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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.

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.

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.

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.

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.

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.

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.

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.

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.

We measure the time dependence of the ratio of decay rates for D-0 -> K+ pi(-) to the Cabibbo- favored decay D-0 -> K- pi(+). The charge conjugate decays are included. A signal of 3: 3 x 10(4) D*(+) -> pi D-+(0), D-0 -> K+ pi(-) decays is obtained with D-0 proper decay times between 0.75 and 10 mean D-0 lifetimes. The data were recorded with the CDF II detector at the Fermilab Tevatron and correspond to an integrated luminosity of 9: 6 fb(-1) for p (p) over bar collisions at root s = 1: 96 TeV. Assuming CP conservation, we search for D-0-(D) over bar (0) mixing and measure the mixing parameters to be R-D = (3: 51 +/- 0: 35) x 10(-3), y' = (4: 3 +/- 4: 3) x 10(-3), and x'(2) = (0: 08 +/- 0: 18) x 10(-3). We report Bayesian probability intervals in the x'(2)- y' plane and find that the significance of excluding the no- mixing hypothesis is equivalent to 6.1 Gaussian standard deviations, providing the second observation of D-0-(D) over bar (0) mixing from a single experiment.

We report an analysis of the Λb0→Λc+π-π+π- decay in a data sample collected by the CDF II detector at the Fermilab Tevatron corresponding to 2.4 fb-1 of integrated luminosity. We reconstruct the currently largest samples of the decay modes Λb0→Λc(2595)+π- (with Λc(2595)+→Λc+π+π-), b0→Λc(2625)+π- (with Λc(2625)+→Λc+π+π-), Λb0→Σc(2455)++π-π- (with Σc(2455)++→Λc+π+), and Λb0→Σc (2455)0π+π- (with Σc(2455)0→Λc+π-) and measure the branching fractions relative to the Λb0→Λc+π- branching fraction. We measure the ratio B(Λb0→Λc +π-π+π-)/ B(Λb0→Λc+π-)=3.04±0.33(stat)-0.55+0.70(syst) which is used to derive B(Λb0→Λc+π-π+π-)=(26.8-11.2+11.9)×10-3.

This article reports a measurement of the production cross section of prompt isolated photon pairs in proton-antiproton collisions at √s=1.96TeV using the CDF II detector at the Fermilab Tevatron collider. The data correspond to an integrated luminosity of 5.36fb -1. The cross section is presented as a function of kinematic variables sensitive to the reaction mechanisms. The results are compared with three perturbative QCD calculations: (1) a leading-order parton shower Monte Carlo, (2) a fixed next-to-leading-order calculation and (3) a next-to-leading-order/next-to-next-to-leading-log resummed calculation. The comparisons show that, within their known limitations, all calculations predict the main features of the data, but no calculation adequately describes all aspects of the data. © 2011 American Physical Society.

Delayed recovery from surgery causes personal suffering and substantial societal and economic costs. Whether immune mechanisms determine recovery after surgical trauma remains ill-defined. Single-cell mass cytometry was applied to serial whole-blood samples from 32 patients undergoing hip replacement to comprehensively characterize the phenotypic and functional immune response to surgical trauma. The simultaneous analysis of 14,000 phosphorylation events in precisely phenotyped immune cell subsets revealed uniform signaling responses among patients, demarcating a surgical immune signature. When regressed against clinical parameters of surgical recovery, including functional impairment and pain, strong correlations were found with STAT3 (signal transducer and activator of transcription), CREB (adenosine 3′,5′-monophosphate response element–binding protein), and NF-κB (nuclear factor κB) signaling responses in subsets of CD14 + monocytes ( R = 0.7 to 0.8, false discovery rate

