I would like to map out how to get connected to SIMnet (specifically McGraw Hill SIMnet)

Using the Wall Street Journal or other reliable technology sources, identify a company in an industry that aligns with your major that uses AI for their business operation.

Prior to completing this assignment, consider that the company you select for this assignment might be used for the Topic 5 assignment. Please review the Topic 5 assignment before selecting a company.

In 400-500 words, describe your selected company, the AI tool/application used for their business operation, and their apparent business goal(s). Briefly explain how digital technology (data, website, app, etc.) enables the AI tool/application used by your selected company.

Prepare this assignment using effective business writing style. Refer to the resource, “Effective Business Writing,” located in Class Resources, for specific guidelines and formatting requirements.

You are required to submit this assignment to LopesWrite. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

Benchmark Information

This benchmark assignment assesses the following programmatic competencies:

BS Business Secondary Education

7.2: Explain the role of information technology and systems within business enterprises.

Task Assignment Library Membership & Resource Access System

Project Overview

You are required to design and implement a complete database system for a university library in the Sultanate of Oman. The system must manage physical books, digital resources, and user access while ensuring data security and compliance with Omans Personal Data Protection Law (PDPL).

The solution must include:

• SQL Database (Relational)
• NoSQL Database (MongoDB)
• Security & Legal Analysis

Use Arabic names for members and staff throughout the system.

Part 1: SQL Database Implementation (50 Marks)

1. Database Design & Normalization

You must:

• Start with UNF (Unnormalized Form)
• Convert step-by-step into:
  • 1NF
  • 2NF
  • 3NF

Requirements:

• Clearly list all attributes
• Identify:
  • Primary Keys (PK)
  • Foreign Keys (FK)
• Explain functional dependencies
• Justify why tables are separated

Example Entities (use Arabic names):

• Members (e.g., Ahmed Al-Harthi, Fatma Al-Balushi)
• Books
• Borrowing Records
• Librarians
• Digital Resources

2. ERD Design

Create a Crows Foot ERD diagram that includes:

• All entities
• Attributes
• PK & FK
• Relationships
• Cardinalities

Ensure:

• No redundancy
• Clear structure

3. SQL Implementation (SSMS)

Write SQL code to:

• Create all tables
• Define:
  • PK, FK
  • Constraints
• Enforce referential integrity

Important:

• Add at least TWO CHECK constraints, for example:
  • Fine amount 0
  • Access duration > 0

4. Insert Sample Data

Insert realistic data using Omani Arabic names, such as:

• Ahmed Al-Harthi
• Salim Al-Rawahi
• Fatma Al-Balushi
• Aisha Al-Zahra

Include:

• Book records
• Borrowing transactions
• Digital access logs

5. Role-Based Access Control (RBAC)

Implement access rules:

Rules:

1. A student can only view their own borrowed books
2. Admin has full access
3. Others (e.g., librarians) have read-only access

Deliverables:

• SQL code for roles/permissions
• Show:
  • Allowed action
  • Denied action

6. SQL Queries

Write and test the following queries:

1. Retrieve the most borrowed books
2. Identify librarians with highest transactions
3. Find members who borrowed but never returned books
4. Identify members with above-average digital resource usage
5. Find digital resources with:
   • Avg usage > 30 minutes
   • Used by at least 3 members
   • Sort descending

Part 2: NoSQL Database (MongoDB) 25 Marks

1. Design NoSQL Model

• Create one collection (e.g., LibraryActivity)
• Store:
  • Member info
  • Borrowing records
  • Digital access logs

Use a document-based structure

2. Insert Data

Insert 5 realistic documents using Arabic names.

3. Queries

Implement:

1. Top 3 most borrowed books
2. Members who:
   • Accessed digital resources
   • Borrowed at least 2 books

4. Justification (Max 200 words)

Explain:

• Why NoSQL is suitable
• Focus on:
  • Flexibility
  • High-volume logs
  • Hybrid system (SQL + NoSQL)

Part 3: Security, Legal & Distributed Systems (25 Marks)

1. PDPL Compliance (Oman)

Analyze two data categories:

A. Library Members Data:

• Personal info
• Borrowing history

B. External Providers:

• Vendors
• Digital subscriptions

For EACH:

• Identify relevant PDPL principles
• Justify one database design decision
• Explain differences in compliance challenges

2. Security Risks

Identify TWO risks, for example:

• Unauthorized data access
• Data leakage

For EACH risk:

• Technical solution (e.g., encryption, access control)
• Organizational solution (e.g., policies, training)

3. CAP Theorem

Explain:

• Consistency
• Availability
• Partition Tolerance

Scenario:

If a branch library in Oman loses connection:

Decide:

• Prioritize Consistency OR Availability

Justify your answer logically.

