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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.

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.

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.]

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.

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.

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.

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.

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.

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...

The town of Yucca Valley located in the southwest part of the Mojave Desert in southern California relies on groundwater pumping from the Warren groundwater basin as its sole source of water supply. This significant dependency has resulted in a large imbalance between groundwater pumpage and natural recharge, causing groundwater levels in the basin to decline more than 90 m from the late 1940s to 1994. Consequently, an artificial recharge program proposed by the Hi-Desert Water District, which provides water service to the town of Yucca Valley, was implemented for the purpose of recovering the groundwater levels; however, the rise in groundwater levels has caused nitrate ( NO3 ) concentration to increase simultaneously. The purpose of this study is to develop an optimal pump and recharge strategy for a planned conjunctive-use project to remove the high-nitrate concentration while maintaining groundwater levels at desired elevations at specified locations as well as meeting water demand. An optimization/ma...

This paper develops an iterative procedure for capacity expansion studies for water distribution systems. We propose a methodology to analyze an existing water distribution system and identify the potential bottlenecks in the system. Based on the results, capacity expansion alternatives are proposed and evaluated for improving the efficiency of water supply. The methodology includes a network flow based optimization model, four evaluation indices, and a series of evaluation steps. We first use a directed graph to configure the water distribution system into a network. The network flow based model optimizes the water distribution in the system so that different expansion alternatives can be evaluated on a comparable basis. This model lends itself to linear programming (LP) and can be easily solved by a standard LP code. The results from the evaluation tool help to identify the bottlenecks in the water distribution system and provide capacity expansion alternatives. A useful complementary tool for decision making is composed of a series of evaluation steps with the bottleneck findings, capacity expansion alternatives, and the evaluation of results. We apply the proposed methodology to the Tou-Qian River Basin, located in the northern region of Taiwan, to demonstrate its applicability in optimization and capacity expansion studies.

This work presents a novel approach for solving groundwater management problems with reduced computational effort. We replace a groundwater flow model governed by a partial differential equation with a simple model governed by an ordinary differential equation. Model reduction is achieved with empirical orthogonal functions, i.e., principal components. Replacement of the full-scale model by a reduced model allows implementation of the embedding approach for optimal groundwater management. Comparing the results obtained with the full-scale simulation model, preliminary analyses show that the reduced model is able to reproduce head variations in the flow domain with good accuracy and, to a certain degree, the sensitivities of head with respect to pumping. A key advantage of the reduced model is that it is simple and easy to solve, and in many instances captures the dominating characteristics of the original model. In view of the many sources of uncertainty influencing groundwater simulation, the accuracy provided by a reduced model may be sufficient for planning purposes. As with other examples of model reduction presented in recent research efforts, the methodology shows promise in presenting general trends, but does not eliminate the need for the original model when more detailed analyses are needed.

A calibrated simulation model is linked with two optimization models to investigate alternatives for enhancing seawater intrusion barrier operations for the Alamitos Barrier Project in Los Angeles. Two types of management problems are analyzed, the optimal scheduling problem (OSP) and the optimal well location problem. The OSP objective is to minimize the total injected water subject to constraints on the state variables: Hydraulic head and chloride concentration at target locations. Two OSP formulations are considered, a pure hydraulic gradient formulation, and a combined hydraulic and transport formulation. When considering all 43 injection wells over a five-year planning horizon, the simulation-optimization model could not significantly improve upon the assigned initial injection rates. However, if a subset of the injection wells is exclusively considered, more favorable injection policies are obtained where less water is injected, compared with either the mean or annual mean derived from the historica...

Reservoir operations play an important role in water resources planning and management. During periods of drought, when inflows to the reservoir are low, it may not be possible to satisfy the target delivery (planned demand), and shortage occurs. Reservoir hedging rules, which include a set of rule curves and rationing factors, are used to guide the operation under such a situation. In particular, hedging rules are employed to balance the current shortage in supply with future storage to avoid severe water shortages in the future. In general, hedging rules are determined at the planning stage, or at the early stage of a reservoir’s operating life. However, after years of operation, the reservoir system, as well as the demand characteristics, may change. Hence, it becomes necessary to reevaluate and update the existing hedging rules to incorporate changes that have taken place. The purpose of this paper is to devise new hedging rules for an existing multireservoir system. An original mixed-integer nonlinea...

