Sequential Procedure Research Articles

Adaptive Multiple Comparison Sequential Design (AMCSD) for clinical trials

ABSTRACT We propose an adaptive sequential testing procedure for clinical trials that test the efficacy of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in one trial. At any interim analyses, sample size re-estimation can be conducted, and any option can be dropped for lack of efficacy or unsatisfactory safety profile. Inference after the trial, including p-value, conservative point estimate and confidence intervals, are provided.

Selecting a significance level in sequential testing procedures for community detection

While there have been numerous sequential algorithms developed to estimate community structure in networks, there is little available guidance and study of what significance level or stopping parameter to use in these sequential testing procedures. Most algorithms rely on prespecifiying the number of communities or use an arbitrary stopping rule. We provide a principled approach to selecting a nominal significance level for sequential community detection procedures by controlling the tolerance ratio, defined as the ratio of underfitting and overfitting probability of estimating the number of clusters in fitting a network. We introduce an algorithm for specifying this significance level from a user-specified tolerance ratio, and demonstrate its utility with a sequential modularity maximization approach in a stochastic block model framework. We evaluate the performance of the proposed algorithm through extensive simulations and demonstrate its utility in controlling the tolerance ratio in single-cell RNA sequencing clustering by cell type and by clustering a congressional voting network.

Closed-Form Harmonic Compensation Using Resonant Controllers Applied to Four-Leg VSI

Harmonic compensation employing resonant controllers applied to voltage source inverters generally requires complex techniques or a trial-and-error process to design the controller parameters. This paper proposes a closed-form solution for designing digital Proportional-Resonant controllers applied to three-phase four-leg inverters. The proposal presents a sequential procedure that uses closed-form expressions to establish the harmonic frequencies at which resonant stages must be added to comply with individual voltage harmonic distortion standards. The method eliminates the need for a trial-and-error process and ensures compliance with THD and individual harmonic distortion limits. The frequencies that require compensation and the gains of the resonant controllers are determined by evaluating the difference that presents the output harmonic impedance of the inverter concerning normalized three-phase harmonic impedance limits defined in this work. Furthermore, this paper adopts the harmonic distortion restrictions imposed by the Standard IEC 61000-2-2, allowing the harmonic impedance limits to be defined for the reference non-linear load indicated in IEC 62040-3. Experimental results are presented to validate the proposed design using a 5 kVA four-leg inverter, feeding balanced, unbalanced, linear, and non-linear loads.

Adaptive-region sequential design with quantitative and qualitative factors in application to HPC configuration

Motivated by the need of finding optimal configuration in the high-performance computing (HPC) system, this work proposes an adaptive-region sequential design (ARSD) for optimization of computer experiments with qualitative and quantitative factors. Experiments with both qualitative and quantitative factors are also encountered in other applications. The proposed ARSD method considers a sequential design criterion under the additive Gaussian process to deal with both qualitative and quantitative factors. Moreover, the adaptiveness of the proposed sequential procedure allows the selection of next design point from the adaptive design region achieving a meaningful balance between exploitation and exploration for optimization. Theoretical justification of the adaptive design region is provided. The performance of the proposed method is evaluated by several numerical examples in simulations. The case study of HPC performance optimization further elaborates the merits of the proposed method.

Managing interruptions in appointment schedules via patient notification

Interruptions in appointment schedules are common in practice, yet many clinics use generic appointment scheduling templates and sub-optimal interruption management strategies that fail to address dynamics aspects of daily healthcare operations. In this study, we consider patient no-shows, patient punctuality, service time variability, random walk-in patient arrivals and other common appointment interruptions to determine optimal single-server, intra-day appointment schedules. Using a modified depth first search (DFS) algorithm that relies on a sample average approximation (SAA) technique for objective function evaluation, we minimize a weighted sum of expected waiting times for scheduled and walk-in patients, physician idle time, and physician overtime. Extensive experimentation shows the proposed algorithm solves realistic problems in reasonable time. Furthermore, we propose a sequential notification procedure (SNP) that serves as a dynamic correcting mechanism to address infrequent and unexpected interruptions with a relatively long duration. Considering possible patient responses to notification, SNP notifies scheduled patients in advance to prevent excessive patient waiting and physician overtime. Furthermore, we propose an easy-to-use patient notification heuristic (NH) that can be adopted by clinics using traditional appointment systems. Experimentation shows that both SNP and NH provide significant value, improving operational outcomes. We demonstrate the value of patient notification increases as interruption levels increase and when patients are responsive to notifications.

