Self-adaptive multi-objective harmony search for optimal design of water distribution networks

NASA Astrophysics Data System (ADS)

Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon

2017-11-01

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Multi-objective parametric optimization of Inertance type pulse tube refrigerator using response surface methodology and non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Rout, Sachindra K.; Choudhury, Balaji K.; Sahoo, Ranjit K.; Sarangi, Sunil K.

2014-07-01

The modeling and optimization of a Pulse Tube Refrigerator is a complicated task, due to its complexity of geometry and nature. The aim of the present work is to optimize the dimensions of pulse tube and regenerator for an Inertance-Type Pulse Tube Refrigerator (ITPTR) by using Response Surface Methodology (RSM) and Non-Sorted Genetic Algorithm II (NSGA II). The Box-Behnken design of the response surface methodology is used in an experimental matrix, with four factors and two levels. The diameter and length of the pulse tube and regenerator are chosen as the design variables where the rest of the dimensions and operating conditions of the ITPTR are constant. The required output responses are the cold head temperature (Tcold) and compressor input power (Wcomp). Computational fluid dynamics (CFD) have been used to model and solve the ITPTR. The CFD results agreed well with those of the previously published paper. Also using the results from the 1-D simulation, RSM is conducted to analyse the effect of the independent variables on the responses. To check the accuracy of the model, the analysis of variance (ANOVA) method has been used. Based on the proposed mathematical RSM models a multi-objective optimization study, using the Non-sorted genetic algorithm II (NSGA-II) has been performed to optimize the responses.

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Emergency strategy optimization for the environmental control system in manned spacecraft

NASA Astrophysics Data System (ADS)

Li, Guoxiang; Pang, Liping; Liu, Meng; Fang, Yufeng; Zhang, Helin

2018-02-01

It is very important for a manned environmental control system (ECS) to be able to reconfigure its operation strategy in emergency conditions. In this article, a multi-objective optimization is established to design the optimal emergency strategy for an ECS in an insufficient power supply condition. The maximum ECS lifetime and the minimum power consumption are chosen as the optimization objectives. Some adjustable key variables are chosen as the optimization variables, which finally represent the reconfigured emergency strategy. The non-dominated sorting genetic algorithm-II is adopted to solve this multi-objective optimization problem. Optimization processes are conducted at four different carbon dioxide partial pressure control levels. The study results show that the Pareto-optimal frontiers obtained from this multi-objective optimization can represent the relationship between the lifetime and the power consumption of the ECS. Hence, the preferred emergency operation strategy can be recommended for situations when there is suddenly insufficient power.

Multi-objective optimization of MOSFETs channel widths and supply voltage in the proposed dual edge-triggered static D flip-flop with minimum average power and delay by using fuzzy non-dominated sorting genetic algorithm-II.

PubMed

Keivanian, Farshid; Mehrshad, Nasser; Bijari, Abolfazl

2016-01-01

D Flip-Flop as a digital circuit can be used as a timing element in many sophisticated circuits. Therefore the optimum performance with the lowest power consumption and acceptable delay time will be critical issue in electronics circuits. The newly proposed Dual-Edge Triggered Static D Flip-Flop circuit layout is defined as a multi-objective optimization problem. For this, an optimum fuzzy inference system with fuzzy rules is proposed to enhance the performance and convergence of non-dominated sorting Genetic Algorithm-II by adaptive control of the exploration and exploitation parameters. By using proposed Fuzzy NSGA-II algorithm, the more optimum values for MOSFET channel widths and power supply are discovered in search space than ordinary NSGA types. What is more, the design parameters involving NMOS and PMOS channel widths and power supply voltage and the performance parameters including average power consumption and propagation delay time are linked. To do this, the required mathematical backgrounds are presented in this study. The optimum values for the design parameters of MOSFETs channel widths and power supply are discovered. Based on them the power delay product quantity (PDP) is 6.32 PJ at 125 MHz Clock Frequency, L = 0.18 µm, and T = 27 °C.

Multi-objective Calibration of DHSVM Based on Hydrologic Key Elements in Jinhua River Basin, East China

NASA Astrophysics Data System (ADS)

Pan, S.; Liu, L.; Xu, Y. P.

2017-12-01

Abstract: In physically based distributed hydrological model, large number of parameters, representing spatial heterogeneity of watershed and various processes in hydrologic cycle, are involved. For lack of calibration module in Distributed Hydrology Soil Vegetation Model, this study developed a multi-objective calibration module using Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (ɛ-NSGAII) and based on parallel computing of Linux cluster for DHSVM (ɛP-DHSVM). In this study, two hydrologic key elements (i.e., runoff and evapotranspiration) are used as objectives in multi-objective calibration of model. MODIS evapotranspiration obtained by SEBAL is adopted to fill the gap of lack of observation for evapotranspiration. The results show that good performance of runoff simulation in single objective calibration cannot ensure good simulation performance of other hydrologic key elements. Self-developed ɛP-DHSVM model can make multi-objective calibration more efficiently and effectively. The running speed can be increased by more than 20-30 times via applying ɛP-DHSVM. In addition, runoff and evapotranspiration can be simulated very well simultaneously by ɛP-DHSVM, with superior values for two efficiency coefficients (0.74 for NS of runoff and 0.79 for NS of evapotranspiration, -10.5% and -8.6% for PBIAS of runoff and evapotranspiration respectively).

Multicomponent pre-stack seismic waveform inversion in transversely isotropic media using a non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Padhi, Amit; Mallick, Subhashis

2014-03-01

Inversion of band- and offset-limited single component (P wave) seismic data does not provide robust estimates of subsurface elastic parameters and density. Multicomponent seismic data can, in principle, circumvent this limitation but adds to the complexity of the inversion algorithm because it requires simultaneous optimization of multiple objective functions, one for each data component. In seismology, these multiple objectives are typically handled by constructing a single objective given as a weighted sum of the objectives of individual data components and sometimes with additional regularization terms reflecting their interdependence; which is then followed by a single objective optimization. Multi-objective problems, inclusive of the multicomponent seismic inversion are however non-linear. They have non-unique solutions, known as the Pareto-optimal solutions. Therefore, casting such problems as a single objective optimization provides one out of the entire set of the Pareto-optimal solutions, which in turn, may be biased by the choice of the weights. To handle multiple objectives, it is thus appropriate to treat the objective as a vector and simultaneously optimize each of its components so that the entire Pareto-optimal set of solutions could be estimated. This paper proposes such a novel multi-objective methodology using a non-dominated sorting genetic algorithm for waveform inversion of multicomponent seismic data. The applicability of the method is demonstrated using synthetic data generated from multilayer models based on a real well log. We document that the proposed method can reliably extract subsurface elastic parameters and density from multicomponent seismic data both when the subsurface is considered isotropic and transversely isotropic with a vertical symmetry axis. We also compute approximate uncertainty values in the derived parameters. Although we restrict our inversion applications to horizontally stratified models, we outline a practical procedure of extending the method to approximately include local dips for each source-receiver offset pair. Finally, the applicability of the proposed method is not just limited to seismic inversion but it could be used to invert different data types not only requiring multiple objectives but also multiple physics to describe them.

A multi-objective genetic algorithm for a mixed-model assembly U-line balancing type-I problem considering human-related issues, training, and learning

NASA Astrophysics Data System (ADS)

Rabbani, Masoud; Montazeri, Mona; Farrokhi-Asl, Hamed; Rafiei, Hamed

2016-12-01

Mixed-model assembly lines are increasingly accepted in many industrial environments to meet the growing trend of greater product variability, diversification of customer demands, and shorter life cycles. In this research, a new mathematical model is presented considering balancing a mixed-model U-line and human-related issues, simultaneously. The objective function consists of two separate components. The first part of the objective function is related to balance problem. In this part, objective functions are minimizing the cycle time, minimizing the number of workstations, and maximizing the line efficiencies. The second part is related to human issues and consists of hiring cost, firing cost, training cost, and salary. To solve the presented model, two well-known multi-objective evolutionary algorithms, namely non-dominated sorting genetic algorithm and multi-objective particle swarm optimization, have been used. A simple solution representation is provided in this paper to encode the solutions. Finally, the computational results are compared and analyzed.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

NASA Astrophysics Data System (ADS)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Multi-objective thermodynamic optimisation of supercritical CO2 Brayton cycles integrated with solar central receivers

NASA Astrophysics Data System (ADS)

Vasquez Padilla, Ricardo; Soo Too, Yen Chean; Benito, Regano; McNaughton, Robbie; Stein, Wes

2018-01-01

In this paper, optimisation of the supercritical CO? Brayton cycles integrated with a solar receiver, which provides heat input to the cycle, was performed. Four S-CO? Brayton cycle configurations were analysed and optimum operating conditions were obtained by using a multi-objective thermodynamic optimisation. Four different sets, each including two objective parameters, were considered individually. The individual multi-objective optimisation was performed by using Non-dominated Sorting Genetic Algorithm. The effect of reheating, solar receiver pressure drop and cycle parameters on the overall exergy and cycle thermal efficiency was analysed. The results showed that, for all configurations, the overall exergy efficiency of the solarised systems achieved at maximum value between 700°C and 750°C and the optimum value is adversely affected by the solar receiver pressure drop. In addition, the optimum cycle high pressure was in the range of 24.2-25.9 MPa, depending on the configurations and reheat condition.

Optimizing the static-dynamic performance of the body-in-white using a modified non-dominated sorting genetic algorithm coupled with grey relational analysis

NASA Astrophysics Data System (ADS)

Wang, Dengfeng; Cai, Kefang

2018-04-01

This article presents a hybrid method combining a modified non-dominated sorting genetic algorithm (MNSGA-II) with grey relational analysis (GRA) to improve the static-dynamic performance of a body-in-white (BIW). First, an implicit parametric model of the BIW was built using SFE-CONCEPT software, and then the validity of the implicit parametric model was verified by physical testing. Eight shape design variables were defined for BIW beam structures based on the implicit parametric technology. Subsequently, MNSGA-II was used to determine the optimal combination of the design parameters that can improve the bending stiffness, torsion stiffness and low-order natural frequencies of the BIW without considerable increase in the mass. A set of non-dominated solutions was then obtained in the multi-objective optimization design. Finally, the grey entropy theory and GRA were applied to rank all non-dominated solutions from best to worst to determine the best trade-off solution. The comparison between the GRA and the technique for order of preference by similarity to ideal solution (TOPSIS) illustrated the reliability and rationality of GRA. Moreover, the effectiveness of the hybrid method was verified by the optimal results such that the bending stiffness, torsion stiffness, first order bending and first order torsion natural frequency were improved by 5.46%, 9.30%, 7.32% and 5.73%, respectively, with the mass of the BIW increasing by 1.30%.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

PubMed

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

Fatigue design of a cellular phone folder using regression model-based multi-objective optimization

NASA Astrophysics Data System (ADS)

Kim, Young Gyun; Lee, Jongsoo

2016-08-01

In a folding cellular phone, the folding device is repeatedly opened and closed by the user, which eventually results in fatigue damage, particularly to the front of the folder. Hence, it is important to improve the safety and endurance of the folder while also reducing its weight. This article presents an optimal design for the folder front that maximizes its fatigue endurance while minimizing its thickness. Design data for analysis and optimization were obtained experimentally using a test jig. Multi-objective optimization was carried out using a nonlinear regression model. Three regression methods were employed: back-propagation neural networks, logistic regression and support vector machines. The AdaBoost ensemble technique was also used to improve the approximation. Two-objective Pareto-optimal solutions were identified using the non-dominated sorting genetic algorithm (NSGA-II). Finally, a numerically optimized solution was validated against experimental product data, in terms of both fatigue endurance and thickness index.

Derivation of optimal joint operating rules for multi-purpose multi-reservoir water-supply system

NASA Astrophysics Data System (ADS)

Tan, Qiao-feng; Wang, Xu; Wang, Hao; Wang, Chao; Lei, Xiao-hui; Xiong, Yi-song; Zhang, Wei

2017-08-01

The derivation of joint operating policy is a challenging task for a multi-purpose multi-reservoir system. This study proposed an aggregation-decomposition model to guide the joint operation of multi-purpose multi-reservoir system, including: (1) an aggregated model based on the improved hedging rule to ensure the long-term water-supply operating benefit; (2) a decomposed model to allocate the limited release to individual reservoirs for the purpose of maximizing the total profit of the facing period; and (3) a double-layer simulation-based optimization model to obtain the optimal time-varying hedging rules using the non-dominated sorting genetic algorithm II, whose objectives were to minimize maximum water deficit and maximize water supply reliability. The water-supply system of Li River in Guangxi Province, China, was selected for the case study. The results show that the operating policy proposed in this study is better than conventional operating rules and aggregated standard operating policy for both water supply and hydropower generation due to the use of hedging mechanism and effective coordination among multiple objectives.

Multi Objective Optimization of Yarn Quality and Fibre Quality Using Evolutionary Algorithm

NASA Astrophysics Data System (ADS)

Ghosh, Anindya; Das, Subhasis; Banerjee, Debamalya

2013-03-01

The quality and cost of resulting yarn play a significant role to determine its end application. The challenging task of any spinner lies in producing a good quality yarn with added cost benefit. The present work does a multi-objective optimization on two objectives, viz. maximization of cotton yarn strength and minimization of raw material quality. The first objective function has been formulated based on the artificial neural network input-output relation between cotton fibre properties and yarn strength. The second objective function is formulated with the well known regression equation of spinning consistency index. It is obvious that these two objectives are conflicting in nature i.e. not a single combination of cotton fibre parameters does exist which produce maximum yarn strength and minimum cotton fibre quality simultaneously. Therefore, it has several optimal solutions from which a trade-off is needed depending upon the requirement of user. In this work, the optimal solutions are obtained with an elitist multi-objective evolutionary algorithm based on Non-dominated Sorting Genetic Algorithm II (NSGA-II). These optimum solutions may lead to the efficient exploitation of raw materials to produce better quality yarns at low costs.

Complexity of line-seru conversion for different scheduling rules and two improved exact algorithms for the multi-objective optimization.

PubMed

Yu, Yang; Wang, Sihan; Tang, Jiafu; Kaku, Ikou; Sun, Wei

2016-01-01

Productivity can be greatly improved by converting the traditional assembly line to a seru system, especially in the business environment with short product life cycles, uncertain product types and fluctuating production volumes. Line-seru conversion includes two decision processes, i.e., seru formation and seru load. For simplicity, however, previous studies focus on the seru formation with a given scheduling rule in seru load. We select ten scheduling rules usually used in seru load to investigate the influence of different scheduling rules on the performance of line-seru conversion. Moreover, we clarify the complexities of line-seru conversion for ten different scheduling rules from the theoretical perspective. In addition, multi-objective decisions are often used in line-seru conversion. To obtain Pareto-optimal solutions of multi-objective line-seru conversion, we develop two improved exact algorithms based on reducing time complexity and space complexity respectively. Compared with the enumeration based on non-dominated sorting to solve multi-objective problem, the two improved exact algorithms saves computation time greatly. Several numerical simulation experiments are performed to show the performance improvement brought by the two proposed exact algorithms.

Multi-objective design of fuzzy logic controller in supply chain

NASA Astrophysics Data System (ADS)

Ghane, Mahdi; Tarokh, Mohammad Jafar

2012-08-01

Unlike commonly used methods, in this paper, we have introduced a new approach for designing fuzzy controllers. In this approach, we have simultaneously optimized both objective functions of a supply chain over a two-dimensional space. Then, we have obtained a spectrum of optimized points, each of which represents a set of optimal parameters which can be chosen by the manager according to the importance of objective functions. Our used supply chain model is a member of inventory and order-based production control system family, a generalization of the periodic review which is termed `Order-Up-To policy.' An auto rule maker, based on non-dominated sorting genetic algorithm-II, has been applied to the experimental initial fuzzy rules. According to performance measurement, our results indicate the efficiency of the proposed approach.

Multi-objective Optimization of Pulsed Gas Metal Arc Welding Process Using Neuro NSGA-II

NASA Astrophysics Data System (ADS)

Pal, Kamal; Pal, Surjya K.

2018-05-01

Weld quality is a critical issue in fabrication industries where products are custom-designed. Multi-objective optimization results number of solutions in the pareto-optimal front. Mathematical regression model based optimization methods are often found to be inadequate for highly non-linear arc welding processes. Thus, various global evolutionary approaches like artificial neural network, genetic algorithm (GA) have been developed. The present work attempts with elitist non-dominated sorting GA (NSGA-II) for optimization of pulsed gas metal arc welding process using back propagation neural network (BPNN) based weld quality feature models. The primary objective to maintain butt joint weld quality is the maximization of tensile strength with minimum plate distortion. BPNN has been used to compute the fitness of each solution after adequate training, whereas NSGA-II algorithm generates the optimum solutions for two conflicting objectives. Welding experiments have been conducted on low carbon steel using response surface methodology. The pareto-optimal front with three ranked solutions after 20th generations was considered as the best without further improvement. The joint strength as well as transverse shrinkage was found to be drastically improved over the design of experimental results as per validated pareto-optimal solutions obtained.

Multi-objective optimal design of sandwich panels using a genetic algorithm

NASA Astrophysics Data System (ADS)

Xu, Xiaomei; Jiang, Yiping; Pueh Lee, Heow

2017-10-01

In this study, an optimization problem concerning sandwich panels is investigated by simultaneously considering the two objectives of minimizing the panel mass and maximizing the sound insulation performance. First of all, the acoustic model of sandwich panels is discussed, which provides a foundation to model the acoustic objective function. Then the optimization problem is formulated as a bi-objective programming model, and a solution algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is provided to solve the proposed model. Finally, taking an example of a sandwich panel that is expected to be used as an automotive roof panel, numerical experiments are carried out to verify the effectiveness of the proposed model and solution algorithm. Numerical results demonstrate in detail how the core material, geometric constraints and mechanical constraints impact the optimal designs of sandwich panels.

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

PubMed

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization. Copyright © 2014 Elsevier B.V. All rights reserved.

Micromagnetic measurement for characterization of ferromagnetic materials' microstructural properties

NASA Astrophysics Data System (ADS)

Zhang, Shuo; Shi, Xiaodong; Udpa, Lalita; Deng, Yiming

2018-05-01

Magnetic Barkhausen noise (MBN) is measured in low carbon steels and the relationship between carbon content and parameter extracted from MBN signal has been investigated. The parameter is extracted experimentally by fitting the original profiles with two Gaussian curves. The gap between two peaks (ΔG) of fitted Gaussian curves shows a better linear relationship with carbon contents of samples in the experiment. The result has been validated with simulation by Monte Carlo method. To ensure the sensitivity of measurement, advanced multi-objective optimization algorithm Non-dominant sorting genetic algorithm III (NSGA III) has been used to fulfill the optimization of the magnetic core of sensor.

Dynamic cellular manufacturing system considering machine failure and workload balance

NASA Astrophysics Data System (ADS)

Rabbani, Masoud; Farrokhi-Asl, Hamed; Ravanbakhsh, Mohammad

2018-02-01

Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators' assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in small-sized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

NASA Astrophysics Data System (ADS)

Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

PubMed

Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

2015-06-18

Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

Novel optimization technique of isolated microgrid with hydrogen energy storage.

PubMed

Beshr, Eman Hassan; Abdelghany, Hazem; Eteiba, Mahmoud

2018-01-01

This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs), Diesel Generator (DG), a Wind Turbine Generator (WTG), Photovoltaic (PV) arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm.

Novel optimization technique of isolated microgrid with hydrogen energy storage

PubMed Central

Abdelghany, Hazem; Eteiba, Mahmoud

2018-01-01

This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs), Diesel Generator (DG), a Wind Turbine Generator (WTG), Photovoltaic (PV) arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm. PMID:29466433

Robust optimization of supersonic ORC nozzle guide vanes

NASA Astrophysics Data System (ADS)

Bufi, Elio A.; Cinnella, Paola

2017-03-01

An efficient Robust Optimization (RO) strategy is developed for the design of 2D supersonic Organic Rankine Cycle turbine expanders. The dense gas effects are not-negligible for this application and they are taken into account describing the thermodynamics by means of the Peng-Robinson-Stryjek-Vera equation of state. The design methodology combines an Uncertainty Quantification (UQ) loop based on a Bayesian kriging model of the system response to the uncertain parameters, used to approximate statistics (mean and variance) of the uncertain system output, a CFD solver, and a multi-objective non-dominated sorting algorithm (NSGA), also based on a Kriging surrogate of the multi-objective fitness function, along with an adaptive infill strategy for surrogate enrichment at each generation of the NSGA. The objective functions are the average and variance of the isentropic efficiency. The blade shape is parametrized by means of a Free Form Deformation (FFD) approach. The robust optimal blades are compared to the baseline design (based on the Method of Characteristics) and to a blade obtained by means of a deterministic CFD-based optimization.

An Open-Source Auto-Calibration Routine Supporting the Stormwater Management Model

NASA Astrophysics Data System (ADS)

Tiernan, E. D.; Hodges, B. R.

2017-12-01

The stormwater management model (SWMM) is a clustered model that relies on subcatchment-averaged parameter assignments to correctly capture catchment stormwater runoff behavior. Model calibration is considered a critical step for SWMM performance, an arduous task that most stormwater management designers undertake manually. This research presents an open-source, automated calibration routine that increases the efficiency and accuracy of the model calibration process. The routine makes use of a preliminary sensitivity analysis to reduce the dimensions of the parameter space, at which point a multi-objective function, genetic algorithm (modified Non-dominated Sorting Genetic Algorithm II) determines the Pareto front for the objective functions within the parameter space. The solutions on this Pareto front represent the optimized parameter value sets for the catchment behavior that could not have been reasonably obtained through manual calibration.

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

PubMed

Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

2017-07-01

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

Enhancing the performance of MOEAs: an experimental presentation of a new fitness guided mutation operator

NASA Astrophysics Data System (ADS)

Liagkouras, K.; Metaxiotis, K.

2017-01-01

Multi-objective evolutionary algorithms (MOEAs) are currently a dynamic field of research that has attracted considerable attention. Mutation operators have been utilized by MOEAs as variation mechanisms. In particular, polynomial mutation (PLM) is one of the most popular variation mechanisms and has been utilized by many well-known MOEAs. In this paper, we revisit the PLM operator and we propose a fitness-guided version of the PLM. Experimental results obtained by non-dominated sorting genetic algorithm II and strength Pareto evolutionary algorithm 2 show that the proposed fitness-guided mutation operator outperforms the classical PLM operator, based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it.

Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II

NASA Astrophysics Data System (ADS)

Dhingra, Sunil; Bhushan, Gian; Dubey, Kashyap Kumar

2014-03-01

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (Pmax)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO x , unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, Pmax), performance (BTE) and emission (CO, NO x , HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NO x , HC, smoke, a multiobjective optimization problem is formulated. Nondominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.

Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm.

PubMed

Yang, Yu; Fritzsching, Keith J; Hong, Mei

2013-11-01

A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra ("good connections"), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra ("bad connections"), and minimizing the number of assigned peaks that have no matching peaks in the other spectra ("edges"). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a larger number of residues. On the other hand, when there are multiple equally good assignments that are significantly different from each other, the modified NSGA-II is less efficient than MC/SA in finding all the solutions. This problem is solved by a combined NSGA-II/MC algorithm, which appears to have the advantages of both NSGA-II and MC/SA. This combination algorithm is robust for the three most difficult chemical shift datasets examined here and is expected to give the highest-quality de novo assignment of challenging protein NMR spectra.

Extended behavioural modelling of FET and lattice-mismatched HEMT devices

NASA Astrophysics Data System (ADS)

Khawam, Yahya; Albasha, Lutfi

2017-07-01

This study presents an improved large signal model that can be used for high electron mobility transistors (HEMTs) and field effect transistors using measurement-based behavioural modelling techniques. The steps for accurate large and small signal modelling for transistor are also discussed. The proposed DC model is based on the Fager model since it compensates between the number of model's parameters and accuracy. The objective is to increase the accuracy of the drain-source current model with respect to any change in gate or drain voltages. Also, the objective is to extend the improved DC model to account for soft breakdown and kink effect found in some variants of HEMT devices. A hybrid Newton's-Genetic algorithm is used in order to determine the unknown parameters in the developed model. In addition to accurate modelling of a transistor's DC characteristics, the complete large signal model is modelled using multi-bias s-parameter measurements. The way that the complete model is performed is by using a hybrid multi-objective optimisation technique (Non-dominated Sorting Genetic Algorithm II) and local minimum search (multivariable Newton's method) for parasitic elements extraction. Finally, the results of DC modelling and multi-bias s-parameters modelling are presented, and three-device modelling recommendations are discussed.

An optimal design of wind turbine and ship structure based on neuro-response surface method

NASA Astrophysics Data System (ADS)

Lee, Jae-Chul; Shin, Sung-Chul; Kim, Soo-Young

2015-07-01

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

PubMed

Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

2015-05-01

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III.

PubMed

Boon, K H; Khalil-Hani, M; Malarvili, M B

2018-01-01

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity. Copyright © 2017 Elsevier B.V. All rights reserved.

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

NASA Astrophysics Data System (ADS)

Alizadeh Afrouzy, Zahra; Paydar, Mohammad Mahdi; Nasseri, Seyed Hadi; Mahdavi, Iraj

2018-03-01

There are many reasons for the growing interest in developing new product projects for any firm. The most embossed reason is surviving in a highly competitive industry which the customer tastes are changing rapidly. A well-managed supply chain network can provide the most profit for firms due to considering new product development. Along with profit, customer satisfaction and production of new products are goals which lead to a more efficient supply chain. As new products appear in the market, the old products could become obsolete, and then phased out. The most important parameter in a supply chain which considers new and developed products is the time that developed and new products are introduced and old products are phased out. With consideration of the factors noted above, this study proposes to design a tri-objective multi-echelon multi-product multi-period supply chain model, which incorporates product development and new product production and their effects on supply chain configuration. The supply chain under consideration is assumed to consist of suppliers, manufacturers, distributors and customer groups. In terms of overcoming NP-hardness of the proposed model and in order to solve the complicated problem, a non-dominated sorting genetic algorithm is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm is developed to validate the results obtained and some test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms.

Multi-objective optimization of discrete time-cost tradeoff problem in project networks using non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Shahriari, Mohammadreza

2016-06-01

The time-cost tradeoff problem is one of the most important and applicable problems in project scheduling area. There are many factors that force the mangers to crash the time. This factor could be early utilization, early commissioning and operation, improving the project cash flow, avoiding unfavorable weather conditions, compensating the delays, and so on. Since there is a need to allocate extra resources to short the finishing time of project and the project managers are intended to spend the lowest possible amount of money and achieve the maximum crashing time, as a result, both direct and indirect costs will be influenced in the project, and here, we are facing into the time value of money. It means that when we crash the starting activities in a project, the extra investment will be tied in until the end date of the project; however, when we crash the final activities, the extra investment will be tied in for a much shorter period. This study is presenting a two-objective mathematical model for balancing compressing the project time with activities delay to prepare a suitable tool for decision makers caught in available facilities and due to the time of projects. Also drawing the scheduling problem to real world conditions by considering nonlinear objective function and the time value of money are considered. The presented problem was solved using NSGA-II, and the effect of time compressing reports on the non-dominant set.

A stochastic conflict resolution model for trading pollutant discharge permits in river systems.

PubMed

Niksokhan, Mohammad Hossein; Kerachian, Reza; Amin, Pedram

2009-07-01

This paper presents an efficient methodology for developing pollutant discharge permit trading in river systems considering the conflict of interests of involving decision-makers and the stakeholders. In this methodology, a trade-off curve between objectives is developed using a powerful and recently developed multi-objective genetic algorithm technique known as the Nondominated Sorting Genetic Algorithm-II (NSGA-II). The best non-dominated solution on the trade-off curve is defined using the Young conflict resolution theory, which considers the utility functions of decision makers and stakeholders of the system. These utility functions are related to the total treatment cost and a fuzzy risk of violating the water quality standards. The fuzzy risk is evaluated using the Monte Carlo analysis. Finally, an optimization model provides the trading discharge permit policies. The practical utility of the proposed methodology in decision-making is illustrated through a realistic example of the Zarjub River in the northern part of Iran.

New mathematical modeling for a location-routing-inventory problem in a multi-period closed-loop supply chain in a car industry

NASA Astrophysics Data System (ADS)

Forouzanfar, F.; Tavakkoli-Moghaddam, R.; Bashiri, M.; Baboli, A.; Hadji Molana, S. M.

2017-11-01

This paper studies a location-routing-inventory problem in a multi-period closed-loop supply chain with multiple suppliers, producers, distribution centers, customers, collection centers, recovery, and recycling centers. In this supply chain, centers are multiple levels, a price increase factor is considered for operational costs at centers, inventory and shortage (including lost sales and backlog) are allowed at production centers, arrival time of vehicles of each plant to its dedicated distribution centers and also departure from them are considered, in such a way that the sum of system costs and the sum of maximum time at each level should be minimized. The aforementioned problem is formulated in the form of a bi-objective nonlinear integer programming model. Due to the NP-hard nature of the problem, two meta-heuristics, namely, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO), are used in large sizes. In addition, a Taguchi method is used to set the parameters of these algorithms to enhance their performance. To evaluate the efficiency of the proposed algorithms, the results for small-sized problems are compared with the results of the ɛ-constraint method. Finally, four measuring metrics, namely, the number of Pareto solutions, mean ideal distance, spacing metric, and quality metric, are used to compare NSGA-II and MOPSO.

A target recognition method for maritime surveillance radars based on hybrid ensemble selection

NASA Astrophysics Data System (ADS)

Fan, Xueman; Hu, Shengliang; He, Jingbo

2017-11-01

In order to improve the generalisation ability of the maritime surveillance radar, a novel ensemble selection technique, termed Optimisation and Dynamic Selection (ODS), is proposed. During the optimisation phase, the non-dominated sorting genetic algorithm II for multi-objective optimisation is used to find the Pareto front, i.e. a set of ensembles of classifiers representing different tradeoffs between the classification error and diversity. During the dynamic selection phase, the meta-learning method is used to predict whether a candidate ensemble is competent enough to classify a query instance based on three different aspects, namely, feature space, decision space and the extent of consensus. The classification performance and time complexity of ODS are compared against nine other ensemble methods using a self-built full polarimetric high resolution range profile data-set. The experimental results clearly show the effectiveness of ODS. In addition, the influence of the selection of diversity measures is studied concurrently.

Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms

NASA Astrophysics Data System (ADS)

Zhong, Shuya; Pantelous, Athanasios A.; Beer, Michael; Zhou, Jian

2018-05-01

Offshore wind farm is an emerging source of renewable energy, which has been shown to have tremendous potential in recent years. In this blooming area, a key challenge is that the preventive maintenance of offshore turbines should be scheduled reasonably to satisfy the power supply without failure. In this direction, two significant goals should be considered simultaneously as a trade-off. One is to maximise the system reliability and the other is to minimise the maintenance related cost. Thus, a non-linear multi-objective programming model is proposed including two newly defined objectives with thirteen families of constraints suitable for the preventive maintenance of offshore wind farms. In order to solve our model effectively, the nondominated sorting genetic algorithm II, especially for the multi-objective optimisation is utilised and Pareto-optimal solutions of schedules can be obtained to offer adequate support to decision-makers. Finally, an example is given to illustrate the performances of the devised model and algorithm, and explore the relationships of the two targets with the help of a contrast model.

Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies.

PubMed

Gazijahani, Farhad Samadi; Ravadanegh, Sajad Najafi; Salehi, Javad

2018-02-01

The inherent volatility and unpredictable nature of renewable generations and load demand pose considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges, this paper proposes a new risk-based multi-objective energy exchange optimization for networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. In so doing, three various risk-based strategies are distinguished by using conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective function. The first function minimizes the operation and maintenance costs, cost of power transaction between upstream network and MGs as well as power loss cost, whereas the second function minimizes the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction method has been implemented to reduce the computational burden. Finally, non-dominated sorting genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and the obtained results show that the presented approach can be considered as an efficient tool for optimal energy exchange optimization of MGs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Evaluation of the influence of dominance rules for the assembly line design problem under consideration of product design alternatives

NASA Astrophysics Data System (ADS)

Oesterle, Jonathan; Lionel, Amodeo

2018-06-01

The current competitive situation increases the importance of realistically estimating product costs during the early phases of product and assembly line planning projects. In this article, several multi-objective algorithms using difference dominance rules are proposed to solve the problem associated with the selection of the most effective combination of product and assembly lines. The list of developed algorithms includes variants of ant colony algorithms, evolutionary algorithms and imperialist competitive algorithms. The performance of each algorithm and dominance rule is analysed by five multi-objective quality indicators and fifty problem instances. The algorithms and dominance rules are ranked using a non-parametric statistical test.

Genetic algorithm-based multi-objective optimal absorber system for three-dimensional seismic structures

NASA Astrophysics Data System (ADS)

Ren, Wenjie; Li, Hongnan; Song, Gangbing; Huo, Linsheng

2009-03-01

The problem of optimizing an absorber system for three-dimensional seismic structures is addressed. The objective is to determine the number and position of absorbers to minimize the coupling effects of translation-torsion of structures at minimum cost. A procedure for a multi-objective optimization problem is developed by integrating a dominance-based selection operator and a dominance-based penalty function method. Based on the two-branch tournament genetic algorithm, the selection operator is constructed by evaluating individuals according to their dominance in one run. The technique guarantees the better performing individual winning its competition, provides a slight selection pressure toward individuals and maintains diversity in the population. Moreover, due to the evaluation for individuals in each generation being finished in one run, less computational effort is taken. Penalty function methods are generally used to transform a constrained optimization problem into an unconstrained one. The dominance-based penalty function contains necessary information on non-dominated character and infeasible position of an individual, essential for success in seeking a Pareto optimal set. The proposed approach is used to obtain a set of non-dominated designs for a six-storey three-dimensional building with shape memory alloy dampers subjected to earthquake.

Optimization of multi-reservoir operation with a new hedging rule: application of fuzzy set theory and NSGA-II

NASA Astrophysics Data System (ADS)

Ahmadianfar, Iman; Adib, Arash; Taghian, Mehrdad

2017-10-01

The reservoir hedging rule curves are used to avoid severe water shortage during drought periods. In this method reservoir storage is divided into several zones, wherein the rationing factors are changed immediately when water storage level moves from one zone to another. In the present study, a hedging rule with fuzzy rationing factors was applied for creating a transition zone in up and down each rule curve, and then the rationing factor will be changed in this zone gradually. For this propose, a monthly simulation model was developed and linked to the non-dominated sorting genetic algorithm for calculation of the modified shortage index of two objective functions involving water supply of minimum flow and agriculture demands in a long-term simulation period. Zohre multi-reservoir system in south Iran has been considered as a case study. The results of the proposed hedging rule have improved the long-term system performance from 10 till 27 percent in comparison with the simple hedging rule, where these results demonstrate that the fuzzification of hedging factors increase the applicability and the efficiency of the new hedging rule in comparison to the conventional rule curve for mitigating the water shortage problem.

Development of closed-loop supply chain network in terms of corporate social responsibility.

PubMed

Pedram, Ali; Pedram, Payam; Yusoff, Nukman Bin; Sorooshian, Shahryar

2017-01-01

Due to the rise in awareness of environmental issues and the depletion of virgin resources, many firms have attempted to increase the sustainability of their activities. One efficient way to elevate sustainability is the consideration of corporate social responsibility (CSR) by designing a closed loop supply chain (CLSC). This paper has developed a mathematical model to increase corporate social responsibility in terms of job creation. Moreover the model, in addition to increasing total CLSC profit, provides a range of strategic decision solutions for decision makers to select a best action plan for a CLSC. A proposed multi-objective mixed-integer linear programming (MILP) model was solved with non-dominated sorting genetic algorithm II (NSGA-II). Fuzzy set theory was employed to select the best compromise solution from the Pareto-optimal solutions. A numerical example was used to validate the potential application of the proposed model. The results highlight the effect of CSR in the design of CLSC.

An improved robust buffer allocation method for the project scheduling problem

NASA Astrophysics Data System (ADS)

Ghoddousi, Parviz; Ansari, Ramin; Makui, Ahmad

2017-04-01

Unpredictable uncertainties cause delays and additional costs for projects. Often, when using traditional approaches, the optimizing procedure of the baseline project plan fails and leads to delays. In this study, a two-stage multi-objective buffer allocation approach is applied for robust project scheduling. In the first stage, some decisions are made on buffer sizes and allocation to the project activities. A set of Pareto-optimal robust schedules is designed using the meta-heuristic non-dominated sorting genetic algorithm (NSGA-II) based on the decisions made in the buffer allocation step. In the second stage, the Pareto solutions are evaluated in terms of the deviation from the initial start time and due dates. The proposed approach was implemented on a real dam construction project. The outcomes indicated that the obtained buffered schedule reduces the cost of disruptions by 17.7% compared with the baseline plan, with an increase of about 0.3% in the project completion time.

Test scheduling optimization for 3D network-on-chip based on cloud evolutionary algorithm of Pareto multi-objective

NASA Astrophysics Data System (ADS)

Xu, Chuanpei; Niu, Junhao; Ling, Jing; Wang, Suyan

2018-03-01

In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.

Development of closed–loop supply chain network in terms of corporate social responsibility

PubMed Central

Pedram, Payam; Yusoff, Nukman Bin; Sorooshian, Shahryar

2017-01-01

Due to the rise in awareness of environmental issues and the depletion of virgin resources, many firms have attempted to increase the sustainability of their activities. One efficient way to elevate sustainability is the consideration of corporate social responsibility (CSR) by designing a closed loop supply chain (CLSC). This paper has developed a mathematical model to increase corporate social responsibility in terms of job creation. Moreover the model, in addition to increasing total CLSC profit, provides a range of strategic decision solutions for decision makers to select a best action plan for a CLSC. A proposed multi-objective mixed-integer linear programming (MILP) model was solved with non-dominated sorting genetic algorithm II (NSGA-II). Fuzzy set theory was employed to select the best compromise solution from the Pareto-optimal solutions. A numerical example was used to validate the potential application of the proposed model. The results highlight the effect of CSR in the design of CLSC. PMID:28384250

Fourier-Mellin moment-based intertwining map for image encryption

NASA Astrophysics Data System (ADS)

Kaur, Manjit; Kumar, Vijay

2018-03-01

In this paper, a robust image encryption technique that utilizes Fourier-Mellin moments and intertwining logistic map is proposed. Fourier-Mellin moment-based intertwining logistic map has been designed to overcome the issue of low sensitivity of an input image. Multi-objective Non-Dominated Sorting Genetic Algorithm (NSGA-II) based on Reinforcement Learning (MNSGA-RL) has been used to optimize the required parameters of intertwining logistic map. Fourier-Mellin moments are used to make the secret keys more secure. Thereafter, permutation and diffusion operations are carried out on input image using secret keys. The performance of proposed image encryption technique has been evaluated on five well-known benchmark images and also compared with seven well-known existing encryption techniques. The experimental results reveal that the proposed technique outperforms others in terms of entropy, correlation analysis, a unified average changing intensity and the number of changing pixel rate. The simulation results reveal that the proposed technique provides high level of security and robustness against various types of attacks.

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

NASA Astrophysics Data System (ADS)

Mozaffari, Ahmad; Vajedi, Mahyar; Chehresaz, Maryyeh; Azad, Nasser L.

2016-03-01

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration

NASA Astrophysics Data System (ADS)

Chiba, Kazuhisa; Obayashi, Shigeru; Morino, Hiroyuki

Data mining is an important facet of solving multi-objective optimization problem. Because it is one of the effective manner to discover the design knowledge in the multi-objective optimization problem which obtains large data. In the present study, data mining has been performed for a large-scale and real-world multidisciplinary design optimization (MDO) to provide knowledge regarding the design space. The MDO among aerodynamics, structures, and aeroelasticity of the regional-jet wing was carried out using high-fidelity evaluation models on the adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. All solutions evaluated during the evolution were analyzed for the tradeoffs and influence of design variables using a self-organizing map to extract key features of the design space. Although the MDO results showed the inverted gull-wings as non-dominated solutions, one of the key features found by data mining was the non-gull wing geometry. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance compared with the original geometry designed in the conventional manner.

A new approach to spike sorting for multi-neuronal activities recorded with a tetrode--how ICA can be practical.

PubMed

Takahashi, Susumu; Anzai, Yuichiro; Sakurai, Yoshio

2003-07-01

Multi-neuronal recording with a tetrode is a powerful technique to reveal neuronal interactions in local circuits. However, it is difficult to detect precise spike timings among closely neighboring neurons because the spike waveforms of individual neurons overlap on the electrode when more than two neurons fire simultaneously. In addition, the spike waveforms of single neurons, especially in the presence of complex spikes, are often non-stationary. These problems limit the ability of ordinary spike sorting to sort multi-neuronal activities recorded using tetrodes into their single-neuron components. Though sorting with independent component analysis (ICA) can solve these problems, it has one serious limitation that the number of separated neurons must be less than the number of electrodes. Using a combination of ICA and the efficiency of ordinary spike sorting technique (k-means clustering), we developed an automatic procedure to solve the spike-overlapping and the non-stationarity problems with no limitation on the number of separated neurons. The results for the procedure applied to real multi-neuronal data demonstrated that some outliers which may be assigned to distinct clusters if ordinary spike-sorting methods were used can be identified as overlapping spikes, and that there are functional connections between a putative pyramidal neuron and its putative dendrite. These findings suggest that the combination of ICA and k-means clustering can provide insights into the precise nature of functional circuits among neurons, i.e. cell assemblies.

A multi-objective simulation-optimization model for in situ bioremediation of groundwater contamination: Application of bargaining theory

NASA Astrophysics Data System (ADS)

Raei, Ehsan; Nikoo, Mohammad Reza; Pourshahabi, Shokoufeh

2017-08-01

In the present study, a BIOPLUME III simulation model is coupled with a non-dominating sorting genetic algorithm (NSGA-II)-based model for optimal design of in situ groundwater bioremediation system, considering preferences of stakeholders. Ministry of Energy (MOE), Department of Environment (DOE), and National Disaster Management Organization (NDMO) are three stakeholders in the groundwater bioremediation problem in Iran. Based on the preferences of these stakeholders, the multi-objective optimization model tries to minimize: (1) cost; (2) sum of contaminant concentrations that violate standard; (3) contaminant plume fragmentation. The NSGA-II multi-objective optimization method gives Pareto-optimal solutions. A compromised solution is determined using fallback bargaining with impasse to achieve a consensus among the stakeholders. In this study, two different approaches are investigated and compared based on two different domains for locations of injection and extraction wells. At the first approach, a limited number of predefined locations is considered according to previous similar studies. At the second approach, all possible points in study area are investigated to find optimal locations, arrangement, and flow rate of injection and extraction wells. Involvement of the stakeholders, investigating all possible points instead of a limited number of locations for wells, and minimizing the contaminant plume fragmentation during bioremediation are new innovations in this research. Besides, the simulation period is divided into smaller time intervals for more efficient optimization. Image processing toolbox in MATLAB® software is utilized for calculation of the third objective function. In comparison with previous studies, cost is reduced using the proposed methodology. Dispersion of the contaminant plume is reduced in both presented approaches using the third objective function. Considering all possible points in the study area for determining the optimal locations of the wells in the second approach leads to more desirable results, i.e. decreasing the contaminant concentrations to a standard level and 20% to 40% cost reduction.

Adaptive compressed sensing of multi-view videos based on the sparsity estimation

NASA Astrophysics Data System (ADS)

Yang, Senlin; Li, Xilong; Chong, Xin

2017-11-01

The conventional compressive sensing for videos based on the non-adaptive linear projections, and the measurement times is usually set empirically. As a result, the quality of videos reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was described. Then an estimation method for the sparsity of multi-view videos was proposed based on the two dimensional discrete wavelet transform (2D DWT). With an energy threshold given beforehand, the DWT coefficients were processed with both energy normalization and sorting by descending order, and the sparsity of the multi-view video can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of video frame effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparsity estimated with the energy threshold provided, the proposed method can ensure the reconstruction quality of multi-view videos.

Multi-stage depressed collector for small orbit gyrotrons

DOEpatents

Singh, Amarjit; Ives, R. Lawrence; Schumacher, Richard V.; Mizuhara, Yosuke M.

1998-01-01

A multi-stage depressed collector for receiving energy from a small orbit gyrating electron beam employs a plurality of electrodes at different potentials for sorting the individual electrons on the basis of their total energy level. Magnetic field generating coils, for producing magnetic fields and magnetic iron for magnetic field shaping produce adiabatic and controlled non-adiabatic transitions of the incident electron beam to further facilitate the sorting.

Multi-stage depressed collector for small orbit gyrotrons

DOEpatents

Singh, A.; Ives, R.L.; Schumacher, R.V.; Mizuhara, Y.M.

1998-07-14

A multi-stage depressed collector for receiving energy from a small orbit gyrating electron beam employs a plurality of electrodes at different potentials for sorting the individual electrons on the basis of their total energy level. Magnetic field generating coils, for producing magnetic fields and magnetic iron for magnetic field shaping produce adiabatic and controlled non-adiabatic transitions of the incident electron beam to further facilitate the sorting. 9 figs.

Hybrid Multi-Objective Optimization of Folsom Reservoir Operation to Maximize Storage in Whole Watershed

NASA Astrophysics Data System (ADS)

Goharian, E.; Gailey, R.; Maples, S.; Azizipour, M.; Sandoval Solis, S.; Fogg, G. E.

2017-12-01

The drought incidents and growing water scarcity in California have a profound effect on human, agricultural, and environmental water needs. California experienced multi-year droughts, which have caused groundwater overdraft and dropping groundwater levels, and dwindling of major reservoirs. These concerns call for a stringent evaluation of future water resources sustainability and security in the state. To answer to this call, Sustainable Groundwater Management Act (SGMA) was passed in 2014 to promise a sustainable groundwater management in California by 2042. SGMA refers to managed aquifer recharge (MAR) as a key management option, especially in areas with high variation in water availability intra- and inter-annually, to secure the refill of underground water storage and return of groundwater quality to a desirable condition. The hybrid optimization of an integrated water resources system provides an opportunity to adapt surface reservoir operations for enhancement in groundwater recharge. Here, to re-operate Folsom Reservoir, objectives are maximizing the storage in the whole American-Cosumnes watershed and maximizing hydropower generation from Folsom Reservoir. While a linear programing (LP) module tends to maximize the total groundwater recharge by distributing and spreading water over suitable lands in basin, a genetic based algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), layer above it controls releases from the reservoir to secure the hydropower generation, carry-over storage in reservoir, available water for replenishment, and downstream water requirements. The preliminary results show additional releases from the reservoir for groundwater recharge during high flow seasons. Moreover, tradeoffs between the objectives describe that new operation performs satisfactorily to increase the storage in the basin, with nonsignificant effects on other objectives.

Raman tweezers in microfluidic systems for analysis and sorting of living cells

NASA Astrophysics Data System (ADS)

Pilát, Zdeněk.; Ježek, Jan; Kaňka, Jan; Zemánek, Pavel

2014-12-01

We have devised an analytical and sorting system combining optical trapping with Raman spectroscopy in microfluidic environment, dedicated to identification and sorting of biological objects, such as living cells of various unicellular organisms. Our main goal was to create a robust and universal platform for non-destructive and non-contact sorting of micro-objects based on their Raman spectral properties. This approach allowed us to collect spectra containing information about the chemical composition of the objects, such as the presence and composition of pigments, lipids, proteins, or nucleic acids, avoiding artificial chemical probes such as fluorescent markers. The non-destructive nature of this optical analysis and manipulation allowed us to separate individual living cells of our interest in a sterile environment and provided the possibility to cultivate the selected cells for further experiments. We used a mixture of polystyrene micro-particles and algal cells to test and demonstrate the function of our analytical and sorting system. The devised system could find its use in many medical, biotechnological, and biological applications.

Raman tweezers in microfluidic systems for analysis and sorting of living cells

NASA Astrophysics Data System (ADS)

Pilát, Zdenëk; Ježek, Jan; Kaňka, Jan; Zemánek, Pavel

2014-03-01

We have devised an analytical and sorting system combining optical trapping with Raman spectroscopy in microfluidic environment in order to identify and sort biological objects, such as living cells of various prokaryotic and eukaryotic organisms. Our main objective was to create a robust and universal platform for non-contact sorting of microobjects based on their Raman spectral properties. This approach allowed us to collect information about the chemical composition of the objects, such as the presence and composition of lipids, proteins, or nucleic acids without using artificial chemical probes such as fluorescent markers. The non-destructive and non-contact nature of this optical analysis and manipulation allowed us to separate individual living cells of our interest in a sterile environment and provided the possibility to cultivate the selected cells for further experiments. We used differently treated cells of algae to test and demonstrate the function of our analytical and sorting system. The devised system could find its use in many medical, biotechnological, and biological applications.

On the Diversity of Linguistic Data and the Integration of the Language Sciences.

PubMed

D'Alessandro, Roberta; van Oostendorp, Marc

2017-01-01

An integrated science of language is usually advocated as a step forward for linguistic research. In this paper, we maintain that integration of this sort is premature, and cannot take place before we identify a common object of study. We advocate instead a science of language that is inherently multi-faceted, and takes into account the different viewpoints as well as the different definitions of the object of study. We also advocate the use of different data sources, which, if non-contradictory, can provide more solid evidence for linguistic analysis. Last, we argue that generative grammar is an important tile in the puzzle.

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

PubMed

Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio

2013-09-01

Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

A Monte-Carlo game theoretic approach for Multi-Criteria Decision Making under uncertainty

NASA Astrophysics Data System (ADS)

Madani, Kaveh; Lund, Jay R.

2011-05-01

Game theory provides a useful framework for studying Multi-Criteria Decision Making problems. This paper suggests modeling Multi-Criteria Decision Making problems as strategic games and solving them using non-cooperative game theory concepts. The suggested method can be used to prescribe non-dominated solutions and also can be used as a method to predict the outcome of a decision making problem. Non-cooperative stability definitions for solving the games allow consideration of non-cooperative behaviors, often neglected by other methods which assume perfect cooperation among decision makers. To deal with the uncertainty in input variables a Monte-Carlo Game Theory (MCGT) approach is suggested which maps the stochastic problem into many deterministic strategic games. The games are solved using non-cooperative stability definitions and the results include possible effects of uncertainty in input variables on outcomes. The method can handle multi-criteria multi-decision-maker problems with uncertainty. The suggested method does not require criteria weighting, developing a compound decision objective, and accurate quantitative (cardinal) information as it simplifies the decision analysis by solving problems based on qualitative (ordinal) information, reducing the computational burden substantially. The MCGT method is applied to analyze California's Sacramento-San Joaquin Delta problem. The suggested method provides insights, identifies non-dominated alternatives, and predicts likely decision outcomes.

A new methodology for surcharge risk management in urban areas (case study: Gonbad-e-Kavus city).

PubMed

Hooshyaripor, Farhad; Yazdi, Jafar

2017-02-01

This research presents a simulation-optimization model for urban flood mitigation integrating Non-dominated Sorting Genetic Algorithm (NSGA-II) with Storm Water Management Model (SWMM) hydraulic model under a curve number-based hydrologic model of low impact development technologies in Gonbad-e-Kavus, a small city in the north of Iran. In the developed model, the best performance of the system relies on the optimal layout and capacity of retention ponds over the study area in order to reduce surcharge from the manholes underlying a set of storm event loads, while the available investment plays a restricting role. Thus, there is a multi-objective optimization problem with two conflicting objectives solved successfully by NSGA-II to find a set of optimal solutions known as the Pareto front. In order to analyze the results, a new factor, investment priority index (IPI), is defined which shows the risk of surcharging over the network and priority of the mitigation actions. The IPI is calculated using the probability of pond selection for candidate locations and average depth of the ponds in all Pareto front solutions. The IPI can help the decision makers to arrange a long-term progressive plan with the priority of high-risk areas when an optimal solution has been selected.

Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

PubMed

Capitanescu, F; Rege, S; Marvuglia, A; Benetto, E; Ahmadi, A; Gutiérrez, T Navarrete; Tiruta-Barna, L

2016-07-15

Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly. Copyright © 2016 Elsevier Ltd. All rights reserved.

To sort or not to sort: the impact of spike-sorting on neural decoding performance

NASA Astrophysics Data System (ADS)

Todorova, Sonia; Sadtler, Patrick; Batista, Aaron; Chase, Steven; Ventura, Valérie

2014-10-01

Objective. Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity. Approach. We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step. Main results. Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior. Significance. Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.

Solving a bi-objective mathematical model for location-routing problem with time windows in multi-echelon reverse logistics using metaheuristic procedure

NASA Astrophysics Data System (ADS)

Ghezavati, V. R.; Beigi, M.

2016-12-01

During the last decade, the stringent pressures from environmental and social requirements have spurred an interest in designing a reverse logistics (RL) network. The success of a logistics system may depend on the decisions of the facilities locations and vehicle routings. The location-routing problem (LRP) simultaneously locates the facilities and designs the travel routes for vehicles among established facilities and existing demand points. In this paper, the location-routing problem with time window (LRPTW) and homogeneous fleet type and designing a multi-echelon, and capacitated reverse logistics network, are considered which may arise in many real-life situations in logistics management. Our proposed RL network consists of hybrid collection/inspection centers, recovery centers and disposal centers. Here, we present a new bi-objective mathematical programming (BOMP) for LRPTW in reverse logistic. Since this type of problem is NP-hard, the non-dominated sorting genetic algorithm II (NSGA-II) is proposed to obtain the Pareto frontier for the given problem. Several numerical examples are presented to illustrate the effectiveness of the proposed model and algorithm. Also, the present work is an effort to effectively implement the ɛ-constraint method in GAMS software for producing the Pareto-optimal solutions in a BOMP. The results of the proposed algorithm have been compared with the ɛ-constraint method. The computational results show that the ɛ-constraint method is able to solve small-size instances to optimality within reasonable computing times, and for medium-to-large-sized problems, the proposed NSGA-II works better than the ɛ-constraint.

Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

PubMed

Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

2016-01-01

Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences

NASA Astrophysics Data System (ADS)

Alderliesten, Tanja; Bosman, Peter A. N.; Sonke, Jan-Jakob; Bel, Arjan

2014-03-01

Currently, two major challenges dominate the field of deformable image registration. The first challenge is related to the tuning of the developed methods to specific problems (i.e. how to best combine different objectives such as similarity measure and transformation effort). This is one of the reasons why, despite significant progress, clinical implementation of such techniques has proven to be difficult. The second challenge is to account for large anatomical differences (e.g. large deformations, (dis)appearing structures) that occurred between image acquisitions. In this paper, we study a framework based on multi-objective optimization to improve registration robustness and to simplify tuning for specific applications. Within this framework we specifically consider the use of an advanced model-based evolutionary algorithm for optimization and a dual-dynamic transformation model (i.e. two "non-fixed" grids: one for the source- and one for the target image) to accommodate for large anatomical differences. The framework computes and presents multiple outcomes that represent efficient trade-offs between the different objectives (a so-called Pareto front). In image processing it is common practice, for reasons of robustness and accuracy, to use a multi-resolution strategy. This is, however, only well-established for single-objective registration methods. Here we describe how such a strategy can be realized for our multi-objective approach and compare its results with a single-resolution strategy. For this study we selected the case of prone-supine breast MRI registration. Results show that the well-known advantages of a multi-resolution strategy are successfully transferred to our multi-objective approach, resulting in superior (i.e. Pareto-dominating) outcomes.

SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Iyengar, P

2016-06-15

Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PETmore » and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.« less

Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows

NASA Astrophysics Data System (ADS)

Srivastav, R. K.; Srinivasan, K.; Sudheer, K.

2009-05-01

Synthetic streamflow data generation involves the synthesis of likely streamflow patterns that are statistically indistinguishable from the observed streamflow data. The various kinds of stochastic models adopted for multi-season streamflow generation in hydrology are: i) parametric models which hypothesize the form of the periodic dependence structure and the distributional form a priori (examples are PAR, PARMA); disaggregation models that aim to preserve the correlation structure at the periodic level and the aggregated annual level; ii) Nonparametric models (examples are bootstrap/kernel based methods), which characterize the laws of chance, describing the stream flow process, without recourse to prior assumptions as to the form or structure of these laws; (k-nearest neighbor (k-NN), matched block bootstrap (MABB)); non-parametric disaggregation model. iii) Hybrid models which blend both parametric and non-parametric models advantageously to model the streamflows effectively. Despite many of these developments that have taken place in the field of stochastic modeling of streamflows over the last four decades, accurate prediction of the storage and the critical drought characteristics has been posing a persistent challenge to the stochastic modeler. This is partly because, usually, the stochastic streamflow model parameters are estimated by minimizing a statistically based objective function (such as maximum likelihood (MLE) or least squares (LS) estimation) and subsequently the efficacy of the models is being validated based on the accuracy of prediction of the estimates of the water-use characteristics, which requires large number of trial simulations and inspection of many plots and tables. Still accurate prediction of the storage and the critical drought characteristics may not be ensured. In this study a multi-objective optimization framework is proposed to find the optimal hybrid model (blend of a simple parametric model, PAR(1) model and matched block bootstrap (MABB) ) based on the explicit objective functions of minimizing the relative bias and relative root mean square error in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi- dimensional parameter space (involving simultaneous exploration of the parametric (PAR(1)) as well as the non-parametric (MABB) components). This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic algorithm - II (NSGA-II). This approach helps in reducing the drudgery involved in the process of manual selection of the hybrid model, in addition to predicting the basic summary statistics dependence structure, marginal distribution and water-use characteristics accurately. The proposed optimization framework is used to model the multi-season streamflows of River Beaver and River Weber of USA. In case of both the rivers, the proposed GA-based hybrid model yields a much better prediction of the storage capacity (where simultaneous exploration of both parametric and non-parametric components is done) when compared with the MLE-based hybrid models (where the hybrid model selection is done in two stages, thus probably resulting in a sub-optimal model). This framework can be further extended to include different linear/non-linear hybrid stochastic models at other temporal and spatial scales as well.

Exploring synergistic benefits of Water-Food-Energy Nexus through multi-objective reservoir optimization schemes.

PubMed

Uen, Tinn-Shuan; Chang, Fi-John; Zhou, Yanlai; Tsai, Wen-Ping

2018-08-15

This study proposed a holistic three-fold scheme that synergistically optimizes the benefits of the Water-Food-Energy (WFE) Nexus by integrating the short/long-term joint operation of a multi-objective reservoir with irrigation ponds in response to urbanization. The three-fold scheme was implemented step by step: (1) optimizing short-term (daily scale) reservoir operation for maximizing hydropower output and final reservoir storage during typhoon seasons; (2) simulating long-term (ten-day scale) water shortage rates in consideration of the availability of irrigation ponds for both agricultural and public sectors during non-typhoon seasons; and (3) promoting the synergistic benefits of the WFE Nexus in a year-round perspective by integrating the short-term optimization and long-term simulation of reservoir operations. The pivotal Shihmen Reservoir and 745 irrigation ponds located in Taoyuan City of Taiwan together with the surrounding urban areas formed the study case. The results indicated that the optimal short-term reservoir operation obtained from the non-dominated sorting genetic algorithm II (NSGA-II) could largely increase hydropower output but just slightly affected water supply. The simulation results of the reservoir coupled with irrigation ponds indicated that such joint operation could significantly reduce agricultural and public water shortage rates by 22.2% and 23.7% in average, respectively, as compared to those of reservoir operation excluding irrigation ponds. The results of year-round short/long-term joint operation showed that water shortage rates could be reduced by 10% at most, the food production rate could be increased by up to 47%, and the hydropower benefit could increase up to 9.33 million USD per year, respectively, in a wet year. Consequently, the proposed methodology could be a viable approach to promoting the synergistic benefits of the WFE Nexus, and the results provided unique insights for stakeholders and policymakers to pursue sustainable urban development plans. Copyright © 2018 Elsevier B.V. All rights reserved.

A systematic approach for watershed ecological restoration strategy making: An application in the Taizi River Basin in northern China.

PubMed

Li, Mengdi; Fan, Juntao; Zhang, Yuan; Guo, Fen; Liu, Lusan; Xia, Rui; Xu, Zongxue; Wu, Fengchang

2018-05-15

Aiming to protect freshwater ecosystems, river ecological restoration has been brought into the research spotlight. However, it is challenging for decision makers to set appropriate objectives and select a combination of rehabilitation acts from numerous possible solutions to meet ecological, economic, and social demands. In this study, we developed a systematic approach to help make an optimal strategy for watershed restoration, which incorporated ecological security assessment and multi-objectives optimization (MOO) into the planning process to enhance restoration efficiency and effectiveness. The river ecological security status was evaluated by using a pressure-state-function-response (PSFR) assessment framework, and MOO was achieved by searching for the Pareto optimal solutions via Non-dominated Sorting Genetic Algorithm II (NSGA-II) to balance tradeoffs between different objectives. Further, we clustered the searched solutions into three types in terms of different optimized objective function values in order to provide insightful information for decision makers. The proposed method was applied in an example rehabilitation project in the Taizi River Basin in northern China. The MOO result in the Taizi River presented a set of Pareto optimal solutions that were classified into three types: I - high ecological improvement, high cost and high benefits solution; II - medial ecological improvement, medial cost and medial economic benefits solution; III - low ecological improvement, low cost and low economic benefits solution. The proposed systematic approach in our study can enhance the effectiveness of riverine ecological restoration project and could provide valuable reference for other ecological restoration planning. Copyright © 2018 Elsevier B.V. All rights reserved.

Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times.

PubMed

Yang, Xin; Zeng, Zhenxiang; Wang, Ruidong; Sun, Xueshan

2016-01-01

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.

Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times

PubMed Central

Zeng, Zhenxiang; Wang, Ruidong; Sun, Xueshan

2016-01-01

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems. PMID:27907163

Markovian queue optimisation analysis with an unreliable server subject to working breakdowns and impatient customers

NASA Astrophysics Data System (ADS)

Liou, Cheng-Dar

2015-09-01

This study investigates an infinite capacity Markovian queue with a single unreliable service station, in which the customers may balk (do not enter) and renege (leave the queue after entering). The unreliable service station can be working breakdowns even if no customers are in the system. The matrix-analytic method is used to compute the steady-state probabilities for the number of customers, rate matrix and stability condition in the system. The single-objective model for cost and bi-objective model for cost and expected waiting time are derived in the system to fit in with practical applications. The particle swarm optimisation algorithm is implemented to find the optimal combinations of parameters in the pursuit of minimum cost. Two different approaches are used to identify the Pareto optimal set and compared: the epsilon-constraint method and non-dominate sorting genetic algorithm. Compared results allow using the traditional optimisation approach epsilon-constraint method, which is computationally faster and permits a direct sensitivity analysis of the solution under constraint or parameter perturbation. The Pareto front and non-dominated solutions set are obtained and illustrated. The decision makers can use these to improve their decision-making quality.

Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu

2013-07-01

Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Exploring the Pareto frontier using multisexual evolutionary algorithms: an application to a flexible manufacturing problem

NASA Astrophysics Data System (ADS)

Bonissone, Stefano R.; Subbu, Raj

2002-12-01

In multi-objective optimization (MOO) problems we need to optimize many possibly conflicting objectives. For instance, in manufacturing planning we might want to minimize the cost and production time while maximizing the product's quality. We propose the use of evolutionary algorithms (EAs) to solve these problems. Solutions are represented as individuals in a population and are assigned scores according to a fitness function that determines their relative quality. Strong solutions are selected for reproduction, and pass their genetic material to the next generation. Weak solutions are removed from the population. The fitness function evaluates each solution and returns a related score. In MOO problems, this fitness function is vector-valued, i.e. it returns a value for each objective. Therefore, instead of a global optimum, we try to find the Pareto-optimal or non-dominated frontier. We use multi-sexual EAs with as many genders as optimization criteria. We have created new crossover and gender assignment functions, and experimented with various parameters to determine the best setting (yielding the highest number of non-dominated solutions.) These experiments are conducted using a variety of fitness functions, and the algorithms are later evaluated on a flexible manufacturing problem with total cost and time minimization objectives.

A multiobjective modeling approach to locate multi-compartment containers for urban-sorted waste

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tralhao, Lino, E-mail: lmlrt@inescc.p; Coutinho-Rodrigues, Joao, E-mail: coutinho@dec.uc.p; Alcada-Almeida, Luis, E-mail: alcada@inescc.p

2010-12-15

The location of multi-compartment sorted waste containers for recycling purposes in cities is an important problem in the context of urban waste management. The costs associated with those facilities and the impacts placed on populations are important concerns. This paper introduces a mixed-integer, multiobjective programming approach to identify the locations and capacities of such facilities. The approach incorporates an optimization model in a Geographical Information System (GIS)-based interactive decision support system that includes four objectives. The first objective minimizes the total investment cost; the second one minimizes the average distance from dwellings to the respective multi-compartment container; the last twomore » objectives address the 'pull' and 'push' characteristics of the decision problem, one by minimizing the number of individuals too close to any container, and the other by minimizing the number of dwellings too far from the respective multi-compartment container. The model determines the number of facilities to be opened, the respective container capacities, their locations, their respective shares of the total waste of each type to be collected, and the dwellings assigned to each facility. The approach proposed was tested with a case study for the historical center of Coimbra city, Portugal, where a large urban renovation project, addressing about 800 buildings, is being undertaken. This paper demonstrates that the models and techniques incorporated in the interactive decision support system (IDSS) can be used to assist a decision maker (DM) in analyzing this complex problem in a realistically sized urban application. Ten solutions consisting of different combinations of underground containers for the disposal of four types of sorted waste in 12 candidate sites, were generated. These solutions and tradeoffs among the objectives are presented to the DM via tables, graphs, color-coded maps and other graphics. The DM can then use this information to 'guide' the IDSS in identifying additional solutions of potential interest. Nevertheless, this research showed that a particular solution with a better objective balance can be identified. The actual sequence of additional solutions generated will depend upon the objectives and preferences of the DM in a specific application.« less

Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization.

PubMed

Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

2016-01-01

Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.

Multi-objective LQR with optimum weight selection to design FOPID controllers for delayed fractional order processes.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu

2015-09-01

An optimal trade-off design for fractional order (FO)-PID controller is proposed with a Linear Quadratic Regulator (LQR) based technique using two conflicting time domain objectives. A class of delayed FO systems with single non-integer order element, exhibiting both sluggish and oscillatory open loop responses, have been controlled here. The FO time delay processes are handled within a multi-objective optimization (MOO) formalism of LQR based FOPID design. A comparison is made between two contemporary approaches of stabilizing time-delay systems withinLQR. The MOO control design methodology yields the Pareto optimal trade-off solutions between the tracking performance and total variation (TV) of the control signal. Tuning rules are formed for the optimal LQR-FOPID controller parameters, using median of the non-dominated Pareto solutions to handle delayed FO processes. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Multi-objective ACO algorithms to minimise the makespan and the total rejection cost on BPMs with arbitrary job weights

NASA Astrophysics Data System (ADS)

Jia, Zhao-hong; Pei, Ming-li; Leung, Joseph Y.-T.

2017-12-01

In this paper, we investigate the batch-scheduling problem with rejection on parallel machines with non-identical job sizes and arbitrary job-rejected weights. If a job is rejected, the corresponding penalty has to be paid. Our objective is to minimise the makespan of the processed jobs and the total rejection cost of the rejected jobs. Based on the selected multi-objective optimisation approaches, two problems, P1 and P2, are considered. In P1, the two objectives are linearly combined into one single objective. In P2, the two objectives are simultaneously minimised and the Pareto non-dominated solution set is to be found. Based on the ant colony optimisation (ACO), two algorithms, called LACO and PACO, are proposed to address the two problems, respectively. Two different objective-oriented pheromone matrices and heuristic information are designed. Additionally, a local optimisation algorithm is adopted to improve the solution quality. Finally, simulated experiments are conducted, and the comparative results verify the effectiveness and efficiency of the proposed algorithms, especially on large-scale instances.

A multi-stakeholder framework for urban runoff quality management: Application of social choice and bargaining techniques.

PubMed

Ghodsi, Seyed Hamed; Kerachian, Reza; Zahmatkesh, Zahra

2016-04-15

In this paper, an integrated framework is proposed for urban runoff management. To control and improve runoff quality and quantity, Low Impact Development (LID) practices are utilized. In order to determine the LIDs' areas and locations, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which considers three objective functions of minimizing runoff volume, runoff pollution and implementation cost of LIDs, is utilized. In this framework, the Storm Water Management Model (SWMM) is used for stream flow simulation. The non-dominated solutions provided by the NSGA-II are considered as management scenarios. To select the most preferred scenario, interactions among the main stakeholders in the study area with conflicting utilities are incorporated by utilizing bargaining models including a non-cooperative game, Nash model and social choice procedures of Borda count and approval voting. Moreover, a new social choice procedure, named pairwise voting method, is proposed and applied. Based on each conflict resolution approach, a scenario is identified as the ideal solution providing the LIDs' areas, locations and implementation cost. The proposed framework is applied for urban water quality and quantity management in the northern part of Tehran metropolitan city, Iran. Results show that the proposed pairwise voting method tends to select a scenario with a higher percentage of reduction in TSS (Total Suspended Solid) load and runoff volume, in comparison with the Borda count and approval voting methods. Besides, the Nash method presents a management scenario with the highest cost for LIDs' implementation and the maximum values for percentage of runoff volume reduction and TSS removal. The results also signify that selection of an appropriate management scenario by stakeholders in the study area depends on the available financial resources and the relative importance of runoff quality improvement in comparison with reducing the runoff volume. Copyright © 2016 Elsevier B.V. All rights reserved.

Uncertainty-Based Multi-Objective Optimization of Groundwater Remediation Design

NASA Astrophysics Data System (ADS)

Singh, A.; Minsker, B.

2003-12-01

Management of groundwater contamination is a cost-intensive undertaking filled with conflicting objectives and substantial uncertainty. A critical source of this uncertainty in groundwater remediation design problems comes from the hydraulic conductivity values for the aquifer, upon which the prediction of flow and transport of contaminants are dependent. For a remediation solution to be reliable in practice it is important that it is robust over the potential error in the model predictions. This work focuses on incorporating such uncertainty within a multi-objective optimization framework, to get reliable as well as Pareto optimal solutions. Previous research has shown that small amounts of sampling within a single-objective genetic algorithm can produce highly reliable solutions. However with multiple objectives the noise can interfere with the basic operations of a multi-objective solver, such as determining non-domination of individuals, diversity preservation, and elitism. This work proposes several approaches to improve the performance of noisy multi-objective solvers. These include a simple averaging approach, taking samples across the population (which we call extended averaging), and a stochastic optimization approach. All the approaches are tested on standard multi-objective benchmark problems and a hypothetical groundwater remediation case-study; the best-performing approach is then tested on a field-scale case at Umatilla Army Depot.

Multi-objective decision-making model based on CBM for an aircraft fleet

NASA Astrophysics Data System (ADS)

Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

NASA Astrophysics Data System (ADS)

Tsai, Wen-Ping; Chang, Fi-John; Chang, Li-Chiu; Herricks, Edwin E.

2015-11-01

Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs.

Design optimization of tailor-rolled blank thin-walled structures based on ɛ-support vector regression technique and genetic algorithm

NASA Astrophysics Data System (ADS)

Duan, Libin; Xiao, Ning-cong; Li, Guangyao; Cheng, Aiguo; Chen, Tao

2017-07-01

Tailor-rolled blank thin-walled (TRB-TH) structures have become important vehicle components owing to their advantages of light weight and crashworthiness. The purpose of this article is to provide an efficient lightweight design for improving the energy-absorbing capability of TRB-TH structures under dynamic loading. A finite element (FE) model for TRB-TH structures is established and validated by performing a dynamic axial crash test. Different material properties for individual parts with different thicknesses are considered in the FE model. Then, a multi-objective crashworthiness design of the TRB-TH structure is constructed based on the ɛ-support vector regression (ɛ-SVR) technique and non-dominated sorting genetic algorithm-II. The key parameters (C, ɛ and σ) are optimized to further improve the predictive accuracy of ɛ-SVR under limited sample points. Finally, the technique for order preference by similarity to the ideal solution method is used to rank the solutions in Pareto-optimal frontiers and find the best compromise optima. The results demonstrate that the light weight and crashworthiness performance of the optimized TRB-TH structures are superior to their uniform thickness counterparts. The proposed approach provides useful guidance for designing TRB-TH energy absorbers for vehicle bodies.

An evolutionary algorithm technique for intelligence, surveillance, and reconnaissance plan optimization

NASA Astrophysics Data System (ADS)

Langton, John T.; Caroli, Joseph A.; Rosenberg, Brad

2008-04-01

To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR) planners must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and constraints to address dynamic collection requirements for assessment. To meet this need we have designed an evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (I2PARS) to support Effects-based Assessment (EBA). I2PARS supports ISR mission planning and dynamic replanning to coordinate assets and optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic replanning of ISR mission plans. I2PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and assets. Intended end users of I2PARS include ISR planners in the Combined Air Operations Centers and Joint Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented along with multi-objective optimization criteria for ISR planning. Promising results of the I2PARS architecture design, early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future research and development, as well as technology transition goals.

Optimal design of dampers within seismic structures

NASA Astrophysics Data System (ADS)

Ren, Wenjie; Qian, Hui; Song, Wali; Wang, Liqiang

2009-07-01

An improved multi-objective genetic algorithm for structural passive control system optimization is proposed. Based on the two-branch tournament genetic algorithm, the selection operator is constructed by evaluating individuals according to their dominance in one run. For a constrained problem, the dominance-based penalty function method is advanced, containing information on an individual's status (feasible or infeasible), position in a search space, and distance from a Pareto optimal set. The proposed approach is used for the optimal designs of a six-storey building with shape memory alloy dampers subjected to earthquake. The number and position of dampers are chosen as the design variables. The number of dampers and peak relative inter-storey drift are considered as the objective functions. Numerical results generate a set of non-dominated solutions.

A novel framework for feature extraction in multi-sensor action potential sorting.

PubMed

Wu, Shun-Chi; Swindlehurst, A Lee; Nenadic, Zoran

2015-09-30

Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently. In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms. Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results. Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates. The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm

NASA Astrophysics Data System (ADS)

Bensghaier, Amani; Romdhane, Lotfi; Benouezdou, Fethi

2012-03-01

This work is focused on the determination of the thumb and the index finger muscle tensions in a tip pinch task. A biomechanical model of the musculoskeletal system of the thumb and the index finger is developed. Due to the assumptions made in carrying out the biomechanical model, the formulated force analysis problem is indeterminate leading to an infinite number of solutions. Thus, constrained single and multi-objective optimization methodologies are used in order to explore the muscular redundancy and to predict optimal muscle tension distributions. Various models are investigated using the optimization process. The basic criteria to minimize are the sum of the muscle stresses, the sum of individual muscle tensions and the maximum muscle stress. The multi-objective optimization is solved using a Pareto genetic algorithm to obtain non-dominated solutions, defined as the set of optimal distributions of muscle tensions. The results show the advantage of the multi-objective formulation over the single objective one. The obtained solutions are compared to those available in the literature demonstrating the effectiveness of our approach in the analysis of the fingers musculoskeletal systems when predicting muscle tensions.

Soil Biota and Litter Decay in High Arctic Ecosystems

NASA Astrophysics Data System (ADS)

González, G.; Rivera, F.; Makarova, O.; Gould, W. A.

2006-12-01

Frost heave action contributes to the formation of non-sorted circles in the High Arctic. Non-sorted circles tend to heave more than the surrounding tundra due to deeper thaw and the formation of ice lenses. Thus, the geomorphology, soils and vegetation on the centers of the patterned-ground feature (non-sorted circles) as compared to the surrounding soils (inter-circles) can be different. We established a decomposition experiment to look at in situ decay rates of the most dominant graminoid species on non-sorted circles and adjacent inter-circle soils along a climatic gradient in the Canadian High Arctic as a component of a larger study looking at the biocomplexity of small-featured patterned ground ecosystems. Additionally, we investigated variation in soil chemical properties and biota, including soil microarthropods and microbial composition and biomass, as they relate to climate, topographic position, and litter decay rates. Our three sites locations, from coldest to warmest, are Isachsen, Ellef Ringnes Island (ER), NU (bioclimatic subzone A); Mould Bay (MB), Prince Patrick Island, NT (bioclimatic subzone B), and Green Cabin (GC), Aulavik National Park, Thomsen River, Banks Island, NT (bioclimatic subzone C). Our sample design included the selection of 15 non-sorted circles and adjacent inter-circle areas within the zonal vegetation at each site (a total of 90 sites), and a second set of 3 non-sorted circles and adjacent inter-circle areas in dry, mesic and wet tundra at each of the sites. Soil invertebrates were sampled at each site using both pitfall traps, soil microbial biomass was determined using substrate induced respiration and bacterial populations were determined using the most probable number method. Decomposition rates were measured using litterbags and as the percent of mass remaining of Carex misandra, Luzula nivalis and Alopecuris alpinus in GC, MB and ER, respectively. Our findings indicate these graminoid species decayed significantly over time at a rate of 10-15 % mass loss / yr during the first year of decay. Decay rates are different in non-sorted circles vs. inter-circle soils along the climatic gradient. In MB, L. nivalis seems to decay faster in the inter-circle soils than in non-sorted circles (0.05

Optimal design of groundwater remediation system using a probabilistic multi-objective fast harmony search algorithm under uncertainty

NASA Astrophysics Data System (ADS)

Luo, Qiankun; Wu, Jianfeng; Yang, Yun; Qian, Jiazhong; Wu, Jichun

2014-11-01

This study develops a new probabilistic multi-objective fast harmony search algorithm (PMOFHS) for optimal design of groundwater remediation systems under uncertainty associated with the hydraulic conductivity (K) of aquifers. The PMOFHS integrates the previously developed deterministic multi-objective optimization method, namely multi-objective fast harmony search algorithm (MOFHS) with a probabilistic sorting technique to search for Pareto-optimal solutions to multi-objective optimization problems in a noisy hydrogeological environment arising from insufficient K data. The PMOFHS is then coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, to identify the optimal design of groundwater remediation systems for a two-dimensional hypothetical test problem and a three-dimensional Indiana field application involving two objectives: (i) minimization of the total remediation cost through the engineering planning horizon, and (ii) minimization of the mass remaining in the aquifer at the end of the operational period, whereby the pump-and-treat (PAT) technology is used to clean up contaminated groundwater. Also, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology. Comprehensive analysis indicates that the proposed PMOFHS can find Pareto-optimal solutions with low variability and high reliability and is a potentially effective tool for optimizing multi-objective groundwater remediation problems under uncertainty.

Optimization of Landscape Services under Uncoordinated Management by Multiple Landowners

PubMed Central

Porto, Miguel; Correia, Otília; Beja, Pedro

2014-01-01

Landscapes are often patchworks of private properties, where composition and configuration patterns result from cumulative effects of the actions of multiple landowners. Securing the delivery of services in such multi-ownership landscapes is challenging, because it is difficult to assure tight compliance to spatially explicit management rules at the level of individual properties, which may hinder the conservation of critical landscape features. To deal with these constraints, a multi-objective simulation-optimization procedure was developed to select non-spatial management regimes that best meet landscape-level objectives, while accounting for uncoordinated and uncertain response of individual landowners to management rules. Optimization approximates the non-dominated Pareto frontier, combining a multi-objective genetic algorithm and a simulator that forecasts trends in landscape pattern as a function of management rules implemented annually by individual landowners. The procedure was demonstrated with a case study for the optimum scheduling of fuel treatments in cork oak forest landscapes, involving six objectives related to reducing management costs (1), reducing fire risk (3), and protecting biodiversity associated with mid- and late-successional understories (2). There was a trade-off between cost, fire risk and biodiversity objectives, that could be minimized by selecting management regimes involving ca. 60% of landowners clearing the understory at short intervals (around 5 years), and the remaining managing at long intervals (ca. 75 years) or not managing. The optimal management regimes produces a mosaic landscape dominated by stands with herbaceous and low shrub understories, but also with a satisfactory representation of old understories, that was favorable in terms of both fire risk and biodiversity. The simulation-optimization procedure presented can be extended to incorporate a wide range of landscape dynamic processes, management rules and quantifiable objectives. It may thus be adapted to other socio-ecological systems, particularly where specific patterns of landscape heterogeneity are to be maintained despite imperfect management by multiple landowners. PMID:24465833

Heterogeneous Multi-Robot Multi-Sensor Platform for Intruder Detection

DTIC Science & Technology

2009-09-15

propagation model, with variance τi: si ~ N(b0i + b1i *logDi, τ i). The initial parameters (b0i, b1i, τ i ) of the model are unknown, and the training...that the advantage of MOO-learned mode would become more significant over time compared with the other mode. 1 2 3 4 5 6 7 0 0.05 0.1 0.15 0.2...nondominated sorting genetic algorithm for multi-objective optimization: NSGA-II,” in Parallel Problem Solving from Nature (PPSN VI), M. Schoenauer

Simulating hydrological processes of a typical small mountainous catchment in Tibetan Plateau

NASA Astrophysics Data System (ADS)

Xu, Y. P.; Bai, Z.; Fu, Q.; Pan, S.; Zhu, C.

2017-12-01

Water cycle of small watersheds with seasonal/permanent frozen soil and snow pack in Tibetan Plateau is seriously affected by climate change. The objective of this study is to find out how much and in what way the frozen soil and snow pack will influence the hydrology of small mountainous catchments in cold regions and how can the performance of simulation by a distributed hydrological model be improved. The Dong catchment, a small catchment located in Tibetan Plateau, is used as a case study. Two measurement stations are set up to collect basic meteorological and hydrological data for the modeling purpose. Annual and interannual variations of runoff indices are first analyzed based on historic data series. The sources of runoff in dry periods and wet periods are analyzed respectively. Then, a distributed hydrology soil vegetation model (DHSVM) is adopted to simulate the hydrological process of Dong catchment based on limited data set. Global sensitivity analysis is applied to help determine the important processes of the catchment. Based on sensitivity analysis results, the Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (ɛ-NSGAII) is finally added into the hydrological model to calibrate the hydrological model in a multi-objective way and analyze the performance of DHSVM model. The performance of simulation is evaluated with several evaluation indices. The final results show that frozen soil and snow pack do play an important role in hydrological processes in cold mountainous region, in particular in dry periods without precipitation, while in wet periods precipitation is often the main source of runoff. The results also show that although the DHSVM hydrological model has the potential to model the hydrology well in small mountainous catchments with very limited data in Tibetan Plateau, the simulation of hydrology in dry periods is not very satisfactory due to the model's insufficiency in simulating seasonal frozen soil.

Limiting Index Sort: A New Non-Dominated Sorting Algorithm and its Comparison to the State-of-the-Art

DTIC Science & Technology

2010-05-01

Skyline Algorithms 2.2.1 Block-Nested Loops A simple way to find the skyline is to use the block-nested loops ( BNL ) algorithm [3], which is the algorithm...by an NDS member are discarded. After every individual has been compared with the NDS, the NDS is the dataset’s skyline. In the best case for BNL ...SFS) algorithm [4] is a variation on BNL that first introduces the idea of initially ordering the individuals by a monotonically increasing scoring

Multi-Objective UAV Mission Planning Using Evolutionary Computation

DTIC Science & Technology

2008-03-01

on a Solution Space. . . . . . . . . . . . . . . . . . . . 41 4.3. Crowding distance calculation. Dark points are non-dominated solutions. [14...SPEA2 was devel- oped by Zitzler [64] as an improvement to the original SPEA algorithm [65]. SPEA2 Figure 4.3: Crowding distance calculation. Dark ...thesis, Los Angeles, CA, USA, 2003. Adviser-Maja J. Mataric . 114 21. Homberger, Joerg and Hermann Gehring. “Two Evolutionary Metaheuristics for the

Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach

NASA Astrophysics Data System (ADS)

Dai, C.; Qin, X. S.; Chen, Y.; Guo, H. C.

2018-06-01

A Gini-coefficient based stochastic optimization (GBSO) model was developed by integrating the hydrological model, water balance model, Gini coefficient and chance-constrained programming (CCP) into a general multi-objective optimization modeling framework for supporting water resources allocation at a watershed scale. The framework was advantageous in reflecting the conflicting equity and benefit objectives for water allocation, maintaining the water balance of watershed, and dealing with system uncertainties. GBSO was solved by the non-dominated sorting Genetic Algorithms-II (NSGA-II), after the parameter uncertainties of the hydrological model have been quantified into the probability distribution of runoff as the inputs of CCP model, and the chance constraints were converted to the corresponding deterministic versions. The proposed model was applied to identify the Pareto optimal water allocation schemes in the Lake Dianchi watershed, China. The optimal Pareto-front results reflected the tradeoff between system benefit (αSB) and Gini coefficient (αG) under different significance levels (i.e. q) and different drought scenarios, which reveals the conflicting nature of equity and efficiency in water allocation problems. A lower q generally implies a lower risk of violating the system constraints and a worse drought intensity scenario corresponds to less available water resources, both of which would lead to a decreased system benefit and a less equitable water allocation scheme. Thus, the proposed modeling framework could help obtain the Pareto optimal schemes under complexity and ensure that the proposed water allocation solutions are effective for coping with drought conditions, with a proper tradeoff between system benefit and water allocation equity.

Strategic rehabilitation planning of piped water networks using multi-criteria decision analysis.

PubMed

Scholten, Lisa; Scheidegger, Andreas; Reichert, Peter; Maurer, Max; Mauer, Max; Lienert, Judit

2014-02-01

To overcome the difficulties of strategic asset management of water distribution networks, a pipe failure and a rehabilitation model are combined to predict the long-term performance of rehabilitation strategies. Bayesian parameter estimation is performed to calibrate the failure and replacement model based on a prior distribution inferred from three large water utilities in Switzerland. Multi-criteria decision analysis (MCDA) and scenario planning build the framework for evaluating 18 strategic rehabilitation alternatives under future uncertainty. Outcomes for three fundamental objectives (low costs, high reliability, and high intergenerational equity) are assessed. Exploitation of stochastic dominance concepts helps to identify twelve non-dominated alternatives and local sensitivity analysis of stakeholder preferences is used to rank them under four scenarios. Strategies with annual replacement of 1.5-2% of the network perform reasonably well under all scenarios. In contrast, the commonly used reactive replacement is not recommendable unless cost is the only relevant objective. Exemplified for a small Swiss water utility, this approach can readily be adapted to support strategic asset management for any utility size and based on objectives and preferences that matter to the respective decision makers. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prediction of protein-protein interaction network using a multi-objective optimization approach.

PubMed

Chowdhury, Archana; Rakshit, Pratyusha; Konar, Amit

2016-06-01

Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

Development of mathematical models and optimization of the process parameters of laser surface hardened EN25 steel using elitist non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Vignesh, S.; Dinesh Babu, P.; Surya, G.; Dinesh, S.; Marimuthu, P.

2018-02-01

The ultimate goal of all production entities is to select the process parameters that would be of maximum strength, minimum wear and friction. The friction and wear are serious problems in most of the industries which are influenced by the working set of parameters, oxidation characteristics and mechanism involved in formation of wear. The experimental input parameters such as sliding distance, applied load, and temperature are utilized in finding out the optimized solution for achieving the desired output responses such as coefficient of friction, wear rate, and volume loss. The optimization is performed with the help of a novel method, Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) based on an evolutionary algorithm. The regression equations obtained using Response Surface Methodology (RSM) are used in determining the optimum process parameters. Further, the results achieved through desirability approach in RSM are compared with that of the optimized solution obtained through NSGA-II. The results conclude that proposed evolutionary technique is much effective and faster than the desirability approach.

Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm

NASA Astrophysics Data System (ADS)

Hao, Yufang; Xie, Shaodong

2018-03-01

Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting-California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.

Multi-Stage Hybrid Rocket Conceptual Design for Micro-Satellites Launch using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kitagawa, Yosuke; Kitagawa, Koki; Nakamiya, Masaki; Kanazaki, Masahiro; Shimada, Toru

The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.

ℓ0 -based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation

NASA Astrophysics Data System (ADS)

Xu, Xia; Shi, Zhenwei; Pan, Bin

2018-07-01

Sparse unmixing aims at recovering pure materials from hyperpspectral images and estimating their abundance fractions. Sparse unmixing is actually ℓ0 problem which is NP-h ard, and a relaxation is often used. In this paper, we attempt to deal with ℓ0 problem directly via a multi-objective based method, which is a non-convex manner. The characteristics of hyperspectral images are integrated into the proposed method, which leads to a new spectra and multi-objective based sparse unmixing method (SMoSU). In order to solve the ℓ0 norm optimization problem, the spectral library is encoded in a binary vector, and a bit-wise flipping strategy is used to generate new individuals in the evolution process. However, a multi-objective method usually produces a number of non-dominated solutions, while sparse unmixing requires a single solution. How to make the final decision for sparse unmixing is challenging. To handle this problem, we integrate the spectral characteristic of hyperspectral images into SMoSU. By considering the spectral correlation in hyperspectral data, we improve the Tchebycheff decomposition function in SMoSU via a new regularization item. This regularization item is able to enforce the individual divergence in the evolution process of SMoSU. In this way, the diversity and convergence of population is further balanced, which is beneficial to the concentration of individuals. In the experiments part, three synthetic datasets and one real-world data are used to analyse the effectiveness of SMoSU, and several state-of-art sparse unmixing algorithms are compared.

Water Quality Planning in Rivers: Assimilative Capacity and Dilution Flow.

PubMed

Hashemi Monfared, Seyed Arman; Dehghani Darmian, Mohsen; Snyder, Shane A; Azizyan, Gholamreza; Pirzadeh, Bahareh; Azhdary Moghaddam, Mehdi

2017-11-01

Population growth, urbanization and industrial expansion are consequentially linked to increasing pollution around the world. The sources of pollution are so vast and also include point and nonpoint sources, with intrinsic challenge for control and abatement. This paper focuses on pollutant concentrations and also the distance that the pollution is in contact with the river water as objective functions to determine two main necessary characteristics for water quality management in the river. These two necessary characteristics are named assimilative capacity and dilution flow. The mean area of unacceptable concentration [Formula: see text] and affected distance (X) are considered as two objective functions to determine the dilution flow by a non-dominated sorting genetic algorithm II (NSGA-II) optimization algorithm. The results demonstrate that the variation of river flow discharge in different seasons can modify the assimilation capacity up to 97%. Moreover, when using dilution flow as a water quality management tool, results reveal that the content of [Formula: see text] and X change up to 97% and 93%, respectively.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.

PubMed

Janko, Vito; Luštrek, Mitja

2017-12-29

The recognition of the user's context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system's energy expenditure and the system's accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

Flexible operation strategy for environment control system in abnormal supply power condition

NASA Astrophysics Data System (ADS)

Liping, Pang; Guoxiang, Li; Hongquan, Qu; Yufeng, Fang

2017-04-01

This paper establishes an optimization method that can be applied to the flexible operation of the environment control system in an abnormal supply power condition. A proposed conception of lifespan is used to evaluate the depletion time of the non-regenerative substance. The optimization objective function is to maximize the lifespans. The optimization variables are the allocated powers of subsystems. The improved Non-dominated Sorting Genetic Algorithm is adopted to obtain the pareto optimization frontier with the constraints of the cabin environmental parameters and the adjustable operating parameters of the subsystems. Based on the same importance of objective functions, the preferred power allocation of subsystems can be optimized. Then the corresponding running parameters of subsystems can be determined to ensure the maximum lifespans. A long-duration space station with three astronauts is used to show the implementation of the proposed optimization method. Three different CO2 partial pressure levels are taken into consideration in this study. The optimization results show that the proposed optimization method can obtain the preferred power allocation for the subsystems when the supply power is at a less-than-nominal value. The method can be applied to the autonomous control for the emergency response of the environment control system.

Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network.

PubMed

Li, Yunjie; Ma, Dongfang; Zhu, Mengtao; Zeng, Ziqiang; Wang, Yinhai

2018-02-01

Identification of the significant factors of traffic crashes has been a primary concern of the transportation safety research community for many years. A fatal-injury crash is a comprehensive result influenced by multiple variables involved at the moment of the crash scenario, the main idea of this paper is to explore the process of significant factors identification from a multi-objective optimization (MOP) standpoint. It proposes a data-driven model which combines the Non-dominated Sorting Genetic Algorithm (NSGA-II) with the Neural Network (NN) architecture to efficiently search for optimal solutions. This paper also defines the index of Factor Significance (F s ) for quantitative evaluation of the significance of each factor. Based on a set of three year data of crash records collected from three main interstate highways in the Washington State, the proposed method reveals that the top five significant factors for a better Fatal-injury crash identification are 1) Driver Conduct, 2) Vehicle Action, 3) Roadway Surface Condition, 4) Driver Restraint and 5) Driver Age. The most sensitive factors from a spatiotemporal perspective are the Hour of Day, Most Severe Sobriety, and Roadway Characteristics. The method and results in this paper provide new insights into the injury pattern of highway crashes and may be used to improve the understanding of, prevention of, and other enforcement efforts related to injury crashes in the future. Copyright © 2017. Published by Elsevier Ltd.

System Architecture For High Speed Sorting Of Potatoes

NASA Astrophysics Data System (ADS)

Marchant, J. A.; Onyango, C. M.; Street, M. J.

1989-03-01

This paper illustrates an industrial application of vision processing in which potatoes are sorted according to their size and shape at speeds of up to 40 objects per second. The result is a multi-processing approach built around the VME bus. A hardware unit has been designed and constructed to encode the boundary of the potatoes, to reducing the amount of data to be processed. A master 68000 processor is used to control this unit and to handle data transfers along the bus. Boundary data is passed to one of three 68010 slave processors each responsible for a line of potatoes across a conveyor belt. The slave processors calculate attributes such as shape, size and estimated weight of each potato and the master processor uses this data to operate the sorting mechanism. The system has been interfaced with a commercial grading machine and performance trials are now in progress.

To sort or not to sort: the impact of spike-sorting on neural decoding performance.

PubMed

Todorova, Sonia; Sadtler, Patrick; Batista, Aaron; Chase, Steven; Ventura, Valérie

2014-10-01

Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity. We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step. Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior. Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.

A master-slave parallel hybrid multi-objective evolutionary algorithm for groundwater remediation design under general hydrogeological conditions

NASA Astrophysics Data System (ADS)

Wu, J.; Yang, Y.; Luo, Q.; Wu, J.

2012-12-01

This study presents a new hybrid multi-objective evolutionary algorithm, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), whereby the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions arose from the evolving nondominated sorting genetic algorithm II (NSGA-II) population. Also, the NPTSGA coupled with the commonly used groundwater flow and transport codes, MODFLOW and MT3DMS, is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large-scale field groundwater remediation system for cleanup of large trichloroethylene (TCE) plume at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface (MPI) is incorporated into the NPTSGA to implement objective function evaluations in distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world application. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.

Pareto frontier analyses based decision making tool for transportation of hazardous waste.

PubMed

Das, Arup; Mazumder, T N; Gupta, A K

2012-08-15

Transportation of hazardous wastes through a region poses immense threat on the development along its road network. The risk to the population, exposed to such activities, has been documented in the past. However, a comprehensive framework for routing hazardous wastes has often been overlooked. A regional Hazardous Waste Management scheme should incorporate a comprehensive framework for hazardous waste transportation. This framework would incorporate the various stakeholders involved in decision making. Hence, a multi-objective approach is required to safeguard the interest of all the concerned stakeholders. The objective of this study is to design a methodology for routing of hazardous wastes between the generating units and the disposal facilities through a capacity constrained network. The proposed methodology uses posteriori method with multi-objective approach to find non-dominated solutions for the system consisting of multiple origins and destinations. A case study of transportation of hazardous wastes in Kolkata Metropolitan Area has also been provided to elucidate the methodology. Copyright © 2012 Elsevier B.V. All rights reserved.

Adaptive surrogate model based multi-objective transfer trajectory optimization between different libration points

NASA Astrophysics Data System (ADS)

Peng, Haijun; Wang, Wei

2016-10-01

An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.

Horsetail matching: a flexible approach to optimization under uncertainty

NASA Astrophysics Data System (ADS)

Cook, L. W.; Jarrett, J. P.

2018-04-01

It is important to design engineering systems to be robust with respect to uncertainties in the design process. Often, this is done by considering statistical moments, but over-reliance on statistical moments when formulating a robust optimization can produce designs that are stochastically dominated by other feasible designs. This article instead proposes a formulation for optimization under uncertainty that minimizes the difference between a design's cumulative distribution function and a target. A standard target is proposed that produces stochastically non-dominated designs, but the formulation also offers enough flexibility to recover existing approaches for robust optimization. A numerical implementation is developed that employs kernels to give a differentiable objective function. The method is applied to algebraic test problems and a robust transonic airfoil design problem where it is compared to multi-objective, weighted-sum and density matching approaches to robust optimization; several advantages over these existing methods are demonstrated.

Development of a novel cell sorting method that samples population diversity in flow cytometry.

PubMed

Osborne, Geoffrey W; Andersen, Stacey B; Battye, Francis L

2015-11-01

Flow cytometry based electrostatic cell sorting is an important tool in the separation of cell populations. Existing instruments can sort single cells into multi-well collection plates, and keep track of cell of origin and sorted well location. However currently single sorted cell results reflect the population distribution and fail to capture the population diversity. Software was designed that implements a novel sorting approach, "Slice and Dice Sorting," that links a graphical representation of a multi-well plate to logic that ensures that single cells are sampled and sorted from all areas defined by the sort region/s. Therefore the diversity of the total population is captured, and the more frequently occurring or rarer cell types are all sampled. The sorting approach was tested computationally, and using functional cell based assays. Computationally we demonstrate that conventional single cell sorting can sample as little as 50% of the population diversity dependant on the population distribution, and that Slice and Dice sorting samples much more of the variety present within a cell population. We then show by sorting single cells into wells using the Slice and Dice sorting method that there are cells sorted using this method that would be either rarely sorted, or not sorted at all using conventional single cell sorting approaches. The present study demonstrates a novel single cell sorting method that samples much more of the population diversity than current methods. It has implications in clonal selection, stem cell sorting, single cell sequencing and any areas where population heterogeneity is of importance. © 2015 International Society for Advancement of Cytometry.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition †

PubMed Central

Janko, Vito

2017-01-01

The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy. PMID:29286301

A Quality Sorting of Fruit Using a New Automatic Image Processing Method

NASA Astrophysics Data System (ADS)

Amenomori, Michihiro; Yokomizu, Nobuyuki

This paper presents an innovative approach for quality sorting of objects such as apples sorting in an agricultural factory, using an image processing algorithm. The objective of our approach are; firstly to sort the objects by their colors precisely; secondly to detect any irregularity of the colors surrounding the apples efficiently. An experiment has been conducted and the results have been obtained and compared with that has been preformed by human sorting process and by color sensor sorting devices. The results demonstrate that our approach is capable to sort the objects rapidly and the percentage of classification valid rate was 100 %.

Utilization of Information Technology for Non Domestic Waste Management in Semarang City

NASA Astrophysics Data System (ADS)

Ali, Muhammad; Hadi, Sudharto P.; Soemantri, Maman

2018-02-01

Garbage problem is often very complex in urban areas. The handling pattern of collecting, transporting and disposing that has been applied up to this day has not yet produced an appropriate solution. This is evident from the data of statistic centre institution in 2015 that 76.31% of the existing waste in the community has not been sorted, while 10.28% sorted to be used and 13.41% sorted to be discarded, showing the community amount of unsorted garbage large enough to necessitate managerial efforts at the waste sources. In designing a systematic and structured waste management system, the generations, compositions, and characteristics of the waste are indispensable. Therefore, a research is conducted on these three dimensions to the non-domestic waste in Semarang City, which involves commercial waste (from the markets, restaurants, and hotels), institutional waste (from the offices and schools). From the research result the average of 0,24kgs/person/day in weight unit of the City's non-domestical waste generation is derived. The waste composition is dominated by organic waste of around 61.95%, while the rest percentage is inorganic. The management policy is directed with the application of Management Information System model based on Information Technology because of the system's abilities to effectuate the waste management.

Multiobjective optimization for Groundwater Nitrate Pollution Control. Application to El Salobral-Los Llanos aquifer (Spain).

NASA Astrophysics Data System (ADS)

Llopis-Albert, C.; Peña-Haro, S.; Pulido-Velazquez, M.; Molina, J.

2012-04-01

Water quality management is complex due to the inter-relations between socio-political, environmental and economic constraints and objectives. In order to choose an appropriate policy to reduce nitrate pollution in groundwater it is necessary to consider different objectives, often in conflict. In this paper, a hydro-economic modeling framework, based on a non-linear optimization(CONOPT) technique, which embeds simulation of groundwater mass transport through concentration response matrices, is used to study optimal policies for groundwater nitrate pollution control under different objectives and constraints. Three objectives were considered: recovery time (for meeting the environmental standards, as required by the EU Water Framework Directive and Groundwater Directive), maximum nitrate concentration in groundwater, and net benefits in agriculture. Another criterion was added: the reliability of meeting the nitrate concentration standards. The approach allows deriving the trade-offs between the reliability of meeting the standard, the net benefits from agricultural production and the recovery time. Two different policies were considered: spatially distributed fertilizer standards or quotas (obtained through multi-objective optimization) and fertilizer prices. The multi-objective analysis allows to compare the achievement of the different policies, Pareto fronts (or efficiency frontiers) and tradeoffs for the set of mutually conflicting objectives. The constraint method is applied to generate the set of non-dominated solutions. The multi-objective framework can be used to design groundwater management policies taking into consideration different stakeholders' interests (e.g., policy makers, agricultures or environmental groups). The methodology was applied to the El Salobral-Los Llanos aquifer in Spain. Over the past 30 years the area has undertaken a significant socioeconomic development, mainly due to the intensive groundwater use for irrigated crops, which has provoked a steady decline of groundwater levels as well as high nitrate concentrations at certain locations (above 50 mg/l.). The results showed the usefulness of this multi-objective hydro-economic approach for designing sustainable nitrate pollution control policies (as fertilizer quotas or efficient fertilizer pricing policies) with insight into the economic cost of satisfying the environmental constraints and the tradeoffs with different time horizons.

Dual-objective optimization of organic Rankine cycle (ORC) systems using genetic algorithm: a comparison between basic and recuperative cycles

NASA Astrophysics Data System (ADS)

Hayat, Nasir; Ameen, Muhammad Tahir; Tariq, Muhammad Kashif; Shah, Syed Nadeem Abbas; Naveed, Ahmad

2017-08-01

Exploitation of low potential waste thermal energy for useful net power output can be done by manipulating organic Rankine cycle systems. In the current article dual-objectives (η_{th} and SIC) optimization of ORC systems [basic organic Rankine cycle (BORC) and recuperative organic Rankine cycle (RORC)] has been done using non-dominated sorting genetic algorithm (II). Seven organic compounds (R-123, R-1234ze, R-152a, R-21, R-236ea, R-245ca and R-601) have been employed in basic cycle and four dry compounds (R-123, R-236ea, R-245ca and R-601) have been employed in recuperative cycle to investigate the behaviour of two systems and compare their performance. Sensitivity analyses show that recuperation boosts the thermodynamic behaviour of systems but it also raises specific investment cost significantly. R-21, R-245ca and R-601 show attractive performance in BORC whereas R-601 and R-236ea in RORC. RORC, due to higher total investment cost and operation & maintenance costs, has longer payback periods as compared to BORC.

The primary cosmic ray mass composition at energies above 10(14) eV

NASA Technical Reports Server (NTRS)

Gawin, J.; Wdowczyk, J.; Kempa, J.

1985-01-01

It is shown in this paper that the experimental data on extensive air showers at the energy interval 10 to the 15th power - 10 to the 17th power eV seems to be described best if it is assumed that the Galactic cosmic rays are described by some sort of a two component picture. The first component is of a mixed composition similar to that at lower energies and the second is dominated by protons. Overall spectrum starts to be enriched in protons at energies about 10 to the 15th power eV bu the effective mass of the primaries remains constant up to energies around 10 to the 16th power eV. That results from the fact that composition gradually changes from multi-component to mixture of protons and heavies. That picture receives also some sort of support from recent observations of relatively high number of nergetic protons in JACEE and Concorde experiments.

A Regionalization Approach to select the final watershed parameter set among the Pareto solutions

NASA Astrophysics Data System (ADS)

Park, G. H.; Micheletty, P. D.; Carney, S.; Quebbeman, J.; Day, G. N.

2017-12-01

The calibration of hydrological models often results in model parameters that are inconsistent with those from neighboring basins. Considering that physical similarity exists within neighboring basins some of the physically related parameters should be consistent among them. Traditional manual calibration techniques require an iterative process to make the parameters consistent, which takes additional effort in model calibration. We developed a multi-objective optimization procedure to calibrate the National Weather Service (NWS) Research Distributed Hydrological Model (RDHM), using the Nondominant Sorting Genetic Algorithm (NSGA-II) with expert knowledge of the model parameter interrelationships one objective function. The multi-objective algorithm enables us to obtain diverse parameter sets that are equally acceptable with respect to the objective functions and to choose one from the pool of the parameter sets during a subsequent regionalization step. Although all Pareto solutions are non-inferior, we exclude some of the parameter sets that show extremely values for any of the objective functions to expedite the selection process. We use an apriori model parameter set derived from the physical properties of the watershed (Koren et al., 2000) to assess the similarity for a given parameter across basins. Each parameter is assigned a weight based on its assumed similarity, such that parameters that are similar across basins are given higher weights. The parameter weights are useful to compute a closeness measure between Pareto sets of nearby basins. The regionalization approach chooses the Pareto parameter sets that minimize the closeness measure of the basin being regionalized. The presentation will describe the results of applying the regionalization approach to a set of pilot basins in the Upper Colorado basin as part of a NASA-funded project.

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

Interactions of Multiple Factors in Creating Small Patterned-Ground Features Across the Arctic Bioclimate Gradient

NASA Astrophysics Data System (ADS)

Walker, D. A.; Epstein, H. E.; Kuss, P.; Michaelson, G. J.; Ping, C. L.; Raynolds, M. K.; Romanovsky, V. E.; Tarnocai, C. T.

2004-12-01

Small patterned-ground landforms are described along a bioclimate gradient in northern Canada and Alaska and summarized in tables and figures showing strength of influence of contraction cracking, differential frost heave, and vegetation - within five bioclimate subzones and four major soil texture classes. In the coldest parts of the Arctic (bioclimate subzones A and B), contraction cracking at small scales (10-30 cm between cracks) is the dominant process and contributes to the formation of hummocky terrain; differential frost heave has a small role here except in course rocky terrain where sorted circles are common. The presence of contraction cracks on all surfaces, wet and dry, and on all soil types indicate that the majority of the contraction cracks are caused by thermal processes and not desiccation. Larger mounds, apparently the result of differential frost heave, occur in some areas of Subzone B where there is more vegetation and peat. In the Middle Arctic (bioclimate subzone C), both small turf hummocks and well-developed non-sorted circles occur. Turf hummocks are dominant on hill slopes; erosion of the inter-hummock areas and accumulation of eolian material on the hummock tops creates taller hummocks. Non-sorted stripes occur on many slopes. In the northern Low Arctic (Subzone D), non-sorted circles are the most common features; and turf hummocks are restricted to small areas - generally steep snow beds. The centers of most frost boils are barren or partially vegetated in Subzone D. In the sourthern Low Arctic (Subzone E), the vegetation is very active and able to colonize and totally cover frost boils. Large vegetated mounds are apparently the remnants of once active frost boils. In areas with more clayey soils of subzones D and E, well-developed tightly packed mounds are common, and frost boils often occur on the tops of the mounds. The spacing of the mound centers is often 2-3 m. Mounds are also common south of treeline. Soil texture affects frost boil morphology and heave characteristics. In silty areas of northern Alaska non-sorted circles have annual differential heave in the order of 20 cm - apparently contributing to the strong patterning in many areas (spotted tundra in the Russian literature). Areas with sandy soil have little differential heave and no frost boils in areas of pure sand; whereas, areas with clayey soils have mound shaped frost boils with little annual heave. Vegetation plays a major role in defining the boundaries of the patterned-ground features, possibly affecting differential frost heave by decreasing the soil temperature and thickness of the active layer in the inter-circle areas; however, at two sites on sandy soils with well-developed non-sorted circles only minor differential soil heave was measured. The cause of the barren centers at these sites is probably unrelated to heave and may be due to the accumulation of salts within the frost-boils. Needle ice is another major contributing cause of barrenness on frost boils and appears to develop most strongly on saturated silts.

Viable cell sorting of dinoflagellates by multi-parametric flow cytometry.

USDA-ARS?s Scientific Manuscript database

Electronic cell sorting for isolation and culture of dinoflagellates and other marine eukaryotic phytoplankton was compared to the traditional method of manually picking of cells using a micropipette. Trauma to electronically sorted cells was not a limiting factor as fragile dinoflagellates, such a...

Comparative analysis of Pareto surfaces in multi-criteria IMRT planning

NASA Astrophysics Data System (ADS)

Teichert, K.; Süss, P.; Serna, J. I.; Monz, M.; Küfer, K. H.; Thieke, C.

2011-06-01

In the multi-criteria optimization approach to IMRT planning, a given dose distribution is evaluated by a number of convex objective functions that measure tumor coverage and sparing of the different organs at risk. Within this context optimizing the intensity profiles for any fixed set of beams yields a convex Pareto set in the objective space. However, if the number of beam directions and irradiation angles are included as free parameters in the formulation of the optimization problem, the resulting Pareto set becomes more intricate. In this work, a method is presented that allows for the comparison of two convex Pareto sets emerging from two distinct beam configuration choices. For the two competing beam settings, the non-dominated and the dominated points of the corresponding Pareto sets are identified and the distance between the two sets in the objective space is calculated and subsequently plotted. The obtained information enables the planner to decide if, for a given compromise, the current beam setup is optimal. He may then re-adjust his choice accordingly during navigation. The method is applied to an artificial case and two clinical head neck cases. In all cases no configuration is dominating its competitor over the whole Pareto set. For example, in one of the head neck cases a seven-beam configuration turns out to be superior to a nine-beam configuration if the highest priority is the sparing of the spinal cord. The presented method of comparing Pareto sets is not restricted to comparing different beam angle configurations, but will allow for more comprehensive comparisons of competing treatment techniques (e.g. photons versus protons) than with the classical method of comparing single treatment plans.

A Novel Sky-Subtraction Method Based on Non-negative Matrix Factorisation with Sparsity for Multi-object Fibre Spectroscopy

NASA Astrophysics Data System (ADS)

Zhang, Bo; Zhang, Long; Ye, Zhongfu

2016-12-01

A novel sky-subtraction method based on non-negative matrix factorisation with sparsity is proposed in this paper. The proposed non-negative matrix factorisation with sparsity method is redesigned for sky-subtraction considering the characteristics of the skylights. It has two constraint terms, one for sparsity and the other for homogeneity. Different from the standard sky-subtraction techniques, such as the B-spline curve fitting methods and the Principal Components Analysis approaches, sky-subtraction based on non-negative matrix factorisation with sparsity method has higher accuracy and flexibility. The non-negative matrix factorisation with sparsity method has research value for the sky-subtraction on multi-object fibre spectroscopic telescope surveys. To demonstrate the effectiveness and superiority of the proposed algorithm, experiments are performed on Large Sky Area Multi-Object Fiber Spectroscopic Telescope data, as the mechanisms of the multi-object fibre spectroscopic telescopes are similar.

Characterization of bottom sediments in the Río de la Plata estuary

NASA Astrophysics Data System (ADS)

Simionato, Claudia G.; Moreira, Diego

2016-04-01

Bottom sediments and surface water samples were collected in the intermediate and outer Río de la Plata Estuary during 2009-2010, in six repeated cruises, with 26 stations each. Samples were processed for grain size using a laser particle size analyzer, and water and organic matter contents. The aim of this work is to analyze this data set to provide a comprehensive and objective characterization of the bottom sediments distribution, to study their composition and to progress in the construction of a conceptual model of the involved physical mechanisms. Principal Components Analysis is applied to the bottom sediments size histograms to investigate the spatial patterns. Variations in grain-size parameters contain information on possible sediment transport patterns, which were analyzed by means of trend vectors. Sediments show a gradational arrangement of textures, sand dominant at the head, silt in the intermediate estuary and clayey silt and clay at its mouth; textures become progressively more poorly sorted offshore, and the water and organic matter contents increase. And seem to be strongly related to the geometry and the hydrodynamics. Along the Northern coast of the intermediate estuary, well sorted medium and fine silt predominates, whereas in the Southern coast, coarser and less sorted silt prevails, due to differences in tidal currents and/or in water pathways. Around Barra del Indio, clay prevails over silt and sand, and the water and organic matter contents reach a maximum, probably due flocculation, and the reduction of the currents. Immediately seawards the salt wedge, net transport reverses its direction and well sorted coarser sand from the adjacent shelf dominates. Relict sediment is observed around the Santa Lucía River, consisting of poorly sorted fine silt and clay. The inferred net transport suggests convergence at the Barra del Indio shoal, which is consistent with the constant growing of the banks.

A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem

NASA Astrophysics Data System (ADS)

Jolai, Fariborz; Assadipour, Ghazal

Crew scheduling is one of the important problems of the airline industry. This problem aims to cover a number of flights by crew members, such that all the flights are covered. In a robust scheduling the assignment should be so that the total cost, delays, and unbalanced utilization are minimized. As the problem is NP-hard and the objectives are in conflict with each other, a multi-objective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimization method. The proposed algorithm provides the decision maker with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Evaluating the performance of the proposed algorithm, three metrics are suggested, and the diversity and the convergence of the achieved Pareto front are appraised. Finally a comparison is made between CellDE and PAES, another meta-heuristic algorithm. The results show the superiority of CellDE.

Detection of Objects Hidden in Highly Scattering Media Using Time-Gated Imaging Methods

NASA Technical Reports Server (NTRS)

Galland, Pierre A.; Wang, L.; Liang, X.; Ho, P. P.; Alfano, R. R.

2000-01-01

Non-intrusive and non-invasive optical imaging techniques has generated great interest among researchers for their potential applications to biological study, device characterization, surface defect detection, and jet fuel dynamics. Non-linear optical parametric amplification gate (NLOPG) has been used to detect back-scattered images of objects hidden in diluted Intralipid solutions. To directly detect objects hidden in highly scattering media, the diffusive component of light needs to be sorted out from early arrived ballistic and snake photons. In an optical imaging system, images are collected in transmission or back-scattered geometry. The early arrival photons in the transmission approach, always carry the direct information of the hidden object embedded in the turbid medium. In the back-scattered approach, the result is not so forth coming. In the presence of a scattering host, the first arrival photons in back-scattered approach will be directly photons from the host material. In the presentation, NLOPG was applied to acquire time resolved back-scattered images under the phase matching condition. A time-gated amplified signal was obtained through this NLOPG process. The system's gain was approximately 100 times. The time-gate was achieved through phase matching condition where only coherent photons retain their phase. As a result, the diffusive photons, which were the primary contributor to the background, were removed. With a large dynamic range and high resolution, time-gated early light imaging has the potential for improving rocket/aircraft design by determining jets shape and particle sizes. Refinements to these techniques may enable drop size measurements in the highly scattering, optically dense region of multi-element rocket injectors. These types of measurements should greatly enhance the design of stable, and higher performing rocket engines.

Improving Robot Locomotion Through Learning Methods for Expensive Black-Box Systems

DTIC Science & Technology

2013-11-01

development of a class of “gradient free” optimization techniques; these include local approaches, such as a Nelder- Mead simplex search (c.f. [73]), and global...1Note that this simple method differs from the Nelder Mead constrained nonlinear optimization method [73]. 39 the Non-dominated Sorting Genetic Algorithm...Kober, and Jan Peters. Model-free inverse reinforcement learning. In International Conference on Artificial Intelligence and Statistics, 2011. [12] George

Myosin Ib modulates the morphology and the protein transport within multi-vesicular sorting endosomes.

PubMed

Salas-Cortes, Laura; Ye, Fei; Tenza, Danièle; Wilhelm, Claire; Theos, Alexander; Louvard, Daniel; Raposo, Graça; Coudrier, Evelyne

2005-10-15

Members of at least four classes of myosin (I, II, V and VI) have been implicated in the dynamics of a large variety of organelles. Despite their common motor domain structure, some of these myosins, however, are non processive and cannot move organelles along the actin tracks. Here, we demonstrate in the human pigmented MNT-1 cell line that, (1) the overexpression of one of these myosins, myosin 1b, or the addition of cytochalasin D affects the morphology of the sorting multivesicular endosomes; (2) the overexpression of myosin 1b delays the processing of Pmel17 (the product of murine silver locus also named GP100), which occurs in these multivesicular endosomes; (3) myosin 1b associated with endosomes coimmunoprecipitates with Pmel17. All together, these observations suggest that myosin 1b controls the traffic of protein cargo in multivesicular endosomes most probably through its ability to modulate with actin the morphology of these sorting endosomes.

Self-organization of sorted patterned ground.

PubMed

Kessler, M A; Werner, B T

2003-01-17

Striking circular, labyrinthine, polygonal, and striped patterns of stones and soil self-organize in many polar and high alpine environments. These forms emerge because freeze-thaw cycles drive an interplay between two feedback mechanisms. First, formation of ice lenses in freezing soil sorts stones and soil by displacing soil toward soil-rich domains and stones toward stone-rich domains. Second, stones are transported along the axis of elongate stone domains, which are squeezed and confined as freezing soil domains expand. In a numerical model implementing these feedbacks, circles, labyrinths, and islands form when sorting dominates; polygonal networks form when stone domain squeezing and confinement dominate; and stripes form as hillslope gradient is increased.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Pareto fronts for multiobjective optimization design on materials data

NASA Astrophysics Data System (ADS)

Gopakumar, Abhijith; Balachandran, Prasanna; Gubernatis, James E.; Lookman, Turab

Optimizing multiple properties simultaneously is vital in materials design. Here we apply infor- mation driven, statistical optimization strategies blended with machine learning methods, to address multi-objective optimization tasks on materials data. These strategies aim to find the Pareto front consisting of non-dominated data points from a set of candidate compounds with known character- istics. The objective is to find the pareto front in as few additional measurements or calculations as possible. We show how exploration of the data space to find the front is achieved by using uncer- tainties in predictions from regression models. We test our proposed design strategies on multiple, independent data sets including those from computations as well as experiments. These include data sets for Max phases, piezoelectrics and multicomponent alloys.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

An optimization method of VON mapping for energy efficiency and routing in elastic optical networks

NASA Astrophysics Data System (ADS)

Liu, Huanlin; Xiong, Cuilian; Chen, Yong; Li, Changping; Chen, Derun

2018-03-01

To improve resources utilization efficiency, network virtualization in elastic optical networks has been developed by sharing the same physical network for difference users and applications. In the process of virtual nodes mapping, longer paths between physical nodes will consume more spectrum resources and energy. To address the problem, we propose a virtual optical network mapping algorithm called genetic multi-objective optimize virtual optical network mapping algorithm (GM-OVONM-AL), which jointly optimizes the energy consumption and spectrum resources consumption in the process of virtual optical network mapping. Firstly, a vector function is proposed to balance the energy consumption and spectrum resources by optimizing population classification and crowding distance sorting. Then, an adaptive crossover operator based on hierarchical comparison is proposed to improve search ability and convergence speed. In addition, the principle of the survival of the fittest is introduced to select better individual according to the relationship of domination rank. Compared with the spectrum consecutiveness-opaque virtual optical network mapping-algorithm and baseline-opaque virtual optical network mapping algorithm, simulation results show the proposed GM-OVONM-AL can achieve the lowest bandwidth blocking probability and save the energy consumption.

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry peak sorting algorithm.

PubMed

Oh, Cheolhwan; Huang, Xiaodong; Regnier, Fred E; Buck, Charles; Zhang, Xiang

2008-02-01

We report a novel peak sorting method for the two-dimensional gas chromatography/time-of-flight mass spectrometry (GC x GC/TOF-MS) system. The objective of peak sorting is to recognize peaks from the same metabolite occurring in different samples from thousands of peaks detected in the analytical procedure. The developed algorithm is based on the fact that the chromatographic peaks for a given analyte have similar retention times in all of the chromatograms. Raw instrument data are first processed by ChromaTOF (Leco) software to provide the peak tables. Our algorithm achieves peak sorting by utilizing the first- and second-dimension retention times in the peak tables and the mass spectra generated during the process of electron impact ionization. The algorithm searches the peak tables for the peaks generated by the same type of metabolite using several search criteria. Our software also includes options to eliminate non-target peaks from the sorting results, e.g., peaks of contaminants. The developed software package has been tested using a mixture of standard metabolites and another mixture of standard metabolites spiked into human serum. Manual validation demonstrates high accuracy of peak sorting with this algorithm.

Efficient Scalable Median Filtering Using Histogram-Based Operations.

PubMed

Green, Oded

2018-05-01

Median filtering is a smoothing technique for noise removal in images. While there are various implementations of median filtering for a single-core CPU, there are few implementations for accelerators and multi-core systems. Many parallel implementations of median filtering use a sorting algorithm for rearranging the values within a filtering window and taking the median of the sorted value. While using sorting algorithms allows for simple parallel implementations, the cost of the sorting becomes prohibitive as the filtering windows grow. This makes such algorithms, sequential and parallel alike, inefficient. In this work, we introduce the first software parallel median filtering that is non-sorting-based. The new algorithm uses efficient histogram-based operations. These reduce the computational requirements of the new algorithm while also accessing the image fewer times. We show an implementation of our algorithm for both the CPU and NVIDIA's CUDA supported graphics processing unit (GPU). The new algorithm is compared with several other leading CPU and GPU implementations. The CPU implementation has near perfect linear scaling with a speedup on a quad-core system. The GPU implementation is several orders of magnitude faster than the other GPU implementations for mid-size median filters. For small kernels, and , comparison-based approaches are preferable as fewer operations are required. Lastly, the new algorithm is open-source and can be found in the OpenCV library.

A Diagnostic Assessment of Evolutionary Multiobjective Optimization for Water Resources Systems

NASA Astrophysics Data System (ADS)

Reed, P.; Hadka, D.; Herman, J.; Kasprzyk, J.; Kollat, J.

