All posts by Abhilash Shukla

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The LEFT and RIGHT (Skeptical view) debate on climate change

The debate on climate change can often be divided into two main camps: those on the “left” and those on the “right.”

People who are considered “left-leaning” (Also termed as Skeptical viewpoint) generally think that climate change is a big problem that needs to be dealt with right away. They usually support ideas like setting a price on carbon and making rules to limit the amount of greenhouse gases that are released. A lot of people on the left also believe that doing something about climate change is the right thing to do morally and ethically, and that countries that have been releasing a lot of greenhouse gases for a long time have a special responsibility to take action.

People who are considered “right-leaning” usually do not believe that climate change is as big of a problem and that it needs any immediate action. They may say that we do not have enough information to be sure that climate change is happening or that it is caused by human activity, or that it would be too expensive to do something about it. Some people on the right may also believe that people should be free to make their own choices and that economic growth is more important than protecting the environment.

It’s important to remember that not everyone who is considered “left” or “right” feels the same way about climate change. There are many different opinions on this topic and some people may not fit into one of these two groups or might hold a more nuanced position. Also, “left” and “right” can mean different things in different countries and situations.

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A note for transitioning from traditional to agile project management

Adopting agile project management? Learn how to excel by aligning team goals, re-evaluating success metrics & fostering collaboration. Say goodbye to traditional constraints and embrace agility for faster results. As a project manager, the transition to agile methodologies can bring about new challenges. Agile emphasizes small teams working in short intervals and continuously learning, which may be a departure from the traditional linear approach to project management that many project managers are accustomed to. However, with the right mindset and approach, project managers can not only adapt but excel in this new environment.

One of the initial steps for project managers leading an agile team is to comprehend the goals and objectives of the organization. By taking the time to understand the reasoning behind the organization’s adoption of agile, project managers can align their team’s goals and objectives with those of the organization. This allows them to demonstrate how organizational agility can aid their team in achieving these goals, and how their team can contribute to the overall success of the organization.

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The dilemma of an entrepreneur acting as a project manager

An entrepreneur who has to also act as a project manager, the days are always filled with a constant act of balancing. In startups and budding businesses, you have to perform those roles side by side. On one hand, you had to lead and manage your teams ensuring that projects are completed on time and within budget, on other hand, you have to focus on growing your business and making sure that it is financially successful as well.

One of the primary challenges I have always heard from people in this dual role is time management, as frankly for an entrepreneur, there are a million things to do and always not enough hours in a day to do them. You have to constantly prioritize and make sure that you are focused on the most important tasks at hand and do not deviate from insignificant activities. Of course, some days such decisions are done right, and sometimes they are not.

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Demonstrating Newton’s laws of motion using Python code

Newton’s laws of motion are a set of three laws that describe the relationship between a body and the forces acting upon it. These laws were developed by Sir Isaac Newton in the 17th century and are still widely used in classical mechanics to describe the motion of objects.

Newton’s laws of motion provide a fundamental framework for understanding the motion of objects and the forces that act upon them.

Here is an example of how you could demonstrate Newton’s laws of motion using Python code:

Newton’s First law of motion with Python code

An object at rest tends to stay at rest, and an object in motion tends to stay in motion with the same speed and in the same direction, unless acted upon by a force.

# define a class to represent an object
class Object:
  def __init__(self, mass, velocity=0):
    self.mass = mass
    self.velocity = velocity
    
  def apply_force(self, force, time):
    # calculate the acceleration of the object
    acceleration = force / self.mass
    # update the velocity of the object based on the acceleration and time elapsed
    self.velocity += acceleration * time
    
# create an object with a mass of 10 kg and initial velocity of 0 m/s
obj = Object(10)
print("Initial velocity:", obj.velocity) # should print 0

# apply a force of 50 N for 1 second
obj.apply_force(50, 1)
print("Velocity after applying force:", obj.velocity) # should print 5 (50 N / 10 kg = 5 m/s^2)

# apply no force for 1 second
obj.apply_force(0, 1)
print("Velocity after applying no force:", obj.velocity) # should still print 5

Newton’s Second law of motion with Python code

Newton’s second law of motion states that the force acting on an object is equal to the mass of the object times its acceleration. In equation form, this is represented as:

F = ma

Where F is the force acting on the object, m is the mass of the object, and a is the acceleration of the object.

