Discrimination of polysorbate 20 by high-performance liquid chromatography-charged aerosol detection and characterization for components by expanding compound database and library

Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.

Determination of Aromatic Components of Rosa davurica Pall. by Headspace Solid Phase Microextraction Combined with GC-MS

[Objectives] To determine the aromatic components of Rosa davurica Pall. [Methods] 42 kinds of aromatic components were identified from the flowers of R. davurica by headspace solid phase microextraction( HS-SPME) combined with gas chromatography-mass spectrometry( GC-MS). The main compounds were alcohols( 54. 88%) and aldehydes( 19. 55%). [Results] The top five components with the highest relative content were phenylethyl alcohol( 12. 69%),geraniol( 9. 85%),citronellol( 8. 80%),nerol( 7. 84%) and 2-n-pentylfuran( 7. 45%). [Conclusions] Headspace solid phase microextraction( HS-SPME) combined with gas chromatography-mass spectrometry( GC-MS) can provide basis for further development and utilization of R. davurica.

Component fractionation of temporal evolution in adsorption-desorption for binary gas mixtures on coals from Haishiwan Coal Mine

Adsorption-desorption experiments on CO2-CH4 gas mixtures with varying compositions have been conducted to study the fractionation characteristics of CO2-CH4 on Haishiwan coal samples. These were carried out at constant temperature but different equilibrium pressure conditions. Based on these experimental results, the temporal evolution of component fractionation in the field was investigated. The results show that the CO2 concentration in the adsorbed phase is always greater than that in the original gas mixture during the desorption process, while CH4 shows the opposite characteristics. This has confirmed that CO2 , with a greater adsorption ability has a predominant position in the competition with CH4 under different pressures. Where gas drainage is employed, the ratio of CO2 to CH4 varies with time and space in floor roadways used for gas drainage, and in the ventilation air in Nos.1 and 2 coal seams, which is consistent with laboratory results.

Determination of bioactive components in the fruits of Cercis chinensis Bunge by HPLC-MS/MS and quality evaluation by principal components and hierarchical cluster analyses

The fruits of leguminous plants Cercis Chinensis Bunge are still overlooked although they have been reported to be antioxidative because of the limited information on the phytochemicals of C.chinensis fruits.A simple,rapid and sensitive HPLC-MS/MS method was developed for the identification and quantitation of the major bioactive components in C.chinensis fruits.Eighteen polyphenols were identified,which are first reported in C.chinensis fruits.Moreover,ten components were simultaneously quantified.The validated quantitative method was proved to be sensitive,reproducible and accurate.Then,it was applied to analyze batches of C.chinensis fruits from different phytomorph and areas.The principal components analysis(PCA)realized visualization and reduction of data set dimension while the hierarchical cluster analysis(HCA)indicated that the content of phenolic acids or all ten components might be used to differentiate C.chinensis fruits of different phytomorph.

Transformer’s Condition Assessment Method Based on Combination of Cloud Matter Element and Principal Component Analysis

With the development of power grid, as one of the key equipment, the transformer’s condition assessment method has always receive attention from experts, scholars concern more and more about the method’s practicality and reliability. In the traditional condition assessment method, due to the characteristics of the transformer’s complex structure, the assessment system is not comprehensive enough, or the assessment system is too complex, the indexes are not easy to quantify, such problems are emerging. The traditional method is complex and the degree of quantification is not enough. Therefore it is necessary to propose a condition assessment method that is easy to carry out the condition assessment work and does not affect the assessment results. In this paper, we propose a method to assess the state of the transformer’s complex structure. First, we establish a comprehensive assessment system, then apply the method of principal component analysis to optimize the index system, and then use the theory of cloud-matter-element. Finally the reliability and rationality of the method are verified by an example.

