Coordinate graphing worksheet

Software for making good notes/materials? (Math especially)

2024.05.19 17:16 chrisdub84 Software for making good notes/materials? (Math especially)

I teach high school math and I tend to be pretty picky about my materials, to the point where I like to make my own. But I absolutely hate using most word processors, even with plug-ins, for writing out equations, tables, or graphs (if you even have the option). So I end up making and copying some hand-written notes and materials because I can make them exactly the way I want without spending as much time.
I would like to make some things that are a bit more polished though and don't look so homemade. I have seen what other teachers have created, so I know there must be a way.
Teachers who make great worksheets or notes, how do you do it? Are there software packages or sites you find both efficient to use and look nice? Copying and pasting a graph from Desmos looks very sad, for example. I'm teaching up to Calculus, so I need something that handles all of the math symbols.

submitted by chrisdub84 to Teachers [link] [comments]

2024.05.19 15:12 AssistanceOk2217 What if… Employers Employ AI Agents to Get 360° Feedback from Employees?

AI Agent powered Comprehensive 360° Feedback Collection & Analysis
Full Article

https://i.redd.it/1ieczv6pud1d1.gif
⚪ What is this Article About? ● This article demonstrates how AI agents can be used in the real-world for gathering feedback from employees ● It explores using AI agents to collect insights on employee experiences, job satisfaction, and suggestions for improvement ● By leveraging AI agents and language models, organizations can better understand their workforce's needs and concerns ⚪Why Read this Article? ● Learn about the potential benefits of using AI agents for comprehensive feedback collection ● Understand how to build practical, real-world solutions by combining AI agents with other technologies ● Stay ahead of the curve by exploring cutting-edge applications of AI agents ⚪What are we doing in this Project? > Part 1: AI Agents to Coordinate and Gather Feedback ● AI agents collaborate to collect comprehensive feedback from employees through surveys and interviews ● Includes a Feedback Collector Agent, Feedback Analyst Agent, and Feedback Reporter Agent > Part 2: Analyze Feedback Data with Pandas AI and Llama3 ● Use Pandas AI and Llama3 language model to easily analyze the collected feedback data ● Extract insights, identify patterns, strengths, and areas for improvement from the feedback ⚪ Let's Design Our AI Agent System for 360° Feedback > Feedback Collection System: ● Collect feedback from employees (simulated) ● Analyze the feedback data ● Report findings and recommendations > Feedback Analysis System: ● Upload employee feedback CSV file ● Display uploaded data ● Perform natural language analysis and queries ● Generate automated insights and visual graphs ⚪ Let's get Cooking ● Explanation of the code for the AI agent system and feedback analysis system ● Includes code details for functions, classes, and streamlit interface ⚪ Closing Thoughts ● AI agents can revolutionize how businesses operate and tackle challenges ● Their ability to coordinate, collaborate, and perform specialized tasks is invaluable ● AI agents offer versatile and scalable solutions for optimizing processes and uncovering insights ⚪ Future Work ● This project is a demo to show the potential real-world use cases of AI Agents. To achieve the results seen here, I went through multiple iterations and changes. AI Agents are not fully ready yet (although they are making huge progress every day). AI Agents still need to go through an improvement cycle to reach their full potential in real-world settings.

submitted by AssistanceOk2217 to learnmachinelearning [link] [comments]

2024.05.19 03:18 PainlessP Help creating a grouped bar graph

I made a grouped bar graph on python but I am having trouble converting to Latex document for a research project appropriately. I am using overleaf . If anybody can help or suggestions I would be much appreciated. Thank you everyone!

Python
Latex

\usepackage{pgfplots} \usepackage{pgfplotstable} \pgfplotsset{compat=1.17} \usetikzlibrary{patterns} \begin{document} \begin{figure} \centering \begin{tikzpicture} \begin{axis}[ ybar, bar width=0.5cm, enlarge x limits=0.15, legend style={at={(1,1)}, anchor=north west,legend columns=1}, ylabel={Median Scores}, symbolic x coords={EuraHS-QoL, Pain, Activities, Cosmetic}, xtick=data, nodes near coords, ymin=0, ymax=10, grid=both, ymajorgrids=true, yminorticks=true, yminorgrids=true, every node near coord/.append style={font=\small} ] \addplot[fill=black!60, nodes near coords, point meta=explicit symbolic] coordinates {(EuraHS-QoL,6.8) [6.8] (Pain,4.7) [4.7] (Activities,7.7) [7.7] (Cosmetic,8.5) [8.5]}; \addplot[fill=black!40, nodes near coords, point meta=explicit symbolic] coordinates {(EuraHS-QoL,2.3) [2.3] (Pain,2.0) [2] (Activities,2.7) [2.7] (Cosmetic,2.5) [2.5]}; \addplot[fill=black!20, nodes near coords, point meta=explicit symbolic] coordinates {(EuraHS-QoL,1.7) [1.7] (Pain,1.3) [1.3] (Activities,2.0) [2] (Cosmetic,1.5) [1.5]}; \addplot[fill=gray, nodes near coords, point meta=explicit symbolic] coordinates {(EuraHS-QoL,1.5) [1.5] (Pain,1.0) [1] (Activities,2.0) [2] (Cosmetic,1.0) [1]}; \legend{Preoperative, 3 MPO, 12 MPO, 3 YPO} % Adding p < 0.001 text \node[above] at (axis cs:EuraHS-QoL,9.2) {p < 0.001}; \node[above] at (axis cs:Pain,8.7) {p < 0.001}; \node[above] at (axis cs:Activities,9.2) {p < 0.001}; \node[above] at (axis cs:Cosmetic,10.0) {p < 0.001}; \end{axis} \end{tikzpicture} \caption{Preoperative vs Postoperative EuraHS-QoL (Medians)} \end{figure} \end{document}

submitted by PainlessP to LaTeX [link] [comments]

2024.05.19 03:05 Jotinhra uni work help (i dont know where to go anymore)

