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Rapid guessing is an aberrant response behavior that commonly occurs in low-stakes assessments with little to no formal consequences for students. Recently, the availability of response time (RT) information in computer-based assessments has motivated researchers to develop various methods to detect rapidly guessed responses systematically. These methods often require researchers to identify an RT threshold subjectively for each item that could distinguish rapid guessing behavior from solution behavior. In this study, we propose a data-driven approach based on random search and genetic algorithm to search for the optimal RT threshold within a predefined search space. We used response data from a low-stakes math assessment administered to over 5000 students in 658 schools across the United States. As we demonstrated how to use our data-driven approach, we also compared its performance with those of the existing threshold-setting methods. The results show that the proposed method could produce viable RT thresholds for detecting rapid guessing in low-stakes assessments. Moreover, compared with the other threshold-setting methods, the proposed method yielded more liberal RT thresholds, flagging a larger number of responses. Implications for practice and directions for future research were discussed. [ABSTRACT FROM AUTHOR]

This study aims to employ the Google Trends searching volume to predict daily COVID-19 confirmed cases in the United States during the beginning and rapidly developing periods of COVID-19. Fourteen keywords related to the COVID-19 pandemic from Google trends in the United States were treated as independent variables. Daily confirmed cases served as dependent variables. This study developed a Light Gradient Boosting Machine with Genetic Algorithms and Google Trends (LGBMGAGT) model in forecasting COVID-19 confirmed cases. Forecasting horizons of one day to seven days ahead were employed to forecast confirmed cases. Furthermore, the Pearson correlation coefficient was used to select essential attributes and forecasting results with and without attributes selection were compared. Numerical results indicated that the proposed LGBMGAGT can obtain more accurate results than the other forecasting models. Therefore, the LGBMGAGT model is a feasible and promising alternative in forecasting COVID-19 confirmed cases. [ABSTRACT FROM AUTHOR]

Near-infrared wavelengths selected by genetic algorithm were used to optimize partial least squares (PLS) regression models for loblolly pine (Pinus taeda L.) from the southeastern United States. Wood properties examined included density (D), microfibril angle, modulus of elasticity and tracheid coarseness (C), radial diameter (R), tangential diameter (T), and wall thickness (w)—measured by SilviScan. The optimization process was run for each property with Agenda 2020 samples utilized for PLS model development and the other sets used for prediction. The number of variables (i.e. wavelengths) varied from 10 to 100 with an optimum number identified by genetic algorithm. When compared to a full data set model (based on 700 wavelengths), calibration and prediction performance of optimized PLS regression models were superior for all properties. Importantly, representative wavelengths for each property were consistently related to recognized bond vibrations observed in specific wood components demonstrating that optimization targets wavelengths directly related to changes in wood chemistry within the examined loblolly pine samples. [ABSTRACT FROM AUTHOR]

The joinpoint regression model (JRM) is used to describe trend changes in many applications and relies on the detection of joinpoints (changepoints). However, the existing joinpoint detection methods, namely, the grid search (GS)-based methods, are computationally demanding, and hence, the maximum number of computable joinpoints is limited. Herein, we developed a genetic algorithm-based joinpoint (GAJP) model in which an explicitly decoupled computing procedure for optimization and regression is used to embed a binary genetic algorithm into the JRM for optimal joinpoint detection. The combinations of joinpoints were represented as binary chromosomes, and genetic operations were performed to determine the optimum solution by minimizing the fitness function, the Bayesian information criterion (BIC) and BIC3 . The accuracy and computational performance of the GAJP model were evaluated via intensive simulation studies and compared with those of the GS-based methods using BIC, BIC3 , and permutation test. The proposed method showed an outstanding computational efficiency in detecting multiple joinpoints. Finally, the suitability of the GAJP model for the analysis of cancer incidence trends was demonstrated by applying this model to data on the incidence of colorectal cancer in the United States from 1975 to 2016 from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Thus, the GAJP model was concluded to be practically feasible to detect multiple joinpoints up to the number of grids without requirement to preassign the number of joinpoints and be easily extendable to cancer trend analysis utilizing large datasets. [ABSTRACT FROM AUTHOR]

Antibiotic-resistant tuberculosis (TB) strains pose a major challenge to TB eradication. Existing US epidemiological models have not fully incorporated the impact of antibiotic-resistance. To develop a more realistic model of US TB dynamics, we formulated a compartmental model integrating single- and multi-drug resistance. We fit twenty-seven parameters to twenty-two years of historical data using a genetic algorithm to minimize a non-differentiable error function. Since counts for several compartments are not available, many parameter combinations achieve very low error. We demonstrate that a crowd of near-best fits can provide compelling new evidence about the ranges of key parameters. While available data is sparse and insufficient to produce point estimates, our crowd of near-best fits computes remarkably consistent predictions about TB prevalence. We believe that our crowd-based approach is applicable to a common problem in mathematical biological research, namely situations where data are sparse and reliable point estimates cannot be directly obtained. [ABSTRACT FROM AUTHOR]

Past research in part family identification has focused mainly on the development of efficient procedures for manufacturing-oriented part family formation in which similarities among parts are established primarily on machine or operation requirements. While these part families are essential in cellular manufacturing, they are not well suited for other areas of production, in particular, part design and process planning. A new part family identification technique using a simple genetic algorithm is proposed in this paper to first determine a set of part family differentiating attributes, and second to use these attributes to guide the formation of part families. The technique is implemented in C using a SUN SPARC workstation 1+. Empirical analyses of the technique on both artificially generated data and a real application are performed and discussed. [ABSTRACT FROM AUTHOR]

