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Hey there, future Angular ninjas! 🥷 If you’re new to Angular and struggling with routing, don’t worry! We’ve got a simple, fun guide that even your lazy friend who never codes can follow. Let's go! 🎉
Open your terminal and type this magic spell:
Set-ExecutionPolicy Unrestricted -Scope CurrentUser
(If your computer starts talking back, run away! Just kidding 😜, this just allows scripts to run.)
ng new routing
Now, check if the server is alive:
ng serve
If everything is working, you should see some Angular awesomeness at localhost:4200. 🎯
Run these one by one in the terminal to create your components:
ng g c home
ng g c contact
ng g c about
ng g c services
Boom 💥! You just made pages like a coding boss.
Open app.routes.ts and change its contents to this:
import { Routes } from '@angular/router';
import { HomeComponent } from './home/home.component';
import { ContactComponent } from './contact/contact.component';
import { AboutComponent } from './about/about.component';
import { ServicesComponent } from './services/services.component';
export const routes: Routes = [
{path:"home", component:HomeComponent},
{path:"contact", component:ContactComponent},
{path:"about", component:AboutComponent},
{path:"services", component:ServicesComponent},
];
Now your app knows where to go when you click a link! 🗺️
Go to app.component.html, erase everything, and add this:
<h1>Testing Routing</h1>
<router-outlet></router-outlet>
Run:
ng serve
Now, check in your browser:
localhost:4200localhost:4200/homelocalhost:4200/contactlocalhost:4200/aboutlocalhost:4200/servicesEnjoy watching the URLs change like magic! 🎩✨
Modify app.routes.ts again:
{path:"", redirectTo:"/home", pathMatch:'full'},
Now, when someone opens your app, they go straight to /home. No confusion, just pure routing genius! 🧠
Modify app.component.ts first:
import { RouterLink, RouterOutlet } from '@angular/router';
imports: [RouterOutlet, RouterLink],
Now, in app.component.html, add this stylish navbar:
<h1>Testing Routing</h1>
<nav>
<a routerLink="/home">Home</a> |
<a routerLink="/contact">Contact</a> |
<a routerLink="/services">Services</a> |
<a routerLink="/about">About</a> |
</nav>
<router-outlet />
Check it in your browser and feel like a pro! 🤩
Modify app.component.html to add active styling:
<h1>Testing Routing</h1>
<nav>
<a routerLink="/home" routerLinkActive="active-home">Home</a> |
<a routerLink="/contact" routerLinkActive="active-contact">Contact</a> |
<a routerLink="/services" routerLinkActive="active-services">Services</a> |
<a routerLink="/about" routerLinkActive="active-link">About</a> |
</nav>
<router-outlet />
Now, create a style sheet to make it pop! 🎨
.active-link {
color: red;
}
.active-home {
color:aqua;
}
.active-about {
color:blue;
}
.active-services {
color: rgb(238, 47, 255);
}
.active-contact {
color:rgb(38, 236, 203);
}
Bam! Your links change colors when clicked! 🌈
STEP 9 :
Create Docker file with the followingcontents
docker build -t angular_practice
docker run -p 8080:4200 angular_practice
Watch this video if you’re stuck: 👉 Programmatic Navigation
Congratulations, you just built a routing system in Angular! 🎉 Now, go flex on your friends! 😎
The Complete AI-Powered Academic Paper Writing Pipeline: From Idea to Publication (2025 Guide)
#AcademicWriting #Research #AI
📍 STAGE 1: RESEARCH IDEATION & PLANNING
🧠 Step 1: Topic Exploration & Research Gap Identification
Tools:
• Iris.ai - Research mapping and gap analysis
• Semantic Scholar - Trend identification
• Connected Papers - Visual literature mapping
• ResearchRabbit - Citation network analysis
• Elicit.org - Research question formulation
🔍 Step 2: Literature Review
Tools:
• Scholarcy - Automated paper summarization
• Zotero - Reference management
• Paper Digest - Quick paper summaries
• SciSpace - PDF analysis and highlighting
• LitMaps - Literature visualization
📊 Step 3: Research Design Planning
Tools:
• ChatGPT - Methodology brainstorming
• Claude - Experimental design feedback
• R Studio AI - Statistical planning
• G*Power AI - Sample size calculation
• Protocol.io - Method documentation
📍 STAGE 2: PAPER DRAFTING
📝 Step 4: Paper Outlining
Tools:
• Writefull Structure - Section organization
• Paperpal Outline - Content planning
• Sciflow - Template-based outlining
• AcademicWriter - Structure suggestions
• Scrivener AI - Content organization
Strategy: Use journal guidelines alongside AI suggestions for optimal structure
✍️ Step 5: First Draft Writing
Tools:
• Writefull - Scientific writing assistance
