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Introduction: Levonorgestrel-releasing intrauterine system (LNG-IUS) is associated with ~50% risk reduction for uterine cancer incidence and can be an effective primary prevention strategy. We aimed to understand current patterns of LNG-IUS use and identify disparities that could inform implementation strategies for more effective and equitable uterine cancer primary prevention. Methods: We analyzed LNG-IUS use among U.S. women aged 18-50 with the 2017-2019 National Survey of Family Growth. Statistical analysis was stratified by race, sociodemographic, and health factors. Predictors of LNG-IUS use were assessed through weighted multivariable logistic regression using the Wald chi-square test. Results: Current LNG-IUS use was significantly lower in Hispanic women compared to White women (AOR 0.65, P=0.049). Compared to women with up to a high school education, LNG-IUS use was higher for women with a college degree or higher in the overall sample (AOR 1.86, P=0.009), White women (AOR 1.91, P=0.04), and Black women (AOR 4.54, P=0.007), but not for Hispanic women (AOR 0.79, P=0.64). All racial/ethnic subgroups had lower odds of LNG-IUS for non-parous women than parous women (all P Conclusions: Hispanic women, women with lower educational levels, and nulliparous women have disproportionately low LNG-IUS use despite being at increased uterine cancer risk. Community-based educational interventions for high-risk women and their providers on the benefits of LNG-IUS on uterine cancer prevention are needed to mitigate uterine cancer disparities. Table 1. Logistic Regression on Predictors for LNG-IUS Use in U.S. Women Aged 18-50 OVERALL (N=4107) WHITE (n=1948) HISPANIC (n=1052) BLACK (n=861) AOR (95% CI) AOR (95% CI) AOR (95% CI) AOR (95% CI) Race Ref 0.65* (0.42-0.98) 0.77 (0.46-1.27) Age 18-24 1.16 (0.55-2.46) 1.15 (0.45-2.94) 13.13* (1.42-121.3) 0.55 (0.11-2.83) 25-34 1.65 (0.88-3.10) 1.46 (0.69-3.13) 31.75** (4.04-249.5) 0.63 (0.18-2.14) 35-44 1.07 (0.55-2.10) 1.10 (0.49-2.47) 13.28* (1.51-116.6) 0.54 (0.14-2.02) 45-50 Ref Ref Ref Ref Education ≤High School Ref Ref Ref Ref Some College 1.56* (1.02-2.51) 1.56 (0.83-2.94) 1.05 (0.47-2.32) 3.90* (1.39-10.96) ≥Bachelor’s 1.86** (1.17-2.97) 1.91* (1.03-3.54) 0.79 (0.30-2.12) 4.54** (1.51-13.65) BMI Normal Ref Ref Ref Ref Overweight 1.44 (0.93-3.22) 1.40 (0.81-2.41) 0.96 (0.35-2.62) 1.54 (0.56-4.22) Obese 0.87 (0.57-1.31) 0.86 (0.52-1.43) 0.68 (0.28-1.63) 1.01 (0.35-2.89) Parity Parous 2.85*** (1.89-4.30) 2.21** (1.33-3.67) 7.30*** (2.61-20.38) 5.38*** (2.27-12.76) Nulliparous Ref Ref Ref Ref Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; Ref, reference group. *P14yo. Citation Format: Paul G. Yeh, Iakovos Toumazis, Charlotte Sun, Karen Lu, Larissa A. Meyer. Disparities in uptake of levonorgestrel-releasing intrauterine system (LNG-IUS): implications for uterine cancer primary prevention. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5769.

Kratom, a psychoactive substance, use is an evolving research area that needs more studies to augment the limited literature. Our study examines the association between kratom use categories and mental health and substance use disorders in the U.S.We used the 2020 National Survey on Drug Use and Health data (N = 32,893), a cross-sectional survey data, on the U.S. population aged 12 years or older. We used STATA/SE version 16 to perform a multinomial logistic regression analysis to assess our study aims.Bisexuals, compared to heterosexuals, had higher risks of kratom use within the past 30 days (relative risk ratio [RRR]= 2.47, 95% CI= 1.07, 5.71). Major depressive episode was positively associated with kratom use more than 30 days ago (RRR= 2.04, 95% CI= 1.24, 3.34). This association was also observed for mild (RRR= 2.04, 95% CI= 1.38, 3.02), moderate (RRR= 2.25, 95% CI= 1.13, 4.51), or severe alcohol use disorder (RRR= 1.88, 95% CI= 1.05, 3.36); and mild (RRR= 1.98, 95% CI= 1.27, 3.11), moderate (RRR= 2.38, 95% CI= 1.27, 4.45), or severe marijuana use disorder (RRR= 2.13, 95% CI= 1.02, 4.47). Illicit drug other than marijuana use disorder was associated positively with kratom use more than 30 days ago (RRR= 2.81, 95% CI= 1.85, 4.26) and kratom use within the past 30 days (RRR= 5.48, 95% CI= 1.50, 20.02).Our findings suggested that identifying as bisexual, experiencing depression, alcohol use disorder, or illicit drug use disorder increased the risks of kratom use. There is a need to consider mental health and substance use disorders and sexual identity in kratom use interventions and policies geared toward reducing or preventing kratom use.