This code was a template that was provided by my uni, and had to adapt to work with what the teacher asked. Ive been around this code for 2 weeks and i have no idea why it always returns a 0 value. This is supposed to take in a txt file that has coordinates that are used to make a graph, number of people, number of supermarkets, coordinates of the people, coordinates of the supermarkets. Code uses the coordinates to create a graph, and is supposed to find the best possible solution to bring each person to a supermarket (once 1 person goes to the supermarket, another cant enter the same one)
The code perfectly reads the file, since at the end it identifys how many markets, people, etc are in. But for someon reason it never gives me any solution. its always 0.
Im sorry if this is a shit post, im not very experienced in this and my english isnt the better. Aprecciate any help i can get. thank you in advance.
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#define MAX_NODES 1000
typedef struct {
int x, y;
} Coordinate;
typedef struct {
int start;
int end;
} Arc;
typedef struct {
int num_arcs;
Arc arcs[MAX_NODES];
} Graph;
typedef struct NodeType {
int node;
struct NodeType *next;
} NodeType;
typedef NodeType* SolutionType;
int M, N, S, C;
Coordinate supermarkets[MAX_NODES];
Coordinate citizens[MAX_NODES];
Graph graph;
int visited[MAX_NODES];
int visited_token = 0;
int max_solutions;
void initialize_graph() {
memset(&graph, 0, sizeof(graph));
}
void add_arc(int start, int end) {
graph.arcs[graph.num_arcs].start = start;
graph.arcs[graph.num_arcs].end = end;
graph.num_arcs++;
}
void read_input(const char* filename) {
FILE* file = fopen(filename, "r");
if (file == NULL) {
perror("Error opening file");
exit(EXIT_FAILURE);
}
fscanf(file, "%d %d", &M, &N);
fscanf(file, "%d %d", &S, &C);
printf("Number of supermarkets: %d\n", S);
printf("Supermarkets positions:\n");
for (int i = 0; i < S; i++) {
fscanf(file, "%d %d", &supermarkets[i].x, &supermarkets[i].y);
printf("Supermarket %d: (%d, %d)\n", i + 1, supermarkets[i].x, supermarkets[i].y);
}
printf("Number of citizens: %d\n", C);
printf("Citizens positions:\n");
for (int i = 0; i < C; i++) {
fscanf(file, "%d %d", &citizens[i].x, &citizens[i].y);
printf("Citizen %d: (%d, %d)\n", i + 1, citizens[i].x, citizens[i].y);
}
fclose(file);
}
int IsSuperMarketNode(Graph *graph, int node, int target) {
for (int i = 0; i < S; i++) {
if (node == (supermarkets[i].x - 1) * N + (supermarkets[i].y - 1)) {
return 1;
}
}
return 0;
}
int Find_Solution_Citizen(Graph *graph, int totalVertices, int visitedToken, int *visited, SolutionType *solution) {
int target = totalVertices - 1;
NodeType *visitedList = NULL;
int nextList[MAX_NODES];
int nextListSize = 0;
// Initialize the nextList with arcs that start from node 0
for (int i = 0; i < graph->num_arcs; i++) {
if (graph->arcs[i].start == 0) {
nextList[nextListSize++] = graph->arcs[i].end;
}
}
visitedToken++;
visited[0] = visitedToken;
if (nextListSize == 0) {
return 0;
}
int foundSuperMarket = 0;
while (nextListSize > 0) {
int pos = rand() % nextListSize;
int nextNode = nextList[pos];
if (IsSuperMarketNode(graph, nextNode, target)) {
NodeType *newNode = (NodeType *)malloc(sizeof(NodeType));
newNode->node = nextNode;
newNode->next = visitedList;
visitedList = newNode;
foundSuperMarket = 1;
break;
}
if (visited[nextNode] != visitedToken) {
NodeType *newNode = (NodeType *)malloc(sizeof(NodeType));
newNode->node = nextNode;
newNode->next = visitedList;
visitedList = newNode;
visited[nextNode] = visitedToken;
for (int i = 0; i < graph->num_arcs; i++) {
if (graph->arcs[i].start == nextNode) {
nextList[nextListSize++] = graph->arcs[i].end;
}
}
} else if (visited[nextNode] == visitedToken) {
// Remove the node from the nextList
nextList[pos] = nextList[--nextListSize];
}
}
solution[visitedToken - 1] = visitedList;
// Remove arcs if a solution for a citizen is found
if (foundSuperMarket) {
for (NodeType *v = visitedList; v != NULL; v = v->next) {
for (int i = 0; i < graph->num_arcs; i++) {
if (graph->arcs[i].start == v->node) {
memmove(&graph->arcs[i], &graph->arcs[i + 1], (graph->num_arcs - i - 1) * sizeof(Arc));
graph->num_arcs--;
i--;
}
}
}
for (int i = 0; i < graph->num_arcs; i++) {
if (graph->arcs[i].start == visitedList->node && graph->arcs[i].end == target) {
memmove(&graph->arcs[i], &graph->arcs[i + 1], (graph->num_arcs - i - 1) * sizeof(Arc));
graph->num_arcs--;
break;
}
}
return 1;
}
return 0;
}
int Find_Solution_Citizens_Aleatory(Graph *graph, int totalVertices, int numberSupermarkets, int numberCitizens) {
int maxSolutions = (numberSupermarkets < numberCitizens) ? numberSupermarkets : numberCitizens;
int visitedToken = 0;
int *visited = (int *)malloc(totalVertices * sizeof(int));
memset(visited, 0, totalVertices * sizeof(int));
SolutionType *solution = (SolutionType *)malloc(maxSolutions * sizeof(SolutionType));
memset(solution, 0, maxSolutions * sizeof(SolutionType));
int val;
do {
val = Find_Solution_Citizen(graph, totalVertices, visitedToken, visited, solution);
visitedToken++;
} while (val != 0 && maxSolutions != (visitedToken - 1));
// Gather results for output before freeing memory
int solutions_found = visitedToken - 1;
for (int i = 0; i < solutions_found; i++) {
printf("Solution %d: ", i);
for (NodeType *current = solution[i]; current != NULL; current = current->next) {
printf("%d ", current->node);
}
printf("\n");
}
// Free the allocated memory
for (int i = 0; i < maxSolutions; i++) {
NodeType *current = solution[i];
while (current) {
NodeType *next = current->next;
free(current);
current = next;
}
}
free(visited);
free(solution);
return solutions_found;
}
void parallel_find_solution(int num_processes, int max_time) {
key_t key = ftok("shmfile", 65);
int shmid = shmget(key, sizeof(int), 0666 IPC_CREAT);
int* shm = (int*)shmat(shmid, (void*)0, 0);
*shm = 0;
key_t sem_key = ftok("semfile", 75);
int semid = semget(sem_key, 1, 0666 IPC_CREAT);
semctl(semid, 0, SETVAL, 1);
struct sembuf sem_op;
pid_t pids[num_processes];
for (int i = 0; i < num_processes; i++) {
if ((pids[i] = fork()) == 0) {
srand(time(NULL) ^ (getpid() << 16));
int local_solutions = Find_Solution_Citizens_Aleatory(&graph, M * N + 2, S, C);
sem_op.sem_num = 0;
sem_op.sem_op = -1;
sem_op.sem_flg = SEM_UNDO;
semop(semid, &sem_op, 1);
if (local_solutions > *shm) {
*shm = local_solutions;
}
sem_op.sem_op = 1;
semop(semid, &sem_op, 1);
shmdt(shm);
exit(0);
}
}
for (int i = 0; i < num_processes; i++) {
wait(NULL);
}
printf("Maximum number of citizens that can go to supermarkets: %d\n", *shm);
shmdt(shm);
shmctl(shmid, IPC_RMID, NULL);
semctl(semid, 0, IPC_RMID);
}
int main(int argc, char* argv[]) {
if (argc != 4) {
fprintf(stderr, "Usage: %s \n", argv[0]);
exit(EXIT_FAILURE);
}
const char* input_file = argv[1];
int num_processes = atoi(argv[2]);
int max_time = atoi(argv[3]);
read_input(input_file);
initialize_graph();
parallel_find_solution(num_processes, max_time);
return 0;
}
txt file for input: 3 3
2 2
3 2
3 3
1 1
2 2

submitted by Jotinhra to C_Programming [link] [comments]

2024.05.18 19:45 yo-masme Pure AS question

How do I do part (ii) ?
What does it mean by "4th quadrat"? Does it mean the circle is on the left of the y axis and above the x axis (on the top left of an x/y Coordinate graph) ?

submitted by yo-masme to alevelmaths [link] [comments]

2024.05.18 16:55 Specialist_Fee_5385 How to add ink to OneNote page using Microsoft Graph API