CNN-based segmentation frameworks for structural component and earthquake damage determinations using UAV images

Buildings undergo various kinds of structural damage during earthquakes,and damage detection and functional assessment of these structures in the aftermath of the events have been challenging issues.Under these circumstances,computer vision techniques offer a promising solution by automating the inspection process.This study presents an effective methodology for automatic structural components and damage detection using unmanned aerial vehicle(UAV)images of damaged buildings.Two types of neural network architectures are considered for appropriate feature extractions in different task detections.The feature pyramid network(FPN)is employed for crack,spall,rebar,and component damage segmentation,while the UNet++network is utilized for the damage state.For network training and validation,a total of 3805 original images of size 1920×1080 pixels are processed by the proposed method and reduced the image pixels.From the FPN,the achieved highest intersection over unions(IoUs)were 0.59,0.93,0.42,and 0.99 for crack,spall,rebar,and components,respectively.These predicted labels were found in close agreement with the labels.Similarly,the UNet++recognized the semantic information and damage state with an IoU of 0.72.This demonstrated the applicability of the proposed method for automated post-earthquake building inspection process accurately without information loss from the original images.

UPLC-QTOF/MS Method for Screening and Determination of 59 Non-labeled Components in Veterinary Drug Preparations

[Objectives]This study was conducted to provide an accurate and reliable method for rapid high-throughput screening of veterinary drug preparations.[Methods]For the matrixes of veterinary drug preparations,high-performance liquid chromatography-high resolution quadrupole time-of-flight mass spectrometry(UPLC-QTOF/MS)was used to establish a fast screening method for 59 non-standard components in five categories of antiviral agents,aminoglycosides,quinolones,sulfonamides,and tetracyclines in veterinary drugs.The target drugs were separated by a Waters ACQUITY UPLC HSS T3 chromatographic column(50 mm×2.1 mm,1.8μm),and data were collected in the positive ion mode.Good separation of the 59 drugs was achieved within 7 min.[Results]In the concentration range of 0-100 ng/ml,each drug showed a good linear relationship,and the correlation coefficients were all greater than 0.999.The detection limits of the 59 drugs were in the range of 0.1-0.5 mg/ml,and the recovery under the addition concentration of 5 mg/ml was in the range of 85.2%-103.8%.[Conclusions]The method is fast,simple,accurate,and highly sensitive,and is suitable for high-throughput screening and qualitative identification of non-standard components in veterinary drug preparations.

Determination of volatile components in cut tobacco with gas chromatography-mass spectrometry and chemometric resolution

Chromatography-mass spectrometry(GC-MS)was used to analyze the volatile components of cut tobacco samples with the help of heuristic evolving latent projections(HELP).After extracting with simultaneous distillation and extraction method,the volatile components in cut tobacco were detected by GC-MS.Then the obtained original two-dimensional data were resolved into pure mass spectra and chromatograms.The qualitative analysis was performed by similarity searches in the national institute of standards and technology(NIST)mass database with the obtained pure mass spectrum of each component and the quantitative results were obtained by calculating the volume of total two-way response.The accuracy of qualitative and quantitative results were greatly improved by using the two-dimensional comprehensive information of chromatograms and mass spectra.107 of 141 separated constituents in the total ion chromatogram of the volatile components were identified and quantified,accounting for about 88.01% of the total content.The result proves that the developed method is powerful for the analysis of complex cut tobacco samples.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

Effects of granulation process variables on the physical properties of dosage forms by combination of experimental design and principal component analysis

The current study was to understand how process variables of high shear wet granulations affect physical properties of granules and tablets. The knowledge gained was intended to be used for Quality-by-Design based process design and optimization. The variables were selected based on the risk assessment as impeller speed, liquid addition rate, and wet massing time. Formulation compositions were kept constant to minimize their influence on granules properties. Multiple linear regression models were built providing understanding of the impact of each variable on granule hardness, Carr’s index, tablet tensile strength, surface mean diameter of granules, and compression behavior. The experimental results showed that the impact of impeller speed was more dominant compared to wet massing time and water addition rate. The results also revealed that quality of granules and tablets could be optimized by adjusting specific process variables(impeller speed 1193 rpm, water spray rate 3.7 ml/min, and wet massing time 2.84 min). Overall desirability was 0.84 suggesting that the response values were closer to the target one. The SEM image of granules showed that spherical and smooth granules produced at higher impeller speed, whereas rough and irregular shape granules at lower speed. Moreover, multivariate data analysis demonstrated that impeller speed and massing time had strong correlation with the granule and tablet properties. In overall, the combined experimental design and principal component analysis approach allowed to better understand the correlation between process variables and granules and tablet attributes.

