Study on Rhizome Crops by Fourier Transform Infrared Spectroscopy Combined with Wavelet Analysis

In order to distinguish 8 kinds of rhizome crops, the 40 samples were studied by Fourier transform infrared spectroscopy （FTIR） combined with wavelet transform （WT）, principal component analysis （PCA） and hieramhical cluster analysis （HCA）. The results showed that the infrared spectra were similar on the whole, but there were differences in peak position, peak shape and peak absorption intensity in the range of 1 800-700 cm-1. The infrared spectra in the range of 1 800-700 cm-1 were selected to perform continuous wavelet transform （CWT） and discrete wavelet transform （DWT）. The 15th-Ievel decomposition coefficients of CWT and the 5=-level detail coefficients of DWT were classified by PCA and HCA. The cumulative contri- bution rates of the first three principal components of CWT and DWT were 93.12% and 89.78%, respectively. The accurate recognition rates of PCA and HCA were all 100%. It is proved that FTIR combined with WT can be used to distinguish different kinds of rhizome crops.

Study on FTIR Spectra of Corn Germs and Endosperms of Three Different Colors Combining with Cluster Analysis

[Objective] This research aimed to study the FTIR spectra of corn germs and endosperms so as to provide a scientific way for identifying corn of different types. [Method] The corn germs and endosperms of three types were studied by using Fourier transform infrared spectroscopy（FTIR） technology, combined with cluster analysis. [Result] The overall characteristics of original FTIR spectra were basically similar within the range of 700-1 800 cm^-1. The FTIR spectra were mainly composed by the absorption peaks of polysaccharides, proteins and lipids. Within the wavelength range of 700-1 800 cm^-1, there were only tiny differences in original FTIR spectra among the corn germs and endosperms of three different types. The spectra were then processed by using first derivative and second derivative. The second derivative spectra were used for hierarchical cluster analysis（HCA）. The results showed that with the wavelength range of 700-1 800 cm^-1, the second derivative spectra of the 52 samples could be better clustered according to the tree types and corn germ and corn endosperm. The clustering correct rate reached 96.1%.[Conclusion] FTIR technology, combined with cluster analysis, can be used to identify different types of corn germs and endosperms, and it is characterized by convenience and rapidness.

Development of a Web-Based GIS of Flood Zones in the Municipality of Ouagadougou in Burkina Faso

This document describes the creation of an informative Web GIS aimed at mitigating the impacts of flooding in the municipality of Ouagadougou, in Burkina Faso, a region that is highly sensitive to climate change. Burkina Faso, which is undergoing rapid urbanization, faces major natural threats, particularly flooding, as demonstrated by the severe floods of 2009 that caused loss of life, injury, structural damage and economic losses in Ouagadougou. The aim of this research is to develop a web map highlighting the municipality’s flood-prone areas, with a view to informing and raising awareness of flood risk reduction. Using the Leaflet JavaScript mapping library, the study uses HTML, CSS and JavaScript to implement web mapping technology. Data on Ouagadougou’s flood zones is generated by a multi-criteria analysis combining Saaty’s AHP method and GIS in QGIS, integrating seven (7) parameters including hydrography, altitude, slope, rainfall, soil types, land use and soil moisture index. QGIS processes and maps the themes, PostgreSQL with PostGIS serves as the DBMS and GeoServer functions as the map server. The Web GIS platform allows users to visualize the different flood risks, from very low to very high, or the high-risk areas specific to Ouagadougou. The AHP calculations classify the municipality into five flood vulnerability zones: very low (24.48%), low (27.93%), medium (23.01%), high (17.11%) and very high (7.47%). Effective risk management requires communication and awareness-raising. This online mapping application serves as a tool for communication, management and flood prevention in Ouagadougou, helping to mitigate flood-related natural disasters.

Study of a Comprehensive Assessment Method for Coal Mine Safety Based on a Hierarchical Grey Analysis

Coal mine safety is a complex system, which is controlled by a number of interrelated factors and is difficult to estimate. This paper proposes an index system of safety assessment based on correlated factors involved in coal mining and a comprehensive evaluation model that combines the advantages of the AHP and a grey clustering method to guarantee the accuracy and objectivity of weight coefficients. First, we confirmed the weight of every index using the AHP, then did a general safety assessment by means of a grey clustering method. This model analyses the status of mining safety both qualitatively and quantitatively. It keeps management and technical groups informed of the situation of the coal production line in real time, which aids in making correct decisions based on practical safety issues. A case study in the application of the model is presented. The results show that the method is applicable and realistic with regard to the core of a coal mine's safety management. Consequently, the safe production of a mine and the awareness of advanced safe production management is accelerated.