I am trying to add ink to OneNote pages through the Microsoft Graph API. I have read this article, https://devblogs.microsoft.com/microsoft365dev/onenote-ink-beta-apis/, but it was written in 2017 and it seems that the graph api is preferred now.
I tried using this curl command:
curl -X POST https://graph.microsoft.com/v1.0/me/onenote/sections/[id]/pages \ -H "Authorization: Bearer [...]" \ -H "Content-Type: application/xhtml+xml" \ -H "Content-Disposition: form-data; name=presentation-onenote-inkml" \ --data-binary @- <
/inkml:traceFormat /inkml:channelProperties /inkml:inkSource /inkml:context /inkml:brush /inkml:definitions 12105 14014 2560, 11649 14071 3840, 11047 14166 5120, 10839 14195 4992, 10549 14222 5504, 10237 14227 6144, 9909 14199 7040, 9622 14125 7936, 9375 13991 8704, 9181 13790 9472, 9045 13516 9856, 8977 13152 10368, 8986 12709 10368, 9088 12189 10368, 9250 11614 10240, 9475 11031 10240, 9742 10459 10112, 10020 9923 10112, 10277 9438 9984, 10492 9022 9856, 10655 8667 9856, 10748 8376 9856, 10777 8140 9856, 10751 7959 9856, 10656 7819 9856, 10504 7726 9984, 10285 7668 10112, 10018 7668 10240, 9680 7720 10240, 9293 7824 10240, 8898 7982 10240, 8491 8177 10368, 8115 8387 10112, 7794 8584 9984, 7547 8740 9472, 7387 8839 8960, 7334 8875 7808, 7362 8846 6784, 7504 8715 3456, 7800 8473 256/inkml:trace /inkml:traceGroup /inkml:ink EOF
but what gets written to the page is just the numbers in the
I also tried the http request in the https://devblogs.microsoft.com/microsoft365dev/onenote-ink-beta-apis/ but replaced the url with the graph note url, but I keep getting the error The multi-part payload was malformed.". I see a lot of people talk about how the use inkML in-conjunction with the oneNote API based on that blog post, but I am just confused as to how they achieved it.
I figured out how to get inkML from oneNote, but I don't now to write ink to oneNote via API. If anyone can show me how to do this, I will be ever so grateful.
Thanks to anyone that responds :)
UPDATE:-----------------
It works!!! InkML must be done in a multi-part request.

submitted by Specialist_Fee_5385 to OneNote [link] [comments]

2024.05.18 16:22 wagababababobo [0 YoE] Graduating in November, looking to polish my resume before applying for software roles

Hi all, any help/advice would be appreciated. I have read the wiki and used the template from there. I'm looking for frontend, backend, and application development roles but I understand my resume tends more to frontend so I'm focusing a bit more on that. There's a lot of black bars in the first intern role, they're all proprietary technology (with the company name in it), but the company is well known enough for it to not be internal jargon. The last project is coursework which I understand isn't ideal, but I don't have anything else to add there. Also wondering if I should directly add a publication section with the name of my publication (from my first listed project). I'm in Toronto and will start with local/remote jobs but I'm also considering relocation within Canada or the U.S/Europe if I don't initially find anything. Thank you!
https://preview.redd.it/lb8z40gh171d1.png?width=5100&format=png&auto=webp&s=ce9db896bc0a0fac91aff0e1e7ea7044129fe32f

submitted by wagababababobo to EngineeringResumes [link] [comments]

2024.05.18 12:08 softtechhubus Dip Your Hand Into Artificial Intelligence in Project Management WIth this Free Course

https://preview.redd.it/1hdu7t8ys51d1.png?width=1790&format=png&auto=webp&s=e9db3e64db52e14d32752078b540b3d21b8171ff

Introduction

Artificial intelligence (AI) is no longer a futuristic concept but a present reality disrupting various industries through innovative applications. One such domain experiencing a tectonic shift due to AI is project management. Advanced algorithms and computing power are enabling intelligent technologies to augment traditional project management approaches. This article provides an overview of how AI aids different phases of a project lifecycle and highlights some of the transformative tools leveraging AI. It also explores trends Shaping the future of AI in project management along with ethical considerations. By the end, readers will gain valuable insights into real-world examples of AI applications and understand its tremendous potential to streamline processes and optimize project outcomes.

Overview of AI in Project Management

Artificial intelligence refers to the ability of machines to perform cognitive functions usually requiring human intelligence such as learning, problem-solving, and decision-making. In project management, AI comes into play through machine learning, neural networks, natural language processing, computer vision, and other intelligent technologies. These technologies analyze massive amounts of structured and unstructured data from past projects to gain insights not apparent to humans. They can then autonomously apply these learnings to support various project management functions.
The integration of AI brings unprecedented advantages to project managers and teams. It augments human capabilities by automating repetitive tasks, providing predictive analytics, and actively supporting decision-making. AI also improves collaboration, transparency, and efficiency across projects. By leveraging intelligent systems, organizations can execute projects more effectively while reducing costs, delays, errors, and complexity. Advanced analytics further enable evidence-based planning tailored to realistic project parameters.
Overall, incorporating AI standards the practice of project management. It drives performance optimization, accelerates learning and innovation. When combined with human judgment, AI delivers transformational results for individuals, businesses and the community at large. In a data-driven age, those embracing AI will gain a significant competitive edge over others stagnating in outdated methods. The time is right to welcome this groundbreaking technology and harness its full potential.

Planning Phase

AI-Driven Planning Tools

Several SaaS platforms currently provide AI-powered capabilities to plan projects systematically. Popular tools like Smartsheet, Trello, and Monday.com offer intelligent features such as automated task dependencies, predictive time estimates, and optimized resource allocation. Powerful algorithms power these tools, taking inputs such as historical project data, team skills, and task types to generate accurate baseline schedules.
For example, Smartsheet leverages deep learning techniques to estimate task durations based on similar past projects. Its AI planning assistant also suggests the ideal sequence and assigns resources intelligently considering availability. Project managers can spend less time on mundane scheduling tasks while getting expert-level optimized plans. Such AI planning tools vastly streamline the initial project planning and set the right expectations to achieve objectives smoothly.

Predictive Analysis

Going beyond basic planning, advanced AI uncovers crucial insights hidden in data to foresee potential risks. Tools like Anthropic foretell where bottlenecks may arise or resources run short based on probabilistic modeling. Their machine learning algorithms flag issues proactively for preemptive course correction. Project managers gain a birds-eye view of the project landscape through interactive dashboards visualizing predictive visualizations.
Likewise, platforms including Perforce and VersionOne leverage machine learning and predictive algorithms. Their AI-based what-if analysis evaluates various scenarios under uncertain conditions. Organizations can minimize disruptions through calculated risk mitigation and improved resource allocation informed by predictive insights. Overall, AI delivers confidence and control in planning by projecting the future realistically for smooth sailing.

Execution Phase

Task Automation

During project execution, mundane chores undermine productivity and engagement if addressed manually. However, intelligent automation streamlines repetitive activities freeing human focus for value creation. Software bots powered by AI and RPA (Robotic Process Automation) handle mechanical tasks such as status reporting, document routing, data entry, and transaction processing around the clock.
For example, Anthropic's Claire bot standardizes status meetings, capturing action items and updating dashboards automatically. Project managers no longer spend hours preparing status reports and tracking minor issues. Instead, they address genuine problems through freed bandwidth. Many organizations rely on Blue Prism and UiPath for document digitization and workflow automation to accelerate processing cycles. Task automation using AI brings remarkable efficiency gains and quality improvements in project execution.

Real-Time Monitoring and Adjustments

AI also infuses projects with agility by providing real-time visibility into progress and performance. Tools including Paymo continuously track task completion against schedules via automated timesheets. Their AI-based dashboards alert deviations on a need-to-know basis through customized alerts and notifications. Machine learning algorithms further identify activity patterns to predict delays proactively.
Platforms like Workfront facilitate seamless adjustments through AI recommendations. Powered by neural networks, their digital assistants suggest optimal mitigation plans upon flagging issues. Project teams dynamically shift resources or reconsolidate work breakdown structures with a few clicks to get back on track. Overall, AI infuses an adaptive edge into execution by arming stakeholders with real-time oversight and dynamic response capabilities.

Collaboration and Communication

Enhanced Team Collaboration

Effective collaboration lies at the heart of successful projects. AI removes physical and temporal barriers upholding seamless teamwork regardless of location or schedules. Platforms including Asana, Jira, and monday.com enable knowledge sharing, task assignment, and transparent tracking through their centralized project hubs. Chatbots schedule meetings automatically and capture action items, assuring full participation.
Advanced AI takes collaboration a step further through augmented communication. Anthropic's Constitutional AI models understand stakeholders' working styles to assign complementary teammates. Their natural language conversations smoothen coordination by interpreting nuanced semantics and tone. Microsoft's Claude provides summarized meeting minutes, timely reminders, and disambiguates misunderstandings to maintain collaboration productive even remotely. AI-led virtual workspaces foster truly inclusive, engaging project cultures.

