Correlation and Pathway Analysis of the Carbon,Nitrogen,and Phosphorus in Soil-Microorganism-Plant with Main Quality Components of Tea(Camellia sinensis)

The contents of carbon(C),nitrogen(N),and phosphorus(P)in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea,such as tea polyphenols,amino acids,and caffeine.However,few studies have quantified the effects of these factors on the main quality components of tea.The study aimed to explore the interactions of C,N,and P in soil-microorganisms-plants and the effects of these factors on the main quality components of tea by using the path analysis method.The results indicated that(1)The contents of C,N,and P in soil,microorganisms,and tea plants were highly correlated and collinear,and showed significant correlations with the main quality components of tea.(2)Optimal regression equations were established to esti-mate tea polyphenol,amino acid,catechin,caffeine,and water extract content based on C,N,and P contents in soil,microorganisms,and tea plants(R^(2)=0.923,0.726,0.954,0.848,and 0.883,respectively).(3)Pathway analysis showed that microbial biomass phosphorus(MBP),root phosphorus,branch nitrogen,and microbial biomass carbon(MBC)were the largest direct impact factors on tea polyphenol,catechin,water extracts,amino acid,and caffeine content,respectively.Leaf carbon,root phosphorus,and leaf nitrogen were the largest indirect impact factors on tea polyphenol,catechin,and water extract content,respectively.Leaf carbon indirectly affected tea polyphenol content mainly by altering MBP content.Root phosphorus indirectly affected catechin content mainly by altering soil organic carbon content.Leaf nitrogen indirectly affected water extract content mainly by altering branch nitrogen content.The research results provide the scientific basis for reasonable fertilization in tea gardens and tea quality improvement.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.

Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method

A crop growth model,integrating genotype,environment,and management factor,was developed to serve as an analytical tool to study the influence of these factors on crop growth,production,and agricultural planning.A major challenge of model application is the optimization and calibration of a considerable number of parameters.Sensitivity analysis(SA) has become an effective method to identify the importance of various parameters.In this study,the extended Fourier Amplitude Sensitivity Test(EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.The outputs for the SA included grain yield and quality(take grain protein content(GPC) as an indicator) at maturity stage,as well as leaf area index,aboveground biomass,and aboveground nitrogen accumulation at the critical process variables.The key results showed that:(1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves;(2) the sensitivity parameters of grain yield and GPC were different,and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield;and(3) the sensitivity analyses of some process variables,including leaf area index,aboveground biomass,and aboveground nitrogen accumulation,should be performed differently.Finally,some parameters,which improve the model’s structure and the accuracy of the process simulation,should not be ignored when maturity output as an objective variable is studied.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

Cookie Baking Process Optimization and Quality Analysis Based on Food 3D Printing

In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.

Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data

At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.

Prediction and sensitivity analysis of the surface quality in Ni60/WC coatings

For laser cladding a large temperature gradient easily weakened the surface quality by generating cracks and irregular coating surfaces,which in turn affected the bearing capacity and corrosion resistance of coatings in the rapid heating and cooling process.The response surface methodology(RSM)was used to predict coating cracks by changing the powder ratio,energy density,and preheating temperature,which obtained the relevant mathematical model.After that,the sensitivity of the crack length to process parameters was analyzed based on the sensitivity analysis method.The effect of Ni60/WC composite powder process parameters on the surface quality was revealed in laser cladding.The crack length first decreased and then increased,and the Smooth decreased with the increased powder ratio.The crack length and Smooth increased with the increased energy density.The crack length decreased and Smooth increased with the increased preheating temperature.Sensitivity analysis showed that the crack length and Smooth were the most sensitive to the powder ratio.Therefore,the process parameters were reasonably selected to control the surface quality.The mathematical model and sensitivity analysis method in the work could improve the surface quality,which provided a theoretical basis for the prediction and control of laser cladding cracks.

Correlation and variability analysis of yield and quality related traits in different peanut varieties across various ecological zones of China