This paper evaluates the impacts of multiple water uses on the operation of a system of existing reservoirs that originally were designed for hydropower generation. The research reported in this paper utilizes a nonlinear optimization model, developed to optimize hydropower production for a system of interconnected reservoirs. This model was modified to include water withdrawals for consumptive uses. The formulated multiobjective optimization problem is solved by employing the weighting method. The formulation considers water allocation policy for consumptive uses and individual hydropower plants. By parametrically varying the weighting coefficients, the tradeoff relationship (Pareto front) between hydropower generation and water demand for consumptive uses was traced out. The modified model is applied to the Sao Francisco River Basin in Brazil, where there are several hydropower plants in operation with a total installed capacity of 10.7 GW. The river basin is an important agricultural land that ...

This paper develops a multicommodity flow model to optimize water distribution and water quality in a regional water supply system. Waters from different sources with different qualities are considered as distinct commodities that concurrently share a single water distribution system. The model can accommodate two-way flow pipes, represented by undirected arcs, and the perfect mixing condition. Additionally, blending requirements are specified at certain control nodes within the system to ensure that downstream users receive the desired water quality. The optimization model is highly nonlinear and solved by a hybrid genetic algorithm (GA). The GA is first used to globally search for the directions of all undirected arcs. Then a generalized reduced gradient (GRG) algorithm embedded in the GA is used to optimize the objective function for fitness evaluation. The proposed methodology was first tested and verified on a hypothetical system and then applied to the regional water distribution system of the Metro...

Increasing demands for water by competing users in semiarid regions pose new challenges for water resources managers. Decision makers must understand the interactions between surface water, groundwater, and the environmental system. Additionally, the decisions made with regard to water transfer and allocation must take into consideration the diverse objectives that include water supply, cost efficiency, and ecosystem protection. The work presented herein demonstrates the use of groundwater simulation and optimization to construct a decision support system (DSS) for solving a groundwater management problem associated with the Upper San Pedro River Basin, located in southeastern Arizona. The case is treated as a multiobjective optimization problem in which environmental objectives are explicitly considered by minimizing the magnitude and extent of drawdown within a prespecified region. The approach adopted uses the constraint method to derive the tradeoffs among three competing objectives. Once the proposed algorithm identifies a set of efficient solutions (alternatives), concepts borrowed from fuzzy set theory are applied to rank the alternatives and to assist decision makers in selecting a suitable policy among them, each of which is optimum with regard to its goal and the corresponding consequences.

A practical monthly optimization model, called SISOPT, is developed for the management and operations of the Brazilian hydropower system. The system, one of the largest in the world, consists of 75 hydropower plants with an installed capacity of 69,375 MW, producing 92% of the nation's electrical power. The system size and nonlinearity pose a real challenge to the modelers. The basic model is formulated in nonlinear programming ~NLP!. The NLP model is the most general formulation and provides a foundation for analysis by other methods. The formulated NLP model was first linearized by two different linearization techniques and solved by linear programming ~LP!. A comparative analysis was made of the results obtained from the linearized and the NLP models. The results show that the simplest linearized model ~referred to as the LP model! without iteration is suitable for planning purposes. For example, the LP model could be used in system capacity expansion studies or to explore various design parameters in connection with feasibility studies, where details in storage variation are not as important as the power production. With a good initial policy provided by the LP model, the successive linear programming ~SLP! model produced excellent results with fast convergence. The NLP model, the most complex and accurate model in the suite, is particularly suited for setting up guidelines for real-time operations using inflow forecast with frequent updating. The performance of the NLP model was checked against the historical operational records, and the comparison yields indica- tions of superior performance.

This research develops a mixed integer linear programming (MILP) model that considers simultaneously both the traditional reservoir rule curves and the hedging rules to manage and operate a multipurpose, multireservoir system. During normal periods of operation, when inflows are plentiful, this optimization model efficiently distributes the available stored water from different reservoirs to meet the planned demands imposed by competing users. However, during periods of drought, or when anticipating a drought, the planned demands cannot be fully met, and a water shortage occurs. By considering the hedging rules along with the rule curves, guidelines are provided for reservoir releases. To minimize the impact of drought, the hedging rules effectively reduce the ongoing water supply to balance with the target storage requirement. The MILP model is applied to a multireservoir system in the southern region of Taiwan, where the results obtained demonstrate the applicability and utility of the model.

