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A large quantity of Steel Balls Automatic precise Counting device is introduced in this paper. Inductance sensors are used for ball detection and STC15F2K60S2 microprocessor is used to do the signal processing. When steel balls are sent to the PVC pipe at the same time, some balls will connect together because of the collision. The sensor will send a connected signal instead of multiple signals if it meets the connected balls. This could cause counting mistake of the device. A correction method is used to solve this problem. The device setup angle could be gotten by using dual sensors to get the average moving speed of the steel balls according to the fixed relations between the setup angle and the speed, which is not affected by whether the steel balls overlap or not because it used two voltage Jump edge to judge the start and end moment of timer. After that, the device setup angle is used to get the standard moving time to single sensor according to the known function relationship. The counting numbers are corrected by using the standard duration to compare with each recording low voltage duration passing single sensor. This method solves the counting mistake problem when a lot of steel balls are pushed into the PVC pipe together. The device improves the efficiency and accuracy of the ball counting. [ABSTRACT FROM AUTHOR]

Recovering a sequence of latent sharp frames from a motion-blurred image is a challenging task. The bio-inspired event camera, which produces an event stream with high temporal resolution, has been exploited to promote the recovery performance. However, recovering sharp sequences with arbitrary temporal scales has been ignored for a long time. Existing works can only recover a fixed number of latent frames from a blurry image once they are trained. In this work, we propose an event-assisted blurry image unfolding framework that can work across arbitrary temporal scales. A bi-directional recurrent network is employed to encode events corresponding to each latent frame, which gathers information over all events in the exposure time. Features of both the blurry image and events are fused together and fed to a bi-directional latent sequence decoder (BiLSD) to produce a sequence of latent sharp frames. Extensive experiments show that the proposed method not only performs favorably against state-of-the-art methods in recovering a fixed number of frames from a blurry image but can be well generalized to arbitrary-temporal-scale blurry image unfolding.

The goal of blurry image deblurring and unfolding task is to recover a single sharp frame or a sequence from a blurry one. Recently, its performance is greatly improved with introduction of a bio-inspired visual sensor, event camera. Most existing event-assisted deblurring methods focus on the design of powerful network architectures and effective training strategy, while ignoring the role of blur modeling in removing various blur in dynamic scenes. In this work, we propose to implicitly model blur in an image by computing blurriness representation with an event-assisted blurriness encoder. The learning of blurriness representation is formulated as a ranking problem based on specially synthesized pairs. Blurriness-aware image unfolding is achieved by integrating blur relevant information contained in the representation into a base unfolding network. The integration is mainly realized by the proposed blurriness-guided modulation and multi-scale aggregation modules. Experiments on GOPRO and HQF datasets show favorable performance of the proposed method against state-of-the-art approaches. More results on real-world data validate its effectiveness in recovering a sequence of latent sharp frames from a blurry image.

Background: Penehyclidine hydrochloride (PHC) has been used for many years as an anticholinergic drug for the treatment of acute organophosphorus pesticide poisoning (AOPP). The purpose of this meta-analysis was to explore whether PHC has advantages over atropine in the use of anticholinergic drugs in AOPP., Methods: We searched Scopus, Embase, Cochrane, PubMed, ProQuest, Ovid, Web of Science, China Science and Technology Journal Database (VIP), Duxiu, Chinese Biomedical literature (CBM), WanFang, and Chinese National Knowledge Infrastructure (CNKI), from inception to March 2022. After all qualified randomized controlled trials (RCTs) were included, we conducted quality evaluation, data extraction, and statistical analysis. Statistics using risk ratios (RR), weighted mean difference (WMD), and standard mean difference (SMD)., Results: Our meta-analysis included 20,797 subjects from 240 studies across 242 different hospitals in China. Compared with the atropine group, the PHC group showed decreased mortality rate (RR=0.20, 95% confidence intervals [ CI]: 0.16-0.25, P  <0.001), hospitalization time (WMD=-3.89, 95% CI: -4.37 to -3.41, P  <0.001), overall incidence rate of complications (RR=0.35, 95% CI: 0.28-0.43, P  <0.001), overall incidence of adverse reactions (RR=0.19, 95% CI: 0.17-0.22, P  <0.001), total symptom disappearance time (SMD=-2.13, 95% CI: -2.35 to -1.90, P  <0.001), time for cholinesterase activity to return to normal value 50-60% (SMD=-1.87, 95% CI: -2.03 to -1.70, P  <0.001), coma time (WMD=-5.57, 95% CI: -7.20 to -3.95, P  <0.001), and mechanical ventilation time (WMD=-2.16, 95% CI: -2.79 to -1.53, P  <0.001)., Conclusion: PHC has several advantages over atropine as an anticholinergic drug in AOPP., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)