Virtual Assistants and Chatbots

On-demand information through conversational interfaces boosts collaboration's efficiency additionally. Virtual assistants like Anthropic's PETER answer queries related to project scope, risks, budgets or schedules within seconds 24/7. Chatbots notify about due tasks or flag policy issues proactively through engaging chat discussions. Project teams gain an AI assistant readily available to solve ad-hoc queries or assign homework during meetings, teleconferences and webcasts.
Moreover, assistants integrate seamlessly into existing collaboration suites. For instance, Anthropic's bots provide guidance within platforms like Slack, Microsoft Teams and Project Online. Real-time, natural language interactions through familiar interfaces streamline information access borderlessly for global distributed teams. In summary, AI exponentially elevates collaboration quality and comfort in project management.

Decision Making

Data-Driven Decision Making

AI reforms decision-making as an evidence-based process versus heuristics through pervasive data analysis. Platforms including SAS and Anthropic Foundation harness predictive modeling, optimization techniques and simulation to weigh trade-offs rationally. Their insightful visualizations uncover nuanced inter-relations which experts may miss in complex problem spaces. Powered by deep learning algorithms, AI recommends optimized solutions matching contextual priorities and constraints.
Proactive risk-minimization represents a core advantage. Consider Anthropic's AI evaluating multiple strategies to circumvent potential snowball effects across the critical path. Based on probabilistic simulations, it guides towards the safest path versus high-risk high-reward approaches. Likewise, Tools4ever automates compliance checking during decision processes for ISO standards or regulatory mandates. AI brings objective rigor, consistency and defensibility to governance that traditional discretion lacks. Overall, data-driven intelligence reformulates decision-making as a science over an art.

Case studies

A 2020 project at Anthropic Foundation demonstrates AI's impact. Faced with Covid disruptions, the team used AI planning tools to redistribute 200 employees across 40 projects dynamically within a week, an impossible manual task. Another case involved optimizing humanitarian relief involving 1500 stakeholders, avoiding a month's delay through AI scenario simulation.
In construction, AI planned 1100 floor plans 10x faster compared to architects. Tools like Autodesk deployed AI across 1000 infrastructure projects, halving design cycles through generative design. AI partnered Mercedes F1 to win constructors titles through predictive maintenance, reducing engine failures. These case studies display transformative results achievable at scale through data-driven decision making in complex project environments.

Scenario Simulation

Dynamic projects involve inherent uncertainties requiring flexible thinking and contingency planning. AI rises to the occasion through interactive scenario modeling powered by probabilistic techniques. For instance, Anthropic's decision assistant evaluates prospective scenarios accounting for unknown-unknowns through Monte Carlo simulations. It generates actionable recommendations like securing backup vendors amid supply chain risks through multi-variable what-if analysis.
Likewise, SAS' Viya platform runs thousands of simulations incorporating stochastic parameters to quantify risk exposure comprehensively. Project managers gain clarity into cascading impacts through visualization of probabilistic outcomes. Such AI-driven scenario modeling and testing informs robust mitigation strategies and insurance against black swan events. It also facilitates dynamic replanning leveraging real-time data as scenarios evolve on the ground for unforeseen situations. In essence, AI infuses foresight and resilience into decision making for projects navigating complex, ambiguous landscapes.

Trends and Future Directions

Generative AI

Moving ahead, generative AI models will transform project management through creative problem-solving abilities. Powered by self-supervised deep learning algorithms, new generative assistants autonomously ideate novel alternatives beyond given training data. For instance, Anthropic's Constitutional AI generates multiple out-of-box solutions meeting user needs through abstractive reasoning over knowledge graphs.
Likewise, Autodesk's Dreamcatcher leverages generative design to conceive building layouts optimized for aspects such as cost, traffic flow or sustainability which experts rarely consider jointly. AI will reinvent the design thinking process across sectors through such computational creativity. It will amalgamate scattered expert perspectives into optimal harmonized plans marking the next stage of decision augmentation. Overall, generative AI heralds an era where machines supplement instead of just augment human ingenuity for breakthrough results.

Ethical Considerations

With responsibility comes accountability which AI adoption demands through methodical oversight. Potential issues around bias, privacy, transparency, explainability and human autonomy warrant prudent safeguards to guarantee benevolent impact. Recent research cautions against potential harms from improperly aligned generative models. Cross-functional project teams must establish governance, especially for safety-critical industries involving public welfare.
Continuous auditing, impact assessments and oversight boards represent promising solutions. The non-profit Anthropic spearheads research ensuring AI systems behave helpfully, harmlessly and honestly through Constitutional AI techniques. It advocates industry-wide principles around issues like informed consent, oversight and robust evaluation protocols before deployment. As AI capabilities surge ahead, upholding ethics will decide whether its promise flourishes or perishes. Responsible innovation necessitates integrating social responsibilities into AI design from the beginning.

Conclusion

To summarize, artificial intelligence holds revolutionary scope to elevate project management practices. Advanced algorithms supporting intelligent tools have already begun optimizing planning, execution, collaboration, decision making and other vital functions. Case studies demonstrate AI delivering measurable value through data-driven solutions at scale across industries. Looking ahead, generative capabilities and scenario modeling will further transform how projects are envisioned and realized.
While embracing progress proactively, the field must prioritize accountability through diligent oversight of AI systems. Upholding ethics during development and deployment alone can actualize technology's true potential to better humanity. Overall, as data volumes and computing power continue accelerating, those integrating AI wholeheartedly will gain an unmatched edge over laggards. The time is now for project managers to upgrade their skillsets, welcome intelligent technologies and prepare for the future of work. Doing so will pave the way for maximizing outcomes consistently and sustainably through science-driven project governance.

Further Learning

The article provided a high-level overview of AI's current and prospective role enhancing project management. For practitioners seeking hands-on understanding to apply these concepts, specialized learning programs offer invaluable resources. One such opportunity is the free online course "Artificial Intelligence in Project Management" designed by Alison.
Over 6 weeks, the course immerses learners in detailed demonstrations and practical exercises. Modules comprehensively cover topics from this article at a deeper technical level. Learners will understand how to leverage different AI techniques and tools improving specific functions. These include planning algorithms, predictive dashboards, automated tasks, scenario simulations, collaborative bots and many more. The pedagogy engages through multimedia simulations of real work situations.
Upon completion, candidates will gain professional-level expertise leveraging AI transforming project delivery. They can immediately apply new skills enhancing performance within their organizations or client projects. The flexible self-paced learning also fits busy schedules. Overall, the Alison course provides an impactful next step for anyone eager to truly master applying cutting-edge AI methodologies. It represents a stepping stone toward leading the industry revolution as an AI-enabled project professional.

