Random Generator Research Articles

Describing randomization in trials included in systematic reviews in orthopaedic surgery.

Systematic reviews of randomized controlled trials (RCTs) are the highest level of evidence used to inform patient care. However, it has been suggested that the quality of randomization in RCTs in orthopaedic surgery may be low. This study aims to describe the quality of randomization in trials included in systematic reviews in orthopaedic surgery. Systematic reviews of RCTs testing orthopaedic procedures published in 2022 were extracted from PubMed, Embase, and the Cochrane Library. A random sample of 100 systematic reviews was selected, and all included RCTs were retrieved. To be eligible for inclusion, systematic reviews must have tested an orthopaedic procedure as the primary intervention, included at least one study identified as a RCT, been published in 2022 in English, and included human clinical trials. The Cochrane Risk of Bias-2 Tool was used to assess random sequence generation as 'adequate', 'inadequate', or 'no information'; we then calculated the proportion of trials in each category. We also collected data to test the association between these categories and characteristics of the RCTs and systematic reviews. We included 917 unique RCTs. We found that 374 RCTs (40.8%) reported adequate sequence generation, 61 (6.7%) were inadequate, 410 (44.7%) lacked information, and 72 (7.9%) were observational studies incorrectly included as RCTs within the systematic review. Publication year, an author with statistical or epidemiological qualifications, and journal impact factor were each associated with adequate randomization. We found that 45 systematic reviews (45%) included at least one inadequately randomized RCT or an observational study incorrectly treated as a RCT. There is evidence of a lack of random allocation in RCTs included in systematic reviews in orthopaedic surgery. The conduct of RCTs and systematic reviews should be improved to minimize the risk of bias from inadequate randomization in RCTs and mislabelling of non-randomized studies as RCTs.

A randomized trial investigating the impact of response expectancy on the counting blessings intervention: the role of optimism as a moderator

IntroductionThis randomized controlled trial aimed to address a knowledge gap concerning the mechanisms responsible for the efficacy of gratitude interventions. Specifically, we investigated how various response expectancies (positive, ambiguous + negative, and no expectancy) impact the efficacy of the “counting blessings” intervention in influencing positive and negative emotions. Additionally, the study explores how optimism levels (high, medium, low) interact with these expectancies to influence intervention efficacy.MethodA total of 529 adult volunteers were recruited through social media and randomly assigned to one of three experimental conditions, Positive Condition (PC), Ambiguous + Negative Condition (ANC), No Expectancy Condition (NEC), using a Random Sequence Generator. Of these, 142 participants completed the seven-day counting blessings intervention, and 111 participated in a follow-up assessment 1 month later. Missing data were addressed using multiple imputation. The main outcomes were changes in positive and negative emotions, with moderation analysis assessing the interaction between optimism levels and response expectancies. The study adhered to the CONSORT guidelines.ResultsWhile no significant interaction was found between experimental conditions and time regarding emotional outcomes (p ˃ 0.05), moderation analysis revealed differential interactions between optimism levels and expectancies, particularly influencing positive emotions (p < 0.009). For participants with low optimism, positive emotions significantly increased from post-intervention to follow-up in the PC (t = −2.42, p < 0.016) and from pre-intervention to post-intervention in the ANC (t = 2.41, p < 0.018). Participants with medium optimism experienced an increase in positive emotions across all conditions from pre-intervention to follow-up and from post-intervention to follow-up (ps < 0.05). High optimism participants showed an increase in positive emotions from pre-intervention to follow-up and post-intervention to follow-up in the PC (t = 2.09, p < 0.038 and t = 3.06, p < 0.003) and NEC c (t = −2.76, p < 0.006 and t = 2.74, p < 0.007).ConclusionOur findings emphasize the effectiveness of a brief gratitude journal and underscore the nuanced role of response expectancy, especially in interaction with the initial level of optimism, in enhancing positive emotions. These results hold significance for both theoretical understanding and clinical applications.

The effect of vitamin D3 supplementation on the incidence of type 2 diabetes in healthy older adults not at high risk for diabetes (FIND): a randomised controlled trial.

