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Objective: This study aimed to explore the effect of different ultrasonic enhancement methods on the quality characteristics of dried lotus root in heat pumps. Methods: The effects of US, CU, and USOD on the drying characteristics, appearance color, microstructure, texture characteristics, nutritional quality and antioxidant characteristics of the lotus root heat pump during the drying process were investigated. Ultrasonic (US), direct contact ultrasound (CU), USOD and heat pump drying (HPD) were treated in series and parallel. The coefficient of variation method was used to comprehensively evaluate the quality of lotus root dried by different ultrasonic enhancement methods. Results: US, CU and USOD treatments had positive effects on the HPD speed of lotus roots. The brightness (L*), polysaccharide content, and hydroxyl radical scavenging rate of lotus root obtained by USOD-HPD were the best, which were 77.98, 51.68 mg/g, and 45.94%, respectively. US-HPD treatment can better retain the content of flavonoids and vitamin C in lotus root. HPD lotus root had the highest polyphenol content and DPPH radical scavenging. The comprehensive evaluation results of the coefficient of variation method showed that the USOD-HPD lotus root had the best quality. Conclusion: The combined drying method of USOD-HPD can effectively improve the quality of lotus root.

Integration of interactive functions into display is an important trend of display technology. Brain‐computer interfaces enable human‐machine interaction by interpreting electroencephalogram signals, among which Steady‐State Visual Evoked Potential (SSVEP) based on liquid crystal displays is a promising BCI type. Currently, LCD faces the limitation of a mere 240Hz refresh rate. However, this constraint can be surpassed by partitioned backlighting (PB), which effortlessly achieves a refresh rate of 1kHz. Comparing with traditional LCD‐SSVEP, this article studies the brain fatigue of PB‐SSVEP by conducting experiments to measure ophthalmic parameters and EEG. The results indicate that under specific frequency, size, and color conditions, PB‐SSVEP induces lighter visual and brain fatigue to a certain extent. [ABSTRACT FROM AUTHOR]

To clarify the moisture migration and microstructure of taro in contact ultrasound enhanced far-infrared radiation drying (CUFRD), low-field nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), paraffin sectioning and microscopic observation techniques were applied to explore the changes of the drying curves, water status, microstructure, porosity, microscopic images and distribution curves of cell microstructure parameters of taro under different ultrasound powers of 0, 40, and 80 W. The results showed that applying contact ultrasound (CU) during drying was beneficial to accelerating the dehydration process and improving the porosity of taro slice. With the augment of CU power, the drying times of taro reduced by 16.67% to 25.00%, and the effective water diffusion coefficient was improved by 14.72% to 31.38%. In addition, the application of CU resulted in an increase firstly and then decrease in the cross-sectional area and perimeter distribution curves of taro cells and a widening of the peak shape of the distribution curve of taro cell roundness. In conclusion, CU application could cause faster internal moisture migration, and higher CU power had more obvious effect on microstructure and cell morphological parameters of taro during CUFRD. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large‐scale FNC as a feature to identify individuals with ASD. This study used a time‐sliding window method to study the dynamic FNC (dFNC) on the resting‐state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10–75 TRs (TR = 2 s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10‐fold cross‐validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD. Lay Summary: In this study, we proposed a new ASD/HC classification framework based on the dynamic FNC (dFNC), obtaining an accuracy of 97.77%. Based on the classification weights, we found that the dFNC patterns of the attention networks (DAN and VAN) played a key role in ASD. In addition, the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly correlated with the clinical symptoms of ASD. [ABSTRACT FROM AUTHOR]