Performance enhancement of metal hydride hydrogen compressors using a novel operating procedure

The H2 refueling infrastructure's capital and operating expenses are mostly driven by the cost of hydrogen compression. To effectively address this issue, thermally driven H2 compression using metal hydrides (MH) may be used. For industrial customers who have the required infrastructure, such as H2 pipelines, low-grade heat sources, etc., metal hydride hydrogen compressors (MHHC) are particularly promising. However, the H2 compression cycle time issue is brought on by the poor thermal conductivity of the MH's powder. In the present study, a novel MHHC operating procedure is examined numerically. This procedure offers two possible options: The first is referred to as a “Multiple-Cycle Simultaneous Procedure’ (MCSiP for short), as the MH beds' sensible cooling (or sensible heating) and hydrogen's absorption (or desorption) processes start simultaneously and the final pressure is reached after a number of sub-cycles of compression. The second one, dubbed the ”Multiple-Cycle Sequential Procedure“ (MCSeP for short), is similar to the MCSiP mode except that the MH beds' sensible cooling (or sensible heating) as well as the hydrogen's absorption (or desorption) start out sequentially. To evaluate the impact of this procedure on the compressor’s performance, a theoretical model was established and effectively validated by comparison with experimental results reported in the literature. The simulation findings show that the suggested procedure compresses more hydrogen mass at high pressure and is faster than the other operating methods. For instance, 140 NL H2 at a pressure of 110 bar can be obtained with a 70% reduction in compression time compared to the commonly used operating mode utilizing 1.0 kg of MmNi4.6Al0.4 as low pressure MH and 0.832 kg of Ti0.99Zr0.01V0.43Fe0.099Cr0.05Mn1.5 as high pressure MH and operating conditions of 20 bar supply pressure, 293 K absorption temperature, and 373 K desorption temperature.

Manganese sources and rates impact plant Mn concentrations and soil Mn fractions

AbstractManganese (Mn) is an essential micronutrient for all organisms. In plants, Mn plays a critical role in photosynthesis and as a structural component of enzymes. In soils, Mn exists as different fractions of varying availability to plants. Mn fertilizers can be used to increase Mn availability to plants but are easily converted from a plant‐available fraction (exchangeable Mn) to an unavailable fraction (Mn‐oxides). Little research has been done in agricultural settings on soil Mn fractions; thus, the objective of this experiment was to study the effect of Mn additions on soil Mn fractions and plant Mn concentration. A greenhouse experiment was conducted by growing soybean (Glycine max) treated with three Mn application rates (recommended or 1×, 10×, and 50×) of two sources (MnSO4 and Mn ethylenediamine tetraacetic acid [MnEDTA]). Soil Mn fractions (Mehlich‐1 extractable, exchangeable, organic‐bound, Mn‐oxide, residual) were quantified via sequential Mn extraction procedures. Bioavailability was evaluated by measuring soybean leaf Mn concentrations. Both fertilizer types increased available soil Mn fractions. Leaf Mn concentration increased with MnSO4 50× application at the V3 stage and decreased with MnEDTA addition at the V3 and R2 stages. Mn additions likely resulted in the conversion of Mn into unavailable fractions. This was amplified by higher application rates, where total Mn increased by 18.5%, but available Mn decreased by 4.3% relative to initial soil values. Thus, our study showed that adding Mn to soils does not necessarily increase plant‐available Mn.

Leveraging external evidence using Bayesian hierarchical model and propensity score in the presence of covariates

In recent decades, there has been growing interest in leveraging external data information for clinical development as it improves the efficiency of the design and inference of clinical trials when utilized properly and more importantly, alleviates potential ethical and recruitment challenges. When it is of interest to augment the concurrent study's control arm using external control data, the potential outcome heterogeneity across data sources, also known as prior-data conflict, should be accounted for. In addition, in the outcome modeling, inclusion of prognostic covariates that may have impact on the outcome can avoid efficiency loss or potential bias. In this paper, we propose a Bayesian hierarchical modeling strategy incorporating covariate-adjusted meta-analytic predictive approach (cMAP) and also introduce a propensity score (PS) based sequential procedure that integrates the cMAP. In the simulation study, the proposed methods are found to have advantages in the estimation, power, and type I error control over the standard methods such as PS matching alone and hierarchical modeling that ignores the covariates. An illustrative example is used to illustrate the procedure.

