The aim of this study was to investigate the effect of multi-frequency power ultrasound (sweeping frequency and pulsed ultrasound (SFPU) and sequential dual frequency ultrasound (SDFU)) on the enzymolysis of corn gluten meal (CGM) and on the structures of the major protein fractions (zein, glutelin) of CGM. The results showed that multi-frequency power ultrasound pretreatments improved significantly (P<0.05) the degree of hydrolysis and conversion rate of CGM. The changes in UV-Vis spectra, fluorescence emission spectra, surface hydrophobicity (H0), and the content of SH and SS groups indicated unfolding of zein and glutelin by ultrasound. The circular dichroism analysis showed that both pretreatments decreased α-helix and increased β-sheet of glutelin. The SFPU pretreatment had little impact on the secondary structure of zein, while the SDFU increased the α-helix and decreased the β-sheet remarkably. Scanning electron microscope indicated that both pretreatments destroyed the microstructures of glutelin and CGM, reduced the particle size of zein despite that the SDFU induced aggregation was observed. In conclusion, multi-frequency power ultrasound pretreatment is an efficient method in protein proteolysis due to its sonochemistry effect on the molecular conformation as well as on the microstructure of protein.
This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. The precoding problem is a joint optimization of analog and digital precoders, and we treat it as a matrix factorization problem with power and constant modulus constraints. Our work presents three main contributions: First, we present a sufficient condition and a necessary condition for hybrid precoding schemes to realize unconstrained optimal precoders exactly when the number of data streams Ns satisfies Ns = min{rank(H), N rf }, where H represents the channel matrix and N rf is the number of radio frequency (RF) chains. Second, we show that the coupled power constraint in our matrix factorization problem can be removed without loss of optimality. Third, we propose a Broyden-Fletcher-Goldfarb-Shanno (BFGS)-based algorithm to solve our matrix factorization problem using gradient and Hessian information. Several numerical results are provided to show that our proposed algorithm outperforms existing hybrid precoding algorithms.
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