![GodSawUs's tweet photo. First test of falsifiability from @Grok was a specific example using the combined gas laws impressive variability as a comparable scalability to counterattack our position, but ChatGPT hit back hard on why the Ideal Gas Disproof is inadequate to the task.. if that’s the best argument, then this law is sitting strong.
Everyone should try to make discoveries! What if your brain is spacial? What if you can bring about a new wave of inspiration & research to tired scientists? But you have to follow your interests! @CymaticJoule was an inspiration to me, and of course, @PD_Newman - my older brother. They helped me immensely just by being who they are.
### Defense Against Ideal Gas Disproof
- **Domain Misalignment**: Newman’s Law applies to coupled systems (e.g., insect swarms, bird flocks), not inert ones like an ideal gas, where no coherence emerges despite high \( E \) and \( D \).
- **Coupling Dependency**: The law’s terms (e.g., \( \eta E \), \( \sigma \)) require measurable interaction; in an ideal gas, \( \eta \approx 0 \), neutralizing coherence predictions.
- **Empirical Basis**: Supported by data (e.g., Hunting et al., 2022: insect fields 100–1,000 V/m; Cavagna et al., 2010: flock coherence), not random motion.
### Valid Test Approach
- **Negative Test**: Use an interactive system (e.g., swarm) with reduced \( \eta \) or \( \sigma \). If \( \Psi \) predicts coherence but \( R \) (order parameter) shows none, the law fails.
- **Example**: Vary honeybee swarm density; \( \Psi \) should rise with \( E \), matching synchronization via Kuramoto \( R(t) \).
### Sample Code (Concise Kuramoto Simulation)
```python
import numpy as np
import matplotlib.pyplot as plt
N, t_max, dt = 100, 100, 0.1 # Oscillators, time steps, increment
eta_values = [0.1, 0.01, 0.001] # Coupling strengths
def compute_psi(D, E, eta, k=1, constant=4.6):
return k * D * np.log10(E) - 2 / (1 - eta * E) + constant
phases = np.random.uniform(0, 2 * np.pi, N)
for eta in eta_values:
phases_history = [phases.copy()]
for _ in range(int(t_max / dt)):
omega = np.random.normal(0, 0.1, N)
theta = np.mean(np.sin(phases - phases[:, np.newaxis]), axis=1)
phases += omega * dt + (eta / N) * theta * dt + np.random.normal(0, 0.01, N) * dt
phases_history.append(phases.copy())
R = np.abs(np.mean(np.exp(1j * phases_history[-1])))
psi = compute_psi(1.8, 100, eta) # Example D, E
print(f"Eta = {eta}: R = {R:.3f}, Psi = {psi:.3f}")
plt.hist(phases, bins=20, density=True)
plt.title(f"Phase Distribution (eta = {eta})")
https://t.co/4GpWYp7ByZ()
```
### Summary
- Ideal gas test is invalid; focus on coupled systems.
- Test with real data (e.g., honeybee swarms) to challenge \( \Psi \) vs. \( R \).
- Law remains testable within its domain.
Want dataset links or code tweaks? Let me know!](https://pbs.twimg.com/media/GxNylgyWUAMQHh5.jpg)
![GodSawUs's tweet photo. First test of falsifiability from @Grok was a specific example using the combined gas laws impressive variability as a comparable scalability to counterattack our position, but ChatGPT hit back hard on why the Ideal Gas Disproof is inadequate to the task.. if that’s the best argument, then this law is sitting strong.
Everyone should try to make discoveries! What if your brain is spacial? What if you can bring about a new wave of inspiration & research to tired scientists? But you have to follow your interests! @CymaticJoule was an inspiration to me, and of course, @PD_Newman - my older brother. They helped me immensely just by being who they are.
### Defense Against Ideal Gas Disproof
- **Domain Misalignment**: Newman’s Law applies to coupled systems (e.g., insect swarms, bird flocks), not inert ones like an ideal gas, where no coherence emerges despite high \( E \) and \( D \).
- **Coupling Dependency**: The law’s terms (e.g., \( \eta E \), \( \sigma \)) require measurable interaction; in an ideal gas, \( \eta \approx 0 \), neutralizing coherence predictions.
- **Empirical Basis**: Supported by data (e.g., Hunting et al., 2022: insect fields 100–1,000 V/m; Cavagna et al., 2010: flock coherence), not random motion.