• Academic Phrasebank AI - Standard phrases
• Grammarly Academic - Real-time writing feedback
• QuillBot Academic - Paraphrasing help
• Ludwig.guru - Academic language suggestions
Writing Workflow:
1. Create section drafts using AI assistance
2. Review and modify content
3. Check technical accuracy
4. Verify citations
5. Polish language
📊 Step 6: Figures & Visualizations
Tools:
• BioRender - Scientific illustrations
• Plotly AI - Interactive charts
• RAWGraphs - Data visualization
• Tableau AI - Complex data plots
• Adobe Illustrator AI - Figure design
📍 STAGE 3: REFINEMENT & EDITING
📌 Step 7: Content Editing
Tools:
• Paperpal - Technical editing
• Grammarly Premium - Style and grammar
• LanguageTool - Language refinement
• Hemingway Editor - Readability check
• ProWritingAid Academic - Deep analysis
Editing Checklist:
□ Technical accuracy
□ Logical flow
□ Citation completeness
□ Figure relevance
□ Methods clarity
🔍 Step 8: Technical Review
Tools:
• StatReviewer - Statistical check
• Penelope.ai - Technical consistency
• SciScore - Methods verification
• iThenticate - Plagiarism check
• Authzen - Author contribution check
Critical: Manual review of all AI suggestions is essential
📑 Step 9: Reference Management
Tools:
• EndNote - Citation organization
• Mendeley Cite - Reference formatting
• Zotero AI - Bibliography management
• CiteDrive - BibTeX management
• ReadCube - Reference library organization
Tip: Double-check all AI-generated citations against original sources
With minimal meteorological data, you can still build a basic model for green energy construction using these steps:
Identify Key Data: Focus on crucial weather parameters:
Collect Data: Use historical or open-source meteorological data from sources like:
Simplify Energy Models:
Site-Specific Adjustments:
Build Predictive Model:
Validate & Optimize: Compare model results with small-scale installations for accuracy.
This approach works as a basic starting point.
with minimum and maximum temperature data, you can build a model to estimate energy needs for maintaining a comfortable classroom for 60 students. Here's a step-by-step approach:
Model the cooling or heating energy required to maintain a comfortable temperature (e.g., 24°C) in a classroom with 60 students.
The heat load includes factors that increase room temperature:
External Temperature Influence:
Heat Gain from Occupants:
External Heat Transfer (Walls and Windows):
Add all sources of heat load:
Qtotal=Qoccupants+QwallsThis gives the energy required to cool or heat the classroom.
Energy Consumption can be calculated using:
Energy (kWh)=1000Qtotal×Operating HoursExample Scenario:
1. Heat from students:
Qoccupants=6000W2. Heat transfer through walls:
Qwalls=U×A×ΔT=1.5×150×(32−24)=1800W3. Total Heat Load:
Qtotal=6000+1800=7800W(7.8kW)4. Energy Consumption:
For 6 hours:
Use Python to automate this calculation. Here’s a simple script:
# Inputs# InputsT_min = 28 # Minimum temperatureT_max = 36 # Maximum temperatureT_comfort = 24 # Desired temperatureU_value = 1.5 # Thermal transmittance (W/m²K)area = 150 # Surface area of walls+roof (m²)occupants = 60heat_per_person = 100 # Heat generated per person (W)hours = 6 # Operating hours# Average temperatureT_avg = (T_min + T_max) / 2# Heat load calculationsdelta_T = T_avg - T_comfortQ_occupants = occupants * heat_per_person # Heat from studentsQ_walls = U_value * area * delta_T # Heat transfer through wallsQ_total = Q_occupants + Q_walls # Total heat load# Energy consumptionenergy_kWh = (Q_total * hours) / 1000# Resultsprint(f"Total Heat Load: {Q_total:.2f} W")print(f"Daily Energy Consumption: {energy_kWh:.2f} kWh")
Let me know if you need further tweaks, bro! 🚀
Cooling load is the amount of heat that needs to be removed to maintain a comfortable 24°C.
Key Heat Sources:
Simplified Formula:
For a classroom, use 1 ton of AC for every 12,000 BTU/hr cooling load.
1 Ton = 3.517 kW (energy).
Cooling load can be calculated as:
Total Cooling Load (BTU/hr)=Qwalls+Qoccupants+QlightsLet’s assume:
Comfort Temp = 24°C.
Heat Gain from Students:
Qoccupants=60×100=6000W(6kW)Heat Gain from Lights:
Qlights=10×50=500W(0.5kW)External Heat Gain (Walls + Roof):
Assume:
Total Cooling Load:
Qtotal=Qoccupants+Qlights+Qwalls Qtotal=6000+500+3600=10,100W(10.1kW)Convert to BTU/hr:
BTU/hr=10.1kW×3412=34,500BTU/hr1 Ton of AC = 12,000 BTU/hr.
For 34,500 BTU/hr:
To reduce AC load:
For a 60-student classroom:
This ensures comfort even in peak summer temperatures, bro! 🌞 Let me know if you need cost estimates or further tweaks. 🚀
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