Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Evaluation of Phytochemical Analysis and Total Phenol Content of Proso Millet and Barnyard Millet

Whole grains of proso and barnyard millets were sequentially extracted using different solvents(hexane,chloroform,ethyl acetate,and methanol).Phytochemical analysis was performed qualitatively,and the total phenolic content in the extracts of proso and barnyard millets was quantified.Alkaloids and cardiac glycosides were identified in all solvent extracts of both millets.Anthraquinone and glycosides yielded negative results in all solvent extracts of both millets.Among all the solvent extracts,methanol extracts of proso and barnyard millets showed the presence of major compounds such as flavonoids,terpenoids,amino acids,tannins,and phenolics compounds.The maximum amount of phenols was found in methanolic extracts of proso and barnyard millets(0.669±0.003 and 0.625±0.003),followed by the chloroform extract of proso and barnyard millets(0.284±0.002 and 0.257±0.003).The minimum amount of phenolics was found in the acetone extract of proso and barnyard millets.The methanol extract of both millets showed the presence of major compounds with high phenolic content.

Multi-omics analysis reveals the pivotal role of phytohormone homeostasis in regulating maize grain water content

Grain water content(GWC)is a key determinant for mechanical harvesting of maize(Zea mays).In our previous research,we identified a quantitative trait locus,qGWC1,associated with GWC in maize.Here,we examined near-isogenic lines(NILs)NILL and NILH that differed at the qGWC1 locus.Lower GWC in NILL was primarily attributed to reduced grain water weight(GWW)and smaller fresh grain size,rather than the accumulation of dry matter.The difference in GWC between the NILs became more pronounced approximately 35 d after pollination(DAP),arising from a faster dehydration rate in NILL.Through an integrated analysis of the transcriptome,proteome,and metabolome,coupled with an examination of hormones and their derivatives,we detected a marked decrease in JA,along with an increase in cytokinin,storage forms of IAA(IAA-Glu,IAA-ASP),and IAA precursor IPA in immature NILL kernels.During kernel development,genes associated with sucrose synthases,starch biosynthesis,and zein production in NILL,exhibited an initial up-regulation followed by a gradual down-regulation,compared to those in NILH.This discovery highlights the crucial role of phytohormone homeostasis and genes related to kernel development in balancing GWC and dry matter accumulation in maize kernels.

A Content Analysis Study Describing Information Regarding Female Genital Mutilation Present in US Medical Textbooks

This study examines the depth of information regarding Female Genital Mutilation (FGM)/Female Cutting (FC) present in US medical textbooks. According to Klein et al. (2018) [1], despite substantial attention regarding the effects of FGM, this practice is still ongoing all over the world. Healthcare providers are very important actors who need to understand FGM in order to provide care to girls and women subjected to it, but the small amount of existing work in this area shows a low level of knowledge and training about FGM/FC among U.S. physicians. The Center for Disease Control and Prevention (CDC) first published the estimated number of girls at risk of FGM/FC in the U.S. to be about 168,000 in 1997;that number had increased to 513,000 girls in the US by 2012 (according to Goldberg et al.) In this study, a small sample of medical textbooks were analyzed for the degree to which the textbooks include information regarding FGM/FC. The results of this study show that US medical textbooks do not include information regarding FGM/FC, suggesting that most medical providers don’t have knowledge and training about FGM/FC. The inadequate information on FGM/FC in medical textbooks reflects a culturally biased lack of recognition of the number of girls and women affected in the US. This maintains an ongoing pattern of silence that reinforces gender inequality. Considering the inadequate information covered in US medical textbooks on FGM/FC, and the concordant lack of provider training to provide advice and care to affected girls and women, incorrect and missed diagnoses may lead to harm to victims of FGM/FC. Providers would be better equipped to care for women and girls if their medical training included information regarding FGM/FC.

Multimodal sentiment analysis for social media contents during public emergencies

Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis,lacking the combination of multimodal contents.In this paper,we propose to combine texts and images generated in the social media to perform sentiment analysis.Design/methodology/approach:We propose a Deep Multimodal Fusion Model(DMFM),which combines textual and visual sentiment analysis.We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis.BiLSTM is employed to generate encoded textual embeddings.To fully excavate visual information from images,a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy.A multimodal fusion method is implemented to fuse textual and visual embeddings completely,producing predicted labels.Findings:We performed extensive experiments on Weibo and Twitter public emergency datasets,to evaluate the performance of our proposed model.Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models.The introduction of images can boost the performance of sentiment analysis during public emergencies.Research limitations:In the future,we will test our model in a wider dataset.We will also consider a better way to learn the multimodal fusion information.Practical implications:We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies.Originality/value:We consider the images posted by online users during public emergencies on social platforms.The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.