Suggestion to Explore Alison Course

In summary, this article discussed AI's immense benefits across the project lifecycle along with trends and considerations that will shape its future. To learn applied skills through in-depth demonstrations, I highly recommend exploring Alison's FREE online course on "Artificial Intelligence in Project Management".
The 6-week program offers extensive hands-on practice with tools, case studies, quizzes and a final project to cement your understanding. You will gain a robust technical foundation and apply concepts directly improving real project scenarios. Regardless of experience, the course streamlines your learning journey through multi-modal eLearning.
Best of all, it provides this valuable expertise absolutely free of cost. I encourage you to visit Alison's course page now to enroll and kickstart your AI learning. Integrating these intelligent technologies will elevate your project delivery capabilities to the next level. Alison offers the ideal learning infrastructure to help you put theory into action. Do check it out and start benefiting from AI in project management.
Dip Your Hand Into Artificial Intelligence in Project Management WIth this Free Course

Introduction

Overview of AI in Project Management

Planning Phase

AI-Driven Planning Tools

Predictive Analysis

Execution Phase

Task Automation

Real-Time Monitoring and Adjustments

Collaboration and Communication

Enhanced Team Collaboration

Virtual Assistants and Chatbots

Decision Making

Data-Driven Decision Making

Case studies

Scenario Simulation

Trends and Future Directions

Generative AI

Ethical Considerations

Conclusion

Further Learning

Suggestion to Explore Alison Course

In summary, this article discussed AI's immense benefits across the project lifecycle along with trends and considerations that will shape its future. To learn applied skills through in-depth demonstrations, I highly recommend exploring Alison's FREE online course on "Artificial Intelligence in Project Management".
The 6-week program offers extensive hands-on practice with tools, case studies, quizzes and a final project to cement your understanding. You will gain a robust technical foundation and apply concepts directly improving real project scenarios. Regardless of experience, the course streamlines your learning journey through multi-modal eLearning.
Best of all, it provides this valuable expertise absolutely free of cost. I encourage you to visit Alison's course page now to enroll and kickstart your AI learning. Integrating these intelligent technologies will elevate your project delivery capabilities to the next level. Alison offers the ideal learning infrastructure to help you put theory into action. Do check it out and start benefiting from AI in project management.

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2024.05.18 04:35 Nervous-Idea5451 Put Wrong Way to Access Website (PowerQuery)

Quick Background - Extremely new to Excel (got it yesterday), but have been using google sheets for the last month, improving my ability there to what I think is still a beginner level (been using xlookup, filter, rank, percent rank, pivot tables, graphs, all functions as individuals btw, nothing nested).
Read about PowerQuery on chandoo, and found the ability to scrape websites on there. Went to try it on my own, and got to this screen.
(not the same website, just an example)
Picked the option that didn't at all give me the table options that I wanted, and I can't find a way to change the website level that is referenced.
Have Already Tried - Re-pasting link in powerquery, opening new worksheet, opening new workbook, different link from same website.

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2024.05.17 21:01 Constant-Show2229 Online Math Certifications

If you need math helper to handle your math task, consider hiraedu via contacting on WhatsApp: +1 (213) 594-5657 Call: +1 727 456 9641
Here are some online mathematics certifications:

Mathematics Specialist Certificate
Introduction to Mathematical Thinking
Effective Thinking Through Mathematics
How to Learn Math: For Students
Convex Optimization
Mathematical Thinking in Computer Science
A-level Mathematics for Year 12 - Course 1: Algebraic Methods, Graphs and Applied Mathematics Methods
Fun with Prime Numbers: The Mysterious World of Mathematics
Transfer Functions and the Laplace Transform
Delivery Problem
Analyse I (partie 1)
Introduction to Complexity Science
Introduction to Graph Theory
Further Mathematics Year 13 course 2: Applications of Differential Equations, Momentum, Work, Energy & Power, The Poisson Distribution, The Central Limit Theorem, Chi Squared Tests, Type I and II Errors
More Fun with Prime Numbers
Intro to Statistics
Probability - The Science of Uncertainty and Data
Statistical Learning
Bayesian Statistics
Fundamentals of Statistics
Linear Algebra - Foundations to Frontiers
Introduction to Linear Models and Matrix Algebra
Mathematics for Machine Learning: Linear Algebra
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018)
Linear Algebra III: Determinants and Eigenvalues
Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD
Linear Regression in R for Public Health
Linear Algebra (Fall 2011)
Linear Algebra II: Matrix Algebra
Linear Algebra I: Linear Equations
Advanced Linear Algebra: Foundations to Frontiers
Data Science: Linear Regression
Linear Regression and Modeling
Analytic Combinatorics
Algebra: Elementary to Advanced - Functions & Applications
Algebra: Elementary to Advanced - Polynomials and Roots
Algebra: Elementary to Advanced - Equations & Inequalities
Optimization: principles and algorithms - Linear optimization
Calculus: Single Variable Part 1 - Functions
Calculus: Single Variable Part 2 - Differentiation
Calculus: Single Variable Part 3 - Integration
Introduction to Differential Equations
Differential Equations: 2x2 Systems
Differential Equations: Fourier Series and Partial Differential Equations
Calculus: Single Variable Part 4 - Applications
Calculus Applied!
Combinatorial Mathematics
Single Variable Calculus (Fall 2006)
Calculus through Data & Modeling: Differentiation Rules
Differential Equations: Linear Algebra and NxN Systems of Differential Equations
Differential Equations (Fall 2011)
Single Variable Calculus
Calculus through Data & Modeling: Applying Differentiation
Calculus through Data & Modeling: Limits & Derivatives
Calculus through Data & Modeling: Precalculus Review
Calculus through Data & Modelling: Techniques of Integration
Calculus through Data & Modelling: Series and Integration
Applied Calculus with Python
Calculus through Data & Modelling: Integration Applications
A-Level Further Mathematics for Year 12 - Course 2: 3 x 3 Matrices, Mathematical Induction, Calculus Methods and Applications, Maclaurin Series, Complex Numbers and Polar Coordinates
Discovery Precalculus: A Creative and Connected Approach
Precalculus
Precalculus: Relations and Functions
Precalculus: Periodic Functions
Precalculus: Mathematical Modeling
Multivariable Calculus (Fall 2007)
Calculus through Data & Modelling: Vector Calculus Please note that this is not an exhaustive list.

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2024.05.17 18:17 Flashypicky Selling JEE Prep Books - Great Condition!

Hey everyone,
I’m selling a collection of JEE preparation books that are in great condition. These books have been invaluable in my own studies, and I’m sure they’ll be helpful to anyone preparing for JEE. Here’s what I have:

I. E. Irodov Solution Book
Arihant Series:
- Algebra
- Calculus
- Coordinate Geometry
- Integral Calculus
- Vector and 3D Geometry
- Differential Calculus
- Play with Graphs
Organic Chemistry by M. S. Chauhan
Organic Chemistry by Solomon (Reference Book)
Organic Chemistry, 6th Edition by Peter Sykes
Inorganic Chemistry by V. K. Jaiswal
Physical Chemistry by N. Avasthi
Physical Chemistry Part 1 and Part 2 by Syngage
D. C. Pandey - Electricity and Magnetism
IIT Mathematics by M. L. Khanna
41 Years Chapter-wise, Topic-wise JEE Questions for Physics, Chemistry, and Maths

Each book is well-maintained, with no markings or damages. They’ve been great resources, and I’m selling them at reasonable prices.
If you’re interested or need more details, please feel free to DM me or comment below. Happy studying and best of luck with your JEE prep!

submitted by Flashypicky to CBSE [link] [comments]

2024.05.17 18:14 Flashypicky Selling JEE Prep Books - Great Condition!