Complement is a major mediator of tissue injury in several types of glomerulonephritis. However, no therapeutic agents that inhibit complement activation are available for human use. sCR1 (TP10, BRL 55736) is a recombinant, soluble human complement receptor 1 (CR1) molecule lacking transmembrane and cytoplasmic domains that inhibits C3 and C5 convertase activity by preferentially binding C4b and C3b. To test the efficacy of sCR1 on complement-mediated glomerulonephritis, rats were pretreated with sCR1 (60 mg/kg per day) before and during the induction of three models of complement-dependent glomerulonephritis (concanavalin A and antithymocyte serum models of proliferative glomerulonephritis, passive Heyman nephritis). Daily sCR1 and complement hemolytic activity levels were measured, and renal histology and urine protein excretion were examined. Mean serum sCR1 levels of 100 to 200 micrograms/mL were maintained with a reduction in complement hemolytic activity to less than 15% in most animals. In the antithymocyte serum model, sCR1-treated animals had significant reductions in mesangiolysis, glomerular platelet and macrophage infiltrates, and proteinuria at 48 h. In the concanavalin A model, sCR1 significantly reduced glomerular C3 and fibrin deposits, platelet infiltrates, and proteinuria at 48 h. In passive Heymann nephritis, proteinuria was also significantly reduced (199 +/- 8.5 versus 125 +/- 16 mg/day, P0.002) at 5 days. It was concluded that sCR1 significantly reduces both morphologic and functional consequences of several different types of complement-mediated glomerulonephritis and deserves evaluation as a potential therapeutic agent in complement-mediated immune glomerular disease in humans.

[1] This paper develops alternative strategies for European call options for water purchase under hydrological uncertainties that can be used by water resources managers for decision making. Each alternative strategy maximizes its own objective over a selected sequence of future hydrology that is characterized by exceedance probability. Water trade provides flexibility and enhances water distribution system reliability. However, water trade between two parties in a regional water distribution system involves many issues, such as delivery network, reservoir operation rules, storage space, demand, water availability, uncertainty, and any existing contracts. An option is a security giving the right to buy or sell an asset; in our case, the asset is water. We extend a flow path-based water distribution model to include reservoir operation rules. The model simultaneously considers both the physical distribution network as well as the relationships between water sellers and buyers. We first test the model extension. Then we apply the proposed optimization model for European call options to the Tainan water distribution system in southern Taiwan. The formulation lends itself to a mixed integer linear programming model. We use the weighing method to formulate a composite function for a multiobjective problem. The proposed methodology provides water resources managers with an overall picture of water trade strategies and the consequence of each strategy. The results from the case study indicate that the strategy associated with a streamflow exceedence probability of 50% or smaller should be adopted as the reference strategy for the Tainan water distribution system.

[1] This study develops a groundwater management model for real-time operation of an aquifer system. A groundwater flow model is allied with a nudging data assimilation algorithm that reduces the forecast error, minimizes the risk of system failure, and improves management strategies. The nudging algorithm treats the unknown private pumping as an additional sink term in the groundwater flow equation and provides a consistently physical interpretation for the identification of pumping rates. The system response due to pumping and injection is represented by a response matrix that is generated by the influence coefficient method. The response matrix (with a much smaller dimension) is used as a reduced model and is embedded directly in the management model as a part of the constraint set. Additionally, the influence coefficient method is utilized to include the nudging effect in the reduced model. The management model optimizes the monthly operation for 12 months into the future and determines the optimal strategy using the information provided by nudging. The management model is updated at the beginning of each month when new head observations and pumping data become available. We also discuss the utility, accuracy, and efficiency of the proposed management model for real-time operation.

