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Life Skills for Everyday Success ~xRay Pixy

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Life skills are the basic abilities we need to handle daily challenges and live a healthy, balanced life. They help us think clearly, manage our feelings, make good decisions, solve problems, and build good relationships with others. The World Health Organization (WHO) highlights 10 important life skills: 1.) Thinking skills: decision-making, problem-solving, creative thinking, critical thinking 2.) Social skills: communication, empathy, interpersonal skills 3.) Emotional skills: self-awareness, coping with emotions, coping with stress Life skills are the tools that make us stronger, wiser, and calmer in real life — at home, in school, at work, and in the community :) Life Skills for Everyday Success ~xRay Pixy https://youtu.be/AMsUfKRl4kw Video Chapters: Life Skills 00:00 Introduction 01:07 Life Skills 09:42 Real Life Challenge 13:44 Task For You #LifeSkills #SuccessTools #StressFreeLiving #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swa...
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WFLO in Python || Optimal Placement of Wind Turbines using PSO in Python...

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Wind turbine optimal placement using particle swarm optimization Implementation in Python. Video Chapters: 00:00 Introduction 00:30 Key Points 03:17 Implementation 05:50 Flowchart 06:32 Code 23:47 Apply PSO 31:53 Output SOURCE CODE import numpy as np import math import random #Probability Distribution Function def PDF(U,k,c): return (k / c) * (U / c)**(k - 1) * math.exp(-((U / c)**k)) #Calculate Alpha def Cal_alpha(Z,Z_o): return 0.5 / math.log (Z/Z_o) #Calculate Full Wake Effect def Full_WE(u_o,a,alpha,X,R_1): return u_o*(1-(2*a/(1+alpha*(X/R_1)**2))) #Calculate Partial Wake Effect def Partial_WE(u_o,a,alpha,X,R_1,A_Partial,A_Total): return u_o * (1-(2*a/(1+alpha*(X/R_1)**2)))*(A_Partial-A_Total) #Calculate No Wake Effect def No_WE(u_o): return u_o #Calculate Power def Power(u,Ideal_Power): if u<3: return 0 elif 3<=u<=12: return Ideal_Power elif 12<=u<=25: return 518.4 else: return 0 #Calcu...
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How to Initialize Population by Good-Point Set in Python ~xRay pixy

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How to Initialize Population by Good-Point Set in Python ~xRay pixy
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GWO Python Code || Grey Wolf Optimizer in Python || ~xRay Pixy

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SOURCE CODE import numpy as np import tkinter as tk import matplotlib.pyplot as plt from tkinter import messagebox def initialization (PopSize,D,LB,UB):     SS_Boundary = len(LB) if isinstance(UB,(list,np.ndarray)) else 1     if SS_Boundary ==1:         Positions = np.random.rand(PopSize,D)*(UB-LB)+LB     else:         Positions = np.zeros((PopSize,D))         for i in range(D):             Positions[:,i]=np.random.rand(PopSize)*(UB[i]-LB[i])+LB[i]     return Positions def GWO(PopSize,MaxT,LB,UB,D,Fobj):     Alpha_Pos = np.zeros(D)     Alpha_Fit = np.inf     Beta_Pos = np.zeros(D)     Beta_Fit = np.inf     Delta_Pos = np.zeros(D)     Delta_Fit = np.inf     Positions = initialization(PopSize,D,UB,LB)     Convergence_curve = np.zeros(MaxT)     l = 0     while l...
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Implement TSP in Python ||Travelling Salesman Problem|| ~xRay Pixy

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Travelling salesman problem implementation in Python. Video Chapters: 00:00 Introduction 00:34 TSP Code 06:51 Calculate the Total Distance 11:17 Find Out the Optimal Route and Minimum Distance 15:03 Output 16:00 Conclusion
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