random number generator matlab rng (MathWorks Inc)
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MathWorks Inc
random number generator matlab rng
Random Number Generator Matlab Rng, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 28 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/random/MATLAB+Report+Generator/pm42784195-450-82-84
Average 93 stars, based on 28 article reviews
Random Number Generator Matlab Rng, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 28 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/random/MATLAB+Report+Generator/pm42784195-450-82-84
Average 93 stars, based on 28 article reviews
random number generator matlab rng - by Bioz Stars,
2026-10
93/100 stars
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Sampling:Article Title: Spatial distribution of soil organic carbon in an Irish salt marsh (Rogerstown Estuary). Article Snippet: .. Subsamples from the CD in this study (n = 60) were randomly taken using the Matlab other:Article Title: Hybrid Harris hawks-optimized random forest model for detecting multi-element geochemical anomalies related to mineralization. Article Snippet: In this regard, the Article Title: On the need of individually optimizing temporal interference stimulation of human brains due to inter-individual variability. Article Snippet: The current at each electrode in the random montage was generated also randomly by the “rand” function in Matlab. Article Title: Performance Evaluation of Four Deep Learning-Based CAD Systems and Manual Reading for Pulmonary Nodules Detection, Volume Measurement, and Lung-RADS Classification Under Varying Radiation Doses and Reconstruction Methods. Article Snippet: Nodule distribution was determined using a Article Title: S1 Supporting Information for “A Novel Kinetic Model to Demonstrate the Independent Effects of ATP and ADP/Pi Concentrations on Sarcomere Function” Article Snippet: We then generate a random number p∗ from a uniform distribution between 0 and 1 using Article Title: On the need of individually optimizing temporal interference stimulation of human brains due to inter-individual variability. Article Snippet: The random montage was generated by “randperm” function in Matlab, and had the same number of electrodes as the optimized montage (4 for 2- pair TI, and K for 2-array TI and HD-TES, where K is the number of electrodes in the optimized montage that have a dosage higher than 10-6 mA). Article Title: D-optimal candexch algorithm-enhanced machine learning UV-spectrophotometry for five-analyte determination in novel anti-glaucoma formulations and ocular fluids: four-color sustainability framework with NQS assessment and UN-SDG integration. Article Snippet: The key novelty was using the D-optimal design generated by Concentration Assay:Article Title: Physics-Constrained Transfer-Matrix Optimization of Diffused Regions in Bifacial p + - n - n + Crystalline-Silicon Solar Cells. Article Snippet: .. Item Symbol or Method Value Front surface concentration NA,s 1017–5 × 1020 cm−3 Base concentration ND,B 1014–1017 cm−3 Rear surface concentration NrearD,s 10 18–5 × 1020 cm−3 Prescribed wafer thicknesses d0 160, 180, and 200 μm Emitter-depth bounds WE 0.05–4.0 μm Rear-field-depth bounds WBSF 0.02–3.0 μm Minimum base thickness WB 5.0 μm Global algorithm MATLAB ga Genetic algorithm Population size — 80 Maximum generations — 100 Maximum stall generations — 30 Function tolerance — 10−6 Initial GA design θref Calibrated reference included Western Blot:Article Title: Physics-Constrained Transfer-Matrix Optimization of Diffused Regions in Bifacial p + - n - n + Crystalline-Silicon Solar Cells. Article Snippet: .. Item Symbol or Method Value Front surface concentration NA,s 1017–5 × 1020 cm−3 Base concentration ND,B 1014–1017 cm−3 Rear surface concentration NrearD,s 10 18–5 × 1020 cm−3 Prescribed wafer thicknesses d0 160, 180, and 200 μm Emitter-depth bounds WE 0.05–4.0 μm Rear-field-depth bounds WBSF 0.02–3.0 μm Minimum base thickness WB 5.0 μm Global algorithm MATLAB ga Genetic algorithm Population size — 80 Maximum generations — 100 Maximum stall generations — 30 Function tolerance — 10−6 Initial GA design θref Calibrated reference included |