[1] Water resources systems management often requires complex mathematical models whose use may be computationally infeasible for many advanced analyses, e.g., optimization, data assimilation, model uncertainty, etc. The computational demand of these analyses can be reduced by approximating the model with a simpler reduced model. Proper Orthogonal Decomposition (POD) is an efficient model reduction technique based on the projection of the original model onto a subspace generated by full-model snapshots. In order to implement this method, an appropriate number of snapshots of the full model must be taken at the appropriate times such that the resulting reduced model is as accurate as possible. Since confined aquifers reach steady state in an exponential manner, we present a simple exponential function that can be used to select snapshots for these types of models. This selection method is then employed to determine the optimal snapshot set for a unit, dimensionless model. The optimal snapshot set is found by maximizing the minimum eigenvalue of the snapshot covariance matrix, a criterion similar to those used in experimental design. The resulting snapshot set can then be translated to any complex, real world model based on a simple, approximate relationship between dimensionless and real-world times. This translation is illustrated using a basin scale model of Central Veneto, Italy. We show that this method is very easy to implement and produces an accurate reduced model that, in the case of Central Veneto, Italy, runs approximately 1,000 times faster than the full model.

[1] This study develops a nudging data assimilation algorithm for estimating unknown pumping from private wells in an aquifer system using measured data of hydraulic head. The proposed algorithm treats the unknown pumping as an additional sink term in the governing equation of groundwater flow and provides a consistent physical interpretation for pumping rate identification. The algorithm identifies the unknown pumping and, at the same time, reduces the forecast error in hydraulic heads. We apply the proposed algorithm to the Las Posas Groundwater Basin in southern California. We consider the following three pumping scenarios: constant pumping rates, spatially varying pumping rates, and temporally varying pumping rates. We also study the impact of head measurement errors on the proposed algorithm. In the case study we seek to estimate the six unknown pumping rates from private wells using head measurements from four observation wells. The results show an excellent rate of convergence for pumping estimation. The case study demonstrates the applicability, accuracy, and efficiency of the proposed data assimilation algorithm for the identification of unknown pumping in an aquifer system.

[1] In this paper we construct an objective-oriented model for conjunctive-use planning of surface water and groundwater for the Warren groundwater basin in southern California. The goal of conjunctive-use planning is to decrease high-nitrate concentration while maintaining groundwater levels at desired elevations and meeting water demand. We formulate a management problem that minimizes the total cost over the proper choices of the time-varying pumping and recharge rates at prespecified wells and surface ponds. To make the solution of the management problem reliable, we must have an accurate simulation model to predict groundwater level and nitrate concentration distributions under different management alternatives. The objective-oriented model construction approach seeks a representative parameter that has the simplest structure and requires the minimum data for identification but can produce reliable results for a given model application. With the data from the Warren groundwater basin, we show how to incorporate management objectives into the construction of an objective-oriented model, identify the parameter structure and its corresponding parameter values, solve the generalized inverse problem effectively by finding the worst-case parameter (WCP), evaluate the sufficiency of existing data, and find a robust experiment design when the existing data are insufficient. Results of this case study show that the presented methodology is useful in practice because (1) data sufficiency can be judged before conducting actual field experiments and (2) the identified WCP drastically reduces the computation time for constructing an objective-oriented model.

This paper describes the development of a numerical model for prediction of soil vapor extraction processes. The major emphasis is placed on field-scale predictions with the objective to advance development of planning tools for design and operation of venting systems. The numerical model solves two-dimensional flow and transport equations for general n-component contaminant mixtures. Flow is limited to the gas phase and local equilibrium partitioning is assumed in tracking contaminants in the immiscible fluid, water, gas, and solid phase. Model predictions compared favorably with analytical solutions and multicomponent column venting experiments. Sensitivity analysis indicates equilibrium phase partitioning is a good assumption in modeling organic liquid volatilization occurring in field venting operations. Mass transfer rates in volatilization from the water phase and contaminant desorption are potentially rate limiting. Simulations of hypothetical field-scale problems show efficiency of venting operations is most sensitive to vapor pressure and the magnitude and distribution of soil permeability.

