When historians debated the causes of Rome's decline and fall, for instance, they were merely debating proximate causes, which was understandable, as the science of energy did not yet exist when . Once scientists began to study the issue, running out of energy became seen as the ultimate cause, even though scientists still argue over environmental causes, for instance, but what some seem to miss in their arguments is that they are all just ways of saying that the civilization ran out of energy, whether humans contributed to the environmental failure (and declining and surplus energy) or not.
The rest of this chapter will trace many important preindustrial developments which helped set the stage for the Industrial Revolution, which is humanity’s fourth and most recent Epochal Event. But until the last few centuries in Europe preceding the Industrial Revolution, the basics among all civilizations did not appreciably change. Agriculture provided a local and stable energy supply that allowed for sedentism, forests were removed to make way for crops, and domestic animals were used to provide labor and/or flesh products, while their manure helped replenish soil nutrients depleted by agriculture. Virtually everywhere that agriculture appeared, so did civilization, with varying levels of urbanity. Elites dominated all civilizations, and they almost always invoked either a divine nature or divine sanction to justify their status, and they always engaged in conspicuous economic consumption. Cities situated on low-energy transportation lanes, which were almost always bodies of water, exploited forested and agricultural hinterlands, which were worked by peasants and slaves, while cities housed professionals and the elite. Forests and agriculture provided the primary energy supply of all preindustrial civilizations, which was usually supplemented with the products and services of domestic animals. All preindustrial civilizations were steeply hierarchical - economically, socially, and politically – and the means of production provided small surpluses that supported a small elite and professional class. Fighting over resources and plunder has been the primary predilection of all civilizations for all time, except for a very brief interlude at the beginnings of .
: In everyday language, a is a rule that must be abided or something that can be relied upon to occur in a particular situation. Scientific laws, on the other hand, are less rigid. They may have exceptions, and, like other scientific knowledge, may be modified or rejected based on new evidence and perspectives. In science, the term usually refers to a generalization about and is a compact way of describing what we'd expect to happen in a particular situation. Some laws are non-mechanistic statements about the relationship among observable phenomena. For example, the ideal gas law describes how the pressure, volume, and temperature of a particular amount of gas are related to one another. It does not describe how gases behave; we know that gases do not precisely conform to the ideal gas law. Other laws deal with phenomena that are not directly observable. For example, the second law of thermodynamics deals with entropy, which is not directly observable in the same way that volume and pressure are. Still other laws offer more mechanistic explanations of phenomena. For example, Mendel's first law offers a of how genes are distributed to gametes and offspring that helps us make about the outcomes of genetic crosses. The term may be used to describe many different forms of scientific knowledge, and whether or not a particular idea is called a law has much to do with its discipline and the time period in which it was first developed.
For example, what the young may think of asprudent planning for the future may be dismissed by the old as livingfor the present; the generations simply scale their lives differently.
: In everyday language, the word usually refers to an educated guess or an idea that we are quite uncertain about. Scientific hypotheses, however, are much more informed than any guess and are usually based on prior experience, scientific background knowledge, preliminary observations, and logic. In addition, hypotheses are often supported by many different lines of evidence in which case, scientists are more confident in them than they would be in any mere "guess." To further complicate matters, science textbooks frequently misuse the term in a slightly different way. They may ask students to make a about the outcome of an experiment (e.g., table salt will dissolve in water more quickly than rock salt will). This is simply a prediction or a guess (even if a well-informed one) about the outcome of an experiment. Scientific hypotheses, on the other hand, have explanatory power they are explanations for phenomena. The idea that table salt dissolves faster than rock salt is not very hypothesis-like because it is not very explanatory. A more scientific (i.e., more explanatory) hypothesis might be "The amount of surface area a substance has affects how quickly it can dissolve. More surface area means a faster rate of dissolution." This hypothesis has some explanatory power it gives us an idea of a particular phenomenon occurs and it is testable because it generates expectations about what we should observe in different situations. If the hypothesis is accurate, then we'd expect that, for example, sugar processed to a powder should dissolve more quickly than granular sugar. Students could examine rates of dissolution of many different substances in powdered, granular, and pellet form to further test the idea. The statement "Table salt will dissolve in water more quickly than rock salt" is not a hypothesis, but an expectation generated by a hypothesis. Textbooks and science labs can lead to confusions about the difference between a hypothesis and an expectation regarding the outcome of a scientific test. To learn more about scientific hypotheses, visit in our section on how science works.
CORRECTION: Perhaps because the Scientific Method and popular portrayals of science emphasize , many people think that science can't be done an experiment. In fact, there are ways to test almost any scientific idea; experimentation is only one approach. Some ideas are best tested by setting up a in a lab, some by making detailed observations of the natural world, and some with a combination of strategies. To study detailed examples of how scientific ideas can be tested fairly, with and without experiments, check out our side trip .
CORRECTION: Some scientists and philosophers have tried to draw a line between "hard" sciences (e.g., chemistry and physics) and "soft" ones (e.g., psychology and sociology). The thinking was that hard science used more rigorous, quantitative methods than soft science did and so were more trustworthy. In fact, the rigor of a scientific study has much more to do with the investigator's approach than with the discipline. Many psychology studies, for example, are carefully controlled, rely on large sample sizes, and are highly quantitative. To learn more about how rigorous and fair tests are designed, regardless of discipline, check out our side trip .
a. Using common sense to disregard the need to test a hypothesis by experimentation
b. Addressing data that do not support hypothesis
c. Having work peer-reviewed before publication
d. None of the above
One of Cotton's objectives was to understand why antibody-producing cells appeared to use only one set of parental genes to produce a functional antibody. Called allelic exclusion, this phenomenon was particularly puzzling as in most cases cells inherit a copy of both sets of genes from their parental cells. What scientists assumed was that in the process of antibody reproduction one gene was silenced while the other was transferred across. By fusing two myeloma cells lines, Cotton and Milstein wanted to see which genes would be transferred and which would be silenced. They were also interested in what effect such fusion would have on the structure of antibodies in terms of their variable and constant regions.
Abstract: Nowadays, geometric constructions are mainly considered in the domain of education whereas the professional occupations where they were traditionally used, like land surveyor or engineering draughtsman, take now advantage of the ability of computers to make complex calculations. Anyway, if we come back to these ancient days when tees, compass and drawing boards were the only instruments in use, construction problems coming from engineering were very similar to those considered in mathematics. All these problems share the same interesting feature: they are invariant under the action of direct isometries (also called rigid body motions). In this talk, I will first recall some basic facts about geometric constructions, and particularly straightedge and compass constructions, through some simple examples. I will also make a small detour through algebra via Wu's method and Galois theory. Then, I will expose the basic techniques used in CAD for decomposing construction problems. These techniques often mix elements of rigidity theory and counting of unknowns and equations on a so-called graph of constraints. I will show that not only the direct isometry group can be used but also translation group or similarity group. Finally, in the light of the previous points, I will discuss some relations between geometric constructions and proofs in geometry.
7. The difference between a prediction and hypothesis is that, unlike a prediction, a hypothesis gives possible explanations of a phenomenon, which can be supported or rejected.