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The aim of the study is to interpret the effects of air‐impingement jet drying (AIJD) on drying kinetics, color, polyphenols, and antioxidation ability of Boletus aereus slices. Page model was most suitable for expressing and predicting AIJD curves of B. aereus slices. The moisture‐effective diffusion coefficient of AIJD ranged from 7.8876 × 10−10 to 2.1426 × 10−9 m2/s, and AIJD also showed high efficiency due to its low activation energy (45.37 kJ/mol). AIJD is better for B. aereus slices than hot air drying (HAD) in accelerating the drying rate (DR) and shortening drying time, and maintaining color. p‐hydroxybenzoic acid, protocatechuic acid, and rutin were identified in B. aereus slices by ultra high‐performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC‐QqQ‐MS). Total polyphenols, flavanone, phenolic acids, and antioxidant activities were significantly lower in dried B. aereus slices than those in fresh B. aereus slices. In AIJD, drying temperature had the greatest effect on the quality of B. aereus slices, and AIJD at 50 °C is the optimum drying condition for B. aereus slices. Practical Application Boletus aereus occurs in many countries all over the world. In this paper, the effect of AIJD on color, polyphenols, and antioxidation ability in B. aereus slices and its drying kinetics were studied. AIJD is an efficient drying method for B. aereus by decreasing its drying time, increasing DR, and protecting the color of B. aereus. These findings have provided important reference basis for people to have a better understanding of AIJD method, which was used to dry B. aereus. This study also provides a new technique for drying B. aereus, which could improve dry efficiency and reduce drying cost.

To avoid the limitation of conventional vehicle magnetorheological (MR) suspension, a variable damping and inertia device is applied in the vehicle suspension with MR technology. A semi-active adaptive MR inerter (AMRI) is discussed. A quarter car suspension model with an AMRI installed in parallel with a double-ended MR damper (D-MRD) is considered. First, the vehicle suspension with variable damping and inertia is analyzed. The prototype of D-MRD and MR variable inertia flywheel (MRVIF) are fabricated and tested respectively. Then, the control model of D-MRD and MRVIF is developed on the basis of test data. An improved Fuzzy PID controller for the semi-active suspension with D-MRD and AMRI is formulated. Numerical simulation is investigated to validate the proposed variable damping and inertia device. The results demonstrate that the performance of the semi-active suspension with D-MRD and AMRI can achieve much better ride comfort than the semi-active suspension with only D-MRD or AMRI.

Due to the dense distribution of tomato fruit with similar morphologies and colors, it is difficult to recognize the maturity stages when the tomato fruit is harvested. In this study, a tomato maturity recognition model, YOLOv5s-tomato, is proposed based on improved YOLOv5 to recognize the four types of different tomato maturity stages: mature green, breaker, pink, and red. Tomato maturity datasets were established using tomato fruit images collected at different maturing stages in the greenhouse. The small-target detection performance of the model was improved by Mosaic data enhancement. Focus and Cross Stage Partial Network (CSPNet) were adopted to improve the speed of network training and reasoning. The Efficient IoU (EIoU) loss was used to replace the Complete IoU (CIoU) loss to optimize the regression process of the prediction box. Finally, the improved algorithm was compared with the original YOLOv5 algorithm on the tomato maturity dataset. The experiment results show that the YOLOv5s-tomato reaches a precision of 95.58% and the mean Average Precision (mAP) is 97.42%; they are improved by 0.11% and 0.66%, respectively, compared with the original YOLOv5s model. The per-image detection speed is 9.2 ms, and the size is 23.9 MB. The proposed YOLOv5s-tomato can effectively solve the problem of low recognition accuracy for occluded and small-target tomatoes, and it also can meet the accuracy and speed requirements of tomato maturity recognition in greenhouses, making it suitable for deployment on mobile agricultural devices to provide technical support for the precise operation of tomato-picking machines.