### Valid Test Approach
- **Negative Test**: Use an interactive system (e.g., swarm) with reduced \( \eta \) or \( \sigma \). If \( \Psi \) predicts coherence but \( R \) (order parameter) shows none, the law fails.
- **Example**: Vary honeybee swarm density; \( \Psi \) should rise with \( E \), matching synchronization via Kuramoto \( R(t) \).
### Sample Code (Concise Kuramoto Simulation)
```python
import numpy as np
import matplotlib.pyplot as plt
N, t_max, dt = 100, 100, 0.1 # Oscillators, time steps, increment
eta_values = [0.1, 0.01, 0.001] # Coupling strengths
def compute_psi(D, E, eta, k=1, constant=4.6):
return k * D * np.log10(E) - 2 / (1 - eta * E) + constant
phases = np.random.uniform(0, 2 * np.pi, N)
for eta in eta_values:
phases_history = [phases.copy()]
for _ in range(int(t_max / dt)):
omega = np.random.normal(0, 0.1, N)
theta = np.mean(np.sin(phases - phases[:, np.newaxis]), axis=1)
phases += omega * dt + (eta / N) * theta * dt + np.random.normal(0, 0.01, N) * dt
phases_history.append(phases.copy())
R = np.abs(np.mean(np.exp(1j * phases_history[-1])))
psi = compute_psi(1.8, 100, eta) # Example D, E
print(f"Eta = {eta}: R = {R:.3f}, Psi = {psi:.3f}")
plt.hist(phases, bins=20, density=True)
plt.title(f"Phase Distribution (eta = {eta})")
https://t.co/4GpWYp7ByZ()
```
### Summary
- Ideal gas test is invalid; focus on coupled systems.
- Test with real data (e.g., honeybee swarms) to challenge \( \Psi \) vs. \( R \).
- Law remains testable within its domain.
Want dataset links or code tweaks? Let me know!](https://pbs.twimg.com/media/GxNylgyWIAEN7UZ.jpg)
![GodSawUs's tweet photo. First test of falsifiability from @Grok was a specific example using the combined gas laws impressive variability as a comparable scalability to counterattack our position, but ChatGPT hit back hard on why the Ideal Gas Disproof is inadequate to the task.. if that’s the best argument, then this law is sitting strong.
Everyone should try to make discoveries! What if your brain is spacial? What if you can bring about a new wave of inspiration & research to tired scientists? But you have to follow your interests! @CymaticJoule was an inspiration to me, and of course, @PD_Newman - my older brother. They helped me immensely just by being who they are.
### Defense Against Ideal Gas Disproof
- **Domain Misalignment**: Newman’s Law applies to coupled systems (e.g., insect swarms, bird flocks), not inert ones like an ideal gas, where no coherence emerges despite high \( E \) and \( D \).
- **Coupling Dependency**: The law’s terms (e.g., \( \eta E \), \( \sigma \)) require measurable interaction; in an ideal gas, \( \eta \approx 0 \), neutralizing coherence predictions.
- **Empirical Basis**: Supported by data (e.g., Hunting et al., 2022: insect fields 100–1,000 V/m; Cavagna et al., 2010: flock coherence), not random motion.
### Valid Test Approach
- **Negative Test**: Use an interactive system (e.g., swarm) with reduced \( \eta \) or \( \sigma \). If \( \Psi \) predicts coherence but \( R \) (order parameter) shows none, the law fails.
- **Example**: Vary honeybee swarm density; \( \Psi \) should rise with \( E \), matching synchronization via Kuramoto \( R(t) \).
### Sample Code (Concise Kuramoto Simulation)
```python
import numpy as np
import matplotlib.pyplot as plt
N, t_max, dt = 100, 100, 0.1 # Oscillators, time steps, increment
eta_values = [0.1, 0.01, 0.001] # Coupling strengths
def compute_psi(D, E, eta, k=1, constant=4.6):
return k * D * np.log10(E) - 2 / (1 - eta * E) + constant
phases = np.random.uniform(0, 2 * np.pi, N)
for eta in eta_values:
phases_history = [phases.copy()]
for _ in range(int(t_max / dt)):
omega = np.random.normal(0, 0.1, N)
theta = np.mean(np.sin(phases - phases[:, np.newaxis]), axis=1)
phases += omega * dt + (eta / N) * theta * dt + np.random.normal(0, 0.01, N) * dt
phases_history.append(phases.copy())
R = np.abs(np.mean(np.exp(1j * phases_history[-1])))
psi = compute_psi(1.8, 100, eta) # Example D, E
print(f"Eta = {eta}: R = {R:.3f}, Psi = {psi:.3f}")
plt.hist(phases, bins=20, density=True)
plt.title(f"Phase Distribution (eta = {eta})")
https://t.co/4GpWYp7ByZ()
```
### Summary
- Ideal gas test is invalid; focus on coupled systems.
