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The majority of the more recent studies have mainly focused on how to achieve energy-efficient goals by optimizing the driving behavior for human-driven vehicles or designing trajectory planning and tacking algorithms for autonomous vehicles. However, the energy-saving advantages of autonomous vehicles have not been quantitatively and theoretically explored. Therefore, this study aims to specifically clarify whether autonomous vehicles use less fuel than human-driven ones. First, the differences in driving behavior, regarding speed control, between autonomous vehicles and human-driven vehicles were compared. The most notable difference between them is that an autonomous vehicle can control the vehicle speed more effectively, with less speed fluctuations than a human driver. Subsequently, the influence of speed fluctuation on vehicle fuel consumption (L/100km) was formulated based on the vehicle specific power (VSP) model. The mathematical deduction showed that the fuel consumption is proportional to the speed fluctuation under the same mean speed. Finally, simulation experiments were conducted under real scenarios. The simulation data showed that the fuel consumption increases almost linearly with the increase in speed fluctuation. Field experiments were also conducted on the fuel consumption of an autonomous vehicle under different driving modes. The experimental data showed that the fuel consumption also increases almost linearly with the increase in speed fluctuation. In the human-driven mode, the fuel consumption increased by 5.6% and 14.7%, respectively, compared with that in the autonomous mode at average speeds of 20km/h and 40km/h. Furthermore, the maximum fuel consumption was up to 60% more when the autonomous vehicle was driven by a driver, as the driving behavior displayed greater speed fluctuations., Author(s): Licheng Zhang [1]; Ting Zhang [1]; Kun Peng [1]; Xiangmo Zhao [1]; Zhigang Xu (corresponding author) [1] 1. Introduction Energy shortage and environmental issues have become increasingly severe worldwide. [...]

ABSTRACT: Adiponectin is an important adipocytokine and plays the roles in multiple metabolic processes via binding its receptors - AdipoR1 and AdipoR2, which has also been found to participate in the regulation of the reproductive system of animals, in particular by influencing the secretion of ovarian steroid hormones. To further investigate the expression of adiponectin and its receptors in follicles after in vitro incubation, and their role in the steroid synthesis of laying hens’ ovaries, we performed qRT-PCR and ELISA to detect the expressions of AdipoQ, AdipoR1, and AidpoR2, and determined the key genes involved in steroidogenesis and the secretion of estradiol (E2) and progesterone (P4) through the in vitro activation of adiponectin (AipoRon) and overexpression or knockdown of AdipoR1 and AdipoR2. Our results revealed that adiponectin and its receptors wildly exist in follicles and granulosa cells, and AdipoRon (5 and 10 µg/mL) had no effect on granulosa cell proliferation and apoptosis but significantly stimulated the secretion of adiponectin and its receptors in granulosa cells after incubation for 24 h. Furthermore, AdipoRon could significantly stimulate the secretion of P4 and inhibit E2 level compared to those of the control group through modulating the key genes expression of steroidogenesis (CYP19A1, StAR, CYP11A1, FSHR, and LHR). The secretion of E2 was also decreased in granulosa cells by the treatments of overexpression and knockdown of AdipoR1/2, however, there was no difference in terms of the level of P4 and StAR expression between them if there was overexpression or knockdown of AdipoR1/2. In addition, it was shown that the secretion of E2 only exhibits a marked drop if co-processing 10 µg/mL AdipoRon and pGMLV AdipoR2 compared to single treatments. Taken together, the study highlighted the role of adiponectin and its receptors in the regulation of steroid synthesis and secretion in ovarian granulosa cells in laying hens.

In the mechanical transmission system gear has a very important role, and Gear surface damage is an important factor affecting gear transmission. Regular inspection of each gear and its surface damage is of great significance for ensuring the stable operation of a whole mechanical system. To improve the identification efficiency and accuracy for gears and their surface damage, In this paper, we propose a gear and its surface damage recognition method based on PyTorch deep learning library, expand the dataset using data augmentation and data extension techniques, compare the performance of gear and its surface damage recognition under three typical models, namely AlexNet, VGG16, and ResNet-101, and optimize each training model through migration learning and hyperparameter comparison. The results show that the speed and accuracy of model training can be improved by applying transfer learning. By extending the dataset using data augmentation techniques, the robustness of the network is improved considerably. When the batch size is 6 and the initial learning rate is set to 0.01, the model training effect is the best, and ResNet has higher recognition accuracy and stability than AlexNet and VGG16, which is more suitable for the classification of gears and their surface damage. In order to find the optimal ResNet model, four ResNet models with different number of layers, ResNet-34, ResNet-50, ResNet-101 and ResNet-152, were compared, among which the ResNet-101 model showed the optimal performance in gear surface damage recognition. [ABSTRACT FROM AUTHOR]

