Analysis on the traditional Chinese medicine syndromes and Six-Meridian Pattern Identification of coronavirus disease 2019

Zhongjing Zhang’s Shang Han Lun(Treatise on Cold-induced Diseases,an ancient Chinese medical book written by Zhongjing Zhang in the Eastern Han Dynasty)was pioneering to conduct the Six-Meridian Pattern Identification,which provided later generations with a theoretical basis for the treatment of Exogenous Febrile Diseases.The coronavirus disease 2019 belongs to the category of“Pestilence”(the general term for virulent infectious epidemic diseases)in“Febrile Diseases”(the general term for acute externally contracted diseases caused by Warm Pathogens,with fever as the chief manifestation,also known as Warm Diseases)and the treatment of coronavirus disease 2019 is mostly based on the Triple Energizer Pattern Identification(the a categorization of Pattern Identification according to the theory of the Triple Energizer)or Defense,Qi,Nutrient and Blood Pattern Identification(the categorization of Febrile Diseases Pattern Identification according to the theory of Defense,Qi,Nutrient and Blood which indicates the stages of the clinical course with corresponding pathological changes).Although the theory of Cold-induced Diseases(the condition caused by Cold,manifested as chills and fever,absence of sweating,headache and floating-tense pulse)and the theory of Febrile Disease seem to be entirely distinct with each other,Zhongjing Zhang’s theory contained Triple Energizer Pattern Identification or Defense,Qi,Nutrient and Blood Pattern Identification,and Triple Energizer Pattern Identification or Defense Qi,Nutrient and Blood Pattern Identification also contained Zhongjing Zhang’s Six-Meridian Pattern Identification(the categorization of Pattern Identification according to the theory of the Six Meridians,applied to the diagnosis of acute Febrile Diseases at different stages,and also useful for the Pattern Identification of other diseases).The basic principles and therapeutic methods of Cold-induced Diseases and Febrile Diseases are intertwined.Therefore,this paper will combine the two theories to analyze the traditional Chinese medicine syndromes of coronavirus disease 2019,and try to use Six-Meridian Pattern Identification to guide the treatment of this disease.

Flow pattern identification in oil wells by electromagnetic image logging

Petroleum production logging needs to determine the interpretation models first and flow pattern identification is the foundation, but traditional flow pattern identification methods have some limitations. In this paper, a new method of flow pattern identification in oil wells by electromagnetic image logging is proposed. First, the characteristics of gas-water and oil-water flow patterns in horizontal and vertical wellbores are picked up. Then, the continuous variation of the two phase flow pattern in the vertical and horizontal pipe space is discretized into continuous fluid distribution models in the pipeline section. Second, the electromagnetic flow image measurement responses of all the eight fluid distribution models are simulated and the characteristic vector of each response is analyzed in order to distinguish the fluid distribution models. Third, the time domain changes of the fluid distribution models in the pipeline section are used to identify the flow pattern. Finally, flow simulation experiments using electromagnetic flow image logging are operated and the experimental and simulated data are compared. The results show that the method can be used for flow pattern identification of actual electromagnetic image logging data.

Identification and spatial patterns of coastal water pollution sources based on GIS and chemometric approach

Comprehensive and joint applications of GIS and chemometric approach were applied in identification and spatial patterns of coastal water pollution sources with a large data set （5 years （2000-2004）, 17 parameters） obtained through coastal water monitoring of Southern Water Control Zone in Hong Kong. According to cluster analysis the pollution degree was significantly different between September-next May （the 1st period） and June-August （the 2nd period）. Based on these results, four potential pollution sources, such as organic/eutrophication pollution, natural pollution, mineral/anthropic pollution and fecal pollution were identified by factor analysis/principal component analysis. Then the factor scores of each monitoring site were analyzed using inverse distance weighting method, and the results indicated degree of the influence by various potential pollution sources differed among the monitoring sites. This study indicated that hybrid approach was useful and effective for identification of coastal water pollution source and spatial patterns.

New pattern recognition system in the e-nose for Chinese spirit identification

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively.

Genome-wide identification and characterization of the JAZ gene family and its expression patterns under various abiotic stresses in Sorghum bicolor

The jasmonate ZIM domain(JAZ)protein belongs to the TIFY((TIF[F/Y]XG)domain protein)family,which is composed of several plant-specific proteins that play important roles in plant growth,development,and defense responses.However,the mechanism of the sorghum JAZ family in response to abiotic stress remains unclear.In the present study,a total of 17 JAZ genes were identified in sorghum using a Hidden Markov Model search.In addition,real-time quantification polymerase chain reaction(RT-qPCR)was used to analyze the gene expression patterns under abiotic stress.Based on phylogenetic tree analysis,the sorghum JAZ proteins were mainly divided into nine subfamilies.A promoter analysis revealed that the SbJAZ family contains diverse types of promoter cis-acting elements,indicating that JAZ proteins function in multiple pathways upon stress stimulation in plants.According to RT-qPCR,SbJAZ gene expression is tissuespecific.Additionally,under cold,hot,polyethylene glycol,jasmonic acid,abscisic acid,and gibberellin treatments,the expression patterns of SbJAZ genes were distinctly different,indicating that the expression of SbJAZ genes may be coordinated with different stresses.Furthermore,the overexpression of SbJAZ1 in Escherichia coli was found to promote the growth of recombinant cells under abiotic stresses,such as PEG 6000,NaCl,and 40℃ treatments.Altogether,our findings help us to better understand the potential molecular mechanisms of the SbJAZ family in sorghum in response to abiotic stresses.