Peanut cultivation in China spans various ecological zones, each with unique environmental conditions. Identifying suitable peanut varieties for these regions has been challenging due to significant phenotypic variations observed across environments. This study, based on a comprehensive analysis of 256 peanut varieties, selected nine representative varieties(Huayu23, Yuanza9102, Silihong, Wanhua2, Zhonghua6, Zhonghua16, Zhonghua21,Zhonghua215, Zhonghua24) for cultivation in five distinct ecological zones including Chengdu, Hefei, Nanjing,Shijiazhuang, and Wuhan. The yield and quality related phenotypic traits of these varieties were thoroughly assessed, revealing a complex interplay between genetic and environmental factors. Principal component analysis(PCA) effectively distinguished varieties based on yield and quality traits. Strong correlations were observed between specific traits, such as seed size and quality components. The G × E interaction was evident, as some varieties consistently performed better in certain environments. Varieties with lower coefficient of variation(CV)values exhibited stable trait expression, making them reliable choices for broad cultivation. In contrast, varieties with higher CV values displayed greater sensitivity to environmental fluctuations, potentially due to specific genetic factors. Two high oleic acid varieties, Zhonghua24 and Zhonghua215, demonstrated remarkable stability in oleic acid content across diverse environments, suggesting the presence of genetic mechanisms that buffer against environmental variations. Overall, this study underscores the importance of selecting peanut varieties based on their adaptability and performance in specific ecological zones. These findings provide valuable insights for peanut breeders and farmers, facilitating informed decisions for improved crop production and quality.

Development and characterization of wheat–Aegilops kotschyi 1U^(k)(1A)substitution line with positive dough quality parameters

Exploring novel high molecular weight glutenin subunits(HMW-GSs)from wild related species is a strategy to improve wheat processing quality.The objective of the present investigation was to identify the chromosomes of the wheatalien introgression line N124,derived from the hybridization between Triticum aestivum with Aegilops kotschyi,and characterize the effects on quality-related traits.Fluorescence in situ hybridization karyotypes showed that N124 is a disomic 1U^(k)(1A)substitution line.Sodium dodecyl sulfate polyacrylamide gel electrophoresis(SDS-PAGE)and reversedphase high-performance liquid chromatography verified N124 expressed two HMW-GSs of the Ae.kotschyi parent.PacBio RNA sequencing and phylogenetic analysis confirmed that the two HMW-GSs were U^(k)x and U^(k)y.Compared to the wheat parent,the substitution line had no obvious agronomic defects except fewer grains per spike but improved several major quality parameters.It can be served as a donor or bridge material for wheat quality improvement.

Water Quality Evaluation of Chapurson Valley in Hunza Nagar, Gilgit Baltistan, Pakistan, Based on Statistical Analysis and Water Quality Index

Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hunza Nagar area of the GB is increasing as a result of anthropogenic activities and tourism. The present study focuses on the public health quality of drinking water of Chapurson valley. The study addressed the fundamental drinking water quality criteria in order to understand the state of the public health in the valley. To ascertain the current status of physico-chemical, metals, and bacteriological parameters, 25 water samples were collected through deterministic sampling strategy and examined accordingly. The physico-chemical parameters of the water samples collected from the valley were found to meet the World Health Organization (WHO) guidelines of drinking water. The water samples showed a pattern of mean metal concentrations in order of Arsenic (As) > Lead (Pb) > Iron (Fe) > Zinc (Zn) > Copper (Cu) > Magnesium (Mg) > Calcium (Ca). As, Cu, Zn, Ca and Mg concentration were under the WHO guidelines range. However, results showed that Pb and Fe are present at much higher concentrations than recommended WHO guidelines. Similarly, the results of the bacteriological analysis indicate that the water samples are heavily contaminated with the organisms of public health importance (including total coliforms (TCC), total faecal coliforms (TFC) and total fecal streptococci (TFS) are more than 3 MPN/100mL). Three principal components, accounting for 48.44% of the total variance, were revealed using principal component analysis (PCA). Bacteriological parameters were shown to be the main determinants of the water quality as depicted by the PCA analysis. The dendrogram of Cluster analysis using the Ward’s method validated the same traits of the sampling locations that were found to be contaminated during geospatial analysis using the Inverse Distance Weight (IDW) method. Based on these findings, it is most likely that those anthropogenic activities and essentially the tourism results in pollution load from upstream channels. Metals may be released into surface and groundwater from a few underlying sources as a result of weathering and erosion. This study suggests that the valley water resources are more susceptible to bacteriological contamination and as such no water treatment facilities or protective measure have been taken to encounter the pollution load. People are drinking the contaminated water without questioning about the quality. It is recommended that the water resources of the valley should be monitored using standard protocol so as to protect not only the public health but to safe guard sustainable tourism in the valley.

Breeding of National Authorized New Wheat Variety Shannong 116 and Supporting Cultivation Technical Measures for High Yield, High Quality and High Efficiency

Shannong 116 is a strong gluten,high yield and multi-resistance wheat variety bred by Shandong Agricultural University,which was approved by the State in 2021 and by Shandong Province in 2022.Shannong 116 combines the excellent characteristics of the female parent(strong gluten,disease resistance and early maturity)and the male parent(high yield,water saving and lodging resistance),with a plant height of 76.9 cm,compact plant type,orderly spike layer and good maturity performance,which is suitable for large-scale promotion and market order planting in Huanghuai wheat area.In this paper,the characteristics of Shannong 116 are analyzed,and cultivation technical measures for high yield,high quality and high efficiency are put forward,in order to provide a technical support for the popularization and application of the variety.

