In this work, a terahertz time-domain spectroscopy (THz-TDS) system was used to measure the thickness of thermal barrier coatings (TBCs) and characterize the interface morphology of TBCs after erosion. Reflection mode, with an angle of incidence of 0, was used for inspection before and after erosion. The refractive index, thickness, and internal structure evolution tendency of the yttria-stabilized zirconia (YSZ) top coat were estimated under consideration of the interaction between the pulsed THz waves and the TBCs. The surface roughness of the top coat surface was considered for the errors analysis in the refractive index and thickness measurement. To reduce the errors introduced by the refractive index change after erosion, two mathematical models were built to assess the thickness loss. Then, the thickness loss was compared with results estimated by the micrometer inspection method. Finally, the basic erosion sample profile with Ra roughness was obtained, and the broadening of THz pulses were suggested as a possible measure for the top coat porosity change, showing that THz waves can be a novel online non-destructive and non-contact evaluation method that can be widely utilized to evaluate the integrity of TBCs applied to gas turbine blades.

Microbial populations in probiotic supplements in China were investigated using the plate count method and PCR–DGGE technique. Our results indicated that total viable cells in most probiotic products could meet the minimum requirement of the Chinese National Standards (106 CFU/mL); however, total viable cell counts of the species detected in the probiotic products did not correspond with the cell number given in the label.

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Effective control of the thickness of the hot-rolled oxide scale on the surface of the steel strip is very vital to ensure the surface quality of steel products. Hence, terahertz nondestructive technology was proposed to measure the thickness of thin oxide scale. The finite difference time domain (FDTD) numerical simulation method was employed to obtain the terahertz time-domain simulation data of oxide scale with various thickness (0–15 μm). Added Gaussian white noise with a Signal Nosie Reduction (SNR) of 10 dB was used when simulating real test signals, using four wavelet denoising methods to reduce noise and to compare their effectiveness. Two machine learning algorithms were adopted to set up models to achieve this goal, including the classical back-propagation (BP) neural network algorithm and the novel extreme learning machine (ELM) algorithm. The principal component analysis (PCA) algorithm and particle swarm optimization (PSO) algorithm were combined to reduce the dimensions of the terahertz time-domain data and improve the robustness of the machine learning model. It could be clearly seen that the novel hybrid PCA-PSO-ELM model possessed excellent prediction performance. Finally, this work proposed a novel, convenient, online, nondestructive, noncontact, safety and high-precision thin oxide scale thickness measuring method that could be employed to improve the surface quality of iron and steel products.