Dynamic Infinity Mapping Framework (DIMF): An Adaptive Oncology Dosing Reinforcement Learning Approach

Dynamic Infinity Mapping Framework (DIMF): An Adaptive Oncology Dosing Reinforcement Learning Approach Author- Shrishti Rastogi Abstract Dynamic Infinity Mapping Framework (DIMF) is a new computational paradigm to manage the stochastic evolution of subpopulations of cancer cells under therapeutic pressure. DIMF combines Markov Decision Processes (MDP), Dynamic Graph Neural Networks (GNNs), and Reinforcement Learning (RL) to offer a powerful framework for optimising the dosage of multiple drugs adaptively. This report outlines the mathematical modelling of state transitions, algorithmic implementation of the RL agent and an empirical calibration using quantitative interaction and toxicity data from the large clinical trials. Our framework shows better ability to cross the balance line between therapeutic effect and total toxicity than static modelling methods. Introduction New challenges for modern oncology are the emergence of acquired resistan...

Cousin Primes & Digital Roots: Interactive Python Tool

Cousin Primes & Digital Roots: Interactive Python Tool

πŸ” Cousin Primes & Their Digital Roots

🎯 What Are Cousin Primes?

Cousin primes are pairs of prime numbers that differ by exactly 4. Examples include (3, 7), (7, 11), and (13, 17). These pairs help us explore prime gaps and distribution patterns in number theory.

πŸ’‘ What Is a Digital Root?

The digital root of a number is the single-digit value obtained by repeatedly summing its digits until only one digit remains. For example:

  • 137 → 1 + 3 + 7 = 11 → 1 + 1 = 2
  • 89 → 8 + 9 = 17 → 1 + 7 = 8

πŸ’» Python Code

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

def digit_root(n):
    while n >= 10:
        n = sum(int(d) for d in str(n))
    return n

def find_cousin_primes_with_roots(lower, upper):
    cousin_pairs = []
    for p in range(lower, upper - 4):
        if is_prime(p) and is_prime(p + 4):
            dr1 = digit_root(p)
            dr2 = digit_root(p + 4)
            cousin_pairs.append(((p, p + 4), (dr1, dr2)))
    return cousin_pairs

def main():
    try:
        lower_limit = int(input("Enter the lower limit: "))
        upper_limit = int(input("Enter the upper limit: "))
        if lower_limit >= upper_limit:
            print("Lower limit must be less than upper limit.")
            return
        pairs_with_roots = find_cousin_primes_with_roots(lower_limit, upper_limit)
        print(f"\nCousin Prime Pairs with Digital Roots from {lower_limit} to {upper_limit}:")
        for (p1, p2), (dr1, dr2) in pairs_with_roots:
            print(f"({p1}, {p2}) → ({dr1}, {dr2})")
    except ValueError:
        print("Please enter valid integers.")

# Run the program
main()

Copy and Try it here!

πŸ“Š Sample Output

Input: Lower = 10, Upper = 50

Output:

(13, 17) → (4, 8)
(19, 23) → (1, 5)
(37, 41) → (1, 5)
(43, 47) → (7, 2)

πŸ” Why It’s Fascinating

Digital roots offer a compact way to analyze numerical behavior. When applied to cousin primes, they reveal patterns, symmetries, and attractors that might otherwise go unnoticed. This blend of number theory and digit analysis is perfect for curious minds.

🌟 Final Thoughts

Try different ranges and observe how digital roots behave across cousin primes. Are certain root pairs more frequent? Do they repeat cyclically? This script is a great way to explore prime behavior and deepen your mathematical intuition.

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