All posts by Abhilash Shukla

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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
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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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Real life scenarios of drones helping in disaster response

Drones have proven to be a valuable tool in disaster response in recent years. They can be used to quickly and efficiently survey areas affected by disasters, such as earthquakes, hurricanes, and wildfires, providing detailed information about the extent of the damage and the location of people in need of assistance. Drones equipped with cameras and other sensors can capture high-resolution images and other data that can be used to assess the damage and plan response and recovery efforts.

In addition, drones can be used to deliver supplies, such as food, water, and medical supplies, to isolated or hard-to-reach areas. They can also be used to search for survivors and provide real-time situational awareness to responders on the ground. Overall, drones have proven to be a valuable asset in disaster response efforts, helping to save lives and reduce the impact of disasters on communities.

There are many things that drones can do for responding efficiently in the case of disaster. This article will capture few of those important activities that can strengthen the disaster response using drones:

Use Case: Mapping

After a disaster strikes, drones can be a valuable tool for emergency responders. They can be used to create detailed maps of the affected areas, highlighting important resources such as hospitals and shelters, as well as evacuation routes. These maps can help responders navigate the chaos and plan for recovery efforts, as well as identify bottlenecks and optimize evacuation efforts.

Drones can also be used to map the distribution of debris, helping responders prioritize clean-up efforts and assess the risk of further damage. In this way, drones provide a bird’s-eye view that can help responders make informed decisions and respond effectively to the crisis at hand.

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Python code to assess carbon contribution on surface temperature

To calculate the contribution of carbon dioxide (CO2) to the current surface temperature of the Earth, we will need to use a combination of physical principles and data on atmospheric concentrations of CO2 and other greenhouse gases. In this article we will try and understand the basics of calculating the carbon concentration affecting the surface temperature using Python programming. But, before let’s do a general outline of the steps we can follow:

  1. Determine the current atmospheric concentrations of CO2 and other greenhouse gases. We can find this information from various sources, including scientific papers, government agencies, and online databases.
  2. Calculate the global mean surface temperature of the Earth. This can be done by using temperature data from a large number of locations around the globe and averaging them.
  3. Determine the amount of energy being absorbed by the Earth’s atmosphere from the sun. This can be calculated using the solar constant, which is the amount of solar energy received by the Earth per unit area per unit time, and the Earth’s albedo, which is the fraction of solar energy reflected by the Earth’s surface and atmosphere.
  4. Calculate the amount of energy being emitted by the Earth back into space. This can be done using the Stefan-Boltzmann law, which states that the rate at which a blackbody (such as the Earth) emits energy is proportional to the fourth power of its temperature.
  5. Calculate the difference between the energy absorbed by the Earth and the energy emitted back into space. This will give us the net energy balance of the Earth, which is the excess energy that is trapped in the Earth’s atmosphere.
  6. Determine the contribution of CO2 and other greenhouse gases to the net energy balance. This can be done by using the absorption and emission spectra of these gases, which describe how they absorb and emit energy at different wavelengths. We can then calculate the amount of energy absorbed and emitted by each gas and add them up to determine the total contribution of all the gases.
  7. Calculate the warming effect of the gases by comparing the net energy balance with and without the contribution of the gases. The difference between the two will give us the warming effect of the gases.

This is a simplified version of the process that scientists use to calculate the warming effect of greenhouse gases. In practice, the calculations are more complex and may involve using advanced computer models and data from a wide range of sources.

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Indian Traditional mathematics equations in Python Code

Indian mathematics has a rich history dating back thousands of years. Some of the key contributions of traditional Indian mathematics include the development of the decimal place-value system and the concept of zero, as well as the development of various equations, trigonometry and algebra. In this article we will attempt to reproduce these equations using Python code.

The Pythagorean theorem

The Pythagorean theorem which states that in a right triangle, the square of the length of the hypotenuse (the side opposite the right angle) is equal to the sum of the squares of the other two sides:

a^2 + b^2 = c^2
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Formulas and equations for calculating change in climate?

There are many different ways to quantify and measure changes in climate, and different formulas and equations are used to calculate different aspects of climate change. Some of the key formulas and equations that are used to calculate changes in climate include:

  1. The Stefan-Boltzmann Law
  2. The greenhouse effect equation
  3. The equilibrium temperature equation
  4. The concentration of Greenhouse gas, or a Pollutant, or Trace gas, or Carbon dioxide (CO2) in the atmosphere, or Nitrous oxide (N2O) in the atmosphere, or SF6, etc.
  5. The global warming potential (GWP) of a greenhouse gas or a pollutant
  6. The carbon footprint of a product or activity
  7. The equilibrium climate sensitivity (ECS)
  8. The atmospheric lifetime of a greenhouse gas, or a pollutant, or a trace gas, or CO2, or CH4, or N2O, or SF6, etc.
  9. The heat capacity of the Earthโ€™s oceans
  10. The global heat budget
  11. The carbon budget
  12. The ozone depletion potential (ODP) of a pollutant
  13. The radiative forcing of a trace gas, or CO2, or N2O, or SF6, etc.
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At current rate of climate change, by when all arctic ice will melt?

It is difficult to predict exactly when all the Arctic ice will melt as it depends on various factors such as global greenhouse gas emissions, the rate at which the Earth’s temperature increases, and the feedback effects of the melting ice. However, it is expected that the Arctic will be free of sea ice in the summer months within the next few decades.

Sea ice extent is a measure of the area of the Earth’s oceans that is covered by sea ice. Sea ice is frozen seawater that forms in the polar regions of the Earth, and it is an important component of the Earth’s climate system. Sea ice forms in the winter when the temperature of the ocean surface drops below the freezing point of seawater, and it melts in the summer when the temperature of the ocean surface rises above the freezing point.

The Arctic sea ice has been melting at an alarming rate in recent years, with the minimum summer sea ice extent (the smallest area of sea ice that is present in the Arctic during the summer) declining by 13% per decade since the late 1970s. In September 2020, the minimum summer sea ice extent reached a new record low, with only 1.44 million square miles (3.74 million square kilometers) of ice remaining. This is the equivalent of losing an area of ice the size of Texas and Oklahoma combined every year.

This graph comparing results from climate models shows that the actual downward trend of Arctic sea ice decline continues to exceed what most models predicted.Courtesy Stroeve et al., Geophysical Research Letters

Climate change is definitely affecting winds and ocean currents, and that these changes can contribute to the melting of Arctic sea ice. As the Earth’s climate warms, it can lead to changes in atmospheric and oceanic circulation patterns, which can affect the strength and direction of winds and ocean currents. These changes can also have a variety of impacts on the Earth’s climate and weather patterns.

The way the change in climate is happening, it can impact the melting of Arctic sea ice is by altering the temperature difference between the equator and the poles. As the Earth’s climate warms, the temperature difference between the equator and the poles is expected to decrease, which could lead to a slowing down of the jet streams, the wind patterns that flow from west to east around the Earth at high altitudes in the mid-latitudes. This could lead to changes in weather patterns, such as more extreme heatwaves and cold snaps in some regions.

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