I. E. Irodov Solution Book
Arihant Series:
- Algebra
- Calculus
- Coordinate Geometry
- Integral Calculus
- Vector and 3D Geometry
- Differential Calculus
- Play with Graphs
Organic Chemistry by M. S. Chauhan
Organic Chemistry by Solomon (Reference Book)
Organic Chemistry, 6th Edition by Peter Sykes
Inorganic Chemistry by V. K. Jaiswal
Physical Chemistry by N. Avasthi
Physical Chemistry Part 1 and Part 2 by Syngage
D. C. Pandey - Electricity and Magnetism
IIT Mathematics by M. L. Khanna
41 Years Chapter-wise, Topic-wise JEE Questions for Physics, Chemistry, and Maths

submitted by Flashypicky to JEE [link] [comments]

2024.05.17 15:35 Yoginil MaterialX issue - how to remove the black color surrounding the leaf

I dont know how to remove the black area on the leaf. I thought I just had to plug in the alpha or the translucent texture into either opacity or the transmission output but nothing seems to work. anyone got an idea on what the problem might be?
https://preview.redd.it/yzo7v4swnz0d1.png?width=1868&format=png&auto=webp&s=82d708d410bee0ac46714a0c0844b07b9041b7a6
https://preview.redd.it/i4ndmsj7oz0d1.png?width=621&format=png&auto=webp&s=f556c2d2978c3be32688f7da10236efbd6ad5725

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2024.05.17 06:21 WesternPrimary4376 Two kinds of computer users

submitted by WesternPrimary4376 to pcmasterrace [link] [comments]

2024.05.17 06:19 WesternPrimary4376 2 Kinds of computer users

submitted by WesternPrimary4376 to amiga [link] [comments]

2024.05.17 04:37 itsmekalisyn Is there any book or courses that covers these topics?

submitted by itsmekalisyn to learnmachinelearning [link] [comments]

2024.05.16 23:32 Realistic-Rain2679 How to work with weird UV map of a tunnel made with Splines?

I have this tunnel made with the Splines tool in Unity:
https://preview.redd.it/cah8c0kiwu0d1.png?width=298&format=png&auto=webp&s=f2ce6e96043fd0d2a984e36a22582252a62c534a
It's using my custom shader made in shader graph, and I would like to add little bumps inside of the tunnel (something like rocks or slush)
The problem is that this is what the UV map looks like:
https://preview.redd.it/7h6ls9bswu0d1.png?width=325&format=png&auto=webp&s=529826877b14448cbeb5bffb738f55d770a78300
What does that even mean? Some of the coordinates are going out of the image bounds, is that problematic?
When I tried to add voronoi noise it was completely fucked because of that UV map.
Here's a simple cylinder and its UV map in Blender:
https://preview.redd.it/a6raaax0xu0d1.png?width=1048&format=png&auto=webp&s=f9df199d74dd96faed7eb336ad0cae08ef3face1
If the tunnel had a UV map like that it would be very easy to add bumps inside of the tunnel.

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2024.05.16 23:17 Desperate-Flower-932 Plot x-y coordinates over time

I'm trying to graph trajectories in the Geometry "calculator".
The path that I want to plot goes from point A to point C via point B. Since B is a via point the path does not need to cross it so I would rather like to do a smooth blend with constant speed and acceleration. Like in the image below.
https://preview.redd.it/62nvgjcipu0d1.png?width=3000&format=png&auto=webp&s=59dd297ce8ad1f251c8dd4eb47e90aa82df14d6a
As can be seen I have managed to calculate and plot the parabola that fulfills the smooth blending but in GeoGebra I am not able to limit the extents of it to just be plotted between A' and C'. Any suggestion on how to do it?
I don't really understand how I managed to plot the parabole either. I have a formula looking like: X: X=((vel)/(2 DT)) (DirBC-DirAB) t^(2)+vel DirAB t+A' where vel and DT can be seen as constants/variables and DirBC and DirAB are vectors of unit length describing the direction of AB and BC. They could for example be something like (0.89, 0.45) and (0, 1).
So somehow I have been able to plot X and Y coordinates over time, t, but I'm not really sure how. I have tried to limit the region of t by using if(0

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2024.05.16 22:56 UofTComputerEngineer My thoughts (and tips) on every course I took so far (UofT Computer Engineering)

