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Objective In order to improve train operational efficiency, it is necessary to monitor metro traction power consumption and establish a relevant energy consumption model for prediction analysis of metro traction power consumption. Method The basic principles of grey prediction model and BP (backpropagation) neural network are introduced. Taking the traction daily electricity consumption data for a typical metro station in Tianjin in June 2021 as example, the grey correlation analysis method is used to select the influencing factors with high correlation to the daily traction power consumption of metro. Based on the GM (1,1) grey prediction model, the short-term traction daily power consumption is predicted. The selected influencing factors with high correlation, the short-term traction daily power consumption predicted by GM (1,1) grey model, and the adjacent historical traction daily power consumption data are used as input for training in BP neural network model to establish the GM-BP grey neural network model. The required short-term metro traction daily power consumption prediction data is generated. Result & Conclusion Compared with conventional GM (1,1) grey prediction model and BP neural network model, the prediction error of short-term traction daily power consumption predicted by the GM-BP grey neural network model shows significant improvement, and can be used as effective metro traction power consumption data for short-term prediction data analysis.

This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target’s posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring.

Double-walled carbon nanotubes have shown competitive properties in broadband optical pulse generation owning to the intrinsic electronic properties. Synchronization of ultrafast optical pulses in multiple wavelengths is a key technique for numerous applications, such as nonlinear frequency conversion, ultrafast pump-probe, coherent Raman scattering spectroscopy, coherent optical synthesis, etc. In this work, we demonstrate the mode-locking and synchronization of 1.55 µm pulses with 1 µm and 1.9 µm pulses via a single saturable absorber based on double-walled carbon nanotubes. The large optical nonlinearity and broadband optical absorption in the double-walled carbon nanotubes enable independent and synchronized mode-locking in >900 nm bandwidth. In addition, we present a creative concept to realize multi-wavelength synchronization from a single laser system. Our results demonstrate a straightforward and feasible approach towards pulse synchronization over ultra-broad bandwidth with flexible wavelength selection in the near-infrared region.

A copper-assisted spheroidization method was developed for preparing spherical intermetallic compound TiAl powder. By introducing copper into TiAl alloy, the brittle L21 Heusler structure TiAlCu2 alloy with low melting point (1050 °C) was produced. After preparing the spherical TiAlCu2 powder through solid–liquid interface de-wetting method, the porous TiAl alloy sphere with atomic ratio about 1:1 was prepared by dealloying the Cu element. Finally, the spherical TiAl alloy powder was prepared by sintering and deoxygenation. Compared with the current mature technology for preparing the spherical TiAl alloy powder, the size distribution of spherical particles can be easily regulated, which can meet the requirements for high-quality additive manufacturing technology. We believe that this method provides a new idea for preparing the spherical TiAl alloy powder and other spherical porous alloys.

Objective To investigate the effect of silenced RBM8A gene on the biological behavior (proliferation, migration, and apoptosis) of human endometrial cancer HEC-1A cells and its possible mechanism. Methods The hairpin shRNA targeted by the RBM8A gene was designed, and the best shRNA silencing fragment was screened. The recombinant lentiviral interference vector carrying the target gene was constructed and used to infect HEC-1A cells. Cells with stable knockdown of RBM8A gene were screened by puromycin as the experimental group (shRBM8A), while the shRNA of nonsense sequence was designed as the control group (shControl). CCK-8 method was used to detect cell proliferation, and flow cytometry was used to detect cell apoptosis. Transwell assay was used to detect cell migration and invasion. Western blot was used to analyze the expression of apoptosis-related proteins and EMT signal transduction pathway related proteins. Results In comparison with the shControl group, after RBM8A knockdown, HEC-1A cell proliferation was reduced, apoptosis was increased, migration and invasion ability were significantly inhibited (P < 0.05), the expression of apoptosis-related proteins cleaved caspase 9 and caspase 3 increased, EMT-related protein E-cadherin expression increased, and Vimentin expression decreased. Conclusion RBM8A gene silencing can inhibit the proliferation, migration, and invasion and promote the apoptosis of endometrial cancer cells. The inhibition of EMT signal transduction pathway may be its mechanism.

Gas-containing renal stones (GCS) are rare urological entities. Current literature has suggested possible associations with premenopausal women, urinary tract infection, and metabolic diseases. We report the case of a 25-year-old young woman with no underlying co-morbidities who had multiple right GCS and suspected emphysematous pyelitis. Antibiotic therapy was initiated to control her urinary tract infection with E. coli. She then underwent elective right flexible ureteroscopy to relieve her ureteropelvic junction obstruction. Complete stone retrieval was achieved and she made an uneventful recovery with no stone recurrence during 1-year follow-up.

The increasing popularity of vehicles has led to traffic congestion and frequent traffic accidents. Intelligent transportation technology is an effective solution to this problem. In order to improve the accuracy and effectiveness of vehicle detection and tracking, this paper combined the improved YOLOv5s model with the optimized DeepSORT tracking algorithm to detect and track vehicles on traffic roads. Firstly, in the detection model of YOLOv5s, the Attention-based Intra-scale Feature Interaction (AIFI) module is introduced to detect vehicles more quickly and accurately. Secondly, the Kalman filtering (KF) algorithm of DeepSORT is optimized to improve the accuracy of predictions of the vehicle state by using the width to replace the length-to-width ratio of the vehicle prediction box in the original KF algorithm. Finally, in the re-recognition network of DeepSORT, the original Convolutional Neural Network (CNN) model is replaced by an improved ResNet36 as the backbone network for feature extraction. The experimental results show that, compared with the original algorithm, when examining the performance of the improved algorithm in terms of target detection, the recall rate, average accuracy (mAP), and detection speed, are increased by 7.7%, 15.5%, and 14.2%, respectively; in terms of multi-object tracking performance, such as multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA), improvements of 14.84% and 9.62%, respectively, are obtained and the total number of times a trajectory is fragmented (Frag) is reduced by 32.52%.These results indicate that the proposed algorithm can meet the requirements of accuracy, real-time detection, and stable vehicle detection and tracking on traffic roads.

Objective: To understand bacterial composition and obtain isolated fermentative strains found in kombucha. Methods: The bacterial composition and dominant strains were analyzed using 16S rDNA high-throughput sequencing technology and traditional microbial culture method. Results: High-throughput sequencing showed that the bacterial community is richer and more diverse than the fungi community. Komagataeibacter, Acetobacter and Lactobacillus were the dominant bacterial strains, while Dekkera and Zygosacchaormyces were the dominant fungal strains. At the same time, 7 strains of acetic acid bacteria, 3 strains of yeast and 1 strain of lactic acid bacteria were isolated by traditional plate culture method, including 1 strain of Komagataeibacter saccharivorans, 1 strain of Acetobacter malorum, 4 strains of Komagataeibacter xylinus, 1 strain of Acetobacter, 2 strains of Zygosaccharomyces bailii, 1 strain of Dekkera bruxelensis and 1 strain of Lactobacillus plantarum. Conclusion: The results from high throughput sequencing were consistent with those obtained from traditional culture method.