2012-04-01

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with 4 or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are provided for which modern MOEAs should serve as tools and benchmarks in the future water resources literature.

[Not Available].

PubMed

Mokeddem, Diab; Khellaf, Abdelhafid

2009-01-01

Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples.

Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

NASA Astrophysics Data System (ADS)

Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

2015-12-01

For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Lopez, Nicolas

This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.

Stochastic Model of Vesicular Sorting in Cellular Organelles

NASA Astrophysics Data System (ADS)

Vagne, Quentin; Sens, Pierre

2018-02-01

The proper sorting of membrane components by regulated exchange between cellular organelles is crucial to intracellular organization. This process relies on the budding and fusion of transport vesicles, and should be strongly influenced by stochastic fluctuations, considering the relatively small size of many organelles. We identify the perfect sorting of two membrane components initially mixed in a single compartment as a first passage process, and we show that the mean sorting time exhibits two distinct regimes as a function of the ratio of vesicle fusion to budding rates. Low ratio values lead to fast sorting but result in a broad size distribution of sorted compartments dominated by small entities. High ratio values result in two well-defined sorted compartments but sorting is exponentially slow. Our results suggest an optimal balance between vesicle budding and fusion for the rapid and efficient sorting of membrane components and highlight the importance of stochastic effects for the steady-state organization of intracellular compartments.

New Multi-objective Uncertainty-based Algorithm for Water Resource Models' Calibration

NASA Astrophysics Data System (ADS)

Keshavarz, Kasra; Alizadeh, Hossein

2017-04-01

Water resource models are powerful tools to support water management decision making process and are developed to deal with a broad range of issues including land use and climate change impacts analysis, water allocation, systems design and operation, waste load control and allocation, etc. These models are divided into two categories of simulation and optimization models whose calibration has been addressed in the literature where great relevant efforts in recent decades have led to two main categories of auto-calibration methods of uncertainty-based algorithms such as GLUE, MCMC and PEST and optimization-based algorithms including single-objective optimization such as SCE-UA and multi-objective optimization such as MOCOM-UA and MOSCEM-UA. Although algorithms which benefit from capabilities of both types, such as SUFI-2, were rather developed, this paper proposes a new auto-calibration algorithm which is capable of both finding optimal parameters values regarding multiple objectives like optimization-based algorithms and providing interval estimations of parameters like uncertainty-based algorithms. The algorithm is actually developed to improve quality of SUFI-2 results. Based on a single-objective, e.g. NSE and RMSE, SUFI-2 proposes a routine to find the best point and interval estimation of parameters and corresponding prediction intervals (95 PPU) of time series of interest. To assess the goodness of calibration, final results are presented using two uncertainty measures of p-factor quantifying percentage of observations covered by 95PPU and r-factor quantifying degree of uncertainty, and the analyst has to select the point and interval estimation of parameters which are actually non-dominated regarding both of the uncertainty measures. Based on the described properties of SUFI-2, two important questions are raised, answering of which are our research motivation: Given that in SUFI-2, final selection is based on the two measures or objectives and on the other hand, knowing that there is no multi-objective optimization mechanism in SUFI-2, are the final estimations Pareto-optimal? Can systematic methods be applied to select the final estimations? Dealing with these questions, a new auto-calibration algorithm was proposed where the uncertainty measures were considered as two objectives to find non-dominated interval estimations of parameters by means of coupling Monte Carlo simulation and Multi-Objective Particle Swarm Optimization. Both the proposed algorithm and SUFI-2 were applied to calibrate parameters of water resources planning model of Helleh river basin, Iran. The model is a comprehensive water quantity-quality model developed in the previous researches using WEAP software in order to analyze the impacts of different water resources management strategies including dam construction, increasing cultivation area, utilization of more efficient irrigation technologies, changing crop pattern, etc. Comparing the Pareto frontier resulted from the proposed auto-calibration algorithm with SUFI-2 results, it was revealed that the new algorithm leads to a better and also continuous Pareto frontier, even though it is more computationally expensive. Finally, Nash and Kalai-Smorodinsky bargaining methods were used to choose compromised interval estimation regarding Pareto frontier.

On the Improvement of Convergence Performance for Integrated Design of Wind Turbine Blade Using a Vector Dominating Multi-objective Evolution Algorithm

NASA Astrophysics Data System (ADS)

Wang, L.; Wang, T. G.; Wu, J. H.; Cheng, G. P.

2016-09-01

A novel multi-objective optimization algorithm incorporating evolution strategies and vector mechanisms, referred as VD-MOEA, is proposed and applied in aerodynamic- structural integrated design of wind turbine blade. In the algorithm, a set of uniformly distributed vectors is constructed to guide population in moving forward to the Pareto front rapidly and maintain population diversity with high efficiency. For example, two- and three- objective designs of 1.5MW wind turbine blade are subsequently carried out for the optimization objectives of maximum annual energy production, minimum blade mass, and minimum extreme root thrust. The results show that the Pareto optimal solutions can be obtained in one single simulation run and uniformly distributed in the objective space, maximally maintaining the population diversity. In comparison to conventional evolution algorithms, VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation for handling complex problems of multi-variables, multi-objectives and multi-constraints. This provides a reliable high-performance optimization approach for the aerodynamic-structural integrated design of wind turbine blade.

Chronology of Islet Differentiation Revealed By Temporal Cell Labeling

PubMed Central

Miyatsuka, Takeshi; Li, Zhongmei; German, Michael S.

2009-01-01

OBJECTIVE Neurogenin 3 plays a pivotal role in pancreatic endocrine differentiation. Whereas mouse models expressing reporters such as eGFP or LacZ under the control of the Neurog3 gene enable us to label cells in the pancreatic endocrine lineage, the long half-life of most reporter proteins makes it difficult to distinguish cells actively expressing neurogenin 3 from differentiated cells that have stopped transcribing the gene. RESEARCH DESIGN AND METHODS In order to separate the transient neurogenin 3 –expressing endocrine progenitor cells from the differentiating endocrine cells, we developed a mouse model (Ngn3-Timer) in which DsRed-E5, a fluorescent protein that shifts its emission spectrum from green to red over time, was expressed transgenically from the NEUROG3 locus. RESULTS In the Ngn3-Timer embryos, green-dominant cells could be readily detected by microscopy or flow cytometry and distinguished from green/red double-positive cells. When fluorescent cells were sorted into three different populations by a fluorescence-activated cell sorter, placed in culture, and then reanalyzed by flow cytometry, green-dominant cells converted to green/red double-positive cells within 6 h. The sorted cell populations were then used to determine the temporal patterns of expression for 145 transcriptional regulators in the developing pancreas. CONCLUSIONS The precise temporal resolution of this model defines the narrow window of neurogenin 3 expression in islet progenitor cells and permits sequential analyses of sorted cells as well as the testing of gene regulatory models for the differentiation of pancreatic islet cells. PMID:19478145

Using Multi-Objective Optimization to Explore Robust Policies in the Colorado River Basin

NASA Astrophysics Data System (ADS)

Alexander, E.; Kasprzyk, J. R.; Zagona, E. A.; Prairie, J. R.; Jerla, C.; Butler, A.

2017-12-01

The long term reliability of water deliveries in the Colorado River Basin has degraded due to the imbalance of growing demand and dwindling supply. The Colorado River meanders 1,450 miles across a watershed that covers seven US states and Mexico and is an important cultural, economic, and natural resource for nearly 40 million people. Its complex operating policy is based on the "Law of the River," which has evolved since the Colorado River Compact in 1922. Recent (2007) refinements to address shortage reductions and coordinated operations of Lakes Powell and Mead were negotiated with stakeholders in which thousands of scenarios were explored to identify operating guidelines that could ultimately be agreed on. This study explores a different approach to searching for robust operating policies to inform the policy making process. The Colorado River Simulation System (CRSS), a long-term water management simulation model implemented in RiverWare, is combined with the Borg multi-objective evolutionary algorithm (MOEA) to solve an eight objective problem formulation. Basin-wide performance metrics are closely tied to system health through incorporating critical reservoir pool elevations, duration, frequency and quantity of shortage reductions in the objective set. For example, an objective to minimize the frequency that Lake Powell falls below the minimum power pool elevation of 3,490 feet for Glen Canyon Dam protects a vital economic and renewable energy source for the southwestern US. The decision variables correspond to operating tiers in Lakes Powell and Mead that drive the implementation of various shortage and release policies, thus affecting system performance. The result will be a set of non-dominated solutions that can be compared with respect to their trade-offs based on the various objectives. These could inform policy making processes by eliminating dominated solutions and revealing robust solutions that could remain hidden under conventional analysis.

Uncued Low SNR Detection with Likelihood from Image Multi Bernoulli Filter

NASA Astrophysics Data System (ADS)

Murphy, T.; Holzinger, M.

2016-09-01

Both SSA and SDA necessitate uncued, partially informed detection and orbit determination efforts for small space objects which often produce only low strength electro-optical signatures. General frame to frame detection and tracking of objects includes methods such as moving target indicator, multiple hypothesis testing, direct track-before-detect methods, and random finite set based multiobject tracking. This paper will apply the multi-Bernoilli filter to low signal-to-noise ratio (SNR), uncued detection of space objects for space domain awareness applications. The primary novel innovation in this paper is a detailed analysis of the existing state-of-the-art likelihood functions and a likelihood function, based on a binary hypothesis, previously proposed by the authors. The algorithm is tested on electro-optical imagery obtained from a variety of sensors at Georgia Tech, including the GT-SORT 0.5m Raven-class telescope, and a twenty degree field of view high frame rate CMOS sensor. In particular, a data set of an extended pass of the Hitomi Astro-H satellite approximately 3 days after loss of communication and potential break up is examined.

Multi-objective Optimization Strategies Using Adjoint Method and Game Theory in Aerodynamics

NASA Astrophysics Data System (ADS)

Tang, Zhili

2006-08-01

There are currently three different game strategies originated in economics: (1) Cooperative games (Pareto front), (2) Competitive games (Nash game) and (3) Hierarchical games (Stackelberg game). Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi-criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.

Multi objective decision making in hybrid energy system design

NASA Astrophysics Data System (ADS)

Merino, Gabriel Guillermo

The design of grid-connected photovoltaic wind generator system supplying a farmstead in Nebraska has been undertaken in this dissertation. The design process took into account competing criteria that motivate the use of different sources of energy for electric generation. The criteria considered were 'Financial', 'Environmental', and 'User/System compatibility'. A distance based multi-objective decision making methodology was developed to rank design alternatives. The method is based upon a precedence order imposed upon the design objectives and a distance metric describing the performance of each alternative. This methodology advances previous work by combining ambiguous information about the alternatives with a decision-maker imposed precedence order in the objectives. Design alternatives, defined by the photovoltaic array and wind generator installed capacities, were analyzed using the multi-objective decision making approach. The performance of the design alternatives was determined by simulating the system using hourly data for an electric load for a farmstead and hourly averages of solar irradiation, temperature and wind speed from eight wind-solar energy monitoring sites in Nebraska. The spatial variability of the solar energy resource within the region was assessed by determining semivariogram models to krige hourly and daily solar radiation data. No significant difference was found in the predicted performance of the system when using kriged solar radiation data, with the models generated vs. using actual data. The spatial variability of the combined wind and solar energy resources was included in the design analysis by using fuzzy numbers and arithmetic. The best alternative was dependent upon the precedence order assumed for the main criteria. Alternatives with no PV array or wind generator dominated when the 'Financial' criteria preceded the others. In contrast, alternatives with a nil component of PV array but a high wind generator component, dominated when the 'Environment' objective or the 'User/System compatibility' objectives were more important than the 'Financial' objectives and they also dominated when the three criteria were considered equally important.

Sperm sex-sorting and preservation for managing the sex ratio and genetic diversity of the southern white rhinoceros (Ceratotherium simum simum).

PubMed

O'Brien, J K; Roth, T L; Stoops, M A; Ball, R L; Steinman, K J; Montano, G A; Love, C C; Robeck, T R

2015-01-01

White rhinoceros ejaculates (n=9) collected by electroejaculation from four males were shipped (10°C, 12h) to develop procedures for the production of chilled and frozen-thawed sex-sorted spermatozoa of adequate quality for artificial insemination (AI). Of all electroejaculate fractions, 39.7% (31/78) exhibited high quality post-collection (≥70% total motility and membrane integrity) and of those, 54.8% (17/31) presented reduced in vitro quality after transport and were retrospectively determined to exhibit urine-contamination (≥21.0μg creatinine/ml). Of fractions analyzed for creatinine concentration, 69% (44/64) were classified as urine-contaminated. For high quality non-contaminated fractions, in vitro parameters (motility, velocity, membrane, acrosome and DNA integrity) of chilled non-sorted and sorted spermatozoa were well-maintained at 5°C up to 54h post-collection, whereby >70% of post-transport (non-sorted) or post-sort (sorted) values were retained. By 54h post-collection, some motility parameters were higher (P<0.05) for non-sorted spermatozoa (total motility, rapid velocity, average path velocity) whereas all remaining motion parameters as well as membrane, acrosome and DNA integrity were similar between sperm types. In comparison with a straw method, directional freezing resulted in enhanced (P<0.05) motility and velocity of non-sorted and sorted spermatozoa, with comparable overall post-thaw quality between sperm types. High purity enrichment of X-bearing (89±6%) or Y-bearing (86±3%) spermatozoa was achieved using moderate sorting rates (2540±498X-spermatozoa/s; 1800±557Y-spermatozoa/s). Collective in vitro characteristics of sorted-chilled or sorted-frozen-thawed spermatozoa derived from high quality electroejaculates indicate acceptable fertility potential for use in AI. Copyright © 2014 Elsevier B.V. All rights reserved.

A study of the asymmetrical distribution of solar activity features on solar and plasma parameters (1967-2016)

NASA Astrophysics Data System (ADS)

El-Borie, M. A.; El-Taher, A. M.; Aly, N. E.; Bishara, A. A.

2018-04-01

The impact of asymmetrical distribution of hemispheric sunspot areas (SSAs) on the interplanetary magnetic field, plasma, and solar parameters from 1967 to 2016 has been studied. The N-S asymmetry of solar-plasma activities based on SSAs has a northern dominance during solar cycles 20 and 24. However, it has a tendency to shift to the southern hemisphere in cycles 21, 22, and 23. The solar cycle 23 showed that the sorted southern SSAs days predominated over the northern days by ˜17%. Through the solar cycles 21-24, the SSAs of the southern hemisphere were more active. In contrast, the northern SSAs predominate over the southern one by 9% throughout solar cycle 20. On the other hand, the average differences of field magnitude for the sorted northern and southern groups during solar cycles 20-24 are statistically insignificant. Clearly, twenty years showed that the solar plasma ion density from the sorted northern group was denser than that of southern group and a highest northern dominant peak occurred in 1971. In contrast, seventeen out of fifty years showed the reverse. In addition, there are fifteen clear asymmetries of solar wind speed (SWS), with SWS (N) > SWS (S), and during the years 1972, 2002, and 2008, the SWS from the sorted northern group was faster than that of southern activity group by 6.16 ± 0.65 km/s, 5.70 ± 0.86 km/s, and 5.76 ± 1.35 km/s, respectively. For the solar cycles 20-24, the grand-averages of P from the sorted solar northern and southern have nearly the same parameter values. The solar plasma was hotter for the sorted northern activity group than the southern ones for 17 years out of 50. Most significant northern prevalent asymmetries were found in 1972 (5.76 ± 0.66 × 103 K) and 1996 (4.7 ± 0.8 × 103 K), while two significant equivalent dominant southern asymmetries (˜3.8 ± 0.3 × 103 K) occurred in 1978 and 1993. The grand averages of sunspot numbers have symmetric activity for the two sorted northern and southern hemispheres through the solar cycles 20 and 21. The sunspots tend to be the southern dominance during the solar cycles 22 and 23, and it shifted during solar cycle 24 to symmetric distribution on both solar hemispheres.

Multi-Frame Convolutional Neural Networks for Object Detection in Temporal Data

DTIC Science & Technology

2017-03-01

maximum 200 words) Given the problem of detecting objects in video , existing neural-network solutions rely on a post-processing step to combine...information across frames and strengthen conclusions. This technique has been successful for videos with simple, dominant objects but it cannot detect objects...Computer Science iii THIS PAGE INTENTIONALLY LEFT BLANK iv ABSTRACT Given the problem of detecting objects in video , existing neural-network solutions rely

A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

PubMed

Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P

2013-01-01

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

PubMed Central

Chichilnisky, E. J.; Simoncelli, Eero P.

2013-01-01

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth. PMID:23671583

Hurricane Sandy's Fingerprint: Ripple Bedforms at an Inner Continental Shelf Sorted Bedform Field Site

NASA Astrophysics Data System (ADS)

DuVal, C.; Trembanis, A. C.; Beaudoin, J. D.; Schmidt, V. E.; Mayer, L. A.

2013-12-01

The hydrodynamics and seabed morphodynamics on the inner continental shelf and near shore environments have increasing relevance with continued development of near shore structures, offshore energy technologies and artificial reef construction. Characterizing the stresses on and response of the seabed near and around seabed objects will inform best practices for structural design, seabed mine and unexploded ordnance detection, and archaeological and benthic habitat studies. As part of an ONR funded project, Delaware's Redbird Reef is being studied for object scour and sorted bedform morphodynamics (Trembanis et al., in press). Central to this study are the effects of large storm events, such as Hurricane Sandy, which have had significant impact on the seafloor. Previous studies of inner shelf bedform dynamics have typically focused on near bed currents and bed stressors (e.g. Trembanis et al., 2004), sorted bedforms (e.g. Green et al., 2004) and object scour (e.g. Quinn, 2006; Trembanis et al., 2007; Mayer et al., 2007), but our understanding of the direct effects of objects and object scour on bedform morphodynamics is still incomplete. With prominent sorted bedform ripple fields, the Delaware Redbird artificial reef site, composed of 997 former New York City subway cars, as well as various military vehicles, tugboats, barges and ballasted tires, has made an ideal study location (Raineault et al., 2013 and 2011). Acoustic mapping of the Redbird reef three days prior to Sandy and two days after the following nor'easter, captured the extensive effects of the storms to the site, while acoustic Doppler current profilers characterized both the waves and bottom currents generated by the storm events. Results of the post-Sandy survey support the theory of sorted bedform evolution proposed by Murray and Thieler (2004). Acoustic imagery analysis indicates a highly energized and mobile bed during the storms, leading to self-organization of bedforms and creation of large orbital ripples. Using the Fingerprint Algorithm technique developed by Skarke and Trembanis (2011), sonar images have been analyzed to quantify ripple orientation, wavelength and defects (e.g. bifurcation and terminations). Correlation to time-series current and wave data shows strong agreement between peak-storm ripple wavelength scaling predictions and Fingerprint Algorithm wavelength measurements of relict ripples, indicating a non-equilibrated response of ripple bedforms to near bed orbital currents. Preliminary results further indicate an increase of ripple bedform defects near seabed objects, and deviations in ripple orientation and wavelength possibly related to current steering and vortices shed from nearby objects. Subsequent surveys and instrument deployments at the site have recorded the burial of these ripple bedforms during low-energy conditions, typical with the cyclical evolution of sorted bedform sites.

Unified selective sorting approach to analyse multi-electrode extracellular data

NASA Astrophysics Data System (ADS)

Veerabhadrappa, R.; Lim, C. P.; Nguyen, T. T.; Berk, M.; Tye, S. J.; Monaghan, P.; Nahavandi, S.; Bhatti, A.

2016-06-01

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.

Unified selective sorting approach to analyse multi-electrode extracellular data

PubMed Central

Veerabhadrappa, R.; Lim, C. P.; Nguyen, T. T.; Berk, M.; Tye, S. J.; Monaghan, P.; Nahavandi, S.; Bhatti, A.

2016-01-01

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators. PMID:27339770

Morphology of single inhalable particle in the air polluted city of Shijiazhuang, China.

PubMed

Wang, Zanhong; Zhang, Lingzhi; Zhang, Yuliang; Zhao, Zhou; Zhang, Sumin

2008-01-01

In the typical air polluted city of Shijiazhuang, single inhalable particle samples in non-heating period, heating period, dust storm days, and snowy days were collected and detected by SEM/EDS (scanning electron microscopy and energy dispersive X-ray spectrometry). The particle morphology was characterized by the 6 shape clusters, which are: irregular square, agglomerate, sphere, floccule, column or stick, and unknown, by quantitative order. The irregular square particles are common in all kinds of samples; sphere particles are more, and column or stick are less in winter samples; in the wet deposit samples, agglomerate and floccule particles are not found. The surface of most particles is coarse with fractal edge, which can provide suitable chemical reaction bed in the polluted atmospheric environment. New formed calcium crystal is found to demonstrate the existence of neutralized reaction, explaining the reason for the high SO2 emission and low acid rain frequency in Shijiazhuang. The three sorts of surface patterns of spheres are smooth, semi-smooth, and coarse, corresponding to the element of Si-dominant, Si-Al-dominant, and Fe-dominant. The soot particle is present as floccule with average size around 10 microm, considerably larger than the former reported results, but wrapped or captured with other fine particles to make its appearance unique and enhance its toxicity potentially. The new formed calcium crystal, the 3 sorts of sphere surface patterns, and the unique soot appearance represent the single inhalable particle's morphology characteristics in Shijiazhuang City.

Resilience of Historical Urban Multi-ethnic Settlement: Entrepreneurship and Religiosity Concept of Gresik City

NASA Astrophysics Data System (ADS)

Ariestadi, Dian; Antariksa; Dwi Wulandari, Lisa; Surjono

2017-12-01

Important aspects in continual development include economic and social developments, as well as environment protection. Social development aspect should concern political aspiration and local socio-culture as resilience of their local wisdom features. A review on urban resilience is more focused on economic and physical concepts, without developing the social concept. Objective of the study was to find out the resilience concept of Gresik City, which was the earliest description of a big trade port city in Indonesia, for example, Jakarta, Surabaya, and Semarang. The study applied morphology approach on spatial settings at historical urban multi-ethnic settlement through physical and non-physical observations, as well as validation through historical records and archives. The descriptive analysis of morphological pattern relates to activities on social, economic, and cultural aspects in order to obtain basic concept of social life. Morphological pattern of Gresik, which is dominated by multi-ethnic settlements, such as Arabs, Chinese, ex-Dutch-colonial, and the natives of Javanese and Madurese, has attracted traders from various nations and ethnics. History of the city as the center of Islamic learning and dissemination has formed the public of Gresik to have basic religious life, which is reflected on Islamic rituals. Settlement domination, which functions as household industries, craftsmanship, and small-scale trading, shows that entrepreneurship activities as socio-economy activities have highly supported daily religious ritual activities. Entrepreneurship and religiosity concept, which is formed and developed through long history of Gresik, represent the resilience of multi-ethnic societies at cities along the North Coast of Java.

Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Babaveisi, Vahid; Paydar, Mohammad Mahdi; Safaei, Abdul Sattar

2018-07-01

This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of maximizing the profit, minimizing the total risk and shortages of products. Since three objective functions are considered, a multi-objective solution methodology can be advantageous. Therefore, several approaches have been studied and an NSGA-II algorithm is first utilized, and then the results are validated using an MOSA and MOPSO algorithms. Priority-based encoding, which is used in all the algorithms, is the core of the solution computations. To compare the performance of the meta-heuristics, random numerical instances are evaluated by four criteria involving mean ideal distance, spread of non-dominance solution, the number of Pareto solutions, and CPU time. In order to enhance the performance of the algorithms, Taguchi method is used for parameter tuning. Finally, sensitivity analyses are performed and the computational results are presented based on the sensitivity analyses in parameter tuning.

Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Babaveisi, Vahid; Paydar, Mohammad Mahdi; Safaei, Abdul Sattar

2017-07-01

This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of maximizing the profit, minimizing the total risk and shortages of products. Since three objective functions are considered, a multi-objective solution methodology can be advantageous. Therefore, several approaches have been studied and an NSGA-II algorithm is first utilized, and then the results are validated using an MOSA and MOPSO algorithms. Priority-based encoding, which is used in all the algorithms, is the core of the solution computations. To compare the performance of the meta-heuristics, random numerical instances are evaluated by four criteria involving mean ideal distance, spread of non-dominance solution, the number of Pareto solutions, and CPU time. In order to enhance the performance of the algorithms, Taguchi method is used for parameter tuning. Finally, sensitivity analyses are performed and the computational results are presented based on the sensitivity analyses in parameter tuning.

Optimal Solutions of Multiproduct Batch Chemical Process Using Multiobjective Genetic Algorithm with Expert Decision System

PubMed Central

Mokeddem, Diab; Khellaf, Abdelhafid

2009-01-01

Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples. PMID:19543537

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

PubMed Central

2010-01-01

Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters. PMID:21034451

Lentiviral gene ontology (LeGO) vectors equipped with novel drug-selectable fluorescent proteins: new building blocks for cell marking and multi-gene analysis.

PubMed

Weber, K; Mock, U; Petrowitz, B; Bartsch, U; Fehse, B

2010-04-01

Vector-encoded fluorescent proteins (FPs) facilitate unambiguous identification or sorting of gene-modified cells by fluorescence-activated cell sorting (FACS). Exploiting this feature, we have recently developed lentiviral gene ontology (LeGO) vectors (www.LentiGO-Vectors.de) for multi-gene analysis in different target cells. In this study, we extend the LeGO principle by introducing 10 different drug-selectable FPs created by fusing one of the five selection marker (protecting against blasticidin, hygromycin, neomycin, puromycin and zeocin) and one of the five FP genes (Cerulean, eGFP, Venus, dTomato and mCherry). All tested fusion proteins allowed both fluorescence-mediated detection and drug-mediated selection of LeGO-transduced cells. Newly generated codon-optimized hygromycin- and neomycin-resistance genes showed improved expression as compared with their ancestors. New LeGO constructs were produced at titers >10(6) per ml (for non-concentrated supernatants). We show efficient combinatorial marking and selection of various cells, including mesenchymal stem cells, simultaneously transduced with different LeGO constructs. Inclusion of the cytomegalovirus early enhancer/chicken beta-actin promoter into LeGO vectors facilitated robust transgene expression in and selection of neural stem cells and their differentiated progeny. We suppose that the new drug-selectable markers combining advantages of FACS and drug selection are well suited for numerous applications and vector systems. Their inclusion into LeGO vectors opens new possibilities for (stem) cell tracking and functional multi-gene analysis.

A Simple Deep Learning Method for Neuronal Spike Sorting

NASA Astrophysics Data System (ADS)

Yang, Kai; Wu, Haifeng; Zeng, Yu

2017-10-01

Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

Transient responses' optimization by means of set-based multi-objective evolution

NASA Astrophysics Data System (ADS)

Avigad, Gideon; Eisenstadt, Erella; Goldvard, Alex; Salomon, Shaul

2012-04-01

In this article, a novel solution to multi-objective problems involving the optimization of transient responses is suggested. It is claimed that the common approach of treating such problems by introducing auxiliary objectives overlooks tradeoffs that should be presented to the decision makers. This means that, if at some time during the responses, one of the responses is optimal, it should not be overlooked. An evolutionary multi-objective algorithm is suggested in order to search for these optimal solutions. For this purpose, state-wise domination is utilized with a new crowding measure for ordered sets being suggested. The approach is tested on both artificial as well as on real life problems in order to explain the methodology and demonstrate its applicability and importance. The results indicate that, from an engineering point of view, the approach possesses several advantages over existing approaches. Moreover, the applications highlight the importance of set-based evolution.

Impact of Spatial Pumping Patterns on Groundwater Management

NASA Astrophysics Data System (ADS)

Yin, J.; Tsai, F. T. C.

2017-12-01

Challenges exist to manage groundwater resources while maintaining a balance between groundwater quantity and quality because of anthropogenic pumping activities as well as complex subsurface environment. In this study, to address the impact of spatial pumping pattern on groundwater management, a mixed integer nonlinear multi-objective model is formulated by integrating three objectives within a management framework to: (i) maximize total groundwater withdrawal from potential wells; (ii) minimize total electricity cost for well pumps; and (iii) attain groundwater level at selected monitoring locations as close as possible to the target level. Binary variables are used in the groundwater management model to control the operative status of pumping wells. The NSGA-II is linked with MODFLOW to solve the multi-objective problem. The proposed method is applied to a groundwater management problem in the complex Baton Rouge aquifer system, southeastern Louisiana. Results show that (a) non-dominated trade-off solutions under various spatial distributions of active pumping wells can be achieved. Each solution is optimal with regard to its corresponding objectives; (b) operative status, locations and pumping rates of pumping wells are significant to influence the distribution of hydraulic head, which in turn influence the optimization results; (c) A wide range of optimal solutions is obtained such that decision makers can select the most appropriate solution through negotiation with different stakeholders. This technique is beneficial to finding out the optimal extent to which three objectives including water supply concern, energy concern and subsidence concern can be balanced.

Recent progress in multi-electrode spike sorting methods

PubMed Central

Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

2017-01-01

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. PMID:28263793

Evaluation of motility, membrane status and DNA integrity of frozen-thawed bottlenose dolphin (Tursiops truncatus) spermatozoa after sex-sorting and recryopreservation.

PubMed

Montano, G A; Kraemer, D C; Love, C C; Robeck, T R; O'Brien, J K

2012-06-01

Artificial insemination (AI) with sex-sorted frozen-thawed spermatozoa has led to enhanced management of ex situ bottlenose dolphin populations. Extended distance of animals from the sorting facility can be overcome by the use of frozen-thawed, sorted and recryopreserved spermatozoa. Although one bottlenose dolphin calf had been born using sexed frozen-thawed spermatozoa derived from frozen semen, a critical evaluation of in vitro sperm quality is needed to justify the routine use of such samples in AI programs. Sperm motility parameters and plasma membrane integrity were influenced by stage of the sex-sorting process, sperm type (non-sorted and sorted) and freezing method (straw and directional) (P<0.05). After recryopreservation, sorted spermatozoa frozen with the directional freezing method maintained higher (P<0.05) motility parameters over a 24-h incubation period compared to spermatozoa frozen using straws. Quality of sperm DNA of non-sorted spermatozoa, as assessed by the sperm chromatin structure assay (SCSA), was high and remained unchanged throughout freeze-thawing and incubation processes. Though a possible interaction between Hoechst 33342 and the SCSA-derived acridine orange was observed in stained and sorted samples, the proportion of sex-sorted, recryopreserved spermatozoa exhibiting denatured DNA was low (6.6±4.1%) at 6 h after the second thawing step and remained unchanged (P>0.05) at 24 h. The viability of sorted spermatozoa was higher (P<0.05) than that of non-sorted spermatozoa across all time points after recryopreservation. Collective results indicate that bottlenose dolphin spermatozoa undergoing cryopreservation, sorting and recryopreservation are of adequate quality for use in AI.

Application of a novel sorting system for equine mesenchymal stem cells (MSCs)

PubMed Central

Radtke, Catherine L.; Nino-Fong, Rodolfo; Esparza Gonzalez, Blanca P.; McDuffee, Laurie A.

2014-01-01

The objective of this study was to validate non-equilibrium gravitational field-flow fractionation (GrFFF), an immunotag-less method of sorting mesenchymal stem cells (MSCs) into subpopulations, for use with MSCs derived from equine muscle tissue, periosteal tissue, bone marrow, and adipose tissue. Cells were collected from 6 young, adult horses, postmortem. Cells were isolated from left semitendinosus muscle tissue, periosteal tissue from the distomedial aspect of the right tibia, bone marrow aspirates from the fourth and fifth sternebrae, and left supragluteal subcutaneous adipose tissue. Aliquots of 800 × 103 MSCs from each tissue source were separated and injected into a ribbon-like capillary device by continuous flow (GrFFF proprietary system). Cells were sorted into 6 fractions and absorbencies [optical density (OD)] were read. Six fractions from each of the 6 aliquots were then combined to provide pooled fractions that had adequate cell numbers to seed at equal concentrations into assays. Equine muscle tissue-derived, periosteal tissue-derived, bone marrow-derived, and adipose tissue-derived mesenchymal stem cells were consistently sorted into 6 fractions that remained viable for use in further assays. Fraction 1 had more cuboidal morphology in culture when compared to the other fractions. Statistical analysis of the fraction absorbencies (OD) revealed a P-value of < 0.05 when fractions 2 and 3 were compared to fractions 1, 4, 5, and 6. It was concluded that non-equilibrium GrFFF is a valid method for sorting equine muscle tissue-derived, periosteal tissue-derived, bone marrow-derived, and adipose tissue-derived mesenchymal stem cells into subpopulations that remain viable, thus securing its potential for use in equine stem cell applications and veterinary medicine. PMID:25355998

Using multi-objective robust decision making to support seasonal water management in the Chao Phraya River basin, Thailand

NASA Astrophysics Data System (ADS)

Riegels, Niels; Jessen, Oluf; Madsen, Henrik

2016-04-01

A multi-objective robust decision making approach is demonstrated that supports seasonal water management in the Chao Phraya River basin in Thailand. The approach uses multi-objective optimization to identify a Pareto-optimal set of management alternatives. Ensemble simulation is used to evaluate how each member of the Pareto set performs under a range of uncertain future conditions, and a robustness criterion is used to select a preferred alternative. Data mining tools are then used to identify ranges of uncertain factor values that lead to unacceptable performance for the preferred alternative. The approach is compared to a multi-criteria scenario analysis approach to estimate whether the introduction of additional complexity has the potential to improve decision making. Dry season irrigation in Thailand is managed through non-binding recommendations about the maximum extent of rice cultivation along with incentives for less water-intensive crops. Management authorities lack authority to prevent river withdrawals for irrigation when rice cultivation exceeds recommendations. In practice, this means that water must be provided to irrigate the actual planted area because of downstream municipal water supply requirements and water quality constraints. This results in dry season reservoir withdrawals that exceed planned withdrawals, reducing carryover storage to hedge against insufficient wet season runoff. The dry season planning problem in Thailand can therefore be framed in terms of decisions, objectives, constraints, and uncertainties. Decisions include recommendations about the maximum extent of rice cultivation and incentives for growing less water-intensive crops. Objectives are to maximize benefits to farmers, minimize the risk of inadequate carryover storage, and minimize incentives. Constraints include downstream municipal demands and water quality requirements. Uncertainties include the actual extent of rice cultivation, dry season precipitation, and precipitation in the following wet season. The multi-objective robust decision making approach is implemented as follows. First, three baseline simulation models are developed, including a crop water demand model, a river basin simulation model, and model of the impact of incentives on cropping patterns. The crop water demand model estimates irrigation water demands; the river basin simulation model estimates reservoir drawdown required to meet demands given forecasts of precipitation, evaporation, and runoff; the model of incentive impacts estimates the cost of incentives as function of marginal changes in rice yields. Optimization is used to find a set of non-dominated alternatives as a function of rice area and incentive decisions. An ensemble of uncertain model inputs is generated to represent uncertain hydrological and crop area forecasts. An ensemble of indicator values is then generated for each of the decision objectives: farmer benefits, end-of-wet-season reservoir storage, and the cost of incentives. A single alternative is selected from the Pareto set using a robustness criterion. Threshold values are defined for each of the objectives to identify ensemble members for which objective values are unacceptable, and the PRIM data mining algorithm is then used to identify input values associated with unacceptable model outcomes.

Flow cytometric sorting of fresh and frozen-thawed spermatozoa in the western lowland gorilla (Gorilla gorilla gorilla).

PubMed

O'Brien, J K; Stojanov, T; Crichton, E G; Evans, K M; Leigh, D; Maxwell, W M C; Evans, G; Loskutoff, N M

2005-08-01

We adapted flow cytometry technology for high-purity sorting of X chromosome-bearing spermatozoa in the western lowland gorilla (Gorilla gorilla gorilla). Our objectives were to develop methodologies for liquid storage of semen prior to sorting, sorting of liquid-stored and frozen-thawed spermatozoa, and assessment of sorting accuracy. In study 1, the in vitro sperm characteristics of gorilla ejaculates from one male were unchanged (P > 0.05) after 8 hr of liquid storage at 15 degrees C in a non-egg yolk diluent (HEPES-buffered modified Tyrode's medium). In study 2, we examined the efficacy of sorting fresh and frozen-thawed spermatozoa using human spermatozoa as a model for gorilla spermatozoa. Ejaculates from one male were split into fresh and frozen aliquots. X-enriched samples derived from both fresh and frozen-thawed human semen were of high purity, as determined by fluorescence in situ hybridization (FISH; 90.7%+/-2.3%, overall), and contained a high proportion of morphologically normal spermatozoa (86.0%+/-1.0%, overall). In study 3, we processed liquid-stored semen from two gorillas for sorting using a modification of methods for human spermatozoa. The sort rate for enrichment of X-bearing spermatozoa was 7.3+/-2.5 spermatozoa per second. The X-enriched samples were of high purity (single-sperm PCR: 83.7%) and normal morphology (79.0%+/-3.9%). In study 4 we examined frozen-thawed gorilla semen, and the sort rate (8.3+/-2.9 X-bearing sperm/sec), purity (89.7%), and normal morphology (81.4%+/-3.4%) were comparable to those of liquid-stored semen. Depending on the male and the type of sample used (fresh or frozen-thawed), 0.8-2.2% of gorilla spermatozoa in the processed ejaculate were present in the X-enriched sample. These results demonstrate that fresh or frozen-thawed gorilla spermatozoa can be flow cytometrically sorted into samples enriched for X-bearing spermatozoa. Copyright 2005 Wiley-Liss, Inc.

Multi-objective possibilistic model for portfolio selection with transaction cost

NASA Astrophysics Data System (ADS)

Jana, P.; Roy, T. K.; Mazumder, S. K.

2009-06-01

In this paper, we introduce the possibilistic mean value and variance of continuous distribution, rather than probability distributions. We propose a multi-objective Portfolio based model and added another entropy objective function to generate a well diversified asset portfolio within optimal asset allocation. For quantifying any potential return and risk, portfolio liquidity is taken into account and a multi-objective non-linear programming model for portfolio rebalancing with transaction cost is proposed. The models are illustrated with numerical examples.

Complexity Optimization and High-Throughput Low-Latency Hardware Implementation of a Multi-Electrode Spike-Sorting Algorithm

PubMed Central

Dragas, Jelena; Jäckel, David; Hierlemann, Andreas; Franke, Felix

2017-01-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction. PMID:25415989

Complexity optimization and high-throughput low-latency hardware implementation of a multi-electrode spike-sorting algorithm.

PubMed

Dragas, Jelena; Jackel, David; Hierlemann, Andreas; Franke, Felix

2015-03-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.

Altered Neural Activity during Semantic Object Memory Retrieval in Amnestic Mild Cognitive Impairment as Measured by Event-Related Potentials.

PubMed

Chiang, Hsueh-Sheng; Mudar, Raksha A; Pudhiyidath, Athula; Spence, Jeffrey S; Womack, Kyle B; Cullum, C Munro; Tanner, Jeremy A; Eroh, Justin; Kraut, Michael A; Hart, John

2015-01-01

Deficits in semantic memory in individuals with amnestic mild cognitive impairment (aMCI) have been previously reported, but the underlying neurobiological mechanisms remain to be clarified. We examined event-related potentials (ERPs) associated with semantic memory retrieval in 16 individuals with aMCI as compared to 17 normal controls using the Semantic Object Retrieval Task (EEG SORT). In this task, subjects judged whether pairs of words (object features) elicited retrieval of an object (retrieval trials) or not (non-retrieval trials). Behavioral findings revealed that aMCI subjects had lower accuracy scores and marginally longer reaction time compared to controls. We used a multivariate analytical technique (STAT-PCA) to investigate similarities and differences in ERPs between aMCI and control groups. STAT-PCA revealed a left fronto-temporal component starting at around 750 ms post-stimulus in both groups. However, unlike controls, aMCI subjects showed an increase in the frontal-parietal scalp potential that distinguished retrieval from non-retrieval trials between 950 and 1050 ms post-stimulus negatively correlated with the performance on the logical memory subtest of the Wechsler Memory Scale-III. Thus, individuals with aMCI were not only impaired in their behavioral performance on SORT relative to controls, but also displayed alteration in the corresponding ERPs. The altered neural activity in aMCI compared to controls suggests a more sustained and effortful search during object memory retrieval, which may be a potential marker indicating disease processes at the pre-dementia stage.

Considering Decision Variable Diversity in Multi-Objective Optimization: Application in Hydrologic Model Calibration

NASA Astrophysics Data System (ADS)

Sahraei, S.; Asadzadeh, M.

2017-12-01

Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.

Plant species composition alters the sign and strength of an emergent multi-predator effect by modifying predator foraging behaviour.

PubMed

Wilby, Andrew; Anglin, Linda Anderson; Nesbit, Christopher M

2013-01-01

The prediction of pest-control functioning by multi-predator communities is hindered by the non-additive nature of species functioning. Such non-additivity, commonly termed an emergent multi-predator effect, is known to be affected by elements of the ecological context, such as the structure and composition of vegetation, in addition to the traits of the predators themselves. Here we report mesocosm experiments designed to test the influence of plant density and species composition (wheat monoculture or wheat and faba bean polyculture) on the emergence of multi-predator effects between Adalia bipunctata and Chrysoperla carnea, in their suppression of populations of the aphid Metopolophium dirhodum. The mesocosm experiments were followed by a series of behavioural observations designed to identify how interactions among predators are modified by plant species composition and whether these effects are consistent with the observed influence of plant species composition on aphid population suppression. Although plant density was shown to have no influence on the multi-predator effect on aphid population growth, plant composition had a marked effect. In wheat monoculture, Adalia and Chrysoperla mixed treatments caused greater suppression of M. dirhodum populations than expected. However this positive emergent effect was reversed to a negative multi-predator effect in wheat and faba bean polyculture. The behavioural observations revealed that although dominant individuals did not respond to the presence of faba bean plants, the behaviour of sub-dominants was affected markedly, consistent with their foraging for extra-floral nectar produced by the faba bean. This interaction between plant composition and predator community composition on the foraging behaviour of sub-dominants is thought to underlie the observed effect of plant composition on the multi-predator effect. Thus, the emergence of multi-predator effects is shown to be strongly influenced by plant species composition, mediated, in this case, by the provision of extra-floral nectar by one of the plant species.

Bullying as strategic behavior: relations with desired and acquired dominance in the peer group.

PubMed

Olthof, Tjeert; Goossens, Frits A; Vermande, Marjolijn M; Aleva, Elisabeth A; van der Meulen, Matty

2011-06-01

To examine whether bullying is strategic behavior aimed at obtaining or maintaining social dominance, 1129 9- to 12-year-old Dutch children were classified in terms of their role in bullying and in terms of their use of dominance oriented coercive and prosocial social strategies. Multi-informant measures of participants' acquired and desired social dominance were also included. Unlike non-bullying children, children contributing to bullying often were bistrategics in that they used both coercive and prosocial strategies and they also were socially dominant. Ringleader bullies also expressed a higher desire to be dominant. Among non-bullying children, those who tended to help victims were relatively socially dominant but victims and outsiders were not. Generally, the data supported the claim that bullying is dominance-oriented strategic behavior, which suggests that intervention strategies are more likely to be successful when they take the functional aspects of bullying behavior into account. Copyright © 2011 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Recent progress in multi-electrode spike sorting methods.

PubMed

Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

2016-11-01

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

VPS35 Mutations in Parkinson Disease

PubMed Central

Vilariño-Güell, Carles; Wider, Christian; Ross, Owen A.; Dachsel, Justus C.; Kachergus, Jennifer M.; Lincoln, Sarah J.; Soto-Ortolaza, Alexandra I.; Cobb, Stephanie A.; Wilhoite, Greggory J.; Bacon, Justin A.; Behrouz, Bahareh; Melrose, Heather L.; Hentati, Emna; Puschmann, Andreas; Evans, Daniel M.; Conibear, Elizabeth; Wasserman, Wyeth W.; Aasly, Jan O.; Burkhard, Pierre R.; Djaldetti, Ruth; Ghika, Joseph; Hentati, Faycal; Krygowska-Wajs, Anna; Lynch, Tim; Melamed, Eldad; Rajput, Alex; Rajput, Ali H.; Solida, Alessandra; Wu, Ruey-Meei; Uitti, Ryan J.; Wszolek, Zbigniew K.; Vingerhoets, François; Farrer, Matthew J.

2011-01-01

The identification of genetic causes for Mendelian disorders has been based on the collection of multi-incident families, linkage analysis, and sequencing of genes in candidate intervals. This study describes the application of next-generation sequencing technologies to a Swiss kindred presenting with autosomal-dominant, late-onset Parkinson disease (PD). The family has tremor-predominant dopa-responsive parkinsonism with a mean onset of 50.6 ± 7.3 years. Exome analysis suggests that an aspartic-acid-to-asparagine mutation within vacuolar protein sorting 35 (VPS35 c.1858G>A; p.Asp620Asn) is the genetic determinant of disease. VPS35 is a central component of the retromer cargo-recognition complex, is critical for endosome-trans-golgi trafficking and membrane-protein recycling, and is evolutionarily highly conserved. VPS35 c.1858G>A was found in all affected members of the Swiss kindred and in three more families and one patient with sporadic PD, but it was not observed in 3,309 controls. Further sequencing of familial affected probands revealed only one other missense variant, VPS35 c.946C>T; (p.Pro316Ser), in a pedigree with one unaffected and two affected carriers, and thus the pathogenicity of this mutation remains uncertain. Retromer-mediated sorting and transport is best characterized for acid hydrolase receptors. However, the complex has many types of cargo and is involved in a diverse array of biologic pathways from developmental Wnt signaling to lysosome biogenesis. Our study implicates disruption of VPS35 and retromer-mediated trans-membrane protein sorting, rescue, and recycling in the neurodegenerative process leading to PD. PMID:21763482

Investigating multi-objective fluence and beam orientation IMRT optimization

NASA Astrophysics Data System (ADS)

Potrebko, Peter S.; Fiege, Jason; Biagioli, Matthew; Poleszczuk, Jan

2017-07-01

Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a ‘bird’s-eye-view’ perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird’s-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters, such as beam fluence and beam angles, were included in the optimization.

Fine-scale multi-species aggregations of oceanic zooplankton

NASA Astrophysics Data System (ADS)

Haury, L. R.; Wiebe, P. H.

1982-07-01

Sixteen Longhurst-Hardy Plankton Recorder tows were taken at different depths in the northwest Atlantic for analysis of fine-scale horizontal patchiness. Abundant species were non-randomly distributed in patches with scales of tens to hundreds of meters. Positive correlations between species abundances dominated, indicating that the patches were multi-species associations. Most horizontal pattern appeared to be of biological origin.

A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

NASA Astrophysics Data System (ADS)

Long, Kim Chenming

Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this application of the proposed algorithm, TSEA, with several state-of-the-art multiobjective optimization algorithms reveals that TSEA outperforms these algorithms by providing retrofit solutions with greater reliability for the same costs (i.e., closer to the Pareto-optimal front) after the algorithms are executed for the same number of generations. This research also demonstrates that TSEA competes with and, in some situations, outperforms state-of-the-art multiobjective optimization algorithms such as NSGA II and SPEA 2 when applied to classic bicriteria test problems in the technical literature and other complex, sizable real-world applications. The successful implementation of TSEA contributes to the safety of aeronautical structures by providing a systematic way to guide aircraft structural retrofitting efforts, as well as a potentially useful algorithm for a wide range of multiobjective optimization problems in engineering and other fields.

Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours

PubMed Central

Boström, Jan; Elger, Christian E.; Mormann, Florian

2016-01-01

Recording extracellulary from neurons in the brains of animals in vivo is among the most established experimental techniques in neuroscience, and has recently become feasible in humans. Many interesting scientific questions can be addressed only when extracellular recordings last several hours, and when individual neurons are tracked throughout the entire recording. Such questions regard, for example, neuronal mechanisms of learning and memory consolidation, and the generation of epileptic seizures. Several difficulties have so far limited the use of extracellular multi-hour recordings in neuroscience: Datasets become huge, and data are necessarily noisy in clinical recording environments. No methods for spike sorting of such recordings have been available. Spike sorting refers to the process of identifying the contributions of several neurons to the signal recorded in one electrode. To overcome these difficulties, we developed Combinato: a complete data-analysis framework for spike sorting in noisy recordings lasting twelve hours or more. Our framework includes software for artifact rejection, automatic spike sorting, manual optimization, and efficient visualization of results. Our completely automatic framework excels at two tasks: It outperforms existing methods when tested on simulated and real data, and it enables researchers to analyze multi-hour recordings. We evaluated our methods on both short and multi-hour simulated datasets. To evaluate the performance of our methods in an actual neuroscientific experiment, we used data from from neurosurgical patients, recorded in order to identify visually responsive neurons in the medial temporal lobe. These neurons responded to the semantic content, rather than to visual features, of a given stimulus. To test our methods with multi-hour recordings, we made use of neurons in the human medial temporal lobe that respond selectively to the same stimulus in the evening and next morning. PMID:27930664

Compressive Failure of Fiber Composites under Multi-Axial Loading

NASA Technical Reports Server (NTRS)

Basu, Shiladitya; Waas, Anthony M.; Ambur, Damodar R.

2006-01-01

This paper examines the compressive strength of a fiber reinforced lamina under multi-axial stress states. An equilibrium analysis is carried out in which a kinked band of rotated fibers, described by two angles, is sandwiched between two regions in which the fibers are nominally straight. Proportional multi-axial stress states are examined. The analysis includes the possibility of bifurcation from the current equilibrium state. The compressive strength of the lamina is contingent upon either attaining a load maximum in the equilibrium response or satisfaction of a bifurcation condition, whichever occurs first. The results show that for uniaxial loading a non-zero kink band angle beta produces the minimum limit load. For multi-axial loading, different proportional loading paths show regimes of bifurcation dominated and limit load dominated behavior. The present results are able to capture the beneficial effect of transverse compression in raising the composite compressive strength as observed in experiments.

Multi-objective optimization of a continuous bio-dissimilation process of glycerol to 1, 3-propanediol.

PubMed

Xu, Gongxian; Liu, Ying; Gao, Qunwang

2016-02-10

This paper deals with multi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. In order to maximize the production rate of 1, 3-propanediol, maximize the conversion rate of glycerol to 1, 3-propanediol, maximize the conversion rate of glycerol, and minimize the concentration of by-product ethanol, we first propose six new multi-objective optimization models that can simultaneously optimize any two of the four objectives above. Then these multi-objective optimization problems are solved by using the weighted-sum and normal-boundary intersection methods respectively. Both the Pareto filter algorithm and removal criteria are used to remove those non-Pareto optimal points obtained by the normal-boundary intersection method. The results show that the normal-boundary intersection method can successfully obtain the approximate Pareto optimal sets of all the proposed multi-objective optimization problems, while the weighted-sum approach cannot achieve the overall Pareto optimal solutions of some multi-objective problems. Copyright © 2015 Elsevier B.V. All rights reserved.

Birth of kids after artificial insemination with sex-sorted, frozen-thawed goat spermatozoa.

PubMed

Bathgate, R; Mace, N; Heasman, K; Evans, G; Maxwell, W M C; de Graaf, S P

2013-12-01

Successful sex-sorting of goat spermatozoa and subsequent birth of pre-sexed kids have yet to be reported. As such, a series of experiments were conducted to develop protocols for sperm-sorting (using a modified flow cytometer, MoFlo SX(®) ) and cryopreservation of goat spermatozoa. Saanen goat spermatozoa (n = 2 males) were (i) collected into Salamon's or Tris catch media post-sorting and (ii) frozen in Tris-citrate-glucose media supplemented with 5, 10 or 20% egg yolk in (iii) 0.25 ml pellets on dry ice or 0.25 ml straws in a controlled-rate freezer. Post-sort and post-thaw sperm quality were assessed by motility (CASA), viability and acrosome integrity (PI/FITC-PNA). Sex-sorted goat spermatozoa frozen in pellets displayed significantly higher post-thaw motility and viability than spermatozoa frozen in straws. Catch media and differing egg yolk concentration had no effect on the sperm parameters tested. The in vitro and in vivo fertility of sex-sorted goat spermatozoa produced with this optimum protocol were then tested by means of a heterologous ova binding assay and intrauterine artificial insemination of Saanen goat does, respectively. Sex-sorted goat spermatozoa bound to sheep ova zona pellucidae in similar numbers (p > 0.05) to non-sorted goat spermatozoa, non-sorted ram spermatozoa and sex-sorted ram spermatozoa. Following intrauterine artificial insemination with sex-sorted spermatozoa, 38% (5/13) of does kidded with 83% (3/5) of kids being of the expected sex. Does inseminated with non-sorted spermatozoa achieved a 50% (3/6) kidding rate and a sex ratio of 3 : 1 (F : M). This study demonstrates for the first time that goat spermatozoa can be sex-sorted by flow cytometry, successfully frozen and used to produce pre-sexed kids. © 2013 Blackwell Verlag GmbH.

Flankers Facilitate 3-Year-Olds' Performance in a Card-Sorting Task

ERIC Educational Resources Information Center

Jordan, Patricia L.; Morton, J. Bruce

2008-01-01

Three-year-old children often act inflexibly in card-sorting tasks by continuing to sort by an old rule after being asked to switch and sort by a new rule. This inflexibility has been variously attributed to age-related constraints on higher order rule use, object redescription, and attention shifting. In 2 experiments, flankers that were…

A stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

USDA-ARS?s Scientific Manuscript database

Proper parameterization enables hydrological models to make reliable estimates of non-point source pollution for effective control measures. The automatic calibration of hydrologic models requires significant computational power limiting its application. The study objective was to develop and eval...

Breakdown voltage reduction by field emission in multi-walled carbon nanotubes based ionization gas sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saheed, M. Shuaib M.; Muti Mohamed, Norani; Arif Burhanudin, Zainal, E-mail: zainabh@petronas.com.my

2014-03-24

Ionization gas sensors using vertically aligned multi-wall carbon nanotubes (MWCNT) are demonstrated. The sharp tips of the nanotubes generate large non-uniform electric fields at relatively low applied voltage. The enhancement of the electric field results in field emission of electrons that dominates the breakdown mechanism in gas sensor with gap spacing below 14 μm. More than 90% reduction in breakdown voltage is observed for sensors with MWCNT and 7 μm gap spacing. Transition of breakdown mechanism, dominated by avalanche electrons to field emission electrons, as decreasing gap spacing is also observed and discussed.

Seeking urbanization security and sustainability: Multi-objective optimization of rainwater harvesting systems in China

NASA Astrophysics Data System (ADS)

Li, Yi; Ye, Quanliang; Liu, An; Meng, Fangang; Zhang, Wenlong; Xiong, Wei; Wang, Peifang; Wang, Chao

2017-07-01

Urban rainwater management need to achieve an optimal compromise among water resource augmentation, water loggings alleviation, economic investment and pollutants reduction. Rainwater harvesting (RWH) systems, such as green rooftops, porous pavements, and green lands, have been successfully implemented as viable approaches to alleviate water-logging disasters and water scarcity problems caused by rapid urbanization. However, there is limited guidance to determine the construction areas of RWH systems, especially for stormwater runoff control due to increasing extreme precipitation. This study firstly developed a multi-objective model to optimize the construction areas of green rooftops, porous pavements and green lands, considering the trade-offs among 24 h-interval RWH volume, stormwater runoff volume control ratio (R), economic cost, and rainfall runoff pollutant reduction. Pareto fronts of RWH system areas for 31 provinces of China were obtained through nondominated sorting genetic algorithm. On the national level, the control strategies for the construction rate (the ratio between the area of single RWH system and the total areas of RWH systems) of green rooftops (ηGR), porous pavements (ηPP) and green lands (ηGL) were 12%, 26% and 62%, and the corresponding RWH volume and total suspended solids reduction was 14.84 billion m3 and 228.19 kilotons, respectively. Optimal ηGR , ηPP and ηGL in different regions varied from 1 to 33%, 6 to 54%, and 30 to 89%, respectively. Particularly, green lands were the most important RWH system in 25 provinces with ηGL more than 50%, ηGR mainly less than 15%, and ηPP mainly between 10 and 30%. Results also indicated whether considering the objective MaxR made a non-significant difference for RWH system areas whereas exerted a great influence on the result of stormwater runoff control. Maximum daily rainfall under control increased, exceeding 200% after the construction of the optimal RWH system compared with that before construction. Optimal RWH system areas presented a general picture for urban development policy makers in China.

Investigation on Reservoir Operation of Agricultural Water Resources Management for Drought Mitigation

NASA Astrophysics Data System (ADS)

Cheng, C. L.

2015-12-01

Investigation on Reservoir Operation of Agricultural Water Resources Management for Drought Mitigation Chung-Lien Cheng, Wen-Ping Tsai, Fi-John Chang* Department of Bioenvironmental Systems Engineering, National Taiwan University, Da-An District, Taipei 10617, Taiwan, ROC.Corresponding author: Fi-John Chang (changfj@ntu.edu.tw) AbstractIn Taiwan, the population growth and economic development has led to considerable and increasing demands for natural water resources in the last decades. Under such condition, water shortage problems have frequently occurred in northern Taiwan in recent years such that water is usually transferred from irrigation sectors to public sectors during drought periods. Facing the uneven spatial and temporal distribution of water resources and the problems of increasing water shortages, it is a primary and critical issue to simultaneously satisfy multiple water uses through adequate reservoir operations for sustainable water resources management. Therefore, we intend to build an intelligent reservoir operation system for the assessment of agricultural water resources management strategy in response to food security during drought periods. This study first uses the grey system to forecast the agricultural water demand during February and April for assessing future agricultural water demands. In the second part, we build an intelligent water resources system by using the non-dominated sorting genetic algorithm-II (NSGA-II), an optimization tool, for searching the water allocation series based on different water demand scenarios created from the first part to optimize the water supply operation for different water sectors. The results can be a reference guide for adequate agricultural water resources management during drought periods. Keywords: Non-dominated sorting genetic algorithm-II (NSGA-II); Grey System; Optimization; Agricultural Water Resources Management.

A novel analytical technique suitable for the identification of plastics.

PubMed

Nečemer, Marijan; Kump, Peter; Sket, Primož; Plavec, Janez; Grdadolnik, Jože; Zvanut, Maja

2013-01-01

The enormous development and production of plastic materials in the last century resulted in increasing numbers of such kinds of objects. Development of a simple and fast technique to classify different types of plastics could be used in many activities dealing with plastic materials such as packaging of food, sorting of used plastic materials, and also, if technique would be non-destructive, for conservation of plastic artifacts in museum collections, a relatively new field of interest since 1990. In our previous paper we introduced a non-destructive technique for fast identification of unknown plastics based on EDXRF spectrometry,1 using as a case study some plastic artifacts archived in the Museum in order to show the advantages of the nondestructive identification of plastic material. In order to validate our technique it was necessary to apply for this purpose the comparison of analyses with some of the analytical techniques, which are more suitable and so far rather widely applied in identifying some most common sorts of plastic materials.

Chip-based droplet sorting

DOEpatents

Beer, Neil Reginald; Lee, Abraham; Hatch, Andrew

2014-07-01

A non-contact system for sorting monodisperse water-in-oil emulsion droplets in a microfluidic device based on the droplet's contents and their interaction with an applied electromagnetic field or by identification and sorting.

Ichthyoplankton assemblages in the Gulf of Nicoya and Golfo Dulce embayments, Pacific coast of Costa Rica.

PubMed

Molina-Ureña, H

1996-12-01

Ichthyoplankton surveys were conducted in December (rainy season), 1993 and February (dry season), 1994, during the RV Victor Hensen German-Costa Rican Expedition to the Gulf of Nicoya and Gulfo Dulce, Costa Rica. Samples from the inner, central, and outer areas of each gulf were collected in oblique tows with a bongo net of 0.6 m mouth diameter, 2.5 m long and 1000-micron mesh. A total of 416 fish larvae of 22 families were sorted out of 14 samples. Stations of both the maximum (11) and the minimum (1) family richness were located in Golfo Dulce. Mean total larval abundances were 124.9 and 197.2 individuals 10 m-2 for the Gulf of Nicoya and Golfo Dulce, respectively, while mean larval densities ranged from 95.3 larvae 10 m-2 in December to 236.7 larvae 10 m-2 in February. However, no statistical differences between gulfs or seasons were detected, due to the high within-group variability. Cluster Analysis, Multi-Dimensional Scaling (MDS), and non-parametric tests showed two well-defined major groups: (1) the Gulf of Nicoya neritic assemblage, represented by Engraulids, Sciaenids, and Gobiids (inner and central stations), and (2) the oceanic assemblage, dominated by Myctophids, Bregmacerotids, Ophiidids, and Trichiurids (outer stations off the Gulf of Nicoya and Golfo Dulce). A third, although less defined group, was an Ophichthid-dominated assemblage (typical in areas nearby coral or rocky reefs). These assemblages closely resemble the clusters based upon adult fish data of the beamtrawl catches of the same cruise. This publication is the first to report on the ichthyoplankton community of Golfo Dulce.

Evaluation of a stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

USDA-ARS?s Scientific Manuscript database

Hydrologic models are essential tools for environmental assessment of agricultural non-point source pollution. The automatic calibration of hydrologic models, though efficient, demands significant computational power, which can limit its application. The study objective was to investigate a cost e...

