High-Temperature Performance of Polymer-Modified Asphalt Mixes: Preliminary Evaluation of the Usefulness of Standard Technical Index in Polymer-Modified Asphalt.

The objectives of this study are to evaluate the high-temperature performance of polymer-modified asphalt and asphalt mixtures, and to investigate if the standard technical indexes are useful in the performance evaluation of the polymer-modified asphalt. There are four typically used polymer-modified asphalt types employed in the study. The standard high-temperature rheological test, such as the temperature sweep test, was used to express the high-temperature performance of the polymer-modified asphalt. Also, considering the non-Newtonian fluid properties of the polymer-modified asphalt, the multiple stress creep recovery (MSCR) and zero-shear viscosity (ZSV) tests were employed for the characterizations. Besides, based on the mixture design of SMA-13, the high temperature of the polymer-modified asphalt mixture was evaluated via Marshall stability and rutting tests. The test results concluded that the ranking of the four kinds of polymer-modified asphalt was different in various laboratory tests. The TB-APAO has the best technical indexes in MSCR and ZSV tests, while the WTR-APAO performed best in the temperature sweep test. In addition, the correlation between the polymer-modified asphalt and the asphalt mixture was very poor. Thus, the present standard technical indexes for the profoundly polymer-modified asphalt mixtures are no longer suitable

Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness

Effects of Emoticons on the Acceptance of Negative Feedback in Computer-Mediated Communication

Delivering negative performance feedback is inevitable in the workplace. However, recipients may feel uncomfortable and behave defensively, and may be unwilling to accept negative feedback mainly because they fear losing face. Such unproductive responses are heightened when negative feedback is delivered through computer-mediated communication (CMC) channels in which many nonverbal cues in face-to-face communication cannot be used to alleviate the concerns of losing face. This study examines the effectiveness of emoticons, which are designed as surrogates for facial expressions in CMC environments, in conveying social and emotional signals of the feedback provider. Specifically, based on the feedback process model and the dissonance reduction theory, this study investigates the differing effects of two types of emoticons (i.e., liking and disliking ones) on the acceptance of negative feedback by considering feedback specificity as a contingent factor. Our results suggest that using liking emoticons increases perceived good intention of the feedback provider and decreases perceived feedback negativity when the feedback is specific; however, it has no significant effect for unspecific feedback. By contrast, our results suggest that using disliking emoticons decreases perceived good intention of the feedback provider and increases perceived feedback negativity when the feedback is unspecific, whereas such effects are not significant for specific feedback. In turn, both perceived good intention of the feedback provider and perceived feedback negativity affect acceptance of the negative feedback

Compliant actuators that mimic biological muscle performance with applications in a highly biomimetic robotic arm

This paper endeavours to bridge the existing gap in muscular actuator design for ligament-skeletal-inspired robots, thereby fostering the evolution of these robotic systems. We introduce two novel compliant actuators, namely the Internal Torsion Spring Compliant Actuator (ICA) and the External Spring Compliant Actuator (ECA), and present a comparative analysis against the previously conceived Magnet Integrated Soft Actuator (MISA) through computational and experimental results. These actuators, employing a motor-tendon system, emulate biological muscle-like forms, enhancing artificial muscle technology. A robotic arm application inspired by the skeletal ligament system is presented. Experiments demonstrate satisfactory power in tasks like lifting dumbbells (peak power: 36W), playing table tennis (end-effector speed: 3.2 m/s), and door opening, without compromising biomimetic aesthetics. Compared to other linear stiffness serial elastic actuators (SEAs), ECA and ICA exhibit high power-to-volume (361 x 10^3 W/m) and power-to-mass (111.6 W/kg) ratios respectively, endorsing the biomimetic design's promise in robotic development

Effect of in vitro gastrointestinal digestion on the chemical composition and antioxidant properties of Ginkgo biloba leaves decoction and commercial capsules

In this study Ginkgo biloba leaves (GBL) decoction and commercial capsules were digested using an in vitro model. Thirty-six active compounds were identified and quantified by HPLC-ESI-MS analysis based on the MS/MS patterns (precursor ions and product ions) and retention times, in comparison with reference standards. Most compounds in GBL showed a significant decrease during intestinal digestion, with an exception of vanillic acid and biflavonoids. Bioaccessibility values of chemical compositions varied between decoction and capsules samples. Also, significant reductions of total flavonoids and total phenolic content was observed after in vitro digestion. Both, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity decreased after gastric digestion, but increased during intestinal digestion. Nevertheless, different behaviour was observed in reducing antioxidant power (FRAP) assay. Compared to the pH of digestion, the influence of digestive enzymes on the chemical composition and antioxidant activity of GBL was relatively minor. Overall, these results may help provide a valid foundation for further investigations on bioactive compounds and the pharmacodynamics of GBL