National Swine Genetic Improvement: An overview of essential program components and organizational structure needed for success

The swine industry in China is a thriving and evolving industry that has shown phenomenal growth over the past 10 years. To insure long term success and viability in a worldwide competitive industry such as pork, there is need for a National Swine Genetic Improvement Program. This program needs to draw on expertise and technology from across the world for its development, but it should be based on the structure of the pig industry in China and be led by Chinese scientists, administrators and producers. National Genetic Improvement requires more than just technology. A successful program of national genetic improvement will require cooperation from the industry and the government. The support for the university system is essential for the success of the pig industry. The university system has a vital role on education (of students, faculty, producers and consumers) as well as research and technology transfer. The government could also have a role in supporting the central test stations and AI stations across the country. An accurate and comprehensive pedigree maintenance system is essential to genetic improvement. And it will be vitally important to be active in the importation of new genetics to sample other populations.

Education Knowledge System Combination Model Based on the Components

Resources are the base and core of education information, but current web education resources have no structure and it is still difficult to reuse them and make them can be self assembled and developed continually. According to the knowledge structure of course and text, the relation among knowledge points, knowledge units from three levels of media material, we can build education resource components, and build TKCM （Teaching Knowledge Combination Model） based on resource components. Builders can build and assemble knowledge system structure and make knowledge units can be self assembled, thus we can develop and consummate them continually. Users can make knowledge units can be self assembled and renewed, and build education knowledge system to satisfy users＇ demand under the form of education knowledge system.

Nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in aerospace community:a comparative analysis

The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.

Touristic Ecological Footprint of Jiufeng National Forest Park in Beijing Based on Component Method

This paper,based on the ecological footprint component method,calculated the touristic ecological footprint of Jiufeng National Forest Park in Beijing.The results showed that in 2013,in Jiufeng National Forest Park,the total touristic ecological footprint was 183.08 hm2,the total ecological capacity was 225.16 hm2,the total touristic ecological surplus was 42.07 hm2,and the average touristic ecological surplus was 0.000 4 hm2 per capita,indicating that tourism in Jiufeng National Forest Park was in ecological surplus and ecological security.However,forest parks in Beijing at large were in ecological deficit.This paper suggested that the tourist flow volume of forest parks with a big ecological deficit should be moved to forest parks with an ecological surplus.Besides,forest parks are expected to strengthen the development and management,improve the availability of forest recreation resource,and promote the environmental protection awareness of tourists,so as to boost the sustainable development of forest park tourism.

Identification of essential language areas by combination of fMRI from different tasks using probabilistic independent component analysis

Functional magnetic resonance imaging (fMRI) has been used to lateralize and localize lan-guage areas for pre-operative planning pur-poses. To identify the essential language areas from this kind of observation method, we pro-pose an analysis strategy to combine fMRI data from two different tasks using probabilistic in-dependent component analysis (PICA). The assumption is that the independent compo-nents separated by PICA identify the networks activated by both tasks. The results from a study of twelve normal subjects showed that a language-specific component was consistently identified, with the participating networks sepa-rated into different components. Compared with a model-based method, PICA’s ability to capture the neural networks whose temporal activity may deviate from the task timing suggests that PICA may be more appropriate for analyzing language fMRI data with complex event-related paradigms, and may be particularly helpful for patient studies. This proposed strategy has the potential to improve the correlation between fMRI and invasive techniques which can dem-onstrate essential areas and which remain the clinical gold standard.

