No abstract

In this study, cardanol, a natural phenol, has been applied to toughen phenolic foam by bisphenol modification. In order to verify the occurrence of Friedel-Craft alkylation between cardanol and phenol on the side chain, FTIR, and NMR had been used to characterize the bisphenol successfully. With the introduction of cardanol, the viscosity of prepolymers increased. The SEM results demonstrated that the some cells with increasingly large size existed, when the dosage of cardanol increased. With respect to the mechanical properties, phenolic foams modified by 10 wt % cardanol increased by 22% in flexural strength and 28% in bending modulus compared to pure phenolic foams, which indicates that the incorporation of cardanol does improve the toughness of phenolic foams. In addition, the effects of different dosage of cardanol on the apparent density and thermal stability of phenolic foams were investigated.

Prior research suggests the complexity of a product choice task is inversely related to the extent of consumers' external information search. The resource-matching perspective holds that cognitive effort (e.g., external information search) is greatest when available cognitive resources (e.g., as determined by self-efficacy) match the cognitive demands of a task (e.g., perceived task complexity). Within a brand-choice context, the relationship between self-efficacy and extent of information search appears nonmonotonic. In support of the resource-matching perspective, consumers conduct the most extensive information search when their self-efficacy matches perceived task difficulty.

No abstract

.A novel hybrid supercapacitor of Ni-CCH@MnNiCo-OH//ZIF-67-C-KOH was successfully fabricated with promising electrochemical storage properties.

Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%.

No abstract

With the wide deployment of wireless sensor networks in smart industrial systems, lots of unauthorized attacking from the adversary are greatly threatening the security and privacy of the entire industrial systems, of which node replication attacks can hardly be defended since it is conducted in the physical layer. To solve this problem, we propose a secure random key distribution scheme, called SRKD, which provides a new method for the defense against the attack. Specifically, we combine a localized algorithm with a voting mechanism to support the detection and revocation of malicious nodes. We further change the meaning of the parameter s to help prevent the replication attack. Furthermore, the experimental results show that the detection ratio of replicate nodes exceeds 90% when the number of network nodes reaches 200, which demonstrates the security and effectiveness of our scheme. Compared with existing state-of-the-art schemes, SRKD also has good storage and communication efficiency.