Universal approximators for multi-objective direct policy search in water reservoir management problems: a comparative analysis

NASA Astrophysics Data System (ADS)

Giuliani, Matteo; Mason, Emanuele; Castelletti, Andrea; Pianosi, Francesca

2014-05-01

The optimal operation of water resources systems is a wide and challenging problem due to non-linearities in the model and the objectives, high dimensional state-control space, and strong uncertainties in the hydroclimatic regimes. The application of classical optimization techniques (e.g., SDP, Q-learning, gradient descent-based algorithms) is strongly limited by the dimensionality of the system and by the presence of multiple, conflicting objectives. This study presents a novel approach which combines Direct Policy Search (DPS) and Multi-Objective Evolutionary Algorithms (MOEAs) to solve high-dimensional state and control space problems involving multiple objectives. DPS, also known as parameterization-simulation-optimization in the water resources literature, is a simulation-based approach where the reservoir operating policy is first parameterized within a given family of functions and, then, the parameters optimized with respect to the objectives of the management problem. The selection of a suitable class of functions to which the operating policy belong to is a key step, as it might restrict the search for the optimal policy to a subspace of the decision space that does not include the optimal solution. In the water reservoir literature, a number of classes have been proposed. However, many of these rules are based largely on empirical or experimental successes and they were designed mostly via simulation and for single-purpose reservoirs. In a multi-objective context similar rules can not easily inferred from the experience and the use of universal function approximators is generally preferred. In this work, we comparatively analyze two among the most common universal approximators: artificial neural networks (ANN) and radial basis functions (RBF) under different problem settings to estimate their scalability and flexibility in dealing with more and more complex problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower production and flood control, is used as a case study. Preliminary results show that the RBF policy parametrization is more effective than the ANN one. In particular, the approximated Pareto front obtained with RBF control policies successfully explores the full tradeoff space between the two conflicting objectives, while most of the ANN solutions results to be Pareto-dominated by the RBF ones.

The scaling behavior of hand motions reveals self-organization during an executive function task

NASA Astrophysics Data System (ADS)

Anastas, Jason R.; Stephen, Damian G.; Dixon, James A.

2011-05-01

Recent approaches to cognition explain cognitive phenomena in terms of interaction-dominant dynamics. In the current experiment, we extend this approach to executive function, a construct used to describe flexible, goal-oriented behavior. Participants were asked to perform a widely used executive function task, card sorting, under two conditions. In one condition, participants were given a rule with which to sort the cards. In the other condition, participants had to induce the rule from experimenter feedback. The motion of each participant’s hand was tracked during the sorting task. Detrended fluctuation analysis was performed on the inter-point time series using a windowing strategy to capture changes over each trial. For participants in the induction condition, the Hurst exponent sharply increased and then decreased. The Hurst exponents for the explicit condition did not show this pattern. Our results suggest that executive function may be understood in terms of changes in stability that arise from interaction-dominant dynamics.

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

NASA Astrophysics Data System (ADS)

Lane, Peter C. R.; Gobet, Fernand

2013-03-01

Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the `speciated non-dominated sorting genetic algorithm' for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.

Fine-scale Patterns in Thaw Depth, Micro-relief, and Ground Cover on Non-sorted Circles and Small Patterned Ground Features Along a Climatic Gradient From Low to High Arctic

NASA Astrophysics Data System (ADS)

Okie, J.; Gould, W. A.; González, G.

2006-12-01

Patterned ground is a ubiquitous feature in the Arctic and the related variation in microtopographic relief strongly affects biotic and abiotic patterns and processes. Patterned ground features are polygenic in origin and are often found superimposed in a complex pattern of multiple features. We investigated the relationship between thaw depth, micro-relief, the cover of vascular, bryophyte, cryptogamic crust and bare ground along transects traversing non-sorted circles and small non-sorted polygons at 8 research sites along a climatic gradient in bioclimatic subzones A-E in the North American Arctic. Non-sorted circles are the result of differential frost heave with circle centers typically showing greater heave during freezing than inter circle areas. Differential heave is a function of climate, soil texture, soil moisture, and vegetation cover. Differential heave and subsidence creates fine-scale gradients in microtopography that affect soil moisture, exposure to winds, and development of vegetation and soils. Non-sorted circles typically range from 20 to 200 cm in diameter and are most common in subzones C-E. Often superimposed on these features are the development of small non-sorted polygons 10-30 cm in diameter, and fine-scale desiccation cracking at a scale of less than 10 cm. These are most common in subzones A-C. We established three 20 m transects in zonal vegetation at each site. Thaw depth, micro-relief, and ground cover were measured at 10 cm intervals along each transect. Additionally, we measured vascular plant beta diversity in a set of 25 x 25 cm quadrates on 15 circles and 15 inter circles at each site. The resulting pattern of thaw depth and micro-relief is correlated with both summer temperatures and vegetation cover. The variability and degree of micro-relief decrease from the Low to the High Arctic. Non-sorted circle centers had deeper active layer than inter circle areas along the gradient. Thaw depths increase linearly with the degree of bare ground and nonlinearly with summer warmth. This unimodal pattern of shallower active layer at the warmest and coldest sites is due to the interaction of climate and the insulating vegetation layer. Greatest thaw depths are found on bare non-sorted circles in subzone C. Beta diversity is greatest in subzone D, where vegetated inter circle areas differ markedly from more barren non- sorted circles.

Simulation of multicomponent light source for optical-electronic system of color analysis objects

NASA Astrophysics Data System (ADS)

Peretiagin, Vladimir S.; Alekhin, Artem A.; Korotaev, Valery V.

2016-04-01

Development of lighting technology has led to possibility of using LEDs in the specialized devices for outdoor, industrial (decorative and accent) and domestic lighting. In addition, LEDs and devices based on them are widely used for solving particular problems. For example, the LED devices are widely used for lighting of vegetables and fruit (for their sorting or growing), textile products (for the control of its quality), minerals (for their sorting), etc. Causes of active introduction LED technology in different systems, including optical-electronic devices and systems, are a large choice of emission color and LED structure, that defines the spatial, power, thermal and other parameters. Furthermore, multi-element and color devices of lighting with adjustable illumination properties can be designed and implemented by using LEDs. However, devices based on LEDs require more attention if you want to provide a certain nature of the energy or color distribution at all the work area (area of analysis or observation) or surface of the object. This paper is proposed a method of theoretical modeling of the lighting devices. The authors present the models of RGB multicomponent light source applied to optical-electronic system for the color analysis of mineral objects. The possibility of formation the uniform and homogeneous on energy and color illumination of the work area for this system is presented. Also authors showed how parameters and characteristics of optical radiation receiver (by optical-electronic system) affect on the energy, spatial, spectral and colorimetric properties of a multicomponent light source.

A modified conjugate gradient method based on the Tikhonov system for computerized tomography (CT).

PubMed

Wang, Qi; Wang, Huaxiang

2011-04-01

During the past few decades, computerized tomography (CT) was widely used for non-destructive testing (NDT) and non-destructive examination (NDE) in the industrial area because of its characteristics of non-invasiveness and visibility. Recently, CT technology has been applied to multi-phase flow measurement. Using the principle of radiation attenuation measurements along different directions through the investigated object with a special reconstruction algorithm, cross-sectional information of the scanned object can be worked out. It is a typical inverse problem and has always been a challenge for its nonlinearity and ill-conditions. The Tikhonov regulation method is widely used for similar ill-posed problems. However, the conventional Tikhonov method does not provide reconstructions with qualities good enough, the relative errors between the reconstructed images and the real distribution should be further reduced. In this paper, a modified conjugate gradient (CG) method is applied to a Tikhonov system (MCGT method) for reconstructing CT images. The computational load is dominated by the number of independent measurements m, and a preconditioner is imported to lower the condition number of the Tikhonov system. Both simulation and experiment results indicate that the proposed method can reduce the computational time and improve the quality of image reconstruction. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

The effect of the involvement of the dominant or non-dominant hand on grip/pinch strengths and the Levine score in patients with carpal tunnel syndrome.

PubMed

Zyluk, A; Walaszek, I

2012-06-01

The Levine questionnaire is a disease-oriented instrument developed for outcome measurement of carpal tunnel syndrome (CTS) management. The objective of this study was to compare Levine scores in patients with unilateral CTS, involving the dominant or non-dominant hand, before and after carpal tunnel release. Records of 144 patients, 126 women (87%) and 18 men (13%) aged a mean of 58 years with unilateral CTS, treated operatively, were analysed. The dominant hand was involved in 100 patients (69%), the non-dominant in 44 (31%). The parameters were analysed pre-operatively, and at 1 and 6 months post-operatively. A comparison of Levine scores in patients with the involvement of the dominant or non-dominant hand showed no statistically significant differences at baseline and any of the follow-up measurements. Statistically significant differences were noted in total grip strength at baseline and at 6 month assessments and in key-pinch strength at 1 and 6 months.

A method for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

NASA Astrophysics Data System (ADS)

Ai, Xueshan; Dong, Zuo; Mo, Mingzhu

2017-04-01

The optimal reservoir operation is in generally a multi-objective problem. In real life, most of the reservoir operation optimization problems involve conflicting objectives, for which there is no single optimal solution which can simultaneously gain an optimal result of all the purposes, but rather a set of well distributed non-inferior solutions or Pareto frontier exists. On the other hand, most of the reservoirs operation rules is to gain greater social and economic benefits at the expense of ecological environment, resulting to the destruction of riverine ecology and reduction of aquatic biodiversity. To overcome these drawbacks, this study developed a multi-objective model for the reservoir operating with the conflicting functions of hydroelectric energy generation, irrigation and ecological protection. To solve the model with the objectives of maximize energy production, maximize the water demand satisfaction rate of irrigation and ecology, we proposed a multi-objective optimization method of variable penalty coefficient (VPC), which was based on integrate dynamic programming (DP) with discrete differential dynamic programming (DDDP), to generate a well distributed non-inferior along the Pareto front by changing the penalties coefficient of different objectives. This method was applied to an existing China reservoir named Donggu, through a course of a year, which is a multi-annual storage reservoir with multiple purposes. The case study results showed a good relationship between any two of the objectives and a good Pareto optimal solutions, which provide a reference for the reservoir decision makers.

Hemispheric Differences in Processing Dichotic Meaningful and Non-Meaningful Words

ERIC Educational Resources Information Center

Yasin, Ifat

2007-01-01

Classic dichotic-listening paradigms reveal a right-ear advantage (REA) for speech sounds as compared to non-speech sounds. This REA is assumed to be associated with a left-hemisphere dominance for meaningful speech processing. This study objectively probed the relationship between ear advantage and hemispheric dominance in a dichotic-listening…

Disruption of visual awareness during the attentional blink is reflected by selective disruption of late-stage neural processing

PubMed Central

Harris, Joseph A.; McMahon, Alex R.; Woldorff, Marty G.

2015-01-01

Any information represented in the brain holds the potential to influence behavior. It is therefore of broad interest to determine the extent and quality of neural processing of stimulus input that occurs with and without awareness. The attentional blink is a useful tool for dissociating neural and behavioral measures of perceptual visual processing across conditions of awareness. The extent of higher-order visual information beyond basic sensory signaling that is processed during the attentional blink remains controversial. To determine what neural processing at the level of visual-object identification occurs in the absence of awareness, electrophysiological responses to images of faces and houses were recorded both within and outside of the attentional blink period during a rapid serial visual presentation (RSVP) stream. Electrophysiological results were sorted according to behavioral performance (correctly identified targets versus missed targets) within these blink and non-blink periods. An early index of face-specific processing (the N170, 140–220 ms post-stimulus) was observed regardless of whether the subject demonstrated awareness of the stimulus, whereas a later face-specific effect with the same topographic distribution (500–700 ms post-stimulus) was only seen for accurate behavioral discrimination of the stimulus content. The present findings suggest a multi-stage process of object-category processing, with only the later phase being associated with explicit visual awareness. PMID:23859644

Shape design of internal cooling passages within a turbine blade

NASA Astrophysics Data System (ADS)

Nowak, Grzegorz; Nowak, Iwona

2012-04-01

The article concerns the optimization of the shape and location of non-circular passages cooling the blade of a gas turbine. To model the shape, four Bezier curves which form a closed profile of the passage were used. In order to match the shape of the passage to the blade profile, a technique was put forward to copy and scale the profile fragments into the component, and build the outline of the passage on the basis of them. For so-defined cooling passages, optimization calculations were carried out with a view to finding their optimal shape and location in terms of the assumed objectives. The task was solved as a multi-objective problem with the use of the Pareto method, for a cooling system composed of four and five passages. The tool employed for the optimization was the evolutionary algorithm. The article presents the impact of the population on the task convergence, and discusses the impact of different optimization objectives on the Pareto optimal solutions obtained. Due to the problem of different impacts of individual objectives on the position of the solution front which was noticed during the calculations, a two-step optimization procedure was introduced. Also, comparative optimization calculations for the scalar objective function were carried out and set up against the non-dominated solutions obtained in the Pareto approach. The optimization process resulted in a configuration of the cooling system that allows a significant reduction in the temperature of the blade and its thermal stress.

A novel fruit shape classification method based on multi-scale analysis

NASA Astrophysics Data System (ADS)

Gui, Jiangsheng; Ying, Yibin; Rao, Xiuqin

2005-11-01

Shape is one of the major concerns and which is still a difficult problem in automated inspection and sorting of fruits. In this research, we proposed the multi-scale energy distribution (MSED) for object shape description, the relationship between objects shape and its boundary energy distribution at multi-scale was explored for shape extraction. MSED offers not only the mainly energy which represent primary shape information at the lower scales, but also subordinate energy which represent local shape information at higher differential scales. Thus, it provides a natural tool for multi resolution representation and can be used as a feature for shape classification. We addressed the three main processing steps in the MSED-based shape classification. They are namely, 1) image preprocessing and citrus shape extraction, 2) shape resample and shape feature normalization, 3) energy decomposition by wavelet and classification by BP neural network. Hereinto, shape resample is resample 256 boundary pixel from a curve which is approximated original boundary by using cubic spline in order to get uniform raw data. A probability function was defined and an effective method to select a start point was given through maximal expectation, which overcame the inconvenience of traditional methods in order to have a property of rotation invariants. The experiment result is relatively well normal citrus and serious abnormality, with a classification rate superior to 91.2%. The global correct classification rate is 89.77%, and our method is more effective than traditional method. The global result can meet the request of fruit grading.

Log sort yard economics, planning, and feasibility

Treesearch

John Rusty Dramm; Robert Govett; Ted Bilek; Gerry L. Jackson

2004-01-01

This publication discusses basic marketing and economic concepts, planning approach, and feasibility methodology for assessing log sort yard operations. Special attention is given to sorting small diameter and underutilized logs from forest restoration, fuels reduction, and thinning operations. A planned programming approach of objectively determining the feasibility...

Lateral-Directional Parameter Estimation on the X-48B Aircraft Using an Abstracted, Multi-Objective Effector Model

NASA Technical Reports Server (NTRS)

Ratnayake, Nalin A.; Waggoner, Erin R.; Taylor, Brian R.

2011-01-01

The problem of parameter estimation on hybrid-wing-body aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aerodynamic control effectors that act in coplanar motion. This adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of flight and simulation data must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, time-decorrelation techniques are applied to a model structure selected through stepwise regression for simulated and flight-generated lateral-directional parameter estimation data. A virtual effector model that uses mathematical abstractions to describe the multi-axis effects of clamshell surfaces is developed and applied. Comparisons are made between time history reconstructions and observed data in order to assess the accuracy of the regression model. The Cram r-Rao lower bounds of the estimated parameters are used to assess the uncertainty of the regression model relative to alternative models. Stepwise regression was found to be a useful technique for lateral-directional model design for hybrid-wing-body aircraft, as suggested by available flight data. Based on the results of this study, linear regression parameter estimation methods using abstracted effectors are expected to perform well for hybrid-wing-body aircraft properly equipped for the task.

Humeral retroversion and shoulder rotational mobility in young handball practitioners

PubMed Central

Quadros, Gustavo Aguiar; Döhnert, Marcelo Baptista

2015-01-01

ABSTRACT OBJECTIVE : To evaluate the prevalence of humeral retroversion and rotational mobility (RHH) in young handball practitioners and non-practitioners. METHODS : This is a cross-sectional study performed with two groups: the handball group, with 14 female students practicing handball and the control group, with 13 young participants non-practicing pitch sports. RESULTS : The handball group presented full rotational movement (FRM) hi-gher than the control group in both the dominant shoulder (p=0.001) and the non-dominant shoulder (p=0.0001). The mobility of active and passive internal rotation was significantly higher in handball players in both shoulders. The handball group presented lower internal rotation range of motion for the dominant shoulder as compared to the non-dominant shoul-der (p=0.001). CONCLUSION : Young handball practitioners, des-pite skeletally immature, showed a higher MRT than the control group. The handball group showed loss of internal rotation (medial) on the dominant shoulder as compared to the non--dominant shoulder. Level of Evidence II, Prospective Study. PMID:27057141

Enhanced detection and visualization of anomalies in spectral imagery

NASA Astrophysics Data System (ADS)

Basener, William F.; Messinger, David W.

2009-05-01

Anomaly detection algorithms applied to hyperspectral imagery are able to reliably identify man-made objects from a natural environment based on statistical/geometric likelyhood. The process is more robust than target identification, which requires precise prior knowledge of the object of interest, but has an inherently higher false alarm rate. Standard anomaly detection algorithms measure deviation of pixel spectra from a parametric model (either statistical or linear mixing) estimating the image background. The topological anomaly detector (TAD) creates a fully non-parametric, graph theory-based, topological model of the image background and measures deviation from this background using codensity. In this paper we present a large-scale comparative test of TAD against 80+ targets in four full HYDICE images using the entire canonical target set for generation of ROC curves. TAD will be compared against several statistics-based detectors including local RX and subspace RX. Even a perfect anomaly detection algorithm would have a high practical false alarm rate in most scenes simply because the user/analyst is not interested in every anomalous object. To assist the analyst in identifying and sorting objects of interest, we investigate coloring of the anomalies with principle components projections using statistics computed from the anomalies. This gives a very useful colorization of anomalies in which objects of similar material tend to have the same color, enabling an analyst to quickly sort and identify anomalies of highest interest.

Enhanced visuo-haptic integration for the non-dominant hand.

PubMed

Yalachkov, Yavor; Kaiser, Jochen; Doehrmann, Oliver; Naumer, Marcus J

2015-07-21

Visuo-haptic integration contributes essentially to object shape recognition. Although there has been a considerable advance in elucidating the neural underpinnings of multisensory perception, it is still unclear whether seeing an object and exploring it with the dominant hand elicits the same brain response as compared to the non-dominant hand. Using fMRI to measure brain activation in right-handed participants, we found that for both left- and right-hand stimulation the left lateral occipital complex (LOC) and anterior cerebellum (aCER) were involved in visuo-haptic integration of familiar objects. These two brain regions were then further investigated in another study, where unfamiliar, novel objects were presented to a different group of right-handers. Here the left LOC and aCER were more strongly activated by bimodal than unimodal stimuli only when the left but not the right hand was used. A direct comparison indicated that the multisensory gain of the fMRI activation was significantly higher for the left than the right hand. These findings are in line with the principle of "inverse effectiveness", implying that processing of bimodally presented stimuli is particularly enhanced when the unimodal stimuli are weak. This applies also when right-handed subjects see and simultaneously touch unfamiliar objects with their non-dominant left hand. Thus, the fMRI signal in the left LOC and aCER induced by visuo-haptic stimulation is dependent on which hand was employed for haptic exploration. Copyright © 2015 Elsevier B.V. All rights reserved.

A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem.

PubMed

Khelifi, Lazhar; Mignotte, Max

2017-08-01

Image segmentation fusion is defined as the set of methods which aim at merging several image segmentations, in a manner that takes full advantage of the complementarity of each one. Previous relevant researches in this field have been impeded by the difficulty in identifying an appropriate single segmentation fusion criterion, providing the best possible, i.e., the more informative, result of fusion. In this paper, we propose a new model of image segmentation fusion based on multi-objective optimization which can mitigate this problem, to obtain a final improved result of segmentation. Our fusion framework incorporates the dominance concept in order to efficiently combine and optimize two complementary segmentation criteria, namely, the global consistency error and the F-measure (precision-recall) criterion. To this end, we present a hierarchical and efficient way to optimize the multi-objective consensus energy function related to this fusion model, which exploits a simple and deterministic iterative relaxation strategy combining the different image segments. This step is followed by a decision making task based on the so-called "technique for order performance by similarity to ideal solution". Results obtained on two publicly available databases with manual ground truth segmentations clearly show that our multi-objective energy-based model gives better results than the classical mono-objective one.

Simultaneous segmentation of the bone and cartilage surfaces of a knee joint in 3D

NASA Astrophysics Data System (ADS)

Yin, Y.; Zhang, X.; Anderson, D. D.; Brown, T. D.; Hofwegen, C. Van; Sonka, M.

2009-02-01

We present a novel framework for the simultaneous segmentation of multiple interacting surfaces belonging to multiple mutually interacting objects. The method is a non-trivial extension of our previously reported optimal multi-surface segmentation. Considering an example application of knee-cartilage segmentation, the framework consists of the following main steps: 1) Shape model construction: Building a mean shape for each bone of the joint (femur, tibia, patella) from interactively segmented volumetric datasets. Using the resulting mean-shape model - identification of cartilage, non-cartilage, and transition areas on the mean-shape bone model surfaces. 2) Presegmentation: Employment of iterative optimal surface detection method to achieve approximate segmentation of individual bone surfaces. 3) Cross-object surface mapping: Detection of inter-bone equidistant separating sheets to help identify corresponding vertex pairs for all interacting surfaces. 4) Multi-object, multi-surface graph construction and final segmentation: Construction of a single multi-bone, multi-surface graph so that two surfaces (bone and cartilage) with zero and non-zero intervening distances can be detected for each bone of the joint, according to whether or not cartilage can be locally absent or present on the bone. To define inter-object relationships, corresponding vertex pairs identified using the separating sheets were interlinked in the graph. The graph optimization algorithm acted on the entire multiobject, multi-surface graph to yield a globally optimal solution. The segmentation framework was tested on 16 MR-DESS knee-joint datasets from the Osteoarthritis Initiative database. The average signed surface positioning error for the 6 detected surfaces ranged from 0.00 to 0.12 mm. When independently initialized, the signed reproducibility error of bone and cartilage segmentation ranged from 0.00 to 0.26 mm. The results showed that this framework provides robust, accurate, and reproducible segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multi-object segmentation problems.

Diagnostic Assessment of the Difficulty Using Direct Policy Search in Many-Objective Reservoir Control

NASA Astrophysics Data System (ADS)

Zatarain-Salazar, J.; Reed, P. M.; Herman, J. D.; Giuliani, M.; Castelletti, A.

2014-12-01

Globally reservoir operations provide fundamental services to water supply, energy generation, recreation, and ecosystems. The pressures of expanding populations, climate change, and increased energy demands are motivating a significant investment in re-operationalizing existing reservoirs or defining operations for new reservoirs. Recent work has highlighted the potential benefits of exploiting recent advances in many-objective optimization and direct policy search (DPS) to aid in addressing these systems' multi-sector demand tradeoffs. This study contributes to a comprehensive diagnostic assessment of multi-objective evolutionary optimization algorithms (MOEAs) efficiency, effectiveness, reliability, and controllability when supporting DPS for the Conowingo dam in the Lower Susquehanna River Basin. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to the system's competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. Seven benchmark and state-of-the-art MOEAs are tested on deterministic and stochastic instances of the Susquehanna test case. In the deterministic formulation, the operating objectives are evaluated over the historical realization of the hydroclimatic variables (i.e., inflows and evaporation rates). In the stochastic formulation, the same objectives are instead evaluated over an ensemble of stochastic inflows and evaporation rates realizations. The algorithms are evaluated in their ability to support DPS in discovering reservoir operations that compose the tradeoffs for six multi-sector performance objectives with thirty-two decision variables. Our diagnostic results highlight that many-objective DPS is very challenging for modern MOEAs and that epsilon dominance is critical for attaining high levels of performance. Epsilon dominance algorithms epsilon-MOEA, epsilon-NSGAII and the auto adaptive Borg MOEA, are statistically superior for the six-objective Susquehanna instance of this important class of problems. Additionally, shifting from deterministic history-based DPS to stochastic DPS significantly increases the difficulty of the problem.

a Heuristic Approach for Multi Objective Distribution Feeder Reconfiguration: Using Fuzzy Sets in Normalization of Objective Functions

NASA Astrophysics Data System (ADS)

Milani, Armin Ebrahimi; Haghifam, Mahmood Reza

2008-10-01

The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. In this paper each objectives is normalized with inspiration from fuzzy sets-to cause optimization more flexible- and formulized as a unique multi-objective function. The genetic algorithm is used for solving the suggested model, in which there is no risk of non-liner objective functions and constraints. The effectiveness of the proposed method is demonstrated through the examples.

A quantitative estimate of schema abnormality in socially anxious and non-anxious individuals.

PubMed

Wenzel, Amy; Brendle, Jennifer R; Kerr, Patrick L; Purath, Donna; Ferraro, F Richard

2007-01-01

Although cognitive theories of anxiety suggest that anxious individuals are characterized by abnormal threat-relevant schemas, few empirical studies have estimated the nature of these cognitive structures using quantitative methods that lend themselves to inferential statistical analysis. In the present study, socially anxious (n = 55) and non-anxious (n = 62) participants completed 3 Q-Sort tasks to assess their knowledge of events that commonly occur in social or evaluative scenarios. Participants either sorted events according to how commonly they personally believe the events occur (i.e. "self" condition), or to how commonly they estimate that most people believe they occur (i.e. "other" condition). Participants' individual Q-Sorts were correlated with mean sorts obtained from a normative sample to obtain an estimate of schema abnormality, with lower correlations representing greater levels of abnormality. Relative to non-anxious participants, socially anxious participants' sorts were less strongly associated with sorts of the normative sample, particularly in the "self" condition, although secondary analyses suggest that some significant results might be explained, in part, by depression and experience with the scenarios. These results provide empirical support for the theoretical notion that threat-relevant self-schemas of anxious individuals are characterized by some degree of abnormality.

Effect of multi-stage inoculation on the bacterial and fungal community structure during organic municipal solid wastes composting.

PubMed

Xi, Beidou; He, Xiaosong; Dang, Qiuling; Yang, Tianxue; Li, Mingxiao; Wang, Xiaowei; Li, Dan; Tang, Jun

2015-11-01

In this study, PCR-DGGE method was applied to investigate the impact of multi-stage inoculation treatment on the community composition of bacterial and fungal during municipal solid wastes (MSW) composting process. The results showed that the high temperature period was extended by the multi-stage inoculation treatment, 1day longer than initial-stage inoculation treatment, and 5days longer than non-inoculation treatment. The temperature of the secondary fermentation increased to 51°C with multi-stage inoculation treatment. The multi-stage inoculation method improved the community diversity of bacteria and fungi that the diversity indexes reached the maximum on the 17days and 20days respectively, avoided the competition between inoculations and indigenous microbes, and enhanced the growth of dominant microorganisms. The DNA sequence indicated that various kinds of uncultured microorganisms with determined ratios were detected, which were dominant microbes during the whole fermentation process. These findings call for further researches of compost microbial cultivation technology. Copyright © 2015 Elsevier Ltd. All rights reserved.

Funds of Knowledge at Work in the Writing Classroom

ERIC Educational Resources Information Center

Street, Chris

2005-01-01

Schools seem to serve certain groups of people over others. Schools become "sorting mechanisms in which select groups of students are favored on the basis of race, class, and gender." Students within the dominant culture gain cultural capital, while students outside the dominant culture are often left without the means to gain entry into…

Reversed polarized delivery of an aquaporin-2 mutant causes dominant nephrogenic diabetes insipidus

PubMed Central

Kamsteeg, Erik-Jan; Bichet, Daniel G.; Konings, Irene B.M.; Nivet, Hubert; Lonergan, Michelle; Arthus, Marie-Françoise; van Os, Carel H.; Deen, Peter M.T.

2003-01-01

Vasopressin regulates body water conservation by redistributing aquaporin-2 (AQP2) water channels from intracellular vesicles to the apical surface of renal collecting ducts, resulting in water reabsorption from urine. Mutations in AQP2 cause autosomal nephrogenic diabetes insipidus (NDI), a disease characterized by the inability to concentrate urine. Here, we report a frame-shift mutation in AQP2 causing dominant NDI. This AQP2 mutant is a functional water channel when expressed in Xenopus oocytes. However, expressed in polarized renal cells, it is misrouted to the basolateral instead of apical plasma membrane. Additionally, this mutant forms heterotetramers with wild-type AQP2 and redirects this complex to the basolateral surface. The frame shift induces a change in the COOH terminus of AQP2, creating both a leucine- and a tyrosine-based motif, which cause the reversed sorting of AQP2. Our data reveal a novel cellular phenotype in dominant NDI and show that dominance of basolateral sorting motifs in a mutant subunit can be the molecular basis for disease. PMID:14662748

Molecular mechanism to recruit galectin-3 into multivesicular bodies for polarized exosomal secretion.

PubMed

Bänfer, Sebastian; Schneider, Dominik; Dewes, Jenny; Strauss, Maximilian T; Freibert, Sven-A; Heimerl, Thomas; Maier, Uwe G; Elsässer, Hans-Peter; Jungmann, Ralf; Jacob, Ralf

2018-05-08

The beta-galactoside binding lectin galectin-3 (Gal3) is found intracellularly and in the extracellular space. Secretion of this lectin is mediated independently of the secretory pathway by a not yet defined nonclassical mechanism. Here, we found Gal3 in the lumen of exosomes. Superresolution and electron microscopy studies visualized Gal3 recruitment and sorting into intraluminal vesicles. Exosomal Gal3 release depends on the endosomal sorting complex required for transport I (ESCRT-I) component Tsg101 and functional Vps4a. Either Tsg101 knockdown or expression of dominant-negative Vps4a E228Q causes an intracellular Gal3 accumulation at multivesicular body formation sites. In addition, we identified a highly conserved tetrapeptide P(S/T)AP motif in the amino terminus of Gal3 that mediates a direct interaction with Tsg101. Mutation of the P(S/T)AP motif results in a loss of interaction and a dramatic decrease in exosomal Gal3 secretion. We conclude that Gal3 is a member of endogenous non-ESCRT proteins which are P(S/T)AP tagged for exosomal release.

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

NASA Astrophysics Data System (ADS)

Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

2013-09-01

The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

Macrophage Sortilin Promotes LDL Uptake, Foam Cell Formation, and Atherosclerosis

PubMed Central

Patel, Kevin M.; Strong, Alanna; Tohyama, Junichiro; Jin, Xueting; Morales, Carlos R.; Billheimer, Jeffery; Millar, John; Kruth, Howard; Rader, Daniel J.

2015-01-01

Rationale Non-coding gene variants at the SORT1 locus are strongly associated with LDL-C levels as well as with coronary artery disease (CAD). SORT1 encodes a protein called sortilin, and hepatic sortilin modulates LDL metabolism by targeting apoB-containing lipoproteins to the lysosome. Sortilin is also expressed in macrophages, but its role in macrophage uptake of LDL and in atherosclerosis independent of plasma LDL-C levels is unknown. Objective To determine the effect of macrophage sortilin expression on LDL uptake, foam cell formation, and atherosclerosis. Methods and Results We crossed Sort1−/− mice onto a ‘humanized’ Apobec1−/−; hAPOB Tg background and determined that Sort1 deficiency on this background had no effect on plasma LDL-C levels but dramatically reduced atherosclerosis in the aorta and aortic root. In order to test whether this effect was a result of macrophage sortilin deficiency, we transplanted Sort1−/−;LDLR−/− or Sort1+/+;LDLR−/− bone marrow into Ldlr−/− mice and observed a similar reduction in atherosclerosis in mice lacking hematopoetic sortilin without an effect on plasma LDL-C levels. In an effort to determine the mechanism by which hematopoetic sortilin deficiency reduced atherosclerosis, we found no effect of sortilin deficiency on macrophage recruitment or LPS-induced cytokine release in vivo. In contrast, sortilin deficient macrophages had significantly reduced uptake of native LDL ex vivo and reduced foam cell formation in vivo, whereas sortilin overexpression in macrophages resulted in increased LDL uptake and foam cell formation. Conclusions Macrophage sortilin deficiency protects against atherosclerosis by reducing macrophage uptake of LDL. Sortilin-mediated uptake of native LDL into macrophages may be an important mechanism of foam cell formation and contributor to atherosclerosis development. PMID:25593281

What does it take to turn a rock into a badlands material?

NASA Astrophysics Data System (ADS)

Kasanin-Grubin, Milica; Nadal Romero, Estela; Della Seta, Marta; Martinez-Murillo, Juan F.

2016-04-01

Badlands can develop under different climatic conditions ranging from arid to humid on materials that have a complex combination of physico-chemical properties. The aim of this study is to determine the critical material properties for badland development based on current knowledge and new data. For that purpose we analyzed, both using the existing and new data, the importance of the distribution of grain size, mineralogical composition, and physico-chemical properties. Generally, the badland materials are most commonly described as "fine - grained" however, the size of the dominant grain fractions is not the solely important parameter. We argue that there is a critical amount of each size fraction (sand, silt and clay) that makes these materials susceptible to erosion. Furthermore, sorting of the material is an important factor in material susceptibility to erosion. The well-sorted fine sediments are generally considered as materials prone to disintegration and piping, while sediments with a large range of sizes and higher degree of packing are more resistant. However, poorly sorted sediments can also be very erodible and are found in badlands. Besides quartz, feldspar and carbonates, clay minerals are always present in badland materials and these minerals are crucial for badland development.The dominant clay mineral determines the behaviour of badland material, regarding swelling/shrinking, dispersion and crust development. Previous studies have shown that pH, SAR (sodium adsorption ratio), TDS (total dissolved salts), PS (percentage of sodium) and ESP (exchangeable sodium percentage) are distinctive parameters for both eroded and non-eroded slopes in badlands. Furthermore, our findings prove that content of organic carbon (Corg) is also a very important parameter and that materials with high SAR are less dispersive if the Corg is above 3%. In conclusion, this study shows that there are a number of thresholds regarding grain size, mineralogical composition and physico-chemical parameters that have to be met to make sediment a badland material.

Evolution of multi-drug resistant HCV clones from pre-existing resistant-associated variants during direct-acting antiviral therapy determined by third-generation sequencing

NASA Astrophysics Data System (ADS)

Takeda, Haruhiko; Ueda, Yoshihide; Inuzuka, Tadashi; Yamashita, Yukitaka; Osaki, Yukio; Nasu, Akihiro; Umeda, Makoto; Takemura, Ryo; Seno, Hiroshi; Sekine, Akihiro; Marusawa, Hiroyuki

2017-03-01

Resistance-associated variant (RAV) is one of the most significant clinical challenges in treating HCV-infected patients with direct-acting antivirals (DAAs). We investigated the viral dynamics in patients receiving DAAs using third-generation sequencing technology. Among 283 patients with genotype-1b HCV receiving daclatasvir + asunaprevir (DCV/ASV), 32 (11.3%) failed to achieve sustained virological response (SVR). Conventional ultra-deep sequencing of HCV genome was performed in 104 patients (32 non-SVR, 72 SVR), and detected representative RAVs in all non-SVR patients at baseline, including Y93H in 28 (87.5%). Long contiguous sequences spanning NS3 to NS5A regions of each viral clone in 12 sera from 6 representative non-SVR patients were determined by third-generation sequencing, and showed the concurrent presence of several synonymous mutations linked to resistance-associated substitutions in a subpopulation of pre-existing RAVs and dominant isolates at treatment failure. Phylogenetic analyses revealed close genetic distances between pre-existing RAVs and dominant RAVs at treatment failure. In addition, multiple drug-resistant mutations developed on pre-existing RAVs after DCV/ASV in all non-SVR cases. In conclusion, multi-drug resistant viral clones at treatment failure certainly originated from a subpopulation of pre-existing RAVs in HCV-infected patients. Those RAVs were selected for and became dominant with the acquisition of multiple resistance-associated substitutions under DAA treatment pressure.

Investigation of trunk muscle activities during lifting using a multi-objective optimization-based model and intelligent optimization algorithms.

PubMed

Ghiasi, Mohammad Sadegh; Arjmand, Navid; Boroushaki, Mehrdad; Farahmand, Farzam

2016-03-01

A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data.

Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses.

PubMed

Zamani, Majid; Demosthenous, Andreas

2014-07-01

Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting.

Sorting drops and cells with acoustics: acoustic microfluidic fluorescence-activated cell sorter.

PubMed

Schmid, Lothar; Weitz, David A; Franke, Thomas

2014-10-07

We describe a versatile microfluidic fluorescence-activated cell sorter that uses acoustic actuation to sort cells or drops at ultra-high rates. Our acoustic sorter combines the advantages of traditional fluorescence-activated cell (FACS) and droplet sorting (FADS) and is applicable for a multitude of objects. We sort aqueous droplets, at rates as high as several kHz, into two or even more outlet channels. We can also sort cells directly from the medium without prior encapsulation into drops; we demonstrate this by sorting fluorescently labeled mouse melanoma cells in a single phase fluid. Our acoustic microfluidic FACS is compatible with standard cell sorting cytometers, yet, at the same time, enables a rich variety of more sophisticated applications.

78 FR 70601 - International Mail Contract

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-26

... Notice of Filing Functionally Equivalent Agreement, November 15, 2013 (collectively, Notice). II. The... Service filed an application for non-public treatment of materials filed under seal (Attachment 1); a... agreement to one previously found to be functionally equivalent to the Inbound Market-Dominant Multi-Service...

Optimization of Land Use Suitability for Agriculture Using Integrated Geospatial Model and Genetic Algorithms

NASA Astrophysics Data System (ADS)

Mansor, S. B.; Pormanafi, S.; Mahmud, A. R. B.; Pirasteh, S.

2012-08-01

In this study, a geospatial model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the infrastructural preference. The model was developed based on multi-agent genetic algorithm. The model was customized to accommodate the constraint set for the study area, namely the resource saving and environmental-friendly. The model was then applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Menderjan Basin in Iran. The first task was to study the dominant crops and economic suitability evaluation of land. Second task was to determine the fitness function for the genetic algorithms. The third objective was to optimize the land use map using economical benefits. The results has indicated that the proposed model has much better performance for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making.

Put your hands up! Gesturing improves preschoolers' executive function.

PubMed

Rhoads, Candace L; Miller, Patricia H; Jaeger, Gina O

2018-09-01

This study addressed the causal direction of a previously reported relation between preschoolers' gesturing and their executive functioning on the Dimensional Change Card Sort (DCCS) sorting-switch task. Gesturing the relevant dimension for sorting was induced in a Gesture group through instructions, imitation, and prompts. In contrast, the Control group was instructed to "think hard" when sorting. Preschoolers (N = 50) performed two DCCS tasks: (a) sort by size and then spatial orientation of two objects and (b) sort by shape and then proximity of the two objects. An examination of performance over trials permitted a fine-grained depiction of patterns of younger and older children in the Gesture and Control conditions. After the relevant dimension was switched, the Gesture group had more accurate sorts than the Control group, particularly among younger children on the second task. Moreover, the amount of gesturing predicted the number of correct sorts among younger children on the second task. The overall association between gesturing and sorting was not reflected at the level of individual trials, perhaps indicating covert gestural representation on some trials or the triggering of a relevant verbal representation by the gesturing. The delayed benefit of gesturing, until the second task, in the younger children may indicate a utilization deficiency. Results are discussed in terms of theories of gesturing and thought. The findings open up a new avenue of research and theorizing about the possible role of gesturing in emerging executive function. Copyright © 2018 Elsevier Inc. All rights reserved.

Research on vehicle routing optimization for the terminal distribution of B2C E-commerce firms

NASA Astrophysics Data System (ADS)

Zhang, Shiyun; Lu, Yapei; Li, Shasha

2018-05-01

In this paper, we established a half open multi-objective optimization model for the vehicle routing problem of B2C (business-to-customer) E-Commerce firms. To minimize the current transport distance as well as the disparity between the excepted shipments and the transport capacity in the next distribution, we applied the concept of dominated solution and Pareto solutions to the standard particle swarm optimization and proposed a MOPSO (multi-objective particle swarm optimization) algorithm to support the model. Besides, we also obtained the optimization solution of MOPSO algorithm based on data randomly generated through the system, which verified the validity of the model.

Using MODIS Cloud Regimes to Sort Diagnostic Signals of Aerosol-Cloud-Precipitation Interactions

PubMed Central

Oreopoulos, Lazaros; Cho, Nayeong; Lee, Dongmin

2018-01-01

Coincident multi-year measurements of aerosol, cloud, precipitation and radiation at near-global scales are analyzed to diagnose their apparent relationships as suggestive of interactions previously proposed based on theoretical, observational, and model constructs. Specifically, we examine whether differences in aerosol loading in separate observations go along with consistently different precipitation, cloud properties, and cloud radiative effects. Our analysis uses a cloud regime (CR) framework to dissect and sort the results. The CRs come from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are defined as distinct groups of cloud systems with similar co-variations of cloud top pressure and cloud optical thickness. Aerosol optical depth used as proxy for aerosol loading comes from two sources, MODIS observations, and the MERRA-2 re-analysis, and its variability is defined with respect to local seasonal climatologies. The choice of aerosol dataset impacts our results substantially. We also find that the responses of the marine and continental component of a CR are frequently quite disparate. Overall, CRs dominated by warm clouds tend to exhibit less ambiguous signals, but also have more uncertainty with regard to precipitation changes. Finally, we find weak, but occasionally systematic co-variations of select meteorological indicators and aerosol, which serves as a sober reminder that ascribing changes in cloud and cloud-affected variables solely to aerosol variations is precarious. PMID:29651373

Using MODIS Cloud Regimes to Sort Diagnostic Signals of Aerosol-Cloud-Precipitation Interactions.

PubMed

Oreopoulos, Lazaros; Cho, Nayeong; Lee, Dongmin

2017-05-27

Coincident multi-year measurements of aerosol, cloud, precipitation and radiation at near-global scales are analyzed to diagnose their apparent relationships as suggestive of interactions previously proposed based on theoretical, observational, and model constructs. Specifically, we examine whether differences in aerosol loading in separate observations go along with consistently different precipitation, cloud properties, and cloud radiative effects. Our analysis uses a cloud regime (CR) framework to dissect and sort the results. The CRs come from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are defined as distinct groups of cloud systems with similar co-variations of cloud top pressure and cloud optical thickness. Aerosol optical depth used as proxy for aerosol loading comes from two sources, MODIS observations, and the MERRA-2 re-analysis, and its variability is defined with respect to local seasonal climatologies. The choice of aerosol dataset impacts our results substantially. We also find that the responses of the marine and continental component of a CR are frequently quite disparate. Overall, CRs dominated by warm clouds tend to exhibit less ambiguous signals, but also have more uncertainty with regard to precipitation changes. Finally, we find weak, but occasionally systematic co-variations of select meteorological indicators and aerosol, which serves as a sober reminder that ascribing changes in cloud and cloud-affected variables solely to aerosol variations is precarious.

Forging Hispanic communities in new destinations: A case study of Durham, NC.

PubMed

Flippen, Chenoa A; Parrado, Emilio A

2012-03-01

The Chicago School of urban sociology and its extension in the spatial assimilation model have provided the dominant framework for understanding the interplay between immigrant social and spatial mobility. However, the main tenets of the theory were derived from the experience of pre-war, centralized cities; scholars falling under the umbrella of the Los Angeles school have recently challenged the extent to which they are applicable to the contemporary urban form, which is characterized by sprawling, decentralized, and multi-nucleated development. Indeed, new immigrant destinations, such as those scattered throughout the American Southeast, are both decentralized and lack prior experience with large scale immigration. Informed by this debate this paper traces the formation and early evolution of Hispanic neighborhoods in Durham, NC, a new immigrant destination. Using qualitative data we construct a social history of immigrant neighborhoods and apply survey and census information to examine the spatial pattern of neighborhood succession. We also model the sorting of immigrants across neighborhoods according to personal characteristics. Despite the many differences in urban form and experience with immigration, the main processes forging the early development of Hispanic neighborhoods in Durham are remarkably consistent with the spatial expectations from the Chicago School, though the sorting of immigrants across neighborhoods is more closely connected to family dynamics and political economy considerations than purely human capital attributes.

Forging Hispanic communities in new destinations: A case study of Durham, NC1

PubMed Central

Flippen, Chenoa A.; Parrado, Emilio A.

2013-01-01

The Chicago School of urban sociology and its extension in the spatial assimilation model have provided the dominant framework for understanding the interplay between immigrant social and spatial mobility. However, the main tenets of the theory were derived from the experience of pre-war, centralized cities; scholars falling under the umbrella of the Los Angeles school have recently challenged the extent to which they are applicable to the contemporary urban form, which is characterized by sprawling, decentralized, and multi-nucleated development. Indeed, new immigrant destinations, such as those scattered throughout the American Southeast, are both decentralized and lack prior experience with large scale immigration. Informed by this debate this paper traces the formation and early evolution of Hispanic neighborhoods in Durham, NC, a new immigrant destination. Using qualitative data we construct a social history of immigrant neighborhoods and apply survey and census information to examine the spatial pattern of neighborhood succession. We also model the sorting of immigrants across neighborhoods according to personal characteristics. Despite the many differences in urban form and experience with immigration, the main processes forging the early development of Hispanic neighborhoods in Durham are remarkably consistent with the spatial expectations from the Chicago School, though the sorting of immigrants across neighborhoods is more closely connected to family dynamics and political economy considerations than purely human capital attributes. PMID:24482612

Designing adaptive operating rules for a large multi-purpose reservoir

NASA Astrophysics Data System (ADS)

Geressu, Robel; Rougé, Charles; Harou, Julien

2017-04-01

Reservoirs whose live storage capacity is large compared with annual inflow have "memory", i.e., their storage levels contain information about past inflows and reservoir operations. Such "long-memory" reservoirs can be found in basins in dry regions such as the Nile River Basin in Africa, the Colorado River Basin in the US, or river basins in Western and Central Asia. There the effects of a dry year have the potential to impact reservoir levels and downstream releases for several subsequent years, prompting tensions in transboundary basins. Yet, current reservoir operation rules in those reservoirs do not reflect this by integrating past climate history and release decisions among the factors that influence operating decisions. This work proposes and demonstrates an adaptive reservoir operating rule that explicitly accounts for the recent history of release decisions, and not only current storage level and near-term inflow forecasts. This implies adding long-term (e.g., multiyear) objectives to the existing short-term (e.g., annual) ones. We apply these operating rules to the Grand Ethiopian Renaissance Dam, a large reservoir under construction on the Blue Nile River. Energy generation has to be balanced with the imperative of releasing enough water in low flow years (e.g., the minimum 1, 2 or 3 year cumulative flow) to avoid tensions with downstream countries, Sudan and Egypt. Maximizing the minimum multi-year releases could be of interest for the Nile problem to minimize the impact on performance of the large High Aswan Dam in Egypt. Objectives include maximizing the average and minimum annual energy generation and maximizing the minimum annual, two year and three year cumulative releases. The system model is tested using 30 stochastically generated streamflow series. One can then derive adaptive release rules depending on the value of one- and two-year total releases with respect to thresholds. Then, there are 3 sets of release rules for the reservoir depending on whether one or both thresholds are not met, vs. only one with a non-adaptive rule. Multi-objective evolutionary algorithms (MOEAs) are used to obtain the Pareto front, i.e., non-dominated adaptive and non-adaptive operating rule sets. Implementing adaptive rules is found to improve the trade-offs between energy generation criteria and minimum release targets. Compared with non-adaptive operations, an adaptive operating policy shows an increase of around 3 and 10 Billion cubic meters in the minimum 1 and 3-year cumulative releases for a given value of the same average annual energy generation.

Composition of the summer photosynthetic pico and nanoplankton communities in the Beaufort Sea assessed by T-RFLP and sequences of the 18S rRNA gene from flow cytometry sorted samples.

PubMed

Balzano, Sergio; Marie, Dominique; Gourvil, Priscillia; Vaulot, Daniel

2012-08-01

The composition of photosynthetic pico and nanoeukaryotes was investigated in the North East Pacific and the Arctic Ocean with special emphasis on the Beaufort Sea during the MALINA cruise in summer 2009. Photosynthetic populations were sorted using flow cytometry based on their size and pigment fluorescence. Diversity of the sorted photosynthetic eukaryotes was determined using terminal-restriction fragment length polymorphism analysis and cloning/sequencing of the 18S ribosomal RNA gene. Picoplankton was dominated by Mamiellophyceae, a class of small green algae previously included in the prasinophytes: in the North East Pacific, the contribution of an Arctic Micromonas ecotype increased steadily northward becoming the only taxon occurring at most stations throughout the Beaufort Sea. In contrast, nanoplankton was more diverse: North Pacific stations were dominated by Pseudo-nitzschia sp. whereas those in the Beaufort Sea were dominated by two distinct Chaetoceros species as well as by Chrysophyceae, Pelagophyceae and Chrysochromulina spp.. This study confirms the importance of Arctic Micromonas within picoplankton throughout the Beaufort Sea and demonstrates that the photosynthetic picoeukaryote community in the Arctic is much less diverse than at lower latitudes. Moreover, in contrast to what occurs in warmer waters, most of the key pico- and nanoplankton species found in the Beaufort Sea could be successfully established in culture.

Stacking Searches for Greater Than 100 MeV Gamma Ray Emission from Radio Galaxies and Seyfert Galaxies

NASA Technical Reports Server (NTRS)

Cillis, A. N.; Hartman, R. C.; Bertsch, D. L.

2003-01-01

The EGRET telescope on CGRO detected more than sixty sources of high-energy gamma radiation associated with active galactic nuclei (AGN). All but one of those belong to the blazar subclass; the only exception is the nearby radio galaxy Centaurus A. Since there is no obvious reason other than proximity to expect Cen A to be the only non-blazar AGN emitting in high-energy gamma rays, we have utilized the "stacking" technique to search for $>100$-MeV emission from two non-blazar AGN subclasses, radio galaxies and Seyfert galaxies. Maps of gamma-ray counts, exposure, and diffuse background have been created, then co-added in varying numbers based on sorts by redshift, 5-GHZ flux density, and optical brightness, and finally tested for gamma-ray emission. No detection significance greater than $2\\sigma$ has been found for any subclass, sorting parameter, or number of objects co-added. Monte Carlo simulations have also been performed, to validate the technique and estimate the significance of the results.

Multi-objective optimization of an arch dam shape under static loads using an evolutionary game method

NASA Astrophysics Data System (ADS)

Meng, Rui; Cheong, Kang Hao; Bao, Wei; Wong, Kelvin Kian Loong; Wang, Lu; Xie, Neng-gang

2018-06-01

This article attempts to evaluate the safety and economic performance of an arch dam under the action of static loads. The geometric description of a crown cantilever section and the horizontal arch ring is presented. A three-objective optimization model of arch dam shape is established based on the arch dam volume, maximum principal tensile stress and total strain energy. The evolutionary game method is then applied to obtain the optimal solution. In the evolutionary game technique, a novel and more efficient exploration method of the game players' strategy space, named the 'sorting partition method under the threshold limit', is presented, with the game profit functions constructed according to both competitive and cooperative behaviour. By way of example, three optimization goals have all shown improvements over the initial solutions. In particular, the evolutionary game method has potentially faster convergence. This demonstrates the preliminary proof of principle of the evolutionary game method.

Exploring student preferences with a Q-sort: the development of an individualized renal physiology curriculum

PubMed Central

Roberts, John K.; Hargett, Charles W.; Nagler, Alisa; Jakoi, Emma

2015-01-01

Medical education reform is underway, but the optimal course for change has yet to be seen. While planning for the redesign of a renal physiology course at the Duke School of Medicine, the authors used a Q-sort survey to assess students' attitudes and learning preferences to inform curricular change. The authors invited first-year medical students at the Duke School of Medicine to take a Q-sort survey on the first day of renal physiology. Students prioritized statements related to their understanding of renal physiology, learning preferences, preferred course characteristics, perceived clinical relevance of renal physiology, and interest in nephrology as a career. By-person factor analysis was performed using the centroid method. Three dominant factors were strongly defined by learning preferences: “readers” prefer using notes, a textbook, and avoid lectures; “social-auditory learners” prefer attending lectures, interactivity, and working with peers; and “visual learners” prefer studying images, diagrams, and viewing materials online. A smaller, fourth factor represented a small group of students with a strong predisposition against renal physiology and nephrology. In conclusion, the Q-sort survey identified and then described in detail the dominant viewpoints of our students. Learning style preferences better classified first-year students rather than any of the other domains. A more individualized curriculum would simultaneously cater to the different types of learners in the classroom. PMID:26330030

Exploring student preferences with a Q-sort: the development of an individualized renal physiology curriculum.

PubMed

Roberts, John K; Hargett, Charles W; Nagler, Alisa; Jakoi, Emma; Lehrich, Ruediger W

2015-09-01

Medical education reform is underway, but the optimal course for change has yet to be seen. While planning for the redesign of a renal physiology course at the Duke School of Medicine, the authors used a Q-sort survey to assess students' attitudes and learning preferences to inform curricular change. The authors invited first-year medical students at the Duke School of Medicine to take a Q-sort survey on the first day of renal physiology. Students prioritized statements related to their understanding of renal physiology, learning preferences, preferred course characteristics, perceived clinical relevance of renal physiology, and interest in nephrology as a career. By-person factor analysis was performed using the centroid method. Three dominant factors were strongly defined by learning preferences: "readers" prefer using notes, a textbook, and avoid lectures; "social-auditory learners" prefer attending lectures, interactivity, and working with peers; and "visual learners" prefer studying images, diagrams, and viewing materials online. A smaller, fourth factor represented a small group of students with a strong predisposition against renal physiology and nephrology. In conclusion, the Q-sort survey identified and then described in detail the dominant viewpoints of our students. Learning style preferences better classified first-year students rather than any of the other domains. A more individualized curriculum would simultaneously cater to the different types of learners in the classroom. Copyright © 2015 The American Physiological Society.

Using a "time machine" to test for local adaptation of aquatic microbes to temporal and spatial environmental variation.

PubMed

Fox, Jeremy W; Harder, Lawrence D

2015-01-01

Local adaptation occurs when different environments are dominated by different specialist genotypes, each of which is relatively fit in its local conditions and relatively unfit under other conditions. Analogously, ecological species sorting occurs when different environments are dominated by different competing species, each of which is relatively fit in its local conditions. The simplest theory predicts that spatial, but not temporal, environmental variation selects for local adaptation (or generates species sorting), but this prediction is difficult to test. Although organisms can be reciprocally transplanted among sites, doing so among times seems implausible. Here, we describe a reciprocal transplant experiment testing for local adaptation or species sorting of lake bacteria in response to both temporal and spatial variation in water chemistry. The experiment used a -80°C freezer as a "time machine." Bacterial isolates and water samples were frozen for later use, allowing transplantation of older isolates "forward in time" and newer isolates "backward in time." Surprisingly, local maladaptation predominated over local adaptation in both space and time. Such local maladaptation may indicate that adaptation, or the analogous species sorting process, fails to keep pace with temporal fluctuations in water chemistry. This hypothesis could be tested with more finely resolved temporal data. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Using MOEA with Redistribution and Consensus Branches to Infer Phylogenies.

PubMed

Min, Xiaoping; Zhang, Mouzhao; Yuan, Sisi; Ge, Shengxiang; Liu, Xiangrong; Zeng, Xiangxiang; Xia, Ningshao

2017-12-26

In recent years, to infer phylogenies, which are NP-hard problems, more and more research has focused on using metaheuristics. Maximum Parsimony and Maximum Likelihood are two effective ways to conduct inference. Based on these methods, which can also be considered as the optimal criteria for phylogenies, various kinds of multi-objective metaheuristics have been used to reconstruct phylogenies. However, combining these two time-consuming methods results in those multi-objective metaheuristics being slower than a single objective. Therefore, we propose a novel, multi-objective optimization algorithm, MOEA-RC, to accelerate the processes of rebuilding phylogenies using structural information of elites in current populations. We compare MOEA-RC with two representative multi-objective algorithms, MOEA/D and NAGA-II, and a non-consensus version of MOEA-RC on three real-world datasets. The result is, within a given number of iterations, MOEA-RC achieves better solutions than the other algorithms.

Non-contact radio frequency shielding and wave guiding by multi-folded transformation optics method

PubMed Central

Madni, Hamza Ahmad; Zheng, Bin; Yang, Yihao; Wang, Huaping; Zhang, Xianmin; Yin, Wenyan; Li, Erping; Chen, Hongsheng

2016-01-01

Compared with conventional radio frequency (RF) shielding methods in which the conductive coating material encloses the circuits design and the leakage problem occurs due to the gap in such conductive material, non-contact RF shielding at a distance is very promising but still impossible to achieve so far. In this paper, a multi-folded transformation optics method is proposed to design a non-contact device for RF shielding. This “open-shielded” device can shield any object at a distance from the electromagnetic waves at the operating frequency, while the object is still physically open to the outer space. Based on this, an open-carpet cloak is proposed and the functionality of the open-carpet cloak is demonstrated. Furthermore, we investigate a scheme of non-contact wave guiding to remotely control the propagation of surface waves over any obstacles. The flexibilities of such multi-folded transformation optics method demonstrate the powerfulness of the method in the design of novel remote devices with impressive new functionalities. PMID:27841358

Multiobjective synchronization of coupled systems

NASA Astrophysics Data System (ADS)

Tang, Yang; Wang, Zidong; Wong, W. K.; Kurths, Jürgen; Fang, Jian-an

2011-06-01

In this paper, multiobjective synchronization of chaotic systems is investigated by especially simultaneously minimizing optimization of control cost and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach that includes a hybrid chromosome representation. The hybrid encoding scheme combines binary representation with real number representation. The constraints on the coupling form are also considered by converting the multiobjective synchronization into a multiobjective constraint problem. In addition, the performances of the adaptive learning method and non-dominated sorting genetic algorithm-II as well as the effectiveness and contributions of the proposed approach are analyzed and validated through the Rössler system in a chaotic or hyperchaotic regime and delayed chaotic neural networks.

Learning the Rules: Observation and Imitation of a Sorting Strategy by 36-Month-Old Children

ERIC Educational Resources Information Center

Williamson, Rebecca A.; Jaswal, Vikram K.; Meltzoff, Andrew N.

2010-01-01

Two experiments were used to investigate the scope of imitation by testing whether 36-month-olds can learn to produce a categorization strategy through observation. After witnessing an adult sort a set of objects by a visible property (their color; Experiment 1) or a nonvisible property (the particular sounds produced when the objects were shaken;…

Does research through Structured Operational Research and Training (SORT IT) courses impact policy and practice?

PubMed Central

Shewade, H. D.; Tripathy, J. P.; Guillerm, N.; Tayler-Smith, K.; Berger, S. Dar; Bissell, K.; Reid, A. J.; Zachariah, R.; Harries, A. D.

2016-01-01

Setting: Structured Operational Research and Training Initiative (SORT IT) courses are well known for their output, with nearly 90% of participants completing the course and publishing in scientific journals. Objective: We assessed the impact of research papers on policy and practice that resulted from six SORT IT courses initiated between July 2012 and March 2013. Design: This was a cross-sectional study involving e-mail-based, self-administered questionnaires and telephone/skype/in-person responses from first and/or senior co-authors of course papers. A descriptive content analysis of the responses was performed and categorised into themes. Results: Of 72 participants, 63 (88%) completed the course. Course output included 81 submitted papers, of which 76 (94%) were published. Of the 81 papers assessed, 45 (55%) contributed to a change in policy and/or practice: 29 contributed to government policy/practice change (20 at national, 4 at subnational and 5 at hospital level), 11 to non-government organisational policy change and 5 to reinforcing existing policy. The changes ranged from modifications of monitoring and evaluation tools, to redrafting of national guidelines, to scaling up existing policies. Conclusion: More than half of the SORT IT course papers contributed to a change in policy and/or practice. Future assessments should include more robust and independent verification of the reported change(s) with all stakeholders. PMID:27051612

Multi-criteria analysis for PM10 planning

NASA Astrophysics Data System (ADS)

Pisoni, Enrico; Carnevale, Claudio; Volta, Marialuisa

To implement sound air quality policies, Regulatory Agencies require tools to evaluate outcomes and costs associated to different emission reduction strategies. These tools are even more useful when considering atmospheric PM10 concentrations due to the complex nonlinear processes that affect production and accumulation of the secondary fraction of this pollutant. The approaches presented in the literature (Integrated Assessment Modeling) are mainly cost-benefit and cost-effective analysis. In this work, the formulation of a multi-objective problem to control particulate matter is proposed. The methodology defines: (a) the control objectives (the air quality indicator and the emission reduction cost functions); (b) the decision variables (precursor emission reductions); (c) the problem constraints (maximum feasible technology reductions). The cause-effect relations between air quality indicators and decision variables are identified tuning nonlinear source-receptor models. The multi-objective problem solution provides to the decision maker a set of not-dominated scenarios representing the efficient trade-off between the air quality benefit and the internal costs (emission reduction technology costs). The methodology has been implemented for Northern Italy, often affected by high long-term exposure to PM10. The source-receptor models used in the multi-objective analysis are identified processing long-term simulations of GAMES multiphase modeling system, performed in the framework of CAFE-Citydelta project.