Vitamin D insufficiency is associated with an elevated risk of type 2 diabetes, but evidence from randomised trials on the benefits of vitamin D supplementation is limited, especially for average-risk populations. The Finnish Vitamin D Trial (FIND) investigated the effects of vitamin D3 supplementation at two different doses on the incidence of type 2 diabetes in a generally healthy older adult population. FIND was a 5 year randomised placebo-controlled, parallel-arm trial among 2271 male and female participants aged ≥60 years and ≥65 years, respectively, from a general Finnish population who were free of CVD or cancer and did not use diabetes medications. The study had three arms: placebo, 1600 IU/day of vitamin D3 or 3200 IU/day of vitamin D3. A non-study group statistician carried out sex-stratified simple randomisation in a 1:1:1 ratio, based on computerised random number generation. The participants, investigators and study staff were masked to group assignment. National health registries were used to collect event data. A representative subcohort of 505 participants had more detailed in-person investigations at months 0, 6, 12 and 24. During the mean follow-up of 4.2 years, there were 38 (5.0%), 31 (4.2%) and 36 (4.7%) type 2 diabetes events in the placebo (n=760), 1600 IU/day vitamin D3 (n=744; vs placebo: HR 0.81; 95% CI 0.50, 1.30) and 3200 IU/day vitamin D3 (n=767; vs placebo: HR 0.92, 95% CI 0.58, 1.45) arms, respectively (p-trend=0.73). When the two vitamin D3 arms were combined and compared with the placebo arm, the HR was 0.86 (95% CI 0.58, 1.29). In the analyses stratified by BMI (<25 kg/m2 [n=813, number of type 2 diabetes events=12], 25-30 kg/m2 [n=1032, number of events=38], ≥30 kg/m2 [n=422, number of events=54]), the HRs in the combined vitamin D3 arms vs the placebo were 0.43 (95% CI 0.14, 1.34), 0.97 (0.50, 1.91) and 1.00 (0.57, 1.75), respectively (p-interaction <0.001). In the subcohort, the mean (SD) baseline serum 25-hydroxyvitamin D3 (25(OH)D3) concentration was 74.5 (18.1) nmol/l. After 12 months, the concentrations were 72.6 (17.7), 99.3 (20.8) and 120.9 (22.1) nmol/l in the placebo, 1600 IU/day vitamin D3 and 3200 IU/day vitamin D3 arms, respectively. In the subcohort, no differences were observed in changes in plasma glucose or insulin concentrations, BMI or waist circumference during the 24 month follow-up (p values ≥0.19). Among generally healthy older adults who are not at high risk for diabetes and who have serum 25(OH)D3 levels that are sufficient for bone health, vitamin D3 supplementation did not significantly reduce the risk of developing diabetes. ClinicalTrials.gov NCT01463813.

Laser-engraved holograms as entropy source for random number generators

Our study introduces a novel approach to true random number generation (TRNG) using speckle patterns generated by laser-engraved holograms on carbon fiber-reinforced polymer (CFRP) composite substrates. Unlike previous methods, our approach simplifies the process by generating the necessary image dataset from a single microscope image of the engraved hologram. We achieve a high extraction ratio of 76 %, demonstrating the effectiveness of our TRNG. Moreover, our method successfully passes rigorous statistical tests proposed by the National Institute of Standards and Technology (NIST), indicating its suitability for cryptographic and secure system applications. This work offers promising implications for enhancing security in various domains, from secure communication networks to IoT devices.

KeyGAN: Synthetic keystroke data generation in the context of digital phenotyping

Objective:This paper aims to introduce and assess KeyGAN, a generative modeling-based keystroke data synthesizer. The synthesizer is designed to generate realistic synthetic keystroke data capturing the nuances of fine motor control and cognitive processes that govern finger-keyboard kinematics, thereby paving the way to support biomarker development for psychomotor impairment due to neurodegeneration. Methods:KeyGAN is designed with two primary objectives: (i) to ensure high realism in the synthetic distributions of the keystroke features and (ii) to analyze its ability to replicate the subtleties of natural typing for enhancing biomarker development. The quality of synthetic keystroke data produced by KeyGAN is evaluated against two keystroke-based applications, TypeNet and nQiMechPD, employed as’referee’ controls. The performance of KeyGAN is compared with a reference random Gaussian generator, testing its ability to fool the biometric authentication method TypeNet, and its ability to characterize fine motor impairment in Parkinson’s Disease using nQiMechPD. Results:KeyGAN outperformed the reference comparator in fooling the biometric authentication method TypeNet. It also exhibited a superior approximation to real data than the reference comparator when using nQiMechPD, showcasing its adaptability and versatility in mimicking early signs of Parkinson’s Disease in natural typing. KeyGAN’s synthetic data demonstrated that almost 20% of real PD samples could be replaced in the training set without a decline in classification performance on the real test set. Low Fréchet Distance (<0.03) and Kullback–Leibler Divergence (<700) between KeyGAN outputs and real data distributions underline the high performance of KeyGAN. Conclusion:KeyGAN presents strong potential as a realistic keystroke data synthesizer, displaying impressive capability to reproduce complex typing patterns relevant to biomarkers for neurological disorders, like Parkinson’s Disease. The ability of its synthetic data to effectively supplement real data for training algorithms without affecting performance implies significant promise for advancing research in digital biomarkers for neurodegenerative and psychomotor disorders.