Investigation and Application of Automatic Fingerprint Identification Based on Fuzzy Pattern Recognition

Fingerprint image is a typical non-restraint image that has some uncertainty, which makes it difficult to perform identification using classical approach. Therefore, fuzzy pattern recognition is applied to match individual query by searching the entire template database. The fuzzy maximum subordinate principle is used to solve shift matching. Through experimenting and analyzing, the approximate principle fuzzy method is employed by selecting fuzzy characteristics and determining the similarity function to achieve the further accuracy. Theoretical and experimental results show this approach is effective and reasonable.

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.

Regional-scale Identification of Three-dimensional Pattern of Vegetation Landscapes

The altitudinal pattern of vegetation is usually identified by field surveys,however,these can only provide discrete data on a local mountain.Few studies identifying and analyzing the altitudinal vegetation pattern on a regional scale are available.This study selected central Inner Mongolia as the study area,presented a method for extracting vegetation patterns in altitudinal and horizontal directions.The data included a vegetation map at a 1∶1 000 000 scale and a digital elevation model at a 1∶250 000 scale.The three-dimensional vegetation pattern indicated the distribution probability for each vegetation type and the transition zones between different vegetation landscapes.From low to high elevations,there were five vegetation types in the southern mountain flanks,including the montane steppe,broad-leaved forest,coniferous mixed forest,montane dwarf-scrub and sub-alpine shrub-meadow.Correspondingly,only four vegetation types were found in the northern flanks,except for the montane steppe.This study could provide a general model for understanding the complexity and diversity of mountain environment and landscape.

Structural Stress Identification Using Fuzzy Pattern Recognition and Information Fusion Technique

In order to ensure the service security of space structures under wind load, the stress identification method based on the combination of fuzzy pattern recognition and information fusion technique is proposed, in which the measurements of limited strain sensors arranged on the structure are used. Firstly, the structure is divided into several regions according to the similarity and the most unfavorable region is selected to be the key region for stress identification, while the different numbers of the strain sensors are located on the key region and the normal regions; secondly, the different stress distributions of the key region are obtained based on the measurements of the strain sensors located on the key region and the normal regions separately, in which the fuzzy pattern recognition is used to identify the different stress distributions; thirdly, the stress distributions obtained by the measurements of sensors in normal regions are selected to calculate the synthesized stress distribution of the key region by D-S evidence theory; fourthly, the weighted fusion algorithm is used to assign the different fusion coefficients to the selected stress distributions obtained by the measurements of the normal regions and the key region, while the synthesized stress distribution of the key region can be obtained. Numerical study on a lattice shell model is carried out to validate the reliability of the proposed stress identification method. The simulated results indicate that the method can improve identification accuracy and be effective by different noise disturbing.

Phase Identification Using Series of Selected Area Diffraction Patterns and Energy Dispersive Spectrometry within TEM

Transmission electron microscopy (TEM) is a very powerful technique for materials characteriza-tion, providing information relating to morphology, composition, and crystal structure. Selected area diffraction patterns (SADPs) are crystallographic data that can be obtained using a TEM in-strument. Conventional identification through SADP/TEM is tricky and tedious, thereby increasing the difficulty of phase identification. To establish a procedure for phase identification of known and unknown phases, in this study we examined two samples: one, a known phase, was Si with alignment;the other, unknown, was the TixOy phase at the 96.4Au-3Ni-0.6Ti interlayer/ yttria-stabilized zirconia (YSZ) interface of a steel/96.4Au-3Ni-0.6Ti interlayer/YSZ joint. The procedures for phase identification of the known and unknown phases are described herein using a series of SADPs and energy dispersive spectrometry within TEM that would be useful for general researchers.

IDENTIFICATION AND SIMULATION OF X-RAY POWDER DIFFRACTION PATTERN

A new method for pattern identification and simulation of X-ray powder diffraction data is described, which can give definite phase composition of the unknown rapidly and correctly. The method is implemented in Microsoft Fortran 77 for the IBM PC/XT/AT personal computer or their compatibles.