Hydropower has been the predominant source of electrical energy supply in Brazil, with significant increasing participation share of thermal and wind power plants. From 2000 to 2012, on average, 91% of Brazilian effective electric generation was provided by hydropower, while the reservoir system operated, on average, at 68% of the active storage. From 2013 to 2020, 73% was supplied by hydropower plants, with only 39% of active storage. Demand increase, delays in expanding the system, and a series of moderate to severe droughts occurred in Brazil over the last eight years have contributed to bringing the power system to its current state of low storage levels and intense dispatch of the thermal plants. The COVID-19 pandemic in 2020 hit the country hard and brought an abrupt reduction in energy demand, with persistent impacts expected for the coming years. On average, 7.5 GW or 9.7% reduction is expected for the next three years in power demand. To deal with the reduction in demand, a well-planned adaptation strategy is urgently needed. It is important to plan how to efficiently combine reduction of dispatching the most expensive thermal plants and recover water levels of reservoir storage and productivity in the complex hydrosystem with over 150 reservoirs. To determine the tradeoff and adaptation strategy, we use the HIDROTERM model, a nonlinear programming optimization model previously developed for planning the operation of the Brazilian hydrothermal system for analysis by comparing results with demand forecasts before and during the pandemic and under different hydrological scenarios. © ASCE.

Multivariate calibration using measurements of multiple water balance components has emerged as a potential solution for improving the performance and realism of large scale hydrologic models. In this study we develop a novel multivariate calibration framework to rigorously test whether incorporation of multiple water balance components into calibration can result in sufficiently accurate (behavioral) solutions for all model responses. Unlike previous studies, we use Bayesian calibration to formally define limits of acceptability or error thresholds in order to distinguish behavioral solutions for each of the incorporated fluxes. We apply the framework in the Mississippi river basin for the calibration of a large scale distributed hydrologic model (Noah-MP) with different combinations of model responses - evapotranspiration (ET), soil moisture (SM), and streamflow (SF). The results of the study show that incorporation of additional fluxes and soil moisture (a storage variable) is not always valuable due to significant trade-offs in accuracy among the model responses. In our experiments, only ET and SF could be simulated simultaneously to a reasonable degree of accuracy. In addition, we quantify the trade-offs in accuracy between the model responses using the concept of Pareto optimality. We find that combining ET with other fluxes entails higher trade-offs in accuracy compared to either SM or SF. Unlike deterministic calibration, with the developed framework we are able to identify deficiencies in model parameterization that lead to significant trade-offs in accuracy, especially between ET and SM. We find that the parameters which are insensitive to individual model responses can influence the trade-off relationship between them.

Computer simulations of groundwater flow and solute transport are often burdened by long runtimes. The simulations are necessarily complex to capture the system dynamics and finely discretized spatial and temporal domains are often needed for solution accuracy and stability. Model reduction allows for the approximation of system state by solving equations in a reduced dimensional space. Proper orthogonal decomposition (POD) is an effective way to reduce the dimensionality of systems of differential equations that are discretized by finite difference or finite element methods. If the problems are nonlinear in nature, the discrete empirical interpolation method (DEIM) has been shown to supplement POD by further reducing the dimension of nonlinear calculations. Here, the combined POD-DEIM approach is shown to work on a problem of 2-dimensional groundwater flow with solute transport exhibiting nonlinear sorption. The application is restricted to largely dispersive problems (low Peclet number). Results show areas of high concentration are effectively identified with mean errors less than 2% of the full model.

