Quantitative traits correlative analysis and growth comparison among different populations of bay scallop,Argopecten irradians

The shell traits and weight traits are measured in cultured populations of bay scallop, Argopecten irradians. The results of regression analysis show that the regression relationships for all the traits are significant (P<0.01). The correlative coefficients between body weight, as well as tissue weight with shell length, shell height and shell width are significant (P<0.05). But the correlative coefficients between the anterior and posterior auricle length with body weight as well as tissue weight are not significant (P>0.05). The multiple regression equation is obtained to estimate live body weight and tissue weight. The above traits except anterior and posterior auricle length are used for the growth and production comparison among three cultured populations, Duncan's new multiple range procedure analysis shows that all the traits in the Lingshuiqiao (LSQ) population are much more significant than those of the other two populations (P<0.01), and there is no significant difference between the Qipanmo (QPM) and Dalijia (DLJ) populations in all traits (P>0.05). The results indicate that the LSQ population has a higher growth rate and is expected to be more productive than the other two populations.

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.

Correlation analysis of human papillomavirus E6/E7 mRNA detection with diagnosis,prognosis and recurrence risk in patients with cervical epithelioma

BACKGROUND Cervical intraepithelial neoplasia(CIN)is an important precursor of cervical cancer.Early detection and treatment can reduce the incidence of cervical cancer.AIM To investigate the detection rate of human papillomavirus(HPV)E6/E7 mRNA in cervical tissue of patients with different types of epithelial cell neoplasia(CIN)and its relationship with CIN progression and diagnosis.METHODS One hundred women with HPV infection detected by cervical exfoliation cytology between January 2022 and January 2023 were retrospectively selected.These patients were graded CIN based on colposcopy and cervical pathology.The positive expression rates of HPV E6/E7 mRNA and HPV[polymerase chain reaction(PCR)-reverse dot crossing]were compared among all groups.Patients with HPV E6/E7 mRNA expression in the grade 1 CIN group were followed up for 1 yr.The relationship between atypical squamous epithelium and high malignant epithelial neoplasia was investigated by univariate and multivariate analysis.RESULTS The diagnostic sensitivity,specificity,and sensitivity of PCR-reverse point hybrid ization technology for secondary CIN were 70.41%,70.66%,and 0.714,respectively.Sensitivity and specificity for secondary CIN were 752%and 7853%,respectively,the area under the curve value was 0.789.Logistic Multifactorial model analysis revealed that the HPV positive rates and the HPV E6/E7 mRNA positive rates were independent risk factors of CIN grade I(P<0.05).In CIN grade I patients with positive for HPV E6/E7 mRNA,in its orientation to grade CIN patients,in its orientation to grade CIN patients,at 69.2%,compared with patients negative for HPV E6/E7 mRNA(30.8%),significant difference(P<0.05).CONCLUSION HPV E6/E7 mRNA and HPV(PCR-reverse dot hybrid)positive expression have a close relationship with CINgrade disease progression and is an independent risk factor for high-grade CIN lesions.

Correlation Analysis Between Changes of D-Dimer Level and Rheumatoid Arthritis Complicated with Interstitial Lung Disease

Objective:To explore the correlation between the change of D-dimer level and rheumatoid arthritis complicated with interstitial lung disease.Methods:From January 2022 to February 2024,20 rheumatoid arthritis patients complicated with interstitial lung disease(interstitial lung disease group),20 rheumatoid arthritis patients without interstitial lung disease(without interstitial lung disease group),and 20 healthy people(control group)in Xijing Hospital were selected for this study.The fasting venous blood of the three groups of subjects was collected and their D-dimer,C-reactive protein(CRP),rheumatoid factor(RF),and erythrocyte sedimentation rate(ESR)were detected.Subsequently,the correlation between each index and rheumatoid arthritis complicated with interstitial lung disease was analyzed.Results:The D-dimer level of the interstitial lung disease group was significantly higher than the other two groups(P<0.05).The D-dimer level of the group without interstitial lung disease was significantly higher than the control group(P<0.05).CRP levels in the interstitial lung disease group and the group without interstitial lung disease were significantly higher than those of the control group(P<0.05).The ESR and RF levels of the interstitial lung disease group were significantly higher than the other two groups(P<0.05).The levels of ESR and RF levels of the group without interstitial lung disease were significantly higher than the control group(P<0.05).Conclusion:D-dimer levels of rheumatoid arthritis patients are higher than those of healthy individuals,and those complicated with interstitial lung disease present even higher levels.This finding shows that there is a correlation between D-dimer levels and rheumatoid arthritis with interstitial lung disease,which may facilitate the evaluation and diagnosis of this disease.

Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

In the past 30 years, Chinese enterprises have been a hot topic of discussion and concern among the general public in terms of economic and social status, ownership structure, business mechanism, and management level. Solving the problem of employment for the people is an important prerequisite for their peaceful living and work, as well as a prerequisite and foundation for building a harmonious society. The employment situation of private enterprises has always been of great concern to the outside world, and these two major jobs have always occupied an important position in the employment field of China that cannot be ignored. With the establishment of the market economy system, individual and private enterprises have become important components of the socialist economy, making significant contributions to economic development and social progress. The rapid development of China’s economy, on the one hand, is the embodiment of the superiority of China’s socialist market economic system, and on the other hand, it is the role of the tertiary industry and private enterprises in promoting the national economy. Since the 1990s, China’s private enterprises have become a new economic growth point for local and even national countries, and are one of the important ways to arrange employment and achieve social stability. This paper studies the employment of private enterprises and individuals from the perspective of statistics, extracts relevant data from China statistical Yearbook, uses the relevant knowledge of statistics to process the data, obtains the conclusion and puts forward relevant constructive suggestions.

Analysis of Short-Shoot Fuji Apple Tree Structure and Correlative Factors

In order to clarify the correlation between short-shoot Fuji apple tree structure and different factors under different trimming and pruning modes,we investigate the trunk taperingness of free-spindle short-shoot Fuji and slender-spindle short-shoot Fuji,respectively,as well as the total thickness,average thickness,total length and average length of small main branches in the standard demonstration apple garden in Xingtang County of Hebei Province. By SPSS analysis,we study the correlation between trunk taperingness of trees with different shapes and the growth indices of their small main branches. The results show that the trunk taperingness of free-spindle short-shoot Fuji apple is negatively correlated with the total thickness,average thickness,total length and average length of small main branches,but the correlation is not significant; the trunk taperingness of slender-spindle short-shoot Fuji apple is negatively correlated with the total thickness but positively correlated with other factors,and the correlation with average length reaches a significant level. The results of this study can provide a scientific basis for guiding the high-density dwarf rootstock short-shoot Fuji apple tree trimming technology.

Product Specification Analysis for Modular Product Design Using Big Sales Data

Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.

Correlation analysis between fault frequency and service time of underground fluid instruments

In this paper, we make a statistical analysis of the fault information of the underground fluid instruments of 12 models in China from January 2021 to May 2022 based on the Pearson correlation coefficient, and compare the fault statistics of the meteorological three-element instruments of 3 models during the study period. The results show that:(1) The numbers of faults of the underground fluid instruments of 12models with different service times are basically positively correlated with the numbers of the corresponding instruments, with good consistency. Moreover, the automatic observation instruments(8models) with more than 30 units are significantly correlated at a 0.05 significance level(95% confidence level). Even at a 0.01 significance level(99% confidence level), there are 7 models(7/8) with significant correlation.(2) The positive and negative correlations between the monthly average number of faults and the corresponding service times of the underground fluid instruments of 12 models with different service times are random, and there are 9 models(75%) with no significant correlation at a 0.05 significance level(95% confidence level), while 12 models(100%) with no significant correlation at a 0.01significance level(99% confidence level).(3) The monthly average numbers of faults of the underground fluid instruments of 12 models are basically 0.02-0.05 times/(unit·month), and the overall fault frequency is low.(4) The fault statistics results of the meteorological three-element instruments of 3 models are consistent with the characteristics of the underground fluid instruments of 12 models. In general,there is no significant correlation between the fault frequency and the service time of underground fluid instruments.(5) The results of this paper demonstrate that the service time of underground fluid instruments cannot be taken as the main reason for whether to update the instruments. Similarly, the fault frequency of the instruments cannot be taken as the main reason for the service life of the instruments in the process of formulating the service life standards of underground fluid instruments.

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.

Correlation analysis of mental health conditions and personality of patients with alcohol addiction

BACKGROUND Alcohol addiction,or alcohol dependence,refers to a psychological state of strong craving for alcohol caused by drinking when both the drinking times and alcohol consumption reach a certain level.Alcohol addiction can cause irreversible damage,leading to mental illness or mental disorders,negative changes in their original personality,and a tendency to safety incidents such as committing suicide or violent attacks on others.Significant attention needs to be given to the mental health of alcohol addicts,which could reflect their abnormal personality traits.However,only a few papers on this issue have been reported in China.AIM To investigate the correlation between mental health and personality in patients with alcohol addiction.METHODS In this single-center observational study,we selected 80 patients with alcohol addiction as the research subjects,according to the criteria of the K10 scale to evaluate the mental health of patients with alcohol addiction,and divided these patients into four groups based on the evaluation results:Good,average,relatively poor and bad.And then analyzed the correlation between mental health conditions and personality characteristics from these four groups of patients.RESULTS The average score of the K10 scale(Kessler 10 Simple Psychological Status Assessment Scale)in 80 patients with alcohol addiction was 25.45 points,the median score was 25 points,the highest score was 50 points,and the lowest score was 11 points.Pearson's analysis showed that the K10 score was positively correlated with the scores of these two subscales,such as the P-subscale and the N-subscale(P<0.05).In contrast,the K10 score had no significant correlation with the scores from the E-subscale and the L-subscale(P>0.05).CONCLUSION The mental health conditions of patients with alcohol addiction are positively correlated with their personality characteristics.