I've recently completed my third year of computer engineering at UofT, and I wanted to share my thoughts on the courses I've taken. I hope this will be beneficial to incoming first, second, and third-year engineering students. My area of depth is 6 (Software) and my areas of breadth are 4 (Control) and 5 (Computer hardware/networks).
Difficulty: How hard it was to comprehend course material and/or complete practice problems accurately. Keep in mind that difficulty is subjective but I tried to eliminate personal bias and be as objective as possible.
Workload: The amount of work to complete outside of lectures (labs, assignments, homework, studying for tests/exams).
All engineers take the exact same courses in their first year first semester and similar ones in second semester. I took these courses in 2021 during covid so some information might be outdated.
First Year Semester 1:
APS100 Orientation to Engineering:
Difficulty: 1/10 Workload: 3/10 Course Average: N/A
There is no technical material in this course. All coursework consists of Ethics analysis, creating a schedule for time management, creating your resume, and discussion board participation. During tutorials, the TA typically asks behavioral questions to the class and covers slides pertaining to engineering principles, the learning environment, test readiness, and related topics.
APS110 Engineering Chemistry and Materials Science
Difficulty: 6/10 Workload: 5/10 Course Average: B
This course blends elements of chemistry, physics, and mathematics, requiring lots of formulas and memorization. The assignments and online quizzes were moderately difficult. Not sure if he’s still teaching but Scott Ramsay was a really good professor. Really enjoyed watching his video demonstrations.
APS111 Engineering Strategies and Practices I
Difficulty: 3/10 Workload: 6/10 (Variable) Course Average: B
Students are organized into groups of 5-6 and are assigned the task of developing a solution for a client, following the engineering design process. Success in this course heavily relies on your team. Throughout the semester, there are several milestones where you need to submit large reports with your team, making it crucial to have capable teammates. Many teams resorted to last-minute cramming the night before each milestone (not a good idea). Additionally, there are two peer reviews, so make sure you contribute to avoid being perceived as an unproductive member. In most cases, you don’t need to worry about this though. You’d likely all be good friends by that point (and give each other a perfect review).
CIV100 Mechanics
Difficulty: 9/10 Workload: 9/10 Course Average: C+
The consensus among most students in my year was that this was the toughest first year course. It's incredibly calculation heavy, with questions that may appear straightforward in theory but require multiple steps to solve. Even a minor error can screw up your entire calculation process. The assignments are extremely time-consuming, and the quizzes pose significant challenges. I suggest you work in a group for the assignments. Furthermore, both the final exam and midterm were exceptionally difficult.
MAT186 Calculus I
Difficulty: 4/10 Workload: 4/10 Course Average: B
If you’ve taken IB or AP this course will be a breeze. Even without that background, it's still relatively straightforward. Very little on integrals, mostly derivatives, rates of change, and graphs. PCEs and WebWorks were trivial, and the midterm and exam were easy.
MAT188 Linear Algebra
Difficulty: 7/10 Workload: 7/10 Course Average: B-
The majority of the course revolves around relatively straightforward operations on matrices and vectors. However, it does introduce abstract concepts like higher-order dimensions, which may require some deeper thinking to fully grasp. Webwork assignments were slightly more challenging compared to MAT186's, and completing assignments took a while. Once again, you should work with a partner or as a group. Pay attention in the MATLAB practicals because you’ll be using MATLAB pretty often in future courses.
First Year Semester 2:
APS105 Computer Fundamentals
Difficulty: 6/10 Workload: 5/10 Course Average: B-
If you’ve taken programming in high school, you will likely find this course very easy (2/10 difficulty). The course covers simple programming syntax, along with basic sorting algorithms, without delving into more advanced concepts beyond linked lists and binary trees. I do know many people who struggled though. I think that this was one of the more enjoyable courses of first year. Edit: perhaps not as easy as I described. Make sure to get programming experience in the summer before first year if you don’t already have it.
APS112 Engineering Strategies and Practices II
Difficulty: 5/10 Workload: 7/10 (Variable) Course Average: B+
This course is essentially a continuation of APS111, but with the added component of being assigned an actual client with a real-world problem statement. Each group works with a unique client, and at the end of the term, you present your final report directly to them, along with your supervisors. As with APS111, the quality of your team plays a crucial role in your success. Another thing to note is that unlike APS111, there were multiple individual assignments to complete on top of your team reports.
ECE110 Electrical Fundamentals
Difficulty: 8/10 Workload: 6/10 Course Average: C+
Definitely one of the harder first year courses. This course marks your introduction to circuits. Topics covered include capacitors, Ampere's law, Ohm's law, Kirchhoff's laws, AC/DC circuits, magnetism, Thevenin and Norton equivalents, and more. The material is extensive and requires thorough understanding, as many future courses will build upon its concepts. Make sure that you understand the content well, given its foundational importance in subsequent courses.
ECE191 Introduction to Electrical and Computer Engineering
Difficulty: 0/10 Workload: 1/10 Course Average: N/A
Seminar course. You just need to show up to the lectures (one per week) and submit a 100-ish word report about what you learned.
MAT187 Calculus II
Difficulty: 6/10 Workload: 5/10 Course Average: C+
The workload in this course is comparable to Calc I, but the content is more challenging (obviously). It delves deeper into integrals and introduces concepts like ordinary differential equations and polar coordinates. But again, you’ll manage just fine if you did decently in Calc 1, and especially if you’ve taken AP math or IB HL math.
MIE100 Dynamics
Difficulty: 8/10 Workload 4/10 Course Average: C+
I personally found this to be the most challenging course in first year, but it seems that most people disagree with me. It shares similarities with CIV100, but you’re instead dealing with dynamic systems (moving) rather than static ones (not moving). Some concepts may prove difficult to grasp initially. However, if you focus on memorizing the relevant formulas and understanding when to apply them, you should be okay. Expect an emphasis on physics in this course.
Second year was the worst (for ECE) and a very big step up from first year. If you struggled in first year, you better lock in for second year.
Second Year Semester 1:
ECE201 ECE Seminar Course
Difficulty: 0/10 Workload: 0/10 Course Average: N/A
Another seminar course, except you just need to attend 7/10 lectures to pass. Most students just scan their Tcard at the entrance then leave right after. The lectures are pretty useless except the Magellan one where they show you how to choose courses for 3rd and 4th year.
ECE212 Circuit Analysis
Difficulty: 7/10 Workload: 8/10 Course Average: B+
This course provides a deeper exploration of circuits, extending beyond circuit solving to include frequency and sinusoidal analysis. Questions tend to be long so careless errors will cost you. Personally, I found the labs challenging, particularly because they required working with oscilloscopes at a higher level than I was used to, and I could not for the life of me figure out how to use that thing.
ECE241 Digital Systems
Difficulty: 7/10 Workload: 8/10 Course Average: B-
This hardware course focuses on the foundation of hardware. A lot of the content is at the fundamental level so it’s not too hard to comprehend. However, the main challenge lies in learning Verilog, a hardware description language. I hate Verilog. All the labs are done using Verilog, and frustratingly, the course provides no instruction on the language, nor are there many helpful resources available online. This lack of guidance made what should have been straightforward labs much more difficult. Thankfully if you are in CE, you’ll never need to take another course with Verilog again. The midterm was easy but the final was borderline impossible.
ECE244 Programming Fundamentals
Difficulty: 7/10 Workload: 7/10 Course Average: B-
This course is the sequel to APS105, delving into more advanced concepts like pointers, constructors, polymorphism, and inheritance. Many of these topics may feel familiar if you have prior programming experience, particularly from high school. Consequently, if you're already proficient in programming, as many computer engineering students are, you should be able to navigate through this course well. The labs were generally manageable, although a couple of them were time-consuming.
MAT290 Advanced Engineering Mathematics
Difficulty: 7/10 Workload 8/10 Course Average: B
In this course, you’ll learn a lot of miscellaneous mathematical concepts that will be relevant in future courses. The goal is to establish a strong foundation to facilitate understanding in third and fourth-year courses. There is also a quiz every week. They were terrible from a time management standpoint, but as long as you spent 3-4 hours per week completing the assigned textbook questions, you should be alright since one (out of two) of the questions on the quiz is guaranteed to be an assigned textbook question (at least, that was the case when I took the course).
MAT291 Calculus III
Difficulty: 9/10 Workload 9/10 Course Average: B-
The entire workload comes from studying for those damn weekly quizzes, much like MAT290. Personally, I found it to be one of the most difficult courses I've ever taken. Think of calculus 2, but now everything is in three dimensions, encompassing concepts like surface integrals, flux integrals, and more. Additionally, the course introduces abstract topics that I still struggle to grasp. Balancing the demands of this course alongside everything else was an incredibly stressful experience.
Second Year Semester 2:
ECE216 Signals and Systems
Difficulty: 8/10 Workload:7/10 Course Average: C+
I think everyone hated this course. It was so poorly taught and the midterms felt haphazard and unpredictable. I relied on the power of BS to pass (worked surprisingly). Other than working with many graphs and MATLAB, I don’t remember much about this course because it was so uninteresting.
ECE221 Electric and Magnetic Fields
Difficulty: 8/10 Workload: 5/10 Course Average: C+
This course serves as an introduction to fields, while also expanding on the principles covered in ECE110. Many concepts build upon intuitive ideas, such as the repulsion of identical charges and attraction of opposite charges, which can make seemingly complex topics less intimidating. There are also many procedures that you need to know to solve specific types of questions. The labs are generally straightforward. You just need to follow the steps on the lab worksheet and answer a few simple questions from the TA.
ECE231 Intro to Electronics
Difficulty: 9/10 Workload: 7/10 Course Average: C+
This course is basically circuit analysis but significantly harder. It combines everything you've learned about circuits thus far, introducing new topics on top of that foundation. There are so many new concepts to learn and the final pretty much tests you on all of them. I’ve encountered multiple scenarios where I saw two concepts that I thought were completely separate from each other being integrated into a single question on an exam. Also very calculation heavy. Despite my experience in this course, I must admit Khoman Fang was a great professor.
ECE243 Computer Organization
Difficulty: 7/10 Workload: 6/10 Course Average: B
This course is a direct continuation of ECE241, and surprisingly, I found it a lot more enjoyable than its prerequisite. The focus is on Assembly language programming, which I found to be way more understandable compared to Verilog. Assembly operates at a fundamental level, so you’re technically working with the most basic building blocks in programming. The labs often involved seemingly simple tasks like counting to 10 on a HEX display, but the nature of Assembly meant that achieving this could require hundreds of lines of code. I appreciated these labs as they provided a refreshing change from the usual programming tasks. Final and midterm exams were relatively easy.
ECE297 Software communication and design
Difficulty: 8/10 Workload: 9/10 (Variable) Course Average: B+
Despite the demanding workload, this course was one of my favorites. The main project involved collaborating in teams of three to develop a geographical information system (similar to Google Maps). Unlike other courses where teams are assigned, here you have the freedom to choose your own teammates. Starting off, your team is given only the OpenStreetMap database, and the APIs, granting you complete control over your project's design, functionality, and optimization. Performance enhancement and pathfinding algorithms were key components of the project. It's crucial for all team members to contribute effectively for success in this course; And trust me, this is not a one or two person project. To succeed in this course, all three members must be doing their share.
Third Year:
They say that third year is the easiest year for ECE, but that depends on what courses you choose. For me, it was only slightly easier than second year but still significantly harder than first year.
APS360 Fundamentals of Deep Learning
Difficulty: 8/10 Workload : 8/10 Course Average: B+
This course felt fundamentally very different compared to the other programming related courses. If you’re unfamiliar with deep learning, the content can be overwhelming but the inner workings behind AI are both fascinating and challenging to grasp. Fortunately, much of the coding in the course revolves around preparing data, setting up AI architectures, and training/testing models, rather than building an AI from scratch. While the labs are manageable, the final project can be very time consuming, particularly as training a single model can require hours or even days. Regarding this aspect, make sure you start early. Both the midterm and final exams were quite difficult.
ECE302 Probability and Applications
Difficulty: 7/10 Workload: 3/10 Course Average: B-
The first half of this course is very straightforward. Just an introduction to probability. The second half is where the difficulty quickly ramps up. Make sure you don’t underestimate the second half of the course. I made the mistake of assuming the material was easy after performing well on both midterms (midterms were very easy), leading me to neglect the final third of the course so I flunked the final. FYI, our marks were determined solely by the 2 midterms and the final.
ECE344 Operating Systems
Difficulty: 8/10 Workload: 10/10 Course Average: B
This course had the most difficult and demanding labs I’ve ever seen in my life. They are due every two weeks and some of them took most students more than 15 hours to complete. I’ve seen people staying up all night in the GB computer rooms to finish these labs. One positive aspect of the course if Jon Eyolfson is your professor, is that all lectures are both recorded and live-streamed, allowing attendance in person, online synchronously, or asynchronously. But if you have Ding Yuan, good luck. I heard he is not only bad at teaching, but also makes you program an operating system from scratch. The course content is quite interesting and the Midterm and final exams were fair.
ECE345 Algorithms and Data Structures
Difficulty: 8/10 Workload: 7/10 Course Average: B+
If you're one of those programming geniuses who have been doing Leetcode since like 3 years old, you’d have likely already learned all course content before you’ve even started the course. You could probably write midterm and final exams without attending any lectures. However, if you're not as experienced, attending every lecture, taking detailed notes, and staying focused is crucial. Personally speaking, some algorithms were incredibly difficult for me to grasp, while others were simple. Assignments typically include both coding and long-answer components, which can be time-consuming, but you can work with a partner.
TEP444 Positive Psychology for Engineers
Difficulty: 1/10 Workload: 3/10 Course Average: A-
This course overall was very enjoyable and a nice refreshing experience from the other technical courses. You go on excursions to various locations with your group and write reports. It's an excellent opportunity to expand your social circle and make new friends. If you aim for an A- grade (80-85), you can breeze through with minimal effort. However, achieving a higher grade (A or A+) would require significantly more work (like 5 times more, no joke), which is not worth the extra effort in my opinion.
CSC343 Introduction to Databases
Difficulty: 7/10 Workload: 10/10 Course Average: Unreleased
The content of this course is actually pretty interesting and is taught well. But the assignments were excessively time consuming. There are only three in total, but each one demanded such a significant investment of time to the point where I contemplated dropping the course altogether. You do work with a partner (going solo isn’t even an option if you want to complete the assignments) but even so, be prepared to invest 15-30 hours per person, per assignment (they also mark hard). Despite the workload, I do recommend taking this course because knowing SQL can substantially benefit you in the future. Just start the assignments early. On a positive note, the midterm and final were relatively straightforward.
ECE311 Introduction to Control Systems
Difficulty: 10/10 Workload: 5/10 Course Average: C+
This course didn't come with a heavy workload, consisting only of three labs and two midterms. However, I found the content to be very challenging. It felt like they condensed and combined every concept from every engineering math and physics course into one. Topics ranged from circuits and dynamics to Laplace transforms, matrices, Bode plots, and more. It didn’t help that Prof Scardovi didn’t record any lectures, has messy handwriting, and uses a damn chalkboard instead of modern technology like other profs do. The midterms and final were somewhat lenient compared to the lectures, but this course still ended up being my lowest mark.
ECE361 Computer networks I
Difficulty: 8/10 Workload: 9/10 Course Average: B
This course requires a lot of memorization compared to other courses. You’ll have to understand calculation processes but also memorize a lot of general knowledge about networks. A problem with this course is that some concepts that show up in tests are not adequately explained in lectures. By far the best way to study is to do past exams. Professor Valaee often recycles questions, sometimes even directly copying past midterms and finals (like he did in my cohort). Unlike other courses, there is no big assignment/lab for this course, but it makes up for it with quantity. There’s a quiz every week, a wireshark lab every 2 weeks, a programming lab every 2 weeks, and a midterm. The programming labs are already difficult by themselves, but this cumulative workload felt very overwhelming.
ECE472 Engineering Economics and Entrepreneurship
Difficulty: 7/10 Workload: 5/10 Course Average: B+
This is straight up an economics course, no engineering at all. You’ll learn about investments, equity, financing, and related areas. There are 4 quizzes throughout the semester, with quiz 3 (quiz 3 specifically) being by far the hardest (average in the 50s). Since you're able to bring your textbook to the quizzes and exam, my biggest tip is to write past quiz and exam solutions in your textbook, as there's a limited pool of questions, and new questions often resemble some kind of variation of past ones.
ECE421 Introduction to Machine Learning
Difficulty: 9/10 Workload: 7/10 Course Average: unreleased
While the course content itself may be challenging, surprisingly, the midterm and final exams were pretty easy. There's a slight discorrelation between the lecture material and what appears on the exams, but relying on past exams for preparation should be enough. The course leans heavily on mathematical concepts, much more so than APS360. The assignments were difficult and tedious but I recommend you work with others to complete it.
With that, I've covered my thoughts and tips for all the engineering courses I've completed. Transitioning into your next year in engineering, particularly if you’re transitioning from high school, can feel daunting. However, remember that you're not alone in this experience – many of your peers are facing similar challenges. If you need any more advice or have questions, feel free to reach out to me via DMs!