Determination of Isoflavone from Soybean Lines Cultivated in Jilin Province and Correlation Analysis between Isoflavone Content and Soybean Quality

[Objective]The aim of this study was to set up a high performance liquid chromatography for rapid determination of isoflavones from soybean and analyze the correlation between isofalvone content and protein or fat content. [Method]The isoflavones were firstly extracted by 80% methanol and then hydrolyzed at 100 ℃. The chromatographic separation adopted a reversed-phase C18 analytical column with binary high-pressure gradient elution,while its analysis time was 25 min and column temperature was 40 ℃. The diode array detector was used for monitoring with wavelength of 260 nm. The correlation between isofalvone content and protein or fat content was analyzed by data processing system Origin 6.0. [Result]The high performance liquid chromatograph for determination of isoflavones from soybean was verified to be accurate and reliable by methodology. The isoflavones of 85 soybean lines cultivated in Jilin Province were determined,and the results primarily showed the characters and ranges of isoflavones from soybean lines cultivated in Jilin Province,while the isoflavone content of soybeans ranged from 2.29 to 4.89 mg/g,and the average content was 3.36 mg/g. The isoflavone content of 5 soybean lines exceeded 4 mg/g,while there was a remarkably negative correlation between isoflavone content and protein content,and there was no significant positive correlation between isoflavone content and fat content. [Conclusion]The isoflavone content of soybean lines cultivated in Jilin Province is higher,so it is feasible for breeding the soybean lines with high isoflavone content and fat contetnt.

Overview of Precious Metal Content Analysis Methods in Automotive Catalytic Converter

With the increasing awareness of environmental protection, people’s concern of pollution issues arising. Vehicles, as the most important means of transportation, its exhaust emission has received considerable attention. The catalytic converter is able to purify harmful substances in exhaust gas. The absolute content of precious metals in the catalytic converter dominates the exhaust gas purification effect. Accurate detection of precious metal content is of great significance for controlling the cost of catalysts, ensuring catalytic performance and recovering precious metals from spent catalysts. We herein summarized several instruments for precious metals content exploration, such as X-ray fluorescence spectrometer (XRF), atomic absorption spectrometer (AAS), inductively coupled plasma emission spectrometer (ICP) and spectrophotometer. In this thesis, the feasibility of using various devices for characterizing precious metal content in catalytic converters is analyzed and their strengths or weaknesses are elaborated.

QTL analysis for ascorbic acid content in strawberry fruit reveals a complex genetic architecture and association with GDP-L-galactose phosphorylase

Strawberry(Fragaria×ananassa)fruits are an excellent source of L-ascorbic acid(AsA),a powerful antioxidant for plants and humans.Identifying the genetic components underlying AsA accumulation is crucial for enhancing strawberry nutritional quality.Here,we unravel the genetic architecture of AsA accumulation using an F1 population derived from parental lines‘Candonga’and‘Senga Sengana’,adapted to distinct Southern and Northern European areas.To account for environmental effects,the F1 and parental lines were grown and phenotyped in five locations across Europe(France,Germany,Italy,Poland and Spain).Fruit AsA content displayed normal distribution typical of quantitative traits and ranged five-fold,with significant differences among genotypes and environments.AsA content in each country and the average in all of them was used in combination with 6,974 markers for quantitative trait locus(QTL)analysis.Environmentally stable QTLs for AsA content were detected in linkage group(LG)3A,LG 5A,LG 5B,LG 6B and LG 7C.Candidate genes were identified within stable QTL intervals and expression analysis in lines with contrasting AsA content suggested that GDP-L-Galactose Phosphorylase FaGGP(3A),and the chloroplast-located AsA transporter gene FaPHT4;4(7C)might be the underlying genetic factors for QTLs on LG 3A and 7C,respectively.We show that recessive alleles of FaGGP(3A)inherited from both parental lines increase fruit AsA content.Furthermore,expression of FaGGP(3A)was two-fold higher in lines with high AsA.Markers here identified represent a useful resource for efficient selection of new strawberry cultivars with increased AsA content.

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.

Content analysis of documents using neural networks: A study of Antarctic science research articles published in international journals

Content analysis of scientific papers emanating from Antarctic science research during the 25 years period （1980-- 2004） has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research articles published in Science Citation Indexed （SCI） journals were used for the study. Normalized co-word matrix from 35 most-used significant words was used to study the semantic association between the words. Structural Equivalence blocks were constructed from these 35 most-used words. Four-block model solution was found to be optimum. The density table was dichotomized using the mean density of the table to derive the binary matrix, which was used to construct the network map. Network maps represent the thematic character of the blocks. The blocks showed preferred connection in establishing semantic relationship with the blocks, characterizing thematic composition of Antarctic science research. The analysis has provided an analytical framework for carrying out studies on the con- tent of scientific articles. The paper has shown the utility of co-word analysis in highlighting the important areas of research in Antarctic science.

Correlation Analysis of SPAD Value with Chlorophyll Content and Economic Yield Traits of Brassica napus L.

[Objective] The aim was to compare differences of SPAD value, chloro- phyll content, agronomic characters, economic characters and yield traits to analyze correlation of SPAD value with other indices and establish regression functions. [Method] Based on 34 Brassica napus L. varieties, SPAD value, chlorophyll content, agronomic characters, economic characters and yield traits were measured and re- gression functions were established according to correlations. [Result] SPAD value, chlorophyll content, agronomic and economic characters and yield traits all achieved significant level in differences among 34 varieties. Specifically, SPAD value was of extremely significant correlation with chlorophyll a and b, total chlorophyll and carotenoid, and the correlation from high to low was chl-b〉chl-z〉chl-a〉chl-x. SPAD value was of significantly positive correlation with total pod number per plant, plant height, seed number per pod, yield per plant and harvest yield, and of insignificant correlation with branch point height, effective branch number, pod density of main stem, and pod length. [Conclusion] It is simple and rapid to predict chlorophyll con- tent, economic characters and yields of Brassica napus L. with SPAD value and re- gression functions.

Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

Education sector has witnessed several changes in the recent past.These changes have forced private universities into fierce competition with each other to get more students enrolled.This competition has resulted in the adoption of marketing practices by private universities similar to commercial brands.To get competitive gain,universities must observe and examine the students’feedback on their own social media sites along with the social media sites of their competitors.This study presents a novel framework which integrates numerous analytical approaches including statistical analysis,sentiment analysis,and text mining to accomplish a competitive analysis of social media sites of the universities.These techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received,major areas for improvement.A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore Pakistan.Experimental results show that diversity of shared content,frequency of posts,and schedule of updates,are the key areas for improvement for the local universities.Based on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university(RIU)Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

Correlation Analysis of Characters of Indica Hybrid Rice with High Content of Amylose

[Objective] The aim was to research and analyze characters and related correlation of indica hybrid rice containing high amylose. [Method] In the research, major characters of 16 species of groups of {ndica hybrid rice and the correlation were explored in rice belts in Sichuan in 2011. [Result] The variation of chalkiness degree was the highest at 26.8%, followed by rate of head rice at 22.89%; varia- tions of head rice rate and chalkiness degree were lower at 0.72% and 2.61%. In addition, rate of unpolished rice was of significantly negative correlation with chalki- ness degree; rate of chalky rice was of extremely positive correlation with chalkiness degree and gel consistency; chalkiness degree was of significantly positive correla- tion with gel consistency; gel consistency was of significantly negative correlation with protein content; amylose content was of insignificant correlation with gel consis- tency （r=0.166 9） of the 16 hybrid groups. These indicated that gel consistency would be still high as the content of amylose grows （AC≥25%）. For example, when the content of anylose in Chuanxiang 29A/Luhui 37, Kangfeng AJ6684, and Lefeng A/SR536 exceeded 27%, the gel consistency was over 80 mm in the research. [Conclusion] The research provides references for improvement of amylose content in breeding hybrid rice.

Accuracy analysis of iron content of galvanized coating using an X-ray fluorescence spectrometer with an L-spectrum

The accuracy(repeatability and reproducibility) of the iron content analysis of galvanized coating using an X-ray fluorescence spectrometer with an L-spectrum is not better than that of flame atomic absorption spectrometry, sometimes it exceeds the quality control limit.Influences, such as current, voltage, equipment(internal circulating water, 10%CH4+90%Ar, and vacuum) checking, instrument monitoring, sample cleaning, and oper-ators, were investigated by means of 6-sigma and lean operations to improve accuracy.

Transcriptome-based analysis of key genes and pathways affecting the linoleic acid content in chickens