This paper presents a systematic approach for solving the management problem of a contaminated groundwater supply system. The proposed methodology begins with the selection of a supply water quality criterion based on a stochastic health risk assessment and concludes with the establishment of a trade-off relationship between the increased management cost and the desired level of protection. In the uncertainty analysis, Guassian quadrature numerical integration is used as an alternative to nested Monte Carlo simulation. Two management approaches—treating the contaminated groundwater with granular activated carbon and using imported water to lower the contamination level in the supply water—are used in a test problem. A random hydraulic conductivity field is used to produce the contamination variability at each extraction well. A trade-off relationship is obtained between the increased management cost and the level of protection by performing the uncertainty analysis at several supply water quality criteria for each of the two management approaches. This study demonstrates that despite the complexity in a contaminated groundwater supply system, a systematic approach can be used to quantify the many uncertainties associated with the management problem.

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

Hydrothermal systems are formed by hydropower plants and different types of thermal power plants, i.e., plants that use different types of fuel. Thermal power plants are very versatile to meet energy demand; therefore, they can be dispatched at any time, with the exception of certain plants, such as nuclear, that are operated on a demand basis. Hydropower plants are also versatile, but exhibit a serious operational constraint, which is water availability. It is clear, therefore, the importance of planning the operation, especially in large hydrothermal systems. In countries where a high percentage of the energy consumed is produced by hydropower, there is a need to determine the degree of connection of the hydro system to increase its effectiveness and efficiency. A large interconnected hydro system can be better managed considering the hydrological diversity occurring in different watersheds. On the other hand, isolated subsystems of smaller sizes, may possibly be able to operate with greater safety. This paper discusses the issue of the energy gain of a hydrothermal system in view of its size. The case study is the national interconnected hydrothermal system in Brazil. The analysis employs an optimization model, HIDROTERM, which has been developed for the optimal planning and operation of the Brazilian hydrothermal system. A comparative analysis is made comparing the Brazilian system operated in a fully interconnected scheme and with some isolated subsystems operating independently. The results can be used to determine the optimal configuration of the hydrothermal system.

In Brazil over the last 12 years, on average 90% of energy consumed has come from hydropower generation. The hydro system includes 150 medium and large hydropower plants. Among these 150 plants, 135 are in operation today and 15 are planned to be operational by the end of 2017. The Brazilian hydro system is completely interconnected. About half of the hydropower plants have large storage capacities to regulate flows. The system is linked with the power grids in Paraguay, Argentina, Venezuela, and Uruguay. Brazil also receives natural gas imports from Bolivia. Each month hydropower generation must be determined. This is a complex decision-making process. The objective is to optimize the entire system operation, which depends on the inflows, storages, forecasts of demands, fuel prices for thermal units, and expansion of the system over a given planning horizon, typically looking ahead five years. Environmental, technical, social, and economic constraints have made it increasingly difficult to project and build new reservoirs with large storage capacities. As a result, newly built hydropower plants are mostly the run-of-river type. To better understand the process, this paper presents a historical survey of the installed capacity of hydropower and storage capacity of the system since 1908 and extends to the planned expansion over the next four years, through 2017. The storage capacity is represented by the concept of maximum stored energy in each reservoir and subsystem. The paper also presents the history of operation over the last 12 years. Data from 2000 and projections through 2017 indicate a significant reduction of relative regulating capacity, represented by the ratio of maximum storage and installed capacity. This reduction has a direct impact on decisions regarding operation and expansion of the complementary thermal system.

Soil vapor extraction is a cost-effective technique for removing volatile organic compounds (VOCs) immobilized in the unsaturated zone. The design of a soil vapor extraction system (SVES) requires that the number and location of extraction wells and the extraction schedule for each extraction well be determined. In this study, schedule optimization for selected active wells is embedded in location optimization to provide an equal basis for system comparison. A combinatorial two-change local search is employed in both optimizations, but with different user-defined properties to address their distinct problem characteristics. The results indicate that the proposed two-change local search is robust and produces reliable designs. Impacts of soil heterogeneity and initial guess of design are evaluated. A detailed analysis of optimization results indicates that extraction well locations dominate the efficiency of contaminant removal. To reduce additional computation, a screening test based on results at the ear...

A methodology is developed for the design of an automatic gate controller for an on-demand canal system. The design is based on the EL-FLO plus RESET control concept developed previously by the U.S. Bureau of Reclamation. Two additional design parameters are included in this study to ensure a smooth upstream gate operation. The basic design parameters are optimized to achieve a stable flow condition in the entire canal system where multiple gates are operated to supply water to meet unexpected turnout demands along the canal. Due to the complexity involved in canal hydraulics, the optimization model is highly nonlinear. Decomposition allied with successive approximations is used for solution. The proposed design methodology, when applied to a section of the Caraibas Project in northeastern Brazil, showed that the proposed methodology is successful in minimizing hydraulic transients in a multipool canal system. A sensitivity analysis of the optimal design is conducted with respect to maximum demand level and the two new design parameters.

