Statistical Tools for Analyzing Water Quality Data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm

Optimal subsampling algorithm for the marginal model with large longitudinal data

Big data is ubiquitous in practices, and it has also led to heavy computation burden. To reduce the calculation cost and ensure the effectiveness of parameter estimators, an optimal subset sampling method is proposed to estimate the parameters in marginal models with massive longitudinal data. The optimal subsampling probabilities are derived, and the corresponding asymptotic properties are established to ensure the consistency and asymptotic normality of the estimator. Extensive simulation studies are carried out to evaluate the performance of the proposed method for continuous, binary and count data and with four different working correlation matrices. A depression data is used to illustrate the proposed method

Robust approach for variable selection with high dimensional Logitudinal data analysis

This paper proposes a new robust smooth-threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. A novel working correlation matrix is proposed to capture correlations within the same subject. The proposed procedure works well when the number of covariates p increases as the number of subjects n increases. The proposed estimates are competitive with the estimates obtained with the true correlation structure, especially when the data are contaminated. Moreover, the proposed method is robust against outliers in the response variables and/or covariates. Furthermore, the oracle properties for robust smooth-threshold estimating equations under "large n, diverging p" are established under some regularity conditions. Extensive simulation studies and a yeast cell cycle data are used to evaluate the performance of the proposed method, and results show that our proposed method is competitive with existing robust variable selection procedures.Comment: 32 pages, 7 tables, 5 figure

An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data

In medical studies, the collected covariates usually contain underlying outliers. For clustered /longitudinal data with censored observations, the traditional Gehan-type estimator is robust to outliers existing in response but sensitive to outliers in the covariate domain, and it also ignores the within-cluster correlations. To take account of within-cluster correlations, varying cluster sizes, and outliers in covariates, we propose weighted Gehan-type estimating functions for parameter estimation in the accelerated failure time model for clustered data. We provide the asymptotic properties of the resulting estimators and carry out simulation studies to evaluate the performance of the proposed method under a variety of realistic settings. The simulation results demonstrate that the proposed method is robust to the outliers existing in the covariate domain and lead to much more efficient estimators when a strong within-cluster correlation exists. Finally, the proposed method is applied to a medical dataset and more reliable and convincing results are hence obtained.Comment: ready for submissio

Settlement Relocation, Urban Construction, and Social Transformation in China’s Central Plain, 2300–1500 B.C.

Settlement relocation occurred repeatedly throughout global human history, often resulting in significant sociopolitical and economic changes. Historical records document the use of settlement relocation as a strategy for social engineering in China no later than the late Shang dynasty (1250–1046 B.C.). We employ placemaking theory to examine social changes associated with population movements to Taosi (2300–1900 B.C.) and Erlitou (1750–1520 B.C.) and the processes of urban construction concomitant to the movements at each site. Furthermore, we employ structuration theory to interpret the process of political knowledge building as concerns settlement relocation and social engineering. Based on our assessment of settlement histories, divisions of space, burial patterns, and community formation, we conclude that the use of settlement relocation as political strategy was formulated during the Taosi and Erlitou eras, and that it was intentionally implemented for political reform by Phase II of Erlitou

The role of echocardiography in prognosis for dysfunction and abandonment of radiocephalic arteriovenous fistula in elderly Chinese patients on hemodialysis

The objective of this study was to examine the impact of cardiac structure and function at baseline on the outcomes associated with arteriovenous fistula (AVF) in patients on hemodialysis (HD). Patients who initiated HD aged ≥70 years and received a mature AVF creation were included retrospectively. Echocardiographic parameters measured within 1 week before AVF creation were acquired. The observational period for each patient was from the point of AVF creation to the last time of follow‐up unless AVF abandonment or death occurred. Kaplan‐Meier and Cox proportional hazard regression analyses were conducted. A total of 82 elderly Chinese HD patients with mature radiocephalic AVF (RCAVF) and EF ≥50% were analyzed. During the median study period of 26.8 (12‐40) months, 42 (51.2%) experienced RCAVF dysfunction and 34 (41.5%) progressed to abandonment. Primary and cumulative patencies at 6, 12, 24, and 36 months were 81%, 73%, 48%, 38%, and 84%, 81%, 68%, 55%, respectively. Left ventricle end‐diastolic volume (LVEDV) ≤103.5 mL (HR = 2.5, P = .019) and the right side of RCAVF (HR = 3.59, P = .003) significantly predicted RCAVF dysfunction. The main pulmonary artery internal diameter (MPAID) ≤21.5 mm (HR = 4.3, P = .001) as well as the right side (HR = 2.95, P = .047) were the independent predictors for RCAVF abandonment. In conclusion, LVEDV, MPAID assessed by echocardiography and the right side of RCAVF, showed significant predictive implications for the outcomes of RCAVF. Disparities among nationalities in the areas of utilization and patency of AVFs necessitate additional studies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156158/2/sdi12871.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156158/1/sdi12871_am.pd