Prediction of offshore wind turbine wake and output power using large eddy simulation and convolutional neural network

Predicting offshore wind turbine wake and output power is crucial for optimizing wind farm layouts and maximizing wind energy production. In recent years, several Computational Fluid Dynamics methods have been developed to predict wind turbine wake and output power and demonstrated good performance compared with traditional analytical models. However, Computational Fluid Dynamics often involve high computational costs in offshore wind farm design because a wide range of offshore wind conditions need to be considered for turbines with different inter-turbine spacings. To ensure both the fidelity and efficiency for predicting offshore wind turbine wake and output power, Large Eddy Simulation and Convolutional Neural Network are utilized in this study. The Large Eddy Simulation effectively integrates the Actuator Line Method and Discretizing and Synthesizing Random Flow Generation to generate wake velocity, wake turbulence intensity, and output power for a stand-alone turbine under different incoming wind speeds and turbulence intensities. Using the generated dataset, Convolutional Neural Network effectively captures the relationship between inputs and outputs for the stand-alone turbine. The predicted wake data for the turbine can then act as input to estimate the output power density and wake characteristics of a downstream turbine. This process can be iteratively applied to predict the wake and output power of each subsequent turbine in a wind farm, supporting the identification of optimal inter-turbine spacing. The proposed method is illustrated using a utility-scale 5 MW wind turbine. The results show that the errors of predicted output power for a stand-alone wind turbine and multiple wind turbines are blew 3 %.

Dynamics and implementation of a functional neuron model with hyperchaotic behavior under electromagnetic radiation

Prolonged exposure to electromagnetic radiation (EMR) may be hazardous to human health and plays a key role in the medical diagnosis of some diseases. And this effect can be better demonstrated by constructing a neuron model of EMR. Flux-controlled memristors can estimate the effects of EMR on neurons. In this work, a discrete flux-controlled memristor is used to estimate the behavior of EMR. Furthermore, the memristor is introduced into a neuron model to explore the effects of EMR on the behavior of neurons. The rich and interesting complex dynamical behavior of the model is investigated using analytical methods from nonlinear theory, including plotting bifurcation diagrams, Lyapunov exponent spectra (LEs) and complexity. By modulating the electromagnetic induction strength k of the memristor, it was observed that the introduction of EMR is able to generate hidden hyperchaotic attractors and initial-boosted behavior. The constructed neuron model is implemented based on a DSP platform. Pseudo random number generators (PRNGs) are further designed and the NIST test results illustrate the excellent random performance of the sequences generated by the neuron model. The properties exhibited in the neuron model under EMR provide a reference solution for EMR in the diagnosis and treatment of certain diseases.

Neural Correlates of Different Randomization Tasks.

In some cases, when we are making decisions, the available choices can appear to be equivalent. When this happens, our choices appear not to be constrained by external factors and, instead, we can believe to be selecting "randomly." Furthermore, randomness is sometimes even explicitly required by task conditions such as in random sequence generation tasks. This is a challenging task that involves the coordination of multiple cognitive processes, which can include the inhibition of habitual choice patterns and monitoring of the running choice sequence. It has been shown that random choices are strongly influenced by the way they are instructed. This raises the question whether the brain mechanisms underlying random selection also differ between different task instructions. To assess this, we measured brain activity while participants were engaging in three different variations of a sequence generation task: On the basis of previous work, participants were instructed to either (1) "generate a random sequence of choices," (2) "simulate a fair coin toss," or (3) "choose freely." Our results reveal a consistent frontoparietal activation pattern that is shared across all tasks. Specifically, increased activity was observed in bilateral inferior and right middle frontal gyrus, left pre-SMA, bilateral inferior parietal lobules, and portions of anterior insular cortex in both hemispheres. Activity in the mental coin toss condition was higher in right dorsolateral prefrontal cortex, left (pre-) SMA, a portion of right inferior frontal gyrus, bilateral superior parietal lobules, and bilateral anterior insula. In addition, our multivariate analysis revealed a distinct region in the right frontal pole to be predictive of the outcome of choices, but only when randomness was explicitly instructed. These results emphasize that different randomization tasks involve both shared and unique neural mechanisms. Thus, even seemingly similar randomization behavior can be produced by different neural pathways.

