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why I feel want to eat something while I read a difficult article

ChatGPT said:

That’s actually a pretty common brain–body response, and it comes down to how your mind handles effort and stress.

When you read something difficult, your brain shifts into high cognitive load mode — working memory, attention, and problem-solving all spike in activity. This can trigger a few things:

1. Energy demand signal – Your brain runs almost entirely on glucose, and although reading doesn’t burn a huge amount of calories, sustained mental effort feels like an energy drain. Your body interprets that as a cue to refuel, making snacks suddenly sound appealing.
2. Stress coping – Challenging reading can activate a mild stress response. Many people unconsciously use eating (especially sweet or crunchy snacks) as a comfort or grounding mechanism.
3. Reward-seeking loop – Struggling with a hard text can feel frustrating. Eating activates your brain’s dopamine reward system, which can momentarily offset that discomfort — so your brain learns: “This is tough… snack time!”
4. Habit association – If you’ve often eaten while studying or reading in the past, the mental strain might automatically trigger that old habit, even if you’re not physically hungry.

If you want to test whether it’s actual hunger or just a mental coping cue, you can try pausing for a moment and drinking water, stretching, or doing a 2-minute unrelated task before reaching for food — if the craving fades, it was likely brain-stress, not fuel need.

Here’s a compact 2-minute “in-flow craving reset” you can do without leaving your seat:

Minute 0–0:30 — Hydrate & Acknowledge

• Take a few slow sips of water or tea.
• Mentally note: “I’m not truly hungry; this is just a brain craving.”

Minute 0:30–1:30 — Micro-movement & sensory shift

• Roll shoulders, stretch neck, wiggle fingers.
• Adjust lighting or glance outside for a quick visual reset.

Minute 1:30–2:00 — Mini reward

• Chew sugar-free gum or take two deep breaths.
• Give your brain a tiny “reward” without food.

talk with gemini pro for 2 hours, I can build the app!
while there are several code generation mistaken which need to dispute.

I’m working with this recently. It was use to interpolate the unmeasured data in environment. I asked chatgpt to explain the algorithm for me.

How are the weights chosen?
Kriging looks at
– Distance: closer points are more relevant
– Spatial correlation: how similar nearby points tend to be (modeled by a variogram)

The variogram describes how values “change” with distance. For example:
If points 1 meter apart are almost always similar → strong correlation
If points quickly become unrelated as you move away → weak correlation

from pykrige.ok import OrdinaryKriging
OK = OrdinaryKriging(
    x, y, values,
    variogram_model='gaussian',
    variogram_parameters=None,
    nlags=6,
    weight=True,
    enable_plotting=False,
    coordinates_type='euclidean'
)
z, ss = OK.execute('grid', gridx, gridy)
z[z < 0] = np.nan   # post-process results by masking negatives

variogram_model: controls how spatial correlation decays including linear, power, gaussian(default), spherical, exponential
variogram_parameters = {‘sill’: 0.8, ‘range’: 10, ‘nugget’: 0.1}
nlags: controls how many lag bins are used to calculate the experimental variogram. More bins is more smoother fitting, but slower.
If weight is True, It weight the variogram fitting by number of point pairs per lag.
coordinates_type
-euclidean: x, y in linear units (e.g., meters)
-geographic: lat/lon in degrees
Execute option: grid for mesh, points for specific locations

#If you want even more control (e.g., kernels, noise, trend), use GaussianProcessRegressor:

from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, WhiteKernel, ConstantKernel

kernel = ConstantKernel() * RBF(length_scale=10.0) + WhiteKernel(noise_level=0.1)
gpr = GaussianProcessRegressor(kernel=kernel)

pd.set_option(‘display.max_columns’, None)
pd.set_option(‘display.width’, 200) # adjust number as needed

Onenote

Microsoft Loop

Notion.so

Obsidian

Notable : import md file, export html,md,pdf

Joblin

Scrapbox.io

説明変数คืออะไร เหมือนจะแปลออกแต่ก็งงๆ แล้วก็มันมีชื่อแถวๆนี้อยู่หลายตัว ตัวไหนเป็นตัวไหนไม่รู้ละ เลยมาเปิดเว็ปเช็คดู

ก็จะมี กลุ่มตัวแปร X ที่เป็น input

• 説明変数：explanatory variable ตัวแปรอธิบาย
• 予測変数：predictor variable ตัวแปรทำนาย
• 独立変数：independent variable ตัวแปรอิสระ

แต่แอบงง 予測変数 ตัวแปรทำนาย แปลมาแล้วชื่อเหมือนจะเป็น output

予測変数とは、結果変数 (目的変数や応答変数とも呼ばれます) を予測するために使用する入力変数です。
ตัวแปรทำนายคือ ตัวแปรinputที่ใช้ทำนายผลลัพธ์…โอเคตามนั้น
มันใช้ในการทำนาย ไม่ใช่ผลลัพธ์จากการทำนาย

ส่วนอีกกลุ่มคือ ตัวแปร Y ที่เป็น output/result มีดังนี้

• 目的変数：response variable ตัวแปรวัตถุประสงค์
• 結果変数：outcome variable ตัวแปรผลลัพธ์
• 従属変数：dependent variable ตัวแปรตาม
• 応答変数：response variable ตัวแปรตอนสนอง

เรามักจะเจอ Y กับ X เป็นคู่ๆตามนี้ (ทำไมเค้าเอา Y นำ X นะ…)

「目的変数＆説明変数」
「従属変数＆独立変数」

reference:
https://yoshida931.hatenablog.com/entry/2018/05/13/232801
https://best-biostatistics.com/correlation_regression/variables.html