A program was developed using a genetic algorithm and automated lookup features to design an efficient passenger rail system for the eastern-half of the United States connecting large cities, metropolitan populations greater than two million, with overnight rail service. The results of the program predicted a passenger starting at the farthest point of the system boards the train at 16:02 on average and arrives at a different point of the system at 07:57 on average the following day, assuming the train travels an average speed of 70 mph. The design used actual distances by train track where possible. The system was modeled with six trains that meet at a hub and exchange passengers and continue on to their destination. The optimal solution had a total one-way minimum distance of 4334 km (2693 miles). Assuming the same ridership that currently exists on a popular train route, ticket prices would average $62 (USD) for a one-way ticket. For this system to be feasible, the government would need to own or lease one set of tracks for all the routes determined, build a hub for passengers to transfer trains near Charleston, WV, and ensure the trains are unimpeded by other trains. Installing tracks that go around cities that the trains do not stop at would be a great benefit also. With advances in communication, GPS, and train control technology, this article points out the benefits of publically available tracks to form a transportation network similar to that found in road, air, and water traffic. [ABSTRACT FROM AUTHOR]

In recent years, residential solar energy consumption of United States under the effect of a series of encouragement policies has exhibited a growth trend characterized by seasonal leap. To predict it, a new grey model based on data grouping and buffer operator is proposed. The model groups on a monthly or quarterly basis are grouped, which are then buffered separately to cope with prediction error caused by seasonal fluctuations and sudden changes in trend. In addition, a genetic algorithm is used to obtain the most appropriate degree of buffering. And then, the predictive effects of classical grey model, grey model based on data grouping, non-linear autoregressive neural network, echo state network, and the proposed model are compared. The results show that the mean absolute percentage errors of predicted results obtained by using these five models are 32.73%, 30.23%, 46.94%, 39.15%, and 6.17%, respectively, implying that the proposed model confers a significant advantage. Compared with the other four models, the new model can more effectively recognize the seasonal fluctuation and structural mutation of time series data. After conducting out-of-sample forecasting, the results demonstrate that the residential solar energy consumption of United States will maintain its rapid growth with an average annual growth rate of 24%. • A prediction model based on data grouping and buffer operator is built. • The parameters of the model are optimized by using Genetic Algorithm. • The solar energy consumption of U.S. residents with leap growth characteristic is accurately forecasted. [ABSTRACT FROM AUTHOR]

Pollution build-up and wash-off processes are often included in urban stormwater quality models. However, these models are often unreliable and have poor performance at large scales and in complicated catchments. This study tried to improve stormwater quality models by adopting the genetic programming (GP) approach to generate new build-up algorithms for three different pollutants (total suspend solids – TSS, total phosphorus – TP and total nitrogen – TN). This was followed by testing of the new models (also traditional build-up and wash-off models as benchmark) using data collected from different catchments in Australia and the USA. The GP approach informed new sets of build-up algorithms with the inclusion of not just the typical antecedent dry weather period (ADWP), but also other less 'traditional' variables - previous rainfall depth for TSS and maximum air temperatures for TP and TN simulation. The traditional models had relatively poor performance (Nash-Sutcliffe coefficient, E < 0.0), except for TP at Gilby Road (GR) (E = 0.21 in calibration and 0.43 in validation). Improved performance was observed using the models with new build-up algorithms informed by GP. Taking TP at GR for example, the best performing model had E of 0.46 in calibration and 0.54 in validation. The best performing models for TSS, TP, and TN are often different, suggesting that specific models shall be used for different pollutants. Insights into further improvements possible for stormwater quality models were given. It is recommended that in addition to the typical build-up and wash-off process, new generations of stormwater quality models should be able to account for the non-conventional pollutant sources (e.g. cross-connections, septic tank leakage, illegal discharges) through stochastic approaches. Emission inventories with information like intensity-frequency-duration (IFD) of pollutant loads from each type of non-conventional source are suggested to be built for stochastic modelling. • GP can generate meaningful build-up algorithms to improve stormwater quality model. • Different stormwater quality models shall be used for different pollutants. • Inclusion of temperature can improve further TP and TN modelling. • Next generation of models shall consider non-conventional pollution sources. [ABSTRACT FROM AUTHOR]

A simple method is presented to carry out a retrospective analysis to examine the development of load-bearing structures. The idea is to eliminate the differences coming from technological changes (such as joints, profiles, loads) by using relative numbers to express the relation of the structures to the possible theoretical solutions under the same circumstances. The method is demonstrated by investigating the impact of historical changes focusing on metal Pratt trusses spanning about 100 ft, located in Indiana, U.S., erected between 1870 and 1937. Data of 87 structures was collected and compared to the results of a multi-objective optimisation computed using a genetic algorithm. Using the relative numbers acquired by evaluating the objective functions for the historical structures, a large timescale optimisation process through history can be visualised. Plotting them on the Pareto-front diagram determined by the genetic algorithm and examining the historical background of the state revealed that the economic and industrial changes, in fact, had a considerable impact on the design trends, which manifests in changes of the weights of the objective functions. [ABSTRACT FROM AUTHOR]