The Brazilian hydrothermal system consists of a completely linked network of 143 medium and large hydropower plants, 888 small hydropower plants, and 1602 thermal and 82 wind power plants, with a total installed capacity of 128,570 MW. The hydropower plants produce, on average, 91% of the total electricity consumed in the country, while the complementary thermal system is dispatched mainly during periods of drought. In our previous studies, we developed a nonlinear programming model, HIDROTERM, to optimize the management and operation of the hydrothermal system, considering individual hydropower plants, thermal generation and exchange, multiple uses of the water, and system expansion. The model is deterministic. In this paper, we develop a new version of HIDROTERM to incorporate the stochasticity of the inflows in the formulation. Additionally, hourly load variation is represented by three levels: peak, base, and low. The proposed approach is based on a two-stage stochastic programming with recourse. In the first stage (first time period) the system passes from its initial state to the final state based on deterministic inflow forecasts. From the second time period on (the second stage), the state of the system branches out according to inflow scenarios. The branching can be scaled up gradually until the end of the planning horizon. New developments are made in the formulation, interface, database and the underlying simulation model. The newly developed integrated decision support system is being tested on the entire Brazilian hydrothermal system. 1 Assistant Professor, University of Sao Paulo; e-mail: rczambon@usp.br; ASCE/EWRI Member 2 Professor, University of Sao Paulo, Brazil; e-mail: mtbarros@usp.br; ASCE Member 3 Professor, University of Campinas; e-mail: franco@fec.unicamp.br 4 Assistant Professor, University of Campinas; e-mail: francato@fec.unicamp.br 5 Consulting Engineer; e-mail: jelopes1@gmail.com 6 Engineer, Companhia Energetica de Sao Paulo; e-mail: luis.nogueira@cesp.com.br 7 Distinguished Professor, UCLA; e-mail: williamy@seas.ucla.edu; ASCE Honorary Member 1563 World Environmental and Water Resources Congress 2013: Showcasing the Future © ASCE 2013

This paper presents a methodology to analyze the reliability of a regional water distribution network. The proposed reliability analysis method focuses on the impact of link failures on source-demand connectivity, which is used as a measure of the mechanical reliability of the network. The mechanical reliability index is computed using the minimum cut-set method. The identification of minimum cut-sets consists of four stages: (1) For source-demand pairs; (2) for individual demand nodes; (3) for a group of demand nodes; and (4) for all demand nodes in the system. By using the multiple-stage approach, the total number of simulations required in the analysis is greatly reduced. Computational efficiency of the minimum cut-set method is further enhanced by using a highly efficient embedded generalized network algorithm to solve the optimization model used in the simulations. The results from the reliability analysis can be used to locate crucial pipelines in the network whose failure will severely impair the source-demand connectivity. The developed methodology is applied to a large-scale regional water distribution network associated with the Metropolitan Water District of Southern California.

Stochastic simulation is applied in a reliability analysis of a regional water distribution network. To complement the connectivity-based reliability analysis presented in the companion paper, the proposed stochastic simulation is aimed at evaluating the impacts of component failures on meeting demand at a certain quantity level. Simulation is conducted in a manner such that a sampling experiment of the system performance is repeated a sufficient number of times and the results are analyzed to obtain the desired reliability index. Given the mean time to failure and repair time of links, a large number of synthetic system conditions are generated and operations of system under the different conditions are simulated. Stochastic simulation of the system is conducted with a highly efficient embedded generalized network flow model (EMNET) to determine the optimal system operations for each scenario. The proposed reliability analysis method is applied to the water distribution network of the Metropolitan Water District of Southern California (MWDSC).

Soil vapor extraction is a cost-effective technique for the removal of volatile organic compounds (VOCs) immobilized in the unsaturated zone. The design of soil vapor extraction system (SVES) requires determination of the number and locations of extraction wells and corresponding extraction rates. At present, few guidelines for design are available. Thus most designs are based on empirical assessments, simplified analytical approximations, or limited trial-and-error simulations. In this paper, three optimization-based design methodologies are introduced: (1) implicit enumeration, (2) systematic reduction, and (3) local search. These methodologies address the combinatorial nature of the design problem and properly evaluate the well interference effects in a multiwell system. Through a benchmark problem, a comparative evaluation of the three methodologies is made with regard to their solution accuracy, computational efficiency, optimality heuristic, and application limitations.