In order to improve the hygroscopicity of Lyocell fibers, the surface of Lyocell fiber was chemically modified by grafting with amino acid and other polar groups using cyanuric chloride as carrier. Moisture absorptions of samples in each group with different experimental conditions such as alkali treatment concentration, soaking time, reaction temperature, reaction time, etc. were analyzed. The morphology, structure and mechanical properties before and after modification were compared. The results showed that treatment with 5% NaOH solution for 30 min, adding the prepared polycarboxylic amino melamine derivative intermediates and reaction at 90 °C for 6 h were the optimum conditions, and Lyocell fiber spunlaced material has the best hygroscopic effect and better mechanical properties, which can meet the use requirements of high hygroscopic spunlaced nonwoven materials. [ABSTRACT FROM AUTHOR]

Introduction Spinal cord injury (SCI) alters the integrity of the spinal cord, which leads to loss of multiple organs’ function including locomotor function. The present study evaluates the protective effect of tabersonine against SCI. Material and methods SCI was induced by traumatic injury and animals were treated with tabersonine 20 and 40 mg/kg, intraperitoneally for the period of 10 days. Tabersonine’s effect was determined by estimating locomotor and neurological function in spinal cord injured rats. Moreover, mediators of inflammation were estimated using enzyme-linked immunosorbent assay (ELISA) and the effect of tabersonine on Notch/inflammasome signaling was estimated by reverse transcription polymerase chain reaction (RT-PCR), western blot assay and immunohistochemistry. Apoptosis of neuronal cells was estimated by staining with Nissl stain on spinal cord tissue in SCI rats. Results Data of the study suggest that neurological and motor functions were improved in the tabersonine treated group compared to the spinal cord injured (SI) group. There was a decrease in the mediators of inflammation in the spinal cord tissue of the tabersonine treated group compared to the SI group. Treatment with tabersonine ameliorates the altered expression of NICD, Nestin and Hes-1 protein and mRNA expression of Notch-1 and Hes-1 in the SCI rats. It was also observed that the tabersonine treated group showed activation of CREB and inhibition of the NLRP-3 pathway in SCI rats. Moreover, apoptosis of neuronal cells was reduced in the tabersonine treated group compared to the SI group. Conclusions Data of the investigation suggest that tabersonine protects against spinal cord injury by activating CREB and reducing NLRP3/Notch signaling.

Summary: Flexible electronics have been rapidly developed in recent years. However, there are still some concerns about the degradation and short life caused by mechanical damage during their operation. Here, we present a self-healing electret nanogenerator (SENG) that can restore its structure and function after mechanical damage, benefiting from a healable polymer with reversible imine bond and the instantaneous healing ability of liquid metal. After destructive testing comprising four cutting-healing cycles the output current of the SENG autonomously recovers to the original values before damage. Finally, as a practical device, the SENG is demonstrated to work in both energy harvesting and human physiology-monitoring applications. This research offers a feasible route for improving reliability and lifetime of electret generators or other flexible electronics that suffer from mechanical damage.

AIM: To evaluate the efficacy of Goji berry supplementation on improving macular pigment, serum zeaxanthin levels and visual acuity in patients with early age-related macular degeneration (AMD). METHODS: A total of 114 patients (aged from 51 to 92y, mean age 69.53±8.41y) with early AMD were enrolled in our prospective, randomized controlled study. The included patients were assigned randomly to the Goji group (n=57) with 25 g of Goji berries supplementation per day for 90d and the control group (n=57) with their normal diet for 90d. Macular pigment optical density (MPOD) was measured using heterochromatic flicker photometry (HFP). The levels of serum lutein (L)/zeaxanthin (Z) were analyzed using high-performance liquid chromatography (HPLC). MPOD, serum L/Z levels and best corrected visual acuity (BCVA) were recorded at baseline and 90d. RESULTS: In the Goji group, there were no statistically significant differences in the serum L levels between the baseline (0.199±0.149 µmol/mL) and 90d (0.203±0.181 µmol/mL) (t=-0.186, P=0.850); however the serum Z levels were increased at 90d (0.101±0.087 µmol/mL) compared with those at the baseline (0.029±0.032 µmol/mL) (t=6.412, P