Different organizations and countries have proposed a variety of policies and strategies to reduce emissions from the shipping industry. These include the Emission Control Area (ECA) policy, carbon tax policy, Vessel Speed Reduction Incentive Program (VSRIP), and Virtual Arrival (VA) strategy. These policies and strategies create additional costs for ships, forcing the ships to change their speeds or routes. This highlights the need to study approaches that optimize the sailing speeds of ships, by coordinating the implementation of the multiple governing policies and strategies. This paper develops a cost model that considers different emission reduction policies and strategies; this is followed by an analysis of their effect on emissions reduction. In addition, the paper proposes an improved quantum genetic algorithm based on a "fork gene" and elite strategy, which is specifically designed for specific shipping-related policies and strategy. The model is applied to a shipping route on the United States (US) West Coast, with three key findings. First, the improved algorithm significantly reduces the total cost, compared with the original alternative, in the context of emission reduction policies and strategies. Second, a single policy can produce positive results in reducing emissions, and the coordinated implementation of three separate policies (ECA, carbon tax, and VSRIP) may not lead to better results than a single policy. Finally, adding the VA strategy above and beyond the three other emission reduction policies may create more benefits in reducing emissions and operating costs. • Impacts from multiple maritime emission reduction regulations are valuated. • Impacts on the ship sailing speed are considered. • Effects of multiple emission regulations on the U.S. Coast are assessed. • There may be mutual exclusivity between emission reduction regulations. [ABSTRACT FROM AUTHOR]

Dispensing of mass prophylaxis can be critical to public health during emergency situations and involves complex decisions that must be made in a short period of time. This study presents a model and solution approach for optimizing point-of-dispensing (POD) location and capacity decisions. This approach is part of a decision support system designed to help officials prepare for and respond to public health emergencies. The model selects PODs from a candidate set and suggests how to staff each POD so that average travel and waiting times are minimized. A genetic algorithm (GA) quickly solves the problem based on travel and queuing approximations (QAs) and it has the ability to relax soft constraints when the dispensing goals cannot be met. We show that the proposed approach returns solutions comparable with other systems and it is able to evaluate alternative courses of action when the resources are not sufficient to meet the performance targets. [ABSTRACT FROM AUTHOR]

Abstract: Compressed air systems account for a significant proportion of industrial energy usage in the European Union and the United States. Improving the energy efficiency of final air consumers is an essential aspect of overall system optimisation. This paper investigates and compares the minimisation of air consumption in linear pneumatic actuator systems using two approaches: dual pressures and the utilisation of expansion energy in compressed air. In order to simplify the implementation of energy saving circuits for industrial end-users, a software program has been developed based on a combination of genetic algorithms and dynamic simulations. The developed program, described in this paper, automates the selection process of optimal design parameters and control input variables for pneumatic cylinders. The robustness of the optimisation software has been experimentally validated. The results from a vertical lifting application case study indicate that the proposed optimisation approaches can reduce compressed air usage by up to 29% per cycle, whilst simultaneously satisfying other common automation design objectives or constraints. The paper concludes with a discussion on methods to increase the application of such energy efficiency measures in pneumatic production machinery. [Copyright &y& Elsevier]

Abstract: The Environment Canada''s Ice Concentration Extractor (ECICE) combines observations from several different satellite sensors to resolve heterogeneous components of a given footprint. To validate the algorithm and demonstrate its applicability, results are presented from combining the enhanced AMSR-E 36.5GHz passive microwave data with dual-polarization QuikSCAT active microwave scatterometer observations of Arctic ice during September to May; 2007/08. Validation is performed using comparison with results from other algorithms in addition to operational ice charts. Three ice types are resolved: young, first-year and multiyear. Total ice concentration from ECICE under cold Arctic winter conditions is in agreement with estimates from previous algorithms such as the enhanced NASA Team. Distribution of multiyear ice concentration from ECICE is presented along with evolution of daily concentration of each ice type. Events of melt-refreeze, which are common during seasonal transition periods, cause misidentification of multiyear ice as first-year ice in the fall. The reverse is observed in the spring. This is a limitation on ice type identification. ECICE is an optimal approach that minimizes the error between observations and predicted concentrations. It provides a confidence measure associated with each ice concentration estimate. It is a generic algorithm, i.e. its applications are not limited to AMSR-E and QuikSCAT. [Copyright &y& Elsevier]

AutoNowcaster (ANC) is an automated system that nowcasts thunderstorms, including thunderstorm initiation. However, its parameters have to be tuned to regional environments, a process that is time consuming, labor intensive, and quite subjective. When the National Weather Service decided to explore using ANC in forecast operations, a faster, less labor-intensive, and objective mechanism to tune the parameters for all the forecast offices was sought. In this paper, a genetic algorithm approach to tuning ANC is described. The process consisted of choosing datasets, employing an objective forecast verifi-cation technique, and devising a fitness function. ANC was modified to create nowcasts offline using weights iteratively generated by the genetic algorithm. The weights were generated by probabilistically combining weights with good fitness, leading to better and better weights as the tuning process proceeded. The nowcasts created by ANC using the automatically determined weights are compared with the nowcasts created by ANC using weights that were the result of manual tuning. It is shown that nowcasts created using the automatically tuned weights are as skilled as the ones created through manual tuning. In addition, automated tuning can be done in a fraction of the time that it takes experts to analyze the data and tune the weights. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to describe refinements to the recently developed GA-ACO method and to show its application to a real world engineering design problem. The GA-ACO method is a genetic algorithm with a new operator, called an ACO operator. ACO stands for Ant Colony Optimization. The ACO operator uses pheromone trails, a method from Ant Colony Optimization to influence the genetic algorithm. The GA-ACO method is used to optimize an engineering design. Engineers produce a preliminary design for a system using a CAD tool. The output of the CAD tool is then translated into a design graph. Many additional characteristics of the design can be represented by labels on the design graph. The GA-ACO method is then used to optimize these labels. This technique can be applied widely to many design optimization problems. The application considered in this paper concerns optimization of designs for efficient assembly. It uses problems in engineering design encountered at Newport News Shipbuilding, the largest shipyard in the United States. We present a comparison of variations of the GA-ACO method with a standard genetic algorithm for this type of problem. [Copyright &y& Elsevier]