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions.

PubMed

Qu, Shaojian; Ji, Ying

2016-01-01

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.

Multi-block analysis coupled to laser-induced breakdown spectroscopy for sorting geological materials from caves.

PubMed

Ammari, Faten; Bassel, Léna; Ferrier, Catherine; Lacanette, Delphine; Chapoulie, Rémy; Bousquet, Bruno

2016-10-01

In this study, multi-block analysis was applied for the first time to LIBS spectra provided by a portable LIBS system (IVEA Solution, France) equipped with three compact Czerny-Turner spectrometers covering the spectral ranges 200-397nm, 398-571nm and 572-1000nm. 41 geological samples taken from a laboratory-cave situated in the "Vézère valley", an area rich with prehistoric sites and decorated caves listed as a UNESCO world heritage in the south west of France, were analyzed. They were composed of limestone and clay considered as underlying supports and of two types of alterations referred as moonmilk and coralloid. Common Components and Specific Weights Analysis (CCSWA) allowed sorting moonmilk and coralloid samples. The loadings revealed higher amounts of magnesium, silicon, aluminum and strontium in coralloids and the saliences emphasized that among the three spectrometers installed in the LIBS instrument used in this work; that covering the range 572-1000nm was less contributive. This new approach for processing LIBS data not only provides good results for sorting geological materials but also clearly reveals which spectral range contains most of the information. This specific advantage of multi-block analysis could lead for some applications to simplify the design and to reduce the size of LIBS instruments. Copyright © 2016 Elsevier B.V. All rights reserved.

Does language dominance affect cognitive performance in bilinguals? Lifespan evidence from preschoolers through older adults on card sorting, Simon, and metalinguistic tasks

PubMed Central

Gathercole, Virginia C. Mueller; Thomas, Enlli M.; Kennedy, Ivan; Prys, Cynog; Young, Nia; Viñas Guasch, Nestor; Roberts, Emily J.; Hughes, Emma K.; Jones, Leah

2014-01-01

This study explores the extent to which a bilingual advantage can be observed for three tasks in an established population of fully fluent bilinguals from childhood through adulthood. Welsh-English simultaneous and early sequential bilinguals, as well as English monolinguals, aged 3 years through older adults, were tested on three sets of cognitive and executive function tasks. Bilinguals were Welsh-dominant, balanced, or English-dominant, with only Welsh, Welsh and English, or only English at home. Card sorting, Simon, and a metalinguistic judgment task (650, 557, and 354 participants, respectively) reveal little support for a bilingual advantage, either in relation to control or globally. Primarily there is no difference in performance across groups, but there is occasionally better performance by monolinguals or persons dominant in the language being tested, and in one case-in one condition and in one age group-lower performance by the monolinguals. The lack of evidence for a bilingual advantage in these simultaneous and early sequential bilinguals suggests the need for much closer scrutiny of what type of bilingual might demonstrate the reported effects, under what conditions, and why. PMID:24550853

Proposal for Implementing Multi-User Database (MUD) Technology in an Academic Library.

ERIC Educational Resources Information Center

Filby, A. M. Iliana

1996-01-01

Explores the use of MOO (multi-user object oriented) virtual environments in academic libraries to enhance reference services. Highlights include the development of multi-user database (MUD) technology from gaming to non-recreational settings; programming issues; collaborative MOOs; MOOs as distinguished from other types of virtual reality; audio…

Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

PubMed

Leibig, Christian; Wachtler, Thomas; Zeck, Günther

2016-09-15

Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

The Role of the Clathrin Adaptor AP-1: Polarized Sorting and Beyond

PubMed Central

Nakatsu, Fubito; Hase, Koji; Ohno, Hiroshi

2014-01-01

The selective transport of proteins or lipids by vesicular transport is a fundamental process supporting cellular physiology. The budding process involves cargo sorting and vesicle formation at the donor membrane and constitutes an important process in vesicular transport. This process is particularly important for the polarized sorting in epithelial cells, in which the cargo molecules need to be selectively sorted and transported to two distinct destinations, the apical or basolateral plasma membrane. Adaptor protein (AP)-1, a member of the AP complex family, which includes the ubiquitously expressed AP-1A and the epithelium-specific AP-1B, regulates polarized sorting at the trans-Golgi network and/or at the recycling endosomes. A growing body of evidence, especially from studies using model organisms and animals, demonstrates that the AP-1-mediated polarized sorting supports the development and physiology of multi-cellular units as functional organs and tissues (e.g., cell fate determination, inflammation and gut immune homeostasis). Furthermore, a possible involvement of AP-1B in the pathogenesis of human diseases, such as Crohn’s disease and cancer, is now becoming evident. These data highlight the significant contribution of AP-1 complexes to the physiology of multicellular organisms, as master regulators of polarized sorting in epithelial cells. PMID:25387275

Retrospective multi-phase non-contrast-enhanced magnetic resonance angiography (ROMANCE MRA) for robust angiogram separation in the presence of cardiac arrhythmia.

PubMed

Kim, Hahnsung; Park, Suhyung; Kim, Eung Yeop; Park, Jaeseok

2018-09-01

To develop a novel, retrospective multi-phase non-contrast-enhanced MRA (ROMANCE MRA) in a single acquisition for robust angiogram separation even in the presence of cardiac arrhythmia. In the proposed ROMANCE MRA, data were continuously acquired over all cardiac phases using retrospective, multi-phase flow-sensitive single-slab 3D fast spin echo (FSE) with variable refocusing flip angles, while an external pulse oximeter was in sync with pulse repetitions in FSE to record real-time information on cardiac cycles. Data were then sorted into k-bin space using the real-time cardiac information. Angiograms were reconstructed directly from k-bin space by solving a constrained optimization problem with both subtraction-induced sparsity and low rank priors. Peripheral MRA was performed in normal volunteers with/without caffeine consumption and a volunteer with cardiac arrhythmia using conventional fresh blood imaging (FBI) and the proposed ROMANCE MRA for comparison. The proposed ROMANCE MRA shows superior performance in accurately delineating both major and small vessel branches with robust background suppression if compared with conventional FBI. Even in the presence of irregular heartbeats, the proposed method exhibits clear depiction of angiograms over conventional methods within clinically reasonable imaging time. We successfully demonstrated the feasibility of the proposed ROMANCE MRA in generating robust angiograms with background suppression. © 2018 International Society for Magnetic Resonance in Medicine.

Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

NASA Astrophysics Data System (ADS)

Akhtar, Taimoor; Shoemaker, Christine

2016-04-01

Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual analytics framework for decision support in selection of one parameter combination from the alternatives identified in Stage 2. HAMS is applied for calibration of flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville watershed in upstate New York. Results from the application of HAMS to Cannonsville indicate that efficient multi-objective optimization and interactive visual and metric based analytics can bridge the gap between the effective use of both automatic and manual strategies for parameter estimation of computationally expensive watershed models.

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

PubMed Central

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-01-01

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

PubMed

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-08-13

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

Hydrodynamic lift of vesicles and red blood cells in flow--from Fåhræus & Lindqvist to microfluidic cell sorting.

PubMed

Geislinger, Thomas M; Franke, Thomas

2014-06-01

Hydrodynamic lift forces acting on cells and particles in fluid flow receive ongoing attention from medicine, mathematics, physics and engineering. The early findings of Fåhræus & Lindqvist on the viscosity change of blood with the diameter of capillaries motivated extensive studies both experimentally and theoretically to illuminate the underlying physics. We review this historical development that led to the discovery of the inertial and non-inertial lift forces and elucidate the origins of these forces that are still not entirely clear. Exploiting microfluidic techniques induced a tremendous amount of new insights especially into the more complex interactions between the flow field and deformable objects like vesicles or red blood cells. We trace the way from the investigation of single cell dynamics to the recent developments of microfluidic techniques for particle and cell sorting using hydrodynamic forces. Such continuous and label-free on-chip cell sorting devices promise to revolutionize medical analyses for personalized point-of-care diagnosis. We present the state-of-the-art of different hydrodynamic lift-based techniques and discuss their advantages and limitations. Copyright © 2014 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beer, Neil Reginald; Lee, Abraham; Hatch, Andrew

A non-contact system for sorting monodisperse water-in-oil emulsion droplets in a microfluidic device based on the droplet's contents and their interaction with an applied electromagnetic field or by identification and sorting.

Design and control of multifunctional sorting and training platform based on PLC control

NASA Astrophysics Data System (ADS)

Wan, Hongqiang; Ge, Shuai; Han, Peiying; Li, Fancong; Zhang, Simiao

2018-05-01

Electromechanical integration, as a multi-disciplinary subject, has been paid much attention by universities and is widely used in the automation production of enterprises. Aiming at the problem of the lack of control among enterprises and the lack of training among colleges and universities, this paper presents a design of multifunctional sorting training platform based on PLC control. Firstly, the structure of the platform is determined and three-dimensional modeling is done. Then design the platform's aerodynamic control and electrical control. Finally, realize the platform sorting function through PLC programming and configuration software development. The training platform can be used to design the practical training experiment, which has a strong advance and pertinence in the electromechanical integration teaching. At the same time, the platform makes full use of modular thinking to make the sorting modules more flexible. Compared with the traditional training platform, its teaching effect is more significant.

Sensor module design and forward and inverse kinematics analysis of 6-DOF sorting transferring robot

NASA Astrophysics Data System (ADS)

Zhou, Huiying; Lin, Jiajian; Liu, Lei; Tao, Meng

2017-09-01

To meet the demand of high strength express sorting, it is significant to design a robot with multiple degrees of freedom that can sort and transfer. This paper uses infrared sensor, color sensor and pressure sensor to receive external information, combine the plan of motion path in advance and the feedback information from the sensors, then write relevant program. In accordance with these, we can design a 6-DOF robot that can realize multi-angle seizing. In order to obtain characteristics of forward and inverse kinematics, this paper describes the coordinate directions and pose estimation by the D-H parameter method and closed solution. On the basis of the solution of forward and inverse kinematics, geometric parameters of links and link parameters are optimized in terms of application requirements. In this way, this robot can identify route, sort and transfer.

Explore the impacts of river flow and quality on biodiversity for water resources management by AI techniques

NASA Astrophysics Data System (ADS)

Chang, Fi-John; Tsai Tsai, Wen-Ping; Chang, Li-Chiu

2016-04-01

Water resources development is very challenging in Taiwan due to her diverse geographic environment and climatic conditions. To pursue sustainable water resources development, rationality and integrity is essential for water resources planning. River water quality and flow regimes are closely related to each other and affect river ecosystems simultaneously. This study aims to explore the complex impacts of water quality and flow regimes on fish community in order to comprehend the situations of the eco-hydrological system in the Danshui River of northern Taiwan. To make an effective and comprehensive strategy for sustainable water resources management, this study first models fish diversity through implementing a hybrid artificial neural network (ANN) based on long-term observational heterogeneity data of water quality, stream flow and fish species in the river. Then we use stream flow to estimate the loss of dissolved oxygen based on back-propagation neural networks (BPNNs). Finally, the non-dominated sorting genetic algorithm II (NSGA-II) is established for river flow management over the Shihmen Reservoir which is the main reservoir in this study area. In addition to satisfying the water demands of human beings and ecosystems, we also consider water quality for river flow management. The ecosystem requirement takes the form of maximizing fish diversity, which can be estimated by the hybrid ANN. The human requirement is to provide a higher satisfaction degree of water supply while the water quality requirement is to reduce the loss of dissolved oxygen in the river among flow stations. The results demonstrate that the proposed methodology can offer diversified alternative strategies for reservoir operation and improve reservoir operation strategies for producing downstream flows that could better meet both human and ecosystem needs as well as maintain river water quality. Keywords: Artificial intelligence (AI), Artificial neural networks (ANNs), Non-dominated sorting genetic algorithm II (NSGA-II), Sustainable water resources management, Flow regime, River ecosystem.

Preschool Children's Performance in Task Switching on the Dimensional Change Card Sort Task: Separating the Dimensions Aids the Ability to Switch

PubMed Central

Diamond, Adele; Carlson, Stephanie M.; Beck, Danielle M.

2006-01-01

Fifty-seven children (53% female) at 3 ages (2½, 3, and 3½ years) were tested on the standard Dimensional Change Card Sort (DCCS) task with integrated stimuli (e.g., a red truck) and on a separated-dimensions version where colorless shapes were presented on a colored background (e.g., a black truck on a red background). Roughly twice as many children successfully switched sorting dimensions when color was a property of the background than when color was a property of the shape itself. Children succeeded 6 months earlier in switching sorting criteria when the dimensions were separated. When evidence of both indecision and accuracy was taken into account, a clear and rich developmental progression emerged. These results support an inhibitory control interpretation of preschoolers' problems on the DCCS task. Diamond theorized that young children can have difficulty integrating features not part of a single object and separating features of a single object so that the object can be categorized first by one attribute and then by another. Preschoolers remain stuck in thinking about objects according to the objects' initially relevant attribute (attentional inertia; Kirkham, Cruess, & Diamond, 2003). To switch perspectives, the old way of thinking about the objects must be inhibited. Separating color and shape reduced the need for such inhibition; a truck was always a truck, and the background was always red. PMID:16144433

Sediment grain-size characteristics and relevant correlations to the aeolian environment in China's eastern desert region.

PubMed

Zhang, Chunlai; Shen, Yaping; Li, Qing; Jia, Wenru; Li, Jiao; Wang, Xuesong

2018-06-15

To identify characteristics of aeolian activity and the aeolian environment in China's eastern desert region, this study collected surface sediment samples from the main desert and sandy lands in this region: the Hobq Desert and the Mu Us, Otindag, Horqin, and Hulunbuir sandy lands. We analyzed the grain-size characteristics and their relationships to three key environmental indicators: drift potential, the dune mobility index, and vegetation cover. The main sediment components are fine to medium sands, with poor (Hulunbuir) to moderate (all other areas) sorting, of unimodal to bimodal distribution. This suggests that improved sorting is accomplished by the loss of both relatively coarser and finer grains. Since 2000, China's eastern desert region has generally experienced low wind energy environmental conditions, resulting in decreased dune activity. In the Hobq Desert, however, the dry climate and sparse vegetation, in conjunction with the most widely distributed mobile dune area in the eastern desert region, have led to frequent and intense aeolian activity, including wind erosion, sand transport, and deposition, resulting in conditions for good sediment sorting. In the Mu Us, Otindag, and Horqin sandy lands, mosaic distribution has resulted from wind erosion-dominated and deposition-dominated aeolian environments. In the Hulunbuir Sandy Land, high precipitation, low temperatures, and steppe vegetation have resulted in well-developed soils; however, strong winds and flat terrain have created an aeolian environment dominated by wind erosion. Copyright © 2018. Published by Elsevier B.V.

Magnetic Polarization Measurements of the Multi-modal Plasma Response to 3D fields in the EAST Tokamak

DOE PAGES

Logan, Nikolas; Cui, L.; Wang, Hui -Hui; ...

2018-04-30

A multi-modal plasma response to applied non-axisymmetric fields has been found in EAST tokamak plasmas. Here, multi-modal means the radial and poloidal structure of an individually driven toroidal harmonic is not fixed. The signature of such a multi-modal response is the magnetic polarization (ratio of radial and poloidal components) of the plasma response field measured on the low field side device mid-plane. A difference in the 3D coil phasing (the relative phase of two coil arrays) dependencies between the two responses is observed in response to n=2 fields in the same plasma for which the n=1 responses are well synchronized.more » Neither the maximum radial nor the maximum poloidal field response to n=2 fields agrees with the best applied phasing for mitigating edge localized modes, suggesting that the edge plasma response is not a dominant component of either polarization. GPEC modeling reproduces the discrepant phasing dependences of the experimental measurements, and confirms the edge resonances are maximized by the coil phasing that mitigates ELMs in the experiments. The model confirms the measured plasma response is not dominated by resonant current drive from the external field. Instead, non-resonant contributions play a large role in the diagnostic signal for both toroidal harmonics n=1 and n=2. The analysis in this paper demonstrates the ability of 3D modeling to connect external magnetic sensor measurements to the internal plasma physics and accurately predict optimal applied 3D field configurations in multi-modal plasmas.« less

Magnetic Polarization Measurements of the Multi-modal Plasma Response to 3D fields in the EAST Tokamak

DOE Office of Scientific and Technical Information (OSTI.GOV)

Logan, Nikolas; Cui, L.; Wang, Hui -Hui

A multi-modal plasma response to applied non-axisymmetric fields has been found in EAST tokamak plasmas. Here, multi-modal means the radial and poloidal structure of an individually driven toroidal harmonic is not fixed. The signature of such a multi-modal response is the magnetic polarization (ratio of radial and poloidal components) of the plasma response field measured on the low field side device mid-plane. A difference in the 3D coil phasing (the relative phase of two coil arrays) dependencies between the two responses is observed in response to n=2 fields in the same plasma for which the n=1 responses are well synchronized.more » Neither the maximum radial nor the maximum poloidal field response to n=2 fields agrees with the best applied phasing for mitigating edge localized modes, suggesting that the edge plasma response is not a dominant component of either polarization. GPEC modeling reproduces the discrepant phasing dependences of the experimental measurements, and confirms the edge resonances are maximized by the coil phasing that mitigates ELMs in the experiments. The model confirms the measured plasma response is not dominated by resonant current drive from the external field. Instead, non-resonant contributions play a large role in the diagnostic signal for both toroidal harmonics n=1 and n=2. The analysis in this paper demonstrates the ability of 3D modeling to connect external magnetic sensor measurements to the internal plasma physics and accurately predict optimal applied 3D field configurations in multi-modal plasmas.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edjabou, Maklawe Essonanawe, E-mail: vine@env.dtu.dk; Jensen, Morten Bang; Götze, Ramona

Highlights: • Tiered approach to waste sorting ensures flexibility and facilitates comparison of solid waste composition data. • Food and miscellaneous wastes are the main fractions contributing to the residual household waste. • Separation of food packaging from food leftovers during sorting is not critical for determination of the solid waste composition. - Abstract: Sound waste management and optimisation of resource recovery require reliable data on solid waste generation and composition. In the absence of standardised and commonly accepted waste characterisation methodologies, various approaches have been reported in literature. This limits both comparability and applicability of the results. In thismore » study, a waste sampling and sorting methodology for efficient and statistically robust characterisation of solid waste was introduced. The methodology was applied to residual waste collected from 1442 households distributed among 10 individual sub-areas in three Danish municipalities (both single and multi-family house areas). In total 17 tonnes of waste were sorted into 10–50 waste fractions, organised according to a three-level (tiered approach) facilitating comparison of the waste data between individual sub-areas with different fractionation (waste from one municipality was sorted at “Level III”, e.g. detailed, while the two others were sorted only at “Level I”). The results showed that residual household waste mainly contained food waste (42 ± 5%, mass per wet basis) and miscellaneous combustibles (18 ± 3%, mass per wet basis). The residual household waste generation rate in the study areas was 3–4 kg per person per week. Statistical analyses revealed that the waste composition was independent of variations in the waste generation rate. Both, waste composition and waste generation rates were statistically similar for each of the three municipalities. While the waste generation rates were similar for each of the two housing types (single-family and multi-family house areas), the individual percentage composition of food waste, paper, and glass was significantly different between the housing types. This indicates that housing type is a critical stratification parameter. Separating food leftovers from food packaging during manual sorting of the sampled waste did not have significant influence on the proportions of food waste and packaging materials, indicating that this step may not be required.« less

Motor and Executive Function Profiles in Adult Residents Environmentally Exposed to Manganese

EPA Science Inventory

Objective: Exposure to elevated levels of manganese (Mn) may be associated with tremor, motor and executive dysfunction (EF), clinically resembling Parkinson’s disease (PD). PD research has identified tremor-dominant (TD) and non-tremor dominant (NTD) profiles. NTD PD pres...

Identification of pre-impact conditions of a cyclist involved in a vehicle-bicycle accident using an optimized MADYMO reconstruction combined with motion capture.

PubMed

Sun, Jie; Li, Zhengdong; Pan, Shaoyou; Feng, Hao; Shao, Yu; Liu, Ningguo; Huang, Ping; Zou, Donghua; Chen, Yijiu

2018-05-01

The aim of the present study was to develop an improved method, using MADYMO multi-body simulation software combined with an optimization method and three-dimensional (3D) motion capture, for identifying the pre-impact conditions of a cyclist (walking or cycling) involved in a vehicle-bicycle accident. First, a 3D motion capture system was used to analyze coupled motions of a volunteer while walking and cycling. The motion capture results were used to define the posture of the human model during walking and cycling simulations. Then, cyclist, bicycle and vehicle models were developed. Pre-impact parameters of the models were treated as unknown design variables. Finally, a multi-objective genetic algorithm, the nondominated sorting genetic algorithm II, was used to find optimal solutions. The objective functions of the walk parameter were significantly lower than cycle parameter; thus, the cyclist was more likely to have been walking with the bicycle than riding the bicycle. In the most closely matched result found, all observed contact points matched and the injury parameters correlated well with the real injuries sustained by the cyclist. Based on the real accident reconstruction, the present study indicates that MADYMO multi-body simulation software, combined with an optimization method and 3D motion capture, can be used to identify the pre-impact conditions of a cyclist involved in a vehicle-bicycle accident. Copyright © 2018. Published by Elsevier Ltd.

Application of sperm sorting and associated reproductive technology for wildlife management and conservation.

PubMed

O'Brien, J K; Steinman, K J; Robeck, T R

2009-01-01

Efforts toward the conservation and captive breeding of wildlife can be enhanced by sperm sorting and associated reproductive technologies such as sperm cryopreservation and artificial insemination (AI). Sex ratio management is of particular significance to species which naturally exist in female-dominated social groups. A bias of the sex ratio towards females of these species will greatly assist in maintaining socially cohesive groups and minimizing male-male aggression. Another application of this technology potentially exists for endangered species, as the preferential production of females can enable propagation of those species at a faster rate. The particular assisted reproductive technology (ART) used in conjunction with sperm sorting for the production of offspring is largely determined by the quality and quantity of spermatozoa following sorting and preservation processes. Regardless of the ART selected, breeding decisions involving sex-sorted spermatozoa should be made in conjunction with appropriate genetic management. Zoological-based research on reproductive physiology and assisted reproduction, including sperm sorting, is being conducted on numerous terrestrial and marine mammals. The wildlife species for which the technology has undergone the most advance is the bottlenose dolphin. AI using sex-sorted fresh or frozen-thawed spermatozoa has become a valuable tool for the genetic and reproductive management of captive bottlenose dolphins with six pre-sexed calves, all of the predetermined sex born to date.

Advancing computational methods for calibration of the Soil and Water Assessment Tool (SWAT): Application for modeling climate change impacts on water resources in the Upper Neuse Watershed of North Carolina

NASA Astrophysics Data System (ADS)

Ercan, Mehmet Bulent

Watershed-scale hydrologic models are used for a variety of applications from flood prediction, to drought analysis, to water quality assessments. A particular challenge in applying these models is calibration of the model parameters, many of which are difficult to measure at the watershed-scale. A primary goal of this dissertation is to contribute new computational methods and tools for calibration of watershed-scale hydrologic models and the Soil and Water Assessment Tool (SWAT) model, in particular. SWAT is a physically-based, watershed-scale hydrologic model developed to predict the impact of land management practices on water quality and quantity. The dissertation follows a manuscript format meaning it is comprised of three separate but interrelated research studies. The first two research studies focus on SWAT model calibration, and the third research study presents an application of the new calibration methods and tools to study climate change impacts on water resources in the Upper Neuse Watershed of North Carolina using SWAT. The objective of the first two studies is to overcome computational challenges associated with calibration of SWAT models. The first study evaluates a parallel SWAT calibration tool built using the Windows Azure cloud environment and a parallel version of the Dynamically Dimensioned Search (DDS) calibration method modified to run in Azure. The calibration tool was tested for six model scenarios constructed using three watersheds of increasing size (the Eno, Upper Neuse, and Neuse) for both a 2 year and 10 year simulation duration. Leveraging the cloud as an on demand computing resource allowed for a significantly reduced calibration time such that calibration of the Neuse watershed went from taking 207 hours on a personal computer to only 3.4 hours using 256 cores in the Azure cloud. The second study aims at increasing SWAT model calibration efficiency by creating an open source, multi-objective calibration tool using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This tool was demonstrated through an application for the Upper Neuse Watershed in North Carolina, USA. The objective functions used for the calibration were Nash-Sutcliffe (E) and Percent Bias (PB), and the objective sites were the Flat, Little, and Eno watershed outlets. The results show that the use of multi-objective calibration algorithms for SWAT calibration improved model performance especially in terms of minimizing PB compared to the single objective model calibration. The third study builds upon the first two studies by leveraging the new calibration methods and tools to study future climate impacts on the Upper Neuse watershed. Statistically downscaled outputs from eight Global Circulation Models (GCMs) were used for both low and high emission scenarios to drive a well calibrated SWAT model of the Upper Neuse watershed. The objective of the study was to understand the potential hydrologic response of the watershed, which serves as a public water supply for the growing Research Triangle Park region of North Carolina, under projected climate change scenarios. The future climate change scenarios, in general, indicate an increase in precipitation and temperature for the watershed in coming decades. The SWAT simulations using the future climate scenarios, in general, suggest an increase in soil water and water yield, and a decrease in evapotranspiration within the Upper Neuse watershed. In summary, this dissertation advances the field of watershed-scale hydrologic modeling by (i) providing some of the first work to apply cloud computing for the computationally-demanding task of model calibration; (ii) providing a new, open source library that can be used by SWAT modelers to perform multi-objective calibration of their models; and (iii) advancing understanding of climate change impacts on water resources for an important watershed in the Research Triangle Park region of North Carolina. The third study leveraged the methodological advances presented in the first two studies. Therefore, the dissertation contains three independent by interrelated studies that collectively advance the field of watershed-scale hydrologic modeling and analysis.

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions

PubMed Central

Qu, Shaojian; Ji, Ying

2016-01-01

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our “worst-case weighted multi-objective game” model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call “robust-weighted Nash equilibrium”. We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications. PMID:26820512

Building Global Capacity for Conducting Operational Research Using the SORT IT Model: Where and Who?

PubMed Central

Zachariah, Rony; Rust, Stefanie; Berger, Selma Dar; Guillerm, Nathalie; Bissell, Karen; Delaunois, Paul; Reid, Anthony J.; Kumar, Ajay M. V.; Olliaro, Piero L.; Reeder, John C.; Harries, Anthony D.; Ramsay, Andrew

2016-01-01

Setting Research capacity is weakest in low and middle-income countries (LMICs) where operational research is highly relevant and needed. Structured Operational Research and Training Initiative (SORT IT) courses have been developed to train participants to conduct and publish operational research and influence policy and practice. Twenty courses were completed in Asia, Africa, Europe and the South Pacific between 2009 and 2014. Objectives In the 20 completed SORT IT courses, to assess where the research was conducted, who was trained, who became facilitators in subsequent courses and course outcomes. Design A cohort study of completed SORT IT courses Results There were 236 participants (41% female) including 64 nationalities who conducted research in 59 countries, mostly from Asia and Africa (mean course duration = 9.7 months). Most participants (68%) were from government health programs and non-governmental agencies. A total of 213(90%) participants completed all milestones successfully with 41(19%) becoming subsequent course facilitators, 88% of whom were from LMICs. Of 228 manuscripts submitted to scientific journals, 197(86%) were either published or in press; in 86%, the principal investigator (first author) was a LMIC national. Papers were published in 23 scientific journals (impact factor 0.5–4.4) and covered 21 disease categories (median publication time = 5.7 months). Published papers (186) had 94,794 cumulative article views/downloads. Article views/downloads for immediate open access articles were double those from closed access journals. Conclusion The SORT IT model has been effective in training personnel to produce relevant operational research in LMICs. It merits continued commitment and support for further scale-up and development. PMID:27505253

Microfluidic EmbryoSort technology: towards in flow analysis, sorting and dispensing of individual vertebrate embryos

NASA Astrophysics Data System (ADS)

Fuad, Nurul M.; Wlodkowic, Donald

2013-12-01

The demand to reduce the numbers of laboratory animals has facilitated the emergence of surrogate models such as tests performed on zebrafish (Danio rerio) or African clawed frog's (Xenopus levis) eggs, embryos and larvae. Those two model organisms are becoming increasingly popular replacements to current adult animal testing in toxicology, ecotoxicology and also in drug discovery. Zebrafish eggs and embryos are particularly attractive for toxicological analysis due their size (diameter 1.6 mm), optical transparency, large numbers generated per fish and very straightforward husbandry. The current bottleneck in using zebrafish embryos for screening purposes is, however, a tedious manual evaluation to confirm the fertilization status and subsequent dispensing of single developing embryos to multitier plates to perform toxicity analysis. Manual procedures associated with sorting hundreds of embryos are very monotonous and as such prone to significant analytical errors due to operator's fatigue. In this work, we present a proofof- concept design of a continuous flow embryo sorter capable of analyzing, sorting and dispensing objects ranging in size from 1.5 - 2.5 mm. The prototypes were fabricated in polymethyl methacrylate (PMMA) transparent thermoplastic using infrared laser micromachining. The application of additive manufacturing processes to prototype Lab-on-a-Chip sorters using both fused deposition manufacturing (FDM) and stereolithography (SLA) were also explored. The operation of the device was based on a revolving receptacle capable of receiving, holding and positioning single fish embryos for both interrogation and subsequent sorting. The actuation of the revolving receptacle was performed using a DC motor and/or microservo motor. The system was designed to separate between fertilized (LIVE) and non-fertilized (DEAD) eggs, based on optical transparency using infrared (IR) emitters and receivers.

Importance of non-flow in mixed-harmonic multi-particle correlations in small collision systems

NASA Astrophysics Data System (ADS)

Huo, Peng; Gajdošová, Katarína; Jia, Jiangyong; Zhou, You

2018-02-01

Recently CMS Collaboration measured mixed-harmonic four-particle azimuthal correlations, known as symmetric cumulants SC (n , m), in pp and p+Pb collisions, and interpreted the non-zero SC (n , m) as evidence for long-range collectivity in these small collision systems. Using the PYTHIA and HIJING models which do not have genuine long-range collectivity, we show that the CMS results, obtained with standard cumulant method, could be dominated by non-flow effects associated with jet and dijets, especially in pp collisions. We show that the non-flow effects are largely suppressed using the recently proposed subevent cumulant methods by requiring azimuthal correlation between two or more pseudorapidity ranges. We argue that the reanalysis of SC (n , m) using the subevent method in experiments is necessary before they can used to provide further evidences for a long-range multi-particle collectivity and constraints on theoretical models in small collision systems.

A game theory-reinforcement learning (GT-RL) method to develop optimal operation policies for multi-operator reservoir systems

NASA Astrophysics Data System (ADS)

Madani, Kaveh; Hooshyar, Milad

2014-11-01

Reservoir systems with multiple operators can benefit from coordination of operation policies. To maximize the total benefit of these systems the literature has normally used the social planner's approach. Based on this approach operation decisions are optimized using a multi-objective optimization model with a compound system's objective. While the utility of the system can be increased this way, fair allocation of benefits among the operators remains challenging for the social planner who has to assign controversial weights to the system's beneficiaries and their objectives. Cooperative game theory provides an alternative framework for fair and efficient allocation of the incremental benefits of cooperation. To determine the fair and efficient utility shares of the beneficiaries, cooperative game theory solution methods consider the gains of each party in the status quo (non-cooperation) as well as what can be gained through the grand coalition (social planner's solution or full cooperation) and partial coalitions. Nevertheless, estimation of the benefits of different coalitions can be challenging in complex multi-beneficiary systems. Reinforcement learning can be used to address this challenge and determine the gains of the beneficiaries for different levels of cooperation, i.e., non-cooperation, partial cooperation, and full cooperation, providing the essential input for allocation based on cooperative game theory. This paper develops a game theory-reinforcement learning (GT-RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria. As the first step to underline the utility of the GT-RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system.

Surface acoustic wave actuated cell sorting (SAWACS).

PubMed

Franke, T; Braunmüller, S; Schmid, L; Wixforth, A; Weitz, D A

2010-03-21

We describe a novel microfluidic cell sorter which operates in continuous flow at high sorting rates. The device is based on a surface acoustic wave cell-sorting scheme and combines many advantages of fluorescence activated cell sorting (FACS) and fluorescence activated droplet sorting (FADS) in microfluidic channels. It is fully integrated on a PDMS device, and allows fast electronic control of cell diversion. We direct cells by acoustic streaming excited by a surface acoustic wave which deflects the fluid independently of the contrast in material properties of deflected objects and the continuous phase; thus the device underlying principle works without additional enhancement of the sorting by prior labelling of the cells with responsive markers such as magnetic or polarizable beads. Single cells are sorted directly from bulk media at rates as fast as several kHz without prior encapsulation into liquid droplet compartments as in traditional FACS. We have successfully directed HaCaT cells (human keratinocytes), fibroblasts from mice and MV3 melanoma cells. The low shear forces of this sorting method ensure that cells survive after sorting.

Extravasation of adhering vesicles

NASA Astrophysics Data System (ADS)

Tordeux, C.; Fournier, J.-B.

2002-12-01

We study how the passage of lipid vesicles through a small pore can be induced by the difference in non-specific adhesion energy between the two sides of the substrate bearing the pore. This process is inspired from the extravasation of cells or liposomes from blood vessels, which involves adhesion binders. We study the adhesion-dominated regime and we show that the passage of a vesicle of volume V and area A is selective in terms of the reduced volume v ~ V/A3/2. Extravasation occurs for adhesion ratios of order unity. We also consider the possibility of pressure-induced extravasation in the presence of adhesion. Finally, we propose a micro-device based on adhesion-induced extravasation, which is designed to sort vesicles according to their deflatedness.

Reference data on reaction time and aging using the Nintendo Wii Balance Board: A cross-sectional study of 354 subjects from 20 to 99 years of age

PubMed Central

Rahbek, Martin T.; Eikhof, Karin D.; Hansen, Mette D.; Søndergaard, Malene; Ryg, Jesper; Andersen, Stig; Jørgensen, Martin G.

2017-01-01

Background Falls among older adults is one of the major public health challenges facing the rapidly changing demography. The valid assessment of reaction time (RT) and other well-documented risk factors for falls are mainly restricted to specialized clinics due to the equipment needed. The Nintendo Wii Balance Board has the potential to be a multi-modal test and intervention instrument for these risk factors, however, reference data are lacking. Objective To provide RT reference data and to characterize the age-related changes in RT measured by the Nintendo Wii Balance Board. Method Healthy participants were recruited at various locations and their RT in hands and feet were tested by six assessors using the Nintendo Wii Balance Board. Reference data were analysed and presented in age-groups, while the age-related change in RT was tested and characterized with linear regression models. Results 354 participants between 20 and 99 years of age were tested. For both hands and feet, mean RT and its variation increased with age. There was a statistically significant non-linear increase in RT with age. The averaged difference between male and female was significant, with males being faster than females for both hands and feet. The averaged difference between dominant and non-dominant side was non-significant. Conclusion This study reported reference data with percentiles for a new promising method for reliably testing RT. The RT data were consistent with previously known effects of age and gender on RT. PMID:29287063

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nagasaka, Y; Matsuoka, S; Azad, A

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. Although many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases and matrices is not available. We firstly identify and mitigate multiple bottlenecks with memory management and thread scheduling on Intel Xeon Phi (Knights Landing or KNL). Specifically targeting multi- and many-core processors, we develop a hash-table-based algorithm and optimize a heap-based shared-memory SpGEMM algorithm. Wemore » examine their performance together with other publicly available codes. Different from the literature, our evaluation also includes use cases that are representative of real graph algorithms, such as multi-source breadth-first search or triangle counting. Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type. We wrap up in-depth evaluation results and make a recipe to give the best SpGEMM algorithm for target scenario. A critical finding is that hash-table-based SpGEMM gets a significant performance boost if the nonzeros are not required to be sorted within each row of the output matrix.« less

Performance evaluation of firefly algorithm with variation in sorting for non-linear benchmark problems

NASA Astrophysics Data System (ADS)

Umbarkar, A. J.; Balande, U. T.; Seth, P. D.

2017-06-01

The field of nature inspired computing and optimization techniques have evolved to solve difficult optimization problems in diverse fields of engineering, science and technology. The firefly attraction process is mimicked in the algorithm for solving optimization problems. In Firefly Algorithm (FA) sorting of fireflies is done by using sorting algorithm. The original FA is proposed with bubble sort for ranking the fireflies. In this paper, the quick sort replaces bubble sort to decrease the time complexity of FA. The dataset used is unconstrained benchmark functions from CEC 2005 [22]. The comparison of FA using bubble sort and FA using quick sort is performed with respect to best, worst, mean, standard deviation, number of comparisons and execution time. The experimental result shows that FA using quick sort requires less number of comparisons but requires more execution time. The increased number of fireflies helps to converge into optimal solution whereas by varying dimension for algorithm performed better at a lower dimension than higher dimension.

Danish A.I. field data with sexed semen.

PubMed

Borchersen, S; Peacock, M

2009-01-01

The objective of this study was to compare conception rates, non-return rates and sex ratios of sexed and conventional semen from the same sires in commercial dairy herds in Denmark. The semen was produced from three bulls from each of the three major dairy breeds in Denmark: Holstein, Jersey and Danish Red Dairy Breed (nine bulls total), in order to answer questions on breeds differences in field results. AI was performed by trained technicians using a minimum of 150 doses of sorted sperm and 50 control doses from each bull. During the trial, a total of 2087 doses were used in 63 herds. The trial showed that the conception rate using sorted semen was 5% points lower than with conventional doses for Danish Reds, 7% points for Jerseys, and 12% points for Holsteins. Translating this into non-return rate revealed differences of 10-20% points among bulls. These differences are thought to be a good indicator of what to expect from commercial use of sexed semen. The sex ratios varied from 89% to 93% female calves among breeds, which on average is consistent with the theoretical average sex ratio of 93% females considering the low number of inseminations.

Development of a Multi-Disciplinary Aerothermostructural Model Applicable to Hypersonic Flight

NASA Technical Reports Server (NTRS)

Kostyk, Chris; Risch, Tim

2013-01-01

The harsh and complex hypersonic flight environment has driven design and analysis improvements for many years. One of the defining characteristics of hypersonic flight is the coupled, multi-disciplinary nature of the dominant physics. In an effect to examine some of the multi-disciplinary problems associated with hypersonic flight engineers at the NASA Dryden Flight Research Center developed a non-linear 6 degrees-of-freedom, full vehicle simulation that includes the necessary model capabilities: aerothermal heating, ablation, and thermal stress solutions. Development of the tool and results for some investigations will be presented. Requirements and improvements for future work will also be reviewed. The results of the work emphasize the need for a coupled, multi-disciplinary analysis to provide accurate

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'.

PubMed

Swindale, Nicholas V; Mitelut, Catalin; Murphy, Timothy H; Spacek, Martin A

2017-02-10

Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

A non-linear optimization programming model for air quality planning including co-benefits for GHG emissions.

PubMed

Turrini, Enrico; Carnevale, Claudio; Finzi, Giovanna; Volta, Marialuisa

2018-04-15

This paper introduces the MAQ (Multi-dimensional Air Quality) model aimed at defining cost-effective air quality plans at different scales (urban to national) and assessing the co-benefits for GHG emissions. The model implements and solves a non-linear multi-objective, multi-pollutant decision problem where the decision variables are the application levels of emission abatement measures allowing the reduction of energy consumption, end-of pipe technologies and fuel switch options. The objectives of the decision problem are the minimization of tropospheric secondary pollution exposure and of internal costs. The model assesses CO 2 equivalent emissions in order to support decision makers in the selection of win-win policies. The methodology is tested on Lombardy region, a heavily polluted area in northern Italy. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of the biology card sorting task to measure conceptual expertise in biology.

PubMed

Smith, Julia I; Combs, Elijah D; Nagami, Paul H; Alto, Valerie M; Goh, Henry G; Gourdet, Muryam A A; Hough, Christina M; Nickell, Ashley E; Peer, Adrian G; Coley, John D; Tanner, Kimberly D

2013-01-01

There are widespread aspirations to focus undergraduate biology education on teaching students to think conceptually like biologists; however, there is a dearth of assessment tools designed to measure progress from novice to expert biological conceptual thinking. We present the development of a novel assessment tool, the Biology Card Sorting Task, designed to probe how individuals organize their conceptual knowledge of biology. While modeled on tasks from cognitive psychology, this task is unique in its design to test two hypothesized conceptual frameworks for the organization of biological knowledge: 1) a surface feature organization focused on organism type and 2) a deep feature organization focused on fundamental biological concepts. In this initial investigation of the Biology Card Sorting Task, each of six analytical measures showed statistically significant differences when used to compare the card sorting results of putative biological experts (biology faculty) and novices (non-biology major undergraduates). Consistently, biology faculty appeared to sort based on hypothesized deep features, while non-biology majors appeared to sort based on either surface features or nonhypothesized organizational frameworks. Results suggest that this novel task is robust in distinguishing populations of biology experts and biology novices and may be an adaptable tool for tracking emerging biology conceptual expertise.

Assessing materials handling and storage capacities in port terminals

NASA Astrophysics Data System (ADS)

Dinu, O.; Roşca, E.; Popa, M.; Roşca, M. A.; Rusca, A.

2017-08-01

Terminals constitute the factual interface between different modes and, as a result, buffer stocks are unavoidable whenever transport flows with different discontinuities meet. This is the reason why assessing materials handling and storage capacities is an important issue in the course of attempting to increase operative planning of logistic processes in terminals. Proposed paper starts with a brief review of the compatibilities between different sorts of materials and corresponding transport modes and after, a literature overview of the studies related to ports terminals and their specialization is made. As a methodology, discrete event simulation stands as a feasible technique for assessing handling and storage capacities at the terminal, taking into consideration the multi-flows interaction and the non-uniform arrivals of vessels and inland vehicles. In this context, a simulation model, that integrates the activities of an inland water terminal and describes the essential interactions between the subsystems which influence the terminal capacity, is developed. Different scenarios are simulated for diverse sorts of materials, leading to bottlenecks identification, performance indicators such as average storage occupancy rate, average dwell or transit times estimations, and their evolution is analysed in order to improve the transfer operations in the logistic process

The Impact of Frontal and Non-Frontal Brain Tumor Lesions on Wisconsin Card Sorting Test Performance

ERIC Educational Resources Information Center

Goldstein, B.; Obrzut, J. E.; John, C.; Ledakis, G.; Armstrong, C. L.

2004-01-01

Several lesion and imaging studies have suggested that the Wisconsin Card Sorting Test (WCST) is a measure of executive dysfunction. However, some studies have reported that this measure has poor anatomical specificity because patients with either frontal or non-frontal focal lesions exhibit similar performance. This study examined 25 frontal, 20…

Bilingual Listeners' Perception of Temporally Manipulated English Passages

ERIC Educational Resources Information Center

Shi, Lu-Feng; Farooq, Nadia

2012-01-01

Purpose: The current study measured, objectively and subjectively, how changes in speech rate affect recognition of English passages in bilingual listeners. Method: Ten native monolingual, 20 English-dominant bilingual, and 20 non-English-dominant bilingual listeners repeated target words in English passages at five speech rates (unprocessed, two…

Upregulation of Immunoproteasome Subunits in Myositis Indicates Active Inflammation with Involvement of Antigen Presenting Cells, CD8 T-Cells and IFNγ

PubMed Central

Ghannam, Khetam; Martinez-Gamboa, Lorena; Spengler, Lydia; Krause, Sabine; Smiljanovic, Biljana; Bonin, Marc; Bhattarai, Salyan; Grützkau, Andreas; Burmester, Gerd-R.

2014-01-01

Objective In idiopathic inflammatory myopathies (IIM) infiltration of immune cells into muscle and upregulation of MHC-I expression implies increased antigen presentation and involvement of the proteasome system. To decipher the role of immunoproteasomes in myositis, we investigated individual cell types and muscle tissues and focused on possible immune triggers. Methods Expression of constitutive (PSMB5, -6, -7) and corresponding immunoproteasomal subunits (PSMB8, -9, -10) was analyzed by real-time RT-PCR in muscle biopsies and sorted peripheral blood cells of patients with IIM, non-inflammatory myopathies (NIM) and healthy donors (HD). Protein analysis in muscle biopsies was performed by western blot. Affymetrix HG-U133 platform derived transcriptome data from biopsies of different muscle diseases and from immune cell types as well as monocyte stimulation experiments were used for validation, coregulation and coexpression analyses. Results Real-time RT-PCR revealed significantly increased expression of immunoproteasomal subunits (PSMB8/-9/-10) in DC, monocytes and CD8+ T-cells in IIM. In muscle biopsies, the immunosubunits were elevated in IIM compared to NIM and exceeded levels of matched blood samples. Proteins of PSMB8 and -9 were found only in IIM but not NIM muscle biopsies. Reanalysis of 78 myositis and 20 healthy muscle transcriptomes confirmed these results and revealed involvement of the antigen processing and presentation pathway. Comparison with reference profiles of sorted immune cells and healthy muscle confirmed upregulation of PSMB8 and -9 in myositis biopsies beyond infiltration related changes. This upregulation correlated highest with STAT1, IRF1 and IFNγ expression. Elevation of T-cell specific transcripts in active IIM muscles was accompanied by increased expression of DC and monocyte marker genes and thus reflects the cell type specific involvement observed in peripheral blood. Conclusions Immunoproteasomes seem to indicate IIM activity and suggest that dominant involvement of antigen processing and presentation may qualify these diseases exemplarily for the evolving therapeutic concepts of immunoproteasome specific inhibition. PMID:25098831

Multi-temporal UAV based data for mapping crop type and structure in smallholder dominated Tanzanian agricultural landscape

NASA Astrophysics Data System (ADS)

Nagol, J. R.; Chung, C.; Dempewolf, J.; Maurice, S.; Mbungu, W.; Tumbo, S.

2015-12-01

Timely mapping and monitoring of crops like Maize, an important food security crop in Tanzania, can facilitate timely response by government and non-government organizations to food shortage or surplus conditions. Small UAVs can play an important role in linking the spaceborne remote sensing data and ground based measurement to improve the calibration and validation of satellite based estimates of in-season crop metrics. In Tanzania most of the growing season is often obscured by clouds. UAV data, if collected within a stratified statistical sampling framework, can also be used to directly in lieu of spaceborne data to infer mid-season yield estimates at regional scales.Here we present an object based approach to estimate crop metrics like crop type, area, and height using multi-temporal UAV based imagery. The methods were tested at three 1km2 plots in Kilosa, Njombe, and Same districts in Tanzania. At these sites both ground based and UAV based data were collected on a monthly time-step during the year 2015 growing season. SenseFly eBee drone with RGB and NIR-R-G camera was used to collect data. Crop type classification accuracies of above 85% were easily achieved.

Field fertility of sex-sorted and non-sorted frozen-thawed stallion spermatozoa.

PubMed

Clulow, J R; Buss, H; Sieme, H; Rodger, J A; Cawdell-Smith, A J; Evans, G; Rath, D; Morris, L H A; Maxwell, W M C

2008-11-01

In the 2004/2005 breeding season, the fertility of sex-sorted (SS) and non-sorted (NS) frozen stallion spermatozoa from two Hannovarian stallions was compared. A hysteroscopic insemination technique [Morris, L.H., Tiplady, C., Allen, W.R., 2003a. Pregnancy rates in mares after a single fixed time hysteroscopic insemination of low numbers of frozen-thawed spermatozoa onto the uterotubal junction. Equine Vet. J. 35, 197-201] was used to deposit low doses (6, 13 or 25 x 10(6) frozen-thawed SS or NS spermatozoa) onto the utero-tubal junction at 32 or 38 h after the administration of Chorulon (2500 IU, Intervet). Fertility was low, with one pregnancy (13 x 10(6) spermatozoa, 500 microL) obtained after artificial insemination with frozen SS spermatozoa (n=29 cycles) which resulted in the birth of a filly. Two pregnancies were obtained in mares inseminated with 6 x 10(6) NS spermatozoa in 250 microL (n=31 cycles). Mares failing to conceive on two experimental cycles were allocated to the conventional insemination group. Insemination with >500 x 10(6) motile NS frozen-thawed spermatozoa, yielded satisfactory per cycle conception rates (35.5%, 22/62) for both stallions combined and was within the values of their normal fertility as quoted by the stud's records. This suggests that the quality of the frozen semen was acceptable and that the freezing processes yielded viable spermatozoa capable of fertilisation. The poor fertility after hysteroscopic insemination with low doses of sex-sorted or non-sorted spermatozoa from the same stallions may be directly attributable to the low dose insemination conditions with frozen-thawed rather than sex-sorted spermatozoa.

Fluence map optimization (FMO) with dose-volume constraints in IMRT using the geometric distance sorting method.

PubMed

Lan, Yihua; Li, Cunhua; Ren, Haozheng; Zhang, Yong; Min, Zhifang

2012-10-21

A new heuristic algorithm based on the so-called geometric distance sorting technique is proposed for solving the fluence map optimization with dose-volume constraints which is one of the most essential tasks for inverse planning in IMRT. The framework of the proposed method is basically an iterative process which begins with a simple linear constrained quadratic optimization model without considering any dose-volume constraints, and then the dose constraints for the voxels violating the dose-volume constraints are gradually added into the quadratic optimization model step by step until all the dose-volume constraints are satisfied. In each iteration step, an interior point method is adopted to solve each new linear constrained quadratic programming. For choosing the proper candidate voxels for the current dose constraint adding, a so-called geometric distance defined in the transformed standard quadratic form of the fluence map optimization model was used to guide the selection of the voxels. The new geometric distance sorting technique can mostly reduce the unexpected increase of the objective function value caused inevitably by the constraint adding. It can be regarded as an upgrading to the traditional dose sorting technique. The geometry explanation for the proposed method is also given and a proposition is proved to support our heuristic idea. In addition, a smart constraint adding/deleting strategy is designed to ensure a stable iteration convergence. The new algorithm is tested on four cases including head-neck, a prostate, a lung and an oropharyngeal, and compared with the algorithm based on the traditional dose sorting technique. Experimental results showed that the proposed method is more suitable for guiding the selection of new constraints than the traditional dose sorting method, especially for the cases whose target regions are in non-convex shapes. It is a more efficient optimization technique to some extent for choosing constraints than the dose sorting method. By integrating a smart constraint adding/deleting scheme within the iteration framework, the new technique builds up an improved algorithm for solving the fluence map optimization with dose-volume constraints.

Removing Barriers for Effective Deployment of Intermittent Renewable Generation

NASA Astrophysics Data System (ADS)

Arabali, Amirsaman

The stochastic nature of intermittent renewable resources is the main barrier to effective integration of renewable generation. This problem can be studied from feeder-scale and grid-scale perspectives. Two new stochastic methods are proposed to meet the feeder-scale controllable load with a hybrid renewable generation (including wind and PV) and energy storage system. For the first method, an optimization problem is developed whose objective function is the cost of the hybrid system including the cost of renewable generation and storage subject to constraints on energy storage and shifted load. A smart-grid strategy is developed to shift the load and match the renewable energy generation and controllable load. Minimizing the cost function guarantees minimum PV and wind generation installation, as well as storage capacity selection for supplying the controllable load. A confidence coefficient is allocated to each stochastic constraint which shows to what degree the constraint is satisfied. In the second method, a stochastic framework is developed for optimal sizing and reliability analysis of a hybrid power system including renewable resources (PV and wind) and energy storage system. The hybrid power system is optimally sized to satisfy the controllable load with a specified reliability level. A load-shifting strategy is added to provide more flexibility for the system and decrease the installation cost. Load shifting strategies and their potential impacts on the hybrid system reliability/cost analysis are evaluated trough different scenarios. Using a compromise-solution method, the best compromise between the reliability and cost will be realized for the hybrid system. For the second problem, a grid-scale stochastic framework is developed to examine the storage application and its optimal placement for the social cost and transmission congestion relief of wind integration. Storage systems are optimally placed and adequately sized to minimize the sum of operation and congestion costs over a scheduling period. A technical assessment framework is developed to enhance the efficiency of wind integration and evaluate the economics of storage technologies and conventional gas-fired alternatives. The proposed method is used to carry out a cost-benefit analysis for the IEEE 24-bus system and determine the most economical technology. In order to mitigate the financial and technical concerns of renewable energy integration into the power system, a stochastic framework is proposed for transmission grid reinforcement studies in a power system with wind generation. A multi-stage multi-objective transmission network expansion planning (TNEP) methodology is developed which considers the investment cost, absorption of private investment and reliability of the system as the objective functions. A Non-dominated Sorting Genetic Algorithm (NSGA II) optimization approach is used in combination with a probabilistic optimal power flow (POPF) to determine the Pareto optimal solutions considering the power system uncertainties. Using a compromise-solution method, the best final plan is then realized based on the decision maker preferences. The proposed methodology is applied to the IEEE 24-bus Reliability Tests System (RTS) to evaluate the feasibility and practicality of the developed planning strategy.

Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

PubMed

Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

2016-01-01

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants

PubMed Central

Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

2016-01-01

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996–2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated. PMID:27010658

The sensitivity of the atmospheric branch of the global water cycle to temperature fluctuations at synoptic to decadal time-scales in different satellite- and model-based products

NASA Astrophysics Data System (ADS)

Nogueira, Miguel

2018-02-01

Spectral analysis of global-mean precipitation, P, evaporation, E, precipitable water, W, and surface temperature, Ts, revealed significant variability from sub-daily to multi-decadal time-scales, superposed on high-amplitude diurnal and yearly peaks. Two distinct regimes emerged from a transition in the spectral exponents, β. The weather regime covering time-scales < 10 days with β ≥ 1; and the macroweather regime extending from a few months to a few decades with 0 <β <1. Additionally, the spectra showed a generally good statistical agreement amongst several different model- and satellite-based datasets. Detrended cross-correlation analysis (DCCA) revealed three important results which are robust across all datasets: (1) Clausius-Clapeyron (C-C) relationship is the dominant mechanism of W non-periodic variability at multi-year time-scales; (2) C-C is not the dominant control of W, P or E non-periodic variability at time-scales below about 6 months, where the weather regime is approached and other mechanisms become important; (3) C-C is not a dominant control for P or E over land throughout the entire time-scale range considered. Furthermore, it is suggested that the atmosphere and oceans start to act as a single coupled system at time-scales > 1-2 years, while at time-scales < 6 months they are not the dominant drivers of each other. For global-ocean and full-globe averages, ρDCCA showed large spread of the C-C importance for P and E variability amongst different datasets at multi-year time-scales, ranging from negligible (< 0.3) to high ( 0.6-0.8) values. Hence, state-of-the-art climate datasets have significant uncertainties in the representation of macroweather precipitation and evaporation variability and its governing mechanisms.

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J

2017-12-01

To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine

Magnetic polarization measurements of the multi-modal plasma response to 3D fields in the EAST tokamak

NASA Astrophysics Data System (ADS)

Logan, N. C.; Cui, L.; Wang, H.; Sun, Y.; Gu, S.; Li, G.; Nazikian, R.; Paz-Soldan, C.

2018-07-01

A multi-modal plasma response to applied non-axisymmetric fields has been found in EAST tokamak plasmas. Here, multi-modal means the radial and poloidal structure of an individually driven toroidal harmonic is not fixed. The signature of such a multi-modal response is the magnetic polarization (ratio of radial and poloidal components) of the plasma response field measured on the low field side device mid-plane. A difference in the 3D coil phasing (the relative phase of two coil arrays) dependencies between the two responses is observed in response to n  =  2 fields in the same plasma for which the n  =  1 responses are well synchronized. Neither the maximum radial nor the maximum poloidal field response to n  =  2 fields agrees with the best applied phasing for mitigating edge localized modes, suggesting that the edge plasma response is not a dominant component of either polarization. GPEC modeling reproduces the discrepant phasing dependences of the experimental measurements, and confirms the edge resonances are maximized by the coil phasing that mitigates ELMs in the experiments. The model confirms the measured plasma response is not dominated by resonant current drive from the external field. Instead, non-resonant contributions play a large role in the diagnostic signal for both toroidal harmonics n  =  1 and n  =  2. The analysis in this paper demonstrates the ability of 3D modeling to connect external magnetic sensor measurements to the internal plasma physics and accurately predict optimal applied 3D field configurations in multi-modal plasmas.

The flaring activity of Markarian 421 during April 2000

NASA Astrophysics Data System (ADS)

Fegan, D. J.; VERITAS Collaboration

2001-08-01

Evidence for correlated TeV γ and X-ray flaring of the extreme blazar Mrk421 during April 2000 is presented and discussed. The remarkably persistent TeV flare of April 30th 2000 (40 σ significance), exhibiting structure over almost six hours of continuous observation, is analysed in detail. 1 Extreme BL Lac objects The most extreme members of the Active Galactic Nucleus (AGN) family are BL Lac objects and optically violently variable (OVV) quasars, collectively known as blazars. These objects are dominated by the presence of relativistic jets. For jets fortuitously aligned with an observers line of sight, emission may exhibit dramatic variability over very short time scales, in turn implying remarkably compact emission regions. For blazars, the Spectral Energy Distribution (SED) is dominated by non-thermal continuum emission, extending from radio to TeV gamma rays. The broadband nature of the blazar emission offers unique insights into energetic physical processes at work in a very compact region, close to the base of the jet and near the underlying central engine, most likely a supermassive black hole. BL Lacs are very effectively characterized on the basis of their SED shape. X-ray and radio flux limited surveys apear to display a bimodal distribution of properties, with LBL (Low-energy peaked, or "Red" BL Lacs) having synchrotron peaks in the IR-optical bands, and HBL (High-energy peaked, or "Blue" BL Lacs) in the UV to soft X-ray band. Recent comprehensive surveys such as DXRBS, REX and RGB have extended, by almost two orders of magnitude, the range of observable synchrotron peak frequencies. For blazar class objects, broadband emission confirms that the synchrotron peak may span the entire IR Xray range, thus accounting for the multi-frequency emission properties of this class of object. Mrk421, Mrk501, 1ES2344 and 1H1426 all exhibit broadband emission properties, high

[Characteristics of arthropod community in alpine cabbage fields].

PubMed

Wang, Xiang-ping; Zhang, Zhong-ning

2007-01-01

The study on the community structure of arthropod in the alpine cabbage fields of Hubei Province showed that the dominant pests were Brevicoryne brassicae, Mamestra brassicae and Plutella xylostella, while the dominant natural enemies were Diaeretiella rapae, Cotesia plutella, Erigonidum gramiaicolum and Syrphus corollae. The richness, diversity index, evenness index and dominance index of pest and natural enemy sub-communities all changed with time. The dominance index of pest sub-community was higher, while its diversity and evenness indices were lower than those of natural enemy sub-community. Based on fuzzy clustering analysis, the pest and natural enemy subcommunities of 14 time sequences were grouped into 4 and 3 sorts, respectively.

Desired Precision in Multi-Objective Optimization: Epsilon Archiving or Rounding Objectives?

NASA Astrophysics Data System (ADS)

Asadzadeh, M.; Sahraei, S.

2016-12-01

Multi-objective optimization (MO) aids in supporting the decision making process in water resources engineering and design problems. One of the main goals of solving a MO problem is to archive a set of solutions that is well-distributed across a wide range of all the design objectives. Modern MO algorithms use the epsilon dominance concept to define a mesh with pre-defined grid-cell size (often called epsilon) in the objective space and archive at most one solution at each grid-cell. Epsilon can be set to the desired precision level of each objective function to make sure that the difference between each pair of archived solutions is meaningful. This epsilon archiving process is computationally expensive in problems that have quick-to-evaluate objective functions. This research explores the applicability of a similar but computationally more efficient approach to respect the desired precision level of all objectives in the solution archiving process. In this alternative approach each objective function is rounded to the desired precision level before comparing any new solution to the set of archived solutions that already have rounded objective function values. This alternative solution archiving approach is compared to the epsilon archiving approach in terms of efficiency and quality of archived solutions for solving mathematical test problems and hydrologic model calibration problems.

Identification of additive, dominant, and epistatic variation conferred by key genes in cellulose biosynthesis pathway in Populus tomentosa†

PubMed Central

Du, Qingzhang; Tian, Jiaxing; Yang, Xiaohui; Pan, Wei; Xu, Baohua; Li, Bailian; Ingvarsson, Pär K.; Zhang, Deqiang

2015-01-01

Economically important traits in many species generally show polygenic, quantitative inheritance. The components of genetic variation (additive, dominant and epistatic effects) of these traits conferred by multiple genes in shared biological pathways remain to be defined. Here, we investigated 11 full-length genes in cellulose biosynthesis, on 10 growth and wood-property traits, within a population of 460 unrelated Populus tomentosa individuals, via multi-gene association. To validate positive associations, we conducted single-marker analysis in a linkage population of 1,200 individuals. We identified 118, 121, and 43 associations (P< 0.01) corresponding to additive, dominant, and epistatic effects, respectively, with low to moderate proportions of phenotypic variance (R2). Epistatic interaction models uncovered a combination of three non-synonymous sites from three unique genes, representing a significant epistasis for diameter at breast height and stem volume. Single-marker analysis validated 61 associations (false discovery rate, Q ≤ 0.10), representing 38 SNPs from nine genes, and its average effect (R2 = 3.8%) nearly 2-fold higher than that identified with multi-gene association, suggesting that multi-gene association can capture smaller individual variants. Moreover, a structural gene–gene network based on tissue-specific transcript abundances provides a better understanding of the multi-gene pathway affecting tree growth and lignocellulose biosynthesis. Our study highlights the importance of pathway-based multiple gene associations to uncover the nature of genetic variance for quantitative traits and may drive novel progress in molecular breeding. PMID:25428896

Multifocal necrotising fasciitis: an overlooked entity?

