PCA Skin Hyaluronic Acid Boosting Serum
是它们家买的最好的东西
奇华顿的NovHyal Biotech G即NAG6P (zo说的生长因子哈哈 是我觉得见效最快的化妆品原料哈哈 但是Tightenyl版本的可能会更好）
促进表皮和真皮生成GAG图二，促进CD44透明质酸受体表达图三，强化基底膜。促进成纤维细胞分化。还合A酸pk了一下，在以上方面都比A酸强虽然只有10天图四。
巴斯夫的PERLAURA 拳根参提取物促进基膜蛋白聚糖串珠素和抗肌萎缩蛋白dystroglycan的表达图五。
Codif的HYDRANOV
1.5%Hydranov刺激表皮生成透明质酸图六
同时提高跨膜蛋白claudin-1(紧密连接蛋白1)的表达，促进聚丝蛋白和神经酰胺的合成，修护皮肤屏障。
Sederma的optim hyal 发酵物中的单糖，分子结构小 ，结构类似于透明质酸，刺激表皮生成透明质酸。
高浓度烟酰胺和赢创的神经酰胺
50kDa的透明质酸 10到1000kDa的透明质酸钠
论文没写 也猜不出来具体是哪些原料
图七和图八是六周后的使用对比
反正高露洁发的文说对皮肤屏障，角质层水合作用，细纹以及皮肤外观的充盈都有改善
用完再来评 主要是也没有皱纹 希望能有niod MHCC2那样充盈的效果就好

#今日财经播报# 【特斯拉人形机器人Optimus再进化，能拿鸡蛋能蹦迪】#特斯拉人形机器人可以蹦迪# 特斯拉发布了一段视频，展示了对其名为Optim us的人形机器人原型所做的改进。在视频的最后，两台机器人在闪烁的灯光下随着电子音乐轻盈起舞。人形机器人是特斯拉开发人工智能(AI)的尝试之一，利用训练有素的神经网络来执行基本任务。在最新的演示中，特斯拉表示已将Optimus的重量减轻了10公斤(22磅)，并展示了机器人灵活拿起和处理鸡蛋的过程，称其所有手指都有触觉感应。在今年9月的另一段视频中，Optimus能够按颜色对积木进行分类。

use machine learning, Python code creat a example of Nucnulear physics

One possible example of using machine learning and Python code to create a nuclear physics application is to use a neural network to predict the binding energy of atomic nuclei. The binding energy is the energy required to break a nucleus into its constituent protons and neutrons, and it is related to the mass defect and the nuclear stability of the nucleus. A neural network is a machine learning model that can learn from data and make predictions based on its learned patterns.

Here is a simplified code snippet that shows how to implement a neural network to predict the binding energy of nuclei using the PyTorch library¹:

```python
# Import PyTorch and other libraries
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Load the nuclear data from a CSV file
# The data contains the atomic number (Z), the mass number (A), and the binding energy per nucleon (BE/A) for each nucleus
data = pd.read_csv("nuclear_data.csv")

# Split the data into features (X) and labels (y)
X = data[["Z", "A"]].values # The features are the atomic number and the mass number
y = data["BE/A"].values # The label is the binding energy per nucleon

# Convert the data to PyTorch tensors
X = torch.from_numpy(X).float()
y = torch.from_numpy(y).float()

# Define the neural network architecture
# The network has two hidden layers with 16 and 8 neurons, respectively, and uses the ReLU activation function
model = torch.nn.Sequential(

torch.nn.Linear(2, 16), # The input layer has 2 neurons (Z and A)

torch.nn.ReLU(), # The activation function for the first hidden layer

torch.nn.Linear(16, 8), # The first hidden layer has 16 neurons

torch.nn.ReLU(), # The activation function for the second hidden layer

torch.nn.Linear(8, 1) # The output layer has 1 neuron (BE/A)
)

# Define the loss function and the optimizer
# The loss function is the mean squared error (MSE) and the optimizer is the stochastic gradient descent (SGD) with a learning rate of 0.01
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Train the neural network for 1000 epochs
epochs = 1000
losses = [] # A list to store the loss values for each epoch
for epoch in range(epochs):

# Forward pass: compute the predicted output by passing the input to the model

y_pred = model(X)

# Compute and print the loss

loss = loss_fn(y_pred, y)

print(f"Epoch {epoch}, Loss: {loss.item()}")

losses.append(loss.item())

# Backward pass: compute the gradient of the loss with respect to the model parameters

optimizer.zero_grad()

loss.backward()

# Update the model parameters using the optimizer

optimizer.step()

# Plot the loss curve
plt.plot(losses)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.show()

# Test the model on some new data
# The new data contains the atomic number (Z), the mass number (A), and the actual binding energy per nucleon (BE/A) for some nuclei
test_data = pd.DataFrame({

"Z": [2, 8, 20, 26, 50, 82, 92],

"A": [4, 16, 40, 56, 118, 208, 238],

"BE/A": [7.07, 7.97, 8.55, 8.79, 8.52, 7.87, 7.57]
})

# Convert the test data to PyTorch tensors
X_test = torch.from_numpy(test_data[["Z", "A"]].values).float()
y_test = torch.from_numpy(test_data["BE/A"].values).float()

# Predict the binding energy per nucleon for the test data using the model
y_pred = model(X_test)

# Print the actual and predicted values
print("Actual\tPredicted")
for i in range(len(y_test)):

print(f"{y_test[i]:.2f}\t{y_pred[i].item():.2f}")

# Calculate the mean absolute error (MAE) for the test data
mae = torch.mean(torch.abs(y_test - y_pred))
print(f"MAE: {mae:.2f}")
```

This code is based on the tutorial from [this website](^2^), which also provides more details and explanations about the nuclear physics and machine learning concepts involved. You can also find more examples of using machine learning for nuclear physics applications on [this GitHub page](^5^). I hope this helps you understand how to use machine learning and Python code to create a nuclear physics application.

Source: Conversation with Bing, 07/12/2023
(1) nuclear-physics · GitHub Topics · GitHub. htps://github.com/topics/nuclear-physics?l=python.
(2) nuclear-physics · GitHub Topics · GitHub. htps://github.com/topics/nuclear-physics.
(3) Nuclear-Physics-with-Machine-Learning · GitHub. htps://github.com/Nuclear-Physics-with-Machine-Learning/.
(4) nuclear · GitHub Topics · GitHub. htps://github.com/topics/nuclear?l=python.
(5) nuclear · GitHub Topics · GitHub. htps://github.com/topics/nuclear.