Retrospective Analysis of External Quality Assessment Data of Humoral (Except Feces) Morphology in Guangxi from 2010 to 2022

[Objectives] The study aims to improve the ability of inspectors to recognize humoral morphology and provide data reference for the improvement of EQA work of humoral morphology by Guangxi Clinical Laboratory Center. [Methods] A retrospective analysis was made on the EQA of humoral morphology by Guangxi Clinical Laboratory Center from 2010 to 2022, and the number of participating laboratories, the rate of return, sample types and maps of EQA of humoral morphology such as urine sediment, vaginal secretions and so on(except feces), and the reasons for the high error rate of the results of the participating laboratories were analyzed. [Results] The number of laboratories participating in the assessment of humoral morphology increased from 98 in 2010 to 371 in 2022. Except for 2011(90.98%), the rate of return was more than 94%, and the highest rate of return was up to 100%. A total of 119 pictures of EQA of humoral morphology were studied, including 101 urine sediment smears, 13 vaginal secretions smears and 5 other smears. The types of specimens were relatively comprehensive. The distribution of the maps was basically reasonable. The urinary sediment was mainly composed of crystalline salts, followed by tubes, and cells and fungi ranked third. The coverage was relatively complete. The error rate of return results from high to low were bacteria(error rate was equal to 40%), tubes and crystalline salts(error rate was close to 29%), and cells and fungi(error rate was about 14%). The main cases with high error rate of return results are as follows: the difference between different tubes was not clear, and the diversity of crystal morphology was not understood enough;the phase division of cells in the same system was wrong. [Conclusions] The analysis and summary of the reasons for the high error rate of the data and results of Guangxi's EQA of humoral morphology is helpful for laboratory physicians to improve their awareness of morphological examination and identify clearly the error type and blurred concept, and lays a foundation for Guangxi Clinical Laboratory Center to carry out EQA of humoral morphology more specifically.

Application of Eye Movement Analysis in Landscape Visual Quality Evaluation

The existing methods of landscape visual quality evaluation is mostly based on people's subjective feelings,and the objectivity and scientificity of evaluation results are insufficient.As an important means of experimental psychology,eye movement technology can show great advantages and potential in landscape visual quality evaluation.On the basis of combing and summarizing the relevant literature,based on the explanation of the technical principle of eye movement analysis,the application field,research content,technical methods and other aspects of eye movement analysis were reviewed.The application prospect of eye movement technology in landscape visual quality evaluation was explored to provide theoretical reference for the in-depth evaluation and research of landscape visual quality evaluation theory.

Outlier Detection of Air Quality for Two Indian Urban Cities Using Functional Data Analysis

Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities turn up dangerous contaminants in our surroundings. This study investigated two years’ worth of air quality and outlier detection data from two Indian cities. Studies on air pollution have used numerous types of methodologies, with various gases being seen as a vector whose components include gas concentration values for each observation per-formed. We use curves to represent the monthly average of daily gas emissions in our technique. The approach, which is based on functional depth, was used to find outliers in the city of Delhi and Kolkata’s gas emissions, and the outcomes were compared to those from the traditional method. In the evaluation and comparison of these models’ performances, the functional approach model studied well.

Preliminary Study on Quality Analysis of Yao Medicine Pileostegia tomentella Hand.-Mazz.

[Objectives]To establish a method for quality analysis of Medicine Pileostegia tomentella Hand.-Mazz.[Methods]According to the testing method of Chinese Pharmacopoeia(2020 Edition),the water content,total ash and alcohol-soluble extract of P.tomentella Hand.-Mazz.were determined.[Results]10 batches of P.tomentella Hand.-Mazz.from different habitats and in different collection time in Guangxi all had the same plant morphology and medicinal properties.The experimental results showed that the water content was 7.92%-10.35%,the total ash content was 5.53%-6.62%,the acid-insoluble ash content was 0.59%-0.76%,and the extract content was 11.63%-16.12%.[Conclusions]The identification method in this study can be used for the quality analysis of P.tomentella Hand.-Mazz.,and the experimental results can provide a scientific basis for the development and utilization of P.tomentella Hand.-Mazz.and the establishment of its quality standard.

Bibliometric analysis of quality of life in peritoneal dialysis patients

Objective:Nowadays,the medical model is constantly changing,and the problem of how to improve the quality of life of peritoneal dialysis patients arises at a historic moment.This paper observes the publication and cooperation of research countries and institutions related to the quality of life of peritoneal dialysis patients,understands the research hotspots,explores the research trends,and provides a reference for further research by scholars.Methods:The search database was the Web of Science core collection,the search terms“peritoneal dialysis”and“quality of life”,the document type“Article or Review”,the language“English”and the period from 1985 to 2022.Results:A total of 1,597 literature were retrieved,and the number of documents showed an annual increase.The United States has the highest volume and centrality of publications and the most cooperation with other countries.The institution with the largest number of articles is the University of Toronto in Canada.The top three most highly cited documents were analyzed to derive the knowledge base of the research area.According to the analysis of keywords,there are high-frequency keywords such as“quality of life”and“peritoneal dialysis”and 11 keyword clusters.Understand the changing trends and frontiers in the research field through timeline maps and mutated words.Conclusion:The comparison of quality of life between peritoneal dialysis and haemodialysis and the study of factors influencing the quality of life is a hot research topic.With the gradual deepening of the research,it is suggested that future research should focus on the intervention research of influencing factors and clarify the intervention measures to improve patients’quality of life.

Influence of protein characteristics and the proportion of gluten on end-use quality in Korean wheat cultivars

The effects of protein characteristics and the proportion of gluten on end-use quality in 13 Korean wheat cultivars for three years were verified in this study. Year, cultivar, and the interaction between the year and the cultivar influenced protein characteristics, the proportion of gluten except for γ-and ω-gliadin using RP-HPLC(reversed-phase high-performance liquid chromatography), and end-use quality. Protein characteristics and the proportion of gluten in Korean wheat cultivars were between those of Australian standard white(ASW) and hard wheat(AH). Korean wheat cultivars exhibited a higher average α+β gliadin proportion than imported wheat, a γ-gliadin proportion similar to that of dark northern spring wheat, and the same ω-gliadin proportion as AH. They showed a bread-loaf volume intermediate between those of ASW and AH and a texture of cooked noodles similar to that of soft white wheat, but less springiness than imported wheat. The cookie diameter of Korean wheat cultivars was similar to that of hard red winter wheat. There was a correlation between bread-loaf volume and protein characteristics, except for the protein content in Korean wheat cultivars. Springiness and cohesiveness of cooked noodles were not correlated with protein characteristics, while hardness was correlated with the protein content and water absorption of a mixograph. Cookie diameter was negatively correlated with the sodium dodecyl sulfate(SDS) sedimentation volume and water absorption of a mixograph. The end-use quality was not correlated with any proportion of gluten composition. Principal component analysis(PCA) showed that the proportion of gluten was not related to the quality of the bread(both PCs, 81.3%), noodle(77.7%), and cookie(82.4%). PCA explained that Keumkang is suitable for superior bread, while Uri is good for cooked noodles and cookies.

Effects of Nitrogen on Grain Yield,Nutritional and Processing Quality of Wheat for Different End Uses

The effects of nitrogen on grain yield, nutritional quality and processing quality of wheat for different end uses were studied in Yangzhou from 2000 to 2002. Increasing N application amount and/or the ratio of topdressing N after elongation stage could raise grain yield, protein content, wet gluten content, sedimentation, falling number, development time and stability time. To produce strong-gluten and medium-gluten wheat, the suitable ratio of basis : tillering : elongation : booting N fertilizer was 3:1:3:3or5:1:2:2 with 180 - 240 kg N ha-1. To produce weak-gluten wheat, the ratio of basis : tillering : elongation N fertilizer should be 7 : 1 : 2 with 180 kg N ha-1 and 240×104 ha-1 seedlings.

Distribution of Grain Hardness in Chinese Wheats and Genetic Analysis

A hundred winter wheat and 41 spring wheat cultivars and advanced lines were used to investigate the distribution of grain hardness in Chinese wheats and correlations between grain hardness and other kernel traits. P1, P2, F1 , F2 and F3 from three crosses, i. e. , Liken2/Yumai2, 85Zhong33/Wenmai6 and 85Zhong33/95Zhong459 were sown to study the genetics of grain hardness. Significant correlation was observed between hardness measured by Single Kernel Characteristic System 4100 (SKCS 4100) and Near Infrared (NIR) Spectroscopy, r ranging from 0.85 to 0.94. Chinese wheat is a mixed population in terms of hardness, ranging from very soft to very hard. For autumn-sown wheat, on average, grain hardness decreases from north to south and spring-sown wheat is dominant with hard type. Hardness is negatively associated with flour color, and its associations with flour yield and ash content differ in winter and spring wheats. Grain hardness is controlled by a major gene and several minor genes with additive effect mostly, but dominant effect is also observed, with heritability of 0.78.