PubMed

El-Khani, Ussamah; Nehme, Jean; Darwish, Ammar; Jamnadas-Khoda, Benjamin; Scerri, Godwin; Heppell, Simon; Bennett, Nicholas

2012-04-01

The aim of the study is to report a case of multi-focal necrotising fasciitis, review research on this subject to identify common aetiological factors and highlight suggestions to improve management. Necrotising fasciitis is a severe, life-threatening soft tissue infection that typically arises from a single area, usually secondary to a minor penetrating injury. Multi-focal necrotising fasciitis, where there is more than one non-contiguous area of necrosis, is much less commonly reported. There are no guidelines specific to the management of multi-focal necrotising fasciitis, and its under-reporting may lead to missed management opportunities. A systematic literature review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. A search of MEDLINE, OLD MEDLINE and the Cochrane Collaboration was performed from 1966 to March 2011 using 16 search terms. All articles were screened for genuine non-contiguous multi-focal necrotising fasciitis. Of the papers that met this criterion, data on patient demographics, likely inciting injury, presentation time-line, microbial agents, sites affected, objective assessment scores, treatment and outcome were extracted. A total of 31 studies met our inclusion criteria and 33 individual cases of multi-focal necrotising fasciitis were included in the quantitative analysis. About half (52%) of cases were type II necrotising fasciitis; 42% of cases had identifiable inciting injuries; 21% of cases developed multi-focal lesions non-synchronously, of which 86% were type II. Nearly all (94%) of cases had incomplete objective assessment scores. One case identified inflammatory imaging findings prior to clinical necrosis. Multifocality in necrotising fasciitis is likely to be associated with type II disease. We postulate that validated objective tools will aid necrotising fasciitis management pathways that will identify high-risk groups for multifocality and advise early pre-emptive imaging. We recommend the adoption of regional multi-focal necrotising fasciitis registers. Copyright © 2011 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

On-chip cell sorting via patterned magnetic traps

NASA Astrophysics Data System (ADS)

Byvank, Tom; Prikockis, Michael; Chen, Aaron; Miller, Brandon; Chalmers, Jeffrey; Sooryakumar, Ratnasingham

2015-03-01

Due to their importance in research for the diagnosis and treatment of cancer, numerous schemes have been developed to sort rare cell populations, e.g., circulating tumor cells (CTCs), from a larger ensemble of cells. Here, we improve upon a previously developed microfluidic device (Lab Chip 13, 1172, (2013)) to increase throughput and sorting purity of magnetically labeled cells. The separation mechanism involves controlling magnetic forces by manipulating the magnetic domain structures of embedded permalloy microdisks with weak external fields. These forces move labeled cells from the input flow stream into an adjacent buffer flow stream. Such magnetically activated transfer separates the magnetic entities from their non-magnetic counterparts as the two flow streams split apart and move toward their respective outputs. Purity of the magnetic output is modulated by the withdrawal rate of the non-magnetic output relative to the inputs. A proof of concept shows that CTCs from metastatic breast cancer patients can be sorted, recovered from the device, and confirmed as CTCs using separate immunofluorescence staining and analysis. With further optimizations, the channel could become a useful device for high purity final sorting of enriched patient cell samples.

On Complex Networks Representation and Computation of Hydrologycal Quantities

NASA Astrophysics Data System (ADS)

Serafin, F.; Bancheri, M.; David, O.; Rigon, R.

2017-12-01

Water is our blue gold. Despite results of discovery-based science keep warning public opinion about the looming worldwide water crisis, water is still treated as a not worth taking resource. Could a different multi-scale perspective affect environmental decision-making more deeply? Can also a further pairing to a new graphical representation of processes interaction sway decision-making more effectively and public opinion consequently?This abstract introduces a complex networks driven way to represent catchments eco-hydrology and related flexible informatics to manage it. The representation is built upon mathematical category. A category is an algebraic structure that comprises "objects" linked by "arrows". It is an evolution of Petri Nets said Time Continuous Petri Nets (TCPN). It aims to display (water) budgets processes and catchment interactions using explicative and self-contained symbolism. The result improves readability of physical processes compared to current descriptions. The IT perspective hinges on the Object Modeling System (OMS) v3. The latter is a non-invasive flexible environmental modeling framework designed to support component-based model development. The implementation of a Directed Acyclic Graph (DAG) data structure, named Net3, has recently enhanced its flexibility. Net3 represents interacting systems as complex networks: vertices match up with any sort of time evolving quantity; edges correspond to their data (fluxes) interchange. It currently hosts JGrass-NewAge components, and those implementing travel time analysis of fluxes. Further bio-physical or management oriented components can be easily added.This talk introduces both graphical representation and related informatics exercising actual applications and examples.

Multi-locus phylogenetics, lineage sorting, and reticulation in Pinus subsection Australes.

PubMed

Gernandt, David S; Aguirre Dugua, Xitlali; Vázquez-Lobo, Alejandra; Willyard, Ann; Moreno Letelier, Alejandra; Pérez de la Rosa, Jorge A; Piñero, Daniel; Liston, Aaron

2018-04-23

Both incomplete lineage sorting and reticulation have been proposed as causes of phylogenetic incongruence. Disentangling these factors may be most difficult in long-lived, wind-pollinated plants with large population sizes and weak reproductive barriers. We used solution hybridization for targeted enrichment and massive parallel sequencing to characterize low-copy-number nuclear genes and high-copy-number plastomes (Hyb-Seq) in 74 individuals of Pinus subsection Australes, a group of ~30 New World pine species of exceptional ecological and economic importance. We inferred relationships using methods that account for both incomplete lineage sorting and reticulation. Concatenation- and coalescent-based trees inferred from nuclear genes mainly agreed with one another, but they contradicted the plastid DNA tree in recovering the Attenuatae (the California closed-cone pines) and Oocarpae (the egg-cone pines of Mexico and Central America) as monophyletic and the Australes sensu stricto (the southern yellow pines) as paraphyletic to the Oocarpae. The plastid tree featured some relationships that were discordant with morphological and geographic evidence and species limits. Incorporating gene flow into the coalescent analyses better fit the data, but evidence supporting the hypothesis that hybridization explains the non-monophyly of the Attenuatae in the plastid tree was equivocal. Our analyses document cytonuclear discordance in Pinus subsection Australes. We attribute this discordance to ancient and recent introgression and present a phylogenetic hypothesis in which mostly hierarchical relationships are overlain by gene flow. © 2018 The Authors. American Journal of Botany is published by Wiley Periodicals, Inc. on behalf of the Botanical Society of America.

Cytokine Signaling in Splenic Leukocytes from Vaccinated and Non-Vaccinated Chickens after Intravenous Infection with Salmonella Enteritidis

PubMed Central

Matulova, Marta; Stepanova, Hana; Sisak, Frantisek; Havlickova, Hana; Faldynova, Marcela; Kyrova, Kamila; Volf, Jiri; Rychlik, Ivan

2012-01-01

In order to design a new Salmonella enterica vaccine, one needs to understand how naive and immune chickens interact differently when exposed to S. enterica. In this study we therefore determined the immune response of vaccinated and non-vaccinated chickens after intravenous infection with Salmonella enterica serovar Enteritidis (S. Enteritidis). Using flow cytometry we showed that 4 days post infection (DPI), counts of CD4 and B-lymphocytes did not change, CD8 and γδ T-lymphocytes decreased and macrophages and heterophils increased in the spleen. When vaccinated and non-vaccinated chickens were compared, only macrophages and heterophils were found in significantly higher counts in the spleens of the non-vaccinated chickens. The non-vaccinated chickens also expressed higher anti-LPS antibodies than the vaccinated chickens. The expression of interleukin (IL)1β, IL6, IL8, IL18, LITAF, IFNγ and iNOS did not exhibit any clear pattern in the cells sorted from the spleens of vaccinated or non-vaccinated chickens. Only IL17 and IL22 showed a differential expression in the CD4 T-lymphocytes of the vaccinated and non-vaccinated chickens at 4 DPI, both being expressed at a higher level in the non-vaccinated chickens. Due to a similar IFNγ expression in the CD4 T-lymphocytes in both the vaccinated and non-vaccinated chickens, and a variable IL17 expression oscillating around IFNγ expression levels, the IL17∶IFNγ ratio in CD4 T-lymphocytes was found to be central for the outcome of the immune response. When IL17 was expressed at higher levels than IFNγ in the non-vaccinated chickens, the Th17 immune response with a higher macrophage and heterophil infiltration in the spleen dominated. However, when the expression of IL17 was lower than that of IFNγ as in the vaccinated chickens, the Th1 response with a higher resistance to S. Enteritidis infection dominated. PMID:22384225

Better Object Recognition and Naming Outcome With MRI-Guided Stereotactic Laser Amygdalohippocampotomy for Temporal Lobe Epilepsy

PubMed Central

Drane, Daniel L.; Loring, David W.; Voets, Natalie L.; Price, Michele; Ojemann, Jeffrey G.; Willie, Jon T.; Saindane, Amit M.; Phatak, Vaishali; Ivanisevic, Mirjana; Millis, Scott; Helmers, Sandra L.; Miller, John W.; Meador, Kimford J.; Gross, Robert E.

2015-01-01

SUMMARY OBJECTIVES Temporal lobe epilepsy (TLE) patients experience significant deficits in category-related object recognition and naming following standard surgical approaches. These deficits may result from a decoupling of core processing modules (e.g., language, visual processing, semantic memory), due to “collateral damage” to temporal regions outside the hippocampus following open surgical approaches. We predicted stereotactic laser amygdalohippocampotomy (SLAH) would minimize such deficits because it preserves white matter pathways and neocortical regions critical for these cognitive processes. METHODS Tests of naming and recognition of common nouns (Boston Naming Test) and famous persons were compared with nonparametric analyses using exact tests between a group of nineteen patients with medically-intractable mesial TLE undergoing SLAH (10 dominant, 9 nondominant), and a comparable series of TLE patients undergoing standard surgical approaches (n=39) using a prospective, non-randomized, non-blinded, parallel group design. RESULTS Performance declines were significantly greater for the dominant TLE patients undergoing open resection versus SLAH for naming famous faces and common nouns (F=24.3, p<.0001, η2=.57, & F=11.2, p<.001, η2=.39, respectively), and for the nondominant TLE patients undergoing open resection versus SLAH for recognizing famous faces (F=3.9, p<.02, η2=.19). When examined on an individual subject basis, no SLAH patients experienced any performance declines on these measures. In contrast, 32 of the 39 undergoing standard surgical approaches declined on one or more measures for both object types (p<.001, Fisher’s exact test). Twenty-one of 22 left (dominant) TLE patients declined on one or both naming tasks after open resection, while 11 of 17 right (non-dominant) TLE patients declined on face recognition. SIGNIFICANCE Preliminary results suggest 1) naming and recognition functions can be spared in TLE patients undergoing SLAH, and 2) the hippocampus does not appear to be an essential component of neural networks underlying name retrieval or recognition of common objects or famous faces. PMID:25489630

Recognition of explosives fingerprints on objects for courier services using machine learning methods and laser-induced breakdown spectroscopy.

PubMed

Moros, J; Serrano, J; Gallego, F J; Macías, J; Laserna, J J

2013-06-15

During recent years laser-induced breakdown spectroscopy (LIBS) has been considered one of the techniques with larger ability for trace detection of explosives. However, despite of the high sensitivity exhibited for this application, LIBS suffers from a limited selectivity due to difficulties in assigning the molecular origin of the spectral emissions observed. This circumstance makes the recognition of fingerprints a latent challenging problem. In the present manuscript the sorting of six explosives (chloratite, ammonal, DNT, TNT, RDX and PETN) against a broad list of potential harmless interferents (butter, fuel oil, hand cream, olive oil, …), all of them in the form of fingerprints deposited on the surfaces of objects for courier services, has been carried out. When LIBS information is processed through a multi-stage architecture algorithm built from a suitable combination of 3 learning classifiers, an unknown fingerprint may be labeled into a particular class. Neural network classifiers trained by the Levenberg-Marquardt rule were decided within 3D scatter plots projected onto the subspace of the most useful features extracted from the LIBS spectra. Experimental results demonstrate that the presented algorithm sorts fingerprints according to their hazardous character, although its spectral information is virtually identical in appearance, with rates of false negatives and false positives not beyond of 10%. These reported achievements mean a step forward in the technology readiness level of LIBS for this complex application related to defense, homeland security and force protection. Copyright © 2013 Elsevier B.V. All rights reserved.

Method for Coating a Tow with an Electrospun Nanofiber

NASA Technical Reports Server (NTRS)

Kohlman, Lee W. (Inventor); Roberts, Gary D. (Inventor)

2015-01-01

Method and apparatus for enhancing the durability as well as the strength and stiffness of prepreg fiber tows of the sort used in composite materials are disclosed. The method involves adhering electrospun fibers onto the surface of such composite materials as filament-wound composite objects and the surface of prepreg fiber tows of the sort that are subsequently used in the production of composite materials of the filament-wound, woven, and braided sorts. The apparatus performs the methods described herein.

Structural and functional characterization of cargo-binding sites on the μ4-subunit of adaptor protein complex 4.

PubMed

Ross, Breyan H; Lin, Yimo; Corales, Esteban A; Burgos, Patricia V; Mardones, Gonzalo A

2014-01-01

Adaptor protein (AP) complexes facilitate protein trafficking by playing key roles in the selection of cargo molecules to be sorted in post-Golgi compartments. Four AP complexes (AP-1 to AP-4) contain a medium-sized subunit (μ1-μ4) that recognizes YXXØ-sequences (Ø is a bulky hydrophobic residue), which are sorting signals in transmembrane proteins. A conserved, canonical region in μ subunits mediates recognition of YXXØ-signals by means of a critical aspartic acid. Recently we found that a non-canonical YXXØ-signal on the cytosolic tail of the Alzheimer's disease amyloid precursor protein (APP) binds to a distinct region of the μ4 subunit of the AP-4 complex. In this study we aimed to determine the functionality of both binding sites of μ4 on the recognition of the non-canonical YXXØ-signal of APP. We found that substitutions in either binding site abrogated the interaction with the APP-tail in yeast-two hybrid experiments. Further characterization by isothermal titration calorimetry showed instead loss of binding to the APP signal with only the substitution R283D at the non-canonical site, in contrast to a decrease in binding affinity with the substitution D190A at the canonical site. We solved the crystal structure of the C-terminal domain of the D190A mutant bound to this non-canonical YXXØ-signal. This structure showed no significant difference compared to that of wild-type μ4. Both differential scanning fluorimetry and limited proteolysis analyses demonstrated that the D190A substitution rendered μ4 less stable, suggesting an explanation for its lower binding affinity to the APP signal. Finally, in contrast to overexpression of the D190A mutant, and acting in a dominant-negative manner, overexpression of μ4 with either a F255A or a R283D substitution at the non-canonical site halted APP transport at the Golgi apparatus. Together, our analyses support that the functional recognition of the non-canonical YXXØ-signal of APP is limited to the non-canonical site of μ4.

Structural and Functional Characterization of Cargo-Binding Sites on the μ4-Subunit of Adaptor Protein Complex 4

PubMed Central

Ross, Breyan H.; Lin, Yimo; Corales, Esteban A.; Burgos, Patricia V.; Mardones, Gonzalo A.

2014-01-01

Adaptor protein (AP) complexes facilitate protein trafficking by playing key roles in the selection of cargo molecules to be sorted in post-Golgi compartments. Four AP complexes (AP-1 to AP-4) contain a medium-sized subunit (μ1-μ4) that recognizes YXXØ-sequences (Ø is a bulky hydrophobic residue), which are sorting signals in transmembrane proteins. A conserved, canonical region in μ subunits mediates recognition of YXXØ-signals by means of a critical aspartic acid. Recently we found that a non-canonical YXXØ-signal on the cytosolic tail of the Alzheimer's disease amyloid precursor protein (APP) binds to a distinct region of the μ4 subunit of the AP-4 complex. In this study we aimed to determine the functionality of both binding sites of μ4 on the recognition of the non-canonical YXXØ-signal of APP. We found that substitutions in either binding site abrogated the interaction with the APP-tail in yeast-two hybrid experiments. Further characterization by isothermal titration calorimetry showed instead loss of binding to the APP signal with only the substitution R283D at the non-canonical site, in contrast to a decrease in binding affinity with the substitution D190A at the canonical site. We solved the crystal structure of the C-terminal domain of the D190A mutant bound to this non-canonical YXXØ-signal. This structure showed no significant difference compared to that of wild-type μ4. Both differential scanning fluorimetry and limited proteolysis analyses demonstrated that the D190A substitution rendered μ4 less stable, suggesting an explanation for its lower binding affinity to the APP signal. Finally, in contrast to overexpression of the D190A mutant, and acting in a dominant-negative manner, overexpression of μ4 with either a F255A or a R283D substitution at the non-canonical site halted APP transport at the Golgi apparatus. Together, our analyses support that the functional recognition of the non-canonical YXXØ-signal of APP is limited to the non-canonical site of μ4. PMID:24498434

Assessing the dynamic biofilm removal of sulfonated phenolics using CP-OCT

NASA Astrophysics Data System (ADS)

Englund, K.; Nikrad, J.; Jones, R.

2017-02-01

Examining the physical mechanisms related to biofilm removal of sulfonated phenolics (SP) is difficult using conventional microscopy techniques. A custom flow cell system integrated with a real time cross polarization optical coherence tomography system investigated the dynamic speed of biofilm removal when oral multi-species biofilms are exposed to SP under sheer stress. The Near infrared 1310-nm CP-OCT system non-destructively imaged fluid immersed oral biofilms at nearly 30 frames/s. This dynamic imaging was able to determine the cohesive and adhesion related disruption of SP on oral biofilms adhering to tooth like surfaces. For multi-species biofilms that are initially grown without the presence of sucrose, the disruption of biofilms on saliva coated hydroxyapatite (HA) is dominated as a adhesive failure at the HA-biofilm interface. For multi-species biofilms that are grown in the presence of sucrose, the disruption is dominated by cohesive disruption followed by adhesive failure. This novel CP-OCT flow cell assay has the potential to examine rapid interactions between anti-biofilm agents and tooth like surfaces.

Linking giant earthquakes with the subduction of oceanic fracture zones

NASA Astrophysics Data System (ADS)

Landgrebe, T. C.; Müller, R. D.; EathByte Group

2011-12-01

Giant subduction earthquakes are known to occur in areas not previously identified as prone to high seismic risk. This highlights the need to better identify subduction zone segments potentially dominated by relatively long (up to 1000 years and more) recurrence times of giant earthquakes. Global digital data sets represent a promising source of information for a multi-dimensional earthquake hazard analysis. We combine the NGDC global Significant Earthquakes database with a global strain rate map, gridded ages of the ocean floor, and a recently produced digital data set for oceanic fracture zones, major aseismic ridges and volcanic chains to investigate the association of earthquakes as a function of magnitude with age of the downgoing slab and convergence rates. We use a so-called Top-N recommendation method, a technology originally developed to search, sort, classify, and filter very large and often statistically skewed data sets on the internet, to analyse the association of subduction earthquakes sorted by magnitude with key parameters. The Top-N analysis is used to progressively assess how strongly particular "tectonic niche" locations (e.g. locations along subduction zones intersected with aseismic ridges or volcanic chains) are associated with sets of earthquakes in sorted order in a given magnitude range. As the total number N of sorted earthquakes is increased, by progressively including smaller-magnitude events, the so-called recall is computed, defined as the number of Top-N earthquakes associated with particular target areas divided by N. The resultant statistical measure represents an intuitive description of the effectiveness of a given set of parameters to account for the location of significant earthquakes on record. We use this method to show that the occurrence of great (magnitude ≥ 8) earthquakes on overriding plate segments is strongly biased towards intersections of oceanic fracture zones with subduction zones. These intersection regions are linked with 8 of the largest 10, 18 of the largest 25, about half of the largest 100 subduction earthquakes, as well as with the 2011 Tohoku-Oki earthquake. Subduction zone intersections with volcanic chains are not found to be associated with a significantly elevated risk for great earthquakes globally. This difference likely arises from subducting fracture zone ridges leading to stronger seismic coupling than subducting volcanic chains.

300 mm arrays and 30 nm Features: Frontiers in Sorting Biological Objects

NASA Astrophysics Data System (ADS)

Austin, Robert; Comella, Brandon; D'Silva, Joseph; Sturm, James

2014-03-01

One of the great challenges in prediction of metastasis is determining when the metastatic process actually begins. It is presumed that this process occurs due to passage of biological objects in the blood from tumor to remote sites. We will discuss our attempts to find both very large objects (circulating tumor cell clumps) and very small (exosomes) using a combination of extremely large scale photolithography on 300 mm wafers and deep-UV lithography to produce sub-100 nm arrays to sort exosomes. These technologies push the envelope of present day academic facilities . Supported by the National Science Foundation and the National Cancer Institute.

Dual-mode nested search method for categorical uncertain multi-objective optimization

NASA Astrophysics Data System (ADS)

Tang, Long; Wang, Hu

2016-10-01

Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

Neural spike sorting using iterative ICA and a deflation-based approach.

PubMed

Tiganj, Z; Mboup, M

2012-12-01

We propose a spike sorting method for multi-channel recordings. When applied in neural recordings, the performance of the independent component analysis (ICA) algorithm is known to be limited, since the number of recording sites is much lower than the number of neurons. The proposed method uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity of one neuron is singled out and then deflated from the recordings. The internal loop implements a sequence of ICA and sorting for removing the noise and all the spikes that are not fired by the targeted neuron. Then a final step is appended to the two nested loops in order to separate simultaneously fired spikes. We solve this problem by taking all possible pairs of the sorted neurons and apply ICA only on the segments of the signal during which at least one of the neurons in a given pair was active. We validate the performance of the proposed method on simulated recordings, but also on a specific type of real recordings: simultaneous extracellular-intracellular. We quantify the sorting results on the extracellular recordings for the spikes that come from the neurons recorded intracellularly. The results suggest that the proposed solution significantly improves the performance of ICA in spike sorting.

DOD Needs to Improve Management and Oversight of Operations at the Theater Retrograde- Camp Arifjan, Kuwait

DTIC Science & Technology

2010-09-30

only at the TRC (includes Retro Sort), Warehouse, and Bulk Yard. 2 Theater Retrograde The Theater Retrograde consists of the TRC (includes Retro Sort...through Kuwait. Figure 1. Proper Flow of Materiel from Iraq through Kuwait Note 1: We reviewed operations at the TRC, Retro Sort, Warehouse...Materiel Processing Instructions CIIC Definition 1 Highest Sensitivity - Non-nuclear missiles and rockets , launcher tube and explosive rounds 2 Highest

New technology for ultrasensitive detection and isolation of rare cells for clinical diagnostics and therapeutics

NASA Astrophysics Data System (ADS)

Leary, James F.; McLaughlin, Scott R.

1995-04-01

A high-speed, 11-parameter, 6-color fluorescence, laser flow cytometer/cell sorter with a number of special and unique features has been built for ultrasensitive detection and isolation of rare cells for clinical diagnostics and therapeutics. The software for real-time data acquisition and sort control, written as C++ programming language modules with a WindowsTM graphical user interface, runs on a 66-MHz 80486 computer joined by an extended bus to 23 sophisticated multi-layered boards of special data acquisition and sorting electronics. Special features include: high-speed (> 100,000 cells/sec) real-time data classification module (U.S. Patent 5,204,884 (1993)); real-time principal component cell sorting; multi-queue signal-processing system with multiple hardware and software event buffers to reduce instrument dead time, LUT charge-pulse definition, high-resolution `flexible' sorting for optimal yield/purity sort strategies (U.S. Patent 5,199,576); pre-focusing optical wavelength correction for a second laser beam; and two trains of three fluorescence detectors-- each adjustable for spatial separation to interrogate only one of two laser beams, syringe- driven or pressure-driven fluidics, and time-windowed parameters. The system has been built to be both expandable and versatile through the use of LUT's and a modular hardware and software design. The instrument is especially useful at detection and isolation of rare cell subpopulations for which our laboratory is well-known. Cell subpopulations at frequencies as small as 10-7 have been successfully studied with this system. Current applications in clinical diagnostics and therapeutics include detection and isolation of (1) fetal cells from material blood for prenatal diagnosis of birth defects, (2) hematopoietic stem and precursor cells for autologous bone marrow transplantation, (3) metastatic breast cancer cells for molecular characterization, and (4) HIV-infected maternal cells in newborn blood to study mother-to-infant vertical transmission of AIDS.

Multi-objective experimental design for (13)C-based metabolic flux analysis.

PubMed

Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel

2015-10-01

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective design should stimulate its application within the field of (13)C-based metabolic flux analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

Importance of non-flow in mixed-harmonic multi-particle correlations in small collision systems

DOE PAGES

Huo, Peng; Gajdosova, Katarina; Jia, Jiangyong; ...

2017-12-18

Recently CMS Collaboration measured mixed-harmonic four-particle azimuthal correlations, known as symmetric cumulants SC(n, m), in pp and p+Pb collisions, and interpreted the non-zero SC(n, m) as evidence for long-range collectivity in these small collision systems. Using the PYTHIA and HIJING models which do not have genuine long-range collectivity, we show that the CMS results, obtained with standard cumulant method, could be dominated by non-flow effects associated with jet and dijets, especially in pp collisions. We show that the non-flow effects are largely suppressed using the recently proposed subevent cumulant methods by requiring azimuthal correlation between two or more pseudorapidity ranges.more » As a result, we argue that the reanalysis of SC(n, m) using the subevent method in experiments is necessary before they can used to provide further evidences for a long-range multi-particle collectivity and constraints on theoretical models in small collision systems.« less

Importance of non-flow in mixed-harmonic multi-particle correlations in small collision systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huo, Peng; Gajdosova, Katarina; Jia, Jiangyong

Recently CMS Collaboration measured mixed-harmonic four-particle azimuthal correlations, known as symmetric cumulants SC(n, m), in pp and p+Pb collisions, and interpreted the non-zero SC(n, m) as evidence for long-range collectivity in these small collision systems. Using the PYTHIA and HIJING models which do not have genuine long-range collectivity, we show that the CMS results, obtained with standard cumulant method, could be dominated by non-flow effects associated with jet and dijets, especially in pp collisions. We show that the non-flow effects are largely suppressed using the recently proposed subevent cumulant methods by requiring azimuthal correlation between two or more pseudorapidity ranges.more » As a result, we argue that the reanalysis of SC(n, m) using the subevent method in experiments is necessary before they can used to provide further evidences for a long-range multi-particle collectivity and constraints on theoretical models in small collision systems.« less

Rmax: A systematic approach to evaluate instrument sort performance using center stream catch☆

PubMed Central

Riddell, Andrew; Gardner, Rui; Perez-Gonzalez, Alexis; Lopes, Telma; Martinez, Lola

2015-01-01

Sorting performance can be evaluated with regard to Purity, Yield and/or Recovery of the sorted fraction. Purity is a check on the quality of the sample and the sort decisions made by the instrument. Recovery and Yield definitions vary with some authors regarding both as how efficient the instrument is at sorting the target particles from the original sample, others distinguishing Recovery from Yield, where the former is used to describe the accuracy of the instrument’s sort count. Yield and Recovery are often neglected, mostly due to difficulties in their measurement. Purity of the sort product is often cited alone but is not sufficient to evaluate sorting performance. All of these three performance metrics require re-sampling of the sorted fraction. But, unlike Purity, calculating Yield and/or Recovery calls for the absolute counting of particles in the sorted fraction, which may not be feasible, particularly when dealing with rare populations and precious samples. In addition, the counting process itself involves large errors. Here we describe a new metric for evaluating instrument sort Recovery, defined as the number of particles sorted relative to the number of original particles to be sorted. This calculation requires only measuring the ratios of target and non-target populations in the original pre-sort sample and in the waste stream or center stream catch (CSC), avoiding re-sampling the sorted fraction and absolute counting. We called this new metric Rmax, since it corresponds to the maximum expected Recovery for a particular set of instrument parameters. Rmax is ideal to evaluate and troubleshoot the optimum drop-charge delay of the sorter, or any instrument related failures that will affect sort performance. It can be used as a daily quality control check but can be particularly useful to assess instrument performance before single-cell sorting experiments. Because we do not perturb the sort fraction we can calculate Rmax during the sort process, being especially valuable to check instrument performance during rare population sorts. PMID:25747337

Non-invasive sex assessment in bovine semen by Raman spectroscopy

NASA Astrophysics Data System (ADS)

De Luca, A. C.; Managó, S.; Ferrara, M. A.; Rendina, I.; Sirleto, L.; Puglisi, R.; Balduzzi, D.; Galli, A.; Ferraro, P.; Coppola, G.

2014-05-01

X- and Y-chromosome-bearing sperm cell sorting is of great interest, especially for animal production management systems and genetic improvement programs. Here, we demonstrate an optical method based on Raman spectroscopy to separate X- and Y-chromosome-bearing sperm cells, overcoming many of the limitations associated with current sex-sorting protocols. A priori Raman imaging of bull spermatozoa was utilized to select the sampling points (head-neck region), which were then used to discriminate cells based on a spectral classification model. Main variations of Raman peaks associated with the DNA content were observed together with a variation due to the sex membrane proteins. Next, we used principal component analysis to determine the efficiency of our device as a cell sorting method. The results (>90% accuracy) demonstrated that Raman spectroscopy is a powerful candidate for the development of a highly efficient, non-invasive, and non-destructive tool for sperm sexing.

Amyloid-like aggregation of provasopressin in diabetes insipidus and secretory granule sorting.

PubMed

Beuret, Nicole; Hasler, Franziska; Prescianotto-Baschong, Cristina; Birk, Julia; Rutishauser, Jonas; Spiess, Martin

2017-01-26

Aggregation of peptide hormone precursors in the trans-Golgi network is an essential process in the biogenesis of secretory granules in endocrine cells. It has recently been proposed that this aggregation corresponds to the formation of functional amyloids. Our previous finding that dominant mutations in provasopressin, which cause cell degeneration and diabetes insipidus, prevent native folding and produce fibrillar aggregates in the endoplasmic reticulum (ER) might thus reflect mislocalized amyloid formation by sequences that evolved to mediate granule sorting. Here we identified two sequences responsible for fibrillar aggregation of mutant precursors in the ER: the N-terminal vasopressin nonapeptide and the C-terminal glycopeptide. To test their role in granule sorting, the glycopeptide was deleted and/or vasopressin mutated to inactivate ER aggregation while still permitting precursor folding and ER exit. These mutations strongly reduced sorting into granules and regulated secretion in endocrine AtT20 cells. The same sequences - vasopressin and the glycopeptide - mediate physiological aggregation of the wild-type hormone precursor into secretory granules and the pathological fibrillar aggregation of disease mutants in the ER. These findings support the amyloid hypothesis for secretory granule biogenesis.

Cone Quasi-Concave Multi-Objective Programming Theory and Dominance Cone Constructions.

DTIC Science & Technology

1988-08-01

generalized cone concavity and nondominated solutions for later use in our development. We also derive some properties of generalized cone concavity. A set S...A-quasiconcave on S" if g(.x 1+(1-X)x 2)-Min{g(x0), g(x 2)) e IntA forallxl*x 2 E SandX e (0,1), where (in ( gi (x’), gi (x2)) Min [g (0i), g (x2 ) - 1...will lead to elimination of the points of J3 by using the dominance cone W3 . Associated with W3 , only the points of J1 and J2 are nondominated, and

Addressing Women's Non-Maternal Healthcare Financing in Developing Countries: What Can We Learn from the Experiences of Rural Indian Women?

PubMed Central

Gopalan, Saji S.; Durairaj, Varatharajan

2012-01-01

Background and Objectives This paper focuses on the inadequate attention on women's non-maternal healthcare in low- and middle-income countries. The study assessed the purchase of and financial access to non-maternal healthcare. It also scoped for mainstreaming household financial resources in this regard to suggest for alternatives. Methods A household survey through multi-stage stratified sampling in the state of Orissa interviewed rural women above 15 years who were neither pregnant nor had any pregnancy-related outcome six weeks preceding the survey. The questions explored on the processes, determinants and outcomes of health seeking for non-maternal ailments. The outcome measures were healthcare access, cost of care and financial access. The independent variables for bivariate and multivariate analyses were contextual factors, health seeking and financing pattern. Results The survey obtained a response rate of 98.64% and among 800 women, 43.8% had no schooling and 51% were above 60 years. Each woman reported at least one episode of non-maternal ailment; financial constraints prevented 68% from receiving timely and complete care. Distress coping measures (e.g. borrowings) dominated the financing source (67.9%) followed by community–based measures (32.1%). Only 6% had financial risk-protection; financial risk of not obtaining care doubled for women aged over 60 years (OR 2.00, 95% CI 0.84–4.80), seeking outpatient consultation (OR 2.01, 95% CI 0.89–4.81), facing unfavourable household response (OR 2.04, 95% CI 1.09–3.83), and lacking other financial alternatives (OR 2.13, 95% CI 1.11–4.07). When it comes to timely mobilization of funds and healthcare seeking, 90% (714) of the households preferred maternal care to non-maternal healthcare. Conclusion The existing financing options enable sub-optimal purchase of women's non-maternal healthcare. Though dominant, household economy extends inadequate attention in this regard owing to its unfavourable approach towards non-maternal healthcare and limited financial capacity and support from other financial resources. PMID:22272262

Relationship between geomorphology and lithotypes of lahar deposit from Chokai volcano, Japan

NASA Astrophysics Data System (ADS)

Minami, Y.; Ohba, T.; Hayashi, S.; Kataoka, K.

2013-12-01

Chokai volcano, located in the northern Honshu arc in Japan, is an andesitic stratovolcano that collapsed partly at ca. 2500 years ago. A post collapse lahar deposit (Shirayukigawa lahar deposit) is distributed in the northern foot of the volcanic edifice. The deposit consists of 16 units of debris flow, hyperconcentrated flow and streamflow deposits. The Shirayukigawa lahar deposit has a total thickness of 30 m and overlies the 2.5-ka Kisakata debris avalanche deposit. Shirayukigawa lahar deposit forms volcanic fan and volcanic apron. The volcanic fan is subdivided into four areas on the basis of slope angles and of geomorphological features: 1) steeply sloped area, 2) moderately sloped area, 3) gently sloped area and 4) horizontal area. From sedimentary facies and structures, each unit of the Shirayukigawa lahar deposit is classified into one of four lithotypes: clast-supported debris flow deposit (Cc), matrix-supported debris flow deposit (Cm1), hyperconcentrated flow deposit (Cm2) and streamflow deposit (Sl). Each type has the following lithological characteristics. The lithotypes are well correlated with the geomorphology of the volcanic fan. The steeply-sloped and the moderately-sloped areas are dominated by Cc, Cm1, and Cm2, and The horizontal area are dominated by Sl. Debris flow deposit (Cc) is massive, very poorly sorted, partly graded, and clast-supported with polymictic clasts dominated by subrounded to rounded volcanic clasts. Matrix is sandy to muddy. Preferred clast orientation are present. Debris flow deposit (Cm1) is massive, very poorly sorted, and matrix-supported with polymictic clasts dominated by subrounded to rounded volcanic clasts. Matrix is sandy to muddy. Some layers exhibit coarse-tail normal/inverse grading. Most clasts are oriented. Hyperconcentrated flow deposit (Cm2) is massive to diffusely laminated, very poorly sorted and matrix-supported with polymictic clasts dominated by subrounded to rounded volcanic rocks. Matrix is sandy. The clasts are randomly distributed in the sandy matrix except for some clast-concentrated lenticular layers. Clasts smaller than 1cm account for about 10 percent of the deposits. Maximum clast size is about 30 cm. Streamflow deposit (Sl) is weakly parallel/cross-laminated, sorted and partly graded. The deposit contains volcanic clasts smaller than 20cm, which clasts are preferentially oriented and account for about 5% of the deposit. Clasts of the deposits consist of altered andesite, fresh andesite, mudstone and sandstone. The sedimentary clasts were derived from the substrate. The proportion of altered andesite clasts decreases upwards through the units. Matrix components in the lower eight units (C-LHR) are different from those of the upper eight units (S-LHR). In C-LHR units, grayish blue clay is dominant in matrix, whereas in S-LHR units, brownish yellow volcanic sand is dominant in matrix. Hydrothermal clay minerals such as smectite, chlorite, pyrophyllite and kaoline group minerals are rich in C-LHR units, whereas they are poor in S-LHR units. The stratigraphic variation in matrix component reflects temporal variation in supplied materials from source region.

PhySortR: a fast, flexible tool for sorting phylogenetic trees in R.

PubMed

Stephens, Timothy G; Bhattacharya, Debashish; Ragan, Mark A; Chan, Cheong Xin

2016-01-01

A frequent bottleneck in interpreting phylogenomic output is the need to screen often thousands of trees for features of interest, particularly robust clades of specific taxa, as evidence of monophyletic relationship and/or reticulated evolution. Here we present PhySortR, a fast, flexible R package for classifying phylogenetic trees. Unlike existing utilities, PhySortR allows for identification of both exclusive and non-exclusive clades uniting the target taxa based on tip labels (i.e., leaves) on a tree, with customisable options to assess clades within the context of the whole tree. Using simulated and empirical datasets, we demonstrate the potential and scalability of PhySortR in analysis of thousands of phylogenetic trees without a priori assumption of tree-rooting, and in yielding readily interpretable trees that unambiguously satisfy the query. PhySortR is a command-line tool that is freely available and easily automatable.

Extensive shared polymorphism at non-MHC immune genes in recently diverged North American prairie grouse

USGS Publications Warehouse

Minias, Piotr; Bateson, Zachary W.; Whittingham, Linda A.; Johnson, Jeff A.; Oyler-McCance, Sara J.; Dunn, Peter O.

2018-01-01

Gene polymorphisms shared between recently diverged species are thought to be widespread and most commonly reflect introgression from hybridization or retention of ancestral polymorphism through incomplete lineage sorting. Shared genetic diversity resulting from incomplete lineage sorting is usually maintained for a relatively short period of time, but under strong balancing selection it may persist for millions of years beyond species divergence (balanced trans-species polymorphism), as in the case of the major histocompatibility complex (MHC) genes. However, balancing selection is much less likely to act on non-MHC immune genes. The aim of this study was to investigate the patterns of shared polymorphism and selection at non-MHC immune genes in five grouse species from Centrocercus and Tympanuchus genera. For this purpose, we genotyped five non-MHC immune genes that do not interact directly with pathogens, but are involved in signaling and regulate immune cell growth. In contrast to previous studies with MHC, we found no evidence for balancing selection or balanced trans-species polymorphism among the non-MHC immune genes. No haplotypes were shared between genera and in most cases more similar allelic variants sorted by genus. Between species within genera, however, we found extensive shared polymorphism, which was most likely attributable to introgression or incomplete lineage sorting following recent divergence and large ancestral effective population size (i.e., weak genetic drift). Our study suggests that North American prairie grouse may have attained relatively low degree of reciprocal monophyly at nuclear loci and reinforces the rarity of balancing selection in non-MHC immune genes.

A SYSTEMATIC SEARCH FOR THE SPECTRA WITH FEATURES OF CRYSTALLINE SILICATES IN THE SPITZER IRS ENHANCED PRODUCTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Rui; Luo, Ali; Liu, Jiaming

2016-06-01

The crystalline silicate features are mainly reflected in infrared bands. The Spitzer Infrared Spectrograph (IRS) collected numerous spectra of various objects and provided a big database to investigate crystalline silicates in a wide range of astronomical environments. We apply the manifold ranking algorithm to perform a systematic search for the spectra with crystalline silicate features in the Spitzer IRS Enhanced Products available. In total, 868 spectra of 790 sources are found to show the features of crystalline silicates. These objects are cross-matched with the SIMBAD database as well as with the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST)/DR2. Themore » average spectrum of young stellar objects shows a variety of features dominated either by forsterite or enstatite or neither, while the average spectrum of evolved objects consistently present dominant features of forsterite in AGB, OH/IR, post-AGB, and planetary nebulae. They are identified optically as early-type stars, evolved stars, galaxies and so on. In addition, the strength of spectral features in typical silicate complexes is calculated. The results are available through CDS for the astronomical community to further study crystalline silicates.« less

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

PubMed Central

Petrus, Marloes L. C.; van Veluw, G. Jerre; Wösten, Han A. B.; Claessen, Dennis

2014-01-01

Streptomycetes are filamentous soil bacteria that are used in industry for the production of enzymes and antibiotics. When grown in bioreactors, these organisms form networks of interconnected hyphae, known as pellets, which are heterogeneous in size. Here we describe a method to analyze and sort mycelial pellets using a Complex Object Parametric Analyzer and Sorter (COPAS). Detailed instructions are given for the use of the instrument and the basic statistical analysis of the data. We furthermore describe how pellets can be sorted according to user-defined settings, which enables downstream processing such as the analysis of the RNA or protein content. Using this methodology the mechanism underlying heterogeneous growth can be tackled. This will be instrumental for improving streptomycetes as a cell factory, considering the fact that productivity correlates with pellet size. PMID:24561666

Starting research in interaction design with visuals for low-functioning children in the autistic spectrum: a protocol.

PubMed

Parés, Narcís; Carreras, Anna; Durany, Jaume; Ferrer, Jaume; Freixa, Pere; Gómez, David; Kruglanski, Orit; Parés, Roc; Ribas, J Ignasi; Soler, Miquel; Sanjurjo, Alex

2006-04-01

On starting to think about interaction design for low-functioning persons in the autistic spectrum (PAS), especially children, one finds a number of questions that are difficult to answer: Can we typify the PAS user? Can we engage the user in interactive communication without generating frustrating or obsessive situations? What sort of visual stimuli can we provide? Will they prefer representational or abstract visual stimuli? Will they understand three-dimensional (3D) graphic representation? What sort of interfaces will they accept? Can we set ambitious goals such as education or therapy? Unfortunately, most of these questions have no answer yet. Hence, we decided to set an apparently simple goal: to design a "fun application," with no intention to reach the level of education or therapy. The goal was to be attained by giving the users a sense of agency--by providing first a sense of control in the interaction dialogue. Our approach to visual stimuli design has been based on the use of geometric, abstract, two-dimensional (2D), real-time computer graphics in a full-body, non-invasive, interactive space. The results obtained within the European-funded project MultiSensory Environment Design for an Interface between Autistic and Typical Expressiveness (MEDIATE) have been extremely encouraging.

A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search.

PubMed

Chang, Yuan-Jyun; Hwang, Wen-Jyi; Chen, Chih-Chang

2016-12-07

The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO). The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

Multiobjective evolutionary optimization of water distribution systems: Exploiting diversity with infeasible solutions.

PubMed

Tanyimboh, Tiku T; Seyoum, Alemtsehay G

2016-12-01

This article investigates the computational efficiency of constraint handling in multi-objective evolutionary optimization algorithms for water distribution systems. The methodology investigated here encourages the co-existence and simultaneous development including crossbreeding of subpopulations of cost-effective feasible and infeasible solutions based on Pareto dominance. This yields a boundary search approach that also promotes diversity in the gene pool throughout the progress of the optimization by exploiting the full spectrum of non-dominated infeasible solutions. The relative effectiveness of small and moderate population sizes with respect to the number of decision variables is investigated also. The results reveal the optimization algorithm to be efficient, stable and robust. It found optimal and near-optimal solutions reliably and efficiently. The real-world system based optimization problem involved multiple variable head supply nodes, 29 fire-fighting flows, extended period simulation and multiple demand categories including water loss. The least cost solutions found satisfied the flow and pressure requirements consistently. The best solutions achieved indicative savings of 48.1% and 48.2% based on the cost of the pipes in the existing network, for populations of 200 and 1000, respectively. The population of 1000 achieved slightly better results overall. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Compromise Programming in forest management

Treesearch

Boris A. Poff; Aregai Tecle; Daniel G. Neary; Brian Geils

2010-01-01

Multi-objective decision-making (MODM) is an appropriate approach for evaluating a forest management scenario involving multiple interests. Today's land managers must accommodate commercial as well as non-commercial objectives that may be expressed quantitatively and/or qualitatively, and respond to social, political, economic and cultural changes. The spatial and...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ethridge, F.G.; Saracino, A.M.; Burns, L.K.

The encompassing sandstones, siltstones, shales and thin conglomerates of the gasified G Coal seam at the North Knobs SDB-UCG site were deposited mainly in fluvial and poorly-drained swamp environments. These beds dip at 65/sup 0/ at the North Knobs site. Thin section and SEM analyses of the sandstones and coarse siltstones show that they are sublithic to subarkosic arenites cemented with clay minerals, calcite hematite, siderite and silica. The sandstones of Unit D directly above the coal seam have the highest concentration of calcite cement, the lowest mean grain size, and are best sorted in terms of quartz grain sizemore » variations; however, they are the worst sorted in terms of sieve size variations. Clay minerals in the sandstones are dominantly kaolinite and smectite with lesser amounts of illite and chlorite. These clays are of secondary origin. Heat alteration is present only in coals and overburden rock from cores that penetrated the cavity. Thermally altered rocks including hornfels, buchite, paralava rock and paralava breccia were found in the bottom of the dipping cavity near the injection well. The high temperature minerals of tridymite, cristobalite, mullite, cordierite, monoclinic pyroxene and high temperature plagioclase indicate that temperatures of at least 1200/sup 0/C to 1400/sup 0/C were attained in the lower part of the burn cavity. The mechanical test on the unaltered and altered overburden rock show that the most important lithologic property controlling rock strength and seismic wave velocity is the amount and type of cement in the rock. Other parameters measured were grain size, amount of clay cement, and porosity; sorting had a secondary effect on the rock strength and seismic wave velocity. There is a non-linear and direct relationship between mechanical strength and ultrasonic wave velocities for the rock tests. 30 references.« less

Differential Internalization Rates and Postendocytic Sorting of the Norepinephrine and Dopamine Transporters Are Controlled by Structural Elements in the N Termini*

PubMed Central

Vuorenpää, Anne; Jørgensen, Trine N.; Newman, Amy H.; Madsen, Kenneth L.; Scheinin, Mika

2016-01-01

The norepinephrine transporter (NET) mediates reuptake of synaptically released norepinephrine in central and peripheral noradrenergic neurons. The molecular processes governing availability of NET in the plasma membrane are poorly understood. Here we use the fluorescent cocaine analogue JHC 1-64, as well as several other approaches, to investigate the trafficking itinerary of NET in live noradrenergic neurons. Confocal imaging revealed extensive constitutive internalization of JHC 1-64-labeled NET in the neuronal somata, proximal extensions and presynaptic boutons. Phorbol 12-myristate 13-acetate increased intracellular accumulation of JHC 1-64-labeled NET and caused a parallel reduction in uptake capacity. Internalized NET strongly colocalized with the “long loop” recycling marker Rab11, whereas less overlap was seen with the “short loop” recycling marker Rab4 and the late endosomal marker Rab7. Moreover, mitigating Rab11 function by overexpression of dominant negative Rab11 impaired NET function. Sorting of NET to the Rab11 recycling compartment was further supported by confocal imaging and reversible biotinylation experiments in transfected differentiated CATH.a cells. In contrast to NET, the dopamine transporter displayed markedly less constitutive internalization and limited sorting to the Rab11 recycling compartment in the differentiated CATH.a cells. Exchange of domains between the two homologous transporters revealed that this difference was determined by non-conserved structural elements in the intracellular N terminus. We conclude that NET displays a distinct trafficking itinerary characterized by continuous shuffling between the plasma membrane and the Rab11 recycling compartment and that the functional integrity of the Rab11 compartment is critical for maintaining proper presynaptic NET function. PMID:26786096

Morphodynamics modelling of bars in channels with graded sediment and sediment supply variation with the Telemac-Mascaret System

NASA Astrophysics Data System (ADS)

Cordier, Florian; Tassi, Pablo; Claude, Nicolas; Crosato, Alessandra; Rodrigues, Stéphane; Pham van Bang, Damien

2017-04-01

Numerical modelling of graded sediment transport in rivers remains a challenge [Siviglia and Crosato, 2016] and only few studies have considered the non-uniform distribution of sediment, although sediment grading is an inherent characteristic of natural rivers. The present work aims at revisiting the morphodynamics module of the Telemac-Mascaret modelling system and to integrate the latest developments to model the effects of non-uniform sediment on i) the sediment transport capacity estimated at the interface between the flow and the riverbed and on ii) the vertical sorting of sediment deposits in response to sediment supply changes. The implementation of these two processes has a key role on the modelling of bar dynamics in aggrading/degrading channels [Blom, 2008]. Numerical modelling of graded sediment transport remains a challenge due to the difficulty to reproduce the non-linear interactions between grains of different shape and size. Application of classical bedload equations usually fails in reproducing relevant transport rates [Recking, 2010 and references therein]. In this work, the graded sediment transport model of Wilcock and Crowe [2003] and the active layer concept of Hirano [1971] for the formulation of the exchange layer are implemented. The ability to reproduce the formation and evolution of graded-sediment bars is assessed on the basis of laboratory experiences from the literature. References: Blom, A., Ribberink, J. S., and Parker, G. 2008. Vertical sorting and the morphodynamics of bed form-dominated rivers: A sorting evolution model. Journal of Geophysical Research: Earth Surface, 113(F1). Lauer, J. W., Viparelli, E., and Piégay, H. 2016. Morphodynamics and sediment tracers in 1-d (mast-1d): 1-d sediment transport that includes exchange with an off-channel sediment reservoir. Advances in Water Resources. Recking, A. 2010. A comparison between flume and field bed load transport data and consequences for surface-based bed load transport prediction. Water Resources Research, 46(3). W03518. Siviglia, A. and Crosato, A. 2016. Numerical modelling of river morphodynamics: latest developments and remaining challenges. Advances in Water Resources, 90:1-9. Wilcock, P. R. and Crowe, J. C. 2003. Surface-based transport model for mixed-size sediment. Journal of Hydraulic Engineering, 129(2):120-128.

The effects of instruction and hand dominance on grip-to-load force coordination in manipulation tasks.

PubMed

Jin, Xin; Uygur, Mehmet; Getchell, Nancy; Hall, Susan J; Jaric, Slobodan

2011-10-31

The force applied upon a vertically oriented hand-held object could be decomposed into two orthogonal and highly coordinated components: the grip force (GF; the component perpendicular to the hand-object contact area that provides friction) and the load force (LF; the parallel component that can move the object or support the body). The aim of this study was to investigate the underexplored effects of task instruction and hand dominance on GF-LF coordination. Sixteen right-handed subjects performed bimanual manipulation against a horizontally oriented instrumented device under different sets of instructions. The tasks involved exertion of ramp-and-hold or oscillation patterns of LF performed symmetrically with two hands, while the instructions regarding individual actions were either similar (pull with both hands) or dissimilar (pull with one hand and hold with another). The results revealed that the instruction "to pull" leads to higher indices of GF-LF coordination than the instruction "to hold", as evidenced by a lower GF-LF ratio, higher GF-LF coupling, and higher GF modulation. The only effect of hand dominance was a moderate time lag of GF relative to LF changes observed in the non-dominant hand. We conclude that the instructions could play an important role in GF-LF coordination and, therefore, they should be taken into account when exploring or routinely testing hand function. Additionally, the results suggest that the neural control of GF of the non-dominant hand could involve some feedback mechanisms. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Multi-dimensional construction of a novel active yolk@conductive shell nanofiber web as a self-standing anode for high-performance lithium-ion batteries

NASA Astrophysics Data System (ADS)

Liu, Hao; Chen, Luyi; Liang, Yeru; Fu, Ruowen; Wu, Dingcai

2015-11-01

A novel active yolk@conductive shell nanofiber web with a unique synergistic advantage of various hierarchical nanodimensional objects including the 0D monodisperse SiO2 yolks, the 1D continuous carbon shell and the 3D interconnected non-woven fabric web has been developed by an innovative multi-dimensional construction method, and thus demonstrates excellent electrochemical properties as a self-standing LIB anode.A novel active yolk@conductive shell nanofiber web with a unique synergistic advantage of various hierarchical nanodimensional objects including the 0D monodisperse SiO2 yolks, the 1D continuous carbon shell and the 3D interconnected non-woven fabric web has been developed by an innovative multi-dimensional construction method, and thus demonstrates excellent electrochemical properties as a self-standing LIB anode. Electronic supplementary information (ESI) available: Experimental details and additional information about material characterization. See DOI: 10.1039/c5nr06531c

Preparation of cherry-picked combinatorial libraries by string synthesis.

PubMed

Furka, Arpád; Dibó, Gábor; Gombosuren, Naran

2005-03-01

String synthesis [1-3] is an efficient and cheap manual method for preparation of combinatorial libraries by using macroscopic solid support units. Sorting the units between two synthetic steps is an important operation of the procedure. The software developed to guide sorting can be used only when complete combinatorial libraries are prepared. Since very often only selected components of the full libraries are needed, new software was constructed that guides sorting in preparation of non-complete combinatorial libraries. Application of the software is described in details.

A Novel and Simple Spike Sorting Implementation.

PubMed

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2017-04-01

Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.

Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

PubMed

Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

2017-03-21

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

Multiple motifs regulate apical sorting of p75 via a mechanism that involves dimerization and higher-order oligomerization

PubMed Central

Youker, Robert T.; Bruns, Jennifer R.; Costa, Simone A.; Rbaibi, Youssef; Lanni, Frederick; Kashlan, Ossama B.; Teng, Haibing; Weisz, Ora A.

2013-01-01

The sorting signals that direct proteins to the apical surface of polarized epithelial cells are complex and can include posttranslational modifications, such as N- and O-linked glycosylation. Efficient apical sorting of the neurotrophin receptor p75 is dependent on its O-glycosylated membrane proximal stalk, but how this domain mediates targeting is unknown. Protein oligomerization or clustering has been suggested as a common step in the segregation of all apical proteins. Like many apical proteins, p75 forms dimers, and we hypothesized that formation of higher-order clusters mediated by p75 dimerization and interactions of the stalk facilitate its apical sorting. Using fluorescence fluctuation techniques (photon-counting histogram and number and brightness analyses) to study p75 oligomerization status in vivo, we found that wild-type p75–green fluorescent protein forms clusters in the trans-Golgi network (TGN) but not at the plasma membrane. Disruption of either the dimerization motif or the stalk domain impaired both clustering and polarized delivery. Manipulation of O-glycan processing or depletion of multiple galectins expressed in Madin-Darby canine kidney cells had no effect on p75 sorting, suggesting that the stalk domain functions as a structural prop to position other determinants in the lumenal domain of p75 for oligomerization. Additionally, a p75 mutant with intact dimerization and stalk motifs but with a dominant basolateral sorting determinant (Δ250 mutant) did not form oligomers, consistent with a requirement for clustering in apical sorting. Artificially enhancing dimerization restored clustering to the Δ250 mutant but was insufficient to reroute this mutant to the apical surface. Together these studies demonstrate that clustering in the TGN is required for normal biosynthetic apical sorting of p75 but is not by itself sufficient to reroute a protein to the apical surface in the presence of a strong basolateral sorting determinant. Our studies shed new light on the hierarchy of polarized sorting signals and on the mechanisms by which newly synthesized proteins are segregated in the TGN for eventual apical delivery. PMID:23637462

Development of a fast multi-line x-ray CT detector for NDT

NASA Astrophysics Data System (ADS)

Hofmann, T.; Nachtrab, F.; Schlechter, T.; Neubauer, H.; Mühlbauer, J.; Schröpfer, S.; Ernst, J.; Firsching, M.; Schweiger, T.; Oberst, M.; Meyer, A.; Uhlmann, N.

2015-04-01

Typical X-ray detectors for non-destructive testing (NDT) are line detectors or area detectors, like e.g. flat panel detectors. Multi-line detectors are currently only available in medical Computed Tomography (CT) scanners. Compared to flat panel detectors, line and multi-line detectors can achieve much higher frame rates. This allows time-resolved 3D CT scans of an object under investigation. Also, an improved image quality can be achieved due to reduced scattered radiation from object and detector themselves. Another benefit of line and multi-line detectors is that very wide detectors can be assembled easily, while flat panel detectors are usually limited to an imaging field with a size of approx. 40 × 40 cm2 at maximum. The big disadvantage of line detectors is the limited number of object slices that can be scanned simultaneously. This leads to long scan times for large objects. Volume scans with a multi-line detector are much faster, but with almost similar image quality. Due to the promising properties of multi-line detectors their application outside of medical CT would also be very interesting for NDT. However, medical CT multi-line detectors are optimized for the scanning of human bodies. Many non-medical applications require higher spatial resolutions and/or higher X-ray energies. For those non-medical applications we are developing a fast multi-line X-ray detector.In the scope of this work, we present the current state of the development of the novel detector, which includes several outstanding properties like an adjustable curved design for variable focus-detector-distances, conserving nearly uniform perpendicular irradiation over the entire detector width. Basis of the detector is a specifically designed, radiation hard CMOS imaging sensor with a pixel pitch of 200 μ m. Each pixel has an automatic in-pixel gain adjustment, which allows for both: a very high sensitivity and a wide dynamic range. The final detector is planned to have 256 lines of pixels. By using a modular assembly of the detector, the width can be chosen as multiples of 512 pixels. With a frame rate of up to 300 frames/s (full resolution) or 1200 frame/s (analog binning to 400 μ m pixel pitch) time-resolved 3D CT applications become possible. Two versions of the detector are in development, one with a high resolution scintillator and one with a thick, structured and very efficient scintillator (pitch 400 μ m). This way the detector can even work with X-ray energies up to 450 kVp.

Circumnuclear Multi-phase Gas in the Circinus Galaxy. I. Non-LTE Calculations of CO Lines

NASA Astrophysics Data System (ADS)

Wada, Keiichi; Fukushige, Ryosuke; Izumi, Takuma; Tomisaka, Kohji

2018-01-01

In this study, we investigate the line emissions from cold molecular gas based on our previous “radiation-driven fountain model,” which reliably explains the spectral energy distribution of the nearest type 2 Seyfert galaxy, the Circinus galaxy. Using a snapshot of the best-fit radiation-hydrodynamic model for the central r≤slant 16 pc, in which non-equilibrium X-ray-dominated region chemistry is solved, we conduct post-processed non-local thermodynamic equilibrium radiation transfer simulations for the CO lines. We obtain a spectral line energy distribution with a peak around J≃ 6, and its distribution suggests that the lines are not thermalized. However, for a given line of sight, the optical depth distribution is highly non-uniform between {τ }ν \\ll 1 and {τ }ν \\gg 1. The CO-to-H2 conversion factor ({X}{CO}), which can be directly obtained from the results and is not a constant, depends strongly on the integrated intensity and differs from the fiducial value for local objects. {X}{CO} exhibits a large dispersion of more than one order of magnitude, reflecting the non-uniform internal structure of a “torus.” In addition, we found that the physical conditions differ between grid cells on a scale of a few parsecs along the observed lines of sight; therefore, a specific observed line ratio does not necessarily represent a single physical state of the interstellar medium.

Identification of key characteristics of male reproductive toxicants as an approach for screening and sorting mechanistic evidence.

EPA Science Inventory

The application of systematic review practices in human health assessment includes integration of multi-disciplinary evidence from epidemiological, experimental, and mechanistic studies. Although mode of action analysis relies on the evaluation of mechanistic and toxicological ou...

Determination of criteria weights in solving multi-criteria problems

NASA Astrophysics Data System (ADS)

Kasim, Maznah Mat

2014-12-01

A multi-criteria (MC) problem comprises of units to be analyzed under a set of evaluation criteria. Solving a MC problem is basically the process of finding the overall performance or overall quality of the units of analysis by using certain aggregation method. Based on these overall measures of each unit, a decision can be made whether to sort them, to select the best or to group them according to certain ranges. Prior to solving the MC problems, the weights of the related criteria have to be determined with the assumption that the weights represent the degree of importance or the degree of contribution towards the overall performance of the units. This paper presents two main approaches which are called as subjective and objective approaches, where the first one involves evaluator(s) while the latter approach depends on the intrinsic information contained in each criterion. The subjective and objective weights are defined if the criteria are assumed to be independent with each other, but if they are dependent, there is another type of weight, which is called as monotone measure weight or compound weights which represent degree of interaction among the criteria. The measure of individual weights or compound weights must be addressed in solving multi-criteria problems so that the solutions are more reliable since in the real world, evaluation criteria always come with different degree of importance or are dependent with each other. As the real MC problems have their own uniqueness, it is up to the decision maker(s) to decide which type of weights and which method are the most applicable ones for the problem under study.

