With the increasing size of wind turbine blades, the need for more sophisticated load control techniques has induced the interest for aerodynamic control systems with build-in intelligence on the blades. New structural concepts have emerged where multifunctional materials, exhibiting a strong coupling between its mechanical response and its electrical behaviour, which work as sensors and actuators, are embedded or bonded to composite laminates for high-performance structural applications. The paper aims to provide a way for modeling the adaptive wind turbine blades and analyze its ability for vibration suppress. This study provides a finite element model of the smart blade for wind turbines. Numerical analysis is performed using finite element method, which is used to calculate the time response of the model. The displacement response from the piezoelectric actuator and piezoelectric sensors is obtained to control the vibration. By using this model, an active vibration method which effectively suppresses the vibrations of the smart blade is designed.

Silver nanoparticles were biosynthesized from Conyzacanadensis leaf extract with the help of a microwave oven. The UV-vis spectrum showed the maximum absorption at 441 nm, corresponding to the surface plasmon resonance of silver nanoparticles. Transmission electron microscope and scanning electron microscope images showed that the synthesized silver nanoparticles were spherical or near-spherical with an average diameter of 43.9 nm. X-ray diffraction demonstrated nanoparticles with a single-phase cubic structure. As-synthesized silver nanoparticles displayed prominent antifungal activity against Bipolaris maydis. The colony inhibition rate reached 88.6% when the concentration of nanosilver colloid was 100 μL·mL−1 (v/v). At such a concentration, no colony formation was observed on the solid plate. The diameter of the inhibition zone was 13.20 ± 1.12 mm. These results lay the foundation for the comprehensive control of plant pathogens using an environmentally friendly approach.

Biochar was prepared from agricultural plant waste, including corn straw (MS), sunflower straw (SS), wheat straw (WS), orange peel (OS), sunflower seed shell (SSS), and chestnut shell (CS) at low temperature in a partially oxygen-limited environment. These biochars were used to adsorb heavy metals and organic pollutants. The results showed that biochar having suitable surface area and microporous area could be obtained from the raw materials at 300 °C under partial oxygen limitation. The total porosity of biochar prepared from corn straw (MS) was 92.8%, and the removal of Pb2+ was 78.6 mg/g. The obtained biochar had good adsorption properties for methylene blue and Pb2+ water of different concentrations, and the adsorption performance of biochar prepared from crop straw was better than that of biochar prepared from plant peel. Thus, it was feasible to prepare biochar and to adsorb harmful substances in water through this process. This study promotes the recycling of agricultural wastes and simplifies the preparation of carbon adsorbents.

As people pay more and more attention to a healthy diet, it has become a consensus to eat more coarse grains. The development of its edible value is of great significance for a healthy human diet and has attracted the attention of many scholars and food processing companies. However, due to the differences in protein composition and structure between corn flour and wheat protein, it is difficult to form a network structure during processing, and the viscoelasticity and flexibility are poor. Based on this, this paper proposes a machine vision-based noodle positioning monitoring method to achieve noodle alignment monitoring in the noodle processing process. First, the images are captured by binocular cameras and preprocessed. Further, feature detection and matching algorithms are used to recover the pose information between binocular cameras, and then the recognition targets are matched. Finally, noodle alignment monitoring during noodle processing is achieved. Experiments show that the detection accuracy of the method proposed in this paper is much higher than the traditional manual detection, which can improve the noodle quality and reduce labor costs.

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