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Environmental Law in Morocco

1. Define environmental governance and explain UNEP’s contribution.

Answer:
Environmental governance refers to the laws, policies, institutions, and decision-making processes that direct how a country manages its natural resources and environmental challenges. It ensures fairness, accountability, and sustainability in environmental management.
UNEP strengthens global environmental governance by helping countries build strong legal frameworks, supporting integrated policies,

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geooooo

Middle East, country of NATO and associated of EU, Turkey. Three examples of crops in Turkey. Oats, citruses, tobacco, cotton Countries of large oil reserves. Saudi Arabia, Iraq. Three examples of main income of oil Extractions. Arab Peninsula Oman, Kuwait, South Arabia. What is kibbutz? Special type of agriculture based on collective ownership of the land. What are Kurds? Nations without territory. Two territories of Palestine. West Bank Gaza Strip. Religion and nationalities in Israel. Religions:

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jejehjeje

Middle East: Turkey (NATO, EU assoc.), crops: olives, citrus, tobacco, cotton. Oil reserves: Iraq, Saudi Arabia. Oil income: Yemen, Kuwait, Oman, Qatar. Kibbutz = collective farming (Israel). Kurds = nation w/out state (Iraq, Turkey, Syria). Palestine = Gaza Strip, West Bank. Religions: Judaism, Christianity, Islam. Nationalities: Jews, Muslims, Christians. South Asia: India independence 1947. Former British India: India, Pakistan, Bangladesh. High illiteracy: Pakistan, Afghanistan. Taliban: Afghanistan.

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gnatologia

Classe I Gli occlusori rappresentano, nella storia, il primo tentativo di disporre di uno strumento che, oltre a mantenere in modo stabile e ripetibile il rappor- to statico di occlusione, permettesse quantomeno il movimento di aper- tura e chiusura delle due arcate dentarie. Le cerniere che simulano l’ATM negli occlusori consentono ai modelli del- le arcate dentarie il solo movimento curvilineo a cerniera di apertura e chiusura. Nella maggior parte dei casi, posteriormente ai modelli questi strumenti

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exam 3

General Links

  • Mixed outcomes: Social media use linked to both positive (connection, identity) and negative (depression, anxiety) outcomes
  • Small effect sizes overall; impact depends on behavior, content, and individual traits
  • U-shaped curve (too high/low = bad; middle=good) captures relationship between social media use and worse mental health

Positive Behaviors

  • Active use (posting, messaging) → social support, self-esteem, positive affect
  • Community building (esp. for marginalized groups) →
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AMS Lecture 3

Information Processing & Reaction Time Revision Sheet

1. Core Concept
The human brain processes information like a computer, in a series of stages from receiving a stimulus to producing a response. The speed of this processing can be measured by Reaction Time (RT).

2. The Information-Processing Model
A three-stage model explaining how sensory input is transformed into motor output.

  • Input: Information from the environment received via the senses.

  • Stimulus Identification Stage: Deciding if a

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Drl2..

Generative Adversarial Network (GAN)
Deep learning model with two NNs — Generator & Discriminator — that compete to create realistic fake data.
“Generator fake data banata hai, Discriminator usse real/fake pehchanta hai.”

Noise → Generator → Fake Data → Discriminator → Real/Fake

Components:
1️⃣ Generator: makes fake data (tries to fool D)
2️⃣ Discriminator: checks data (real or fake)

Working:

  • G generates fake samples

  • D detects fake vs real

  • Both train together →

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deep ren forse

🧠 Recurrent Neural Network (RNN)
A neural network for sequential data (time series, speech, text).
It has memory of past inputs to affect current output.

Simple: “RNN past data yaad rakh kar next output predict karta hai.”

x1 → x2 → x3 → ...
↓ ↓ ↓ h1 → h2 → h3 →
... ↓ ↓ ↓ y1 y2 y3

Stepwise:1️⃣ Input – Sequential data (text/audio)
2️⃣ Hidden Layer – Current input + previous hidden state
  Eq: hₜ = f(Wxₜ + Uhₜ₋₁ + b)
3️⃣

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AI-ML

1.Explain the Expectation-Maximization (EM) algorithm and its application to Gaussian Mixture Models.

The Expectation-Maximization (EM) algorithm is an iterative method used to estimate parameters in statistical models that involve latent (hidden) variables, such as missing data or unobserved groupings. It is especially useful for fitting models like Gaussian Mixture Models (GMMs), where the data is assumed to come from a mixture of several Gaussian distributions, but the assignment of each data

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