Qualitative-quantitative identification and functional zoning analysis of production-living-ecological space: a case study of Urban Agglomeration in Central Yunnan, China.

Territorial space exhibits multiple functional attributes, which comprise production, living, and ecological functions usually. Optimizing the production-living-ecological space (PLES) has become the key to territorial and spatial planning; the scientific identification of the PLES lays a foundation for space optimization and has important guiding significance in territorial spatial zoning. To achieve the integration of macro-scale and micro-scale PLES, with the Urban Agglomeration in Central Yunnan as the research area in this study, the PLES functional identification systems from the administrative unit scale and the grid scale are constructed. The types of PLES are determined by integrating qualitative and quantitative evaluation results and using an improved primacy index model from a composite spatial perspective. On that basis, the division of comprehensive zoning is achieved for land use functions through kernel density analysis. As indicated by the results, the model is capable of reflecting the macro background of the PLES functions in administrative regions while characterizing the micro differences at the grid level in administrative units. There are significant differences in the production, living, and ecological functional spaces in the Urban Agglomeration. Production functions are concentrated in the central and northeastern, living functions are concentrated in the central, and ecological functions are concentrated in the western and northeastern, with significantly consistent or complementary spatial distributions of each other. The PLES of Urban Agglomeration includes production space (PS), ecological space (ES), production-living space (P-LS), production-ecological space (P-ES), living-ecological space (L-ES), and production-living-ecological space (P-L-ES), placing a focus on ES, P-ES, and P-L-ES, which marks significant differences in spatial distribution among different spatial types. The study area is divided into 24 functional zones, which are classified into 6 categories, and optimization paths are proposed. This study will provide a reference for territorial and spatial planning in spatial functional zoning, spatial pattern optimization, and land management applications.

Research on Improved Quantitative Identification Algorithm in Odor Source Searching Based on Gas Sensor Array.

In order to improve the precision of gas detection and develop valid search strategies, the improved quantitative identification algorithm in odor source searching was researched based on the gas sensor array. The gas sensor array was devised corresponding to the artificial olfactory system, and the one-to-one response mode to the measured gas was set up with its inherent cross-sensitive properties. The quantitative identification algorithms were researched, and the improved Back Propagation algorithm was proposed combining cuckoo algorithm and simulated annealing algorithm. The test results prove that using the improved algorithm to obtain the optimal solution -1 at the 424th iteration of the Schaffer function with 0% error. The gas detection system designed with MATLAB was used to obtain the detected gas concentration information, then the concentration change curve may be achieved. The results show that the gas sensor array can detect the concentration of alcohol and methane in the corresponding concentration detection range and show a good detection performance. The test plan was designed, and the test platform in a simulated environment in the laboratory was found. The concentration prediction of experimental data selected randomly was made by the neural network, and the evaluation indices were defined. The search algorithm and strategy were developed, and the experimental verification was carried out. It is testified that the zigzag searching stage with an initial angle of 45° is with fewer steps, faster searching speed, and a more exact position to discover the highest concentration point.

Quantitative Detection of Tank Floor Defects by Pseudo-Color Imaging of Three-Dimensional Magnetic Flux Leakage Signals.

Highly integrated three-dimensional magnetic sensors have just been developed and have been used in some fields, such as angle measurement of moving objects. The sensor used in this paper is a three-dimensional magnetic sensor with three Hall probes highly integrated inside; 15 sensors are used to design the sensor array and then measure the magnetic field leakage of the steel plate; the three-dimensional component characteristics of the magnetic field leakage are used to determine the defect area. Pseudo-color imaging is the most widely used in the imaging field. In this paper, color imaging is used to process magnetic field data. Compared with analyzing the three-dimensional magnetic field information obtained directly, this paper converts the magnetic field information into color image information through pseudo-color imaging and then obtains the color moment characteristic values of the color image in the defect area. Moreover, the least-square support-vector machine and particle swarm optimization (PSO-LSSVM) algorithm are used to quantitatively identify the defects. The results show that the three-dimensional component of the magnetic field leakage can effectively determine the area range of defects, and it is feasible to use the color image characteristic value of the three-dimensional magnetic field leakage signal to identify defects quantitatively. Compared with a single component, the three-dimensional component can effectively improve the identification rate of defects.

