Answer:
Land is considered the primary factor of production. Land is rich in coal, water and petroleum, which are used for generating power. Land is required to construct factories and industries to carry out the production process. A nation's economic wealth is directly related to the richness of its natural resources.
Explanation:
In the wars that the “founding fathers”, the main leaders of the colonists at that time, stood out.
It was these leaders who drafted, in 1776, the Declaration of Independence of the United States and, after the war, in 1787, composed the so-called Bill Of Rights, that is, the Bill of Rights, the Constitution of the United States of America, which prevails until today. For this reason, they were called "founding fathers", or "fouding fathers", as they are considered to be those who gave the United States a political-legal architecture, claiming its right to exist as an independent nation-state.
The main names among the “founding fathers” are: <u>John and Samuel Adams, George Washington (who became the first president), Thomas Jefferson, George Clymer, Benjamin Franklin, George Tylor and George Rea.</u>
Ok I’m not an expert or anything but most because of fear or trauma when being assaulted or violated many victims mentality is to shut down or shove there problems down for most don’t know who or how to tell anyone. And when some ppl do come toward they most likely want to remain anonymous for the fear of loved ones or the public to know and revealing a offender often is believed to get them in trouble by said offender or their community especially if it’s a more conservative area
dzmitry bahdanau, kyunghyun cho, and yoshua bengio. 2014. neural machine translation by jointly learning to align and
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance.
The models proposed as of late for brain machine interpretation frequently have a place with a group of encoder-decoders and comprises of an encoder that encodes a source sentence into a fixed-length vector from which a decoder creates an interpretation.
In this paper, we guess that the utilization of a fixed-length vector is a bottleneck in working on the exhibition of this essential encoder-decoder engineering, and propose to broaden this by permitting a model to naturally (delicate )look for parts of a source sentence that are pertinent to anticipating an objective word, without shaping these parts as a hard section unequivocally.
With this new methodology, we accomplish an interpretation execution equivalent to the current cutting edge state put together framework with respect to the undertaking of English-to-French interpretation. Moreover, subjective examination uncovers that the (delicate )arrangements found by the model concur well with our instinct.
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