LAS PENAS
¿Cuál es la función principal de la pena en un Estado social y democrático de Derecho? La función principal de la pena es la prevención de los delitos. Se orienta a la protección de los bienes jurídicos, respetando el principio de proporcionalidad y basándose en la culpabilidad. La función de prevención es básica y se infiere de los artículos 1º y 2º de la Constitución Política.
¿Cómo se vincula la pena con la teoría del delito? La pena y la teoría del delito tienen
Water cycle
the most of Earth's water is in the oceans. The sun drives the water cycle by heating water, causing it to evaporate into the air. Ice and snow can also sublimate directly into water vapor. As air rises, it cools and condenses into clouds. Air currents move these clouds globally. Within clouds, tiny water particles collide, grow, and eventually fall as precipitation (rain, snow, etc.). Snow can accumulate as ice caps and glaciers, storing water for thousands of years. In warmer climates,...
| # | Key Term | Quick Meaning | Example |
|---|---|---|---|
| 1 | Subject | The doer of the action. | She runs fast. |
| 2 | Verb | An action or state word. | She runs. |
| 3 | Auxiliary Verbs | Helping verbs. | She is running. |
| 4 | Predicate | The verb and all info about the subject. | The dog barked loudly. |
| 5 | Subject-Verb Agreement | Verb must match the subject (singular/plural). | He walks, they walk. |
| 6 | Simple Sentence | One complete idea (IC). | I love pizza. |
| 7 | Capital Letters | Start sentences |
Demand refers to the quantity of a good or service that consumers are willing and able to purchase at different prices during a given period of time.
🔑 Key Determinants of Demand (Factors Affecting Demand):
1. Price of the Good (Own Price)
Law of Demand: As the price of a good decreases, the quantity demanded increases (and vice versa), all else being equal.
2. Income of the Consumer
Normal Goods: Demand increases as income increases.
Inferior Goods: Demand decreases as income increases.
3. Prices
A scatter diagram (or scatter plot) is a graphical representation of two variables where each point represents an observation consisting of paired values from two datasets. The horizontal axis (X-axis) represents one variable, and the vertical axis (Y-axis) represents the other.
Each point (xi,yi)(x_i, y_i)(xi,yi) is plotted on the graph for the corresponding values of the two variables.
Mesoamérica:
Zona geográfica y cultural que incluye parte de México y Centroamérica.
Características: agricultura (maíz), politeísmo, escritura jeroglífica, calendario.
Cosmovisión:
Relación entre dioses, naturaleza y humanos.
Ciclicidad del tiempo y sacrificios como ofrenda a los dioses.
Aportaciones de 3 culturas:
Olmeca: calendario, escritura, cabezas colosales.
Maya: sistema numérico con el cero, astronomía, arquitectura avanzada.
Chemical vapour deposition, a hybrid method using chemicals in vapour phase is conventionally used to obtain coatings of a variety of inorganic or organic materials.It is widely used in industry because of relatively simple instrumentation, ease of processing, possibility of depositing different types of materials and economical viability.Under certain deposition conditions nanocrystalline films or single crystalline films are possible. There are many variants of CVD like Metallo Organic CVD (MOCVD)
Your Address] 4 June 2025 MP’s Name] House of Commons London SW1A 0AA Dear [MP’s Name],
Re: When the world is ablaze, will we douse the flames or dare to speak There are moments in history when silence ceases to be neutral — it becomes an act of surrender. Today is such a moment. As injustice swells and the climate buckles under the weight of human greed, what future are we preserving if we teach the next generation to suffer in silence? Young people should not merely be permitted to protest
Implementation of KNN ALGORITHM -LAB PROGRAM
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
importnumpy as np
# Load the Iris dataset
dataset = load_iris()
# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2, random_state=0)
print("Training labels:", y_train)
# Initialize and fit the KNN classifier
kn = KNeighborsClassifier(
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