A framework of simulation models for including human exposure to contaminants in regional scale aquifer management problems is presented. The framework includes horizontal flow and transport of contaminant plumes in the aquifer and multiple-pathway human exposure. Well water from the aquifer simulation model is used as the source of contaminant in the human exposure model. The exposure pathways considered for regional analysis are ingestion of foods grown using well water as part of the irrigation supply; ingestion and dermal absorption of contaminants in tap water; and inhalation of vaporized contaminants while bathing. An environmental compartment model is used to track the contaminant in irrigation water into the soil layers in contact with food products. The simulation framework is demonstrated in a study of the sensitivity of exposure to various aquifer and water supply parameters. The region used is hypothetical; however, the parameters are typical of California.

Using a generalized network formulation, the nonlinear evaporation loss function of a reservoir is directly incorporated in a water-supply-optimization model. The nonlinear function is first piecewise-linearized and the resulting line segments are represented by a series of arcs. An appropriate arc-flow multiplier is used to reflect the slope in each line segment. A convex penalty function is then used to preserve the order of arc-flow fulfillment. The proposed formulation can be applied to model other forms of losses in a water-supply system and it eliminates iterative solutions as required by a pure network formulation. The overall management model is solved by an embedded generalized network algorithm (EMNET), which is designed to solve a linear-programming problem with an embedded generalized network substructure. Together with our previous study EMNET is proven to be an efficient tool in solving general water-supply-optimization problems.

In recent years, considerable progress has been made in the development of fast network flow algorithms aiming at solving linear programming problems with network substructures. Recognizing the fact that the basic structure of a water-supply system is a network, network algorithms have been used for system operation and management. However, to date, most algorithms are designed to solve transshipment problems in a pure network setting with total demand being equal to total supply. The non–network-type constraints and variables are precluded from the network models. Consequently, network models are used to perform optimization only for the network portion of the water-supply system under certain overall operational guidelines. In this study, an algorithm, EMNET, is introduced for solving the regional-water-supply-system optimization that corresponds to a generalized network problem with additional non–network-type constraints and non–network-type variables. The multiperiod, multiobjective optimization mode...

A water-supply-distribution-system planning model has been developed using a directed graph algorithm as its pre- and postprocessors and a linear programming (LP) procedure as an “intelligent” system operator at its core. The directed graph, as a preprocessor, is used to produce a mathematical representation of the distribution system in question, to check the consistency and correctness of the network topology before an input file is created for the LP procedure. In the postprocessing mode, the directed graph generates files for graphical displays of model results and performs graph analyses such as depth- and breadth-first traversals, cut set and connectivity for evaluating water-quality blending, tracking of source-to-demand contributions, system reliability, and lifeline objectives. Unlike the conventional formulation of a resource-allocation problem, the demand requirements, storage capacities, and other system constraints are removed from the constraint set and translated into a composite objective function. This methodology was successfully applied to the Metropolitan Water District of Southern California’s (MWD’s) distribution system. The results indicate that the combination of using an LP procedure and graph algorithm is a very versatile tool for solving large-scale water-distribution problems. This paper documents the concept used in developing the system-planning model and the results of its application to a simplified example as well as application to four case studies involving the entire MWD’s distribution system.

There are many conflicts involved in the operation of a hydrothermal system mainly due to competitive uses of water. That is the case of Sao Francisco River located in the northeast region in Brazil. The hydro system in this region has an installed capacity of 10.7 GW and supports 13% of the Brazilian hydro electrical power. Due to the scarcity of water in this region the operation of this system has been questioned in the last few years. The competitive uses of water in this region are water supply, irrigation, animal raising and environmental protection. These demands can be attended by a proposed large water transfer system from Sao Francisco river to other watersheds in the region. The electrical sector is planning to review the reservoir operation rules in order to attend these demands. This paper presents results from a study conducted to evaluate the impacts of the hydro production caused by the multipurpose uses of water in the Sao Francisco River. The study was conducted by applying the HIDROTERM model, which is an optimization model that maximizes hydropower production and minimizes thermo production based on fossil fuels. A new objective function was added to the HIDROTERM model in order to evaluate the proposed water transfer system. The trade-offs among energy, water supply, irrigation and environment protection are presented, along with other relevant discussions.