Effect of Particle Size on the Wear Property of Magnetorheological Fluid

Aiming to study the effect of particle size on the wear property of magnetorheological fluid (MRF), experiment materials, preparation process, and test methods are elaborated, and three different MRF samples consisting of particles of different size are prepared. Test experiments are carried out and the effect of particle size on the wear property of MRF is discussed. Moreover, the microstructures of particles extracted from MRF obtained before and after the wear experiments are observed by scanning electron microscope (SEM). Experimental results show that the particle size has a significant effect on wear property of MRF. Furthermore, the MRF with particles of 1.5–2.8 μm diameter on average is good for the requirement of engineering applications

Impact of the central refractive index dip of fibers on high-power applications

Central refractive index dip is a common phenomenon in the fibers fabricated by the modified chemical vapor deposition (MCVD) technology, which is the main fabrication technique for high-power laser fibers. In this paper, we present a numerical analysis of the dip effect on high-power-related parameters for the first time, to the best of our knowledge. Three aspects including mode field parameter, beam quality, and bending performance are studied under different dip parameters and bending radii. It is found that the dip is possible to increase the effective mode area and the bending loss, which offers a flexible way to suppress the non-linear effects and filter the higher-order modes by optimizing the dip parameters. Besides, different from the mode area and bending loss, beam quality exhibits an interesting trend when the dip radius increases. The results could facilitate a comprehensive understanding of the dip fiber properties, which also offer guidance to evaluate and design the fiber with central refractive index dip for high-power applications

Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees

Data accessibility statement: Full census data are available upon reasonable request from the ForestGEO data portal, http://ctfs.si.edu/datarequest/ We thank Margie Mayfield, three anonymous reviewers and Jacob Weiner for constructive comments on the manuscript. This study was financially supported by the National Key R&D Program of China (2017YFC0506100), the National Natural Science Foundation of China (31622014 and 31570426), and the Fundamental Research Funds for the Central Universities (17lgzd24) to CC. XW was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB3103). DS was supported by the Czech Science Foundation (grant no. 16-26369S). Yves Rosseel provided us valuable suggestions on using the lavaan package conducting SEM analyses. Funding and citation information for each forest plot is available in the Supplementary Information Text 1.Peer reviewedPostprin

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction.

ObjectiveProtoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue. Approach: We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the Enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification. Main results: The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618. Qualitative results also demonstrated that our approach addressed the limit-view issue. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. The processing time was reduced to 6 seconds, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. Significance: Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in-vivo 3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes.

Study on Friction Characteristics of Slipper Pair of Large Displacement High-Pressure Piston Pump

The reference value of the oil film thickness and friction coefficient of the slipper pair is critical to the development of the piston pump, especially for 750 mL/r displacement piston pumps. To explore the computing method and range of the reference value mentioned applicable to 750 mL/r displacement piston pumps, this study aims to propose the modified calculation model of the oil film thickness based on the real clearance flowrate and obtain the value range of the friction coefficient of the slipper pair. Through the friction test of the slipper pair, the mean deviation ratio of the oil film thickness between the modified value, theoretical value, and the measured value was calculated and compared, respectively. The variation law of the friction under the influence of different speeds and working pressures was analyzed. Finally, the range of the equivalent friction coefficient with the upper and lower limit surfaces was obtained. The results show that the mean deviation ratio between the modified oil film thickness value and the measured value is mainly within 6%, while that of the theoretical method is mainly from 6% to 8%, and the mean of the difference between the two deviation ratios is about 3%, verifying the feasibility of the modified model used for the calculation of the reference value. Meanwhile, the value of the equivalent friction coefficient fluctuates in the range of 0.006–0.018, which is affected more significantly by the working pressure than the speed, suggesting that the working pressure can be given priority as the design basis of the friction coefficient for 750 mL/r displacement piston pumps

Impact of the channel length on molybdenum disulfide field effect transistors with hafnia-based high- k dielectric gate

International audienceField effect transistors (FETs) using two-dimensional molybdenum disulfide (MoS 2) as the channel material has been considered one of the most potential candidates for future complementary metal-oxide-semiconductor technology with low power consumption. However, the understanding of the correlation between the device performance and material properties, particularly for devices with scaling-down channel lengths, is still insufficient. We report in this paper back-gate FETs with chemical-vapor-deposition grown and transferred MoS 2 and Zr doped HfO 2 ((Hf,Zr)O 2 , HZO) high-k dielectric gates with channel lengths ranging from 10 to 30 μm with a step of 5 μm. It has been demonstrated that channels with the length to width ratio of 0.2 lead to the most superior performance of the FETs. The MoS 2 /HZO hybrid FETs show a stable threshold voltage of ∼1.5 V, current on/off ratio of >10 4 , and field effect mobility in excess of 0.38 cm 2 V −1 s −1. The impact of the channel lengths on FET performance is analyzed and discussed in depth. A hysteresis loop has been observed in the I ds − Vgs characteristics of the hybrid FETs, which has been further studied and attributed to the charge effect at the interfaces. The HZO films show a relatively weak ferroelectric orthorhombic phase and thus serve mainly as the high-k dielectric gate. Charge trapping in the HZO layer that might induce hysteresis has been discussed. Our results show that MoS 2 /HZO hybrid FETs possess great potential in future low power and high-speed integrated circuits, and future work will focus on further improvement of the transistor performances using ferroelectric HZO films and the study of devices with even shorter MoS 2 channels