Thermal information is not differentiated in our memory; rather it is retained as a quality, or underlying tone, associated with the whole experience of the place. It contributes to our sense of the particular personality, or spirit, that we identify with that place. In remembering the spirit of a place, we can anticipate that if we return, we will have the same sense of comfort or relaxation as before.
There is an underlying assumption that the best thermal environment never needs to be noticed, and that once an objectively "comfortable" thermal environment has been provided, all of our thermal needs will have been met. The use of all of our extremely sophisticated environmental control systems is directed to this one end—to produce standard comfort zone conditions.
Textbooks on water-system engineering state that supply mains are generally installed on the north side of the street in the Northern Hemisphere and on the south side in the Southern Hemisphere, so that the sun will warm them. In both hemispheres they are supposed to be on the east side of north-south streets, on the premise that the afternoon sun is warmer than the morning sun.
The predicted mean vote (PMV) was developed by Povl Ole Fanger at Kansas State University and the Technical University of Denmark as an empirical fit to the human sensation of thermal comfort. It was later adopted as an ISO standard. It predicts the average vote of a large group of people on the a seven-point thermal sensation scale where:
Miners seek valuable nuggets of ore buried in the earth, but have no control over what is out there or how hard it is to extract the nuggets from their surroundings. ... Similarly, data miners seek to uncover valuable nuggets of information buried within massive amounts of data.
Farmers cultivate the land to maximize their yield. They manipulate the environment to their advantage using irrigation, pest control, crop rotation, fertilizer, and more. Small-scale designed experiments let them determine whether these treatments are effective. Similarly, data farmers manipulate simulation models to their advantage, using large-scale designed experimentation to grow data from their models in a manner that easily lets them extract useful information.