A new damping ratio identification method based on pattern search

In order to improve the effectiveness of traditional time domain identification methods in identifying damping ratios, a new damping ratio identification method based on pattern search is proposed by fluctuating the reliable natural frequency obtained through traditional time domain identification methods by about 10% to build the boundary conditions, using all the initial identification results to establish the free decay response of the system, and using the pattern search method to correct the initial identification results with the residual sum of squares between the free decay response and the actually measured free-decay signal as the objective function. The proposed method deals with the actually measured free-decay signal with curve fitting and avoids enlarging the identified error caused by intermediate conversion, so it can effectively improve the identified accuracy of damping ratios. Simulations for a room-sized vibration isolation foundation show that the relative errors of analyzed three damping ratios are down to 1.05%, 1.51% and 3.7% by the proposed method from 8.42%, 5.85% and 8.5% by STD method when the noise level is 10%.

Identification of Patterns of Consumption through the Daily Mean Outdoor Temperature

The identification and recognition of patterns in the context of building is a necessary feedback to create intelligent buildings. In this context, the key is empowering the systems with learning elements to make decisions. The challenge is detected element to predict the human behavior in the building. Daily mean outdoor temperature is one of the variables with incidence in the human comfort due to the weather adaptation of the users. In this paper it analyzed the consumption in an office respect to the internal temperature and the daily mean temperature through cluster techniques. The cluster can be used as a forecasting of consumption.

Flow pattern identification based on a single-wire capacitance probe

To identify flow patterns in horizontal gas-liquid flows,a single-wire capacitance probe was used for voltage output for the first time.Regardless of the measurement accuracy of water layer height,the statistic parameters of the voltage-time traces were compared within the same sampling time of 5s under different flow patterns,including maximum,minimum,range,and average.The results show that most of flow pattens were accurately identified except for some transition lines.

Application of Computer Vision Technique to Maize Variety Identification

Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted.

Rapid Identification of Rice Samples Using an Electronic Nose

Four rice samples of long grain type were tested using an electronic nose (Cyranose-320).Samples of 5 g of each variety of rice were placed individually in vials and were analyzed with the electronic nose unit consisting of 32 polymer sensors.The Cyranose-320 was able to differentiate between varieties of rice.The chemical composition of the rice odors for differentiating rice samples needs to be investigated.The optimum parameter settings should be considered during the Cyranose-320 training process especially for multiple samples,which are helpful for obtaining an accurate training model to improve identification capability.Further,it is necessary to investigate the E-nose sensor selection for obtaining better classification accuracy.A re- duced number of sensors could potentially shorten the data processing time,and could be used to establish an application pro- cedure and reduce the cost for a specific electronic nose.Further research is needed for developing analytical procedures that adapt the Cyranose-320 as a tool for testing rice quality.

Automated detection and identification of white-backed planthoppers in paddy fields using image processing

A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.

Bohai crude oil identification by gas chromatogram fingerprinting quantitative analysis coupled with cluster analysis

By gas chromatogram, six crude oils fingerprinting distributed in four oilfields and four oil platforms were analyzed and the corre- sponding normal paraffin hydrocarbon （ including pristane and phytane） concentration was obtained by the internal standard methed. The normal paraffin hydrocarbon distribution patterns of six crude oils were built and compared. The cluster analysis on the normal paraffin hydrocarbon concentration was conducted for classification and some ratios of oils were used for oils comparison. The results indicated： there was a clear difference within different crude oils in different oil fields and a small difference between the crude oils in the same oil platform. The normal paraffin hydrocarbon distribution pattern and ratios, as well as the cluster analysis on the nomad paraffin hydrocarbon concentration can have a better differentiation result for the crude oils with small difference than the original gas chromatogram.

Spatial Identification of Housing Vacancy in China

The housing vacancy rate(HVR)is an important index in assessing the healthiness of residential real estate market.In China,it is hardly to take advantage of the basic data of real estate information due to the opaque of those data.In this paper,the HVR is estimated to two scales.At the grid level,urban area ratio was calculated by nighttime images after eliminating outliers of nighttime images and night light intensity of non-residential pixels in mixed pixels by a proposed modified optimal threshold method,and built-up areas in each pixel were extracted from the land-cover data.Then,the HVR is calculated by comparing the light intensity of specific grid with the light intensity of full occupancy rate regions.At the administrative scale,the GCI(‘ghost city’index)is constructed by calculating the ratio of the total light radiation intensity of a city to the total construction land area of the city.The overall spatial differentiation pattern of the vacant houses in the national prefecture level administrative regions is analyzed.The following conclusions were drawn:vacant housing is rare in certain eastern coastal cities and regions in China with relatively fast economic development.Cities based on exhausted resources,some mountainous cities,and cities with relatively backward economic development more typically showed high levels of housing vacancy.The GCI of prefecture-level administrative units gradually declined from north to south,whereas the east-west distribution showed a parabolic shape.As city level decreased,the GCI registered a gradual upward trend.China’s urban housing vacancy can be divided into five categories:industry or resources driven,government planned,epitaxy expansionary,environmental constraint and speculative activate by combining the spatial distribution of housing vacancy with the factors of natural environment,social economic development level,and population density into consideration.

Gas monitoring data anomaly identification based on spatio-temporal correlativity analysis

Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.