Monitoring and analysis of blasting vibration in tunnel excavation of nuclear power plant

Vibration monitoring of blasting excavation of drainage tunnel in Lufeng Nuclear Power Plant is carried out and the data of blasting vibration is analyzed in this paper. The results show that: (1) The vertical vibration velocity of the rock mass is greater than the horizontal radial and horizontal tangential vibration velocity (2) The blasting vibration velocity of rock mass decreases with distance, which is affected by rock structure and explosive quantity. The monitoring research in this paper has guiding significance for vibration prediction and control in tunnel blasting excavation

Analysis of female drivers’ ECG characteristics within the context of connected vehicles

Purpose – This study aims to analyze the differences of electrocardiograph (ECG) characteristics for female drivers in calm and anxious states during driving. Design/methodology/approach – The authors used various materials (e.g. visual materials, auditory materials and olfactory materials) to induce drivers’ mood states (calm and anxious), and then conducted the real driving experiments and driving simulations to collect driver’s ECG signal dynamic data. Physiological changes in ECG during the stimulus process were recorded using PSYLAB software. The paired T-test analysis was conducted to determine if there is a significant difference in driver’s ECG characteristics between calm and anxious states during driving. Findings – The results show significant differences in the characteristic parameters of female driver’s ECG signals, including (average heart rate), (atrioventricular interval), (percentage of NN intervals > 50ms), (R wave average peak), (Root mean square of successive), (Q wave average peak) and ( S wave average peak), in time domain, frequency domain and waveform in emotional states of calmness and anxiety. Practical implications – Findings of this work show that ECG can be used to identify driver’s anxious and calm states during driving. It can be used for the development of personalized driver assistance system and driver warning system. Originality/value – Only a few attempts have been made on the influence of human emotions on physiological signals in the transportation field. Hence, there is a need for transport scholars to begin to identify driver’s ECG characteristics under different emotional states. This study will analyze the differences of ECG characteristics for female drivers in calm and anxious states during driving to provide a theoretical basis for developing the intelligent and connected vehicles

Antibacterial sensitizers from natural plants: A powerful weapon against methicillin-resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is a drug-resistant bacterium that can cause a range of infections with high morbidity and mortality, including pneumonia, etc. Therefore, development of new drugs or therapeutic strategies against MRSA is urgently needed. Increasing evidence has shown that combining antibiotics with “antibacterial sensitizers” which itself has no effect on MRSA, is highly effective against MRSA. Many studies showed the development of antibacterial sensitizers from natural plants may be a promising strategy against MRSA because of their low side effects, low toxicity and multi-acting target. In our paper, we first reviewed the resistance mechanisms of MRSA including “Resistance to Beta-Lactams”, “Resistance to Glycopeptide antibiotics”, “Resistance to Macrolides, Aminoglycosides, and Oxazolidinones” etc. Moreover, we summarized the possible targets for antibacterial sensitizers against MRSA. Furthermore, we reviewed the synergy effects of active monomeric compounds from natural plants combined with antibiotics against MRSA and their corresponding mechanisms over the last two decades. This review provides a novel approach to overcome antibiotic resistance in MRSA

Analysis on monitoring and controlling techniques about blasting vibration effect of open channel in Taishan nuclear power station

The blasting for bedrock excavation on land for open channel project has a great influence on lock gate in Taishan Nuclear Power Station, therefore, based on blasting vibration monitoring data, the attenuation law of blasting vibration signal has been studied through regression analysis of practical test data by Sadaovsk empirical formula and corresponding time-frequency characteristics was analyzed by Empirical Mode Decomposition based on Hilbert-Huang transform. As for those monitoring data, the results of blast vibration velocity for vertical direction are generally larger than horizontal radial and horizontal tangential direction in the near field of blasting source and the peak particle velocity of vertical direction is usually lower than horizontal radial and horizontal tangential direction in the far field of blasting source; at the same time, their main vibration frequency mostly vary from 10 Hz to 80 Hz which is much higher than natural frequency of lock gate and is beneficial to structural safety and stability of surrounding rock mass for reducing the probability of resonance. To ensure the safety of lock gate, it is of great significance to control maximum explosive weight per delay in advance for different distance from monitoring point to the explosion source according to Safety Regulations for Blasting (GB6722-2014), which shows the excellent effect on blasting damage control of the lock gate and surrounding rock mass. The results from the analysis can be for reference to similar blasting design and blasting construction

A high-Q metasurface signal isolator for 1.5T surface coil magnetic resonance imaging on the go

The combination of surface coils and metamaterials remarkably enhance magnetic resonance imaging (MRI) performance for significant local staging flexibility. However, due to the coupling in between, impeded signal-to-noise ratio (SNR) and low-contrast resolution, further hamper the future growth in clinical MRI. In this paper, we propose a high-Q metasurface decoupling isolator fueled by topological LC loops for 1.5T surface coil MRI system, increasing the magnetic field up to fivefold at 63.8 MHz. We have employed a polarization conversion mechanism to effectively eliminate the coupling between the MRI metamaterial and the radio frequency (RF) surface transmitter-receiver coils. Furthermore, a high-Q metasurface isolator was achieved by taking advantage of bound states in the continuum (BIC) for extremely high-field MRI and spectroscopy. An equivalent physical model of the miniaturized metasurface design was put forward through LC circuit analysis. This study opens up a promising route for the easy-to-use and portable surface coil MRI scanners

Identification of three species commonly known as “daqingye” by internal leaf anatomy and high-performance liquid chromatography analyses

The macroscopic and microscopic morphologies and indigo and indirubin concentration of the traditional Chinese medicine herbs Isatis indigotica Fort., Polygonum tinctorium Ait., and Baphicacanthus cusia (Nees) Bremek, all commonly known as “daqingye”, were determined and compared. The morphological analyses indicated that I. indigotica has leaves with winged petioles and no glandular hairs or crystals, P. tinctorium has leaves with membranous ocrea and clusters of calcium oxalate, and B. cusia has palisade cells in the mesophyll running over the main vein and single cells containing calcium carbonate crystals. Indigo and indirubin are chemical constituents that have been previously isolated from daqingye and were selected in this study as identification markers for high-performance liquid chromatography analysis due to their pharmacological activities. The chromatographic results showed that indigo and indirubin concentration varied significantly among the three species: high concentration of both indigo and indirubin were observed in I. indigotica, the highest concentration among the three daqingye plants was found in P. tinctorium but with low levels of indirubin, and the concentration of indigo and indirubin was quite low in B. cusia. In summary, three different species commonly known as daqingye were accurately distinguished by morphological observation, internal leaf anatomy analysis, and chromatographic analysis

Identification and validation of an immune-related gene prognostic signature for clear cell renal carcinoma

Clear Cell Renal Carcinoma (ccRCC) accounts for nearly 80% of renal carcinoma cases, and immunotherapy plays an important role in ccRCC therapy. However, the responses to immunotherapy and overall survival for ccRCC patients are still hard to predict. Here, we constructed an immune-related predictive signature using 19 genes based on TCGA datasets. We also analyzed its relationships between disease prognosis, infiltrating immune cells, immune subtypes, mutation load, immune dysfunction, immune escape, etc. We found that our signature can distinguish immune characteristics and predict immunotherapeutic response for ccRCC patients with better prognostic prediction value than other immune scores. The expression levels of prognostic genes were determined by RT-qPCR assay. This signature may help to predict overall survival and guide the treatment for patients with ccRCC

Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods

The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike–pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection