Genetic Variability and Phenotypic Correlations Study among Grain Quality Traits and Mineral Elements Concentrations in Colored and Non-Colored Rice (Oryza sativa L.)

Twenty-four rice genotypes were examined to assess genetic variability,heritability,and correlations for seven-grain quality traits,eight nutritional elements,and protein.ANOVA revealed significant differences for the quality traits studied.For every trait under study,the phenotypic coefficient of variation was higher than the correspon-dence genotypic coefficient of variation.Heritability in a broad sense varied from 29.75%for grain length to 98.31%for the elongation trait.Hulling percentage recovery had a highly significant positive correlation with milling and head rice percentage.Consequently,milling percentage had a highly positive correlation with head rice percentage.In amylose percentage,all the genotypes belonged to low amylose except the Hassawi-1 variety,which had intermediate amylose content.Mineral nutrition contents of magnesium(Mg),sodium(Na),potas-sium(K),calcium(Ca),copper(Cu),manganese(Mn),zinc(Zn),iron(Fe),or protein percentage gave different variations for 24 rice genotypes under all the nutritional elements.Among the 24 genotypes,ten rice genotypes–HighNutrient-1,HighNutrient-2,HighNutrient-9,HighNutrient-8,HighNutrient-3,Hassawi-2,HighNutrient-7,HighNutrient-6,Hassawi-1,and HighNutrient-4–had the highest heist value for all nutritional and protein con-tents,and could be used as a donor to improving new varieties.There was a positive and significant correlation between magnesium Mg,K,Zn and Fe.Consequently,K had a positive correlation with zinc Zn,Fe,and protein percentage.Clustering analysis was divided into two groups:thefirst group included all genotypes rich in nutri-ents,while the remaining genotypes with low nutritional content were included in the second group.

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.

Combining ability of Egyptian cotton(Gossypium barbadense L.)reveals genetic potential for improved yield and fiber quality

Background As the most widely cultivated fiber crop,cotton production depends on hybridization to unlock the yield potential of current varieties.A deep understanding of genetic dissection is crucial for the cultivation of enhanced hybrid plants with desired traits,such as high yield and fine fiber quality.In this study,the general combining ability(GCA)and specific combining ability(SCA)of yield and fiber quality of nine cotton parents(six lines and three testers)and eighteen F1 crosses produced using a line×tester mating design were analyzed.Results The results revealed significant effects of genotypes,parents,crosses,and interactions between parents and crosses for most of the studied traits.Moreover,the effects of both additive and non-additive gene actions played a notably significant role in the inheritance of most of the yield and fiber quality attributes.The F1 hybrids of(Giza 90×Aust)×Giza 86,Uzbekistan 1×Giza 97,and Giza 96×Giza 97 demonstrated superior performance due to their favorable integration of high yield attributes and premium fiber quality characteristics.Path analysis revealed that lint yield has the highest positive direct effect on seed cotton yield,while lint percentage showed the highest negative direct effect on seed cotton yield.Principal component analysis identified specific parents and hybrids associated with higher cotton yield,fiber quality,and other agronomic traits.Conclusion This study provides insights into identifying potential single-and three-way cross hybrids with superior cotton yield and fiber quality characteristics,laying a foundation for future research on improving fiber quality in cotton.

Sepsis one-hour bundle management combined with psychological intervention on negative emotion and sleep quality in patients with sepsis

BACKGROUND Sepsis is a serious infectious disease caused by various systemic inflammatory responses and is ultimately life-threatening.Patients usually experience depression and anxiety,which affect their sleep quality and post-traumatic growth levels.AIM To investigate the effects of sepsis,a one-hour bundle(H1B)management was combined with psychological intervention in patients with sepsis.METHODS This retrospective analysis included 300 patients with sepsis who were admitted to Henan Provincial People’s Hospital between June 2022 and June 2023.According to different intervention methods,the participants were divided into a simple group(SG,n=150)and combined group(CG,n=150).H1B management was used in the SG and H1B management combined with psychological intervention was used in the CG.The changes of negative emotion,sleep quality and post-traumatic growth and prognosis were compared between the two groups before(T0)and after(T1)intervention.RESULTS After intervention(T1),the scores of the Hamilton Anxiety scale and Hamilton Depression scale in the CG were significantly lower than those in the SG(P<0.001).Sleep time,sleep quality,sleep efficiency,daytime dysfunction,sleep disturbance dimension score,and the total score in the CG were significantly lower than those in the SG(P<0.001).The appreciation of life,mental changes,relationship with others,personal strength dimension score,and total score of the CG were significantly higher than those of the SG(P<0.001).The scores for mental health,general health status,physiological function,emotional function,physical pain,social function,energy,and physiological function in the CG were significantly higher than those in the SG(P<0.001).The mechanical ventilation time,intensive care unit stay time,and 28-d mortality of the CG were significantly lower than those of the SG(P<0.05).CONCLUSION H1B management combined with psychological intervention can effectively alleviate the negative emotions of patients with sepsis and increase their quality of sleep and life.