Catalytic Ozone Oxidation of Petrochemical Secondary Effluent: Mechanism, Application and Future Development

Petrochemical secondary effluent contains toxic and refractory organic compounds, which are difficult to be further treated by traditional biological process. In China, most of the advanced treatment units have been built recently by catalytic ozone oxidation process to achieve the high-quality effluent. In this chapter, the mechanism and reaction process of catalytic ozone oxidation of petrochemical secondary effluent will be introduced in detail. With the operation of the catalytic ozone oxidation tank, a series of problems which are not taken into account at the beginning of the design have arisen. The chapter will talk about the problems concerning the biological flocs, colloidal macromolecule organic compounds, ozone mass transfer, and catalysts based on practical applications. In the last part of the chapter, the development trends of catalytic ozone oxidation of petrochemical secondary effluent will also be discussed

Physical activity improves the visual–spatial working memory of individuals with mild cognitive impairment or Alzheimer’s disease: a systematic review and network meta-analysis

ObjectiveOur network meta-analysis aimed to ascertain the effect of physical activity on the visual–spatial working memory of individuals with mild cognitive impairment and Alzheimer’s disease as well as to propose tailored exercise interventions for each group.MethodsEmploying a frequentist approach, we performed a network meta-analysis to compare the effectiveness of different exercise interventions in improving the visual–spatial working memory of individuals with mild cognitive impairment and Alzheimer’s disease. Subsequently, we explored the moderating variables influencing the effectiveness of the exercise interventions through a subgroup analysis.ResultsWe included 34 articles involving 3,074 participants in the meta-analysis, comprised of 1,537 participants from studies on mild cognitive impairment and 1,537 participants from studies on Alzheimer’s disease. The articles included exhibited an average quality score of 6.6 (score studies) and 6.75 (reaction time [RT] studies), all passing the inconsistency test (p > 0.05). In the mild cognitive impairment literature, mind–body exercise emerged as the most effective exercise intervention (SMD = 0.61, 95% CI: 0.07–1.14). In Alzheimer’s disease research, aerobic exercise was identified as the optimal exercise intervention (SMD = 0.39, 95% CI: 0.06–0.71).ConclusionThe results of the subgroup analysis suggest that the most effective approach to enhancing the visual–spatial working memory of individuals with mild cognitive impairment entails exercising at a frequency of three or more times per week for over 60 min each time and at a moderate intensity for more than 3 months. Suitable exercise options include mind–body exercise, multicomponent exercise, resistance exercise, and aerobic exercise. For individuals with Alzheimer’s disease, we recommend moderately intense exercise twice per week for over 90 min per session and for a duration of 3 months or longer, with exercise options encompassing aerobic exercise and resistance exercise

Extrauterine growth restriction in preterm infants: Postnatal growth pattern and physical development outcomes at age 3–6 years

ObjectivesTo investigate the postnatal growth trajectories of preterm infants and evaluate the association between extrauterine growth restriction (EUGR) at discharge and adverse physical growth outcomes at age 3–6 years.MethodsPremature infants admitted to Shanghai Children’s Medical Center within 24 h after birth from 1 January 2016 to 31 December 2018 were enrolled. Neonatal complications, nutrition support, and anthropometric data were collected and analyzed to diagnose EUGR on different definitions at discharge. The weight and the height of each subject were collected by telephone investigation from 1 September 2021 to 31 November 2021 to access the incidences of overweight/obesity, short stature, and thinness at age 3–6 years.ResultsA total of 527 preterm infants were included in the final sample. The overall mean weight and height Z-scores were –0.37 ± 0.97 SD and –0.29 ± 1.18 SD at birth, and increased to –0.03 ± 1.11 SD and 0.13 ± 1.2 SD at follow-up, respectively. The logistic regression analysis indicated longitudinal EUGR on head circumference as the risk factor of overweight or obesity, cross-sectional EUGR on height as the risk factor of short stature, and delayed EN as the risk factor of thinness.ConclusionThe growth trajectories of the preterm newborns tended toward the normal direction. Longitudinal EUGR on the head circumference and cross-sectional EUGR on height at discharge were associated with adverse physical growth outcomes at age 3–6 years