Python code that calculates the mass and acceleration of an object based on the given variables:

# Define the initial and final velocities of the object
initial_velocity = 10  # m/s
final_velocity = 20  # m/s

# Define the time elapsed
time_elapsed = 5  # s

# Calculate the change in velocity
delta_v = final_velocity - initial_velocity

# Calculate the acceleration of the object
acceleration = delta_v / time_elapsed

# Print the calculated acceleration
print("Acceleration:", acceleration, "m/s^2")

# Define the force acting on the object
force = 50  # N

# Calculate the mass of the object
mass = force / acceleration

# Print the calculated mass
print("Mass:", mass, "kg")

This code will output the following values:

  • Acceleration: 4 m/s^2
  • Mass: 12.5 kg

The variables initial_velocity, final_velocity, and time_elapsed can be changed to different values to calculate the acceleration and mass for different scenarios.

Now the below Python code demonstrates this principle by calculating the force required to accelerate a 1 kilogram object at a rate of 1 meter per second squared:

# Define the mass of the object in kilograms
mass = 1

# Define the acceleration of the object in meters per second squared
acceleration = 1

# Calculate the force required to accelerate the object
force = mass * acceleration

# Print the calculated force
print(force)

This code will output the result 1, which represents the force required to accelerate a 1 kilogram object at a rate of 1 meter per second squared.

Newton’s Third law of motion with Python code

For every action, there is an equal and opposite reaction.

import matplotlib.pyplot as plt
import numpy as np

# Constants
GRAVITY = 9.81  # m/s^2
MASS = 1.0      # kg

# Initial conditions
y_0 = 0         # m
v_0 = 10        # m/s
t_0 = 0         # s
dt = 0.01       # s

# Create lists to store the position and velocity of the ball at each time step
y_positions = [y_0]
v_velocities = [v_0]
times = [t_0]

# Loop through time steps and calculate the position and velocity of the ball at each step
while y_positions[-1] >= 0:
    # Calculate the acceleration of the ball due to gravity
    acceleration = -GRAVITY

    # Calculate the velocity at the current time step
    v_current = v_velocities[-1] + acceleration * dt

    # Calculate the position at the current time step
    y_current = y_positions[-1] + v_current * dt

    # Append the current position, velocity, and time to the lists
    y_positions.append(y_current)
    v_velocities.append(v_current)
    times.append(times[-1] + dt)

# Plot the position of the ball over time
plt.plot(times, y_positions)
plt.xlabel('Time (s)')
plt.ylabel('Position (m)')
plt.show()

This code simulates the motion of a ball being dropped from a height of y_0 meters with an initial velocity of v_0 meters per second. The ball is subjected to the force of gravity, which is represented by the acceleration GRAVITY meters per second squared. The code uses a while loop to step through the simulation, calculating the position and velocity of the ball at each time step using the equations of motion:

acceleration = force / mass
velocity = velocity + acceleration * dt
position = position + velocity * dt

At each time step, the ball experiences a force equal to its mass times the acceleration due to gravity (F = ma). This force is the action, and the reaction is the equal and opposite force that the ground exerts on the ball. The code plots the position of the ball over time, showing how it bounces back up after hitting the ground due to the equal and opposite reaction force.