Fractionations of heavy metals and their correlations with magnetic susceptibility in soil from a typical alluvial island in the lower yangtze river, China

The use of magnetic susceptibility (χ) as a means of assessing heavy metal pollution in soils has been explored by researchers, yielding varying results in terms of the correlations between χ with heavy metals. The efficacy of χ as an indicator of soil heavy metal pollution remains a topic of debate. This study aims to elucidate the inter-relationships between χ, iron oxides, and heavy metals in soil through the application of a modified 5-step sequential extraction procedure (SEP), and to identify an effective approach for assessing metal concentrations in soil using magnetic susceptibility measurements. The soil samples were collected from a typical alluvial island in the lower Yangtze River, China, and a total of 6 forms (exchangeable and acid soluble fraction, easily reducible fraction, oxidizable fraction, amorphous iron oxide, crystallized iron oxyhydroxides and residual fraction) were partitioned and their heavy metal concentrations and χ were analyzed. The results show that crystalline Fe oxyhydroxides and residual fractions are the two uppermost fractions of heavy metals. By combining the fractionation of elements with the variation of χ of the soil during the processing of SEP, it was inferred that the external input of Fe, Pb, Cr and Cd in the soil likely originated from the vicinal steel production. The correlation analysis revealed a significant correlation between heavy metal concentrations and χ in the residual fraction, whereas no significant correlations were observed between the concentrations of heavy metals and χ in the bulk soil samples. It is recommended that the evaluation of heavy metal contamination in the soil neighboring industrial sites can be conducted via magnetic susceptibility measurements subsequent to the elimination of crystalline iron oxyhydroxides.

Arsenic movement and fractionation in agricultural soils which received wastewater from an adjacent industrial site for 50 years

Arsenic (As) is an element with important environmental and human health implications due to its toxic properties. It is naturally occurring since it is contained in minerals, but it can also be enriched and distributed in the environment by anthropogenic activities. This paper reports on the historic As contamination of agricultural soils in one of the most important national relevance site for contamination in Italy, the so-called SIN Brescia-Caffaro, in the city of Brescia, northern Italy. These agricultural areas received As through the use of irrigation waters from wastewater coming from a factory of As-based pesticides (lead and calcium arsenates, sodium arsenite). Pesticide production started in 1920 and ended in the ‘70. Concentrations in the areas are generally beyond the legal threshold values for different soil uses and are up to >200 mg/kg. Arsenic contamination was studied to assess the long-time trend and the dynamics related to the vertical movement of As down to 1 m depth and its horizontal diffusion with surface irrigation in the entire field. Arsenic fractionation analysis (solid phase speciation by sequential extraction procedure) was also performed on samples collected from these areas and employed in greenhouse experiments with several plant species to evaluate the long-term contamination and the role of plant species in modifying As availability in soil. The results of this work can help in the evaluation of the conditions controlling the vertical transfer of As towards surface aquifers, the bioaccumulation likelihood in the agricultural food chain and the selection of sustainable remediation techniques such as phytoextraction.

Adaptive information-based methods for determining the co-integration rank in heteroskedastic VAR models

Standard methods, such as sequential procedures based on Johansen’s (pseudo-)likelihood ratio (PLR) test, for determining the co-integration rank of a vector autoregressive (VAR) system of variables integrated of order one can be significantly affected, even asymptotically, by unconditional heteroskedasticity (non-stationary volatility) in the data. Known solutions to this problem include wild bootstrap implementations of the PLR test or the use of an information criterion, such as the BIC, to select the co-integration rank. Although asymptotically valid in the presence of heteroskedasticity, these methods can display very low finite sample power under some patterns of non-stationary volatility. In particular, they do not exploit potential efficiency gains that could be realized in the presence of non-stationary volatility by using adaptive inference methods. Under the assumption of a known autoregressive lag length, Boswijk and Zu develop adaptive PLR test based methods using a non-parametric estimate of the covariance matrix process. It is well-known, however, that selecting an incorrect lag length can significantly impact on the efficacy of both information criteria and bootstrap PLR tests to determine co-integration rank in finite samples. We show that adaptive information criteria-based approaches can be used to estimate the autoregressive lag order to use in connection with bootstrap adaptive PLR tests, or to jointly determine the co-integration rank and the VAR lag length and that in both cases they are weakly consistent for these parameters in the presence of non-stationary volatility provided standard conditions hold on the penalty term. Monte Carlo simulations are used to demonstrate the potential gains from using adaptive methods and an empirical application to the U.S. term structure is provided.