General Instructions

• Use Microsoft Word (.docx)
• Include:
  • SQL code as TEXT (not screenshots)
  • Output screenshots
• Word count:
  • ~2000 words total
  • Activity 3 600 words
• References:
  • 46 academic sources (Harvard style)
• Ensure:
  • Plagiarism < 15%
  • AI detection = 0%

Expected Outcome

The final work should demonstrate:

• Strong database design
• Correct SQL implementation
• Proper NoSQL usage
• Understanding of Omans legal framework
• Awareness of security and distributed systems

Final Project: Low-Power and Energy-Aware Microprocessor Design

This project challenges you to delve into the world of low-power and energy-aware microprocessor design. You will critically analyze seminal research, evaluate cutting-edge techniques, and investigate how industry leaders address power consumption. Your final submission will be a comprehensive technical report that demonstrates your mastery of this critical aspect of computer architecture.

This project will enable you to gain a comprehensive understanding of current trends, challenges, and innovations in microprocessor and GPU chip design. Specifically, you will delve deeply into the intricate world of chip architecture, gaining insights into the fundamental principles that drive the development and optimization of these critical components in modern computing.

Throughout the project, you will engage in critical thinking and problem-solving, applying theoretical concepts to practical scenarios. You will work collaboratively, simulating the real-world environment of multidisciplinary engineering teams. The culmination of your efforts will showcase your ability to synthesize information and contribute original ideas to the field of microprocessor and GPU chip design. This project will not only equip you with technical expertise but also prepare you for future challenges in the rapidly evolving landscape of computing technology.

Industry and Real-World Analysis

You will focus your attention on identifying technical designs from industry microprocessor manufacturers.

Deliverable 1: Identify Two Select Industry Leaders:

a) Choose two majorchip manufacturers known for their focus on low-power designs, such as ARM,Intel, Qualcomm, Samsung, and Texas Instruments. These companies have a trackrecord of innovation in low-power chip design and offer a wide range ofproducts that cater to different applications. Explain your choices.

Deliverable 2: Comparative Analysis:

• a) Examinetechnical documentation, patent filings, and product specifications to identifythe specific low-power strategies employed by each company. Look for commonthemes and unique approaches across the different manufacturers.
• b) Compare andcontrast their approaches. Discuss the trade-offs they make betweenperformance, power, and other factors such as cost, size, and reliability. Forexample, some companies may prioritize performance, while others might focus onpower efficiency.
• C) Analyze howtheir strategies align with or diverge from the academic research you’vestudied. Identify any gaps between theory and practice and potential areas forfuture research and development.

Deliverables:

For each deliverable, include a 1100 words technical report that includes tables, charts, and graphs as necessary. Support your sources.

R Programming Random Forest / Gradient Boosting

Completion requirements

For this assignment you will write an R program to complete the tasks given below. You will hand in two files for this assignment.

• A File with your R program. This file should contain only the code (no output) and must have the typical r extension. No other file extensions will be accepted. The reason is that the assignment be graded based on your R code and not the output file. The output file will be used to verify the code commands. Also, please make sure that all comments, discussion, and conclusions regarding results are also annotated as part of your code.
• A PDF/DOC file with your output code. We are giving you more flexibility regarding how you want to present your output (tables, plots, etc.). You can either use RMD files that combine code, narrative txt, and plots or you can use word document with copy and paste from the R platform you are using. However, please remember that all output (tables, plots, comments, conclusions, etc.) shown in this file has to be generated by the same R code that you submit. This is important! Output shown that is generated using a separate code or output shown that is not supported by the submitted code will not be graded. Screenshots will not be accepted.

• R Data Set: HMEQ_Scrubbed.csv (in the zip file attached).
• The Data Dictionary in the zip file.

Note: The HMEQ_Scrubbed.csv file is a simple scrubbed file from the previous week homework. If you did more advanced scrubbing of data for last week, you may use your own data file instead. You might get better accuracy! If you decide to use your own version of HMEQ_Scrubbed.csv, please hand it in along with the other deliverables.