Startup companies contributing to climate change mitigation

There are a number of startups out there that are tackling climate change head on. These companies come from different industries and are using a variety of methods to cut down on greenhouse gases and lessen the effects of climate change. This article showcases some of these innovative firms and their efforts to protect our planet. Please note, its just a small list and I do not have sponsorship from any one of them ๐Ÿ™‚

One company has developed a technology that captures and sequesters CO2 from industrial processes and uses it to make concrete stronger and more durable. Another company specializes in developing and operating facilities that convert waste into clean energy. There is a company that manufactures inverters and other technologies that help optimize the performance of solar energy systems. There is also a company that provides electric vehicle charging infrastructure and related services. Another company has developed a technology that captures CO2 from industrial emissions and uses it to produce chemicals and fuels.

So the idea is to share a small sample of many startup companies that are focused on developing and commercializing innovative technologies and services that directly or indirectly help reduce greenhouse gas emissions and mitigate the impacts of climate change.

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Electrifying world with Solar: How much Surface Area required?

Electricity powers our daily lives and drives economic growth, so it’s no surprise that some countries consume more electricity than others. According to recent data, the top 10 countries with the highest total electricity consumption are China, the United States, India, Russia, Japan, Germany, South Korea, Iran, Saudi Arabia, and Canada. These countries have high demand for electricity due to factors such as large populations, industrialization, and economic development.

It has been an interesting exercise to mathematically assume how much surface area would be required to install solar panels in these countries to meet their electricity needs. However, please do understand that this article is purely an interesting hypothesis and not a concrete recommendation in any sense. It’s just a mere area-based assumption to see how much land we might need to electrify a country or this entire world.

China, the United States, and India are the largest consumers of electricity globally, with China alone accounting for almost 20% of total global electricity consumption. Russia, Japan, and Germany also have large and developed economies, which contribute to their high levels of electricity consumption. South Korea, Iran, Saudi Arabia, and Canada also consume relatively large amounts of electricity due to their populations, industrial bases, and economic development. I assume that you possess the basic understanding that electricity consumption doesn’t necessarily reflect a country’s prosperity or well-being, but it is a significant indicator of economic and industrial activity.

Top 10 countries with the highest total electricity consumption (2019):

  1. China – 9,596 billion kWh
  2. United States – 4,178 billion kWh
  3. India – 3,599 billion kWh
  4. Russia – 1,295 billion kWh
  5. Japan – 1,196 billion kWh
  6. Germany – 647 billion kWh
  7. South Korea – 593 billion kWh
  8. Iran – 423 billion kWh
  9. Saudi Arabia – 358 billion kWh
  10. Canada – 347 billion kWh

Again, I am referring my last quote before banging on the complete article is that the ranking of countries by electricity consumption may change depending on the data source and time frame being considered for these assumptions. It is also important to remember that a country’s electricity consumption does not necessarily reflect its level of development or well-being.”.

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Solving Euler’s formula for polyhedra, Navier-Stokes equations, and Feynman path integral using Python

Euler’s formula, the Navier-Stokes equations, and the Feynman path integral are three important concepts in the fields of mathematics and physics. Just to clarify, they are not a tall connected to one another directly, however, they are all related to our understanding of the world around us. We will be using the Python programming language to explore and solve these concepts. My assumption is that you understand all these formula’s and hence attempting to solve it using Python.

Euler’s formula is a tool that helps us understand the structure of three-dimensional shapes called polyhedra. It tells us the relationship between the number of vertices, edges, and faces that make up a polyhedron.

The Navier-Stokes equations, on the other hand, are used to study the movement of fluids. These equations are a set of mathematical statements that describe how fluids behave and are used to model the flow of liquids and gases in many different situations.

The Feynman path integral, named after physicist Richard Feynman, is a way of understanding the behavior of particles at the quantum level. It allows physicists to make predictions about the actions of particles by considering all of the possible paths they might take and calculating the chances of each one occurring.

