Physical Stability of Octenyl Succinate–Modified Polysaccharides and Whey Proteins for Potential Use as Bioactive Carriers in Food Systems

The high cost and potential toxicity of biodegradable polymers like poly(lactic‐co‐glycolic)acid (PLGA) has increased the interest in natural and modified biopolymers as bioactive carriers. This study characterized the physical stability (water sorption and state transition behavior) of selected starch and proteins: octenyl succinate–modified depolymerized waxy corn starch (DWxCn), waxy rice starch (DWxRc), phytoglycogen, whey protein concentrate (80%, WPC), whey protein isolate (WPI), and α‐lactalbumin (α‐L) to determine their potential as carriers of bioactive compounds under different environmental conditions. After enzyme modification and particle size characterization, glass transition temperature and moisture isotherms were used to characterize the systems. DWxCn and DWxRc had increased water sorption compared to native starch. The level of octenyl succinate anhydrate (OSA) modification (3% and 7%) did not reduce the water sorption of the DWxCn and phytoglycogen samples. The Guggenheim–Andersen–de Boer model indicated that native waxy corn had significantly (P < 0.05) higher water monolayer capacity followed by 3%‐OSA‐modified DWxCn, WPI, 3%‐OSA‐modified DWxRc, α‐L, and native phytoglycogen. WPC had significantly lower water monolayer capacity. All Tg values matched with the solid‐like appearance of the biopolymers. Native polysaccharides and whey proteins had higher glass transition temperature (Tg) values. On the other hand, depolymerized waxy starches at 7%‐OSA modification had a “melted” appearance when exposed to environments with high relative humidity (above 70%) after 10 days at 23 °C. The use of depolymerized and OSA‐modified polysaccharides blended with proteins created more stable blends of biopolymers. Hence, this biopolymer would be suitable for materials exposed to high humidity environments in food applications.     

液压支架立柱拆装机构设计

拆柱机由电动机匹配乳化液泵将电能转化为泵的液压能,带动液压马达回转作为拆装柱的动力。     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

Electrocatalytic Reduction of CO2 to Ethylene by Molecular Cu‐Complex Immobilized on Graphitized Mesoporous Carbon

The electrochemical reduction of carbon dioxide (CO₂) to hydrocarbons is a challenging task because of the issues in controlling the efficiency and selectivity of the products. Among the various transition metals, copper has attracted attention as it yields more reduced and C2 products even while using mononuclear copper center as catalysts. In addition, it is found that reversible formation of copper nanoparticle acts as the real catalytically active site for the conversion of CO₂ to reduced products. Here, it is demonstrated that the dinuclear molecular copper complex immobilized over graphitized mesoporous carbon can act as catalysts for the conversion of CO₂ to hydrocarbons (methane and ethylene) up to 60%. Interestingly, high selectivity toward C2 product (40% faradaic efficiency) is achieved by a molecular complex based hybrid material from CO₂ in 0.1 m KCl. In addition, the role of local pH, porous structure, and carbon support in limiting the mass transport to achieve the highly reduced products is demonstrated. Although the spectroscopic analysis of the catalysts exhibits molecular nature of the complex after 2 h bulk electrolysis, morphological study reveals that the newly generated copper cluster is the real active site during the catalytic reactions.