Grey Clustering Evaluation on Regional Eco-environmental Quality Based on Normalized Index Value

[Objective] The aim was to assess regional eco-environmental quality by means of grey clustering method based on normalized index value. [Method] Eco-environmental quality in Chaohu basin was assessed by using grey clustering method based on normalized index value, and the evaluation results were compared with those of unascertained measure method to verify the feasibility of grey clustering method used to evaluate regional eco-environmental quality. [Result] In the grey clustering assessment method based on normalized index value, indices whose standard normalized values in the same grade were close to each other were classified into one class and had the same whitening function, which reduced the number of whitening functions. Grey clustering method based on normalized index value was used to assess eco-environmental quality in Chaohu basin, and the evaluation results were basically in accordance with those of unascertained measure method, namely eco-environmental quality in Hefei, Chaohu and Lu’an belonged to the third (pass), fourth (worse) and fifth grade (bad), except for one grade difference in overall basin, and the results showed that the method had practicality and could be applied to assess regional eco-environmental quality. [Conclusion] The study could provide theoretical foundation for the establishment of comprehensive management countermeasures of regional ecological environment.

Water quality assessment for Ulansuhai Lake using fuzzy clustering and pattern recognition

Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application.

Spatial quality evaluation for drinking water based on GIS and ant colony clustering algorithm

To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.

Evaluation and classification of residential greenbelt quality based on factor analysis & clustering analysis:An example of Xinxiang City,China

Five factors expressing greenbelt quality and one factor expressing quantity were adopted for evaluation of the residential greenbelt, and the AHP （Analytical Hierarchy Process） method was used to determine the value of factors. Thirty residential areas were selected as the samples. Two principal components were extracted and their expression was constructed by method of factor anlysis, therefore, quality evaluation of residential greenbelt was obtained. The accuracy of the function and implement quality classification toward the residential greenbelts in Xinxiang City were validated by clustering analysis method. The results showed that the greenbelt quality of fourteen residential areas was higher than the average level, of which eleven were newly-built residential areas. The 30 residential areas were classified into three types according to their greenbelt features and their formation by clustering analysis method. Finally rational proposal basing on aforesaid evaluating results was proposed for construction and renewal of residential greenbelt, upon which directive basis was provided for construction and renewal of residential greenbelt.

Detection and Mapping of Water Quality Variation in the Godavari River Using Water Quality Index, Clustering and GIS Techniques

The objective of this research is to develop a tool for planning and managing the water quality of River Godavari. This is achieved by classifying the pollution levels of Godavari River into several categories using water quality index and a clustering approach that ensure simple but accurate information about the pollution levels and water characteristics at any point in Godavari River in Maharashtra. The derived water quality indices and clusters were then visualized by using a Geographical Information System to draw thematic maps of Godavari River, thus making GIS as a decision support system. The obtained maps may assist the decision makers in managing and controlling pollution in the Godavari River. This also provides an effective overview of those spots in the Godavari River where intensified monitoring activities are required. Consequently, the obtained results make a major contribution to the assessment of the State’s water quality monitoring network. Three significant groups (less polluted, moderately and highly polluted sites) were detected by Cluster Analysis method. The results of Discriminant Analysis revealed that five parameters?i.e.?pH, Dissolved Oxygen (DO), Faecal Coliform (FC), Total Coliform (TC) and Ammonical Nitrogen (NH3-N) were necessary for analysis in spatial variation. Using discriminant function developed in the analysis, 100% of the original sites were correctly classified.