Optimization of Streptomyces caelestis Culture Conditions and Preliminarily Determination of Its Antifungal Components

Streptomyces caelestis FAS is an actinomyces strain isolated with Ustilago scitaminea as an indicator. With GAUZE s medium as the basic medium, carbon source and nitrogen source in the medium and ventilation volume were optimized, and the antifungal substances produced by S. caelestis FAS were preliminarily determined through organic solvent extraction and ammonium sulfate precipitation. The results showed that the antifungal components produced by S. caelestis FAS were proteins, and the optimal culture conditions were using corn flour as carbon source and KNO 3 as nitrogen source and culturing in 250 ml triangular flask added with 70 ml of culture medium. The crude protein obtained under these culture conditions had the best antifungal effect with an inhibition zone diameter of 25 mm. This study will provide a scientific basis for further study on the antifungal mechanism and application of S. caelestis FAS.

Determination of Fourier Components of Spatial Correlation Function of Dielectric Susceptibility of Random Medium

In this work, we present a new method of directly determining Fourier components of the spatial correlation function of the dielectric susceptibility of random medium. The method is based on the analysis of the ratio of the spectrum of the light scattered by the spatial correlation components of the dielectric susceptibility of tissue to the spectrum of light scattered by the randomly distributed scatterers which are independent on the value of the spectrum of the incident light and the direction of the observation. The results may find wide applications in areas such as in biomedical diagnosis.

Epidemiological characteristics of hyperuricemia in metabolic syndrome and its different components in the physical examination population

Objective:To explore the epidemiological characteristics of hyperuricemia(HUA)in the metabolic syndrome(MS)and its different components in the physical examination population.Methods:Subjects who underwent medical check-ups at a hospital health management center from June 2021 to March 2023 were included in the study.To analyze the prevalence of HUA in MS and its different components,further,stratify the study population by gender and assess the serum uric acid(SUA)levels and prevalence of HUA in people with different numbers of MS components and the combination of MS components with the highest prevalence of HUA in both sexes.Logistic regression was used to analyze the risk of HUA in people with different numbers of MS components.Result:A total of 66,520 individuals were enrolled in the study.SUA levels(t=-82.947,P<0.001)and HUA prevalence(χ^(2)=3421.632,P<0.001)were significantly higher in the MS group than in the Non-MS group.SUA levels and prevalence of HUA were significantly higher in abdominal obesity,hypertension,decreased HDL-C and evaluated TG than in normal subjects(P<0.001),while there were gender differences in SUA levels and HUA prevalence in diabetic patients,with significantly lower SUA levels and HUA prevalence in men with diabetes than in those with normal blood glucose,an opposite result in women.SUA levels and HUA prevalence gradually increased with the increasing number of MS components in women,whereas in men,such a trend was only observed in MS1-MS4.The combination of MS components with the highest prevalence of HUA was abdominal obesity+hypertension+decreased HDL-C+evaluated TG(54.35%)in men and abdominal obesity+hypertension+diabetes+decreased HDL-C+evaluated TG(41.46%)in women.Logistic regression analysis showed that after adjusting for gender,age and ethnicity,the risk of HUA increased with the number of MS components in women,while in men,the risk of HUA increased continuously from MS1-MS4.Further adjustment for BMI,elevated TC,elevated LDL-C,and coronary artery disease,the results remained consistent.Conclusion:MS and its components are risk factors for HUA in the physical examination population,with different combinations of MS components having different correlations with HUA,and the risk of developing HUA correlates with the number of abnormal MS components.

Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.

The Simultaneous Determination of Five Components Including Acetaminophen by Ridge Regression Spectrophotometry

Ridge regression spectrophotometry(LHG)is used for thesimultaneous determination of five components(acetaminophen,p-aminophenol, caffeine, chlorphenamine maleate and guaifenesin)incough syr- up. The computer program of LHG is based on VB language.The difficulties in overlapping of absorption spectrums of fivecompounds are overcome by this procedure. The experimental resultsshow that the recovery of each component is in the range from97.9/100 to 103.3/100 and each component obtains satisfactory resultswithout any pre-separation.