Integrating Energy-Efficient Train Control in railway Vertical Alignment Optimization: A novel Mixed-Integer Linear Programming approach

Incorporating train control into the railway design process enables a practical and comprehensive evaluation of the lifecycle utility of a track profile. This paper proposes a novel integrated approach, termed EETC-VAO, which combines railway track Vertical Alignment Optimization (VAO) and Energy-Efficient Train Control (EETC). Initially formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, EETC-VAO aims to meet various geometric constraints and simultaneously minimize construction costs, traction energy consumption, and section running times in both directions. The model is subsequently reformulated into an equivalent Mixed-Integer Linear Programming (MILP) model using linearization methods and is further enhanced with valid inequalities, logic cuts, and a warm start algorithm with random velocity generation. The model has been extensively tested across a variety of case studies and train types, from synthetic small-scale scenarios to challenging real-world cases spanning from 3 to 71.2 km. Our findings demonstrate that operational costs can be significantly reduced with only marginal increases in construction costs. The integrated approach achieves reductions in total lifecycle costs of up to 40%, revealing a critical trade-off between construction and operational expenses. Notably, our results also indicate that lower construction costs do not inherently conflict with reduced operational costs, emphasizing the critical importance of integrating the train control scheme into the VAO problem.

To the logical foundations of random number generator construction

Abstract This study explores fundamental aspects of probability theory within the framework of constructing random sequences or random number generators. We propose an interpretation of probability spaces and operations on formal events through the theory of formal languages, utilizing string manipulation techniques. As part of the research, we present a direct implementation of the discussed concepts in the form of a program that generates random numbers of the required type by processing signals from a sound card. Additionally, the problem of primality testing, which is particularly relevant to practical cryptographic applications, is addressed. We critically examine common misconceptions regarding the properties of Carmichael numbers and the application of Fermat’s Little Theorem. Furthermore, we propose an efficient primality testing algorithm.

Observation and Manipulation of Self‐Chaos in Disordered Optical System

AbstractOptical chaos is an attractive topic due to its unique dynamics and has been widely investigated in external‐cavity lasers. While chaotic behavior is hindered by undesired periodicity from external feedback. Although a self‐chaotic micro‐laser based on nonlinear interaction of internal modes can eliminate the periodicity, the inevitable characteristic frequency related to well‐defined cavity limits the improvement of chaotic performance. By virtue of the inherent randomness, disordered optical system can naturally avoid characteristic frequency and is deemed an ideal platform for generating self‐chaos. Here, the dynamical evolution process of self‐chaos in disordered optical system is observed, and self‐chaotic behavior can be flexibly manipulated by altering the interaction strength among random modes. Simultaneously, by adopting Erbium‐Raman hybrid gain, chaotic bandwidth can be synergistically enhanced to 38 GHz, which is successfully employed for higher‐speed true random bits generation and a scheme of local information encryption with higher‐quality. This work paves the way for investigating complex chaotic dynamics in disordered systems and showcases great potentialities within information security applications.

Phased therapeutic intervention improves long-term prognosis of new hemodialysis patients.

To explore the effect of phased therapeutic intervention in improving the long-term prognosis of new hemodialysis(HD) patients. This was a retrospective study. A total of 90 new HD patients in Qinhuangdao Haigang Hospital from June 2021 to June 2022 were included and stratified according to their compliance. They were grouped using a random number generator. The control group was given routine nursing intervention, while the intervention group was intervened with phased change nursing intervention. The effects of different intervention modes on patient compliance, biochemical indicators, cardiac function indicators and inflammatory cytokines were compared. At T0, the compliance of the two groups was improved compared with that before the intervention. The compliance of the intervention group at T1 and T2 (71.11%, 91.11%) was higher than that of the control group(48.89%, 75.56%), with statistically significant differences(p< 0.05). At T0, the levels of hemoglobin(Hb), hematocrit(Hct), serum iron(SI), transferrin saturation (TS) and left ventricular ejection fraction(LVEF) all increased compared with those before intervention(p< 0.05), At T1 and T2, Hb, Hct, SI, TS and LVEF in the intervention group were higher than those in the control group(p< 0.05). At T1 and T2, LVEDD, LVESD and LVWT in the intervention group were smaller than those in the control group(p< 0.05). Phased therapeutic intervention can significantly enhance the compliance of new HD patients, as well as further improve their anemia and cardiac function, and reduce inflammatory responses. Therefore, it is worthy of clinical application.