Air Quality Prediction Based on Kohonen Clustering and ReliefF Feature Selection

Air quality prediction is an important part of environmental governance.The accuracy of the air quality prediction also affects the planning of people’s outdoor activities.How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention,pollution control and pollution early warning.In this paper,we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants.Firstly,the data of air pollutants in different cities are collected by a sliding window technology,and the data of different cities in the sliding window are clustered by Kohonen method to find the same tends in air pollutants.On this basis,combined with the weather data,we use the ReliefF method to extract the characteristics of climate factors that helpful for prediction.Finally,different types of air pollutants and corresponding extracted the characteristics of climate factors are used to train different sub models.The experimental results of different algorithms with different air pollutants show that this method not only improves the accuracy of air quality prediction,but also improves the operation efficiency.

Maternal and Child Health Care Quality Assessment: An Improved Approach Using K-Means Clustering

High maternal and child deaths in developing countries are frequently linked to poor health services provided to pregnant women and children. To improve the quality of maternal, neonatal and child health (MNCH) services, the government and other stakeholders in MNCH emphasize the importance of quality assessment. However, effective quality assessment approaches are mostly lacking in most developing countries, particularly in Tanzania. This study, therefore, aimed at developing a quality assessment approach that can effectively assess and report on the quality of MNCH services. Due to the need for a good quality assessment approach that suits a resource-constrained environment, machine learning-based approach was proposed and developed. K-means algorithm was used to develop a clustering model that groups MNCH data and performs cluster summarization to discover the knowledge portrayed in each group on the quality of MNCH services. Results confirmed the clustering model’s ability to assign the data points into appropriate clusters;cluster analysis with the collaboration of MNCH experts successfully discovered insights on the quality of services portrayed by each group.

Application of Time Serial Model in Water Quality Predicting

Water resources are an indispensable and valuable resource for human survival and development.Water quality predicting plays an important role in the protection and development of water resources.It is difficult to predictwater quality due to its random and trend changes.Therefore,amethod of predicting water quality which combines Auto Regressive Integrated Moving Average(ARIMA)and clusteringmodelwas proposed in this paper.By taking thewater qualitymonitoring data of a certain river basin as a sample,thewater quality Total Phosphorus(TP)index was selected as the prediction object.Firstly,the sample data was cleaned,stationary analyzed,and white noise analyzed.Secondly,the appropriate parameters were selected according to the Bayesian Information Criterion(BIC)principle,and the trend component characteristics were obtained by using ARIMA to conduct water quality predicting.Thirdly,the relationship between the precipitation and the TP index in themonitoring water field was analyzed by the K-means clusteringmethod,and the random incremental characteristics of precipitation on water quality changes were calculated.Finally,by combining with the trend component characteristics and the random incremental characteristics,the water quality prediction results were calculated.Compared with the ARIMA water quality prediction method,experiments showed that the proposed method has higher accuracy,and its Mean Absolute Error(MAE),Mean Square Error(MSE),and Mean Absolute Percentage Error(MAPE)were respectively reduced by 44.6%,56.8%,and 45.8%.

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.

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

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.

云南不同品种核桃果实品质分析与综合评价

为了探究云南主栽核桃和薄壳山核桃果实的品质评价指标,选取27个核桃品种果实,对矿质元素、蛋白脂、粗脂肪等12个营养指标进行变异和相关性分析,并采用主成分和聚类分析方法,进行分类与综合评价。结果表明:12个营养指标的含量变异较为丰富,变异系数的变化范围为5%~83%,其中VC变异系数最大,粗脂肪变异系数最小;9种矿质元素平均含量从高到低依次为:K>P>Mg>Ca>Mn>Na>Fe>Zn>Cu,蛋白质含量均值为15.62%,脂肪含量均值为68.63%,VC的平均含量为4.66 mg/100 g;相关性分析表明,12个营养指标之间,P、Fe、Mg、Ca、Cu均与蛋白质呈极显著(P<0.01)正相关;主成分分析进行综合评价筛选出品质最佳的核桃品种为‘香茶’‘永平’‘晚熟’和‘小圆果’,薄壳山核桃中品种‘卡多’综合表现较好;聚类分析结果显示,可将其分为两大类,即富含矿质元素类群和高脂肪含量类群;相关性分析和主成分分析结果显示,K、Fe、Mg、Ca、Cu元素、蛋白质、脂肪为评价云南不同品种核桃品质的关键性指标。薄壳山核桃适合榨油,22种主栽核桃适合直接食用或开发富矿质元素和高蛋白的功能饮料。研究明确了云南核桃品质评价的关键指标并探明品质特性,提供科学分类方法,为云南核桃品质评价体系的构建奠定理论依据。