Map-matching algorithms integrate data from positioning sensors with a digital map in order, first, to identify the road link on which a vehicle is traveling, and second, to determine the vehicle's location on that link. Due to errors in positioning sensors, digital maps, and the map-matching (MM) process, MM algorithms sometimes fail to identify the correct road segment from the candidate segments. This phenomenon is known as mismatching. Identification of the wrong road link may mislead users and degrade the performance of a location-based intelligent transportation system (ITS) and services. The main objective of this article is to improve a topological map-matching (tMM) algorithm by error detection, correction, and performance re-evaluation. Errors in a tMM algorithm were determined using data comprising 62,887 positioning points collected in three different countries (the United Kingdom, the United States, and India). After map matching, each mismatched case was examined to identify the primary causes of the mismatches. A number of strategies were developed and applied to reduce the risk of mismatching thus enhancing the tMM algorithm. An independent data set of 5,256 positioning points collected in and around Nottingham, UK, was employed to re-evaluate the performance of the enhanced tMM algorithm. The original tMM algorithm correctly identified the vehicle's position 96.5% of the time; after enhancement this increased to 97.8%. This compares very well with the performance of tMM algorithms reported in the literature. The enhanced tMM algorithm developed in this research is simple, fast, efficient, and easy to implement. Since the accuracy offered by the enhanced algorithm is found to be high, the developed algorithm has potential to be implemented in real-time location-based ITS applications. [ABSTRACT FROM AUTHOR]

Background: The SF-6D and EQ-5D are widely used generic index measures as health-related quality of life. We assessed within-subject agreement between SF-6D and EQ-5D utilities with different preference weights, and their validities in measuring Chinese rural residents, before and after standardization scores. Methodology/Principal Findings: Rural residents over 18 years old were interviewed using EQ-5D and SF-6D in Jiangsu Province, China. EQ-5D utility-scoring algorithms were used from three conversion tables from the United Kingdom, Japan, and the United States. Validities, Sensitivity and agreement between instruments were computed and compared. Factors affecting utility difference were explored with multiple liner regression models. Scores with standardization intervals of 0-1 in the two instruments were analyzed by the use of the above methods again. In 929 respondents, relative efficiency statistic and receiver operating characteristic curves analysis showed SF-6D to be the more efficient, followed by the EQ-5D model in Japan weights. Bland-Altman plot analysis showed paired SF-6D/EQ-5D in UK weights had better agreement. Though some risk factors were found, multiple liner regression demonstrated most coefficients were weaker than 0.2, and all R2 values were less than 0.06. Standardization did not significantly influence these results except scores' value. Conclusions/Significance: SF-6D and next EQ-5D in Japan weights could be used for Chinese rural residents. Further research with larger sample size of population is needed to establish and determine the feasibility of standardization score. [ABSTRACT FROM AUTHOR]

The current Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network is approaching 20 years of age, leading researchers to begin exploring new opportunities for a next-generation network in the United States. With a vast list of requirements for a new weather radar network, research has provided various approaches to the design and fabrication of such a network. Additionally, new weather radar networks in other countries, as well as networks on smaller scales, must balance a large number of variables in order to operate in the most effective way possible. To offer network designers an objective analysis tool for such decisions, a coverage optimization technique, utilizing a genetic algorithm with a focus on low-level coverage, is presented. Optimization is achieved using a variety of variables and methods, including the use of climatology, population density, and attenuation due to average precipitation conditions. A method to account for terrain blockage in mountainous regions is also presented. Various combinations of multifrequency radar networks are explored, and results are presented in the form of a coverage-based cost-benefit analysis, with considerations for total network lifetime cost. [ABSTRACT FROM AUTHOR]

Major roads and bridges in the United States are aging and deteriorating, which is causing significant human and economic losses. The required investments to rehabilitate these transportation networks exceed available funds and budgets. The rehabilitation efforts of these aging networks, therefore, need to be optimized to maximize their net benefits and reduce the effect of construction works on the traveling public. This paper presents the development of an innovative model aimed at aiding decision makers in planning and optimizing highway rehabilitation programs. This model provides new and unique capabilities, which include: allocating limited financial resources to competing highway rehabilitation projects, measuring the effect of rehabilitation efforts on network performance and road user savings, analyzing the expected benefits and costs of rehabilitation programs, and generating optimal tradeoffs between maximizing rehabilitation benefits and minimizing network service disruption using a genetic algorithm (GA)-based optimization module. An application example is analyzed to evaluate the performance of the proposed model and demonstrate its capabilities in identifying a wide range of optimal rehabilitation programs, in which each provides a unique and nondominated tradeoff between maximizing rehabilitation benefits and minimizing service disruption. This allows decision makers in departments of transportation to select and implement the rehabilitation programs that address their specific societal needs. [ABSTRACT FROM AUTHOR]