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network.

To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model.

[Quantitative identification of Q-markers of Euphorbiae Humifusae Herba based on AHP-CRITIC comprehensive weighting method].

This study investigated the quality markers(Q-markers) of Euphorbiae Humifusae Herba based on the analytic hierarchy process(AHP)-criteria importance through intercriteria correlation(CRITIC) comprehensive weighting method. The Q-markers evaluation system was constructed based on the AHP-CRITIC comprehensive weighting method with quantitative identification of Q-markers of Euphorbiae Humifusae Herba as the target layer. The index weights of the factor layer and the control layer were integrated based on the weights of three indicators(effectiveness, testability, and specificity) in the factor layer calculated by the AHP method and weights of eight indicators(anti-inflammatory inhibitory rate, coagulation shortening rate, anti-cancer inhibition rate, component degree value, component test batch, component average content, content variation coefficient, and number of medicinal materials retrieved according to components) in the control layer calculated by the CRITIC method. The comprehensive score of the chemical components of Euphorbiae Humifusae Herba was weighted and ranked to identify the Q-markers of Euphorbiae Humifusae Herba. In terms of comprehensive scores, top 10 potential Q-markers of Euphorbiae Humifusae Herba were ranked as cynaroside > quercetin > gallic acid > apigenin > luteolin > apigenin-7-O-glucoside > quercetin-7-O-glucoside > ellagic acid > astragalin > ethyl gallate. This study provides a reference for the quality control of Euphorbiae Humifusae Herba and a methodological reference for the quantitative identification of Q-markers of Chinese medicine.

A quantitative identification method based on CWT and CNN for external and inner broken wires of steel wire ropes.

The detection of broken wires in steel wire ropes is of great significance for the production safety. However, the existing identification techniques mainly focus on the external broken wires problem. Here, the artificial feature extraction is one of the most important method, while only the prior knowledge of the artificial feature extraction method is adequate, the identification precision can be satisfied. Therefore, it is still a challenge to realize intelligent diagnosis for the broken wires. Besides, the identification of internal broken wires problem is still not well solved. In this paper, a quantitative identification method based on continuous wavelet transform (CWT) and convolutional neural network (CNN) is proposed to solve the internal and external broken wires identification problem. The key technology of this research is that the fault information from the time-frequency images converted by the magnetic flux leakage (MFL) signals can be automatically extracted through a designed CNN. The main innovation is that the complex signal processing work can be eliminated and the internal and external broken wires can be accurately identified simultaneously by combining CWT and CNN. The experimental results of a steel wire rope test rig are compared with the traditional recognition method, which shows that the proposed method achieved significant improvement on detection accuracy and recognition performance.

[Identification of quality markers of Qixuehe Capsules based on analytic hierarchy process and entropy weight methods].

Qixuehe Capsules is a compound Chinese patent medicine developed for treating the disorder of Qi and blood(a common etiology of gynecological disease), which has remarkable effects on smoothing liver and regulating Qi, activating blood circulation, and relieving pain. However, due to its complex prescriptions(15 herbs) and multiple effects, the quality control of Qixuehe Capsules has always been a bottleneck problem limiting its sustainable development. Therefore, this study adopted the traditional Chinese medicine Q-markers quantitative identification system established previously by our research group based on the combination of analytic hierarchy process and entropy weight methods. With the different effects of Qixuehe Capsules as the entry point, the comprehensive scores of chemical ingre-dients in Qixuehe Capsules under the items of effectiveness(smoothing liver and regulating qi, activating blood circulation, and relieving pain), testability and specificity were calculated and integrated, respectively. Subsequently, through the analysis of compatibility relationship of Qixuehe Capsules, 15 active ingredients with high comprehensive scores were found to be the top Q-mar-kers of Qixuehe Capsules, including ferulic acid, quercetin, caffeic acid, kaempferol, rutin, Z-ligustilide, senkyunolide Ⅰ, vanillic acid, protocatechuic acid, chlorogenic acid, rosmarinic acid, senkyunolide A, gallic acid, tetrahydropalmatine and eugenol. Collectively, this study not only provided scientific evidence for further research on the improvement and standardization of quality standards of Qixuehe Capsules but also provided methodological references for the quantitative identification of Q-markers of multi-effect traditional Chinese medicine formulae.