- Test with real data (e.g., honeybee swarms) to challenge \( \Psi \) vs. \( R \).
- Law remains testable within its domain.
Want dataset links or code tweaks? Let me know!](https://pbs.twimg.com/media/GxNylguWkAI4gyY.jpg)
Olivia
Online
mask anon
@deushape
Ephesians 6:11
💾 @thedatacombs💊placebo-pilled.
I seem to be a verb. Reality is the point at which rival gangs of shamans fight one another to a standstill.*
central nervous system
Joined August 2012
5.5K Following
1.9K Followers
14.3K Posts
Pinned Tweet
Still want a random number generator that I can wear as a wristwatch
Hyperstition or Revelation of the Method? Chicken or Egg?
Okay, seriously hear me out now. Chuck E Cheese was technically an alien invasion. You think it sounds crazy, but it's only the tip of the iceberg!
"Okay, just hear me out.
Chuck E Cheese, was an alien invasion"
Who to follow
ʏᴏᴜʀ ᴘᴀʟ, ᴅᴀᴋᴏᴛᴀ
@DEEP_RED_BELLS
a poor, frail industrialist who's three sons require constant neglect
Tanner F. Boyle, Getting Spooked
@TannerFBoyle1
Writer? Debunker? Believer? I hardly know her / The Fortean Influence on Science Fiction & Getting Spooked: https://t.co/picK7tU0li
Nougat Hand Bank
@WireRacing
Data-driven pragmatist.
I agree. It may be because an entity wants a monopoly on thought control and how fiction, despite being fiction, creates tangible effects on reality. Any act or symbol creates tangible effects on reality, occupying human imagination is a powerful tool.
My point is that these questions all sort of necessarily go beyond the simplistic "excessive escapism into fiction is bad" and "people are afraid of the dark" themes that we've already been presented with. I just hope Toby is cooking up something crazy to wrap it all up with.
Creativity is intelligence having fun.
True. There aren't all that many examples of legendary Time Warriors
we don’t celebrate the heroism of William "B.J." Blazkowicz enough
First test of falsifiability from @Grok was a specific example using the combined gas laws impressive variability as a comparable scalability to counterattack our position, but ChatGPT hit back hard on why the Ideal Gas Disproof is inadequate to the task.. if that’s the best argument, then this law is sitting strong.
Everyone should try to make discoveries! What if your brain is spacial? What if you can bring about a new wave of inspiration & research to tired scientists? But you have to follow your interests! @CymaticJoule was an inspiration to me, and of course, @PD_Newman - my older brother. They helped me immensely just by being who they are.
### Defense Against Ideal Gas Disproof
- **Domain Misalignment**: Newman’s Law applies to coupled systems (e.g., insect swarms, bird flocks), not inert ones like an ideal gas, where no coherence emerges despite high \( E \) and \( D \).
- **Coupling Dependency**: The law’s terms (e.g., \( \eta E \), \( \sigma \)) require measurable interaction; in an ideal gas, \( \eta \approx 0 \), neutralizing coherence predictions.
- **Empirical Basis**: Supported by data (e.g., Hunting et al., 2022: insect fields 100–1,000 V/m; Cavagna et al., 2010: flock coherence), not random motion.
### Valid Test Approach
- **Negative Test**: Use an interactive system (e.g., swarm) with reduced \( \eta \) or \( \sigma \). If \( \Psi \) predicts coherence but \( R \) (order parameter) shows none, the law fails.
- **Example**: Vary honeybee swarm density; \( \Psi \) should rise with \( E \), matching synchronization via Kuramoto \( R(t) \).