Usage of air jigging for multi-component separation of construction and demolition waste.

PubMed

Ambrós, Weslei Monteiro; Sampaio, Carlos Hoffmann; Cazacliu, Bogdan Grigore; Miltzarek, Gerson Luis; Miranda, Leonardo R

2017-02-01

The use of air jigging for performing multi-component separation in the treatment of mixed construction and demolition waste was studied. Sorting tests were carried out with mixtures of equal bulk volume of concrete and brick in which fixed quantities of unwanted materials - gypsum, wood and paper - were added. Experimental results have demonstrated the possibility to use air jigging to carry out both the removal of low-density contaminants and the concrete concentration in only one process step. In relation to the removal of contaminants only, the overall performance of jigging process can be comparable with that of commercial air classifiers and automatic sorting systems. Also, the initial content of contaminants seems does not have a significant effect on the separation extent. These results are of particular importance for recycling plants processing as they represent an alternative to optimize the use of air jigs. Further investigation is needed in order to evaluate the practical feasibility of such method. Copyright © 2016 Elsevier Ltd. All rights reserved.

The influence of joint technologies on ELV recyclability.

PubMed

Soo, Vi Kie; Compston, Paul; Doolan, Matthew

2017-10-01

Stricter vehicle emission legislation has led to the increasing use of lightweight materials and multi-material concepts to reduce the vehicle mass. To account for the complexity of multi-material vehicle designs, the choice of joining techniques used is becoming more diverse. Moreover, the different material combinations, and their respective joining methods play an important role in determining the potential of full material separation in a closed-loop system. This paper evaluates the types of joining technologies used in the automotive industry, and identifies those that hinder the sorting of ELV materials. The study is based on an industrial shredding trial of car doors. Observations from the case study showed that steel screws and bolts are increasingly used to combine different material types and are less likely to be perfectly liberated during the shredding process. The characteristics of joints that lead to impurities and valuable material losses, such as joint strength, material type, size, diameter, location, and protrusion level, can influence the material liberation in the current sorting practices and thus, lead to ELV waste minimisation. Additionally, the liberation of joints is also affected by the density and thickness of materials being joined. Correlation analyses are carried out to further support the influence of mechanical screws and bolts on material separation efficiencies. The observations are representative of the initial phases of current global ELV sorting practices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination

PubMed Central

2012-01-01

Background Principal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed for hardware implementations of PCA. General Hebbian algorithm (GHA) has been proposed for calculating PCs of neuronal spikes in our previous work, which eliminates the needs of computationally expensive covariance analysis and eigenvalue decomposition in conventional PCA algorithms. However, large memory resources are still inherently required for storing a large volume of aligned spikes for training PCs. The large size memory will consume large hardware resources and contribute significant power dissipation, which make GHA difficult to be implemented in portable or implantable multi-channel recording micro-systems. Method In this paper, we present a new algorithm for PCA-based spike sorting based on GHA, namely stream-based Hebbian eigenfilter, which eliminates the inherent memory requirements of GHA while keeping the accuracy of spike sorting by utilizing the pseudo-stationarity of neuronal spikes. Because of the reduction of large hardware storage requirements, the proposed algorithm can lead to ultra-low hardware resources and power consumption of hardware implementations, which is critical for the future multi-channel micro-systems. Both clinical and synthetic neural recording data sets were employed for evaluating the accuracy of the stream-based Hebbian eigenfilter. The performance of spike sorting using stream-based eigenfilter and the computational complexity of the eigenfilter were rigorously evaluated and compared with conventional PCA algorithms. Field programmable logic arrays (FPGAs) were employed to implement the proposed algorithm, evaluate the hardware implementations and demonstrate the reduction in both power consumption and hardware memories achieved by the streaming computing Results and discussion Results demonstrate that the stream-based eigenfilter can achieve the same accuracy and is 10 times more computationally efficient when compared with conventional PCA algorithms. Hardware evaluations show that 90.3% logic resources, 95.1% power consumption and 86.8% computing latency can be reduced by the stream-based eigenfilter when compared with PCA hardware. By utilizing the streaming method, 92% memory resources and 67% power consumption can be saved when compared with the direct implementation of GHA. Conclusion Stream-based Hebbian eigenfilter presents a novel approach to enable real-time spike sorting with reduced computational complexity and hardware costs. This new design can be further utilized for multi-channel neuro-physiological experiments or chronic implants. PMID:22490725

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

NASA Astrophysics Data System (ADS)

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution.

PubMed

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-06

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

PubMed Central

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining. PMID:28059147

Bridging the Divide: Linking Genomics to Ecosystem Responses to Climate Change: Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Melinda D.

2014-03-15

Over the project period, we have addressed the following objectives: 1) assess the effects of altered precipitation patterns (i.e., increased variability in growing season precipitation) on genetic diversity of the dominant C4 grass species, Andropogon gerardii, and 2) experimentally assess the impacts of extreme climatic events (heat wave, drought) on responses of the dominant C4 grasses, A. gerardii and Sorghastrum nutans, and the consequences of these response for community and ecosystem structure and function. Below is a summary of how we have addressed these objectives. Objective 1 After ten years of altered precipitation, we found the number of genotypes ofmore » A. gerardii was significantly reduced compared to the ambient precipitation treatments (Avolio et al., 2013a). Although genotype number was reduced, the remaining genotypes were less related to one another indicating that the altered precipitation treatment was selecting for increasingly dissimilar genomes (based on mean pairwise Dice distance among individuals). For the four key genotypes that displayed differential abundances depending on the precipitation treatment (G1, G4, and G11 in the altered plots and G2 in the ambient plots), we identified phenotypic differences in the field that could account for ecological sorting (Avolio & Smith, 2013a). The three altered rainfall genotypes also have very different phenotypic traits in the greenhouse in response to different soil moisture availabilities (Avolio and Smith, 2013c). Two of the genotypes that increased in abundance in the altered precipitation plots had greater allocation to root biomass (G4 and G11), while G1 allocated more biomass aboveground. These phenotypic differences among genotypes suggests that changes in genotypic structure between the altered and the ambient treatments has likely occurred via niche differentiation, driven by changes in soil moisture dynamics (reduced mean, increased variability and changes in the depth distribution of soil moisture) under a more variable precipitation regime, rather than reduced population numbers (A. gerardii tiller densities did not differ between altered and ambient treatments; p = 0.505) or a priori differences in genotype richness (Avolio et al.2013a). This ecological sorting of genotypes, which accounts for 40% of all sampled individuals in the altered plots, is an important legacy of the press chronic climate changes in the RaMPs experiment. Objective 2 In May 2010, we established the Climate Extremes Experiment at the Konza Prairie Biological Station. For the experiment, a gradient of temperatures, ranging from ambient to extreme, were imposed in 2010 and 2011 as a mid-season heat wave under well-watered or severe drought conditions. This study allowed us for the first time to examine species-specific thresholds of responses to climate extremes and assess how these phenotypic responses may impact selection of particular genotypes, with the ultimate goal of linking alterations in individual performance and genetic diversity to ecosystem structure and functioning. We found that tallgrass prairie was resistant to heat waves, but it was not resistant to extreme drought, which reduced aboveground net primary productivity (ANPP) below the lowest level measured in this grassland in almost thirty years (Hoover et al. in press(a)). This extreme reduction in ecosystem function was a consequence of reduced productivity of both C4 grasses and C3 forbs. This reduction in biomass of the C4 grasses (Andropogon gerardii and Sorghastrum nutans) was, in part, due to significant reductions in photosynthesis, leaf water potential and productivity with drought in the dominant grasses species, with S. nutans was more sensitive than A. gerardii to drought (Hoover et al. in press(b)). However, the dominant forb was negatively impacted by the drought more than the dominant grasses, and this led to a reordering of species abundances within the plant community. Although this change in community composition persisted post-drought, ANPP recovered completely the year after drought due to rapid demographic responses by the dominant grass, compensating for loss of the dominant forb. Overall, our results show that an extreme reduction in ecosystem function attributable to a climate extreme (e.g., low resistance) does not preclude rapid ecosystem recovery. Given that dominance by a few species is characteristic of most ecosystems, knowledge of the traits of these species and their responses to climate extremes will be key for predicting future ecosystem dynamics and function. In addition, our research suggests that water stress will dominate photosynthetic and productivity responses caused by discrete drought and heat wave events, rather than direct or additive effects of heat stress, with differential sensitivity in these grasses altering future ecosystem function.« less

Multi-Method Provenance Analysis of Namibian Desert Sand

NASA Astrophysics Data System (ADS)

Vermeesch, P.; Garzanti, E.

2014-12-01

Mineralogical, geochemical and geochronological provenance proxies each have their own strengths and weaknesses: a. Bulk geochemistry, framework petrography and heavy mineral compositions can differentiate between source areas characterised by different lithologies, but are sensitive to hydraulic sorting and chemical alteration. b. Detrital zircon U-Pb geochronology is insensitive to winnowing effects, but is 'blind' to lithologies devoid of zircon and cannot differentiate between first cycle and recycled sediments. c. Cosmogenic neon isotopes can be used to identify different generations of surface exposure while simultaneously tracking different magmatic sources. The challenge is then to combine these different proxies into a self consistent story, and do so in as objective a manner as possible. We here present a case study of Namibia's Namib Sand Sea and Skeleton Coast ergs, in which all the aforementioned methods have been combined using a three-way multidimensional scaling (aka INDividual Differences SCALing or INDSCAL) analysis: 1. Each of the datasets was represented by a 'dissimilarity matrix' of pairwise distances between samples. 2. The set of these matrices was fed into the INDSCAL algorithm, which produces two pieces of graphical output: the 'group configuration', which is a scatter plot or 'map' in which similar samples plot close together and dissimilar samples plot far apart, and the 'proxy weights', in which not the samples but the proxies are plotted according to the weight they attached to the 'group configuration' axes. The INDSCAL map of the Namibia dataset indicates that (a) long-shore drift of Orange River sediments dominates the coastal sediment compositions all along the Namibian coast until Angola, and (b) that light and heavy minerals tell complementary parts of the provenance story.

An Analysis of Published Preschool Language Programs.

ERIC Educational Resources Information Center

Bartlett, Elsa Jaffe

For purposes of analysis, preschool language programs can be sorted into four general categories according to the dominant type of learning activity (1) Pattern practice, (2) Cognitive verbalization, (3) Discussion, (4) Role play. Along with definitions of language, the program types differ in the kinds of interactions which occur between teacher…

φ-evo: A program to evolve phenotypic models of biological networks.

PubMed

Henry, Adrien; Hemery, Mathieu; François, Paul

2018-06-01

Molecular networks are at the core of most cellular decisions, but are often difficult to comprehend. Reverse engineering of network architecture from their functions has proved fruitful to classify and predict the structure and function of molecular networks, suggesting new experimental tests and biological predictions. We present φ-evo, an open-source program to evolve in silico phenotypic networks performing a given biological function. We include implementations for evolution of biochemical adaptation, adaptive sorting for immune recognition, metazoan development (somitogenesis, hox patterning), as well as Pareto evolution. We detail the program architecture based on C, Python 3, and a Jupyter interface for project configuration and network analysis. We illustrate the predictive power of φ-evo by first recovering the asymmetrical structure of the lac operon regulation from an objective function with symmetrical constraints. Second, we use the problem of hox-like embryonic patterning to show how a single effective fitness can emerge from multi-objective (Pareto) evolution. φ-evo provides an efficient approach and user-friendly interface for the phenotypic prediction of networks and the numerical study of evolution itself.

Study on Impact Acoustic—Visual Sensor-Based Sorting of ELV Plastic Materials

PubMed Central

Huang, Jiu; Tian, Chuyuan; Ren, Jingwei; Bian, Zhengfu

2017-01-01

This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles’ (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41.4% for PP/EPDM scraps as well as 62.4% for ABS, and 70.8% for ABS/PC scraps. Within the diameter range of 8-13 mm, only 25% of PP and 27% of PP/EPDM scraps, as well as 43% of ABS, and 47% of ABS/PC scraps were finally separated. This research proposes a new approach for sensor-aided automatic recognition and sorting of black plastic materials, it is an effective method for ASR reduction and recycling. PMID:28594341

Study on Impact Acoustic-Visual Sensor-Based Sorting of ELV Plastic Materials.

PubMed

Huang, Jiu; Tian, Chuyuan; Ren, Jingwei; Bian, Zhengfu

2017-06-08

This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles' (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41.4% for PP/EPDM scraps as well as 62.4% for ABS, and 70.8% for ABS/PC scraps. Within the diameter range of 8-13 mm, only 25% of PP and 27% of PP/EPDM scraps, as well as 43% of ABS, and 47% of ABS/PC scraps were finally separated. This research proposes a new approach for sensor-aided automatic recognition and sorting of black plastic materials, it is an effective method for ASR reduction and recycling.

A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings.

PubMed

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2015-01-01

The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.

Interaction sorting method for molecular dynamics on multi-core SIMD CPU architecture.

PubMed

Matvienko, Sergey; Alemasov, Nikolay; Fomin, Eduard

2015-02-01

Molecular dynamics (MD) is widely used in computational biology for studying binding mechanisms of molecules, molecular transport, conformational transitions, protein folding, etc. The method is computationally expensive; thus, the demand for the development of novel, much more efficient algorithms is still high. Therefore, the new algorithm designed in 2007 and called interaction sorting (IS) clearly attracted interest, as it outperformed the most efficient MD algorithms. In this work, a new IS modification is proposed which allows the algorithm to utilize SIMD processor instructions. This paper shows that the improvement provides an additional gain in performance, 9% to 45% in comparison to the original IS method.

Sperm sexing in sheep and cattle: the exception and the rule.

PubMed

de Graaf, S P; Beilby, K H; Underwood, S L; Evans, G; Maxwell, W M C

2009-01-01

Flow cytometric sorting for the preselection of sex has progressed considerably in the 20 years since its inception. This technique has allowed the production of pre-sexed offspring in a multitude of species and become a commercial success in cattle around the world. However, due to the stress inherent to the sex-sorting process, sex-sorted spermatozoa are widely recognized as functionally compromised in terms of their fertilizing lifespan within the female reproductive tract as a result of reduced motility and viability and changed functional state. These characteristics, when compared to non-sorted controls, are manifest in vivo as lower fertility. However, improvements to the technology and a greater understanding of its biological impact have facilitated recent developments in sheep, showing sex-sorting is capable of selecting a functionally superior population in terms of both in vitro and in vivo function. These results are reviewed in the context of recent developments in other species and the reasons for success after artificial insemination with sex-sorted ram spermatozoa are discussed.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Effects of straw processing and pen overstocking on the growth performance and sorting characteristics of diets offered to replacement Holstein dairy heifers

USDA-ARS?s Scientific Manuscript database

The effects of pen-stocking density and straw processing on the growth performance of Holstein dairy heifers housed in a free-stall system are not well understood. Our objectives were to evaluate these factors on the growth performance, feed-bunk sorting behaviors, daily behavioral traits, and hygie...

Interannual variability of sorted bedforms in the coastal German Bight (SE North Sea)

NASA Astrophysics Data System (ADS)

Mielck, F.; Holler, P.; Bürk, D.; Hass, H. C.

2015-12-01

Sorted bedforms are ubiquitous on the inner continental shelves worldwide. They are described as spatially-grain-size-sorted features consisting of small rippled medium-to-coarse sand and can remain stable for decades. However, the knowledge about their genesis and development is still fragmentary. For this study, a representative investigation area (water depth<15 m) located on the shelf west of the island of Sylt (SE North Sea, Germany) was periodically surveyed with hydroacoustic means (i.e. sidescan sonar, multibeam echo sounder, and sub-bottom profiler) during 2010-2014. Since this area is influenced by tidal and wind-driven currents, the aim was to detect and examine interannual variabilities in the characteristics of the prevailing sorted bedforms. Our measurements reveal sinuous stripes of rippled medium sand which are embedded in shallow symmetrical depressions. These domains are surrounded by relatively smooth fine-sand areas. These sorted bedforms were identified as flow-transverse features that are maintained by ebb and flood currents of almost equal strengths that flow in opposite directions. This bidirectional flow field generates sharp boundaries between the medium- and fine-sand domains in both current directions. Further to the north, where flood currents are dominant, asymmetric sorted bedforms were detected which show sharp boundaries only in flood-current direction. Comparisons between the measurements of the different years show no significant variations in morphology and distribution of the sorted bedforms. However, variations of the boundaries between the medium and the fine-sand domains were observed. Additionally, new minor sorted bedforms and rippled excavation marks as well as new fine-sand areas developed and disappeared occasionally. It can be supposed that such sediment winnowing and focusing processes take place during periodically recurring storm surges, which change the shapes of the features. Moreover, variations in alignments and sizes of the small ripple formations were detected. They seem to indicate the directions and intensities of previous storm events.

SortNet: learning to rank by a neural preference function.

PubMed

Rigutini, Leonardo; Papini, Tiziano; Maggini, Marco; Scarselli, Franco

2011-09-01

Relevance ranking consists in sorting a set of objects with respect to a given criterion. However, in personalized retrieval systems, the relevance criteria may usually vary among different users and may not be predefined. In this case, ranking algorithms that adapt their behavior from users' feedbacks must be devised. Two main approaches are proposed in the literature for learning to rank: the use of a scoring function, learned by examples, that evaluates a feature-based representation of each object yielding an absolute relevance score, a pairwise approach, where a preference function is learned to determine the object that has to be ranked first in a given pair. In this paper, we present a preference learning method for learning to rank. A neural network, the comparative neural network (CmpNN), is trained from examples to approximate the comparison function for a pair of objects. The CmpNN adopts a particular architecture designed to implement the symmetries naturally present in a preference function. The learned preference function can be embedded as the comparator into a classical sorting algorithm to provide a global ranking of a set of objects. To improve the ranking performances, an active-learning procedure is devised, that aims at selecting the most informative patterns in the training set. The proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state-of-the-art algorithms.

Modelling of Robotized Manufacturing Systems Using MultiAgent Formalism

NASA Astrophysics Data System (ADS)

Foit, K.; Gwiazda, A.; Banaś, W.

2016-08-01

The evolution of manufacturing systems has greatly accelerated due to development of sophisticated control systems. On top of determined, one way production flow the need of decision making has arisen as a result of growing product range that are manufactured simultaneously, using the same resources. On the other hand, the intelligent flow control could address the “bottleneck” problem caused by the machine failure. This sort of manufacturing systems uses advanced control algorithms that are introduced by the use of logic controllers. The complex algorithms used in the control systems requires to employ appropriate methods during the modelling process, like the agent-based one, which is the subject of this paper. The concept of an agent is derived from the object-based methodology of modelling, so it meets the requirements of representing the physical properties of the machines as well as the logical form of control systems. Each agent has a high level of autonomy and could be considered separately. The multi-agent system consists of minimum two agents that can interact and modify the environment, where they act. This may lead to the creation of self-organizing structure, what could be interesting feature during design and test of manufacturing system.

Measuring persistence to hormonal therapy in patients with breast cancer: accounting for temporary treatment discontinuation.

PubMed

Huiart, Laetitia; Ferdynus, Cyril; Dell'Aniello, Sophie; Bakiri, Naciba; Giorgi, Roch; Suissa, Samy

2014-08-01

Several studies have been conducted to estimate persistence to hormonal therapy among women with breast cancer (BC). Most studies focus on first treatment discontinuation. Patients, however, can have numerous periods of treatment discontinuation or treatment exposure. Our objective is to estimate persistence to tamoxifen in patients with BC while accounting for temporary treatment discontinuations and this by using multi-state (MS) models. A cohort of 10,806 women with BC having received at least one prescription of tamoxifen between 1998 and 2008 was constituted from the UK General Practice Research Database. We fitted a semi-Markov model with three states to estimate the probability of being off treatment over a 5-year period while accounting for temporary treatment discontinuations (transition between on treatment and off treatment) and competing risks (recurrence of BC or death). Non-persistence, as estimated from the MS model, ranged from 12.1% (95% confidence interval [95%CI]: 9.2-15.1) at 1 year to 14.9% (95%CI: 11.7-18.1) at 5 years. Estimations of non-persistence based on the Kaplan-Meier model were higher, i.e., 29.3% (95%CI: 28.1-30.6) at 5 years, as well as those obtained from a competing risk model, i.e., 24.0% (95%CI: 22.9-25.1). Most temporary discontinuations (94.7%) lasted less than 6 months. Temporary treatment discontinuations are frequent and should be accounted for when measuring adherence to treatment. MS models can provide a useful framework for this sort of analysis insofar as they help describe patients' complex behavior. This may help tailor interventions that improve persistence to hormonal therapy among women with BC. Copyright © 2014 John Wiley & Sons, Ltd.

Stochastic injection-strategy optimization for the preliminary assessment of candidate geological storage sites

NASA Astrophysics Data System (ADS)

Cody, Brent M.; Baù, Domenico; González-Nicolás, Ana

2015-09-01

Geological carbon sequestration (GCS) has been identified as having the potential to reduce increasing atmospheric concentrations of carbon dioxide (CO2). However, a global impact will only be achieved if GCS is cost-effectively and safely implemented on a massive scale. This work presents a computationally efficient methodology for identifying optimal injection strategies at candidate GCS sites having uncertainty associated with caprock permeability, effective compressibility, and aquifer permeability. A multi-objective evolutionary optimization algorithm is used to heuristically determine non-dominated solutions between the following two competing objectives: (1) maximize mass of CO2 sequestered and (2) minimize project cost. A semi-analytical algorithm is used to estimate CO2 leakage mass rather than a numerical model, enabling the study of GCS sites having vastly different domain characteristics. The stochastic optimization framework presented herein is applied to a feasibility study of GCS in a brine aquifer in the Michigan Basin (MB), USA. Eight optimization test cases are performed to investigate the impact of decision-maker (DM) preferences on Pareto-optimal objective-function values and carbon-injection strategies. This analysis shows that the feasibility of GCS at the MB test site is highly dependent upon the DM's risk-adversity preference and degree of uncertainty associated with caprock integrity. Finally, large gains in computational efficiency achieved using parallel processing and archiving are discussed.

A novel scene-based non-uniformity correction method for SWIR push-broom hyperspectral sensors

NASA Astrophysics Data System (ADS)

Hu, Bin-Lin; Hao, Shi-Jing; Sun, De-Xin; Liu, Yin-Nian

2017-09-01

A novel scene-based non-uniformity correction (NUC) method for short-wavelength infrared (SWIR) push-broom hyperspectral sensors is proposed and evaluated. This method relies on the assumption that for each band there will be ground objects with similar reflectance to form uniform regions when a sufficient number of scanning lines are acquired. The uniform regions are extracted automatically through a sorting algorithm, and are used to compute the corresponding NUC coefficients. SWIR hyperspectral data from airborne experiment are used to verify and evaluate the proposed method, and results show that stripes in the scenes have been well corrected without any significant information loss, and the non-uniformity is less than 0.5%. In addition, the proposed method is compared to two other regular methods, and they are evaluated based on their adaptability to the various scenes, non-uniformity, roughness and spectral fidelity. It turns out that the proposed method shows strong adaptability, high accuracy and efficiency.

Teleoperated robotic sorting system

DOEpatents

Roos, Charles E.; Sommer, Jr., Edward J.; Parrish, Robert H.; Russell, James R.

2008-06-24

A method and apparatus are disclosed for classifying materials utilizing a computerized touch sensitive screen or other computerized pointing device for operator identification and electronic marking of spatial coordinates of materials to be extracted. An operator positioned at a computerized touch sensitive screen views electronic images of the mixture of materials to be sorted as they are conveyed past a sensor array which transmits sequences of images of the mixture either directly or through a computer to the touch sensitive display screen. The operator manually "touches" objects displayed on the screen to be extracted from the mixture thereby registering the spatial coordinates of the objects within the computer. The computer then tracks the registered objects as they are conveyed and directs automated devices including mechanical means such as air jets, robotic arms, or other mechanical diverters to extract the registered objects.

Teleoperated robotic sorting system

DOEpatents

Roos, Charles E.; Sommer, Edward J.; Parrish, Robert H.; Russell, James R.

2000-01-01

A method and apparatus are disclosed for classifying materials utilizing a computerized touch sensitive screen or other computerized pointing device for operator identification and electronic marking of spatial coordinates of materials to be extracted. An operator positioned at a computerized touch sensitive screen views electronic images of the mixture of materials to be sorted as they are conveyed past a sensor array which transmits sequences of images of the mixture either directly or through a computer to the touch sensitive display screen. The operator manually "touches" objects displayed on the screen to be extracted from the mixture thereby registering the spatial coordinates of the objects within the computer. The computer then tracks the registered objects as they are conveyed and directs automated devices including mechanical means such as air jets, robotic arms, or other mechanical diverters to extract the registered objects.

Cost-effectiveness of 64-slice CT angiography compared to conventional coronary angiography based on a coverage with evidence development study in Ontario.

PubMed

Goeree, Ron; Blackhouse, Gord; Bowen, James M; O'Reilly, Daria; Sutherland, Simone; Hopkins, Robert; Chow, Benjamin; Freeman, Michael; Provost, Yves; Dennie, Carole; Cohen, Eric; Marcuzzi, Dan; Iwanochko, Robert; Moody, Alan; Paul, Narinder; Parker, John D

2013-10-01

Conventional coronary angiography (CCA) is the standard diagnostic for coronary artery disease (CAD), but multi-detector computed tomography coronary angiography (CTCA) is a non-invasive alternative. A multi-center coverage with evidence development study was undertaken and combined with an economic model to estimate the cost-effectiveness of CTCA followed by CCA vs CCA alone. Alternative assumptions were tested in patient scenario and sensitivity analyses. CCA was found to dominate CTCA, however, CTCA was relatively more cost-effective in females, in advancing age, in patients with lower pre-test probabilities of CAD, the higher the sensitivity of CTCA and the lower the probability of undergoing a confirmatory CCA following a positive CTCA. RESULTS were very sensitive to alternative patient populations and modeling assumptions. Careful consideration of patient characteristics, procedures to improve the diagnostic yield of CTCA and selective use of CCA following CTCA will impact whether CTCA is cost-effective or dominates CCA.

Environmental species sorting dominates forest-bird community assembly across scales.

PubMed

Özkan, Korhan; Svenning, Jens-Christian; Jeppesen, Erik

2013-01-01

Environmental species sorting and dispersal are seen as key factors in community assembly, but their relative importance and scale dependence remain uncertain, as the extent to which communities are consistently assembled throughout their biomes. To address these issues, we analysed bird metacommunity structure in a 1200-km(2) forested landscape (Istranca Forests) in Turkish Thrace at the margin of the Western Palaearctic (WP) temperate-forest biome. First, we used spatial regressions and Mantel tests to assess the relative importance of environmental and spatial factors as drivers of local species richness and composition within the metacommunity. Second, we analysed species' abundance-occupancy relationship across the metacommunity and used null models to assess whether occupancy is determined by species' environmental niches. Third, we used generalized linear models to test for links between species' metacommunity-wide occupancy and their broader WP regional populations and assessed whether these links are consistent with environmental species sorting. There was strong environmental control on local species richness and composition patterns within the metacommunity, but non-environmental spatial factors had also an important joint role. Null model analyses on randomized communities showed that species' occupancy across the metacommunity was strongly determined by species' environmental niches, with occupancy being related to niche position marginality. Species' metacommunity-wide occupancy correlated with their local abundance as well as with their range size and total abundance for the whole WP, suggesting that the same assembly mechanisms act consistently across local to regional scales. A species specialization index that was estimated by bird species' habitat use across France, incorporating both niche position and breadth, was significantly related to species' occupancy and abundance at both metacommunity and WP regional scales. Hence, the same niche-related assembly mechanisms appear to act consistently across the WP region. Overall, our results suggest that the structure of the Istranca Forest' bird metacommunity was predominantly controlled by environmental species sorting in a manner consistent with the broader WP region. However, variability in local community structure was also linked to purely spatial factors, albeit more weakly. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.

Near-infrared-spectroscopic study on processing of sounds in the brain; a comparison between native and non-native speakers of Japanese.

PubMed

Tsunoda, Koichi; Sekimoto, Sotaro; Itoh, Kenji

2016-06-01

Conclusions The result suggested that mother tongue Japanese and non- mother tongue Japanese differ in their pattern of brain dominance when listening to sounds from the natural world-in particular, insect sounds. These results reveal significant support for previous findings from Tsunoda (in 1970). Objectives This study concentrates on listeners who show clear evidence of a 'speech' brain vs a 'music' brain and determines which side is most active in the processing of insect sounds, using with near-infrared spectroscopy. Methods The present study uses 2-channel Near Infrared Spectroscopy (NIRS) to provide a more direct measure of left- and right-brain activity while participants listen to each of three types of sounds: Japanese speech, Western violin music, or insect sounds. Data were obtained from 33 participants who showed laterality on opposite sides for Japanese speech and Western music. Results Results showed that a majority (80%) of the MJ participants exhibited dominance for insect sounds on the side that was dominant for language, while a majority (62%) of the non-MJ participants exhibited dominance for insect sounds on the side that was dominant for music.

Real World Cognitive Multi-Tasking and Problem Solving: A Large Scale Cognitive Architecture Simulation Through High Performance Computing-Project Casie

DTIC Science & Technology

2008-03-01

computational version of the CASIE architecture serves to demonstrate the functionality of our primary theories. However, implementation of several other...following facts. First, based on Theorem 3 and Theorem 5, the objective function is non -increasing under updating rule (6); second, by the criteria for...reassignment in updating rule (7), it is trivial to show that the objective function is non -increasing under updating rule (7). A Unified View to Graph

Multi-objective and Perishable Fuzzy Inventory Models Having Weibull Life-time With Time Dependent Demand, Demand Dependent Production and Time Varying Holding Cost: A Possibility/Necessity Approach

NASA Astrophysics Data System (ADS)

Pathak, Savita; Mondal, Seema Sarkar

2010-10-01

A multi-objective inventory model of deteriorating item has been developed with Weibull rate of decay, time dependent demand, demand dependent production, time varying holding cost allowing shortages in fuzzy environments for non- integrated and integrated businesses. Here objective is to maximize the profit from different deteriorating items with space constraint. The impreciseness of inventory parameters and goals for non-integrated business has been expressed by linear membership functions. The compromised solutions are obtained by different fuzzy optimization methods. To incorporate the relative importance of the objectives, the different cardinal weights crisp/fuzzy have been assigned. The models are illustrated with numerical examples and results of models with crisp/fuzzy weights are compared. The result for the model assuming them to be integrated business is obtained by using Generalized Reduced Gradient Method (GRG). The fuzzy integrated model with imprecise inventory cost is formulated to optimize the possibility necessity measure of fuzzy goal of the objective function by using credibility measure of fuzzy event by taking fuzzy expectation. The results of crisp/fuzzy integrated model are illustrated with numerical examples and results are compared.

Interactions between hydrology and water chemistry shape bacterioplankton biogeography across boreal freshwater networks

PubMed Central

Niño-García, Juan Pablo; Ruiz-González, Clara; del Giorgio, Paul A

2016-01-01

Disentangling the mechanisms shaping bacterioplankton communities across freshwater ecosystems requires considering a hydrologic dimension that can influence both dispersal and local sorting, but how the environment and hydrology interact to shape the biogeography of freshwater bacterioplankton over large spatial scales remains unexplored. Using Illumina sequencing of the 16S ribosomal RNA gene, we investigate the large-scale spatial patterns of bacterioplankton across 386 freshwater systems from seven distinct regions in boreal Québec. We show that both hydrology and local water chemistry (mostly pH) interact to shape a sequential structuring of communities from highly diverse assemblages in headwater streams toward larger rivers and lakes dominated by fewer taxa. Increases in water residence time along the hydrologic continuum were accompanied by major losses of bacterial richness and by an increased differentiation of communities driven by local conditions (pH and other related variables). This suggests that hydrology and network position modulate the relative role of environmental sorting and mass effects on community assembly by determining both the time frame for bacterial growth and the composition of the immigrant pool. The apparent low dispersal limitation (that is, the lack of influence of geographic distance on the spatial patterns observed at the taxonomic resolution used) suggests that these boreal bacterioplankton communities derive from a shared bacterial pool that enters the networks through the smallest streams, largely dominated by mass effects, and that is increasingly subjected to local sorting of species during transit along the hydrologic continuum. PMID:26849312

Interactions between hydrology and water chemistry shape bacterioplankton biogeography across boreal freshwater networks.

PubMed

Niño-García, Juan Pablo; Ruiz-González, Clara; Del Giorgio, Paul A

2016-07-01

Disentangling the mechanisms shaping bacterioplankton communities across freshwater ecosystems requires considering a hydrologic dimension that can influence both dispersal and local sorting, but how the environment and hydrology interact to shape the biogeography of freshwater bacterioplankton over large spatial scales remains unexplored. Using Illumina sequencing of the 16S ribosomal RNA gene, we investigate the large-scale spatial patterns of bacterioplankton across 386 freshwater systems from seven distinct regions in boreal Québec. We show that both hydrology and local water chemistry (mostly pH) interact to shape a sequential structuring of communities from highly diverse assemblages in headwater streams toward larger rivers and lakes dominated by fewer taxa. Increases in water residence time along the hydrologic continuum were accompanied by major losses of bacterial richness and by an increased differentiation of communities driven by local conditions (pH and other related variables). This suggests that hydrology and network position modulate the relative role of environmental sorting and mass effects on community assembly by determining both the time frame for bacterial growth and the composition of the immigrant pool. The apparent low dispersal limitation (that is, the lack of influence of geographic distance on the spatial patterns observed at the taxonomic resolution used) suggests that these boreal bacterioplankton communities derive from a shared bacterial pool that enters the networks through the smallest streams, largely dominated by mass effects, and that is increasingly subjected to local sorting of species during transit along the hydrologic continuum.

Navy/Marine Corps innovative science and technology developments for future enhanced mine detection capabilities

NASA Astrophysics Data System (ADS)

Holloway, John H., Jr.; Witherspoon, Ned H.; Miller, Richard E.; Davis, Kenn S.; Suiter, Harold R.; Hilton, Russell J.

2000-08-01

JMDT is a Navy/Marine Corps 6.2 Exploratory Development program that is closely coordinated with the 6.4 COBRA acquisition program. The objective of the program is to develop innovative science and technology to enhance future mine detection capabilities. The objective of the program is to develop innovative science and technology to enhance future mine detection capabilities. Prior to transition to acquisition, the COBRA ATD was extremely successful in demonstrating a passive airborne multispectral video sensor system operating in the tactical Pioneer unmanned aerial vehicle (UAV), combined with an integrated ground station subsystem to detect and locate minefields from surf zone to inland areas. JMDT is investigating advanced technology solutions for future enhancements in mine field detection capability beyond the current COBRA ATD demonstrated capabilities. JMDT has recently been delivered next- generation, innovative hardware which was specified by the Coastal System Station and developed under contract. This hardware includes an agile-tuning multispectral, polarimetric, digital video camera and advanced multi wavelength laser illumination technologies to extend the same sorts of multispectral detections from a UAV into the night and over shallow water and other difficult littoral regions. One of these illumination devices is an ultra- compact, highly-efficient near-IR laser diode array. The other is a multi-wavelength range-gateable laser. Additionally, in conjunction with this new technology, algorithm enhancements are being developed in JMDT for future naval capabilities which will outperform the already impressive record of automatic detection of minefields demonstrated by the COBAR ATD.

Thermofluid Analysis of Magnetocaloric Refrigeration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abdelaziz, Omar; Gluesenkamp, Kyle R; Vineyard, Edward Allan

While there have been extensive studies on thermofluid characteristics of different magnetocaloric refrigeration systems, a conclusive optimization study using non-dimensional parameters which can be applied to a generic system has not been reported yet. In this study, a numerical model has been developed for optimization of active magnetic refrigerator (AMR). This model is computationally efficient and robust, making it appropriate for running the thousands of simulations required for parametric study and optimization. The governing equations have been non-dimensionalized and numerically solved using finite difference method. A parametric study on a wide range of non-dimensional numbers has been performed. While themore » goal of AMR systems is to improve the performance of competitive parameters including COP, cooling capacity and temperature span, new parameters called AMR performance index-1 have been introduced in order to perform multi objective optimization and simultaneously exploit all these parameters. The multi-objective optimization is carried out for a wide range of the non-dimensional parameters. The results of this study will provide general guidelines for designing high performance AMR systems.« less

Feature point based 3D tracking of multiple fish from multi-view images

PubMed Central

Qian, Zhi-Ming

2017-01-01

A feature point based method is proposed for tracking multiple fish in 3D space. First, a simplified representation of the object is realized through construction of two feature point models based on its appearance characteristics. After feature points are classified into occluded and non-occluded types, matching and association are performed, respectively. Finally, the object's motion trajectory in 3D space is obtained through integrating multi-view tracking results. Experimental results show that the proposed method can simultaneously track 3D motion trajectories for up to 10 fish accurately and robustly. PMID:28665966

Feature point based 3D tracking of multiple fish from multi-view images.

PubMed

Qian, Zhi-Ming; Chen, Yan Qiu

2017-01-01

A feature point based method is proposed for tracking multiple fish in 3D space. First, a simplified representation of the object is realized through construction of two feature point models based on its appearance characteristics. After feature points are classified into occluded and non-occluded types, matching and association are performed, respectively. Finally, the object's motion trajectory in 3D space is obtained through integrating multi-view tracking results. Experimental results show that the proposed method can simultaneously track 3D motion trajectories for up to 10 fish accurately and robustly.

HLRW management during MR reactor decommissioning in NRC 'Kurchatov Institute'

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chesnokov, Alexander; Ivanov, Oleg; Kolyadin, Vyacheslav

2013-07-01

A program of decommissioning of MR research reactor in the Kurchatov institute started in 2008. The decommissioning work presumed a preliminary stage, which included: removal of spent fuel from near reactor storage; removal of spent fuel assemble of metal liquid loop channel from a core; identification, sorting and disposal of radioactive objects from gateway of the reactor; identification, sorting and disposal of radioactive objects from cells of HLRW storage of the Kurchatov institute for radwaste creating form the decommissioning of MR. All these works were performed by a remote controlled means with use of a remote identification methods of highmore » radioactive objects. A distribution of activity along high radiated objects was measured by a collimated radiometer installed on the robot Brokk-90, a gamma image of the object was registered by gamma-visor. Spectrum of gamma radiation was measured by a gamma locator and semiconductor detector system. For identification of a presence of uranium isotopes in the HLRW a technique, based on the registration of characteristic radiation of U, was developed. For fragmentation of high radiated objects was used a cold cutting technique and dust suppression system was applied for reduction of volume activity of aerosols in air. The management of HLRW was performed by remote controlled robots Brokk-180 and Brokk-330. They executed sorting, cutting and parking of high radiated part of contaminated equipment. The use of these techniques allowed to reduce individual and collective doses of personal performed the decommissioning. The average individual dose of the personnel was 1,9 mSv/year in 2011, and the collective dose is estimated by 0,0605 man x Sv/year. Use of the remote control machines enables reducing the number of working personal (20 men) and doses. X-ray spectrometric methods enable determination of a presence of the U in high radiated objects and special cans and separation of them for further spent fuel inspection. The sorting of radwaste enabled shipping of the LLRW and ILRW to special repositories and keeping of the HLRW for decay in the Kurchatov institute repository. (authors)« less

Global dynamic optimization approach to predict activation in metabolic pathways.

PubMed

de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R

2014-01-06

During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.

Quantification of Radiation Exposure of Non-Dominant Index for the Surgeon Performing Sentinel Lymph-Node Removal Procedure.

PubMed

Peştean, Claudiu; Larg, Maria Iulia; Bărbuş, Elena; Bădulescu, Claudiu; Piciu, Doina

2018-01-01

Sentinel lymph-node scintigraphy is a useful method for accurate staging of different tumors and a helpful tool in personalized therapy for oncological patients. The radiation exposure for surgical staff has been a concern since the sentinel lymph-node detection method was developed. The objective of the study was to determine and quantify the exposure to radiation of the non-dominant index for the surgeon performing sentinel lymph-node removal and to determine, if there is an irradiation risk imposed during the surgical procedure. We performed a study over a period of one year, where we evaluated the exposure of surgeon's non-dominant index during 196 sentinel lymph-node removal procedures. The pharmaceutical was administrated via subcutaneous injection in four peritumoral or perilesional injection sites. The equipment we used consisted of EuroProbe3 for sentinel lymph-node detection and ring TLD dosimeter placed on the surgeon's non-dominant index. The clinical distribution was: 104 melanomas, 84 breast carcinomas, 6 vulvar carcinomas and 2 penial carcinomas. The administered activity showed an average of 39.55 MBq (SD ± 1.96) Tc-99m nanoalbumin compound. The non-dominant index exposure ranged between 0.10 mSv and 0.13 mSv/month with a cumulative dose of 1.31 mSv/year, thus 6.69 µSv per procedure. The surgeon received a minimal dose for the non-dominant index. The values we recorded did not pose any additional concerns or restrictions, the exposure being under the limits and constraints established by regulations, close to the detectability limit of the dosimeter. The procedure is safe in terms of radiation protection, respecting the limitation and optimization principles. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Effects of Overstocking at the Feedbunk on the Growth Performance and Sorting Characteristics of a Forage-based Diet Offered for Ad-libitum Intake to Replacement Holstein Dairy Heifers

USDA-ARS?s Scientific Manuscript database

Various forms of overcrowding are common in heifer-rearing operations. Our objectives for this study were to evaluate the effects of overstocking at the feedbunk (100, 133, 160, or 200% of capacity) on the growth performance, feedbunk-sorting behaviors, and hygiene of 128 gravid Holstein heifers (47...

Object recognition through a multi-mode fiber

NASA Astrophysics Data System (ADS)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

Isolation of Human Induced Pluripotent Stem Cell-Derived Dopaminergic Progenitors by Cell Sorting for Successful Transplantation

PubMed Central

Doi, Daisuke; Samata, Bumpei; Katsukawa, Mitsuko; Kikuchi, Tetsuhiro; Morizane, Asuka; Ono, Yuichi; Sekiguchi, Kiyotoshi; Nakagawa, Masato; Parmar, Malin; Takahashi, Jun

2014-01-01

Summary Human induced pluripotent stem cells (iPSCs) can provide a promising source of midbrain dopaminergic (DA) neurons for cell replacement therapy for Parkinson’s disease. However, iPSC-derived donor cells inevitably contain tumorigenic or inappropriate cells. Here, we show that human iPSC-derived DA progenitor cells can be efficiently isolated by cell sorting using a floor plate marker, CORIN. We induced DA neurons using scalable culture conditions on human laminin fragment, and the sorted CORIN+ cells expressed the midbrain DA progenitor markers, FOXA2 and LMX1A. When transplanted into 6-OHDA-lesioned rats, the CORIN+ cells survived and differentiated into midbrain DA neurons in vivo, resulting in significant improvement of the motor behavior, without tumor formation. In particular, the CORIN+ cells in a NURR1+ cell-dominant stage exhibited the best survival and function as DA neurons. Our method is a favorable strategy in terms of scalability, safety, and efficiency and may be advantageous for clinical application. PMID:24672756

Unmixing the Materials and Mechanics Contributions in Non-resolved Object Signatures

DTIC Science & Technology

2008-09-01

abundances from hyperspectral or multi-spectral time - resolved signatures. A Fourier analysis of temporal variation of material abundance provides...factorization technique to extract the temporal variation of material abundances from hyperspectral or multi-spectral time - resolved signatures. A Fourier...approximately one hundred wavelengths in the visible spectrum. The frame rate for the instrument was not large enough to collect time resolved data. However

Moving beyond "Shut up and Learn"

ERIC Educational Resources Information Center

Watkins, Chris

2016-01-01

This article analyses the sort of classroom talk that leads to effective learning, and some of the forces which operate against such practices. It starts with an analysis of the classroom context and the dominant patterns of interaction. These cause processes of learning to be hidden. It then develops by an analysis of effective learning,…

A fully automated non-external marker 4D-CT sorting algorithm using a serial cine scanning protocol.

PubMed

Carnes, Greg; Gaede, Stewart; Yu, Edward; Van Dyk, Jake; Battista, Jerry; Lee, Ting-Yim

2009-04-07

Current 4D-CT methods require external marker data to retrospectively sort image data and generate CT volumes. In this work we develop an automated 4D-CT sorting algorithm that performs without the aid of data collected from an external respiratory surrogate. The sorting algorithm requires an overlapping cine scan protocol. The overlapping protocol provides a spatial link between couch positions. Beginning with a starting scan position, images from the adjacent scan position (which spatial match the starting scan position) are selected by maximizing the normalized cross correlation (NCC) of the images at the overlapping slice position. The process was continued by 'daisy chaining' all couch positions using the selected images until an entire 3D volume was produced. The algorithm produced 16 phase volumes to complete a 4D-CT dataset. Additional 4D-CT datasets were also produced using external marker amplitude and phase angle sorting methods. The image quality of the volumes produced by the different methods was quantified by calculating the mean difference of the sorted overlapping slices from adjacent couch positions. The NCC sorted images showed a significant decrease in the mean difference (p < 0.01) for the five patients.

The relationship between children's sensitivity to dominant and non-dominant patterns of lexical stress and reading accuracy.

PubMed

Arciuli, Joanne

2017-05-01

This study reports on a new task for assessing children's sensitivity to lexical stress for words with different stress patterns and demonstrates that this task is useful in examining predictors of reading accuracy during the elementary years. In English, polysyllabic words beginning with a strong syllable exhibit the most common or dominant pattern of lexical stress (e.g., "coconut"), whereas polysyllabic words beginning with a weak syllable exhibit a less common non-dominant pattern (e.g., "banana"). The new Aliens Talking Underwater task assesses children's ability to match low-pass filtered recordings of words to pictures of objects. Via filtering, phonetic detail is removed but prosodic contour information relating to lexical stress is retained. In a series of two-alternative forced choice trials, participants see a picture and are asked to choose which of two filtered recordings matches the name of that picture; one recording exhibits the correct lexical stress of the target word, and the other recording reverses the pattern of stress over the initial two syllables of the target word rendering it incorrect. Target words exhibit either dominant stress or non-dominant stress. Analysis of data collected from 192 typically developing children aged 5 to 12years revealed that sensitivity to non dominant lexical stress was a significant predictor of reading accuracy even when age and phonological awareness were taken into account. A total of 76.3% of variance in children's reading accuracy was explained by these variables. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Observations of plan-view sand ripple behavior and spectral wave climate on the inner shelf of San Pedro Bay, California

USGS Publications Warehouse

Xu, J. P.

2005-01-01

Concurrent video images of sand ripples and current meter measurements of directional wave spectra are analyzed to study the relations between waves and wave-generated sand ripples. The data were collected on the inner shelf off Huntington Beach, California, at 15 m water depth, where the sea floor is comprised of well-sorted very fine sands (D50=92 ??m), during the winter of 2002. The wave climate, which was controlled by southerly swells (12-18 s period) and westerly wind waves (5-10 s period), included three wave types: (A) uni-modal, swells only; (B) bi-modal, swells dominant; and (C) bi-modal, wind-wave dominant. Each wave type has distinct relations with the plan-view shapes of ripples that are classified into five types: (1) sharp-crested, two-dimensional (2-D) ripples; (2) sharp-crested, brick-pattern, 3-D ripples; (3) bifurcated, 3-D ripples; (4) round-crested, shallow, 3-D ripples; and (5) flat bed. The ripple spacing is very small and varies between 4.5 and 7.5 cm. These ripples are anorbital as ripples in many field studies. Ripple orientation is only correlated with wave directions during strong storms (wave type C). In a poly-modal, multi-directional spectral wave environment, the use of the peak parameters (frequency, direction), a common practice when spectral wave measurements are unavailable, may lead to significant errors in boundary layer and sediment transport calculations. ?? 2004 Elsevier Ltd. All rights reserved.

Predictor sort sampling and one-sided confidence bounds on quantiles

Treesearch

Steve Verrill; Victoria L. Herian; David W. Green

2002-01-01

Predictor sort experiments attempt to make use of the correlation between a predictor that can be measured prior to the start of an experiment and the response variable that we are investigating. Properly designed and analyzed, they can reduce necessary sample sizes, increase statistical power, and reduce the lengths of confidence intervals. However, if the non- random...

Motives of College Women for Participating in Physical Activities.

ERIC Educational Resources Information Center

Lundegren, Herberta

One hundred and fifty-one college women, 88 non-physical education majors, and 63 physical education majors were given a 75-item Q-sort of statements on motives for participation in physical activity and a background questionnaire that elicited demographic data and information on sports activity experience. The Q-sort data for each major group…

Full-color large-scaled computer-generated holograms for physical and non-physical objects

NASA Astrophysics Data System (ADS)

Matsushima, Kyoji; Tsuchiyama, Yasuhiro; Sonobe, Noriaki; Masuji, Shoya; Yamaguchi, Masahiro; Sakamoto, Yuji

2017-05-01

Several full-color high-definition CGHs are created for reconstructing 3D scenes including real-existing physical objects. The field of the physical objects are generated or captured by employing three techniques; 3D scanner, synthetic aperture digital holography, and multi-viewpoint images. Full-color reconstruction of high-definition CGHs is realized by RGB color filters. The optical reconstructions are presented for verifying these techniques.

The multi-sensory approach as a geoeducational strategy

NASA Astrophysics Data System (ADS)

Musacchio, Gemma; Piangiamore, Giovanna Lucia; Pino, Nicola Alessandro

2014-05-01

Geoscience knowledge has a strong impact in modern society as it relates to natural hazards, sustainability and environmental issues. The general public has a demanding attitude towards the understanding of crucial geo-scientific topics that is only partly satisfied by science communication strategies and/or by outreach or school programs. A proper knowledge of the phenomena might help trigger crucial inquiries when approaching mitigation of geo-hazards and geo-resources, while providing the right tool for the understanding of news and ideas floating from the web or other media, and, in other words, help communication to be more efficient. Nonetheless available educational resources seem to be inadequate in meeting the goal, while research institutions are facing the challenge to experience new communication strategies and non-conventional way of learning capable to allow the understanding of crucial scientific contents. We suggest the use of multi-sensory approach as a successful non-conventional way of learning for children and as a different perspective of learning for older students and adults. Sense organs stimulation are perceived and processed to build the knowledge of the surrounding, including all sorts of hazards. Powerfully relying in the sense of sight, Humans have somehow lost most of their ability for a deep perception of the environment enriched by all the other senses. Since hazards involve emotions we argue that new ways to approach the learning might go exactly through emotions that one might stress with a tactile experience, a hearing or smell stimulation. To test and support our idea we are building a package of learning activities and exhibits based on a multi-sensory experience where the sight is not allowed.

4D CT sorting based on patient internal anatomy

NASA Astrophysics Data System (ADS)

Li, Ruijiang; Lewis, John H.; Cerviño, Laura I.; Jiang, Steve B.

2009-08-01

Respiratory motion during free-breathing computed tomography (CT) scan may cause significant errors in target definition for tumors in the thorax and upper abdomen. A four-dimensional (4D) CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. The current 4D CT techniques require retrospective sorting of the reconstructed CT slices oversampled at the same couch position. Most sorting methods depend on external surrogates of respiratory motion recorded by extra instruments. However, respiratory signals obtained from these external surrogates may not always accurately represent the internal target motion, especially when irregular breathing patterns occur. We have proposed a new sorting method based on multiple internal anatomical features for multi-slice CT scan acquired in the cine mode. Four features are analyzed in this study, including the air content, lung area, lung density and body area. We use a measure called spatial coherence to select the optimal internal feature at each couch position and to generate the respiratory signals for 4D CT sorting. The proposed method has been evaluated for ten cancer patients (eight with thoracic cancer and two with abdominal cancer). For nine patients, the respiratory signals generated from the combined internal features are well correlated to those from external surrogates recorded by the real-time position management (RPM) system (average correlation: 0.95 ± 0.02), which is better than any individual internal measures at 95% confidence level. For these nine patients, the 4D CT images sorted by the combined internal features are almost identical to those sorted by the RPM signal. For one patient with an irregular breathing pattern, the respiratory signals given by the combined internal features do not correlate well with those from RPM (correlation: 0.68 ± 0.42). In this case, the 4D CT image sorted by our method presents fewer artifacts than that from the RPM signal. Our 4D CT internal sorting method eliminates the need of externally recorded surrogates of respiratory motion. It is an automatic, accurate, robust, cost efficient and yet simple method and therefore can be readily implemented in clinical settings.

A new zero-inflated negative binomial methodology for latent category identification.

PubMed

Blanchard, Simon J; DeSarbo, Wayne S

2013-04-01

We introduce a new statistical procedure for the identification of unobserved categories that vary between individuals and in which objects may span multiple categories. This procedure can be used to analyze data from a proposed sorting task in which individuals may simultaneously assign objects to multiple piles. The results of a synthetic example and a consumer psychology study involving categories of restaurant brands illustrate how the application of the proposed methodology to the new sorting task can account for a variety of categorization phenomena including multiple category memberships and for heterogeneity through individual differences in the saliency of latent category structures.

Rapid fabrication of three-dimensional structures for dielectrophoretic sorting of lipid-containing organisms

NASA Astrophysics Data System (ADS)

Schor, Alisha R.; Buie, Cullen R.

2016-10-01

In this work, we demonstrate a microfluidic particle sorter consisting of three-dimensional, conducting microposts. Our sorter uses dielectrophoresis (DEP) to sort high- and low-lipid phenotypes of the yeast Yarrowia lipolytica. Y. lipolytica is one of the many microorganisms being explored as a hydrocarbon source for biodiesel, Omega-3 additives, and other products derived from fatty acids. A rapid, non-destructive, lipid-based sorting tool would accelerate the commercialization of these products. Our device consists of an array of 105, 25 μm wide gold microposts that span the height of a 15 μm channel. This array generates an electric field in a microfluidic device that is uniform through the channel height, but has a custom-shaped non-uniformity in the horizontal directions. This is crucial in order to achieve continuous sorting using DEP, as it ensures all cells are exposed to the same conditions throughout the channel height. By using very low currents (100 μA), we are able to electroplate these post arrays in fewer than 15 min. This is an order of magnitude improvement over previous reports of electroplated microstructures. With an applied signal of 250 MHz, 2.6 V pp in our device, we separate a heterogeneous population with a purity of 97.8% in the low-lipid stream and 71.4% in the high-lipid stream. The high-lipid stream purity can be improved by adjusting the spacing of the array. This unique protocol for the rapid fabrication of 3D microstructures has enabled the creation of a non-invasive sorting tool for genetically engineered, lipid-producing organisms. The ability to screen organisms based on lipid content will alleviate one of the major bottlenecks in commercialization of microbial biofuels.

Multi-sensor millimeter-wave system for hidden objects detection by non-collaborative screening

NASA Astrophysics Data System (ADS)

Zouaoui, Rhalem; Czarny, Romain; Diaz, Frédéric; Khy, Antoine; Lamarque, Thierry

2011-05-01

In this work, we present the development of a multi-sensor system for the detection of objects concealed under clothes using passive and active millimeter-wave (mmW) technologies. This study concerns both the optimization of a commercial passive mmW imager at 94 GHz using a phase mask and the development of an active mmW detector at 77 GHz based on synthetic aperture radar (SAR). A first wide-field inspection is done by the passive imager while the person is walking. If a suspicious area is detected, the active imager is switched-on and focused on this area in order to obtain more accurate data (shape of the object, nature of the material ...).

Axon-Axon Interactions Regulate Topographic Optic Tract Sorting via CYFIP2-Dependent WAVE Complex Function.

PubMed

Cioni, Jean-Michel; Wong, Hovy Ho-Wai; Bressan, Dario; Kodama, Lay; Harris, William A; Holt, Christine E

2018-03-07

The axons of retinal ganglion cells (RGCs) are topographically sorted before they arrive at the optic tectum. This pre-target sorting, typical of axon tracts throughout the brain, is poorly understood. Here, we show that cytoplasmic FMR1-interacting proteins (CYFIPs) fulfill non-redundant functions in RGCs, with CYFIP1 mediating axon growth and CYFIP2 specifically involved in axon sorting. We find that CYFIP2 mediates homotypic and heterotypic contact-triggered fasciculation and repulsion responses between dorsal and ventral axons. CYFIP2 associates with transporting ribonucleoprotein particles in axons and regulates translation. Axon-axon contact stimulates CYFIP2 to move into growth cones where it joins the actin nucleating WAVE regulatory complex (WRC) in the periphery and regulates actin remodeling and filopodial dynamics. CYFIP2's function in axon sorting is mediated by its binding to the WRC but not its translational regulation. Together, these findings uncover CYFIP2 as a key regulatory link between axon-axon interactions, filopodial dynamics, and optic tract sorting. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Hierarchical Object-based Image Analysis approach for classification of sub-meter multispectral imagery in Tanzania

NASA Astrophysics Data System (ADS)

Chung, C.; Nagol, J. R.; Tao, X.; Anand, A.; Dempewolf, J.

2015-12-01

Increasing agricultural production while at the same time preserving the environment has become a challenging task. There is a need for new approaches for use of multi-scale and multi-source remote sensing data as well as ground based measurements for mapping and monitoring crop and ecosystem state to support decision making by governmental and non-governmental organizations for sustainable agricultural development. High resolution sub-meter imagery plays an important role in such an integrative framework of landscape monitoring. It helps link the ground based data to more easily available coarser resolution data, facilitating calibration and validation of derived remote sensing products. Here we present a hierarchical Object Based Image Analysis (OBIA) approach to classify sub-meter imagery. The primary reason for choosing OBIA is to accommodate pixel sizes smaller than the object or class of interest. Especially in non-homogeneous savannah regions of Tanzania, this is an important concern and the traditional pixel based spectral signature approach often fails. Ortho-rectified, calibrated, pan sharpened 0.5 meter resolution data acquired from DigitalGlobe's WorldView-2 satellite sensor was used for this purpose. Multi-scale hierarchical segmentation was performed using multi-resolution segmentation approach to facilitate the use of texture, neighborhood context, and the relationship between super and sub objects for training and classification. eCognition, a commonly used OBIA software program, was used for this purpose. Both decision tree and random forest approaches for classification were tested. The Kappa index agreement for both algorithms surpassed the 85%. The results demonstrate that using hierarchical OBIA can effectively and accurately discriminate classes at even LCCS-3 legend.

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Montazeri, A.; West, C.; Monk, S. D.; Taylor, C. J.

2017-04-01

This paper concerns the problem of dynamic modelling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model-orientated research using the same machine, the paper develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimise the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivisation of an output error single-performance index. The developed algorithm utilises a multi-objective genetic algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivisation has improved convergence of the estimated parameters compared to the single-objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

Accretion-driven turbulence in filaments - I. Non-gravitational accretion

NASA Astrophysics Data System (ADS)

Heigl, S.; Burkert, A.; Gritschneder, M.

2018-03-01

We study accretion-driven turbulence for different inflow velocities in star-forming filaments using the code RAMSES. Filaments are rarely isolated objects and their gravitational potential will lead to radially dominated accretion. In the non-gravitational case, accretion by itself can already provoke non-isotropic, radially dominated turbulent motions responsible for the complex structure and non-thermal line widths observed in filaments. We find that there is a direct linear relation between the absolute value of the total density-weighted velocity dispersion and the infall velocity. The turbulent velocity dispersion in the filaments is independent of sound speed or any net flow along the filament. We show that the density-weighted velocity dispersion acts as an additional pressure term, supporting the filament in hydrostatic equilibrium. Comparing to observations, we find that the projected non-thermal line width variation is generally subsonic independent of inflow velocity.

Self-organized sorting limits behavioral variability in swarms

PubMed Central

Copenhagen, Katherine; Quint, David A.; Gopinathan, Ajay

2016-01-01

Swarming is a phenomenon where collective motion arises from simple local interactions between typically identical individuals. Here, we investigate the effects of variability in behavior among the agents in finite swarms with both alignment and cohesive interactions. We show that swarming is abolished above a critical fraction of non-aligners who do not participate in alignment. In certain regimes, however, swarms above the critical threshold can dynamically reorganize and sort out excess non-aligners to maintain the average fraction close to the critical value. This persists even in swarms with a distribution of alignment interactions, suggesting a simple, robust and efficient mechanism that allows heterogeneously mixed populations to naturally regulate their composition and remain in a collective swarming state or even differentiate among behavioral phenotypes. We show that, for evolving swarms, this self-organized sorting behavior can couple to the evolutionary dynamics leading to new evolutionarily stable equilibrium populations set by the physical swarm parameters. PMID:27550316

Self-organized sorting limits behavioral variability in swarms

NASA Astrophysics Data System (ADS)

Copenhagen, Katherine; Quint, David A.; Gopinathan, Ajay

2016-08-01

Swarming is a phenomenon where collective motion arises from simple local interactions between typically identical individuals. Here, we investigate the effects of variability in behavior among the agents in finite swarms with both alignment and cohesive interactions. We show that swarming is abolished above a critical fraction of non-aligners who do not participate in alignment. In certain regimes, however, swarms above the critical threshold can dynamically reorganize and sort out excess non-aligners to maintain the average fraction close to the critical value. This persists even in swarms with a distribution of alignment interactions, suggesting a simple, robust and efficient mechanism that allows heterogeneously mixed populations to naturally regulate their composition and remain in a collective swarming state or even differentiate among behavioral phenotypes. We show that, for evolving swarms, this self-organized sorting behavior can couple to the evolutionary dynamics leading to new evolutionarily stable equilibrium populations set by the physical swarm parameters.