Comparison of Gini indices using sequential approach: Application to the U.S. Small Business Administration data

The comparison of Gini inequality indices is an important study related to regional imbalance in equality. In the design of such a study, both the cost constraints and variability of the difference of inequality indices play an active role. In this article, we compare the Gini inequality indices for two regions under cost constraints leveraging on the concept of sequential analysis. Without prior knowledge of the statistical distributions of the data in the two regions of interest, the optimal sample sizes that balance the cost constraint and the accuracy of the comparison cannot be calculated under a fixed-sample-size methodology. Therefore, in this article, we develop a sequential procedure for comparing Gini indices for two regions under a budget constraint. With no specific assumption about the population distribution of the data, we examine and prove the large-sample properties offered by the proposed purely sequential procedure. Further, we use extensive simulations to empirically examine the characteristics of this procedure and illustrate its application using data on the Paycheck Protection Program loan from the U.S. Small Business Administration for the states of Connecticut and Rhode Island.

Long-term disease interactions amongst surgical patients: a population cohort study

BackgroundThe average age of the surgical population continues to increase, as does prevalence of long-term diseases. However, outcomes amongst multi-morbid surgical patients are not well described. MethodsWe included adults undergoing non-obstetric surgical procedures in the English National Health Service between January 2010 and December 2015. Patients could be included multiple times in sequential 90-day procedure spells. Multi-morbidity was defined as presence of two or more long-term diseases identified using a modified Charlson comorbidity index. The primary outcome was 90-day postoperative death. Secondary outcomes included emergency hospital readmission within 90 days. We calculated age- and sex-adjusted odds ratios (OR) with 95% confidence intervals (CI) using logistic regression. We compared the outcomes associated with different disease combinations. ResultsWe identified 20 193 659 procedure spells among 13 062 715 individuals aged 57 (standard deviation 19) yr. Multi-morbidity was present among 2 577 049 (12.8%) spells with 195 965 deaths (7.6%), compared with 17 616 610 (88.2%) spells without multi-morbidity with 163 529 deaths (0.9%). Multi-morbidity was present in 1 902 859/16 946 808 (11.2%) elective spells, with 57 663 deaths (2.7%, OR 4.9 [95% CI: 4.9–4.9]), and 674 190/3 246 851 (20.7%) non-elective spells, with 138 302 deaths (20.5%, OR 3.0 [95% CI: 3.0–3.1]). Emergency readmission followed 547 399 (22.0%) spells with multi-morbidity compared with 1 255 526 (7.2%) without. Multi-morbid patients accounted for 57 663/114 783 (50.2%) deaths after elective spells, and 138 302/244 711 (56.5%) after non-elective spells. The rate of death varied five-fold from lowest to highest risk disease pairs. ConclusionOne in eight patients undergoing surgery have multi-morbidity, accounting for more than half of all postoperative deaths. Disease interactions amongst multi-morbid patients is an important determinant of patient outcome.

Effect of Grain Size Distribution of Soil on Immobilization of Cadmium and Nickel in Contaminated Soil Using Nano Zerovalent Iron: A Factorial Design and Response Surface Methodology Approach

Urban soils get contaminated due to the unscientific disposal of industrial effluents and solid waste, contaminating the soil with heavy metals. Contaminated soils need to be remediated to avoid the ingestion of toxic heavy metals. The immobilization of heavy metals is one of the methods to remediate contaminated soils. The primary purpose of this study was to explore the effect of grain size distribution (GSD) of soil on the efficiency of immobilization of toxic heavy metals in soil when nano zerovalent iron (nZVI) is used to immobilize the metals. Preliminary studies conducted with the 23 factorial design method confirmed that GSD is a significant factor along with the level of contamination and nZVI dosage in the immobilization of heavy metals in soil. Experiments were conducted with cadmium (Cd) and nickel (Ni), the toxic heavy metals, and three soils with different finer particles than 75 µm. Immobilization efficiency was obtained by comparing the leachability of contaminated and treated soil samples using the toxicity characteristic leaching procedure. Reduction in available fraction and variation in the speciation of heavy metals was observed through the sequential extraction procedure. The response surface methodology was adopted for analysis with three levels of GSD, contamination, and nZVI dosage. For Cd, a reduced cubic model was the best fit with a coefficient of determination (R-square) of 0.93, and for Ni, a quadratic model was the best fit with an R-square of 0.92. Both these models were validated experimentally. The efficiency variation followed an optimum curve concerning GSD, contamination level, and the nZVI dosage. For multimetal contamination, the immobilization efficiency gradually decreased from 10% finer to 90% finer soil, both in the case of Cd and Ni. It was observed that for both single metal and multimetal contamination, the interaction between soil and the nZVI plays a vital role in immobilization along with the factors considered.

