Real-time and accurate calibration detection of gout stones based on terahertz and Raman spectroscopy

Gout is a metabolic disease that can result in the formation of gout stones. It is essential to promptly identify and confirm the type of gout stone to alleviate pain and inflammation in patients and prevent complications associated with gout stones. Traditional detection methods, such as X-ray, ultrasound, CT scanning, and blood uric acid measurement, have limitations in early diagnosis. Therefore, this article aims to explore the use of micro Raman spectroscopy, Fourier transform infrared spectroscopy, and Terahertz time-domain spectroscopy systems to detect gout stone samples. Through comparative analysis, Terahertz technology and Raman spectroscopy have been found to provide chemical composition and molecular structure information of different wavebands of samples. By combining these two technologies, faster and more comprehensive analysis and characterization of samples can be achieved. In the future, handheld portable integrated testing instruments will be developed to improve the efficiency and accuracy of testing. Furthermore, this article proposes establishing a spectral database of gout stones and urinary stones by combining Raman spectroscopy and Terahertz spectroscopy. This database would provide accurate and comprehensive technical support for the rapid diagnosis of gout in clinical practice

A Novel Reliability Evaluation Method Based on RBD and AHP for Industrial Network Systems

Since each component has different impacts on reliability of the industrial network system, a multi-layer reliability evaluation method was proposed in this paper. Firstly, in order to construct multi-layer reliability evaluation system, a framework of industrial network system was introduced based on analytic hierarchy process (AHP). Secondly, a multi-layer reliability evaluation model with weight coefficient of components was proposed based on reliability block diagram (RBD). Thirdly, the simple rule-based fuzzy judgment and risk priority number (RPN) were applied to determining weight coefficient. Last, a reliability evaluation case of the industrial network system for an electronic automatic assembly line was studied. It shows that the proposed method is more reasonable than the conventional reliability analysis method, and the reliability prediction result is consistent with the engineering practice

A Novel Reliability Evaluation Method Based on RBD and AHP for Industrial Network Systems

Since each component has different impacts on reliability of the industrial network system, a multi-layer reliability evaluation method was proposed in this paper. Firstly, in order to construct multi-layer reliability evaluation system, a framework of industrial network system was introduced based on analytic hierarchy process (AHP). Secondly, a multi-layer reliability evaluation model with weight coefficient of components was proposed based on reliability block diagram (RBD). Thirdly, the simple rule-based fuzzy judgment and risk priority number (RPN) were applied to determining weight coefficient. Last, a reliability evaluation case of the industrial network system for an electronic automatic assembly line was studied. It shows that the proposed method is more reasonable than the conventional reliability analysis method, and the reliability prediction result is consistent with the engineering practice

Color measurement of tea leaves at different drying periods using hyperspectral imaging technique.

This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods

Hyperspectral imaging.

<p>Hyperspectral imaging.</p

Measured vs. predicted values of calibration and prediction by CARS-LS-SVM and SPA-LS-SVM models, respectively.

<p>(a): CARS-LS-SVM-ΔL*; (b): CARS-LS-SVM-Δa*; (c): CARS-LS-SVM-Δb*; (d): SPA-LS-SVM-ΔL*; e): SPA-LS-SVM-Δa*; (f): SPA-LS-SVM-Δb*.</p

Reference values of color (ΔL*, Δa* and Δb*) of tea leaves in calibration and prediction sets.

<p>Reference values of color (ΔL*, Δa* and Δb*) of tea leaves in calibration and prediction sets.</p

Performance of models in calibration and prediction for predicting color (ΔL*, Δa* and Δb*) using different preprocessing methods.

<p>Performance of models in calibration and prediction for predicting color (ΔL*, Δa* and Δb*) using different preprocessing methods.</p

Schematic diagram of the hyperspectral imaging system.

<p>Schematic diagram of the hyperspectral imaging system.</p

Effective wavelengths recommended by CARS and SPA, respectively.

<p>Effective wavelengths recommended by CARS and SPA, respectively.</p