analyzed the time series generated by schizophrenic patients and control subjects using three nonlinear methods of time series: CTM from the scatter plots of first differences of data, approximate entropy (ApEn), and Lempel-Ziv (LZ) complexity. Then he concluded that this test could be a complementary tool to help physicians in the estimation of cognitive-motor dysfunction in schizophrenic patients. He used chaotic dynamic attractors and the second-order difference plots of the time series, and calculation of the correlation dimension and the central tendency measure (CTM) parameter. The time series generated by tapping a key for schizophrenic patients had a lower complexity and variability than the healthy subjects. He showed that the tapping rhythm of schizophrenic patients differ from healthy subjects ( P ANOVA < 0.001). in classification of schizophrenic patients from healthy subjects. The first assessment of this idea is done in a study by Hornero et al. Several studies in cognitive psychology show that the generation of random rhythms is a cognitive task and has enough information for discrimination of different clinical populations.Ī closely related task to number generation is random rhythms of tapping a key. When someone is asked to generate random numbers, a cognitive load is implied, since there is a close interaction between executive memory and internalized decision-making mechanisms. Analysis of random numbers generated by people to investigate short-term memory function and attention is a well-explored area of psychological research. Because of this process, the random sequence generated by people, is biased compared to true random numbers. If numbers are not written down, then the mind can only review those numbers held in short-term memory which causes pattern suppression. Generally, generation of random numbers activate certain areas of the brain. Recently, new statistical processing methods have been applied to discover this ability of human beings and in most of them man has been known as a bad random number generator. Baddeley investigated this field extensively. During the random number generation, each subject has his/her own strategy and must partially memorize the previous numbers and choose the next one based on his/her own conception of randomness. The rand command was updated in Maple 2015.The Longman Dictionary of Contemporary English describes “random” as “happening or chosen without any definite plan, aim, or pattern.” A sequence of numbers is said to be random, if the next element cannot be predicted from the previous one.įor many years, scientists have been checking the ability of human beings to generate true random numbers. To sample from non-uniform probability distributions, the Statistics function can be used. To generate more complex Maple objects, the RandomTools function can be used. The algorithm used by rand in Maple versions up to and including 9.5 has been moved into the RandomTools package as RandomTools. The use of the _seed global variable to seed the generator is deprecated. The random number generator used by rand can be seeded by using the randomize or RandomTools functions. It is more efficient to make these calls directly than to call rand. Rand calls RandomTools or RandomTools depending on whether or not a number or procedure is to be returned. However, since all random number generators use the same underlying random number sequence, calls to one random number generator will affect the random numbers returned from another. More than one random number generator may be used at the same time, because rand(a.b) returns a Maple procedure. If r is a single integer, the call rand(r) is the abbreviated form of rand(0.r-1). If r is a numeric range a.b and one of a or b is not an integer, the call rand(a.b) returns a procedure which, when called, generates random floating-point numbers in the range a.b. If the parameter r is an integer range a.b, the call rand(a.b) returns a procedure which, when called, generates random integers in the range a.b. With no arguments, the call rand() returns a random integer sampled uniformly from the range 0 to 10 12 - 1. (optional) numeric range or positive integer
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