unknown person coming out of the darkness. But this paradigm has changed since the evolution of acquaintance rape and ‘date rape’. Date rape is a sexual assault that occurs when the victim and perpetrator are involved in dating relationships, ranging from the first date to a steady dating relationship. [Sally K. Ward, Jane Stapleton, and Jennifer Leatherman] On the basis of the information received and collated from Central Michigan University, the U.S. Dept. of Justice, the Kansas State University Alcohol and other Drug Education Service and other sources, the University of Tennessee prepared a report which revealed that 25% of women were victims of rape and attempted rape whereas 85% rapes occurred on campuses as an acquaintance/ date rape and 42% of college women who were raped did not disclose. [Sandra Choron and Harry Choron] Under the study conducted by Towson State University, it was believed that 33.6% of campus sexual assaults were reported to the Police where the rest went unreported. Campus rape victims are often away from the home for the first time in a new environment and after being victimized by sexual assault, many campus rape victims feel a loss of trust in themselves or others and transfer to another school or dropout [Allan M. Hoffman, John H. Schuh, Robert H. Fenske]

Date rape can happen to anyone, you, me, or anyone around. Anyone would like to go on for a date to have some wonderful time only to discover at the end of the evening that her date can’t take no for an answer. It is estimated that 90 percent of all rapes are never reported. of those that are reported about 60 percent of the victims know their assailants. Although date rape is a problem among females of all ages, it is especially prevalent among girls who are in their final years of high school. American statistics highlight that most victims are between the ages of sixteen and nineteen, with the second-highest rate between the ages of twenty and twenty-four’.

Hi, need to submit a 1750 words paper on the topic Use of Profit and Loss Account, Balance Sheet, and Cash Flow Statement. Profit and Loss Account is an important financial statement from the lender’s point of view in order to arrive at a lending decision. This financial statement evaluates the operational and other activities of an entity over a fiscal period, though it may be drawn for any period of business activities say for a month, quarter, half-year, or for a year.

Profit and loss account contains information about revenue (or sales), costs of sales, beginning and ending inventories, operational expenses details, interest and other financial expenses, and information about taxes as well. A comparative analysis of revenue or sales from the previous few years of profit and loss accounts will provide the marketability trends of the product being dealt in. In the case of a restaurant, revenue will be from the sale of eatables and drinks. If sales are continuously rising there is every possibility that fresh loans when invested in the business will bring more revenue to generate extra profits. Cost of sales provides an idea about the nature of expenditure involved that directly fluctuate with sales and their effect on profitability. If for example loaned funds are used for purchasing kitchen equipment of the restaurant, then depreciation on machinery being manufacturing cost will enhance the costs of sales and thus reduce the margins if expected increase in revenue is not generated. That is why a ratio generated by profit and loss account called ‘Gross Profit Margin’ has gained importance in analyzing the results, particularly during the inflationary period. “The gross profit margin measures the percentage of each sales dollar remaining after the firm has paid for its good.” (Lawrence J. Gitman, page 67) The expenditure recorded in profit and loss account after calculating the gross profits are called below the line expenses and help in computing operating and net profits. Mostly these are overheads and the lender can have an idea about fixed and variable nature of overheads like rent of the restaurant, insurance expenses are fixed and have to bear out even whatever the level of sales or profits, etc

The expenditure recorded in profit and loss account after calculating the gross profits are called below the line expenses and help in computing operating and net profits.

COVID-19 Open Research Dataset Challenge (CORD-19)

An AI challenge with AI2, CZI, MSR, Georgetown, NIH & The White House

(1) FULL-LENGTH PROJECT

Dataset Description

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 44,000 scholarly articles, including over 29,000 with full text, about COVID-19, SARS-CoV-2, and related corona viruses. This freely available dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. There is a growing urgency for these approaches because of the rapid acceleration in new coronavirus literature, making it difficult for the medical research community to keep up.