Multivariate analysis of fluorescence and source identification of dissolved organic matter in Jiaozhou Bay, China

Hierarchical clustering analysis and principal component analysis （PCA） methods were used to assess the similarities and dissimilarities of the entire Excitation-emission matrix spectroscopy （EEMs） data sets of samples collected from Jiaozhou Bay, China. The results demonstrate that multivariate analysis facilitates the complex data treatment and spectral sorting processes, and also enhances the probability to reveal otherwise hidden information concerning the chemical characteristics of the dissolved organic matter （DOM）. The distribution of different water samples as revealed by multivariate results has been used to track the movement of DOM material in the study area, and the interpretation is supported by the results obtained from the numerical simulation model of substance tracing technique, which show that the substance discharged by Haibo River can be distributed in Jiaozhou Bay.

Derringer desirability and kinetic plot LC-column comparison approach for MS-compatible lipopeptide analysis

Lipopeptides are currently re-emerging as an interesting subgroup in the peptide research field, having historical applications as antibacterial and antifungal agents and new potential applications as antiviral, antitumor, immune-modulating and cell-penetrating compounds. However, due to their specific structure, chromatographic analysis often requires special buffer systems or the use of trifluoroacetic acid, limiting mass spectrometry detection. Therefore, we used a traditional aqueous/acetonitrile based gradient system, containing 0.1% （m/v） formic acid, to separate four pharmaceutically relevant lipopeptides （polymyxin B1, caspofungin, daptomycin and gramicidin A1）, which were selected based upon hierarchical cluster analysis （HCA） and principal component analysis （PCA）.In total, the performance of four different C18 columns, including one UPLC column, were evaluated using two parallel approaches. First, a Derringer desirability function was used, whereby six single and multiple chromatographic response values were rescaled into one overall D-value per column. Using this approach, the YMC Pack Pro C18 column was ranked as the best column for general MS-compatible lipopeptide separation. Secondly, the kinetic plot approach was used to compare the different columns at different flow rate ranges. As the optimal kinetic column performance is obtained at its maximal pressure, the length elongation factor λ（Pmax/Pexp） was used to transform the obtained experimental data （retention times and peak capacities） and construct kinetic performance limit （KPL） curves, allowing a direct visual and unbiased comparison of the selected columns, whereby the YMC Triart C18 UPLC and ACE C18 columns performed as best. Finally, differences in column performance and the （dis）advantages of both approaches are discussed.

Water Quality Assessment in the Bamoun Plateau, Western-Cameroon: Hydrogeochemical Modelling and Multivariate Statistical Analysis Approach

This study focuses on the geochemical and bacteriological investigation of surface and ground water in the Bamoun plateau (Western-Cameroon). During the period from September 2013 to August 2014, 71 samples were collected from two springs, one borehole, four wells and the Nchi stream for analysis of major elements. In order to obtain the characteristics of the various species of bacteria, 7 samples were selected. The analytical method adopted for this study is the conventional hydrochemical technic and multivariate statistical analysis, coupled with the hydrogeochemical modelling. The results revealed that, water from the zone under study are acidic to basic, very weakly to weakly mineralized. Four types of water were identified: 1) CaMg-HCO3;2) CaMg-Cl-SO4;3) NaCl-SO4 and 4) NaK-HCO3. The major elements were all listed in the World Health Organization guidelines for drinking water quality, except for nitrates which was found at a concentration > 50 mg /l NO-3 in the borehole F401. As for the hydrobiological aspect, the entire sample contained all the bacteriological species except for spring S301 and well P401. According to the hydrogeochemical modelling, the Gibbs model and multivariate statistical tests, the quality of surface and ground water of the Foumban locality is influenced by two important factors: 1) the natural factors characterized by the water-rock interaction, evapotranspiration/crystallization, 2) the anthropogenic factors such as: uncontrolled discharges of liquid and solid effluents of all kinds and without any prior treatment within the ground and the strong urbanization accompanied by lack of sanitation and insufficient care.

The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS

Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.

Mathematical Tools of Cluster Analysis

The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.

