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Glass fiber‐reinforced polypropylene composites (GF/PP) were fabricated in this study by direct fiber feeding injection molding technology (DFFIM) equipped with specially designed plasticizing units. By analyzing the fiber length distribution, scanning electron microscopy micrographs, and the mechanical properties of GF/PP composites, the effect of three process parameters, namely the number of fiber bundles, screw rotational speed, and injection pressure, on the properties of GF/PP composites fabricated by DFFIM were investigated. The results show that the mechanical properties of GF/PP composites gradually increase as the fiber content increases and the screw rotational speed decreases; however, when the fiber content exceeds a certain range, the enhancement effect of mechanical property is reduced due to the increase of fiber breakage and fiber aggregation. Appropriately increasing the injection pressure can increase the fiber length and make the fiber and PP more tightly bonded, and the mechanical properties of GF/PP composites gradually improve, but the fiber breakage phenomenon is more severe under excessive injection pressure, the mechanical property enhancement effect is reduced or even the mechanical property is decreased due to the decrease of the fiber length. [ABSTRACT FROM AUTHOR]

In the real-world scenario, data often have a long-tailed distribution and training deep neural networks on such an imbalanced dataset has become a great challenge. The main problem caused by a long-tailed data distribution is that common classes will dominate the training results and achieve a very low accuracy on the rare classes. Recent work focuses on improving the network representation ability to overcome the long-tailed problem, while it always ignores adapting the network classifier to a long-tailed case, which will cause the "incompatibility" problem of network representation and network classifier. In this paper, we use knowledge distillation to solve the long-tailed data distribution problem and fully optimize the network representation and classifier simultaneously. We propose multiexperts knowledge distillation with class-balanced sampling to jointly learn high-quality network representation and classifier. Also, a channel activation-based knowledge distillation method is also proposed to improve the performance further. State-of-the-art performance on several large-scale long-tailed classification datasets shows the superior generalization of our method. [ABSTRACT FROM AUTHOR]

1,7‐Bis(4‐hydroxyphenyl)‐1,4‐heptadien‐3‐one (EB30) is a diarylheptanoid‐like compound isolated from Viscum coloratum. This curcumin analog exhibits significant cytotoxic activity against HeLa, SGC‐7901, and MCF‐7 cells. However, little is known about the anticancer effects and mechanisms of EB30 in human lung cancer. The current study reports that EB30 significantly reduced the cell viability of A549 and NCI‐H292 human lung cancer cells. Further examination revealed that EB30 not only induced cell cycle arrest and promoted the generation of reactive oxygen species (ROS) but also induced cell apoptosis through the intrinsic and extrinsic signaling pathways. Furthermore, EB30 upregulated the expression levels of p‐ERK1/2 and p‐P90RSK, whereas downregulating the phosphorylation of Akt and P70RSK. Cell viability was further inhibited by the combination of EB30 with LY294002 (a specific PI3K inhibitor) or U0126 (a MEK inhibitor). The current study indicates that EB30 is a potential anticancer agent that induces cell apoptosis via suppression of the PI3K/Akt pathway and activation of the ERK1/2 pathway. The results of this study indicated that EB30 could decrease cell viability, induce apoptosis, and cell cycle arrest. The induction of apoptosis was found to be mediated through the mitochondrial intrinsic pathway and extrinsic pathway. The apoptotic effects of EB30 were mediated by suppressing the PI3K/Akt pathway, whereas activating the ERK1/2 signaling. [ABSTRACT FROM AUTHOR]