Quantitative identification of TCM Q-markers based on analytical hierarchy and the entropy weight comprehensive method by taking Shaoyao Gancao decoction as an example / 药学学报

The quality markers (Q-markers) of traditional Chinese medicine (TCM) have become a topic of interest in TCM research in recent years. Nonetheless, there is still no consensus on how to scientifically characterize TCM Q-markers. Our study establishes an identification method for TCM Q-markers based on the analytical hierarchy process (AHP) and the entropy weight comprehensive method. By constructing an evaluation system encompassing the target layer, the factor layer and the control layer, AHP can be used to analyze the weight of three core TCM quality attributes, including effectiveness, testability and specificity. Following that, the entropy weight method is employed to analyze the specific indicators for each attribute based on the literature and experimental data. Finally, the comprehensive weight of each index is obtained by combining the two weights, and the comprehensive weight and the specific value of each component is multiplied and summed to obtain the integrated score ranking, and thereby identify the TCM Q-markers. Taking Shaoyao Gancao decoction as an example, the analysis revealed that the top 8 components are as follows: paeoniflorin > quercetin > albiflorin > glycyrrhizic acid > naringenin > liquiritin > oxypaeoniflorin > benzoylpaeoniflorin, and can be identified as Q-markers of Shaoyao Gancao decoction. This study not only provides support for the establishment of quality standards and process quality control of TCM formulae, but also provides innovative ideas and methods for quantitative evaluation and accurate identification of TCM Q-markers.

Identification of quality markers of Qixuehe Capsules based on analytic hierarchy process and entropy weight methods / 中国中药杂志

Qixuehe Capsules is a compound Chinese patent medicine developed for treating the disorder of Qi and blood(a common etiology of gynecological disease), which has remarkable effects on smoothing liver and regulating Qi, activating blood circulation, and relieving pain. However, due to its complex prescriptions(15 herbs) and multiple effects, the quality control of Qixuehe Capsules has always been a bottleneck problem limiting its sustainable development. Therefore, this study adopted the traditional Chinese medicine Q-markers quantitative identification system established previously by our research group based on the combination of analytic hierarchy process and entropy weight methods. With the different effects of Qixuehe Capsules as the entry point, the comprehensive scores of chemical ingre-dients in Qixuehe Capsules under the items of effectiveness(smoothing liver and regulating qi, activating blood circulation, and relieving pain), testability and specificity were calculated and integrated, respectively. Subsequently, through the analysis of compatibility relationship of Qixuehe Capsules, 15 active ingredients with high comprehensive scores were found to be the top Q-mar-kers of Qixuehe Capsules, including ferulic acid, quercetin, caffeic acid, kaempferol, rutin, Z-ligustilide, senkyunolide Ⅰ, vanillic acid, protocatechuic acid, chlorogenic acid, rosmarinic acid, senkyunolide A, gallic acid, tetrahydropalmatine and eugenol. Collectively, this study not only provided scientific evidence for further research on the improvement and standardization of quality standards of Qixuehe Capsules but also provided methodological references for the quantitative identification of Q-markers of multi-effect traditional Chinese medicine formulae.

A measurement, quantitative identification and estimation method(QINRW) of non-rainfall water component by lysimeter.

Non-rainfall water (NRW) has an important impact on the ecosystem, especially in arid and semi-arid areas. It is also an important component in the surface water cycle. Currently, there is not any instrument that can directly measure NRW and it can only be estimated by observation data. Presently, there is no standard method available to estimate each constituents of NRW. With some research not distinguishing each component of NRW, this inaccurate methodology will consequently lead to a greater scope for statistical error. Naturally, this compounds the difficulty in evaluating the role of NRW on the ecosystem and land surface water cycle. Therefore, this paper proposes a new methodology for separating NRW components, which is called QINRW(A Quantitative Identification method for NRW). Based on lysimeter data and combined with meteorological data, this method distinguishes the physical properties of each component of NRW. Consequently, the amount of NRW can be obtained. It is also suitable for microlysimeter data to be applied in QINRW. The advantages of QINRW are three points: •It is more accurate for excluding the precipitation and dry deposition information from lysimeter data, which was not mentioned in previous studies;•It can obtain each component of NRW;•The identification process is more rigorous and clear in theory so far.