This assignment is an extension of the Week 4 assignment. The difference is that this assignment will now incorporate Random Forest and Gradient Boosting models.

Step 1: Read in the Data

• Read the data into R
• List the structure of the data (str)
• Execute a summary of the data
• Print the first six records

Step 2: Classification Models

• Using the code discussed in the lecture, split the data into training and testing data sets.
• Create a Decision Tree model using the rpart library to predict the variable TARGET_BAD_FLAG
• Create a Random Forest model using the randomForest library to predict the variable TARGET_BAD_FLAG
• Create a Gradient Boosting model using the gbm library to predict the variable TARGET_BAD_FLAG
• All model parameters such as tree depth are up to you.
• Do not use TARGET_LOSS_AMT to predict TARGET_BAD_FLAG.
• Plot the Decision Tree and list the important variables for the tree.
• List the important variables for the Random Forest and include the variable importance plot.
• List the important variables for the Gradient Boosting model and include the variable importance plot.
• Using the testing data set, create a ROC curves for all models. They must all be on the same plot.
• Display the Area Under the ROC curve (AUC) for all models.
• Rerun with different training and testing data at least three times.
• Determine which model performed best and why you believe this.
• Write a brief summary of which model you would recommend using. Note that this is your opinion. There is no right answer. You might, for example, select a less accurate model because it is faster or easier to interpret.

Step 3: Regression Decision Tree

• Using the code discussed in the lecture, split the data into training and testing data sets.
• Create a Decision Tree model using the rpart library to predict the variable TARGET_LOSS_AMT
• Create a Random Forest model using the randomForest library to predict the variable TARGET_LOSS_AMT
• Create a Gradient Boosting model using the gbm library to predict the variable TARGET_LOSS_AMT
• All model parameters such as tree depth are up to you.
• Do not use TARGET_BAD_FLAG to predict TARGET_LOSS_AMT.
• Plot the Decision Tree and list the important variables for the tree.
• List the important variables for the Random Forest and include the variable importance plot.
• List the important variables for the Gradient Boosting model and include the variable importance plot.
• Using the testing data set, calculate the Root Mean Square Error (RMSE) for all models.
• Rerun with different training and testing data at least three times.
• Determine which model performed best and why you believe this.
• Write a brief summary of which model you would recommend using. Note that this is your opinion. There is no right answer. You might, for example, select a less accurate model because it is faster or easier to interpret.

Step 4: Probability / Severity Model Decision Tree (Push Yourself!)

• Using the code discussed in the lecture, split the data into training and testing data sets.
• Use any model from Step 2 in order to predict the variable TARGET_BAD_FLAG
• Develop three models: Decision Tree, Random Forest, and Gradient Boosting to predict the variable TARGET_LOSS_AMT using only records where TARGET_BAD_FLAG is 1.
• Select one of the models to predict damage.
• List the important variables for both models.
• Using your models, predict the probability of default and the loss given default.
• Multiply the two values together for each record.
• Calculate the RMSE value for the Probability / Severity model.
• Rerun at least three times to be assured that the model is optimal and not over fit or under fit.
• Comment on how this model compares to using the model from Step 3. Which one would your recommend using?

Essential Activities:

1. Watch all the training videos
2. Execute the example code while watching the training videos.

Notes:

1. This assignment is due Sunday at 11:59 PM EST

February 4 2026, 3:13 PM

follow the instructions of what to do for the access

What is C Programming?

C programming is a general-purpose programming language used to create software, systems, and applications. It is one of the oldest and most important languages in computer science.

A PDF outlining your recommendations should be submitted before the deadline.

The goal of this project is to identify major events that have large scale impact on Internet connectivity for individual networks or even entire countries. In this project we will learn:

1) How to leverage tools and resources, so that we can understand how a large-scale event is reflected on Internet connectivity data

2) How to perform measurements so we can measure ourselves multiple aspects of the events impact

In this project, we will use PyBGPStream to interact with the BGP protocol. The goal is to gain a better understanding of the BGP protocol itself and to experience how researchers and engineers have been using BGPStream to get insights.

More specifically, we will interact with the BGP protocol using a newly developed tool that gives us the option to both browse BGP data in real-time and go back in time and browse through historic BGP data.

additional files will need to be shared via google drive