Purpose To examine the costs and cost savings associated with a large, urban teleretinal screening program for diabetic retinopathy (DR). Design Retrospective analysis. Participants Eighteen thousand twenty-five patients (36 050 eyes) screened via the Harris Health System (HHS) DR teleretinal screening program between June 2013 and April 2014. Methods Activity-based costing applied to the operational screening pathway was implemented to determine the cost of screening. Actual costs were calculated based on retrospective chart review and figures obtained from the HHS and Centers for Medicare and Medicaid Services. Theoretical costs of in-clinic examinations and delayed intervention were compared with actual costs of screening and treatment to determine costs savings. Main Outcome Measures Costs and cost savings in United States dollars were estimated. Results The per-patient cost of teleretinal screening itself was found to be $27.35, whereas the average total cost (factoring in treatment) per patient was determined to be $43.14. The physical examination-only and treatment-only models yielded cost savings estimates of $2 047 442.53 and $1 148 597.35, respectively. Conclusions The cost savings yielded by the HHS DR teleretinal screening program compared with conventional screening are substantial and corroborate the findings of similar studies that have analyzed teleretinal screening. Additionally, it can be presumed that there are additional indirect economic benefits resulting from earlier detection and treatment of disease.

Multiple uncertainties exist in the optimal flood control decision-making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood control decision making that constitute the Forecast-Optimization-Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two-step process. We propose a novel SMAA-TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision-making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk-informed decisions with higher reliability.

Estimating parameters accurately in groundwater models for aquifers is challenging because the models are non-explicit solutions of complex partial differential equations. Modern research methods, such as Monte Carlo methods and metaheuristic algorithms, for searching an efficient design to estimate model parameters require hundreds, if not thousands of model calls, making the computational cost prohibitive. One method to circumvent the problem and gain valuable insight on the behavior of groundwater is to first apply a Galerkin method and convert the system of partial differential equations governing the flow to a discrete problem and then use a Proper Orthogonal Decomposition to project the high-dimensional model space of the original groundwater model to create a reduced groundwater model with much lower dimensions. The reduced model can be solved several orders of magnitude faster than the full model and able to provide an accurate estimate of the full model. The task is still challenging because the optimization problem is non-convex, non-differentiable and there are continuous variables and integer-valued variables to optimize. Following convention, heuristic algorithms and a combination is used search to find efficient designs for the reduced groundwater model using various optimality criteria. The main goals are to introduce new design criteria and the concept of design efficiency for experimental design research in hydrology. The two criteria have good utility but interestingly, do not seem to have been implemented in hydrology. In addition, design efficiency is introduced. Design efficiency is a method to assess how robust a design is under a change of criteria. The latter is an important issue because the design criterion may be subjectively selected and it is well known that an optimal design can perform poorly under another criterion. It is thus desirable that the implemented design has relatively high efficiencies under a few criteria. As applications, two heuristic algorithms are used to find optimal designs for a small synthetic aquifer design problem and a design problem for a large-scale groundwater model and assess their robustness properties to other optimality criteria. The results show the proof of concept is workable for finding a more informed and efficient model-based design for a water resource study.

Purpose: To report long-term outcomes of primary intraocular lens (IOL) placement in patients aged 7 to 24 months. Methods: This was a retrospective study of 27 consecutive patients (28 eyes) aged 7 to 24 months who underwent cataract surgery with primary IOL placement. Results: Average follow-up was 62.7 ± 41.7 months and the mean age of surgery was 14.4 ± 5.6 months. Mean final visual acuity was 1.02 ± 0.72 logMAR (20/209). Adverse events occurred in 7 eyes (25%) and included visual axis opacification in 6 eyes and pupillary block glaucoma in 1 eye. Seven patients (25.9%) required additional intraocular surgery. Strabismus was present in 19 patients (70.4%). Better stereopsis was correlated with better final acuity. Conclusions: Cataract surgery with IOL placement in patients aged 7 to 24 months is associated with few complications. Visual axis opacification is the most frequent adverse event. [ J Pediatr Ophthalmol Strabismus . 2017;54(3):149–155.]