Compatibility Rules of Neonatal Parenteral Nutrition Prescriptions Based on Association Rules and Hierarchical Cluster Analysis

[Objectives]To explore the compatibility rules of neonatal parenteral nutrition(PN)prescriptions based on association rules and hierarchical cluster analysis,thereby providing a reference for standardizing neonatal parenteral nutrition supportive therapy.[Methods]The data about neonatal PN formulations prepared by the Pharmacy Intravenous Admixture Services(PIVAS)of the Affiliated Hospital of Chengde Medical University from July 2015 to June 2021 were collected.The general information of the prescriptions and the frequency of drug use were analyzed with Excel 2019;the boxplot of drug dosing was drawn using GraphPad 8.0 software;and SPSS Modeler 18.0 and SPSS Statistics 26.0 were used to perform association rules and hierarchical cluster analysis.[Results]A total of 11488 PN prescriptions were collected from 1421 newborns,involving 18 kinds of drugs,which were divided into 11 types of nutrients.Association rules analysis yielded 84 nutrient substance combinations.The combination of fat emulsion-water-soluble vitamins-fat-soluble vitamins-glucose-amino acids had the highest confidence(99.95%).The hierarchical cluster analysis divided nutrients into 5 types.[Conclusions]The prescriptions of PN for newborns were composed of five types of nutrients:amino acids,fat emulsion,glucose,water-soluble vitamins,and fat-soluble vitamins.According to the lack of electrolytes and trace elements,appropriate drugs can be chosen to meet nutritional demands.This study provides reference basis for reasonable selection of drugs for neonatal PN prescriptions and further standardization of PN supportive therapy in newborns.

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

Optimum Designing of Gas Distribution Networks of Ilam Province by Using GIS Network and Spatial Analysis

Undoubtedly, pipeline transport is considered as significant economical artery of country and national valuable resources, so it is necessary to use latest technologies, major standards and instructions and the best human resources in designing, operation and supervision in construction and also protection of it. Also, all authorities and involved of construction and operation of gas industries installation should observe safety criteria, health and environment and aware of them ever. In fact, in designing of these programs, in addition to technical and economical points, environmental characteristics should be considered in order to their construction has minimum damage to environment. On the other hand, common and traditional approaches of pipeline routing are based on using costly and time-consuming methods. In these methods, it is not easily to use all effective parameters in determining optimum way. According to capability of analysis of network spatial information systems in incorporation of spatial data, for using all effective parameters in routing, this environment is used, therefore weighted overlay analysis (Boleyn, index and fuzzy) and shortest path are modeled for finding optimum path of pipeline in GIS environment.

Hierarchical Visual Analysis and Steering Framework for Astrophysical Simulations

A framework for accelerating modern long-running astrophysical simulations is presented, which is based on a hierarchical architecture where computational steering in the high-resolution run is performed under the guide of knowledge obtained in the gradually refined ensemble analyses. Several visualization schemes for facilitating ensemble management, error analysis, parameter grouping and tuning are also integrated owing to the pluggable modular design. The proposed approach is prototyped based on the Flash code, and it can be extended by introducing userdefined visualization for specific requirements. Two real-world simulations, i.e., stellar wind and supernova remnant, are carried out to verify the proposed approach.

Safety Risk Assessment of Overturning Construction of Towering Structure Based on Cloud Matter–Element Coupled Model

Rapid urbanization has led to a surge in the number of towering structures,and overturning is widely used because it can better accommodate the construction of shaped structures such as variable sections.The complexity of the construction process makes the construction risk have certain randomness,so this paper proposes a cloudbased coupled matter-element model to address the ambiguity and randomness in the safety risk assessment of overturning construction of towering structures.In the pretended model,the digital eigenvalues of the cloud model are used to replace the eigenvalues in the matter–element basic element,and calculate the cloud correlation of the risk assessment metrics through the correlation algorithm of the cloud model to build the computational model.Meanwhile,the improved hierarchical analysis method based on the cloud model is used to determine the weight of the index.The comprehensive evaluation scores of the evaluation event are then obtained through the weighted average method,and the safety risk level is determined accordingly.Through empirical analysis,(1)the improved hierarchical analysis method based on the cloud model can incorporate the data of multiple decisionmakers into the calculation formula to determine theweights,which makes the assessment resultsmore credible;(2)the evaluation results of the cloud-basedmatter-element coupledmodelmethod are basically consistent with those of the other two commonly used methods,and the confidence factor is less than 0.05,indicating that the cloudbased physical element coupled model method is reasonable and practical for towering structure overturning;(3)the cloud-based coupled element model method,which confirms the reliability of risk level by performing Spearman correlation on comprehensive assessment scores,can provide more comprehensive information of instances compared with other methods,and more comprehensively reflects the fuzzy uncertainty relationship between assessment indexes,which makes the assessment results more realistic,scientific and reliable.