We offer further evidence on the relevance of technical trading in exchange-rate markets using daily data for 95 currencies against the US dollar. To that end, we investigate the profitability of a simple technical trading rule based on Taylor's (1980) price trend model, generating optimal one-step-ahead forecasts of returns using genetic algorithms. These trading rules, that bear similarity to the popular trading rules based on moving averages, overcome the buy-and-hold strategy in 25 of 39 cases where trends are detected, even in the presence of transaction costs. [ABSTRACT FROM AUTHOR]

We test for the existence of trends in exchange-rate series for 95 currencies against the US dollar. To that end, we make use of Taylor's (1980) price trend model that, instead of focusing on the mean reverting behaviour of exchange rates measured over a long horizon, concentrates on the short-term pattern of the price trend. Employing a maximum likelihood method and a genetic algorithm to estimate the model parameters, in 39 of the 95 cases considered we find evidence in favour of the presence of trends, the trends being more frequent in intermediate exchange-rate regimes. [ABSTRACT FROM AUTHOR]

Variational data assimilation methods optimize the match between an observed and a predicted field. These methods normally require information on error variances of both the analysis and the observations, which are sometimes difficult to obtain for transport and dispersion problems. Here, the variational problem is set up as a minimization problem that directly minimizes the root mean squared error of the difference between the observations and the prediction. In the context of atmospheric transport and dispersion, the solution of this optimization problem requires a robust technique. A genetic algorithm (GA) is used here for that solution, forming the GA-Variational (GA-Var) technique. The philosophy and formulation of the technique is described here. An advantage of the technique includes that it does not require observation or analysis error covariances nor information about any variables that are not directly assimilated. It can be employed in the context of either a forward assimilation problem or used to retrieve unknown source or meteorological information by solving the inverse problem. The details of the method are reviewed. As an example application, GA-Var is demonstrated for predicting the plume from a volcanic eruption. First the technique is employed to retrieve the unknown emission rate and the steering winds of the volcanic plume. Then that information is assimilated into a forward prediction of its transport and dispersion. Concentration data are derived from satellite data to determine the observed ash concentrations. A case study is made of the March 2009 eruption of Mount Redoubt in Alaska. The GA-Var technique is able to determine a wind speed and direction that matches the observations well and a reasonable emission rate. [ABSTRACT FROM AUTHOR]

Abstract: Based on historical records, the genetic landscape of the Bahamian archipelago is presumed to be complex and to exhibit island-specific characteristics, yet the genetic composition of the island chain, which could corroborate or refute these past accounts, remains poorly defined. As such, the current investigation was undertaken to genetically characterize 5 Bahamian populations representing the Northwest (Grand Bahama and Abaco) and Central (Eleuthera, Exuma and Long Island) Bahamas across the 15 autosomal Identifiler loci routinely employed in forensic analyses. Altogether, our findings suggest that Bahamians are a genetically heterogeneous group, with each island sampled receiving differential contributions from African, European, East Asian and Native American sources. Even though the strongest genetic signal in all 5 collections emanates from continental Africa, inter-island differentiation is noted in both the Structure and admixture analyses. The presence of alleles not in common among the 5 insular populations also signals genetic heterogeneity among the islands of the archipelago. This is especially the case when considering the Long Island population, which exhibits statistically significant genetic differences in relation to the other Bahamian collections and the New World groups of African descent (Afro-American and Afro-Caribbean) in the G-test pair-wise comparisons, even after application of the Bonferroni adjustment. [Copyright &y& Elsevier]

Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA-II based search method. The model was tested for the selection and placement of BMPs in Wildcat Creek Watershed, a corn dominated watershed located in northcentral Indiana, to reduce nitrogen, phosphorus, sediment, and pesticide losses from the watershed. The Pareto optimal fronts (plotted as spider plots) generated between the optimized objective functions can be used to make management decisions to achieve desired water quality goals with minimum BMP implementation and maintenance cost for the watershed. Also these solutions were geographically mapped to show the locations where various BMPs should be implemented. The solutions with larger pollution reduction consisted of buffer filter strips that lead to larger pollution reduction with greater costs compared to other alternatives. [ABSTRACT FROM AUTHOR]

Abstract: We develop and validate a medium-term solar irradiance forecasting model by adopting predicted meteorological variables from the US National Weather Service’s (NWS) forecasting database as inputs to an Artificial Neural Network (ANN) model. Since the inputs involved are the same as the ones available from a recently validated forecasting model, we include mean bias error (MBE), root mean square error (RMSE), and correlation coefficient (R 2) comparisons between the more established forecasting model and the proposed ones. An important component of our study is the development of a set of criteria for selecting relevant inputs. The input variables are selected using a version of the Gamma test combined with a genetic algorithm. The solar geotemporal variables are found to be critically important, while the most relevant meteorological variables include sky cover, probability of precipitation, and maximum and minimum temperatures. Using the relevant input sets identified by the Gamma test, the developed forecasting models improve RMSEs for GHI by 10–15% over the reference model. Prediction intervals based on regression of the squared residuals on the input variables are also derived. [Copyright &y& Elsevier]