Past research has demonstrated that a reduced-order model (ROM) can be two-to-three orders of magnitude smaller than the original model and run considerably faster with acceptable error. A standard method to construct the system matrices for a ROM is Proper Orthogonal Decomposition (POD), which projects the system matrices from the full model space onto a subspace whose range spans the full model space but has a much smaller dimension than the full model space. This projection can be prohibitively expensive to compute if it must be done repeatedly, as with a Monte Carlo simulation. We propose a Fast Algorithm to reduce the computational burden of constructing the system matrices for a parameterized, reduced-order groundwater model (i.e. one whose parameters are represented by zones or interpolation functions). The proposed algorithm decomposes the expensive system matrix projection into a set of simple scalar-matrix multiplications. This allows the algorithm to efficiently construct the system matrices of a POD reduced-order model at a significantly reduced computational cost compared with the standard projection-based method. The developed algorithm is applied to three test cases for demonstration purposes. The first test case is a small, two-dimensional, zoned-parameter, finite-difference model; the second test case is a small, two-dimensional, interpolated-parameter, finite-difference model; and the third test case is a realistically-scaled, two-dimensional, zoned-parameter, finite-element model. In each case, the algorithm is able to accurately and efficiently construct the system matrices of the reduced-order model.

Risk analysis plays an important role in decision making for real-time flood control operation of complex flood control systems. A typical flood control system consists of reservoirs, river channels, and downstream control points. The system generally is characterized by nonlinearity and large scale. Additionally, the input variables are mostly stochastic. Because of the dimensionality problem, generally, it would not be possible to carry out risk analysis without decomposition. In this paper, we propose a decomposition-integration approach whereby the original complex flood control system is decomposed into a number of independent subsystems. We conduct risk analysis for each subsystem and then integrate the results by means of combination theory of stochastic processes. We evaluate the propagation of uncertainties through the complex flood control system and calculate the risk of reservoir overtopping, as well as the risk of flooding at selected downstream control points. We apply the proposed methodology to a flood control system in the middle reaches of the Huaihe River basin in China. The results show that the proposed method is practical and provides a way to estimate the risks in real-time flood control operation of a complex flood control system. This article is protected by copyright. All rights reserved.

Nonlinear groundwater flow models have the propensity to be overly complex leading to burdensome computational demands. Reduced modeling techniques are used to develop an approximation of the original model that has smaller dimensionality and faster run times. The reduced model proposed is a combination of proper orthogonal decomposition (POD) and the discrete empirical interpolation method (DEIM). Solutions of the full model (snapshots) are collected to represent the physical dynamics of the system and Galerkin projection allows the formulation of a reduced model that lies in a subspace of the full model. Interpolation points are added through DEIM to eliminate the reduced model's dependence on the dimension of the full model. POD is shown to effectively reduce the dimension of the full model and DEIM is shown to speed up the solution by further reducing the dimension of the nonlinear calculations. To show the concept can work for unconfined groundwater flow model, with added nonlinear forcings, one-dimensional and two-dimensional test cases are constructed in MODFLOW-OWHM. POD and DEIM are added to MODFLOW as a modular package. Comparing the POD and the POD-DEIM reduced models, the experimental results indicate similar reduction in dimension size with additional computation speed up for the added interpolation. The hyper-reduction method presented is effective for models that have fine discretization in space and/or time as well as nonlinearities with respect to the state variable. The dual reduction approach ensures that, once constructed, the reduced model can be solved in an equation system that depends only on reduced dimensions.

We develop a Bayesian hierarchical model (BHM) for estimating the statistical parameters for monthly average streamflows. We assume monthly average streamflow can be characterized by a three-parameter log-normal distribution (LN3). The three underlying statistical parameters are shift, shape, and location. When estimating a parameter, such as the shape, of a given month the BHM utilizes historical observations not only from the month under consideration but also from all other months. This is different from traditional statistical parameter estimation methods that only use historical observations for the month under consideration. We apply the proposed BHM for parameter estimation to eight watersheds in the United States, where historical unimpaired streamflows have been collected. We also carry out parameter estimation using traditional methods, such as the maximum likelihood estimation, the method of moments, and the L-moment method. Using cross-validation with test data log-likelihood as the measure of performance, the results show that BHM outperforms traditional estimation methods. In addition, we show that as available observation data decreases, the more the proposed method improves relative to traditional methods. Since BHM utilizes information contained in the entire data set, it is especially suited for parameter estimation where historical observations are limited. Furthermore, we conduct a comparative analysis between BHM and an autoregressive model to demonstrate the advantage of BHM.

