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As stress is related to many mental and physical health problems, monitoring stress and its management is getting increasingly important in modern societies. Because of the advantage of convolutional neural network (CNN) in automatic feature learning, this study is proposed to use CNN to achieve accurate and fast detection of acute cognitive stress from heart rate variability (HRV). The traditional mental arithmetic calculation was adopted as the stressor for a total of twenty participants, during which one-lead electrocardiogram (ECG) was acquired. Six conventional HRV methods for inferring cognitive stress were extracted from the ECG signals, and their performance in identifying acute cognitive stress was compared with the proposed CNN-based method. The experimental results showed that with a super-short (10 s) time window, the detection error rate of CNN was 17.3%, which is significantly better than the performance of all six conventional HRV methods (> 7.2%, p < 0.01). Further analysis showed that the improvement achieved by the proposed CNN methods mainly came from the decrease in false stress sample detection. This study demonstrated the possibility of super-short windows and the advantage of CNN on acute cognitive stress detection. Its outcome would benefit practical applications of real-time stress detection via HRV. INDEX TERMS Cognitive stress, electrocardiogram (ECG), heart rate variability (HRV), convolutional neural network (CNN).

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Synthetic hydrogels with hydrophobic interactions, which show excellent mechanical performance and good anti-swelling ability in saltwater, have great potential in various industries, such as soft robots, 3D printing, and wearable sensors. Normally, hydrophobic molecules inside a hydrophobic hydrogel tend to aggregate to form a large hydrophobic domain, leading to a phase separation phenomenon because water is a poor solvent of the hydrophobic domain. This aggregation, however, inhibits the adhesion of the hydrophobic hydrogel to various dry materials and thus limits its application in device and sensor industries. In this study, we report the synthesis of hybrid hydrogels with ionically and hydrophobically cross-linked networks. This novel hybrid hydrogel can strongly adhere to various substrates, such as glass, polypropylene, silicone, wood, and polytetrafluoroethylene, with a maximum adhesion strength measured to be 100 kPa. Meanwhile, this hybrid hydrogel can be stretched beyond 8–10 times of its initial length. We attribute this observed strong adhesion and high toughness properties to the synergy of electrostatic interactions and hydrophobic associations. With the strong adhesion and excellent tensile performance, these hydrogels may serve as a model system to explore the strong adhesion mechanism of hydrophobic hydrogels and expand the scope of hydrogel applications.

The application of carbon-silica dual phase fillers (CSDPF) to natural rubber compound was investigated. It was found that these new fillers give significantly better overall performances in comparison with the conventional fillers—carbon black and silica. In a typical truck-tread compound, due to its high polymer—filler interaction and lower filler—filler interaction, the CSDPF E shows a comparable laboratory abrasion resistance and more than 40% reduction in tan δ at 70 °C, a parameter for rolling resistance, compared to compound filled with its carbon black counterpart, N1 10. These properties can, to a certain degree, be further improved by the addition of a small amount of coupling agent, bis(3-triethoxysilylpropyl)tetrasulfane (TESPT). In the case of wet skid resistance measured using the British Portable Skid Tester, the data show that CSDPF gives better performance than the conventional fillers, with and without coupling agent.

Chemical studies on the constituents of Dracaena cochinchinensis led to the discovery of eight new flavonoid derivatives (1–8) along with 14 known compounds (9–22). The identification and structural elucidation of these isolates were based on spectral analyses. All isolates were tested for antibacterial activities against Helicobacter pylori (ATCC43504) and thrombin inhibitory effects. As a result, new flavonoid derivatives 6 and 7 and (2S)-4′,7-dihydroxy-8-methylflavan (11) were found to be most efficacious against H. pylori (ATCC43504) with MIC values of 29.5, 29.5, and 31.3 µM, respectively, and the seven new flavonoid derivatives (1–7) and one known biflavonoid (9) were observed to exhibit moderate thrombin inhibitory activity.