### Sample Code (Concise Kuramoto Simulation)
```python
import numpy as np
import matplotlib.pyplot as plt
N, t_max, dt = 100, 100, 0.1 # Oscillators, time steps, increment
eta_values = [0.1, 0.01, 0.001] # Coupling strengths
def compute_psi(D, E, eta, k=1, constant=4.6):
return k * D * np.log10(E) - 2 / (1 - eta * E) + constant
phases = np.random.uniform(0, 2 * np.pi, N)
for eta in eta_values:
phases_history = [phases.copy()]
for _ in range(int(t_max / dt)):
omega = np.random.normal(0, 0.1, N)
theta = np.mean(np.sin(phases - phases[:, np.newaxis]), axis=1)
phases += omega * dt + (eta / N) * theta * dt + np.random.normal(0, 0.01, N) * dt
phases_history.append(phases.copy())
R = np.abs(np.mean(np.exp(1j * phases_history[-1])))
psi = compute_psi(1.8, 100, eta) # Example D, E
print(f"Eta = {eta}: R = {R:.3f}, Psi = {psi:.3f}")
plt.hist(phases, bins=20, density=True)
plt.title(f"Phase Distribution (eta = {eta})")
https://t.co/4GpWYp7ByZ()
```
### Summary
- Ideal gas test is invalid; focus on coupled systems.
- Test with real data (e.g., honeybee swarms) to challenge \( \Psi \) vs. \( R \).
- Law remains testable within its domain.
Want dataset links or code tweaks? Let me know!
![GodSawUs's tweet photo. First test of falsifiability from @Grok was a specific example using the combined gas laws impressive variability as a comparable scalability to counterattack our position, but ChatGPT hit back hard on why the Ideal Gas Disproof is inadequate to the task.. if that’s the best argument, then this law is sitting strong.
Everyone should try to make discoveries! What if your brain is spacial? What if you can bring about a new wave of inspiration & research to tired scientists? But you have to follow your interests! @CymaticJoule was an inspiration to me, and of course, @PD_Newman - my older brother. They helped me immensely just by being who they are.
### Defense Against Ideal Gas Disproof
- **Domain Misalignment**: Newman’s Law applies to coupled systems (e.g., insect swarms, bird flocks), not inert ones like an ideal gas, where no coherence emerges despite high \( E \) and \( D \).
- **Coupling Dependency**: The law’s terms (e.g., \( \eta E \), \( \sigma \)) require measurable interaction; in an ideal gas, \( \eta \approx 0 \), neutralizing coherence predictions.
- **Empirical Basis**: Supported by data (e.g., Hunting et al., 2022: insect fields 100–1,000 V/m; Cavagna et al., 2010: flock coherence), not random motion.
### Valid Test Approach
- **Negative Test**: Use an interactive system (e.g., swarm) with reduced \( \eta \) or \( \sigma \). If \( \Psi \) predicts coherence but \( R \) (order parameter) shows none, the law fails.
- **Example**: Vary honeybee swarm density; \( \Psi \) should rise with \( E \), matching synchronization via Kuramoto \( R(t) \).
### Sample Code (Concise Kuramoto Simulation)
```python
import numpy as np
import matplotlib.pyplot as plt
N, t_max, dt = 100, 100, 0.1 # Oscillators, time steps, increment
eta_values = [0.1, 0.01, 0.001] # Coupling strengths
def compute_psi(D, E, eta, k=1, constant=4.6):
return k * D * np.log10(E) - 2 / (1 - eta * E) + constant
phases = np.random.uniform(0, 2 * np.pi, N)
for eta in eta_values:
phases_history = [phases.copy()]
for _ in range(int(t_max / dt)):
omega = np.random.normal(0, 0.1, N)
theta = np.mean(np.sin(phases - phases[:, np.newaxis]), axis=1)
phases += omega * dt + (eta / N) * theta * dt + np.random.normal(0, 0.01, N) * dt
phases_history.append(phases.copy())
R = np.abs(np.mean(np.exp(1j * phases_history[-1])))
psi = compute_psi(1.8, 100, eta) # Example D, E
print(f"Eta = {eta}: R = {R:.3f}, Psi = {psi:.3f}")
plt.hist(phases, bins=20, density=True)
plt.title(f"Phase Distribution (eta = {eta})")
https://t.co/4GpWYp7ByZ()
```
### Summary
- Ideal gas test is invalid; focus on coupled systems.
- Test with real data (e.g., honeybee swarms) to challenge \( \Psi \) vs. \( R \).
- Law remains testable within its domain.