“四链”深度融合下战略性新兴产业高质量发展战略研究

推动创新链、产业链、资金链、人才链深度融合,提升产业创新能力是我国战略性新兴产业迈向全球产业链中高端,实现高质量发展的关键。本文通过系统分析战略性新兴产业高质量发展的本质要求及内涵特征,研判全球战略性新兴产业发展格局变化及趋势,分析我国战略性新兴产业发展现状、问题及挑战,提出“创新驱动、企业主体、开放融合”的战略性新兴产业高质量发展思路和“体系融合、协同发展,自主可控、开放创新,企业主体、市场机制,前瞻布局、高端引领”的发展原则,研究提出各领域重点发展方向及“系统性提升、融合式创新、市场化推动、引领性发展”的发展策略,以期为战略性新兴产业高质量发展提供参考。

基于主成分分析和聚类分析的李子果实品质综合评价

为了解不同品种李子的品质特性,本文选取12个品种李子作为研究对象,分别从外观、理化及糖酸组成等方面对果实品质进行了对比分析,同时采用主成分分析和聚类分析对李子品质性状进行综合评价。结果表明,不同品种李子外观、理化和糖酸组成等指标均表现出丰富的多样性。糖酸组成、色泽、单果重、果实密度和果形指数等是评价李子综合品质的关键性指标。12个品种中‘紫皇’(ZH)‘圣雪珀’(SXP)‘爱丽丝’(ALS)‘香李’(XL)‘香甜李’(XTL)5个品种综合评分为正值,品质较好。其中,ZH和SXP品质特征为出汁率、可溶性固形物、总糖含量及色泽品质高;ALS品质特征为总糖、总甜度、甜酸比和糖酸比最高;XL和XTL品质特征为可溶性固形物含量、糖酸比、甜酸比高,但出汁率低。该研究结果可作为不同用途李子品种筛选的重要参考依据。

省级医院优质医疗资源下沉实践与思考

全面深化医疗供给侧结构性改革,作为全周期医疗健康服务体系的重要一环,省级公立医院积极推进多形式医联体建设,促进优质医疗资源均衡布局和扩容下沉,构建分级诊疗服务格局。笔者以河南省人民医院“医院-政府”托管县级公立医院实践为案例,探析其经验做法,从体制机制、技术支撑、人才能力方面剖析存在的问题,并提出针对性建议。

Unveiling Citizens’ Perceptions, Attitudes, and Behaviors towards Air Quality: Insights from a Survey in the Po Basin Area

Air pollution has far-reaching environmental and social consequences, requiring the active participation of individual citizens in improving air quality by means of emission-reducing behaviors. This research examines the relationship between citizens’ knowledge, perceptions of air quality, attitudes towards policy measures, and intentions to adopt environmentally-friendly behaviors to combat air pollution. A comprehensive survey is conducted among a representative sample from seven regions in the Po basin area: Emilia-Romagna, Friuli-Venezia Giulia, Lombardy, Piedmont, Province of Trento, Valle d’Aosta, and Veneto. The survey aims at profiling participants based on their level of information, perceptions of air pollution, and attitudes towards emission-reducing behaviors. Cluster analysis identifies meaningful differences among citizen groups in terms of their awareness and intentions to engage in specific behaviors. Four distinct clusters emerge, each characterized by varying levels of willingness to embrace pro-environmental behaviors and support air quality improvement initiatives. By examining these profiles, the study uncovers patterns in citizens’ awareness, concerns, and acceptance of environmentally-friendly practices. The findings offer valuable insights for policymakers to develop targeted interventions, policies, and communication strategies.