To assess the change of central corneal thickness (CCT) in the treatment of orthokeratology in patients with myopia.A systematic search of all relevant studies published through April 2014 was conducted, and 95% confidence intervals (CI) of CCT change were calculated. Random or fixed-effects models were used according to heterogeneity. Publication bias of the articles was evaluated using funnel plots and Begg test.A total of 10 studies with 239 patients (339 eyes) from clinical studies were included. Central corneal thickness reduced significantly from 1 day to 1 week by 5.73 μm (95% CI, 1.75-9.70 μm; P=0.005), and a significant mean reduction of 5.89 μm also occurred from 1 day to 1 month (95% CI, 3.50-8.29 μm; P<0.001). No significant reduction was found between 1 week and 1 month (P=0.32).Our meta-analysis demonstrated that most reduction of CCT occurred during the first week and remained thinner for 1 month. Further randomized controlled trials with larger sample sizes, standardized outcome measurements, and different follow-up periods are warranted to find the precise change.
Three-dimensional coronary magnetic resonance angiography (CMRA) demands reconstruction algorithms that can significantly suppress the artifacts from a heavily undersampled acquisition. While unrolling-based deep reconstruction methods have achieved state-of-the-art performance on 2D image reconstruction, their application to 3D reconstruction is hindered by the large amount of memory needed to train an unrolled network. In this study, we propose a memory-efficient deep compressed sensing method by employing a sparsifying transform based on a pre-trained artifact estimation network. The motivation is that the artifact image estimated by a well-trained network is sparse when the input image is artifact-free, and less sparse when the input image is artifact-affected. Thus, the artifact-estimation network can be used as an inherent sparsifying transform. The proposed method, named De-Aliasing Regularization based Compressed Sensing (DARCS), was compared with a traditional compressed sensing method, de-aliasing generative adversarial network (DAGAN), model-based deep learning (MoDL), and plug-and-play for accelerations of 3D CMRA. The results demonstrate that the proposed method improved the reconstruction quality relative to the compared methods by a large margin. Furthermore, the proposed method well generalized for different undersampling rates and noise levels. The memory usage of the proposed method was only 63% of that needed by MoDL. In conclusion, the proposed method achieves improved reconstruction quality for 3D CMRA with reduced memory burden.
In this paper, we present a vision-based robot programming system for pick-and-place tasks that can generate programs from human demonstrations. The system consists of a detection network and a program generation module. The detection network leverages convolutional pose machines to detect the key-points of the objects. The network is trained in a simulation environment in which the train set is collected and auto-labeled. To bridge the gap between reality and simulation, we propose a design method of transform function for mapping a real image to synthesized style. Compared with the unmapped results, the Mean Absolute Error (MAE) of the model completely trained with synthesized images is reduced by 23% and the False Negative Rate FNR (FNR) of the model fine-tuned by the real images is reduced by 42.5% after mapping. The program generation module provides a human-readable program based on the detection results to reproduce a real-world demonstration, in which a longshort memory (LSM) is designed to integrate current and historical information. The system is tested in the real world with a UR5 robot on the task of stacking colored cubes in different orders.
Optical clocks have been demonstrated to be good choices to redefine the SI ``second.'' With this goal, we have constructed a quantum-logic-based ${}^{27}$$\mathrm{Al}$${}^{+}$ ion optical clock with a fractional frequency uncertainty of $1.6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}18}$. A ${}^{25}$$\mathrm{Mg}$${}^{+}$ ion is used as the logic ion for sympathetic cooling, state readout, and frequency-shift measurement of the ${}^{27}$$\mathrm{Al}$${}^{+}$ ion. The stability of the optical clock is $2.6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}15}/\sqrt{\ensuremath{\tau}}$, measured with a self-comparison method.
Adapting the mastered manipulation skill to novel objects is still challenging for robots. Recent works have attempted to endow the robot with the ability to adapt to unseen tasks by leveraging meta-learning. However, these methods are data-hungry in the training phase, which limits their application in the real world. In this paper, we propose Meta-Residual Policy Learning (MRPL) to reduce the cost of policy learning and adaptation. During meta-training, MRPL accelerates the learning process by focusing on the residual task-shared knowledge that is hard to be embedded in the hand-engineered controller. During testing, MRPL achieves fast adaptation on similar unseen tasks through fusing task-specific knowledge in the demonstration with task-shared knowledge in the learned policy. We conduct a series of simulated and real-world peg-in-hole tasks to evaluate the proposed method. The experimental results demonstrate that MRPL outperforms prior methods in robot skill adaptation. Code for this work is available at https://github.com/Bartopt/code4MRPL .
Accurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yield prediction in China’s main wheat-producing areas. We tested a combination of short-term WF data from the China Meteorological Administration to predict in-season yield at different forecast lengths. The results showed that explicitly incorporating WF data can improve the accuracy in crop yield predictions [Root Mean Square Error (RMSE) = 0.517 t/ha] compared to using only remote sensing data (RMSE = 0.624 t/ha). After comparing a series of WF data with different time series lengths, we found that adding 25 days of WF data can achieve the highest yield prediction accuracy. Specifically, the highest accuracy (RMSE = 0.496 t/ha) is achieved when predictions are made on Day of The Year (DOY) 215 (40 days before harvest). Our study established a deep-learning model which can be used for early yield prediction at the county level, and we have proved that weather forecast data can also be applied in data-driven deep-learning yield prediction tasks.
The expression of peroxiredoxin 2 and vascular endothelial growth factor receptor 2 (VEGFR2) was detected in pterygium to investigate whether they are involved in the pathogenesis or recurrence of pterygium and to evaluate the association between peroxiredoxin 2 and VEGFR2 in pterygium.Ten normal bulbar conjunctivae, 35 primary pterygia, and 35 recurrent pterygia were obtained. Formalin-fixed, paraffin-wax-embedded tissues were analyzed by immunohistochemistry with peroxiredoxin 2 and VEGFR2 antibodies.There was no statistical difference between primary pterygia and recurrent pterygia in terms of age and sex (P = 0.685; P = 0.811). The expression rate of peroxiredoxin 2 (94.3%, 66/70) and VEGFR2 (61.4%, 43/70) was increased in pterygia compared with normal conjunctivae (negative). The expression of peroxiredoxin 2 in recurrent pterygia (negative 0, weak 0, moderate 27, strong 8) was higher than that in primary pterygia (negative 6, weak 16, moderate 13, strong 0) (P < 0.001). The expression of VEGFR2 in recurrent pterygia (negative 4, weak 5, moderate 12, strong 4) was higher than that in primary pterygia (negative 23, weak 10, moderate 1, strong 1) (P < 0.001). The expression of peroxiredoxin 2 was consistent with that of VEGFR2 in pterygium (r = 0.348, P = 0.006).Overexpression of peroxiredoxin 2 and VEGFR2 in pterygium might be involved in the pathogenesis or recurrence of pterygium. The increase of VEGFR2 might be related to the increase of peroxiredoxin 2 in response to excessive reactive oxygen species from ultraviolet exposure.
Abstract With the increasing penetration of distributed power sources, the stochastic and fluctuating nature of distributed power sources poses a great challenge to the reactive power optimization of the distribution network system. In this study, a dynamic reactive power optimization model with two-stage robust optimization is established, proposing whether the energy storage is charged or discharged. The number of groups of group-switching capacitors is taken as the variables in the first stage. The power of the energy storage charging and discharging and the amount of static reactive power compensator compensation are placed in the second stage. The control strategy in the first stage ensures that the control strategy in the second stage can maintain the safe and stable operation of the distribution network under the worst scenarios. The grid-storage joint optimization technology based on distributed architecture establishes an optimization planning model for the distribution network energy storage system with the goal of optimal technical and economic performance of the transmission and distribution network and considering the constraints of safe and stable operation of the transmission and distribution network, respectively. The PG&E-69 node system arithmetic example is used to verify the effectiveness and feasibility of the proposed model and algorithm. The results of the arithmetic example show that the strategy obtained based on the robust optimization model can achieve voltage magnitude stability within the safety range of 1.0-1.05 p.u. in the simulation scenario that has the best economy. At the same time, the mismatches of each distribution system under cooperative planning are all 0, which indicates that the proposed optimization strategy can fully cooperate with the resources of transmission and distribution networks, promote the safe consumption of clean energy, effectively improve the economy of transmission and distribution networks, and achieve the goal of “mutual benefit and win-win”.
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