Linoleic acid is an essential polyunsaturated fatty acid that cannot be synthesized by humans or animals themselves and can only be obtained externally.The amount of linoleic acid present has an impact on the quality and flavour of meat and indirectly affects consumer preference.However,the molecular mechanisms influencing the deposition of linoleic acid in organisms are not clear.As the molecular mechanisms of linoleic acid deposition are not well understood,to investigate the main effector genes affecting the linoleic acid content,this study aimed to screen for hub genes in slow-type yellow-feathered chickens by transcriptome sequencing(RNA-Seq)and weighted gene coexpression network analysis(WGCNA).We screened for candidate genes associated with the linoleic acid content in slow-type yellow-feathered broilers.A total of 399 Tiannong partridge chickens were slaughtered at 126 days of age,fatty acid levels were measured in pectoral muscle,and pectoral muscle tissue was collected for transcriptome sequencing.Transcriptome sequencing results were combined with phenotypes for WGCNA to screen for candidate genes.KEGG enrichment analysis was also performed on the genes that were significantly enriched in the modules with the highest correlation.A total of 13310 genes were identified after quality control of transcriptomic data from 399 pectoral muscle tissues.WGCNA was performed,and a total of 26 modules were obtained,eight of which were highly correlated with the linoleic acid content.Four key genes,namely,MDH2,ATP5B,RPL7A and PDGFRA,were screened according to the criteria|GS|>0.2 and|MM|>0.8.The functional enrichment results showed that the genes within the target modules were mainly enriched in metabolic pathways.In this study,a large-sample-size transcriptome analysis revealed that metabolic pathways play an important role in the regulation of the linoleic acid content in Tiannong partridge chickens,and MDH2,ATP5B,RPL7A and PDGFRA were screened as important candidate genes affecting the linoleic acid content.The results of this study provide a theoretical basis for selecting molecular markers and comprehensively understanding the molecular mechanism affecting the linoleic acid content in muscle,providing an important reference for the breeding of slow-type yellowfeathered broiler chickens.

Shadow banking:a bibliometric and content analysis

This study reports on our systematic review of 2008–2021 literature on shadow banking.We present an overview of the shadow banking sector,wherein we discuss the definitions,evolution,functions,and specific activities that comprise it.We conducted a bibliometric analysis using the VOSviewer bibliometric tool on articles collected from the Scopus database,after which we conducted content analysis on top articles from leading sources,and identified four major streams of shadow banking literature.Additionally,we identified gaps in the literature and proposed seven research questions to be addressed in future studies to advance knowledge of the shadow banking sector.The findings of this review may serve as a robust reference for scholars researching various aspects of shadow banking to develop our understanding of this sector.

An Analysis of Cultural Contents in Middle School English Textbooks GO FOR IT

The Chinese EFL textbook GO FOR IT is widely used in most state-owned middle schools of China.According to the national English Curriculum of in China,cultural contents should be provided.The cultural contents are analyzed from two aspects—cultural awareness through verbal communication and the cultural awareness through non-verbal communication.Specifically,appellations and greetings,polite expressions,the dining patterns,cultural imageries of animals are analyzed in this thesis.

Correlation analysis between major nutritional components and resistant starch content in wheat

With 94 spring wheat cultivars as experimental materials, the correlations between the content of resistant starch （RS） in uncooked flour and cooked flour, and the apparent amylose content （AAC）, protein, lipid were investigated. The results showed that RS contents in both the uncooked flour and cooked flour assumed significantly positive correlation with AAC, and significantly negative cor- relation with protein content; and they were proved to be not significantly correlated with lipid content. RS content in uncooked flour was significantly correlated with that in cooked flour. These results provided references for the genetic improvement of wheat cultivars.

QTL Analysis of the Oil Content and the Hull Content in Brassica napus L.

The QTLs of the oil content and the hull content were analyzed in Brassica napus L. by constructing the linkage map. The F2：6RIL population with 188 lines, derived from the cross of GH06 × P147, was used as the mapping population. The SRAP, SSR, AFLP, and TRAP markers were used to construct the linkage map, and the composite interval mapping （CIM） to identify the quantitative trait loci associated with the oil content and the hull content. 300 markers were integrated into 19 linkage groups, covering 1 248.5 cM in total. Seven QTLs were found to be responsible for the oil content with the single contribution to phenotypic variance ranging from 3.73 to 10.46%; four QTLs were found for the hull content with the single contribution to phenotypic variance ranging from 4.89 to 6.84%. The yellow-seeded Brassica napus L. has the advantage of higher oil content and the hull content has a significant effect on the oil content. In addition, the SRAP marker is good for detecting QTL.

Multiscale analysis of the contents of palmatine in the Nature populations of Phellodendron amurense in Northeast China

Palmatine is a valuable ingredient in Chinese medicine that is produced by Phellodendron amurense Rupr. The contents of palmatine content in root bark, trunk bark, perennial branch bark, annual branches, and leaves of the trees with different ages and geographies in Northeast China were measured by high-performance liquid chromatography. The contents of palmatine in the barks of root, trunk, and perennial branch were significantly higher than those in annual branches and leaves. The contents of palmatine in trunk bark and root bark from Lesser Khingan Mountains increased with age, which is significantly opposite to other three vegetation types. The contents of palmatine in perennial branch bark, annual bark and leaves had no significant reg- ularity. Moreover, the contents of palmatine in samples of root bark, trunk bark, perennial branch bark and annual bark varied significantly with latitude. The nature populations of P. amurense growing at low latitude contained significantly more palmatine than those growing at high latitude. These results provide a scientific basis for the reasonable cultivation and efficient utilization of P. amurense.