[1] When the structure of an aquifer is complex and unknown, determining the complexity of model structure and identifying its spatially distributed parameters become very difficult. This two-paper series describes an approach for objective-oriented model construction that can identify a model with simplified structure and assure its reliability for predetermined model applications. The theoretical basis of the methodology includes the definition of structure error, the conditions of structure reducibility, the calculation of the worst-case parameter (WCP), and the solution of a generalized inverse problem. In the first paper of the series we extend the basic theory established in our previous works from zonation parameterization to any kind of parameterization. The extension makes the methodology more general and complete. We present an effective direct search algorithm that can form a nonnested structure series with an optimized pattern for solving the generalized inverse problem. We use numerical examples to show how the WCP depends on the structure complexity, prior information, boundary conditions, and the objectives of model application. Generally speaking, with more prior information, simpler boundary conditions, fewer objectives, and lower accuracy requirement, the WCP will have a simpler shape. As a result, a reliable model can be identified with less observation data. In part 2 of this series we will give the conditions of structure reducibility and the method for robust experimental design. A robust experimental design is independent of the true parameter and can provide sufficient information for identifying an objective-oriented model.

Kreweras considered the problem of counting noncrossing partitions of the set {1, 2, · · · , n}, whose elements are arranged into a cycle in its natural order, into p parts of given sizes n1, n2, · · · , np (but not specifying which part gets which size). He gave a beautiful and surprising result whose proof resorts to a recurrence relation. In this paper we give a direct, entirely bijective, proof starting from the same initial idea as Kreweras’ proof.

[1] Designing a pumping observation experiment for parameter identification in groundwater modeling is a very challenging problem because the parameter structure, such as the hydraulic conductivity of a heterogeneous aquifer, is usually unknown. Generally, to identify a more complex parameter structure requires more observation data. Traditional experimental design methods have two disadvantages: First, the sufficiency of information provided by a design is not considered in the design stage. Second, the decision variables often are dependent on the unknown parameter because of the nonlinearity of the model. As a result, a sequential design observation process should be used, but this often is impractical in groundwater modeling. This paper presents a new methodology for experimental design that can find a minimum cost design to provide sufficient information for identifying both the parameter structure and parameter values. Sequential Gaussian simulation is used to generate different realizations for the unknown parameter. For each possible realization the number of observation wells is increased gradually during the design process until the information provided by the design is sufficient. The selection criterion for locating a new observation well is the maximization of the information content for parameter identification. The overall sufficiency of a design is assessed by Monte Carlo simulation. The proposed methodology is explained in detail by a numerical example.

[1] This research proposes a generalized parameterization method (referred to as GP) and Bayesian estimation for parameter heterogeneity characterization and identification in groundwater modeling. GP unifies zonation and interpolation through a set of weighting coefficients and is capable of creating a zonation structure, a continuous distribution, or a mixed structure. GP shows greater flexibility not only in manipulating the highly complex spatial distribution but also in identifying the parameter structure. With GP, parameter structure identification seeks to identify the parameter dimension, parameter pattern, parameter values, as well as the values of the weighting coefficients simultaneously through a set of basis points. Additionally, this study develops an embedded genetic algorithm (GA) for solving the structure identification problem. A Bayesian estimator that estimates the basis point values as well as the values of the weighing coefficients is embedded in the GA, which searches for the best basis point locations. We demonstrate the inverse methodology by a numerical example in which the distributed transmissivity in a two-dimensional confined aquifer is identified. We calculate the Jacobian matrix by the adjoint state method. With GP we have successfully identified the transmissivity structure with four basis points that results in a good fitting in groundwater heads and captures the nonsmooth characteristic as well as the trend of the true transmissivity field. We compare GP with Voronoi tessellation (zonation) and natural neighbor interpolation. Results show that GP outperforms the other two parameterization methods in that GP identified the transmissivity field with a smaller parameter uncertainty along with a sufficiently small fitting residual and without over-parameterization.