Study on a Novel Fault Damage Degree Identification Method Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy.

A damage degree identification method based on high-order difference mathematical morphology gradient spectrum entropy (HMGSEDI) is proposed in this paper to solve the problem that fault signal of rolling bearings are weak and difficult to be quantitatively measured. In the HMGSEDI method, on the basis of mathematical morphology gradient spectrum and spectrum entropy, the changing scale influence of structure elements to damage degree identification is thoroughly analyzed to determine its optimal scale range. The high-order difference mathematical morphology gradient spectrum entropy is then defined in order to quantitatively describe the fault damage degree of bearing. The discrimination concept of fault damage degree is defined to quantitatively describe the difference between the high-order differential mathematical entropy and the general mathematical morphology entropy in order to propose a fault damage degree identification method. The vibration signal of motors under no-load and load states are used to testify the effectiveness of the proposed HMGSEDI method. The experiment shows that high-order differential mathematical morphology entropy can more effectively identify the fault damage degree of bearings and the identification accuracy of fault damage degree can be greatly improved. Therefore, the HMGSEDI method is an effective quantitative fault damage degree identification method, and provides a new way to identify fault damage degree and fault prediction of rotating machinery.

Quantitative Index and Abnormal Alarm Strategy Using Sensor-Dependent Vibration Data for Blade Crack Identification in Centrifugal Booster Fans.

Centrifugal booster fans are important equipment used to recover blast furnace gas (BFG) for generating electricity, but blade crack faults (BCFs) in centrifugal booster fans can lead to unscheduled breakdowns and potentially serious accidents, so in this work quantitative fault identification and an abnormal alarm strategy based on acquired historical sensor-dependent vibration data is proposed for implementing condition-based maintenance for this type of equipment. Firstly, three group dependent sensors are installed to acquire running condition data. Then a discrete spectrum interpolation method and short time Fourier transform (STFT) are applied to preliminarily identify the running data in the sensor-dependent vibration data. As a result a quantitative identification and abnormal alarm strategy based on compound indexes including the largest Lyapunov exponent and relative energy ratio at the second harmonic frequency component is proposed. Then for validation the proposed blade crack quantitative identification and abnormality alarm strategy is applied to analyze acquired experimental data for centrifugal booster fans and it has successfully identified incipient blade crack faults. In addition, the related mathematical modelling work is also introduced to investigate the effects of mistuning and cracks on the vibration features of centrifugal impellers and to explore effective techniques for crack detection.

Rapid simultaneous identification and quantitation of Staphylococcus aureus and Pseudomonas aeruginosa directly from bronchoalveolar lavage specimens using automated microscopy.

Diagnosis of ventilator-assisted pneumonia (VAP) requires pathogen quantitation of respiratory samples. Current quantitative culture methods require overnight growth, and pathogen identification requires an additional step. Automated microscopy can perform rapid simultaneous identification and quantitation of live, surface-immobilized bacteria extracted directly from patient specimens using image data collected over 3 h. Automated microscopy was compared to 1 µL loop culture and standard identification methods for Staphylococcus aureus and Pseudomonas spp. in 53 remnant bronchoalveolar lavage specimens. Microscopy identified 9/9 S. aureus and 7/7 P. aeruginosa in all specimens with content above the VAP diagnostic threshold. Concordance for specimens containing targets above the diagnostic threshold was 13/16, with concordance for sub-diagnostic content of 86/90. Results demonstrated that automated microscopy had higher precision than 1 µL loop culture (range ~0.55 log versus ≥1 log), with a dynamic range of ~4 logs (~10(3) to 10(6) CFU/mL).