Many uncertainties are associated with real-time reservoir flood control operation and introduce risks for decision making. A multi-objective risk management model is proposed to optimize the real-time flood control operation of a parallel reservoir system during flood season. The model considers the combined impact of reservoir inflow and lateral inflow uncertainties and flood control operation risks. It consists of three key components: a risk optimal operation submodel, a risk assessment submodel, and an optimization submodel. The risk optimal operation submodel takes into account the uncertainties and establishes an operation model that considers two competing objectives of minimizing the risk of upstream flooding and minimizing the risk of downstream flooding. The risk assessment submodel calculates the risks based on a stochastic differential equation (SDE). The final optimization submodel embeds the risk assessment model in the risk optimal operation model and is solved by the Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The proposed methodology is applied to a real flood control system in the middle reaches of the Huaihe River basin in China. The results show that the developed multi-objective risk management model can provide operation schedules that satisfy flood control objectives and simultaneously minimize the overall risks. The Pareto front of the proposed objectives demonstrates the tradeoff among the competing objectives. The model can be used as a decision-making tool for conducting risk management for real-time reservoir flood control operation during flood season. The decision makers can choose the operation schedule according to their risk preference and updated hydrological information.

We develop an experimental design algorithm to select locations for a network of observation wells that provide the maximum robust information about unknown hydraulic conductivity in a confined, anisotropic aquifer. Since the information that a design provides is dependent on an aquifer's hydraulic conductivity, a robust design is one that provides the maximum information in the worst-case scenario. The design can be formulated as a max–min optimization problem. The problem is generally non-convex, non-differentiable, and contains integer variables. We use a Genetic Algorithm (GA) to perform the combinatorial search. We employ proper orthogonal decomposition (POD) to reduce the dimension of the groundwater model, thereby reducing the computational burden posed by employing a GA. The GA algorithm exhaustively searches for the robust design across a set of hydraulic conductivities and finds an approximate design (called the High Frequency Observation Well Design) through a Monte Carlo-type search. The results from a small-scale 1-D test case validate the proposed methodology. We then apply the methodology to a realistically-scaled 2-D test case.

To investigate the range of renal near-infrared spectroscopy (NIRS) measures in neonates undergoing therapeutic hypothermia for hypoxic ischemic encephalopathy (HIE) and to determine the association between renal NIRS measures and the development of acute kidney injury (AKI).A retrospective chart review was conducted of neonates with moderate to severe HIE who received therapeutic hypothermia at a tertiary care center from 2014 to 2016. Neonates had routine continuous NIRS monitoring of cerebral and renal saturation (Rsat) as part of their clinical care for 72 hours of cooling and until 24 hours after rewarming. The outcome of AKI was defined by an abnormal rate of decline of serum creatinine over the first 5 days of life. Mixed effects models determined the association between renal NIRS measures and AKI over time.Of 38 neonates with HIE undergoing cooling, 15 (39%) developed AKI. Rsat was lower than cerebral saturation during cooling (P .01), but Rsat increased over time after rewarming, while renal oxygen extraction levels decreased (P .0001). Neonates with AKI had higher Rsat levels (P .01) compared with those without AKI after 24 hours of life. Using receiver operating characteristic curves, Rsat75% by 24-48 hours predicted AKI with a sensitivity of 79% and specificity of 82% (area under the receiver operating characteristic curve = 0.76).Throughout cooling, neonates with AKI had higher Rsat measures than those without AKI. These differences may reflect lower oxygen extraction by the injured kidney. NIRS monitoring of Rsat may identify neonates with HIE at risk of developing AKI.

Projection-based model reduction techniques have been shown to be very effective for reducing the computational burden of high-dimensional groundwater simulations, but only applied to confined groundwater flow. A new methodology is proposed that reduces the dimension of a discretized, transient, unconfined groundwater-flow model. This unconfined model reduction technique is based on Galerkin projection and the Newton formulation of MODFLOW. The method is implemented following the standard package design and code structure that MODFLOW employs for all its features. When the package is invoked within MODFLOW it can collect snapshots, produce a basis, construct the reduced model and propagate the reduced model forward in time. The new formulation accurately represents the water-table surface under a variety of nonlinear settings, such as intraborehole flow from a Multi-Node Well. The unconfined model reduction is applied to four test cases to illustrate its flexibility in handling nonlinear features. Several test cases are discussed to demonstrate the unconfined model reduction applicability. The final test case applies the new model reduction methodology to a scoping MODFLOW model of Santa Barbara, CA composed of 113,578 cells, which requires solving 113,578 equations per time step, and reduces it to 127 equations.