Anthropogenic disturbance of aquatic biodiversity and water quality of an urban river in Penang, Malaysia

Malaysia's rapid economic and demographic development have placed negative pressure on its water supplies and the quality of the Juru River, which is close to the nation's capital and its major source of water. Healthy aquatic ecosystems are supported by physicochemical properties and biological diversity. This study evaluated the anthropogenic impacts on aquatic biodiversity, especially plankton, fish, and macrobenthos, as well as the water quality of the Juru River in the Penang area. Aquatic biodiversity and river water parameters were collected from ten sampling stations along the Juru River. Seven variables were used to assess the physicochemical environment: pH, temperature, total suspended solids (TSS), salinity, dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand. At each sampling station, the total number of plankton, fish, and macrobenthic taxa were counted and analyzed. The relationships between the physicochemical parameters and aquatic biodiversity were investigated with biotypological analysis, principal component analysis, hierarchical cluster analysis, and linear regression analysis. These analyses showed that the richness and diversity indices were generally influenced by salinity, temperature, TSS, BOD, and pH. The data obtained in this study supported the bioindicator concept. The findings, as they related to scientifically informed conservation, could serve as a model for Juru River management, as well as for river management throughout Malaysia and other tropical Asian countries.

Application of PCA and HCA to the Structure-Activity Relationship Study of Fluoroquinolones

Density functional theory （DFT） was used to calculate molecular descriptors （properties） for 12 fluoro-quinolone with anti-S.pneumoniae activity. Principal component analysis （PCA） and hierarchical cluster analysis （HCA） were employed to reduce dimensionality and investigate in which variables should be more effective for classifying fluoroquinolones according to their degree of an-S.pneumoniae activity. The PCA results showed that the variables ELUMO, Q3, Q5, QA, logP, MR, VOL and △EHL of these compounds were responsible for the anti-S.pneumoniae activity. The HCA results were similar to those obtained with PCA.The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with antiS.pneumoniae activity. By using the chemometric results, 6 synthetic compounds were analyzed through the PCA and HCA and two of them are proposed as active molecules with anti-S.pneumoniae, which is consistent with the results of clinic experiments.

Study on Land Use Regionalization in Henan Province

According to the natural ecology and socio-economic conditions in Henan Province, a land use regionalization index system with 6 factors and 24 factor layers was constructed by combining with the characteristics of land use in Henan Province. Expert scoring method was used to determine the weights of the indicators. Based on the similarities and differences of these factors in the index system at county （city, district） levels, hierarchical clustering method was used to make the quantitative analysis to the land use regionalization in Henan Province. And constrastive analysis and qualitative analysis were made to the regionalization scheme by combining with the acutal conditions in the counties （cities, districts）, and finally, Henan Province was classified into 6 regions.

DNA Sequence Classification Based on the Side Chain Radical Polarity of Amino Acids

The features of DNA sequence fragments were extracted from the distribution density of the condons in the individual cases of DNA sequence fragments. Based on the polarity of side chain radicals of amino acids molecules, the amino acids were classified into five categories, and the frequencies of these five categories were calculated. This kind of feature extraction based on the biological meanings not only took the content of basic groups into consideration, but also considered the marshal ing sequence of the basic groups. The hierarchical clustering analysis and BP neural network were used to classify the DNA sequence fragments. The results showed that the classification results of these two kinds of algo-rithms not only had high accuracy, but also had high consistence, indicating that this kind of feature extraction was superior over the traditional feature extraction which only took the features of basic groups into consideration.

Land Use Regionalization of Rural Settlements in China

This paper compartmentalizes regional land use of rural settlements in China by employing a hierarchical clustering method.The statistic data are sourced from the National Bureau of Statistics of China(NBSC) and the data of land use change from the Ministry of Land and Resources of China(MLRC).The population of rural settlement decreases from the southeast to the northwest of China and the density of rural settlement decreases from the east to the west of China.Land-use scale of rural settlement,the proportion of one-storey houses and the average household area decrease from the north to the south of China.The ratio of area of cultivated land to rural settlement is high in the northeast and southwest of China but low in the southeast of China.The land use regionalization of rural settlement can be divided into four regions,namely:the northern region of China,Qinghai-Tibet,Yunnan-Guizhou,and the middle and eastern region of China.The northern region of China and the middle and eastern region of China can be further divided into nine sub-regions:Xinjiang,Northeast China,Ningxia and Inner Mongolia,North China,the south of the Changjiang(Yantze) River and Sichuan Basin,Jiangsu-Shanghai,South China,the Loess Plateau,and Guangxi.In consideration of the significant regional differences,it is proposed that different policies should be implemented regarding the utilization and management of rural settlements.

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.