[1] This paper develops two global-local optimization methods for three-dimensional parameter structure identification in groundwater modeling. Parameter structure identification is formulated in terms of solving a generalized inverse problem, which allows for a determination of an appropriate level of parameter structure complexity, and the identification of its pattern and the associated parameter values. The structure complexity is determined by calculating the parameter structure error measured in the prediction space or management space, while the structure pattern and the associated parameter values are identified simultaneously by minimizing the fitting residual measured in the observation space. The former requires solving a continuous maximum-minimum (max-min) optimization problem, and the latter requires solving a combinatorial optimization problem. In this paper, Voronoi zonation is used to parameterize the unknown distributed parameter. For each given level of structure complexity a sequential global-local optimization method, which consists of a genetic algorithm, a quasi-Newton method, and local search, is developed to solve the combinatorial problem. In addition, the continuous max-min problem is transformed to a hierarchical optimization problem and solved by a hybrid global-local method. Sensitivities of state variables to the unknown parameters are calculated by the sensitivity equations. The validity and applicability of the proposed methodology are demonstrated by numerical experiments. We have shown that the choice of an objective function in model application impacts the determination of the parameter structure complexity.

Alpha-thalassemia is the most common single gene disorder in Taiwan and Southeast China. The majority of alpha-thalassemic mutations in this area are alpha-thalassemia 1. Homozygous alpha-thalassemia 1 has been recognized as the most important cause of hydrops fetalis. To investigate the incidence of alpha-thalassemia 1 mutations and to characterize its molecular defects, cord blood electrophoresis was performed on 2,000 newborns, of which 99 (5%) cases were found to have hemoglobin (Hb) Bart's levels3.0%. A methodology using biphasic polymerase chain reaction (PCR) with nesting primers was developed to characterize the alpha-thalassemia 1 Southeast Asia type (SEA) deletion in the cases with detectable Hb Bart's levels. The SEA deletion was found in 92 (93%) of 99 cases. Prenatal screening was performed on couples with abnormal hematologic indices, and PCR was used to detect couples heterozygous for SEA deletion. Prenatal diagnosis was performed in 21 cases, and four cases were found to have a homozygous SEA deletion. This strategy can be applied to couples who need prenatal genetic counseling for alpha-thalassemia major in this area.

[1] This paper investigates the experimental design problem in the context of estimating the location and dimensions of a dissolving dense nonaqueous phase liquid (DNAPL) pool in an aquifer. The design problem deals with the selection of the number and location of observation wells that minimizes the parameter uncertainty while minimizing installation and sampling costs. The solution of this multiobjective optimization problem is achieved by solving a series of combinatorial optimization problems in which, for each imposed budget constraint, the optimal combination of wells is found. The combinatorial optimization problems are solved by a genetic algorithm for two different design strategies: one where the prior estimates are not updated and one where the parameter updating is performed at the end of each design stage (sequential design). In the latter case, parameter updating is obtained by executing an inverse model based on concentration values collected at the locations specified by the previous optimal design. The algorithms are tested for a controlled, bench-scale pool dissolution experiment. The results demonstrate that the sequential design performs better in terms of final parameter estimates. Three optimality criteria associated with experimental design for model parameter estimation are compared: the A optimality, D optimality, and E optimality. Among the three criteria, the A and E optimalities produce better estimates of the pool location and size, which is justified by appreciably different variances associated with the parameters and their high degree of correlation.

Groundwater simulation models are physically based mathematical models derived from Darcy's law and the law of conservation of mass. Various established solution techniques utilizing either the finite difference or the finite element method, or a combination of both, are available for solving the governing equation of the model, provided that model parameters and initial and boundary conditions are properly specified. With the advancement of numerical computing and various user-friendly interface software, groundwater simulation models are now commonly used by groundwater planners to assist in the decision making in a variety of water resources management problems. In simulation,groundwater models are used to predict (1) the hydraulic or water quality response of the aquifer system to a set of pumping and recharge schedules and (2) the probable hydrologic and environmental impacts associated with groundwater development. Because it can only consider a limited number of management alternatives, the simulation approach generally does not identify the optimal pumping/recharge schedules in the context of all the objectives and constraints. A recent advancement in groundwater modeling has been the development of management models that combine optimization and simulation. In contrast to simulation, optimization models identify optimal planning, design,and operational policies for the groundwater system. And because the simulation model is incorporated in a constraint set of the management model, the optimal decisions not only define the optimal pumping/recharge schedules, but also predict the time and spatial variation in the hydraulic head.