Summary The objective of this study is to develop a method that combines signal analysis with a groundwater simulation model for the characterization and quantification of regional groundwater pumping. We first use independent component analysis to identify the characteristics of the main pumping types from hourly groundwater head observations. Then we fit the drawdown variation at each observation well caused by each pumping type with a calibrated groundwater simulation model. We then use the results to estimate the quantity of each pumping type. We apply the proposed method to a regional aquifer system in Sijhou Township, located in the Jhuoshuei River alluvial fan in central-western Taiwan. The independent component analysis identifies three main pumping types: Agricultural type, Industrial type A, and Industrial type B. All three pumping types have the same sine-wave shape with one-day frequency, but differ in peak time, valley time, and peak-to-valley amplitude. Agricultural pumping occurs during daytime farming hours, while industrial pumping occurs during off-peak hours to take advantage of much lower nighttime electricity rates. Quantitative results indicate that the total amount of groundwater pumping in Sijhou is 37,565 tons per day, of which more than 58% is for agricultural use, while the remaining 42% is for industrial use. The percentage of industrial pumping is higher than expected, which may imply that industry use of groundwater is changing. The results indicate a large amount of pumping takes place at depths of more than 40 m. For further investigation, the installation of more monitoring wells is recommended at deeper aquifers.

A stochastic programming with recourse model requires the consequences of recourse actions be modeled for all possible realizations of the stochastic variables. Continuous stochastic variables are approximated by scenario trees. This paper evaluates the impact of scenario tree reduction on model performance for hydropower operations and suggests procedures to determine the optimal level of scenario tree reduction. We first establish a stochastic programming model for the optimal operation of a cascaded system of reservoirs for hydropower production. We then use the neural gas method to generate scenario trees and employ a Monte Carlo method to systematically reduce the scenario trees. We conduct in-sample and out-of-sample tests to evaluate the impact of scenario tree reduction on the objective function of the hydropower optimization model. We then apply a statistical hypothesis test to determine the significance of the impact due to scenario tree reduction. We develop a stochastic programming with recourse model and apply it to real-time operation for hydropower production to determine the loss in solution accuracy due to scenario tree reduction. We apply the proposed methodology to the Qingjiang cascade system of reservoirs in China. The results show: (1) The neural gas method preserves the mean value of the original streamflow series but introduces bias to variance, cross variance and lag-one co-variance due to information loss when the original tree is systematically reduced; (2) Reducing the scenario number by as much as 40% results in insignificant change in the objective function and solution quality, but significantly reduces computational demand. This article is protected by copyright. All rights reserved.

Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.

This paper examines the impact of short-term operation on long-term energy production. We propose a multiobjective optimization model for the short-term, daily operation of a system of cascade reservoirs. The two objectives considered in the daily model are: (1) minimizing the total amount of water released and (2) maximizing the stored energy in the system. Optimizing short-term operation without considering its impact on long-term energy production does not guarantee maximum energy production in the system. Therefore, a major goal of this paper is to identify desirable short-term operation strategies that, at the same time, optimize long-term energy production. First, we solve the daily model for 1 month (30 days) using a nondominated genetic algorithm (NSGAII). We then use the nondominated solutions obtained by NSGAII to assess the impact on long-term energy production using a monthly model. We use historical monthly inflows to characterize the inflow variability. We apply the proposed methodology to the Qingjiang cascade system of reservoirs in China. The results show: (1) in average hydrology scenarios, the solution maximizing stored energy produces the most overall long-term energy production; (2) in moderately wet hydrology scenarios, the solution minimizing water released outperforms the maximizing stored energy solution; and (3) when extremely wet hydrology scenarios are expected, a compromise solution is the best strategy.

A new methodology is proposed for the development of parameter-independent reduced models for transient groundwater flow models. The model reduction technique is based on Galerkin projection of a highly discretized model onto a subspace spanned by a small number of optimally chosen basis functions. We propose two greedy algorithms that iteratively select optimal parameter sets and snapshot times between the parameter space and the time domain in order to generate snapshots. The snapshots are used to build the Galerkin projection matrix, which covers the entire parameter space in the full model. We then apply the reduced subspace model to solve two inverse problems: a deterministic inverse problem and a Bayesian inverse problem with a Markov Chain Monte Carlo (MCMC) method. The proposed methodology is validated with a conceptual one-dimensional groundwater flow model. We then apply the methodology to a basin-scale, conceptual aquifer in the Oristano plain of Sardinia, Italy. Using the methodology, the full model governed by 29,197 ordinary differential equations is reduced by two to three orders of magnitude, resulting in a drastic reduction in computational requirements.