Random generation of three-dimensional realistic ballast particles using generative adversarial networks

Natural ballast particles exhibit a wide array of shape characteristics and follow complex probability distributions, which are difficult to quantify using explicit formulas due to their implicit nature. Traditional methods for generating ballast particles are limited to shapes that adhere to specific shape indices. In this study, generative adversarial networks (GANs) were utilized to randomly generate realistic three-dimensional (3D) ballast particles. Representative ballast particle shapes were captured using 3D structured light scanning technology. The scanned shapes were then voxelized and augmented to create a comprehensive dataset representing various ballast particle shapes. This dataset served to train the generator and discriminator components of the GANs. The trained generator successfully produced 2,000 detailed 3D ballast particles. Further refinement of these particles was achieved using Gaussian blur, followed by the Laplace smoothing algorithms and marching cubes algorithm for surface reconstruction. The authenticity of the generated ballast particles was validated by comparing their shape indices with those of natural ballast particles, thus demonstrating the effectiveness of the trained GANs model. The generated ballast particles are suitable for use as templates in discrete element method (DEM) simulations, specifically for clumps and polyhedrons.

Enhancing security and randomness in cryptography and non-cryptographic applications with ORNA algorithm

Abstract In this paper, we advocate for using PRNGs to generate long sequences of statistically random numbers, also known as pseudorandom numbers. It has several practical applications, including cryptography, weather-based stimulation, procedural environment noise generation in games and casinos, and scientific simulations of real-world problems, like city traffic systems that use it to stimulate cars. One distinctive feature of our approach is the PRNG algorithm we propose; this algorithm has successfully produced statistically sound long-term sequences. The properties of the PRNG are assessed using several statistical methods. Our work presents the Oscillating Random Number Algorithm (ORNA), a new kind of random number generator, and examines both the statistical tests proposed by NIST and the results that ORNA has generated. The following also describes the several preceding algorithms developed, including the Mersenne Twister Algorithm (MT19937) and the Permuted Congruential Algorithm (PCG128), and compares ORNA’s graphical efficiency to theirs. In this work, we compared ORNA to MT19937 and PCG128, and found that ORNA excels according to all 16 statistical tests. ORNA has three primary benefits, namely, superior statistical performance, quicker code execution, and simpler code implementation due to the algorithm’s reduced complexity.

Quality of the systematic reviews in cochrane multiple sclerosis related articles

Introduction: To enhance the assessment of the systematic reviews and meta-analyses performed by the Cochrane Multiple Sclerosis (MS) Group. Methods: Our study was conducted on 57 systematic reviews and meta-analyses related to MS, published by the Cochrane database until July 2023. Results: We found that the most encountered risk of bias was the low-risk domain, associated with Selective Reporting (data reporting), and followed by an unclear outcome for Allocation Concealment (selection bias). In contrast, Blinding of Participants and Personnel (performance bias) showed the highest risk of bias. Also, we concluded that up to 2015, the most prevalent risk of bias was ‘low outcome’ for Selective Reporting (data reporting). However, from 2016 till 2023, the most common risk of bias shifted to ‘low outcome’ for Random Sequence Generation (selection bias). Conclusion: Despite significant enhancements in improving the quality of studies, there is still a far way to achieve the ideal quality.

A novel algorithm for generating random fiber distributions for fiber reinforced composites based on lattice partition

AbstractThis paper propose a random fiber distribution generation algorithm based on lattice partition, which addresses the issue of limited increase in fiber volume fraction caused by the random movement of fibers in previous methods. By partitioning the representative volume element (RVE) window into lattices and moving fibers directionally, the proposed algorithm significantly increases the number of fibers within the RVE window. The generated fiber distribution achieves a fiber volume fraction of up to 80%, with an average execution time of 243.4 s. The influence of algorithm parameters on the randomness of fiber distribution is examined through statistical analysis of the generated distributions. Finite element models with various parameters are established to predict the elastic properties of the composites. The average relative error between the predicted values and the experimental results is 8.6%, demonstrating the effectiveness of the proposed algorithm. This algorithm offers a simple and effective approach for generating fiber distributions in the numerical study of the micromechanics of composites.Highlights A new lattice‐partition method is proposed to generate fiber distributions. The fiber volume fraction of generated fiber distribution is up to 80%. The randomness of fiber distribution is analyzed through statistical analysis. The algorithm predicts elastic properties with an average relative error of 8.6%.

