Quality prediction of whole-grain rice noodles using backpropagation artificial neural network.

BACKGROUND: Whole-grain rice noodles are a kind of healthy food with rich nutritional value, and their product quality has a notable impact on consumer acceptability. The quality evaluation model is of great significance to the optimization of product quality. However, there are few methods that can establish a product quality prediction model with multiple preparation conditions as inputs and various quality evaluation indexes as outputs. In this study, an artificial neural network (ANN) model based on a backpropagation (BP) algorithm was used to predict the comprehensive quality changes of whole-grain rice noodles under different preparation conditions, which provided a new way to improve the quality of extrusion rice products. RESULTS: The results showed that the BP-ANN using the Levenberg-Marquardt algorithm and the optimal topology (4-11-8) gave the best performance. The correlation coefficients (R2) for the training, validation, testing, and global data sets of the BP neural network were 0.927, 0.873, 0.817, and 0.903, respectively. In the validation test, the percentage error in the quality prediction of whole-grain rice noodles was within 10%, indicating that the BP-ANN could accurately predict the quality of whole-grain rice noodles prepared under different conditions. CONCLUSION: This study showed that the quality prediction model of whole-grain rice noodles based on the BP-ANN algorithm was effective, and suitable for predicting the quality of whole-grain rice noodles prepared under different conditions. © 2024 Society of Chemical Industry.

Comparison and discussion of behavior and pathology of four kinds of cerebral palsy disease models.

Explore the differences in behavioral and pathological manifestations of rat models of cerebral palsy made by different methods and discuss what types of studies these models are suitable for. Behavioral evaluation and pathological section observation were used to observe and evaluate the model. Conclusion: except for the absence of data of bilateral common carotid artery ligation rats, the other three methods could all achieve a successful cerebral palsy disease model for both behavioral and pathological. For researchers, the selection of intraperitoneal infection model in pregnant rats or unilateral ischemia and hypoxia model in infant rats is sufficient to meet the experimental needs, whereas the selection of the combined method for modeling does not show enough advantages, which not only causes the waste of financial and human resources but also increases the possibility of experimental error made by intervention factors.

Enhancing flashover performance of alumina/epoxy spacers by adaptive surface charge regulation using graded conductivity coating.

Surface flashover properties of alumina/epoxy spacers, involving a surface charge accumulation process, are critical for the safe and reliable operation of a high-voltage direct-current (HVDC) gas-insulated transmission line (GIL). This study reports surface charging behavior and flashover performance of alumina/epoxy spacers with different surface conductivity graded coating (SCGC) schemes in SF6/N2 mixtures under DC stress. Four kinds of SCGC schemes, i.e. localized coating near high voltage (HV-coating), near grounded electrode (GND-coating), at the middle of spacer surface (SPM-coating) and near both high voltage and grounded electrode (HV-GND-coating), are designed by partially spraying SiC/epoxy composites on the spacer surface. Surface charge distribution patterns exhibit varied features with different SCGC schemes. The HV-coating and GND-coating schemes lead to aggravated homo-charge and hetero-charge accumulation respectively, whereas in the SPM-coating scheme surface charge shows a multi-tier distribution pattern with alternating polarity. A transition of the dominant surface charge mechanism from bulk conductivity to surface conductivity with increasing conductivity on the coated area is found. Flashover performance differs a lot with different SCGC schemes: the HV-coating and HV-GND-coating schemes increase the flashover voltage while the SPM-coating and GND-coating schemes degrade it. The optimal surface insulation strength is achieved in the HV-coating scheme with a coating width of about 10 mm. The impact of different SCGC schemes on flashover performance is revealed based on the electric field analysis by considering the effect of surface charges.

Robust Takagi-Sugeno fuzzy control for fractional order hydro-turbine governing system.

A robust fuzzy control method for fractional order hydro-turbine governing system (FOHGS) in the presence of random disturbances is investigated in this paper. Firstly, the mathematical model of FOHGS is introduced, and based on Takagi-Sugeno (T-S) fuzzy rules, the generalized T-S fuzzy model of FOHGS is presented. Secondly, based on fractional order Lyapunov stability theory, a novel T-S fuzzy control method is designed for the stability control of FOHGS. Thirdly, the relatively loose sufficient stability condition is acquired, which could be transformed into a group of linear matrix inequalities (LMIs) via Schur complement as well as the strict mathematical derivation is given. Furthermore, the control method could resist random disturbances, which shows the good robustness. Simulation results indicate the designed fractional order T-S fuzzy control scheme works well compared with the existing method.