Call to Action

We are issuing a call to action to the world’s artificial intelligence experts to develop text and data mining tools that can help the medical community develop answers to high priority scientific questions. The CORD-19 dataset represents the most extensive machine-readable coronavirus literature collection available for data mining to date. This allows the worldwide AI research community the opportunity to apply text and data mining approaches to find answers to questions within, and connect insights across, this content in support of the ongoing COVID-19 response efforts worldwide. There is a growing urgency for these approaches because of the rapid increase in coronavirus literature, making it difficult for the medical community to keep up.

A list of our initial key questions can be found under the Tasks section of this dataset. These key scientific questions are drawn from the NASEM’s SCIED (National Academies of Sciences, Engineering, and Medicine’s Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats) research topics and the World Health Organization’s R&D Blueprint for COVID-19.

Many of these questions are suitable for text mining, and we encourage researchers to develop text mining tools to provide insights on these questions.

In this project, you will follow your own interests to create a portfolio worthy single-frame viz or multi-frame data story that will be shared in your presentation. You will use all the skills taught in this course to complete this project step-by-step, with guidance from your instructors along the way. You will first create a project proposal to identify your goals for the project, including the question you wish to answer or explore with data. You will then find data that will provide the information you are seeking. You will then import that data into Tableau and prepare it for analysis. Next you will create a dashboard that will allow you to explore the data in depth and identify meaningful insights. You will then give structure to your data story by writing the story arc in narrative form. Finally, you will consult your design checklist to craft the final viz or data story in Tableau. This is your opportunity to show the world what you’re capable of – so think big, and have confidence in your skills!

Kaggle Website:

https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge

Assignment Length (word count): 10-15 pages.

References: At least 10 peer-reviewed, scholarly journal references.

Specific TASK:

Create summary tables that address relevant factors related to COVID-19

COVID-19 Open Research Dataset Challenge (CORD-19) Round #2

Paul Mooney · 3 Submissions · 9 days to go

67

Task Details

Create summary tables that address relevant factors related to COVID-19

Specifically, we want to know what the literature reports about:

• Effectiveness of case isolation/isolation of exposed individuals (i.e. quarantine)
• Effectiveness of community contact reduction
• Effectiveness of inter/inner travel restriction
• Effectiveness of school distancing
• Effectiveness of workplace distancing
• Effectiveness of a multifactorial strategy prevent secondary transmission
• Seasonality of transmission
• How does temperature and humidity affect the transmission of 2019-nCoV?
• Significant changes in transmissibility in changing seasons?
• Effectiveness of personal protective equipment (PPE)

And we also want to know what the literature reports about the following questions that we added only very recently:

• This is where you will find new questions, if applicable

Expected Submission

Article summary tables. Specifically, the expected submission will be one or more .CSV files saved to the output folder of a Kaggle Notebook. The .CSV files should contain summary tables that follow the table formatting that is both described and demonstrated in the folder that is titled target_tables. For this specific task, article summary tables should follow the table formatting: Group 2 – Relevant Factors.

Date Study Study Link Journal Study Type Factors Influential Excerpt Measure of Evidence Added on DOI CORD_UID

There should be one .CSV file per target table (and one or more .CSV file per notebook), and the title of the .CSV files should be the same as the titles of the target tables. It may be advantageous to extract larger excerpts instead of specific values and, likewise, it may be advantageous for values to be prefixed by a human-readable indication of the location of that same value within the full-text document (in square brackets [] to facilitate error-checking).

To be valid, a submission must be contained in one or more Kaggle notebooks made public on or before the submission deadline. Participants are free to use additional datasets in addition to the official Kaggle dataset, but those datasets must also be publicly available in order for the submission to be valid. Previous versions of the article summary tables can be found in the folder that is titled target_tables.

An ideal submission will be able to: (1) recreate the target tables; (2) create new summary tables; and/or (3) append new rows to the old tables in order to add: (A) newly published articles; or (B) previously overlooked articles. Notebook authors should attempt to minimize the number of missing values within each new row while also attempting to avoid any errors or inaccuracies. See the Kaggle COVID-19 Contributions page for an example of what an article summary table might look like.

Data Set :

https://www.kaggle.com/datasets