Pumpkin Leaf Extract Crop Waste as a New Degradable and Environmentally Friendly Corrosion Inhibitor.

The pumpkin leaf was extracted by the decoction method, and it was used as an eco-friendly, nontoxic inhibitor of copper in 0.5 M H2SO4 corrosion media. To evaluate the composition and protective capacity of the pumpkin leaf extract, Fourier infrared spectroscopy, electrochemical testing, XPS, AFM, and SEM were employed. The results showed that the pumpkin leaf extract (PLE) is an effective cathode corrosion inhibitor, exhibiting exceptional protection for copper within a specific temperature range. The corrosion inhibition efficiency of the PLE against copper reached 89.98% when the concentration of the PLE reached 800 mg/L. Furthermore, when the temperature and soaking time increased, the corrosion protection efficiency of 800 mg/L PLE on copper consistently remained above 85%. Analysis of the morphology also indicated that the PLE possesses equally effective protection for copper at different temperatures. Furthermore, XPS analysis reveals that the PLE molecules are indeed adsorbed to form an adsorption film, which is consistent with Langmuir monolayer adsorption. Molecular dynamics simulations and quantum chemical calculations were conducted on the main components of the PLE.

Effects of protoscoleces excretory-secretory products of Echinococcus granulosus on hepatocyte growth, function, and glucose metabolism.

Cystic echinococcosis (CE) is one of the most widespread and harmful zoonotic parasitic diseases, which most commonly affects the liver. In this study, we characterized multiple changes in mouse hepatocytes following treatment with excretory-secretory products (ESPs) of Echinococcus granulosus protoscoleces (Eg-PSCs) by a factorial experiment. The cell counting kit-8 assay (CCK-8), the 5-ethynyl-2'-deoxyuridine (EdU) assay, and flow cytometry were used to detect the growth of hepatocytes. Inverted microscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) were used to observe the morphology and ultrastructure of hepatocytes. An automatic biochemical analyzer and an ELISA detection kit were used to determine six conventional hepatocyte enzymatic indices, the levels of five hepatocyte-synthesized substances, and the contents of glucose and lactate. Western blot analysis was conducted to analyze the protein expression of three apoptosis-related proteins, Bax, Bcl-2, cleaved caspase-3, and six glucose metabolism pathways rate-limiting enzymes in hepatocytes. The results showed that ESPs inhibited hepatocyte proliferation and promoted hepatocyte apoptosis. The cell membrane and microvilli of hepatocytes changed, and the nucleus, mitochondria and rough endoplasmic reticulum were damaged to varying degrees. The contents of iron, albumin (ALB), uric acid (UA) and urea were increased, and the activities of six enzymes in hepatocytes were increased except for the decrease of transferrin (TRF). The expression levels of all six key enzymes in the glucose metabolism pathway in hepatocytes were reduced. Our characterization provides a basis for further research on the pathogenesis, prevention and treatment of CE.

Design of an interval type-2 fuzzy neural network sliding mode robust controller for higher stability of magnetic spacecraft attitude control.

This paper designs an interval type-2 fuzzy neural network sliding mode robust controller (IT2FNNSMRC) to improve the stability of the vibrational angle of the orbital plane in magnetic rigid spacecraft attitude control. The control system consists of an interval type-2 fuzzy neural network (IT2FNN) controller, a PD controller, and a robust controller in parallel connection. The IT2FNN controller, as a nonlinear regulator, compensates the nonlinearity of the controlled object; the PD controller, as a feedback controller, ensures the global asymptotic stability of the control system; the robust controller inhibits input load disturbance. The IT2FNN controller hereof has a self-organizing function which enables it to automatically determine the network structure and parameters online. At the stage of IT2FNN structure learning, the standard on rule growth is set according to the incentive intensities of IT2FNN rule premises. A new rule is generated when the incentive intensities of rules are all smaller than a certain threshold; next, a significance index is set for each rule. When the significance index of some rule decays to a certain threshold, the corresponding rule shall be deleted to achieve the goals of optimizing IT2FNN structure and reducing system complexity. At the stage of parameter learning, adaptive adjustment of IT2FNN parameters is made via the sliding mode control theory learning algorithm, and the stabilities of the algorithm and control system are proven using Lyapunov function. Finally, the proposed control scheme is used in the control of a magnetic rigid spacecraft, as compared to three other designed control methods. Simulation results show that IT2FNNSMRC has superior control precision and stability. And the IT2FNN which adopts the proposed learning algorithm can address uncertainty satisfactorily, with higher computational implementability.

Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network.

This study introduces a novel self-organizing recurrent interval type-2 fuzzy neural network (SRIT2FNN) for the construction of a soft sensor model for a complex chemical process. The proposed SRIT2FNN combines interval type-2 fuzzy logic systems (IT2FLSs) and recurrent neural networks (RNNs) to improve the modeling precision. The Gaussian interval type-2 membership function is used to describe the antecedent part of the SRIT2FNN fuzzy rule, and the consequent part is of the Mamdani type with an interval random number. An adaptive optimal clustering number of fuzzy kernel clustering algorithm based on a Gaussian kernel validity index (GKVI-AOCN-FKCM) is developed to determine the structure of the SRIT2FNN and fuzzy rule antecedent parameters, and the parameter learning of SRIT2FNN used the gradient descent method. Finally, the proposed SRIT2FNN is applied to the soft sensor modeling of ethylene cracking furnace yield in a typical chemical process. Comparisons between the SRIT2FNN and conventional fuzzy neural network (FNN) and interval type-2 fuzzy neural network (IT2FNN) are made via simulation experiments. The results show that the proposed SRIT2FNN performs better than the conventional FNN and IT2FNN.