Analysis of mental health status and related factors in patients with acute cerebral infarction

BACKGROUND Acute cerebral infarction(ACI)is characterized by a high incidence of morbidity,disability,recurrence,death and heavy economic burden,and has become a disease of concern in global researchers.As ACI has serious effects on patients’physical status,life and economy,often causing anxiety,depression and other psychological problems,these problems can lead to the aggravation of physical symptoms;thus,it is very important to understand the factors affecting the mental health of these patients.AIM To understand the elements that affect the mental health of patients who have suffered an ACI.METHODS A questionnaire survey was conducted among patients with ACI admitted to three tertiary hospitals(Quanzhou First Hospital,Fuqing City Hospital Affiliated to Fujian Medical University,and the 900 Hospital of the Joint Service Support Force of the People’s Liberation Army of China)in Fujian Province from January 2022 to December 2022 using the convenience sampling method.ACI inpatients who met the inclusion criteria were selected.Informed consent was obtained from the patients before the investigation,and a face-to-face questionnaire survey was conducted using a unified scale.The questionnaire included a general situation questionnaire,Zung’s self-rating depression scale and Zung’s self-rating anxiety scale.All questionnaires were checked by two researchers and then the data were input and sorted using Excel software.The general situation of patients with ACI was analyzed by descriptive statistics,the influence of variables on mental health by the independent sample t test and variance analysis,and the influencing factors on psychological distress were analyzed by multiple stepwise regression.RESULTS The average age of the 220 patients with ACI was 68.64±10.74 years,including 142 males and 78 females.Most of the patients were between 60 and 74 years old,the majority had high school or technical secondary school education,most lived with their spouse,and most lived in cities.The majority of patients had a personal income of 3001 to 5000 RMB yuan per month.The new rural cooperative medical insurance system had the largest number of participants.Most stroke patients were cared for by their spouses and of these patients,52.3%had previously smoked.Univariate analysis showed that gender,age,residence,course of disease,number of previous chronic diseases and smoking history were the main factors affecting the anxiety scores of patients with ACI.Age,living conditions,monthly income,course of disease and knowledge of disease were the primary variables influencing the depression score in patients with ACI.The findings of multivariate analysis revealed that the course of disease and gender were the most important factors influencing patients’anxiety scores,and the course of disease was also the most important factor influencing patients’depression scores.CONCLUSION Long disease course and female patients with ACI were more likely to have psychological problems such as a high incidence of emotional disorders.These groups require more attention and counseling.

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.

Analysis on Residents’ Satisfaction and Its Influencing Factors with Water Environment Management: Based on the Data from Xiaoqing River

Xiaoqing River is one of the key rivers in the Yellow River Basin, and its management satisfaction is the content that the government should consider when formulating policies. This paper concentrates on residents’ satisfaction of water environment management in Jinan section of Xiaoqing River, using questionnaires to find out the problems and effects of Xiaoqing River management. Based on the correlation analysis of the questionnaire data, the results show that five factors including the impact of water pollution, understanding of Xiaoqing River governance, willingness to participate in Xiaoqing River governance, policy publicity, and government regulation have a positive impact on the satisfaction of Xiaoqing River water environment governance. Finally, the paper puts forward some countermeasures and suggestions to increase residents’ satisfaction from five aspects, such as increasing publicity efforts, paying attention to the cultivation of public participation consciousness, etc.

The Establishment of Mathematical Models for the Composition Analysis and Identification of Ancient Glass Products

Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Impacts of climate change and human activities on vegetation dynamics on the Mongolian Plateau, East Asia from 2000 to 2023

The Mongolian Plateau in East Asia is one of the largest contingent arid and semi-arid areas of the world.Under the impacts of climate change and human activities,desertification is becoming increasingly severe on the Mongolian Plateau.Understanding the vegetation dynamics in this region can better characterize its ecological changes.In this study,based on Moderate Resolution Imaging Spectroradiometer(MODIS)images,we calculated the kernel normalized difference vegetation index(kNDVI)on the Mongolian Plateau from 2000 to 2023,and analyzed the changes in kNDVI using the Theil-Sen median trend analysis and Mann-Kendall significance test.We further investigated the impact of climate change on kNDVI change using partial correlation analysis and composite correlation analysis,and quantified the effects of climate change and human activities on kNDVI change by residual analysis.The results showed that kNDVI on the Mongolian Plateau was increasing overall,and the vegetation recovery area in the southern region was significantly larger than that in the northern region.About 50.99%of the plateau showed dominant climate-driven effects of temperature,precipitation,and wind speed on kNDVI change.Residual analysis showed that climate change and human activities together contributed to 94.79%of the areas with vegetation improvement.Appropriate human activities promoted the recovery of local vegetation,and climate change inhibited vegetation growth in the northern part of the Mongolian Plateau.This study provides scientific data for understanding the regional ecological environment status and future changes and developing effective ecological protection measures on the Mongolian Plateau.