Clinical impact of customised positive airway pressure (PAP) therapy interfaces versus usual care in the treatment of patients with sleep-disordered breathing (3DPiPPIn): a randomised controlled trial protocol

IntroductionSleep-disordered breathing affects 1.6 million people in the UK. The recognised treatment is positive airway pressure (PAP) therapy, delivered via a generic conventional interface (mask). PAP therapy improves morbidity, mortality...

GAN-based pseudo random number generation optimized through genetic algorithms

Pseudo-random number generators (PRNGs) are deterministic algorithms that generate sequences of numbers approximating the properties of random numbers, which are widely utilized in various fields. In this paper, we present a Genetic Algorithm Optimized Generative Adversarial Network (hereinafter referred to as GAGAN), which is designed for the effective training of discrete generative adversarial networks. In situations where non-differentiable activation functions, such as the modulo operation, are employed and traditional gradient-based backpropagation methods are inapplicable, genetic algorithms are utilized to optimize the parameters of the generator network. Based on this framework, we propose a novel recursive PRNG. Given that a PRNG can be constructed from one-way functions and their associated hardcore predicates, our proposed generator consists of two neural networks that simulate these functions and serve as the state transition function and the output function, respectively. The proposed PRNG has been rigorously tested using stringent benchmarks such as the NIST Statistical Test Suite (SP800-22) and the Chinese standard for random number generation (GM/T 0005-2021). Additionally, it has demonstrated outstanding performance in terms of Hamming distance. The results indicate that the proposed GAN-based PRNG has achieved a high degree of randomness and is highly sensitive to variations in the input.

Generation of algebraic data type values using evolutionary algorithms

Automatic data generation is a key component of automated software testing. Random generation of test input data can uncover some bugs in software, but its effectiveness decreases when those inputs must satisfy complex properties in order to be meaningful. In this work, we study an evolutionary approach to generate values that can be encoded as algebraic data types plus additional properties. First, the approach is illustrated with the generation of sorted lists. Then, we generalize the technique to arbitrary algebraic data type definitions. Finally, we consider the problem of constrained data types where the data must satisfy some nontrivial property, using the well-known example of red-black trees for our experiments. This example will allow us to introduce the main principles of evolutionary algorithms and how these principles can be applied to obtain valid, nontrivial samples of a given data structure. Our experiments have revealed that this evolutionary approach is able to improve diversity, and increase the size of valid generated values with respect to simple random sampling techniques.

Energy‐Efficient Hardware Implementation of Spiking‐Restricted Boltzmann Machines Using Pseudo‐Synaptic Sampling

Stochastic sampling is performed to reduce hardware energy consumption and prevent overfitting by reducing parameters, because not all data are required for learning. In this study, a new approach, pseudo‐synaptic sampling (PS2) method, which approximates the conventional synaptic sampling machine (S2M) method through a hardware‐friendly implementation while demonstrating superior efficiency, is introduced. By sampling in front of neurons rather than at each synapse, the PS2 method improves hardware energy efficiency and ensures scalability. Furthermore, it improves energy and area efficiency by eliminating the additional circuit required by other techniques, such as the random walk (RW) method previously used which requires an additional circuit to frequently charge/discharge the membrane potential. Herein, the average firing rate equation for the S2M method is modified to suit the experimental conditions of this study. Through this numerical simulations, it is confirmed that the activation function of the PS2 method aligns with that of the S2M method and verified that the PS2 method can implement stochasticity for restricted Boltzmann machine (RBM) neurons. Experimental validation of the PS2 method, compared to the RW method, for Modified National Institute of Standards and Technology database (MNIST) training and inference on field‐programmable‐gate‐array‐implemented spiking RBM chips reveals promising results. In an MNIST 100‐handwritten digit experiment, the PS2 method exhibits on‐chip training accuracy (92%) comparable to that of the RW method (93%). Furthermore, in the energy consumption analysis, it is shown that the PS2 method reduces power consumption by 94.94% compared to the RW method, highlighting its enhanced power efficiency due to a reduced number of circuit elements. In the investigation into the impact of increasing the frequency at which random bits are generated, it is shown that the RW method experiences accuracy degradation even with slight increases, whereas the PS2 method maintains accuracy over significantly longer periods. This enables further power reduction by allowing for a longer period during random bit generation. In this study, a foundation is laid for maximizing the energy efficiency of spiking neural network processors by optimizing internal noise generation mechanisms.