Optimal Energy Flow in Integrated Electricity and Gas Systems With Injection of Alternative Gas

This paper proposes an optimal energy flow technique for the integrated electricity and gas systems (IEGS) with injections of alternative gases. Firstly, we develop a novel optimal energy flow model of IEGS with alternative gases, where the physical characteristics (e.g., specific gravity) of the gas mixtures are modeled as variables to reflect the impacts of alternative gas injections more accurately. Security indices are introduced to restrain the gas composition variation. Then, convex optimization techniques are tailored to transfer the original highly nonlinear and nonconvex optimization problem into a tractable form. An advanced sequential programming procedure is proposed with self-adaptive convergence criteria to better balance the feasibility and convergency. Finally, the IEEE 24-bus RTS and Belgium gas transmission system, and the practical 160-bus Britain gas system are used to validate the proposed techniques. It can be observed from numerical studies that the computation efficiency of the proposed solution methods is 96.44% faster than traditional mixed-integer nonlinear solvers. The injection of alternative gas can cause up to 4.32% variations in the nodal gas pressure. Nonetheless, under various uncertainties from renewable generation, load level, etc., the proposed optimal energy flow model can maintain the security of IEGS.

Learning and Predicting Shape Deviations of Smooth and Non-Smooth 3D Geometries Through Mathematical Decomposition of Additive Manufacturing

In additive manufacturing (AM), final product geometries are often deformed or distorted. The deviations of three-dimensional (3D) shapes from their intended designs can be represented as 2D surfaces in a R³ space, which constitutes a complicated set of data for learning and predicting geometric quality. Patterns of deviation surfaces vary with shape geometries, sizes/volumes, materials, and AM processes. Our previous work has established an engineering-informed convolution framework to learn shape deviation from a small set of training products built with the same material and process. It incorporates the characteristics of the layer-wise shape forming process through a convolution formulation and the size factor for a category of smooth 3D shapes such as domes or cylinders. This study extends this fabrication-aware learning framework to a larger class of products including both smooth and non-smooth surfaces (polyhedral shapes). The key idea of learning heterogeneous deviation surface data under a unified model is to establish the association between the deviation profiles of smooth base shapes and those of non-smooth polyhedral shapes. The association, which is characterized by a novel 3D cookie-cutter function, views polyhedral shapes as being carved out from smooth base shapes. In essence, the AM process of building non-smooth shapes is mathematically decomposed into two steps: additively fabricate smooth base shapes using a convolution learning framework, and then subtract extra materials using a cookie-cutter function. The proposed joint learning framework of shape deviation data reflects this decomposition by adopting a sequential model estimation procedure. The model learning procedure first establishes the convolution model to capture the effects of layer-wise fabrication and sizes, and then estimates the 3D cookie-cutter function to realize geometric differences between smooth and non-smooth shapes. A new Gaussian process model is proposed to consider the spatial correlation among neighboring regions within a 3D shape and across different shapes. The case study demonstrates the feasibility and prospects of prescriptive learning of complex 3D shape deviations in AM and extension to broader engineering surface data.