In the new open access environment, in pursuit of profit, power producers tend to operate closer to the security boundaries and consequently, the voltage instability, which is caused by insufficient reactive power support, threats system security and reliability. This paper presents a day-ahead reactive power market based on uniform auction price scheme considering voltage security. First, expected payment function (EPF), identified earlier in the literature to construct a bidding framework for synchronous generators, is modified. Then, the modified EPF is used as the objective function of optimal power flow problem to clear reactive power market. Finally, the OPF, including overload, voltage drop and voltage stability margin in its constraints, is solved by binary coded genetic algorithm. The validity of the proposed reactive power market is studied based on the IEEE 24-bus reliability test system. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with a fair ramp metering problem which takes into account average travel delay distribution among on-ramps for an expressway system comprising expressways, on-ramps and off-ramps. A novel spatial equity index is defined to measure the evenness of travel delay distribution among on-ramps within the predefined on-ramp groups. An ideal fair ramp metering problem therefore aims to find an optimal dynamic ramp metering rate solution that not only minimizes the total system delay, but also maximizes the equity indexes associated to the groups. Some of these objectives, however, contradict with each other, and their Pareto-optimality is explored. The fair ramp metering problem proposed in this paper is formulated as a multiobjective optimization model incorporating a modified cell-transmission model (MCTM) that captures dynamic traffic flow pattern with ramp metering operations. The MCTM then is embedded in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the multiobjective optimization model. Finally, the Interstate I-210W expressway-ramp network in the United States is adopted to assess the methodology proposed in this paper. [Copyright &y& Elsevier]

Abstract: In this paper, the traditional automatic generation control loop with modifications is incorporated for simulating automatic generation control (AGC) in restructured power system. Federal energy regulatory commission (FERC) encourages an open market system for price based operation. FERC has issued a notice for proposed rulemaking of various ancillary services. One of these ancillary services is load following with frequency control which comes broadly under Automatic Generation Control in deregulated regime. The concept of DISCO participation matrix is used to simulate the bilateral contracts in the three areas and four area diagrams. Hybrid particle swarm optimization is used to obtain optimal gain parameters for optimal transient performance. [Copyright &y& Elsevier]

Abstract: The West Nile virus (WNV) is an infectious disease spreading rapidly throughout the United States, causing illness among thousands of birds, animals, and humans. Yet, we only have a rudimentary understanding of how the mosquito-borne virus operates in complex avian–human environmental systems coupled with risk factors. The large array of multidimensional risk factors underlying WNV incidences is environmental, built-environment, socioeconomic, and existing mosquito abatement policies. Therefore it is essential to identify an optimal number of risk factors whose management would result in effective disease prevention and containment. Previous models built to select important risk factors assumed a priori that there is a linear relationship between these risk factors and disease incidences. However, it is difficult for linear models to incorporate the complexity of the WNV transmission network and hence identify an optimal number of risk factors objectively. There are two objectives of this paper, first, use combination of genetic algorithm (GA) and computational neural network (CNN) approaches to build a model incorporating the non-linearity between incidences and hypothesized risk factors. Here GA is used for risk factor (variable) selection and CNN for model building mainly because of their ability to capture complex relationships with higher accuracy than linear models. The second objective is to propose a method to measure the relative importance of the selected risk factors included in the model. The study is situated in the metropolitan area of Minnesota, which had experienced significant outbreaks from 2002 till present. [Copyright &y& Elsevier]

The article presents information on genetic programming approach to technical trading. The article discusses about small-cap stocks, and their share of responsibility for incorrect pricing and a sufficient condition for profitable technical trading. It also explores the results of applying the technology of genetic programming (GP) to three popular U.S. stock portfolios namely, the large-cap Russell 1000 index, the small-cap Russell 2000 index, and the broad-based Russell 3000 index respectively.

The article discusses a hybrid genetic algorithm (HGA) which combines the standard genetic algorithm (GA) with a local search (LS) operator for the design of monitoring networks. HGA was applied using data from ground level ozone in the U.S. environmental monitoring networks. Results show that HGA outperforms simulated annealing (SA) and GA in terms of quality of the solutions and reliability. However, computation time of HGA was noted to be longer than the SA and GA in a single-processor environment.

Polynomial regression models of the first, second, and third order were used to fit milk production deviations of daughters in Mexico on Canadian and US predicted transmitting ability values for 305-d mature-equivalent milk production (kg). For the pairs Canada-Mexico and Mexico-United States, 40 and 73 bulls with a minimum reliability of 0.75 were analyzed, respectively. Genetic correlations between pairs of countries were also estimated. The parameters were evaluated for all data, and for sires grouped according to the mean of the average phenotypic milk production (high and low) of their daughters' herd mates. Quadratic and cubic effects were not significant in any analysis. From linear regression models, slopes of Mexican daughter deviations on US and Canadian predicted transmitting abilities were 1.01 and 0.93, respectively. Slopes were greater but intercepts were smaller for the high versus low level of production of the sires' herd mates in Mexico. A greater difference between the genetic correlations was found for the high versus low environmental level than for the low level (0.79 vs. 0.70) for Mexico-US data compared with Canada-Mexico data (0.81 vs. 0.78). Genetic correlations between Mexico and the United States (0.74), and between Mexico and Canada (0.77), were smaller than the genetic correlation between the same Canadian and US sires (0.92), suggesting the presence of a moderate degree of genotype-environment interaction for milk production between Canada and the United States, and Mexico. [ABSTRACT FROM AUTHOR]