This paper reports the approach and results of calibrating a two-dimensional hydrodynamics model. The model was applied to Humboldt Bay, California, and calibrated with synoptic tidal data at four locations. The model calibration was done by using both a trial-and-error approach and a parameter identification (PI) method. For the given finite-difference grid resolution and field observations, the calibration attempt revealed that the two methods produced two different sets of parameters, but with almost identical comparisons between the model solutions and observations. The study results indicate that the appropriate range of model parameter values can be more efficiently identified by parameter identification method, and the best calibration strategy is to use both methods conjunctively.

A two beam null method has been used to measure gain at 10.6 μ in a pure CO2 discharge for several gas pressures, flow velocities, and discharge currents. It is shown that the presence of CO, produced by dissociation of CO2, leads to the dominant population inversion mechanisms. Carbon monoxide is found to enhance the gain both by increasing the population of the upper level and by aiding in the depletion of the lower level population.Highest gain values were found for gas pressures of about 1 Torr and current values of the order of 10 mA. The maximum gain obtained was 40% per meter in a sealed system.

A technique for encapsulating CdSe films to obtain the flat band condition at the semiconductor surface is discussed and the results of Hall studies on suitably encapsulated films are presented. Results obtained with these films are consistent with a crystallite–grain-boundary model, where the mobility is limited by intercrystalline barriers, when the carrier concentration is not too large.

Purpose To define the dose-limiting toxicity (DLT), maximum tolerated dose (MTD) and pharmacokinetics (PK) of PEP02, a novel liposome-encapsulated irinotecan, in patients with advanced refractory solid tumors. Methods Patients were enrolled in cohorts of one to three to receive escalating dose of PEP02 in a phase I trial. PEP02, from 60 to 180 mg/m2, was given as a 90-min intravenous infusion, every 3 weeks. Results A total of 11 patients were enrolled into three dose levels: 60 (one patient), 120 (six patients) and 180 mg/m2 (four patients). DLT was observed in three patients, one at 120 mg/m2 (grade 3 catheter-related infection) and two at 180 mg/m2 (grade 4 neutropenia lasting for >3 days in one, grade 4 hematological toxicities and grade 4 diarrhea in the other). MTD was determined as 120 mg/m2. Comparing with those after free-form irinotecan in the literature, the dose-normalized PK of SN-38 (the active metabolite) after PEP02 was characterized by lower Cmax, prolonged terminal half-life and higher AUC but with significant inter-individual variation. One patient who died of treatment-related toxicity had significantly higher Cmax and AUC levels of SN-38 than those of the other three patients at 180 mg/m2. Post hoc pharmacogenetic study showed that the patient had a combined heterozygosity genotype of UGT1A1*6/*28. Two patients had objective tumor response. Conclusions PEP02 apparently modified the PK parameters of irinotecan and SN-38 by liposome encapsulation. The MTD of PEP02 monotherapy at 3-week interval is 120 mg/m2, which will be the recommended dose for future studies.

A protein which is recognized by an antibody to human amniotic epithelial basement membrane was identified at the basal lamina of human epidermis by immunohistology. This protein was localized at the lamina lucida of human epidermal basement membrane by immunoelectron microscopy. Studies of normal human keratinocyte cultures and epidermal wound healing suggested that the protein was probably produced by keratinocytes. By immunoblotting, a basic apparent isoelectric pH (pHiapp = 7.3) protein band of 37 kD was seen. These data indicate that this 37 kD protein, clearly different from other known basement membrane components, is present in simple and stratified epithelia of ectodermal origin, and is associated with hemidesmosomes.