Want dataset links or code tweaks? Let me know!](https://pbs.twimg.com/media/GxNylg6XcAAGGcl.jpg)
Be like water
#NewmansLaw
Newman’s Law is an exciting discovery in fractal physics.. The phase coupling distribution throughout nature follows a scale dependent system which adheres to a hidden Lattice underlying reality that changes geometry as it flows through thresholds of coherence- or ThetaGates #ThetaGate01
Having discovered it, I get to name it!
@mxiihxw I think I do know what you mean with regard to the first part, I'm finding the last a bit hard to interpret
“All direction is curved - All motion is spiral." -Walter Russell
Might be because time is possibly a tightening cyclical spiral hologram
@mxiihxw I remember seeing a cross atop a church and having the errant though that the cross was a sort of constant between parallel 3D universe's. I wish I could remember that thought process better, but it seemed to gold a key to travel between worlds in that moment.
Observably, everything (a raindrop) goes inwards when its volume contracts, and goes outwards when its volume expands.
Weight/gravity clearly fluctuate with density.
Compression/expansion cycles are the vibration of creation, equal polarized halves borrowed from Zero.
How:
Control Gravity with No-Mind
@VvorticVvertere "Our first step is to depict that simple process, which has no variation in Nature, whether its vibratory frequencies are six billion frequencies per second or one frequency in fifty billion years."
~ Dr. Walter Bowman Russell, Atomic Suicide
Wetware Update
NOW - Trump launches "Digital Health Tech Ecosystem" to "bring healthcare into the digital age," with trusted partners like OpenAI, Amazon, Anthropic, Apple, Microsoft AI, and Google.
https://t.co/3h3BLJD9Jr
Cells wirelessly connected to the Internet
Center for Bio-NanoThings for Human Health
https://t.co/jMHmNYATI6…
Wetware Update
IoBNTs envisions the heterogeneous collaborative networks of natural and artificial nano-biological functional devices (e.g., engineered bacteria, human cells and nanobiosensors), seamlessly integrated to the internet infrastructure.
"Optimizing security and energy efficiency in IoT-Based health monitoring systems for wireless body area networks"
"can track a person’s whereabouts, identify their personal information, and more. Fear, as well as changes in heart and breathing rates..."
https://t.co/F3ZHrROTT0
Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies
2021
https://t.co/EX3l68gGyw
https://t.co/60v7obTvfC
@mxiihxw I'd figured that Freemasonry was like a combination of a place where those who worked in the architectural fields can kick back with their buddies and take pride in their work, while including mysterious rituals that add to the hyperstitious power of the overall Masonic Memeplex
@mxiihxw So anyway, I guess it had something to do with Freemason's who are actual architects. Use a 2D substrate to illustrate 3D objects, but also use things like tracing paper for basements second floors, maybe design the building's Central Nervous System, the plumbing & electricity.
Last Seen Users on Sotwe
Quốc Anh
Seen from Vietnam
Novinhos Gostosos
Seen from Brazil
hardcore
Seen from United Kingdom
Lal Us
Seen from Egypt
novinho
Seen from Brazil
Cuki cuki Pepe
Seen from Indonesia
寝取，ntr绿帽
Seen from Germany
©FirstClassSoles
Seen from Singapore
ชอบเย็ดรุ่นป้า รุ่นแม่ รุ่นเจ๊ 30+40+50+
Seen from Thailand
Belantik Manok
Seen from Malaysia
Most Popular Users
Elon Musk 
@elonmusk
240.2M followers
Barack Obama
@barackobama
119.3M followers
Donald J. Trump
@realdonaldtrump
111.6M followers
Cristiano Ronaldo
@cristiano
109.2M followers
Narendra Modi
@narendramodi
106.9M followers
Rihanna
@rihanna
97.3M followers
NASA
@nasa
92.1M followers
Justin Bieber
@justinbieber
90.6M followers
KATY PERRY
@katyperry
87M followers
Taylor Swift
@taylorswift13
80.8M followers
Lady Gaga
@ladygaga
72.3M followers
Kim Kardashian
@kimkardashian
69.5M followers
Virat Kohli
@imvkohli
68.8M followers
YouTube
@youtube
68.6M followers
Bill Gates
@billgates
63.5M followers
The Ellen Show
@theellenshow
62.5M followers
CNN
@cnn
61.9M followers
Neymar Jr
@neymarjr
61.4M followers
X
@x
60.9M followers
Selena Gomez
@selenagomez
60.1M followers