Bibliometric Analysis of the Thinking Styles in Math and Its' Implication on Science Learning

The exploration of mathematical thinking styles is a vital area of investigation, particularly concerning its influence on science education within the classroom, given the significant role mathematics plays in advancing scientific understanding. The examination of this subject holds great interest, and its pertinence strongly bolsters prospective teaching and research endeavors. This research aims to perform a bibliometric scrutiny of mathematical thinking styles and their implication on science learning. The focus of this bibliometric inquiry is to elucidate and scrutinize literature congruent with the concept of mathematical thinking styles and their alignment with science learning. The SCOPUS repository is employed as the primary source of document references. Document selection and analysis were conducted using specific keywords in the 'document search' section. Employing diverse document screening methodologies pertinent to mathematical thinking styles and their implication on science learning, a corpus of relevant documents addressing the subject matter was identified. The sequential screening procedures and document findings are discussed comprehensively in this article. Fundamentally, articles pertinent to the bibliometric analysis theme, 'mathematical thinking styles and their implications for science learning,' underscore the significance of delving into students' mathematical thinking styles. Variances in these cognitive styles pose significant challenges for educators' pedagogical approaches in both mathematics and science instruction. This constitutes a pivotal implication of the present study, necessitating educators to adeptly navigate diverse mathematical thinking styles when structuring pedagogy in science and mathematics. Ultimately, this study stands as a pivotal reference for future investigations delving into themes associated with mathematical thinking styles.

Multiresolution functional characterization and correction of biofouling for improved biosensing efficacy

Multielectrode electrochemical biosensors promise on-the-spot inspection of target compounds in biofluids, reducing costs in personalized healthcare. However, sensor sensitivity may decrease after each use due to biofouling, where chemical attachments on sensor electrodes curtail sensing signals. Current biofouling characterization techniques rely on time-consuming offline tests and analysis, making them impractical for on-the-spot signal correction. Alternatively, we propose to statistically model and correct the biofouling-induced signal changes. However, in addition to biofouling, the signals are influenced by multiple sources of variation, each with different levels of impact. To effectively characterize and separate biofouling effects from the major sources of variability, we establish a multiresolution functional mixed-effect model based on domain knowledge. A biosensing signal is first decomposed into a smooth trend and local peaks. The smooth trend models the effects of population-level biofluid composition, as well as patient and electrode effects to isolate variability sources. Changes in local peak location and amplitude indicate biofouling. These local peaks are modeled using a sparse subset of high-order functional terms. By modeling the changes of those high-order terms, we can characterize and predict the biofouling between consecutive measurements. We propose a sequential parameter estimation procedure that ensures model identifiability. A nonparametric regression model is developed for biofouling prediction. The proposed strategy is validated through simulation and real case studies, effectively correcting biofouling-affected signals from new patients.

Estimating Average Causal Effects from Patient Trajectories

In medical practice, treatments are selected based on the expected causal effects on patient outcomes. Here, the gold standard for estimating causal effects are randomized controlled trials; however, such trials are costly and sometimes even unethical. Instead, medical practice is increasingly interested in estimating causal effects among patient (sub)groups from electronic health records, that is, observational data. In this paper, we aim at estimating the average causal effect (ACE) from observational data (patient trajectories) that are collected over time. For this, we propose DeepACE: an end-to-end deep learning model. DeepACE leverages the iterative G-computation formula to adjust for the bias induced by time-varying confounders. Moreover, we develop a novel sequential targeting procedure which ensures that DeepACE has favorable theoretical properties, i.e., is doubly robust and asymptotically efficient. To the best of our knowledge, this is the first work that proposes an end-to-end deep learning model tailored for estimating time-varying ACEs. We compare DeepACE in an extensive number of experiments, confirming that it achieves state-of-the-art performance. We further provide a case study for patients suffering from low back pain to demonstrate that DeepACE generates important and meaningful findings for clinical practice. Our work enables practitioners to develop effective treatment recommendations based on population effects.

The distribution and enrichment of trace elements in surface and core sediments from the Changjiang River Estuary, China: Evidence for anthropogenic inputs and enhanced availability of rare earth elements (REE)

Huge amount of trace metals emitted through manmade activities are carried by the Changjiang River into the East China Sea. Most of them deposit in the Changjiang River Estuary and threaten the regional aquatic environment. In this study, major and trace elements of 34 archive surface sediments and two cores are examined. Sequential extraction procedures were also performed on surface sediments from 12 sites. We found that Tl, Tm, Er show distinct accumulation in surface sediments in the order of Tm > Tl > Er. Particularly, abnormally elevated HREE are observed mainly in those sites near the mouth of the estuary. Most elements exhibit an obvious reduction in the upper 30 cm of core B8, reflecting a decrease of sediment discharge from Changjiang River runoff. The increase of some trace elements recorded in the upper 20 cm of core C3 demonstrates a distinct local anthropogenic input in recent years.