Composix® Kugel® (CK) mesh is a prosthetic available since 2000 for ventral hernia repair. A recent voluntary, class I recall of CK has caused concern regarding implantable medical devices and has caused many patients to seek medical attention and advice related to their hernia repair. Surgeons are required to answer many patient inquiries and address their concerns. Although the Food and Drug Administration (FDA) warnings can help guide surgeons in dealing with events such as this recall, detailed information and algorithms are lacking. We present information regarding the composite mesh recall and propose an algorithm for the management of patients who have had this mesh implanted. [ABSTRACT FROM AUTHOR]

The article focuses on the use of aggregative fitness genetic algorithm (AFGA) to develop the most effective bargaining strategies of market-driven agents (MDAs) in the U.S. It presents a series of experiments that are formulated to determine the most successful strategies and compare the performance of MDAs against GA-MDAs. In addition, it compares the strengths and weaknesses of GA-MDAs with related systems. The authors concluded that GA-MDAs achieved higher expected utilities and higher success rates and they reached agreements with fewer negotiation rounds than MDAs.

The design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy's Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis. [ABSTRACT FROM AUTHOR]

The article presents the findings from a study that addressed the efficacy of five optimization algorithms for parameter calibration of the hydrologic model, Soil and Water Assessment Tool (SWAT), as applied to four watersheds in the U.S. It discusses the performance of each optimization algorithm, which includes genetic algorithm (GA), shufflex complex evolution (SCE), particle swarm optimization (PSO), differential evolution (DE) and artificial immune system (AIS). It notes that PSO algorithm is opted when given a limited computational time, while GA is preferred when there is more time for such process.

We compare the numerical methods that are most widely applied in the computation of the standard business cycle model with flexible labor. The numerical techniques imply economically insignificant differences with regard to business cycle summary statistics. Furthermore, these results are robust with regard to the choice of the functional form of the utility function and the model's parameterization. In addition, the extended path approach, albeit time-consuming, and the Galerkin projection are found to be the most accurate methods, given that we have not used function approximations beyond the second degree. [ABSTRACT FROM AUTHOR]

Ecosystem carbon (C) uptake is determined largely by C residence times and increases in net primary production (NPP). Therefore, evaluation of C uptake at a regional scale requires knowledge on spatial patterns of both residence times and NPP increases. In this study, we first applied an inverse modeling method to estimate spatial patterns of C residence times in the conterminous United States. Then we combined the spatial patterns of estimated residence times with a NPP change trend to assess the spatial patterns of regional C uptake in the United States. The inverse analysis was done by using the genetic algorithm and was based on 12 observed data sets of C pools and fluxes. Residence times were estimated by minimizing the total deviation between modeled and observed values. Our results showed that the estimated C residence times were highly heterogeneous over the conterminous United States, with most of the regions having values between 15 and 65 years; and the averaged C residence time was 46 years. The estimated C uptake for the whole conterminous United States was 0.15 P g C a[sup-1]. Large portions of the taken C were stored in soil for grassland and cropland (47-70%) but in plant pools for forests and woodlands (73-82%). The proportion of C uptake in soil was found to be determined primarily by C residence times and be independent of the magnitude of NPP increase. Therefore, accurate estimation of spatial patterns of C residence times is crucial for the evaluation of terrestrial ecosystem C uptake. [ABSTRACT FROM AUTHOR]

This article discusses the use of a genetic programming , evolutionary algorithm-based modeling approach for flood hydrographs of the Walla Walla River in the U.S. According to the authors, the Muskingum method for single peak storage-discharge relationships is not adequate for routing flood hydrographs in natural channels with a non-linear relationship between storage and discharge with multiple peaks. They argue that the Genetic Programming hydrologic model establishes the routing relationship in a simple and easy mathematical form.

Coccidioidomycosis is an endemic infectious disease in western North American deserts caused by the dimorphic ascomycete Coccidioides spp. Even though there has been an increase in the number of reported cases in the last years, few positive isolations have been obtained from soil samples in endemic areas for the disease. This low correlation between epidemiological and environmental data prompted us to better characterize the fundamental ecological niche of this important fungal pathogen. By using a combination of environmental variables and geospatially referenced points, where positive isolations had been obtained in southern California and Arizona (USA) and Sonora (Mexico), we have applied Genetic Algorithm for Rule Set Production (GARP) and Geographical Information Systems (GIS) to characterize the most likely ecological conditions favorable for the presence of the fungus. This model, based on environmental variables, allowed us to identify hotspots for the presence of the fungus in areas of southern California, Arizona, Texas, Baja California, and northern Mexico, whereas an alternative model based on bioclimatic variables gave us much broader probable distribution areas. We have overlapped the hotspots obtained with the environmental model with the available epidemiological information and have found a high match. Our model suggests that the most probable fundamental ecological niche for Coccidioides spp. is found in the arid lands of the North American deserts and provides the methodological basis to further characterize the realized ecological niche of Coccidioides spp ., which would ultimately contribute to design smart field-sampling strategies. [ABSTRACT FROM AUTHOR]

Abstract: In homeland security applications, it is often necessary to characterize the source location and strength of a potentially harmful contaminant. Correct source characterization requires accurate meteorological data such as wind direction. Unfortunately, available meteorological data is often inaccurate or unrepresentative, having insufficient spatial and temporal resolution for precise modeling of pollutant dispersion. To address this issue, a method is presented that simultaneously determines the surface wind direction and the pollutant source characteristics. This method compares monitored receptor data to pollutant dispersion model output and uses a genetic algorithm (GA) to find the combination of source location, source strength, and surface wind direction that best matches the dispersion model output to the receptor data. A GA optimizes variables using principles from genetics and evolution. The approach is validated with an identical twin experiment using synthetic receptor data and a Gaussian plume equation as the dispersion model. Given sufficient receptor data, the GA is able to reproduce the wind direction, source location, and source strength. Additional runs incorporating white noise into the receptor data to simulate real-world variability demonstrate that the GA is still capable of computing the correct solution, as long as the magnitude of the noise does not exceed that of the receptor data. [Copyright &y& Elsevier]