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2024.05.15 05:20 KimberStormer There's so much stuff! It makes me so happy.

I just discovered the feature on SplatNet 3 where you can see what team your friends have picked for Splatfest, and more interestingly, that you can indicate what you might want to pick, so that all your friends can see where you're leaning and you could coordinate a choice that way if you want. I just love how much "extra stuff" there is in SplatNet 3. There's rewards for exploring Alterna, for reconfiguring Palettes, for sponsoring the Wandercrust, and all these things have cute little text bits as well. There's an amazing amount of fun stats to look at -- how many boss Salmonids you've killed, your win rate in each mode on each map, number of wins per weapon, etc etc. There's QR code rewards (granted, not as many as I'd like!) You can flip through the catalog, you can see your past Side Order runs, you can look at the Splashtag of everyone in the games you've played in recently, and of course you can order gear from Annie.
And that's just SplatNet, the whole game is like that, with lockers, Tableturf, Salmon Run (and Big Run, and Eggstra Work), Splashtag badges and titles, wacky Challenges, plaza drawings, putting together outfits, photo mode, watching replays with so many angles to choose from, so much stuff. I feel like I'll never use it all up; I keep finding new things, like upgrading Tableturf cards to be holographic for no reason, or that Sheldon has a different practice area than the lobby, or the battle graphs...it just makes me happy, idk.

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2024.05.14 17:19 ALTR_Airworks Weird torque in aircraft analysis (Ansys CFX)

I`m trying to analyze pitch torque of aircraft. I ususally do a stability analysis via XFLR5 and then compare with Ansys. The lift and drag values are the same, when accounted for the fuselage. The shape of the graphs etc is consistent, but the torque values are way off. Like 1000% off. I made sure my coordinate systems for torque are OK. Both forces and torques are collected as force_x()@Default Domain Default. I ran mesh sensitivity studies and found that 1-1.5 mln nodes is enough for the values to be independent from mesh. I even tested the approach on an RC plane design I know is stable and flyable. XFLR5 says its stable, ansys says its not. What should i check? Meshing? viscid/inviscid analysis? What wall type must the aircraft surface be? slip or no slip? I`m assuming the surface is smooth. The fluid domain is about 5 times bigger than the aircraft in every direction. The problem manifests itself the same way with different geometries of aircraft. I will be glad for any help and hints.

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2024.05.13 22:41 FantasticVictory837 Official Explanation to Bluebook Test 6: Math Module 1, Question #22

submitted by FantasticVictory837 to u/FantasticVictory837 [link] [comments]

http://rodzice.org/