Quality of random number generators significantly affects results of Monte Carlo simulations for organic and biological systems

Click, Timothy H.; Liu, Aibing; Kaminski, George A.

doi:10.1002/jcc.21638

Cited by 37 publications

(31 citation statements)

References 10 publications

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“…The pick from the list is generated by a 15‐bit LCG with a = 12453 and c = 2213, normalized by 512 and incremented by one (to obtain a random number between 1 and 64). The BOSS program uses a similar strategy to generate pseudorandom numbers …”

Section: Methodsmentioning

confidence: 99%

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Comparison of a quantum random number generator with pseudorandom number generators for their use in molecular Monte Carlo simulations

Ghersi

Parakh

Mezei³

2017

J Comput Chem

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Four pseudorandom number generators were compared with a physical, quantum-based random number generator using the NIST suite of statistical tests, which only the quantum-based random number generator could successfully pass. We then measured the effect of the five random number generators on various calculated properties in different Markov-chain Monte Carlo simulations. Two types of systems were tested: conformational sampling of a small molecule in aqueous solution and liquid methanol under constant temperature and pressure. The results show that poor quality pseudorandom number generators produce results that deviate significantly from those obtained with the quantum-based random number generator, particularly in the case of the small molecule in aqueous solution setup. In contrast, the widely used Mersenne Twister pseudorandom generator and a 64-bit Linear Congruential Generator with a scrambler produce results that are statistically indistinguishable from those obtained with the quantum-based random number generator. © 2017 Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

“…The second system studied in a recent publication dealing with random number generators included liquid methanol under constant pressure and temperature (METH216). Methanol was modeled with the Charmm general force field .…”

Section: Methodsmentioning

confidence: 99%

Comparison of a quantum random number generator with pseudorandom number generators for their use in molecular Monte Carlo simulations

Ghersi

Parakh

Mezei³

2017

J Comput Chem

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“…[45–48] Due to their stochastic nature, MC methods require a reliable source of random numbers. [49] The default random number generator used in GMCT is the Mersenne Twister,[50] because it offers very good performance in terms of speed and quality of the generated random numbers.…”

Section: Monte Carlo Simulation Methods In Gmctmentioning

confidence: 99%

Sirolimus‐ versus paclitaxel‐eluting stents for unselected patients with coronary artery disease

Park

2010

Cathet Cardio Intervent

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Millauer et al. reported that sirolimus-eluting stent (SES) decreased the incidence of major adverse cardiac events over 2 years compared with paclitaxel-eluting stent (PES) in 904 unselected patients with coronary artery stenosis [1]. In spite of a nonrandomized study design, a selection bias may be minimized, because this study has been conducted before the publication of randomized studies comparing SES and PES. The major finding of this study was in line with the previous randomized or registry studies showing the superior benefit of SES over PES in the reduction of restenosis and subsequent repeat revascularization [2]. In the meta-analysis, SES significantly reduced the risk of reintervention by 26% (P 5 0.001) and stent thrombosis by 34% (P 5 0.02) [2]. Because of this finding and anticipation of better safety and deliverability, the first generation drug-eluting stent (DES) has increasingly been exchanged with the second-generation DES in the current practices.The risk of mortality or myocardial infarction has not been affected by the type of DES. However, when it comes to the incidence of repeat revascularization, there may be a difference among the diverse DES. The previous studies having mandatory angiographic follow-up showed that the limus-analog DES decreased the late loss and reintervention rate by the stronger inhibition of neointima compared with PES [2,3]. In contrast, a randomized study without mandatory angiographic surveillance showed no difference in the clinical outcomes between SES and PES [4]. This study of Millauer et al, however, suggests that greater reduction of late loss can improve clinical outcomes by the reduction of repeat revascularization in unselected patients even when angiographic follow-up was not routinely performed.A superior benefit of SES in reduction of restenosis or repeat revascularization did not exist for a subgroup of diabetic patients. Although the mechanism is not clear, the clinical impact of SES may be less pronounced because multiple comorbidities and extensive coronary disease in diabetic patients independently influence the clinical outcomes [5]. In addition, the risk of reintervention may be underestimated, because medical treatment is occasionally adopted for complex patterns of restenosis [3]. Alternatively, antimitotic activity of limus-analog DES may be attenuated in the presence of hyperglycemia. Nonetheless, it is noteworthy that this finding of the subgroup analysis can only be regarded as exploratory. This study and all other studies showing the similarity in repeat reavascularization rate between SES and PES were performed as the post hoc subgroup analysis without adequate statistical power. In fact, results from the previous randomized studies have consistently proposed that SES may offer an angiographic and clinical advantage over PES even in patients with diabetes [6,7]. Further large randomized studies to compare diverse type of DES are required in diabetic population.

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“…The truth is that every PRNG shows its weakness in some particular application. Indeed PRNGs are often found to be the cause of erroneous stochastic simulations and calculations [11,13]. As for cryptographic purposes, all major families of PRNGs have been cryptanalyzed so far [32], and use of PRNG where an RNG should be used will therefore present a big security risk for the protocol in question.…”

Section: A C T E Dmentioning

confidence: 99%

Random Number Generators Basedon EEG Non-linear and ChaoticCharacteristics

Nguyen

Tran

et al. 2018

JCSM

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Current electroencephalogram (EEG)-based methods in security have been mainly used for person authentication and identification purposes only. The non-linear and chaotic characteristics of EEG signal have not been taken into account. In this paper, we propose a new method that explores the use of these EEG characteristics in generating random numbers. EEG signal and its wavebands are transformed into bit sequences that are used as random number sequences or as seeds for pseudo-random number generators. EEG signal has the following advantages: 1) it is noisy, complex, chaotic and non-linear in nature, 2) it is very difficult to mimic because similar mental tasks are person dependent, and 3) it is almost impossible to steal because the brain activity is sensitive to the stress and the mood of the person and an aggressor cannot force the person to reproduce his/her mental pass-phrase. Our experiments were conducted on the four EEG datasets: AEEG, Alcoholism, DEAP and GrazA 2008. The randomness of the generated bit sequences was tested at a high level of significance by comprehensive battery of tests recommended by the National Institute of Standard and Technology (NIST) to verify the quality of random number generators, especially in cryptography application. Our experimental results showed high average success rates for all wavebands and the highest rate is 99.17% for the gamma band.

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Quality of random number generators significantly affects results of Monte Carlo simulations for organic and biological systems

Cited by 37 publications

References 10 publications

Comparison of a quantum random number generator with pseudorandom number generators for their use in molecular Monte Carlo simulations

Comparison of a quantum random number generator with pseudorandom number generators for their use in molecular Monte Carlo simulations

Sirolimus‐ versus paclitaxel‐eluting stents for unselected patients with coronary artery disease

Random Number Generators Basedon EEG Non-linear and ChaoticCharacteristics

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