Solving Euler’s formula for polyhedra using Python code

This is a Python function that checks if a three-dimensional shape, called a polyhedron, follows a certain rule called Euler’s formula. To use the function, you need to give it three whole numbers, which represent the number of flat faces, straight edges, and points on the polyhedron. The function will then tell you if the polyhedron follows Euler’s formula by giving you either ‘True’ or ‘False’:

def satisfies_euler(V, E, F):
  return V - E + F == 2
Continue reading Solving Euler’s formula for polyhedra, Navier-Stokes equations, and Feynman path integral using Python

Why poor experience the most severe effects of climate change?

Climate change affects everyone, but it disproportionately impacts vulnerable and marginalized communities, including the poor. These communities often have fewer resources and less political power to adapt to and mitigate the impacts of climate change.

For example, in a coastal community where fishing is a major source of income, rising sea levels and more frequent storms may make it more difficult for fishermen to go out to sea, leading to a loss of income. Without the financial means to adapt, such as by investing in more durable boats or finding alternative sources of income, these individuals and their families may be at risk of poverty and food insecurity.

According to a report by the United Nations Office for Disaster Risk Reduction (UNDRR), between 1995 and 2015, more than 95% of all deaths caused by natural disasters occurred in developing countries. During this period, more than 1.3 million people died as a result of natural disasters, and more than 4.4 billion people were affected.

It is difficult to determine the exact percentage of people living in poverty worldwide, as definitions of poverty and methods of measuring it vary across countries and regions. However, according to the World Bank, as of 2021, about 9.2% of the global population, or about 689 million people, lived in extreme poverty, defined as living on less than $1.90 per day. This represents a significant reduction from 1990, when more than 35% of the global population lived in extreme poverty.

It is important to address the needs and concerns of poor communities in the context of climate change and to ensure that they have the resources and support they need to adapt to and mitigate the impacts of a changing climate.

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Why child birth control is often talked with Climate Change?

These days, it is not uncommon for people to talk about this or to show the concerns about child birth control in the face of climate change and other environmental challenges. People may simply be uncertain about what the future will hold and this has become a reason for them to be hesitant to bring the children into a world that is facing such significant challenges.

Now many people don’t think like this, and the black-and-white reason could be that few people are too sensitive about the climate change, whereas few aren’t. In this article, I will try to understand and help bring my perspectives on why people would be thinking that way.

A word of caution

This article is written from my own individual perspective and have psychological, geographical, conceptual, and overall belief that I possess. The article is quite lengthy, as there were many elements that I couldn’t resist to write about. Please read at your own wisdom.

There are several reasons why some people may be hesitant to have children due to environmental concerns. Fear of environmental degradation is one such reason. Climate change and other environmental problems can harm human health and well-being. Some people may worry that their child will face a difficult future due to environmental challenges.

Another reason is fear of resource depletion. People may be concerned about the availability of resources such as food, water, and energy in the future and how having a child may impact these resources. Also, there is the fear of overpopulation. Population growth can put pressure on the environment and natural resources. Some people may be hesitant to have children out of concern for the impact their family may have on the planet.

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Solving 5 mathematical Conjecture puzzles with Python code

There are many complex math puzzles that have stumped mathematicians and puzzle enthusiasts alike. Here are 5 mathematical conjecture puzzles that I have attempted to explain and solve using Python:

The Collatz Conjecture

The Collatz conjecture is a mathematical problem that involves a sequence of positive integers that are generated according to a specific rule. The conjecture states that for any positive integer, the sequence will eventually reach the number 1, regardless of the starting number.

Here is a simple Python function that generates the Collatz sequence for a given starting number:

def collatz(n):
    while n != 1:
        print(n, end=", ")
        if n % 2 == 0:
            n = n // 2
        else:
            n = 3*n + 1
    print(1)

To use this function, you would simply call it with a positive integer as the argument, like this:

collatz(10)

This would output the following sequence:

10, 5, 16, 8, 4, 2, 1

The conjecture has been verified for many starting numbers, but it has not been proven for all positive integers. Despite much effort, a general proof or counterexample has not yet been found.

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