  Entropy is a famous and well established concept in physics and engineering
that can be used for explanation of basic fundamentals as well it finds
applications in several areas, from quantum physics to astronomy, from network
communication to medical image processing, for example. Now, entropy meets its
dual, the disentropy. As such, the disentropy can be used everywhere entropy is
used, offering a different point of view: since entropy is a measure of
disorder or uncertainty, disentropy is a measure of order or certainty. Thus,
important concepts of physics can be rewritten using disentropy instead of
entropy. Although there is a large range of problems that can be solved using
entropy or disentropy, there are situations where only the disentropy can be
used. This happens because the disentropy can provide a real output value when
its argument is negative, while the entropy cannot. Thus, it is possible to
calculate, for example, the disentropy of quasi-probability distributions like
the Wigner function of highly quantum states. In this direction, the present
work shows applications of the disentropy in a small list of problems: quantum
and classical information theory, black hole thermodynamics, image processing
and number theory.
