A survey of path planning of industrial robots based on rapidly exploring random trees

Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm

Direct Stimulation of Adult Neural Stem/Progenitor Cells In Vitro and Neurogenesis In Vivo by Salvianolic Acid B

Background: Small molecules have been shown to modulate the neurogenesis processes. In search for new therapeutic drugs, the herbs used in traditional medicines for neurogenesis are promising candidates. Methodology and Principal Findings: We selected a total of 45 natural compounds from Traditional Chinese herbal medicines which are extensively used in China to treat stroke clinically, and tested their proliferation-inducing activities on neural stem/progenitor cells (NSPCs). The screening results showed that salvianolic acid B (Sal B) displayed marked effects on the induction of proliferation of NSPCs. We further demonstrated that Sal B promoted NSPCs proliferation in dose- and time-dependent manners. To explore the molecular mechanism, PI3K/Akt, MEK/ERK and Notch signaling pathways were investigated. Cell proliferation assay demonstrated that Ly294002 (PI3K/Akt inhibitor), but neither U0126 (ERK inhibitor) nor DAPT (Notch inhibitor) inhibited the Sal B-induced proliferation of cells. Western Blotting results showed that stimulation of NSPCs with Sal B enhanced the phosphorylation of Akt, and Ly294002 abolished this effect, confirming the role of Akt in Sal B mediated proliferation of NSPCs. Rats exposed to transient cerebral ischemia were treated for 4 weeks with Sal B from the 7th day after stroke. BrdU incorporation assay results showed that exposure Sal B could maintain the proliferation of NSPCs after cerebral ischemia. Morris water maze test showed that delayed post-ischemic treatment with Sal B improved cognitive impairment after stroke in rats

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information

Problem definition: The intelligent transportation system (ITS) plays a vital role in the construction of smart cities. For the past few years, traffic flow prediction has been a hot study topic in the field of transportation. Facing the rapid increase in the amount of traffic information, finding out how to use dynamic traffic information to accurately predict its flow has become a challenge. Methodology: Thus, to figure out this issue, this study put forward a multistep prediction model based on a convolutional neural network and bidirectional long short-term memory (BILSTM) model. The spatial characteristics of traffic data were considered as input of the BILSTM model to extract the time series characteristics of the traffic. Results: The experimental results validated that the BILSTM model improved the prediction accuracy in comparison to the support vector regression and gated recurring unit models. Furthermore, the proposed model was comparatively analyzed in terms of mean absolute error, mean absolute percentage error, and root mean square error, which were reduced by 30.4%, 32.2%, and 39.6%, respectively. Managerial implications: Our study provides useful insights into predicting the short-term traffic flow on highways and will improve the management of traffic flow optimization

Short-Term Traffic Flow Prediction Based on a K-Nearest Neighbor and Bidirectional Long Short-Term Memory Model

In the previous research on traffic flow prediction models, most of the models mainly studied the time series of traffic flow, and the spatial correlation of traffic flow was not fully considered. To solve this problem, this paper proposes a method to predict the spatio-temporal characteristics of short-term traffic flow by combining the k-nearest neighbor algorithm and bidirectional long short-term memory network model. By selecting the real-time traffic flow data observed on high-speed roads in the United Kingdom, the K-nearest neighbor algorithm is used to spatially screen the station data to determine the points with high correlation and then input the BILSTM model for prediction. The experimental results show that compared with SVR, LSTM, GRU, KNN-LSTM, and CNN-LSTM models, the model proposed in this paper has better prediction accuracy, and its performance has been improved by 77%, 19%, 18%, 22%, and 13%, respectively. The proposed K-nearest neighbor-bidirectional long short-time memory model shows better prediction performance

Salvianolic acid B promoted NSPCs proliferation in a PI3K/Akt -independent pathway.

<p>NSPCs were cultured in the proliferation medium containing Sal B (20 µM) in the presence and absence of the PI3K inhibitor Ly294002 (20 µM), MEK inhibitor U0126 (10 µM) or Notch inhibitor DAPT (10 µM) for 2 days. Cell survival was assessed by MTS assay. Data represent the mean ± S.D. from three independent experiments. **<i>P</i><0.01 as compared with control,##<i>P</i><0.01 as compared with Sal B-treated cells.</p

Delayed post-ischemic treatment with salvianolic acid B improved cognitive impairment in Morris water maze task.

<p>(A) Place learning with multiple trials. There was a decrease in escape latencies with training in all three groups. (B) In the transfer task, the escape latencies (mean ± S.D.) are compared among sham-operated, untreated ischemic, and Sal B treated groups (n = 8). And the animals from the Sal B-treated group spent more time in the quadrant that contained the escape platform during the place learning than untreated group. *<i>P</i><0.05 as compared with sham group, #<i>P</i><0.05 as compared with model group.</p

Screening for NSPCs proliferation-inducing natural materials.

<p>The proliferation-inducing activities on NSPCs of a total of 45 natural compounds, which were from medicinal materials extensively used in China to treat stroke clinically, were tested using a MTS assay, and the results are expressed in fold change relative to the corresponding controls. The proliferation-inducing effect of the most potent compound, Sal B (A) and berberine (B), were indicated by the arrow and its structure is shown in the inset. Data represent the mean ± S.D. from three independent experiments. **Significant difference from the control group at <i>P</i><0.01.</p