Fifty Years Ahead of the Private Sector by James Pate Williams, Jr, BA, BS, MSwE, PhD

I have heard some former or want to be National Security Officers suggest that the National Security Agency is and has been since 1952 fifty years ahead of the private sector in many active areas of research. I have clear evidence based on one 1941 document that the United States Navy was at least fifty years ahead of the war-by -wireless technology in the Ordnance Pamphlet 770 which was printed in October 1941:

https://eugeneleeslover.com/USN-GUNS-AND-RANGE-TABLES/OP-770-1.html

The approximate maximum range of the 16-inch by 50 caliber fast battleship class Iowa artillery is around 24 miles at a maximum elevation of 45 degrees. The horizon can be calculated to be at height of 60 feet above sea level to be 1.22 * (60)^(1/2) = 9.45 miles. So, the question is how can you guarantee an accurate shot at 24 miles even with the crude radar of the day? One uses a spotter aircraft to give the accurate longitude-latitude coordinates of the enemy ship. Thus, we have GPS gunnery in 1930s-1940s technology.  We are a country of warfare geniuses in the United States of North America.

I am sure there are many other examples of the U.S. getting a lot of bang for our tax dollars.

Realistic Predator-Prey Model by James Pate Williams, Jr., BA, BS, MSwE, PhD

Realistic Predator Prey Model

dN / dt = N * [r * (1 – N / K) – k * P / (N + D)] (Prey Equation)

dP/ dt = P * [s * (1 – h * P / N)] (Predator Equation)

To reduce the six-parameter model to a more manageable three parameter one introduce the following variables and parameters

u(w) = N(t) / K

v(w) = h * P(t) / K

w = r * t

a = k / (h * r)

b = s / r

d = D / K

du / dw = u – (1 – u) – a * u * v / (u + d)

dv / dw = b * v * (1 – v / u)

In my program a = alpha, d = beta, and b = gamma

More Fractals by James Pate Williams, Jr., BA, BS, MSwE, PhD

All the images were generated using the complex polynomial as shown below:

z1 = z0 ^ n + c0

Where c0 and z0 are random complex numbers of the form:

z = x + i * y

where x and y are real numbers and i is the imaginary square root -1. The first three images use n = 3, the next one n = 4, the next four n = 5, and the last three n = 6.

z1 = z0 ^ 2 + c0 = z0 * z0 + c0

Entrapment and Potential Ethics Violations Email and Post by James Pate Williams, Jr., BA, BS, MSwE, PhD

Some law enforcement officer using a Facebook profile and telephone number and text is trying to entrap in some type of crime (probably a sexual deviant crime). The Facebook profile is NAME REDACTED and the telephone number might be in Huntsville, Alabama TELEPHONE NUMBER REDACTED. This NAME REDACTED character or persona is on the dating website ZOOSK also. He/She maybe an Alabama Bureau of Investigation (ABI) or Tennessee Bureau of Investigation (TBI) or Federal Bureau of Investigation (FBI) agent. He/she claims to be living in Knoxville, Tennessee now. There are only two females that I am really interested in and they are: Dr. Yvonne Maria or Marie Greene, MD, age 51 (?), CDC (?), and Merry Sue Bracken, age 44 (?), last I read she was with Pathways Center Carrollton, Georgia, Mental Health Outpatient Clinic (?). I have Merry’s contact information, but it is an ethics violation to call or write a letter to her. Unfortunately, I must stick with Pathways since I have loyalty to my psychiatric service provider even though they do not know what the hell they are doing most of the time. Dr. Scott Andrews of LaGrange, Georgia refused to take on my case for a second time in November 2019.

I sent this unredacted email to the Central Intelligence Agency so they might call off the state wolf hounds. I am a patriot lone wolf software developer, not a treasonous hacker bastard, and I am not a threat to myself and/or others.

Decision Tree Learner by James Pate Williams, Jr., BA, BS, MSwE, PhD

I am sure that the brilliant scientists at our national laboratories (Argonne, Los Alamos, Jet Propulsion, Oakridge, etc.) have numerous parameter-less models of our current pandemic, but I offer one more, a decision tree learner.

Attributes

  1. Age
  2. Assets (a measure of wealth and the ability to afford great healthcare)
  3. City (or Urban or Rural)
  4. Employment Status
  5. Gender
  6. Health (Likert Scale 1 [bad] – 10 [excellent])
  7. State or territory
  8. Wears a mask in shopping public (yes, no)
  9. Workplace (home, office, outdoors)

Build an ID3 or C4 decision tree using data from 50 days past (training examples), different set of 25 examples as a test set.