Impact of climate and human activity on NDVI of various vegetation types in the Three-River Source Region, China

The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level.On the basis of an in-depth study of the vegetation evolution in the TRSR from 2000 to 2022,we conducted a detailed analysis of the feedback mechanism of vegetation growth to climate change and human activity for different vegetation types.During the growing season,the spatiotemporal variations of normalized difference vegetation index(NDVI)for different vegetation types in the TRSR were analyzed using the Moderate Resolution Imaging Spectroradiometer(MODIS)-NDVI data and meteorological data from 2000 to 2022.In addition,the response characteristics of vegetation to temperature,precipitation,and human activity were assessed using trend analysis,partial correlation analysis,and residual analysis.Results indicated that,after in-depth research,from 2000 to 2022,the TRSR's average NDVI during the growing season was 0.3482.The preliminary ranking of the average NDVI for different vegetation types was as follows:shrubland(0.5762)>forest(0.5443)>meadow(0.4219)>highland vegetation(0.2223)>steppe(0.2159).The NDVI during the growing season exhibited a fluctuating growth trend,with an average growth rate of 0.0018/10a(P<0.01).Notably,forests displayed a significant development trend throughout the growing season,possessing the fastest rate of change in NDVI(0.0028/10a).Moreover,the upward trends in NDVI for forests and steppes exhibited extensive spatial distributions,with significant increases accounting for 95.23%and 93.80%,respectively.The sensitivity to precipitation was significantly enhanced in other vegetation types other than highland vegetation.By contrast,steppes,meadows,and highland vegetation demonstrated relatively high vulnerability to temperature fluctuations.A further detailed analysis revealed that climate change had a significant positive impact on the TRSR from 2000 to 2022,particularly in its northwestern areas,accounting for 85.05%of the total area.Meanwhile,human activity played a notable positive role in the southwestern and southeastern areas of the TRSR,covering 62.65%of the total area.Therefore,climate change had a significantly higher impact on NDVI during the growing season in the TRSR than human activity.

Evaluation and Influencing Factors on Particle Agglomeration in RAP

Asphalt extraction test and scanning electron microscopy(SEM) were used for analysis of agglomerations of reclaimed asphalt pavement(RAP) particles. In order to quantify the agglomeration degree of RAP, the fineness modulus ratio(FMR) and the percentage loss index(PLI) were proposed. In addition, grey correlation analysis was conducted to discuss the relationship between particle agglomerations and RAP size,asphalt content(AC), and surface area. Two indexes indicate that the agglomeration degree increases in general as the RAP size reduces. This can be attributed to that particles are prone to agglomeration in the case of higher AC. Based on the SEM images and the material composition of RAP, the particle agglomeration in RAP can be classified into weak agglomeration and strong agglomeration. Grey correlation analysis shows that AC is the crucial factor affecting the agglomeration degree and RAP variability. In order to produce consistent and stable reclaimed mixtures, disposal measures of RAP are suggested to lower the AC of RAP.

Monitoring and evaluation of the water quality of the Lower Neches River, Texas, USA

Increasing bacteria levels in the Lower Neches River caused by Hurricane Harvey has been of a serious concern.This study is to analyze the historical water sampling measurements and real-time water quality data collected with wireless sensors to monitor and evaluate water quality under different hydrological and hydraulic conditions.The statistical and Pearson correlation analysis on historical water samples determines that alkalinity,chloride,hardness,conductivity,and pH are highly correlated,and they decrease with increasing flow rate due to dilution.The flow rate has positive correlations with Escherichia coli,total suspended solids,and turbidity,which demonstrates that runoff is one of the causes of the elevated bacteria and sediment loadings in the river.The correlation between E.coli and turbidity indicates that turbidity greater than 45 nephelometric turbidity units in the Neches River can serve as a proxy for E.coli to indicate the bacterial outbreak.A series of statistical tools and an innovative two-layer data smoothing filter are developed to detect outliers,fill missing values,and filter spikes of the sensor measurements.The correlation analysis on the sensor data illustrates that the elevated sediment/bacteria/algae in the river is either caused by the first flush rain and heavy rain events in December to March or practices of land use and land cover.Therefore,utilizing sensor measurements along with rainfall and discharge data is recommended to monitor and evaluate water quality,then in turn to provide early alerts on water resources management decisions.