Arhat Vachan 2002 04

SR No.: 526554
Original Sutra Author: N/A
Author: Anupam Jain
Publisher: Kundkund Gyanpith Indore
Publication Year: 2002
Total Pages: 148
Language: Hindi
Classification: Magazine, India_Arhat Vachan, & India
File Size: 9 MB

Page 76

existed for some time and decayed in course of time. Each niṣeka contains eight types of karma paramānus.
Graphic Representation of Niseka
The position of any niseka can be represented graphically. Let time be taken on X-axis and number of nisekas as mass number equipped with recoil energy on Y-axis, the Z-axis be taken as configuration axis. The 3-dimensional space so formed is considered as karmic space or karmas. The position of niseka is represented by N, where i represents instants (time unit), j the mass number and k the configuration (prakṛti).
Z
We observe that since there is decay of niṣekas, the mass number on Y-axis decreases with time as a step function.
0
Matrix Representation
The karma paramānus which posses least amount of energy is called indivisible corresponding section (Avibhagt praticcheda) for short ICS. The class of karma paramānus possessing same number of ICS is called variform (varganā). A minimum variform is one which has least number of ICS. The set having one more ICS then minimum variform is called second variform. In this way the sequence of the variforms is increasing, the number of ICS increases in arithmatic progression with common difference one. The group of such variforms is known as minimum supervariforms (spardhaka). The number of ICS in the next variforms increases by two and the group will form the second supervariform with common difference two. Similarly for third supervariform the common difference will be four. In general for the n supoervariform common difference will be 21. The set of supervariforms is a geometric regression and the geometric regression length is the number of supervariforms.
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The karma matrix whose elements are karma paramānus, has w rows and s columns, where w stands for number of instants and s for the number of supervariforms. The matrix of the first geometric regression assumes the form:
Arhat Vacana, 14(2-3), 2002