Increasing demand for water in urban areas and agricultural zones in arid and semi-arid coastal regions has urged planners and regulators to look for alternative renewable water sources. Seawater reverse osmosis desalination plants have become an essential supply source for the production of freshwater in such regions. However, disposal of hypersaline wastes from the plants in many of these regions has not been fully and properly addressed. A simulation–optimization approach is proposed to design a system for safe disposal of brine wastes. We use a hydrodynamic model to assess the initial dilution of hypersaline effluent discharged into coastal waters. A regression model is developed to relate the input and output parameters of the simulation model. We then formulate an optimization model to determine the design of a brine disposal system with multiport diffusers in which the regression model replaces the simulation model. The design parameters are the length, diameter and number of ports of the disposal system. Given the parameter uncertainty, a chance-constrained programming model is used. This simulation–optimization framework provides planners with effective tools that meet environmental permitting requirements and restrictions, while achieving cost savings and adequate hydrodynamic performance. A case study demonstrates the applicability of the proposed methodology.

Streamflow scenario tree reduction is essential for alleviating the computational burden of a stochastic programming with recourse model. This paper develops a new streamflow scenario tree reduction method aimed at preserving important statistical moment information and maintaining streamflow scenario probability. Specifically, we first employ a neural gas algorithm for scenario tree generation, then establish a stepwise conditional Monte Carlo sampling method for systemically reducing the number of scenarios from the full tree. We then develop a regularized optimization model based on ridge regression and moment matching to determine the posterior scenario probability. We apply the proposed method to the Qingjiang cascade reservoir system in China. The results show that the reduced tree with 35% reduction level can still maintain robust moment preservations, including the mean, variance, lag-one covariance, cross-site covariance, and scenario probability. Additionally, the stability test indicates that the proposed conditional Monte Carlo sampling method is stable and converges within a reasonable number of scenario combinations.

Background & Aims Radiofrequency ablation (RFA) is effective treatment for Barrett’s esophagus (BE). However, some patients have recurrence after complete eradication of intestinal metaplasia (CEIM). We investigated the incidence of and factors associated with BE recurrence, with or without neoplasia, after RFA and CEIM using data from the national Veterans Affairs (VA) healthcare system. Methods We performed a retrospective cohort study of Veterans with BE treated by RFA from 2005 through 2016 with follow-up endoscopy. Subjects were followed until BE recurrence, neoplasia, death until October 2016. CEIM, BE recurrence, and factors associated with recurrence were identified by review of medical records. We calculated incidence rates of BE recurrence, with and without neoplasia, after CEIM and identified predictors using Cox proportional hazards models. Results We identified 430 Veterans with BE who were treated with RFA; of these 337 achieved CEIM (78.4%). Most were men (98.6%), White (83.7%), and 66.0% had baseline dysplasia. Of those with CEIM, 98 patients (29.1%) had recurrence of BE during a total 906.0 patient-years of follow-up (median 1.9 years) after CEIM (incidence, 10.8%/patient-year). Dysplasia developed in 20 patients (2.2%/patient-year) and cancer in 3 patients (0.3%/patient-year). Baseline dysplasia (hazard ratio [HR], 1.71; 95% CI, 1.03–2.84) and long-segment BE (HR, 1.59; 95% CI, 1.01–2.51) increased risk of BE recurrence whereas treatment at high-volume RFA facilities reduced risk of BE recurrence (for quartile 4 vs quartile 1: HR, 0.19; 95% CI, 0.05–0.68). Conclusions In a nationwide VA system study of outcomes of RFA for BE, we associated baseline dysplasia, long-segment BE, and treatment at low-volume RFA centers with recurrence of BE after CEIM. The findings call for performing these procedures in high-volume centers.

[1] An optimal experimental design algorithm is developed to select locations for a network of observation wells that provide maximum information about unknown groundwater pumping in a confined, anisotropic aquifer. The design uses a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. The formulated optimization problem is non-convex and contains integer variables necessitating a combinatorial search. Given a realistic large-scale model, the size of the combinatorial search required can make the problem difficult, if not impossible, to solve using traditional mathematical programming techniques. Genetic algorithms (GAs) can be used to perform the global search; however, because a GA requires a large number of calls to a groundwater model, the formulated optimization problem still may be infeasible to solve. As a result, proper orthogonal decomposition (POD) is applied to the groundwater model to reduce its dimensionality. Then, the information matrix in the full model space can be searched without solving the full model. Results from a small-scale test case show identical optimal solutions among the GA, integer programming, and exhaustive search methods. This demonstrates the GA's ability to determine the optimal solution. In addition, the results show that a GA with POD model reduction is several orders of magnitude faster in finding the optimal solution than a GA using the full model. The proposed experimental design algorithm is applied to a realistic, two-dimensional, large-scale groundwater problem. The GA converged to a solution for this large-scale problem.

