ABSTRACT
The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.
ABSTRACT
AIMS: Mathematical models were created which predict the growth of spoilage bacteria in response to various preservation systems. METHODS AND RESULTS: A Box-Behnken design included five variables: pH (2.8, 3.3, 3.8), titratable acidity (0.20%, 0.40%, 0.60%), sugar (8.0, 12.0, 16.0 * Brix), sodium benzoate concentration (100, 225, 350 ppm), and potassium sorbate concentration (100, 225, 350 ppm). Duplicate samples were inoculated with a bacterial cocktail (100 microl 50 ml(-1)) consisting of equal proportions of Acinetobacter calcoaceticus and Gluconobacter oxydans (5 x 10(5) cfu ml(-1) each). Bacteria from the inoculated samples were enumerated on malt extract agar at zero, one, two, four, six, and eight weeks. CONCLUSION: The pH, titratable acidity, sugar content, sodium benzoate, and potassium sorbate levels were all significant factors in predicting the growth of spoilage bacteria. SIGNIFICANCE AND IMPACT OF THE STUDY: This beverage spoilage model can be used to predict microbial stability in new beverage product development and potentially reduce the cost and time involved in microbial challenge testing.