The article presents a study investigating the invasion risk of Chinese mitten crabs in the North American coastal habitats. The study used a combined reported quantities of ballast water discharge with environmental matching to demonstrate relative invasion risk for different U.S. ports. It was found out that the ports of Norfolk in Virginia, Baltimore in Maryland and Portland in Oregon have the greatest relative risk of introduction and establishment of Chinese mitten crabs because they have the greatest volumes of ballast water from source areas known to support the species and have the highest environmental match for both the environmental niche models.

This study presents a genetic algorithm approach to detecting financial statement fraud. The study uses a sample comprising a target class of 51 companies accused by the Securities and Exchange Commission of improperly recognizing revenue and a peer class of 339 companies matched on industry and size (revenue). Variables include 76 comparative metrics, based on specific financial metrics and ratios that capture company performance in the context of historical and industry performance, and nine company characteristics. Time-based patterns detected by the genetic algorithm accurately classify 63% of the target class companies and 95% of the peer class companies. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Many forested steeplands in the western United States display a legacy of disturbances due to timber harvest, mining or wildfires, for example. Such disturbances have caused accelerated hillslope erosion, leading to increased sedimentation in fish-bearing streams. Several restoration techniques have been implemented to address these problems in mountain catchments, many of which involve the removal of abandoned roads and re-establishing drainage networks across road prisms. With limited restoration funds to be applied across large catchments, land managers are faced with deciding which areas and problems should be treated first, and by which technique, in order to design the most effective and cost-effective sediment reduction strategy. Currently most restoration is conducted on a site-specific scale according to uniform treatment policies. To create catchment-scale policies for restoration, we developed two optimization models - dynamic programming and genetic algorithms - to determine the most cost-effective treatment level for roads and stream crossings in a pilot study basin with approximately 700 road segments and crossings. These models considered the trade-offs between the cost and effectiveness of different restoration strategies to minimize the predicted erosion from all forest roads within a catchment, while meeting a specified budget constraint. The optimal sediment reduction strategies developed by these models performed much better than two strategies of uniform erosion control which are commonly applied to road erosion problems by land managers, with sediment savings increased by an additional 48 to 80 per cent. These optimization models can be used to formulate the most cost-effective restoration policy for sediment reduction on a catchment scale. Thus, cost savings can be applied to further restoration work within the catchment. Nevertheless, the models are based on erosion rates measured on past restoration sites, and need to be updated as additional monitoring studies evaluate long-term basin response to erosion control treatments. [ABSTRACT FROM AUTHOR]

Homeland security challenges civil engineers to develop solutions to protect critical infrastructure systems from intentional attacks. The water infrastructure system is one of eight critical infrastructures defined in the report by the President’s Commission on Critical Infrastructure Protection (PDD 1998). This paper offers a model for an operational response strategy to mitigate consequences of intentional physical attacks on the water infrastructure system. An intentional physical attack occurs when a water infrastructure facility is nonfunctional because of physical destruction of the facility by individuals or groups with malicious intentions. Three indices are presented in this paper to measure the consequences of intentional physical attacks: (a) the degree of the disruption of critical infrastructure services, (b) economic loss, and (c) the number of people affected. Then, a multiobjective genetic algorithm is used to find solutions to affect minimum consequences among these indicators. Evaluation of the model using two water infrastructure networks indicates that the genetic algorithms can successfully identify Pareto-optimal solutions to reduce the negative consequences of different attack scenarios. These solutions represent a pool of strategies to be incorporated in emergency plans. In addition, the modified nondominated sorting genetic algorithms (NSGA-II) developed in this study can significantly improve the quality of the solution early in the evolution process. This can be very critical when the time available to obtain mitigation plans is very limited. [ABSTRACT FROM AUTHOR]

The potential distribution of alien species in a novel habitat often is difficult to predict because factors limiting species distributions may be unique to the new locale. Eragrostis lehmanniana is a perennial grass purposely introduced from South Africa to Arizona, USA in the 1930s; by the 1980s, it had doubled its extent. Based on environmental characteristics associated with its introduced and native range, researchers believed that E. lehmanniana had reached the limits of its distribution by the early 1990s. We collected data on E. lehmanniana locations from various land management agencies throughout Arizona and western New Mexico and found new records that indicate that E. lehmanniana has continued to spread. Also, we employed two modelling techniques to determine the current potential distribution and to re-investigate several environmental variables related to distribution. Precipitation and temperature regimes similar to those indicated by past research were the most important variables influencing model output. The potential distribution of E. lehmanniana mapped by both models was 71,843 km2 and covers a large portion of southeastern and central Arizona. Logistic regression (LR) predicted a potential distribution of E. lehmanniana more similar to this species current distribution than GARP based on average temperature, precipitation, and grassland species composition and recorded occurrences. Results of a cross-validation assessment and extrinsic testing showed that the LR model performed as well or better than GARP based on sensitivity, specificity, and kappa indices. [ABSTRACT FROM AUTHOR]