Background PEP02 is a novel highly stable liposomal nanocarrier formulation of irinotecan. This randomized phase II study evaluated the efficacy and safety of single agent PEP02 compared with irinotecan or docetaxel in the second-line treatment of advanced oesophago-gastric (OG) cancer. Patients and methods Patients with locally advanced/metastatic disease who had failed one prior chemotherapy regimen were randomly assigned to PEP02 120 mg/m2, irinotecan 300 mg/m2 or docetaxel (Taxotere) 75 mg/m2 every 3 weeks. The primary end point was objective response rate (ORR). Simon's two-stage design was used and the ORR of interest was 20% (α = 0.05, type II error β = 0.10, null hypothesis of ORR was 5%). Results Forty-four patients per arm received treatment, and 124 were assessable for response. The ORR statistical threshold for the first stage was reached in all arms. In the intent-to-treat (ITT) population, ORRs were 13.6% (6/44), 6.8% (3/44) and 15.9% (7/44) in the PEP02, irinotecan and docetaxel arms, respectively. The median progression-free survival (PFS) and overall survival were similar between the trial arms. Commonest grade 3–4 adverse event reported was diarrhoea in the PEP02 and irinotecan groups (27.3% versus 18.2%). Conclusion The ORR associated with PEP02 was comparable with docetaxel and numerically greater than that of irinotecan. PEP02 warrants further evaluation in the advanced gastric cancer setting.

[1] We present a model-order reduction technique that overcomes the computational burden associated with the application of Monte Carlo methods to the solution of the groundwater flow equation with random hydraulic conductivity. The method is based on the Galerkin projection of the high-dimensional model equations onto a subspace, approximated by a small number of pseudo-optimally chosen basis functions (principal components). To obtain an efficient reduced-order model, we develop an offline algorithm for the computation of the parameter-independent principal components. Our algorithm combines a greedy algorithm for the snapshot selection in the parameter space and an optimal distribution of the snapshots in time. Moreover, we introduce a residual-based estimation of the error associated with the reduced model. This estimation allows a considerable reduction of the number of full system model solutions required for the computation of principal components. We demonstrate the robustness of our methodology by way of numerical examples, comparing the empirical statistics of the ensemble of the numerical solutions obtained using the traditional Monte Carlo method and our reduced model. The numerical results show that our methodology significantly reduces the computational requirements (CPU time and storage) for the solution of the Monte Carlo simulation, ensuring a good approximation of the mean and variance of the head. The analysis of the empirical probability density functions at the observation wells suggests that our reduced model produces good results and is most accurate in the regions with large drawdown.

The regional water resources planning contains a variety of proposed projects. Precisely estimating the regional system yield could be difficult. Incorrectly computed system yield will cause the scheduling strategy to be not correctly screened. This research imbedded a combined regional simulation-optimization model, namely Generalized Water Allocation Simulation Model (GWASIM), for evaluating system yield in binary dynamic programming (DP) analysis as a scheduling model to determine the optimal development strategy of the master plan of a regional water supply system. To achieve a complete scheduling analysis, this paper investigated: (1) applying GWASIM to effectively and efficiently determine the precise system yields of many different combinations of proposed projects while meeting design criteria of acceptable shortage; (2) solving a non-pure sequencing problem with a DP based scheduling model since some projects may not be necessarily developed, or even no feasible combination of proposed projects can meet the demand; (3) clarifying the amortized construction cost to be accounted in economic analysis if the economic life of a project exceeds the scheduling horizon. In addition, the computation efficiencies of scheduling analysis and system yield analysis, which are significantly improved with DP and simulation-optimization model respectively, are discussed in the paper. This study devised the master plan of the Jilong river system in northern Taiwan as an example. The optimal scheduling strategy of minimum developing cost was precisely determined.

This paper presents the development of a mathematical model to optimize the management and operation of the Brazilian hydrothermal system. The system consists of a large set of individual hydropowe...