Personal Universes: revealing community college students' competences though their organization of the cosmos

NASA Astrophysics Data System (ADS)

Buck Bracey, Zoë

2017-10-01

In this article I present a study on learners' conceptions in cosmology by situating the results in the context of broader historical and sociocultural themes. Participants were community college students in California from non-dominant cultural and linguistic backgrounds finishing their first semester of astronomy. Data were collected through a drawing activity and card sort given during clinical-style interviews. This type of work is typically done from the perspective of conceptual change theory, using drawings to reveal student "misconceptions." I argue that in analyzing this kind of data, we need to come from the perspective that students are competent, and put their conceptions in context. I begin by presenting traditional frameworks for evaluating and describing learning, all of which rely on an outdated "banking" or "transmission" model of learning that puts an over-emphasis on the performance and attributes of individuals. Not only do these theories provide an incomplete picture of what learning looks like, they create and reify unnecessary divides between "scientific" and "unscientific" that can contribute to student alienation from the world of science. To illustrate this, I present my own results as a window into the logic of learners' assumptions within a sociocultural context, and suggest ways to support their learning trajectories, rather than figuring out how to unlearn their misconceptions. Through this analysis, I hope to show how taking student conceptions out of sociocultural context can potentially exclude students from non-dominant cultural and linguistic backgrounds from science.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks.

PubMed

Chen, Huan-Yuan; Chen, Chih-Chang; Hwang, Wen-Jyi

2017-09-28

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

PubMed Central

Chen, Huan-Yuan; Chen, Chih-Chang

2017-01-01

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859

Retrograde Signaling from Progranulin to Sort1 Counteracts Synapse Elimination in the Developing Cerebellum.

PubMed

Uesaka, Naofumi; Abe, Manabu; Konno, Kohtarou; Yamazaki, Maya; Sakoori, Kazuto; Watanabe, Takaki; Kao, Tzu-Huei; Mikuni, Takayasu; Watanabe, Masahiko; Sakimura, Kenji; Kano, Masanobu

2018-02-21

Elimination of redundant synapses formed early in development and strengthening of necessary connections are crucial for shaping functional neural circuits. Purkinje cells (PCs) in the neonatal cerebellum are innervated by multiple climbing fibers (CFs) with similar strengths. A single CF is strengthened whereas the other CFs are eliminated in each PC during postnatal development. The underlying mechanisms, particularly for the strengthening of single CFs, are poorly understood. Here we report that progranulin, a multi-functional growth factor implicated in the pathogenesis of frontotemporal dementia, strengthens developing CF synaptic inputs and counteracts their elimination from postnatal day 11 to 16. Progranulin derived from PCs acts retrogradely onto its putative receptor Sort1 on CFs. This effect is independent of semaphorin 3A, another retrograde signaling molecule that counteracts CF synapse elimination. We propose that progranulin-Sort1 signaling strengthens and maintains developing CF inputs, and may contribute to selection of single "winner" CFs that survive synapse elimination. Copyright © 2018 Elsevier Inc. All rights reserved.

Decomposition-Based Decision Making for Aerospace Vehicle Design

NASA Technical Reports Server (NTRS)

Borer, Nicholas K.; Mavris, DImitri N.

2005-01-01

Most practical engineering systems design problems have multiple and conflicting objectives. Furthermore, the satisfactory attainment level for each objective ( requirement ) is likely uncertain early in the design process. Systems with long design cycle times will exhibit more of this uncertainty throughout the design process. This is further complicated if the system is expected to perform for a relatively long period of time, as now it will need to grow as new requirements are identified and new technologies are introduced. These points identify a need for a systems design technique that enables decision making amongst multiple objectives in the presence of uncertainty. Traditional design techniques deal with a single objective or a small number of objectives that are often aggregates of the overarching goals sought through the generation of a new system. Other requirements, although uncertain, are viewed as static constraints to this single or multiple objective optimization problem. With either of these formulations, enabling tradeoffs between the requirements, objectives, or combinations thereof is a slow, serial process that becomes increasingly complex as more criteria are added. This research proposal outlines a technique that attempts to address these and other idiosyncrasies associated with modern aerospace systems design. The proposed formulation first recasts systems design into a multiple criteria decision making problem. The now multiple objectives are decomposed to discover the critical characteristics of the objective space. Tradeoffs between the objectives are considered amongst these critical characteristics by comparison to a probabilistic ideal tradeoff solution. The proposed formulation represents a radical departure from traditional methods. A pitfall of this technique is in the validation of the solution: in a multi-objective sense, how can a decision maker justify a choice between non-dominated alternatives? A series of examples help the reader to observe how this technique can be applied to aerospace systems design and compare the results of this so-called Decomposition-Based Decision Making to more traditional design approaches.

Multi-decadal trend and space-time variability of sea level over the Indian Ocean since the 1950s: impact of decadal climate modes

NASA Astrophysics Data System (ADS)

Han, W.; Stammer, D.; Meehl, G. A.; Hu, A.; Sienz, F.

2016-12-01

Sea level varies on decadal and multi-decadal timescales over the Indian Ocean. The variations are not spatially uniform, and can deviate considerably from the global mean sea level rise (SLR) due to various geophysical processes. One of these processes is the change of ocean circulation, which can be partly attributed to natural internal modes of climate variability. Over the Indian Ocean, the most influential climate modes on decadal and multi-decadal timescales are the Interdecadal Pacific Oscillation (IPO) and decadal variability of the Indian Ocean dipole (IOD). Here, we first analyze observational datasets to investigate the impacts of IPO and IOD on spatial patterns of decadal and interdecadal (hereafter decal) sea level variability & multi-decadal trend over the Indian Ocean since the 1950s, using a new statistical approach of Bayesian Dynamical Linear regression Model (DLM). The Bayesian DLM overcomes the limitation of "time-constant (static)" regression coefficients in conventional multiple linear regression model, by allowing the coefficients to vary with time and therefore measuring "time-evolving (dynamical)" relationship between climate modes and sea level. For the multi-decadal sea level trend since the 1950s, our results show that climate modes and non-climate modes (the part that cannot be explained by climate modes) have comparable contributions in magnitudes but with different spatial patterns, with each dominating different regions of the Indian Ocean. For decadal variability, climate modes are the major contributors for sea level variations over most region of the tropical Indian Ocean. The relative importance of IPO and decadal variability of IOD, however, varies spatially. For example, while IOD decadal variability dominates IPO in the eastern equatorial basin (85E-100E, 5S-5N), IPO dominates IOD in causing sea level variations in the tropical southwest Indian Ocean (45E-65E, 12S-2S). To help decipher the possible contribution of external forcing to the multi-decadal sea level trend and decadal variability, we also analyze the model outputs from NCAR's Community Earth System Model (CESM) Large Ensemble Experiments, and compare the results with our observational analyses.

Shapes, scents and sounds: quantifying the full multi-sensory basis of conceptual knowledge.

PubMed

Hoffman, Paul; Lambon Ralph, Matthew A

2013-01-01

Contemporary neuroscience theories assume that concepts are formed through experience in multiple sensory-motor modalities. Quantifying the contribution of each modality to different object categories is critical to understanding the structure of the conceptual system and to explaining category-specific knowledge deficits. Verbal feature listing is typically used to elicit this information but has a number of drawbacks: sensory knowledge often cannot easily be translated into verbal features and many features are experienced in multiple modalities. Here, we employed a more direct approach in which subjects rated their knowledge of objects in each sensory-motor modality separately. Compared with these ratings, feature listing over-estimated the importance of visual form and functional knowledge and under-estimated the contributions of other sensory channels. An item's sensory rating proved to be a better predictor of lexical-semantic processing speed than the number of features it possessed, suggesting that ratings better capture the overall quantity of sensory information associated with a concept. Finally, the richer, multi-modal rating data not only replicated the sensory-functional distinction between animals and non-living things but also revealed novel distinctions between different types of artefact. Hierarchical cluster analyses indicated that mechanical devices (e.g., vehicles) were distinct from other non-living objects because they had strong sound and motion characteristics, making them more similar to animals in this respect. Taken together, the ratings align with neuroscience evidence in suggesting that a number of distinct sensory processing channels make important contributions to object knowledge. Multi-modal ratings for 160 objects are provided as supplementary materials. Copyright © 2012 Elsevier Ltd. All rights reserved.

Towards Distributed Intelligence: A High Level Definition

DTIC Science & Technology

2004-12-01

Some of the first research in multi-robot systems came in the foraging /sorting area by Parker [77] and Beckers [9] and was likely fueled by the bio...chapter 24, pages 28–39. Artificial Intelligence at MIT. The MIT Press, 1989. 15. R.A. Brooks. Robotic Science, chapter 11, The Whole Iguana , pages 432

Designing Realistic Human Behavior into Multi-Agent Systems

DTIC Science & Technology

2001-09-01

different results based on some sort of randomness built into it, a trend can be looked at over time and a success or failure rate can be...simulation remains in that state, very different results can be achieved each simulation run. An analyst can look at success and failure over a long

Increasing the Utility of Information Systems in Schools: Lessons from the Literature. Multilevel Evaluation Systems Project.

ERIC Educational Resources Information Center

Herman, Joan

The Multilevel Evaluation Systems Project is exploring the requirements for information systems that could help teachers and administrators sort through, analyze, and apply comprehensive information about their students, community, instructional processes, and outcomes to improve their schools. Toward this end, a multi-disciplinary literature…

Multi-Axis Identifiability Using Single-Surface Parameter Estimation Maneuvers on the X-48B Blended Wing Body

NASA Technical Reports Server (NTRS)

Ratnayake, Nalin A.; Koshimoto, Ed T.; Taylor, Brian R.

2011-01-01

The problem of parameter estimation on hybrid-wing-body type aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aero- dynamic control effectors that act in coplanar motion. This fact adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of system inputs must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, asymmetric, single-surface maneuvers are used to excite multiple axes of aircraft motion simultaneously. Time history reconstructions of the moment coefficients computed by the solved regression models are then compared to each other in order to assess relative model accuracy. The reduced flight-test time required for inner surface parameter estimation using multi-axis methods was found to come at the cost of slightly reduced accuracy and statistical confidence for linear regression methods. Since the multi-axis maneuvers captured parameter estimates similar to both longitudinal and lateral-directional maneuvers combined, the number of test points required for the inner, aileron-like surfaces could in theory have been reduced by 50%. While trends were similar, however, individual parameters as estimated by a multi-axis model were typically different by an average absolute difference of roughly 15-20%, with decreased statistical significance, than those estimated by a single-axis model. The multi-axis model exhibited an increase in overall fit error of roughly 1-5% for the linear regression estimates with respect to the single-axis model, when applied to flight data designed for each, respectively.

The Narrowing Gap in New York City Teacher Qualifications and Its Implications for Student Achievement in High-Poverty Schools

ERIC Educational Resources Information Center

Boyd, Donald; Lankford, Hamilton; Loeb, Susanna; Rockoff, Jonah; Wyckoff, James

2008-01-01

Understanding what makes an effective teacher, as well as how teachers sort by their effectiveness across schools, is central to understanding and addressing student achievement gaps. Prior studies have found substantial sorting of teachers across schools, with the schools with the highest proportions of poor, non-white, and low-scoring students…

Time-varying economic dominance in financial markets: A bistable dynamics approach

NASA Astrophysics Data System (ADS)

He, Xue-Zhong; Li, Kai; Wang, Chuncheng

2018-05-01

By developing a continuous-time heterogeneous agent financial market model of multi-assets traded by fundamental and momentum investors, we provide a potential mechanism for generating time-varying dominance between fundamental and non-fundamental in financial markets. We show that investment constraints lead to the coexistence of a locally stable fundamental steady state and a locally stable limit cycle around the fundamental, characterized by a Bautin bifurcation. This provides a mechanism for market prices to switch stochastically between the two persistent but very different market states, leading to the coexistence and time-varying dominance of seemingly controversial efficient market and price momentum over different time periods. The model also generates other financial market stylized facts, such as spillover effects in both momentum and volatility, market booms, crashes, and correlation reduction due to cross-sectional momentum trading. Empirical evidence based on the U.S. market supports the main findings. The mechanism developed in this paper can be used to characterize time-varying economic dominance in economics and finance in general.

Time-varying economic dominance in financial markets: A bistable dynamics approach.

PubMed

He, Xue-Zhong; Li, Kai; Wang, Chuncheng

2018-05-01

By developing a continuous-time heterogeneous agent financial market model of multi-assets traded by fundamental and momentum investors, we provide a potential mechanism for generating time-varying dominance between fundamental and non-fundamental in financial markets. We show that investment constraints lead to the coexistence of a locally stable fundamental steady state and a locally stable limit cycle around the fundamental, characterized by a Bautin bifurcation. This provides a mechanism for market prices to switch stochastically between the two persistent but very different market states, leading to the coexistence and time-varying dominance of seemingly controversial efficient market and price momentum over different time periods. The model also generates other financial market stylized facts, such as spillover effects in both momentum and volatility, market booms, crashes, and correlation reduction due to cross-sectional momentum trading. Empirical evidence based on the U.S. market supports the main findings. The mechanism developed in this paper can be used to characterize time-varying economic dominance in economics and finance in general.

Multi-objective optimization of solid waste flows: environmentally sustainable strategies for municipalities.

PubMed

Minciardi, Riccardo; Paolucci, Massimo; Robba, Michela; Sacile, Roberto

2008-11-01

An approach to sustainable municipal solid waste (MSW) management is presented, with the aim of supporting the decision on the optimal flows of solid waste sent to landfill, recycling and different types of treatment plants, whose sizes are also decision variables. This problem is modeled with a non-linear, multi-objective formulation. Specifically, four objectives to be minimized have been taken into account, which are related to economic costs, unrecycled waste, sanitary landfill disposal and environmental impact (incinerator emissions). An interactive reference point procedure has been developed to support decision making; these methods are considered appropriate for multi-objective decision problems in environmental applications. In addition, interactive methods are generally preferred by decision makers as they can be directly involved in the various steps of the decision process. Some results deriving from the application of the proposed procedure are presented. The application of the procedure is exemplified by considering the interaction with two different decision makers who are assumed to be in charge of planning the MSW system in the municipality of Genova (Italy).

Non-linear multi-objective model for planning water-energy modes of Novosibirsk Hydro Power Plant

NASA Astrophysics Data System (ADS)

Alsova, O. K.; Artamonova, A. V.

2018-05-01

This paper presents a non-linear multi-objective model for planning and optimizing of water-energy modes for the Novosibirsk Hydro Power Plant (HPP) operation. There is a very important problem of developing a strategy to improve the scheme of water-power modes and ensure the effective operation of hydropower plants. It is necessary to determine the methods and criteria for the optimal distribution of water resources, to develop a set of models and to apply them to the software implementation of a DSS (decision-support system) for managing Novosibirsk HPP modes. One of the possible versions of the model is presented and investigated in this paper. Experimental study of the model has been carried out with 2017 data and the task of ten-day period planning from April to July (only 12 ten-day periods) was solved.

The Influence of the Knowledge Base on the Development of Mnemonic Strategies.

ERIC Educational Resources Information Center

Ornstein, Peter A.; Naus, Mary J.

A dominant theme in cognitive psychology is that prior knowledge in long-term memory has a strong influence on an individual's cognitive processing. Citing numerous memory studies with children, knowledge base effects are presented as part of a broader picture of memory development. Using the sort/recall procedure (asking subjects to group sets of…

Crunch Seen on Ed. Issues after Election

ERIC Educational Resources Information Center

Klein, Alyson

2012-01-01

From the White House to Capitol Hill, the winners in this week's elections won't have much time to savor their victories. Even as federal policymakers sort out the political landscape, the remainder of 2012 and the early months of 2013 are likely to be dominated by divisive, unresolved issues with broad consequences for K-12 and higher…

Tuning magnetofluidic spreading in microchannels

NASA Astrophysics Data System (ADS)

Wang, Zhaomeng; Varma, V. B.; Wang, Z. P.; Ramanujan, R. V.

2015-12-01

Magnetofluidic spreading (MFS) is a phenomenon in which a uniform magnetic field is used to induce spreading of a ferrofluid core cladded by diamagnetic fluidic streams in a three-stream channel. Applications of MFS include micromixing, cell sorting and novel microfluidic lab-on-a-chip design. However, the relative importance of the parameters which govern MFS is still unclear, leading to non-optimal control of MFS. Hence, in this work, the effect of various key parameters on MFS was experimentally and numerically studied. Our multi-physics model, which combines magnetic and fluidic analysis, showed excellent agreement between theory and experiment. It was found that spreading was mainly due to cross-sectional convection induced by magnetic forces, and can be enhanced by tuning various parameters. Smaller flow rate ratio, higher magnetic field, higher core stream or lower cladding stream dynamic viscosity, and larger magnetic particle size can increase MFS. These results can be used to tune magnetofluidic spreading in microchannels.

Contributions to singing ability by the posterior portion of the superior temporal gyrus of the non-language-dominant hemisphere: First evidence from subdural cortical stimulation, Wada testing, and fMRI

PubMed Central

Suarez, Ralph O.; Golby, Alexandra; Whalen, Stephen; Sato, Susumu; Theodore, William H.; Kufta, Conrad V.; Devinsky, Orrin; Balish, Marshall; Bromfield, Edward B.

2009-01-01

INTRODUCTION Although the substrates that mediate singing abilities in the human brain are not well understood, invasive brain mapping techniques used for clinical decision making such as intracranial electrocortical testing and Wada testing offer a rare opportunity to examine music-related function in a select group of subjects, affording exceptional spatial and temporal specificity. METHODS We studied eight patients with medically refractory epilepsy undergoing indwelling subdural electrode seizure focus localization. All patients underwent Wada testing for language lateralization. Functional assessment of language and music tasks was done by electrode grid cortical stimulation. One patient was also tested non-invasively with functional MRI. Functional organization of singing ability compared to language ability was determined based on four regions-ofinterest: left and right inferior frontal gyrus (IFG), and left and right posterior superior temporal gyrus (pSTG). RESULTS In some subjects, electrical stimulation of dominant pSTG can interfere with speech and not singing, whereas stimulation of non-dominant pSTG area can interfere with singing and not speech. Stimulation of the dominant IFG tends to interfere with both musical and language expression, while non-dominant IFG stimulation was often observed to cause no interference with either task; and finally, that stimulation of areas adjacent to but not within non-dominant pSTG typically does not affect either ability. FMRI mappings of one subject revealed similar music/language dissociation with respect to activation asymmetry within the regions-of-interest. CONCLUSION Despite inherent limitations with respect to strictly research objectives, invasive clinical techniques offer a rare opportunity to probe musical and language cognitive processes of the brain in a select group of patients. PMID:19570530

Differences in Multi-Modal Ultrasound Imaging between Triple Negative and Non-Triple Negative Breast Cancer.

PubMed

Li, Ziyao; Tian, Jiawei; Wang, Xiaowei; Wang, Ying; Wang, Zhenzhen; Zhang, Lei; Jing, Hui; Wu, Tong

2016-04-01

The objective of this study was to identify multi-modal ultrasound imaging parameters that could potentially help to differentiate between triple negative breast cancer (TNBC) and non-TNBC. Conventional ultrasonography, ultrasound strain elastography and 3-D ultrasound (3-D-US) findings from 50 TNBC and 179 non-TNBC patients were retrospectively reviewed. Immunohistochemical examination was used as the reference gold standard for cancer subtyping. Different ultrasound modalities were initially analyzed to define TNBC-related features. Subsequently, logistic regression analysis was applied to TNBC-related features to establish models for predicting TNBC. TNBCs often presented as micro-lobulated, markedly hypo-echoic masses with an abrupt interface (p = 0.015, 0.0015 and 0.004, compared with non-TNBCs, respectively) on conventional ultrasound, and showed a diminished retraction pattern phenomenon in the coronal plane (p = 0.035) on 3-D-US. Our findings suggest that B-mode ultrasound and 3-D-US in multi-modality ultrasonography could be a useful non-invasive technique for differentiating TNBCs from non-TNBCs. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Diagnostic Utility of the Shortened Version of the Wisconsin Card Sorting Test in Patients With Sporadic Late Onset Alzheimer Disease.

PubMed

Sánchez, Juan Luis; Martín, Javier; López, Carolina

2017-12-01

The classic version of the Wisconsin Card Sorting Test (WCST) consists of correctly sorting 128 cards according to changing sorting criteria. Its application is costly in terms of the time employed, with all the negative consequences this entails (decrease in motivation, frustration, and fatigue). The main objective of this study was to test the usefulness of the shortened version of the WCST as compared to the full test by analyzing the equivalence between the two decks comprising the full 128-card version on a sample of patients diagnosed with sporadic late onset Alzheimer disease (SLOAD) and to check its clinical usefulness. The variables showed equivalence between the two decks and their ability to differentiate between the control group (CG) and the Alzheimer disease (AD) group. The scores obtained suggest equivalence between decks and that the application of only the first deck is sufficient.

Effect of Hydrograph Characteristics on Vertical Grain Sorting in Gravel Bed Rivers

NASA Astrophysics Data System (ADS)

Hassan, M. A.; Parker, G.; Egozi, R.

2005-12-01

This study focuses on the formation of armour layers over a range of hydrologic conditions that includes two limiting cases; a relatively flat hydrograph that represents conditions produced by continuous snowmelt and a sharply peaked hydrograph that represents conditions associated with flash floods. To achieve our objective we analyzed field evidence, conducted flume experiments and performed numerical simulations. Sediment supply appears to be a first-order control on bed surface armouring, while the shape of the hydrograph plays a secondary role. All constant hydrograph experiments developed a well-armored structured surface while short asymmetrical hydrographs did not show substantial vertical sorting. All symmetrical hydrographs show some degree of sorting, and the sorting tended to become more pronounced with longer duration. Using the numerical framework of Parker, modified Powell, et al. and Wilcock and Crowe, we were able to achieve similar results.

USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES

EPA Science Inventory

Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...

Co-assembly of Viral Envelope Glycoproteins Regulates Their Polarized Sorting in Neurons

PubMed Central

Mardones, Gonzalo A.; Bonifacino, Juan S.

2014-01-01

Newly synthesized envelope glycoproteins of neuroinvasive viruses can be sorted in a polarized manner to the somatodendritic and/or axonal domains of neurons. Although critical for transneuronal spread of viruses, the molecular determinants and interregulation of this process are largely unknown. We studied the polarized sorting of the attachment (NiV-G) and fusion (NiV-F) glycoproteins of Nipah virus (NiV), a paramyxovirus that causes fatal human encephalitis, in rat hippocampal neurons. When expressed individually, NiV-G exhibited a non-polarized distribution, whereas NiV-F was specifically sorted to the somatodendritic domain. Polarized sorting of NiV-F was dependent on interaction of tyrosine-based signals in its cytosolic tail with the clathrin adaptor complex AP-1. Co-expression of NiV-G with NiV-F abolished somatodendritic sorting of NiV-F due to incorporation of NiV-G•NiV-F complexes into axonal transport carriers. We propose that faster biosynthetic transport of unassembled NiV-F allows for its proteolytic activation in the somatodendritic domain prior to association with NiV-G and axonal delivery of NiV-G•NiV-F complexes. Our study reveals how interactions of viral glycoproteins with the host's transport machinery and between themselves regulate their polarized sorting in neurons. PMID:24831812

In situ real-time imaging of self-sorted supramolecular nanofibres

NASA Astrophysics Data System (ADS)

Onogi, Shoji; Shigemitsu, Hajime; Yoshii, Tatsuyuki; Tanida, Tatsuya; Ikeda, Masato; Kubota, Ryou; Hamachi, Itaru

2016-08-01

Self-sorted supramolecular nanofibres—a multicomponent system that consists of several types of fibre, each composed of distinct building units—play a crucial role in complex, well-organized systems with sophisticated functions, such as living cells. Designing and controlling self-sorting events in synthetic materials and understanding their structures and dynamics in detail are important elements in developing functional artificial systems. Here, we describe the in situ real-time imaging of self-sorted supramolecular nanofibre hydrogels consisting of a peptide gelator and an amphiphilic phosphate. The use of appropriate fluorescent probes enabled the visualization of self-sorted fibres entangled in two and three dimensions through confocal laser scanning microscopy and super-resolution imaging, with 80 nm resolution. In situ time-lapse imaging showed that the two types of fibre have different formation rates and that their respective physicochemical properties remain intact in the gel. Moreover, we directly visualized stochastic non-synchronous fibre formation and observed a cooperative mechanism.

The Power of Exclusion using Automated Osteometric Sorting: Pair-Matching.

PubMed

Lynch, Jeffrey James; Byrd, John; LeGarde, Carrie B

2018-03-01

This study compares the original pair-matching osteometric sorting model (J Forensic Sci 2003;48:717) against two new models providing validation and performance testing across three samples. The samples include the Forensic Data Bank, USS Oklahoma, and the osteometric sorting reference used within the Defense POW/MIA Accounting Agency. A computer science solution to generating dynamic statistical models across a commingled assemblage is presented. The issue of normality is investigated showing the relative robustness against non-normality and a data transformation to control for normality. A case study is provided showing the relative exclusion power of all three models from an active commingled case within the Defense POW/MIA Accounting Agency. In total, 14,357,220 osteometric t-tests were conducted. The results indicate that osteometric sorting performs as expected despite reference samples deviating from normality. The two new models outperform the original, and one of those is recommended to supersede the original for future osteometric sorting work. © 2017 American Academy of Forensic Sciences.

Applications and interpretation of computer-assisted sperm analyses and sperm sorting methods in assisted breeding and comparative research.

PubMed

Holt, William V; O'Brien, Justine; Abaigar, Teresa

2007-01-01

Theoretical and practical knowledge of sperm function is an essential requirement in almost every aspect of modern reproductive technology, if the overarching objective is the eventual production of live offspring. Artificial insemination (AI) techniques depend on the availability of high quality semen, whether fresh, diluted and stored, or frozen. Assessing such semen for quality and the likelihood of fertility is therefore also important, as much time, resources and effort can easily be wasted by using poor samples. Some semen technologies are aimed not at quality assessment, but at attempting to skew the breeding outcomes. Sex preselection by separating the male- and female-bearing spermatozoa using flow cytometry is now practised routinely in the agricultural industry, but speculatively it may eventually be possible to use other genetic markers besides the sex chromosomes. A moment's reflection shows that although sex-biasing flow cytometry technology is well developed and generally fulfils its purpose if presorting of sperm quality is adequate, other technologies aimed specifically at semen assessment are also sophisticated but provide inadequate data that say little about fertility. This is especially true of instrumentation for objective sperm motility assessment. Here we aim to examine this technological paradox and suggest that although the sperm assessment equipment might be sophisticated, the shortcomings probably lie largely with inappropriate objectives and data interpretation. We also aim to review the potential value and use of sperm sexing technology for non-domestic species, arguing in this case that the limitations also lie less with the technology itself than with the applications envisaged. Finally, the potential application of a sorting method directed at motility rather than sperm DNA content is discussed.

The effects of hoechst 33342 staining and the male sample donor on the sorting efficiency of canine spermatozoa.

PubMed

Rodenas, C; Lucas, X; Tarantini, T; Del Olmo, D; Roca, J; Vazquez, J M; Martinez, E A; Parrilla, I

2014-02-01

The aim of this study was to evaluate the influence of Hoechst 33342 (H-42) concentration and of the male donor on the efficiency of sex-sorting procedure in canine spermatozoa. Semen samples from six dogs (three ejaculates/dog) were diluted to 100 × 10(6) sperm/ml, split into four aliquots, stained with increasing H-42 concentrations (5, 7.5, 10 and 12.5 μl, respectively) and sorted by flow cytometry. The rates of non-viable (FDA+), oriented (OS) and selected spermatozoa (SS), as well as the average sorting rates (SR, sorted spermatozoa/s), were used to determine the sorting efficiency. The effects of the sorting procedure on the quality of sorted spermatozoa were evaluated in terms of total motility (TM), percentage of viable spermatozoa (spermatozoa with membrane and acrosomal integrity) and percentage of spermatozoa with reacted/damaged acrosomes. X- and Y-chromosome-bearing sperm populations were identified in all of the samples stained with 7.5, 10 and 12.5 μl of H-42, while these two populations were only identified in 77.5% of samples stained with 5 μl. The values of OS, SS and SR were influenced by the male donor (p < 0.01) but not by the H-42 concentration used. The quality of sorted sperm samples immediately after sorting was similar to that of fresh samples, while centrifugation resulted in significant reduction (p < 0.05) in TM and in the percentage of viable spermatozoa and a significant increase (p < 0.01) in the percentage of spermatozoa with damage/reacted acrosomes. In conclusion, the sex-sorting of canine spermatozoa by flow cytometry can be performed successfully using H-42 concentrations between 7.5 and 12.5 μl. The efficiency of the sorting procedure varies based on the dog from which the sperm sample derives. © 2013 Blackwell Verlag GmbH.

Multi-scale Quantitative Precipitation Forecasting Using ...

EPA Pesticide Factsheets

Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals difficult to be detected at a local scale as it could cause large uncertainties when using linear correlation analysis only. This paper explores the relationship between global SST and terrestrial precipitation with respect to long-term non-stationary teleconnection signals during 1981-2010 over three regions in North America and one in Central America. Empirical mode decomposition as well as wavelet analysis is utilized to extract the intrinsic trend and the dominant oscillation of the SST and precipitation time series in sequence. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant SST regions are extracted based on the correlation coefficient. With these characterized associations, individual contribution of these SST forcing regions linked to the related precipitation responses are further quantified through nonlinear modeling with the aid of extreme learning machine. Results indicate that the non-leading SST regions also contribute a salient portion to the terrestrial precipitation variability compared to some known leading SST regions. In some cases, these

Design and Development of a High Speed Sorting System Based on Machine Vision Guiding

NASA Astrophysics Data System (ADS)

Zhang, Wenchang; Mei, Jiangping; Ding, Yabin

In this paper, a vision-based control strategy to perform high speed pick-and-place tasks on automation product line is proposed, and relevant control software is develop. Using Delta robot to control a sucker to grasp disordered objects from one moving conveyer and then place them on the other in order. CCD camera gets one picture every time the conveyer moves a distance of ds. Objects position and shape are got after image processing. Target tracking method based on "Servo motor + synchronous conveyer" is used to fulfill the high speed porting operation real time. Experiments conducted on Delta robot sorting system demonstrate the efficiency and validity of the proposed vision-control strategy.

Detecting Slums from Quick Bird Data in Pune Using AN Object Oriented Approach

NASA Astrophysics Data System (ADS)

Shekhar, S.

2012-07-01

We have been witnessing a gradual and steady transformation from a pre dominantly rural society to an urban society in India and by 2030, it will have more people living in urban than rural areas. Slums formed an integral part of Indian urbanisation as most of the Indian cities lack in basic needs of an acceptable life. Many efforts are being taken to improve their conditions. To carry out slum renewal programs and monitor its implementation, slum settlements should be recorded to obtain an adequate spatial data base. This can be only achieved through the analysis of remote sensing data with very high spatial resolution. Regarding the occurrences of settlement areas in the remote sensing data pixel-based approach on a high resolution image is unable to represent the heterogeneity of complex urban environments. Hence there is a need for sophisticated method and data for slum analysis. An attempt has been made to detect and discriminate the slums of Pune city by describing typical characteristics of these settlements, by using eCognition software from quick bird data on the basis of object oriented approach. Based on multi resolution segmentation, initial objects were created and further depend on texture, geometry and contextual characteristics of the image objects, they were classified into slums and non-slums. The developed rule base allowed the description of knowledge about phenomena clearly and easily using fuzzy membership functions and the described knowledge stored in the classification rule base led to the best classification with more than 80% accuracy.

Conversion of proteins from a non-polarized to an apical secretory pattern in MDCK cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vogel, Lotte K.; Larsen, Jakob E.; Hansen, Martin

2005-05-13

Previously it was shown that fusion proteins containing the amino terminus of an apical targeted member of the serpin family fused to the corresponding carboxyl terminus of the non-polarized secreted serpin, antithrombin, are secreted mainly to the apical side of MDCK cells. The present study shows that this is neither due to the transfer of an apical sorting signal from the apically expressed proteins, since a sequence of random amino acids acts the same, nor is it due to the deletion of a conserved signal for correct targeting from the non-polarized secreted protein. Our results suggest that the polarity ofmore » secretion is determined by conformational sensitive sorting signals.« less

Handling of boar spermatozoa during and after flow cytometric sex-sorting process to improve their in vitro fertilizing ability.

PubMed

del Olmo, D; Parrilla, I; Gil, M A; Maside, C; Tarantini, T; Angel, M A; Roca, J; Martinez, E A; Vazquez, J M

2013-09-01

The objective of this study was to develop an adequate sperm handling protocol in order to obtain a sex-sorted sperm population with an optimal fertilizing ability. For this purpose, different aspects of the sorting procedure were examined. The effects of the high dilution rates (experiment 1), type of collection medium used (experiment 2), and sheath fluid composition (experiment 3) on sorted boar sperm quality and function were evaluated. Sperm quality was assessed by motility and viability tests, whereas sperm function was evaluated by an in vitro fertilization assay which determined the penetration and polyspermy rates as well as the mean number of sperm penetrating each oocyte. In experiment 1, the results obtained indicated that the high dilution rates did not cause a decrease either in the sperm quality parameters evaluated or the in vitro fertilization ability of spermatozoa. In experiment 2, although sperm quality was not affected, fertilizing ability was compromised after sorting, regardless of the collection medium that was used. In the experiment 3, all groups displayed adequate sperm quality values, but higher in vitro fertility parameters were obtained for spermatozoa sorted in presence of EDTA in the sheath fluid and egg yolk (EY) in the collection media when compared with those sorted in absence of these protective agents. No differences in penetration rates between unsorted highly diluted (control) and sorted sperm in the presence of EDTA and EY were observed. In conclusion, fertilizing ability was compromised in sex-sorted sperm. The addition of EDTA to sheath fluid and EY to collection medium improved boar sperm fertilizing ability, and both agents should be included as essential media components in future studies. Copyright © 2013 Elsevier Inc. All rights reserved.

Contribution of non-volatile and aroma fractions to in-mouth sensory properties of red wines: wine reconstitution strategies and sensory sorting task.

PubMed

Sáenz-Navajas, María-Pilar; Campo, Eva; Avizcuri, José Miguel; Valentin, Dominique; Fernández-Zurbano, Purificación; Ferreira, Vicente

2012-06-30

This work explores to what extent the aroma or the non-volatile fractions of red wines are responsible for the overall flavor differences perceived in-mouth. For this purpose, 14 samples (4 commercial and 10 reconstituted wines), were sorted by a panel of 30 trained assessors according to their sensory in-mouth similarities. Reconstituted wines were prepared by adding the same volatile fraction (coming from a red wine) to the non-volatile fraction of 10 different red wines showing large differences in perceived astringency. Sorting was performed under three different conditions: (a) no aroma perception: nose-close condition (NA), (b) retronasal aroma perception only (RA), and (c) allowing retro- and involuntary orthonasal aroma perception (ROA). Similarity estimates were derived from the sorting and submitted to multidimensional scaling (MDS) followed by hierarchical cluster analysis (HCA). Results have clearly shown that, globally, aroma perception is not the major driver of the in-mouth sensory perception of red wine, which is undoubtedly primarily driven by the perception of astringency and by the chemical compounds causing it, particularly protein precipitable proanthocyanidins (PAs). However, aroma perception plays a significant role on the perception of sweetness and bitterness. The impact of aroma seems to be more important whenever astringency, total polyphenols and protein precipitable PAs levels are smaller. Results also indicate that when a red-black fruit odor nuance is clearly perceived in conditions in which orthonasal odor perception is allowed, a strong reduction in astringency takes place. Such red-black fruit odor nuance seems to be the result of a specific aroma release pattern as a consequence of the interaction between aroma compounds and the non-volatile matrix. Copyright © 2011 Elsevier B.V. All rights reserved.

Optically enhanced acoustophoresis

NASA Astrophysics Data System (ADS)

McDougall, Craig; O'Mahoney, Paul; McGuinn, Alan; Willoughby, Nicholas A.; Qiu, Yongqiang; Demore, Christine E. M.; MacDonald, Michael P.

2017-08-01

Regenerative medicine has the capability to revolutionise many aspects of medical care, but for it to make the step from small scale autologous treatments to larger scale allogeneic approaches, robust and scalable label free cell sorting technologies are needed as part of a cell therapy bioprocessing pipeline. In this proceedings we describe several strategies for addressing the requirements for high throughput without labeling via: dimensional scaling, rare species targeting and sorting from a stable state. These three approaches are demonstrated through a combination of optical and ultrasonic forces. By combining mostly conservative and non-conservative forces from two different modalities it is possible to reduce the influence of flow velocity on sorting efficiency, hence increasing robustness and scalability. One such approach can be termed "optically enhanced acoustophoresis" which combines the ability of acoustics to handle large volumes of analyte with the high specificity of optical sorting.

Dynamics modeling and periodic control of horizontal-axis wind turbines

NASA Astrophysics Data System (ADS)

Stol, Karl Alexander

2001-07-01

The development of large multi-megawatt wind turbines has increased the need for active feedback control to meet multiple performance objectives. Power regulation is still of prime concern but there is an increasing interest in mitigating loads for these very large, dynamically soft and highly integrated power systems. This work explores the opportunities for utilizing state space modeling, modal analysis, and multi-objective controllers in advanced horizontal-axis wind turbines. A linear state-space representation of a generic, multiple degree-of-freedom wind turbine is developed to test various control methods and paradigms. The structural model, SymDyn, provides for limited flexibility in the tower, drive train and blades assuming a rigid component architecture with joint springs and dampers. Equations of motion are derived symbolically, verified by numerical simulation, and implemented in the Matlab with Simulink computational environment. AeroDyn, an industry-standard aerodynamics package for wind turbines, provides the aerodynamic load data through interfaced subroutines. Linearization of the structural model produces state equations with periodic coefficients due to the interaction of rotating and non-rotating components. Floquet theory is used to extract the necessary modal properties and several parametric studies identify the damping levels and dominant dynamic coupling influences. Two separate issues of control design are investigated: full-state feedback and state estimation. Periodic gains are developed using time-varying LQR techniques and many different time-invariant control designs are constructed, including a classical PID controller. Disturbance accommodating control (DAC) allows the estimation of wind speed for minimization of the disturbance effects on the system. Controllers are tested in simulation for multiple objectives using measurement of rotor position and rotor speed only and actuation of independent blade pitch. It is found that periodic control is capable of reducing cyclic blade bending moments while regulating speed but that optimal performance requires additional sensor information. Periodic control is also the only design found that could successfully control the yaw alignment although blade loads are increased as a consequence. When speed regulation is the only performance objective then a time-invariant state-space design or PID is appropriate.

Ancient Egypt: History 380.

ERIC Educational Resources Information Center

Turk, Laraine D.

"Ancient Egypt," an upper-division, non-required history course covering Egypt from pre-dynastic time through the Roman domination is described. General descriptive information is presented first, including the method of grading, expectation of student success rate, long-range course objectives, procedures for revising the course, major…

An efficient dynamic load balancing algorithm

NASA Astrophysics Data System (ADS)

Lagaros, Nikos D.

2014-01-01

In engineering problems, randomness and uncertainties are inherent. Robust design procedures, formulated in the framework of multi-objective optimization, have been proposed in order to take into account sources of randomness and uncertainty. These design procedures require orders of magnitude more computational effort than conventional analysis or optimum design processes since a very large number of finite element analyses is required to be dealt. It is therefore an imperative need to exploit the capabilities of computing resources in order to deal with this kind of problems. In particular, parallel computing can be implemented at the level of metaheuristic optimization, by exploiting the physical parallelization feature of the nondominated sorting evolution strategies method, as well as at the level of repeated structural analyses required for assessing the behavioural constraints and for calculating the objective functions. In this study an efficient dynamic load balancing algorithm for optimum exploitation of available computing resources is proposed and, without loss of generality, is applied for computing the desired Pareto front. In such problems the computation of the complete Pareto front with feasible designs only, constitutes a very challenging task. The proposed algorithm achieves linear speedup factors and almost 100% speedup factor values with reference to the sequential procedure.

Space power subsystem automation technology

NASA Technical Reports Server (NTRS)

Graves, J. R. (Compiler)

1982-01-01

The technology issues involved in power subsystem automation and the reasonable objectives to be sought in such a program were discussed. The complexities, uncertainties, and alternatives of power subsystem automation, along with the advantages from both an economic and a technological perspective were considered. Whereas most spacecraft power subsystems now use certain automated functions, the idea of complete autonomy for long periods of time is almost inconceivable. Thus, it seems prudent that the technology program for power subsystem automation be based upon a growth scenario which should provide a structured framework of deliberate steps to enable the evolution of space power subsystems from the current practice of limited autonomy to a greater use of automation with each step being justified on a cost/benefit basis. Each accomplishment should move toward the objectives of decreased requirement for ground control, increased system reliability through onboard management, and ultimately lower energy cost through longer life systems that require fewer resources to operate and maintain. This approach seems well-suited to the evolution of more sophisticated algorithms and eventually perhaps even the use of some sort of artificial intelligence. Multi-hundred kilowatt systems of the future will probably require an advanced level of autonomy if they are to be affordable and manageable.

Design and analysis on robotic arm for serving hazard container

NASA Astrophysics Data System (ADS)

Razali, Zol Bahri; Kader, Mohamed Mydin M. Abdul; Yi, Khoo Zern; Daud, Mohd Hisam

2017-09-01

This paper presents about design, analyses development and fabrication of robotic arm for sorting multi-material. The major problem that urges the initiation of the project is the fact that manufacturing industry is growing at relatively faster rate. Most of the company produce high load robotic arm. Less company creates light weight, and affordable robotic arm. As the result, light weight and affordable robot is developing to cover this issue. Plastic material was used to construct the body of the robotic arm, and an optical sensor was implemented to provide basic recognition of object to be carried. The robotic arm used five servomotors for overall operation; four for its joints, and one for the gripping mechanism. The gripper was designed and fabricated using Perspex due to the light weight and high strength of the material. The operation of the robotic arm was governed by Basic Stamp programming sequence and the device was expected to differentiate material and other objects based on reflective theory, and perform subsequent operations afterwards. The SolidWorks was used to model the detail design of the robotic arm, and to simulate the motion of the device.

Dynamics of particulate organic matter composition in coastal systems: Forcing of spatio-temporal variability at multi-systems scale

NASA Astrophysics Data System (ADS)

Liénart, Camilla; Savoye, Nicolas; David, Valérie; Ramond, Pierre; Rodriguez Tress, Paco; Hanquiez, Vincent; Marieu, Vincent; Aubert, Fabien; Aubin, Sébastien; Bichon, Sabrina; Boinet, Christophe; Bourasseau, Line; Bozec, Yann; Bréret, Martine; Breton, Elsa; Caparros, Jocelyne; Cariou, Thierry; Claquin, Pascal; Conan, Pascal; Corre, Anne-Marie; Costes, Laurence; Crouvoisier, Muriel; Del Amo, Yolanda; Derriennic, Hervé; Dindinaud, François; Duran, Robert; Durozier, Maïa; Devesa, Jérémy; Ferreira, Sophie; Feunteun, Eric; Garcia, Nicole; Geslin, Sandrine; Grossteffan, Emilie; Gueux, Aurore; Guillaudeau, Julien; Guillou, Gaël; Jolly, Orianne; Lachaussée, Nicolas; Lafont, Michel; Lagadec, Véronique; Lamoureux, Jézabel; Lauga, Béatrice; Lebreton, Benoît; Lecuyer, Eric; Lehodey, Jean-Paul; Leroux, Cédric; L'Helguen, Stéphane; Macé, Eric; Maria, Eric; Mousseau, Laure; Nowaczyk, Antoine; Pineau, Philippe; Petit, Franck; Pujo-Pay, Mireille; Raimbault, Patrick; Rimmelin-Maury, Peggy; Rouaud, Vanessa; Sauriau, Pierre-Guy; Sultan, Emmanuelle; Susperregui, Nicolas

2018-03-01

In costal systems, particulate organic matter (POM) results from a multiplicity of sources having their respective dynamics in terms of production, decomposition, transport and burial. The POM pool experiences thus considerable spatial and temporal variability. In order to better understand this variability, the present study employs statistical multivariate analyses to investigate links between POM composition and environmental forcings for a panel of twelve coastal systems distributed along the three maritime regions of France and monitored weekly to monthly for 1 to 8 years. At multi-system scale, two main gradients of POC composition have been identified: a 'Continent-Ocean' gradient associated with hydrodynamics, sedimentary dynamics and depth of the water column, and a gradient of trophic status related to nutrient availability. At local scale, seasonality of POC composition appears to be station-specific but still related to part of the above-mentioned forcings. A typology of systems was established by coupling spatial and temporal variability of POC composition. Four groups were highlighted: (1) the estuarine stations where POC composition is dominated by terrestrial POM and driven by hydrodynamics and sedimentary processes, (2) the oligotrophic systems, characterized by the contribution of diazotrophs due to low nutrient availability, and the marine meso/eutroph systems whose POC composition is (3) either deeply dominated by phytoplankton or (4) dominated by phytoplankton but where the contribution of continental and benthic POC is not negligible and is driven by hydrodynamics, sedimentary processes and the height of the water column. Finally, the present study provides several insights into the different forcings to POM composition and dynamics in temperate coastal systems at local and multi-system scales. This work also presents a methodological approach that establishes statistical links between forcings and POM composition, helping to gain more objectively insight of forcings.

Joint Intelligence Operations Center (JIOC) Baseline Business Process Model & Capabilities Evaluation Methodology

DTIC Science & Technology

2012-03-01

Targeting Review Board OPLAN Operations Plan OPORD Operations Order OPSIT Operational Situation OSINT Open Source Intelligence OV...Analysis Evaluate FLTREPs MISREPs Unit Assign Assets Feedback Asset Shortfalls Multi-Int Collection Political & Embasy Law Enforcement HUMINT OSINT ...Embassy Information OSINT Manage Theater HUMINT Law Enforcement Collection Sort Requests Platform Information Agency Information M-I Collect

Changes in Patient Sorting to Nursing Homes under Public Reporting: Improved Patient Matching or Provider Gaming?

PubMed Central

Werner, Rachel M; Konetzka, R Tamara; Stuart, Elizabeth A; Polsky, Daniel

2011-01-01

Objective To test whether public reporting in the setting of postacute care in nursing homes results in changes in patient sorting. Data Sources/Study Setting All postacute care admissions from 2001 to 2003 in the nursing home Minimum Data Set. Study Design We test changes in patient sorting (or the changes in the illness severity of patients going to high- versus low-scoring facilities) when public reporting was initiated in nursing homes in 2002. We test for changes in sorting with respect to pain, delirium, and walking and then examine the potential roles of cream skimming and downcoding in changes in patient sorting. We use a difference-in-differences framework, taking advantage of the variation in the launch of public reporting in pilot and nonpilot states, to control for underlying trends in patient sorting. Principal Findings There was a significant change in patient sorting with respect to pain after public reporting was initiated, with high-risk patients being more likely to go to high-scoring facilities and low-risk patients more likely to go to low-scoring facilities. There was also an overall decrease in patient risk of pain with the launch of public reporting, which may be consistent with changes in documentation of pain levels (or downcoding). There was no significant change in sorting for delirium or walking. Conclusions Public reporting of nursing home quality improves matching of high-risk patients to high-quality facilities. However, efforts should be made to reduce the incentives for downcoding by nursing facilities. PMID:21105869

Online sorting of recovered wood waste by automated XRF-technology: part II. Sorting efficiencies.

PubMed

Hasan, A Rasem; Solo-Gabriele, Helena; Townsend, Timothy

2011-04-01

Sorting of waste wood is an important process practiced at recycling facilities in order to detect and divert contaminants from recycled wood products. Contaminants of concern include arsenic, chromium and copper found in chemically preserved wood. The objective of this research was to evaluate the sorting efficiencies of both treated and untreated parts of the wood waste stream, and metal (As, Cr and Cu) mass recoveries by the use of automated X-ray fluorescence (XRF) systems. A full-scale system was used for experimentation. This unit consisted of an XRF-detection chamber mounted on the top of a conveyor and a pneumatic slide-way diverter which sorted wood into presumed treated and presumed untreated piles. A randomized block design was used to evaluate the operational conveyance parameters of the system, including wood feed rate and conveyor belt speed. Results indicated that online sorting efficiencies of waste wood by XRF technology were high based on number and weight of pieces (70-87% and 75-92% for treated wood and 66-97% and 68-96% for untreated wood, respectively). These sorting efficiencies achieved mass recovery for metals of 81-99% for As, 75-95% for Cu and 82-99% of Cr. The incorrect sorting of wood was attributed almost equally to deficiencies in the detection and conveyance/diversion systems. Even with its deficiencies, the system was capable of producing a recyclable portion that met residential soil quality levels established for Florida, for an infeed that contained 5% of treated wood. Copyright © 2010 Elsevier Ltd. All rights reserved.

Optimization of cascading failure on complex network based on NNIA

NASA Astrophysics Data System (ADS)

Zhu, Qian; Zhu, Zhiliang; Qi, Yi; Yu, Hai; Xu, Yanjie

2018-07-01

Recently, the robustness of networks under cascading failure has attracted extensive attention. Different from previous studies, we concentrate on how to improve the robustness of the networks from the perspective of intelligent optimization. We establish two multi-objective optimization models that comprehensively consider the operational cost of the edges in the networks and the robustness of the networks. The NNIA (Non-dominated Neighbor Immune Algorithm) is applied to solve the optimization models. We finished simulations of the Barabási-Albert (BA) network and Erdös-Rényi (ER) network. In the solutions, we find the edges that can facilitate the propagation of cascading failure and the edges that can suppress the propagation of cascading failure. From the conclusions, we take optimal protection measures to weaken the damage caused by cascading failures. We also consider actual situations of operational cost feasibility of the edges. People can make a more practical choice based on the operational cost. Our work will be helpful in the design of highly robust networks or improvement of the robustness of networks in the future.

Hello darkness my old friend: the fading of the nearby TDE ASASSN-14ae

NASA Astrophysics Data System (ADS)

Brown, Jonathan S.; Shappee, Benjamin J.; Holoien, T. W.-S.; Stanek, K. Z.; Kochanek, C. S.; Prieto, J. L.

2016-11-01

We present late-time optical spectroscopy taken with the Large Binocular Telescope's Multi-Object Double Spectrograph, an improved All-Sky Automated Survey for SuperNovae pre-discovery non-detection, and late-time Swift observations of the nearby (d = 193 Mpc, z = 0.0436) tidal disruption event (TDE) ASASSN-14ae. Our observations span from ˜20 d before to ˜750 d after discovery. The proximity of ASASSN-14ae allows us to study the optical evolution of the flare and the transition to a host-dominated state with exceptionally high precision. We measure very weak Hα emission 300 d after discovery (LH α ≃ 4 × 1039 erg s-1) and the most stringent upper limit to date on the Hα luminosity ˜750 d after discovery (LH α ≲ 1039 erg s-1), suggesting that the optical emission arising from a TDE can vanish on a time-scale as short as 1 yr. Our results have important implications for both spectroscopic detection of TDE candidates at late times, as well as the nature of TDE host galaxies themselves.

Non-cavitating propeller noise modeling and inversion

NASA Astrophysics Data System (ADS)

Kim, Dongho; Lee, Keunhwa; Seong, Woojae

2014-12-01

Marine propeller is the dominant exciter of the hull surface above it causing high level of noise and vibration in the ship structure. Recent successful developments have led to non-cavitating propeller designs and thus present focus is the non-cavitating characteristics of propeller such as hydrodynamic noise and its induced hull excitation. In this paper, analytic source model of propeller non-cavitating noise, described by longitudinal quadrupoles and dipoles, is suggested based on the propeller hydrodynamics. To find the source unknown parameters, the multi-parameter inversion technique is adopted using the pressure data obtained from the model scale experiment and pressure field replicas calculated by boundary element method. The inversion results show that the proposed source model is appropriate in modeling non-cavitating propeller noise. The result of this study can be utilized in the prediction of propeller non-cavitating noise and hull excitation at various stages in design and analysis.

Sorting waves and associated eigenvalues

NASA Astrophysics Data System (ADS)

Carbonari, Costanza; Colombini, Marco; Solari, Luca

2017-04-01

The presence of mixed sediment always characterizes gravel bed rivers. Sorting processes take place during bed load transport of heterogeneous sediment mixtures. The two main elements necessary to the occurrence of sorting are the heterogeneous character of sediments and the presence of an active sediment transport. When these two key ingredients are simultaneously present, the segregation of bed material is consistently detected both in the field [7] and in laboratory [3] observations. In heterogeneous sediment transport, bed altimetric variations and sorting always coexist and both mechanisms are independently capable of driving the formation of morphological patterns. Indeed, consistent patterns of longitudinal and transverse sorting are identified almost ubiquitously. In some cases, such as bar formation [2] and channel bends [5], sorting acts as a stabilizing effect and therefore the dominant mechanism driving pattern formation is associated with bed altimetric variations. In other cases, such as longitudinal streaks, sorting enhances system instability and can therefore be considered the prevailing mechanism. Bedload sheets, first observed by Khunle and Southard [1], represent another classic example of a morphological pattern essentially triggered by sorting, as theoretical [4] and experimental [3] results suggested. These sorting waves cause strong spatial and temporal fluctuations of bedload transport rate typical observed in gravel bed rivers. The problem of bed load transport of a sediment mixture is formulated in the framework of a 1D linear stability analysis. The base state consists of a uniform flow in an infinitely wide channel with active bed load transport. The behaviour of the eigenvalues associated with fluid motion, bed evolution and sorting processes in the space of the significant flow and sediment parameters is analysed. A comparison is attempted with the results of the theoretical analysis of Seminara Colombini and Parker [4] and Stecca, Siviglia and Blom [6]. [1] Kuhnle, R.A. and Southard, J.B. 1988. Bed Load Transport Fluctuations in a Gravel Bed Laboratory Channel. Water Resources Research, 24(2), 247-260. [2] Lanzoni, S. and Tubino, M. 1999. Grain sorting and bar instability. Journal of Fluid Mechanics. 393, 149-174. [3] Recking, A., Frey, P., Paquier, A. and Belleudy, P. 2009. An experimental investigation of mechanisms involved in bed load sheet production and migration. Journal of Geophysical Research, 114, F03010. [4] Seminara, G., Colombini, M. and Parker, G. 1996. Nearly pure sorting waves and formation of bedload sheets. Journal of Fluid Mechanics. 312, (1996), 253-278. [5] Seminara, G., Solari, L. and Tubino, M. 1997. Finite amplitude scour and grain sorting in wide channel bends. XXVII IAHR Congress, San Francisco, 1445-1450. [6] Stecca, G., Siviglia, A. and Blom, A. 2014. Mathematical analysis of the Saint-Venant-Hirano model for mixed-sediment morphodynamics. Water Resources Research, 50, 7563-7589. [7] Whiting, P.J., Dietrich, W.E., Leopold, L. B., Drake, T. G. and Shreve, R.L. 1988. Bedload sheets in heterogeneous sediment. Geology, 16, 105-108.

A Note on Evolutionary Algorithms and Its Applications

ERIC Educational Resources Information Center

Bhargava, Shifali

2013-01-01

This paper introduces evolutionary algorithms with its applications in multi-objective optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed with their advantages and disadvantages. We also discuss constrained multiobjective evolutionary algorithms and their applications in various areas.

Intracellular trafficking of silicon particles and logic-embedded vectors

NASA Astrophysics Data System (ADS)

Ferrati, Silvia; Mack, Aaron; Chiappini, Ciro; Liu, Xuewu; Bean, Andrew J.; Ferrari, Mauro; Serda, Rita E.

2010-08-01

Mesoporous silicon particles show great promise for use in drug delivery and imaging applications as carriers for second-stage nanoparticles and higher order particles or therapeutics. Modulation of particle geometry, surface chemistry, and porosity allows silicon particles to be optimized for specific applications such as vascular targeting and avoidance of biological barriers commonly found between the site of drug injection and the final destination. In this study, the intracellular trafficking of unloaded carrier silicon particles and carrier particles loaded with secondary iron oxide nanoparticles was investigated. Following cellular uptake, membrane-encapsulated silicon particles migrated to the perinuclear region of the cell by a microtubule-driven mechanism. Surface charge, shape (spherical and hemispherical) and size (1.6 and 3.2 μm) of the particle did not alter the rate of migration. Maturation of the phagosome was associated with an increase in acidity and acquisition of markers of late endosomes and lysosomes. Cellular uptake of iron oxide nanoparticle-loaded silicon particles resulted in sorting of the particles and trafficking to unique destinations. The silicon carriers remained localized in phagosomes, while the second stage iron oxide nanoparticles were sorted into multi-vesicular bodies that dissociated from the phagosome into novel membrane-bound compartments. Release of iron from the cells may represent exocytosis of iron oxide nanoparticle-loaded vesicles. These results reinforce the concept of multi-functional nanocarriers, in which different particles are able to perform specific tasks, in order to deliver single- or multi-component payloads to specific sub-cellular compartments.Mesoporous silicon particles show great promise for use in drug delivery and imaging applications as carriers for second-stage nanoparticles and higher order particles or therapeutics. Modulation of particle geometry, surface chemistry, and porosity allows silicon particles to be optimized for specific applications such as vascular targeting and avoidance of biological barriers commonly found between the site of drug injection and the final destination. In this study, the intracellular trafficking of unloaded carrier silicon particles and carrier particles loaded with secondary iron oxide nanoparticles was investigated. Following cellular uptake, membrane-encapsulated silicon particles migrated to the perinuclear region of the cell by a microtubule-driven mechanism. Surface charge, shape (spherical and hemispherical) and size (1.6 and 3.2 μm) of the particle did not alter the rate of migration. Maturation of the phagosome was associated with an increase in acidity and acquisition of markers of late endosomes and lysosomes. Cellular uptake of iron oxide nanoparticle-loaded silicon particles resulted in sorting of the particles and trafficking to unique destinations. The silicon carriers remained localized in phagosomes, while the second stage iron oxide nanoparticles were sorted into multi-vesicular bodies that dissociated from the phagosome into novel membrane-bound compartments. Release of iron from the cells may represent exocytosis of iron oxide nanoparticle-loaded vesicles. These results reinforce the concept of multi-functional nanocarriers, in which different particles are able to perform specific tasks, in order to deliver single- or multi-component payloads to specific sub-cellular compartments. Electronic supplementary information (ESI) available: Confocal microscopy image showing internalized negative particles, and movie of the intracellular migration of silicon particles. See DOI: 10.1039/c0nr00227e

ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery.

PubMed

Partl, Christian; Lex, Alexander; Streit, Marc; Strobelt, Hendrik; Wassermann, Anne-Mai; Pfister, Hanspeter; Schmalstieg, Dieter

2014-12-01

Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms.

An object-oriented and quadrilateral-mesh based solution adaptive algorithm for compressible multi-fluid flows

NASA Astrophysics Data System (ADS)

Zheng, H. W.; Shu, C.; Chew, Y. T.

2008-07-01

In this paper, an object-oriented and quadrilateral-mesh based solution adaptive algorithm for the simulation of compressible multi-fluid flows is presented. The HLLC scheme (Harten, Lax and van Leer approximate Riemann solver with the Contact wave restored) is extended to adaptively solve the compressible multi-fluid flows under complex geometry on unstructured mesh. It is also extended to the second-order of accuracy by using MUSCL extrapolation. The node, edge and cell are arranged in such an object-oriented manner that each of them inherits from a basic object. A home-made double link list is designed to manage these objects so that the inserting of new objects and removing of the existing objects (nodes, edges and cells) are independent of the number of objects and only of the complexity of O( 1). In addition, the cells with different levels are further stored in different lists. This avoids the recursive calculation of solution of mother (non-leaf) cells. Thus, high efficiency is obtained due to these features. Besides, as compared to other cell-edge adaptive methods, the separation of nodes would reduce the memory requirement of redundant nodes, especially in the cases where the level number is large or the space dimension is three. Five two-dimensional examples are used to examine its performance. These examples include vortex evolution problem, interface only problem under structured mesh and unstructured mesh, bubble explosion under the water, bubble-shock interaction, and shock-interface interaction inside the cylindrical vessel. Numerical results indicate that there is no oscillation of pressure and velocity across the interface and it is feasible to apply it to solve compressible multi-fluid flows with large density ratio (1000) and strong shock wave (the pressure ratio is 10,000) interaction with the interface.

Characterizing Variability in the Distribution of High-Frequency Acoustic Backscattering in a Shallow Water Coastal Region

DTIC Science & Technology

2007-08-01

zooplankton scatterer types, perhaps dominated by copepods and gas- bearing siphonophores (Lawson et al., 2004). Similar analyses of multi-frequency...in this Gulf of Maine study region, gas-bearing siphonophores dominated scattering at all four BIOMAPER-II frequencies (Lavery et al., in press...with multi-frequency acoustics. 2. Away from these locations, other zooplankton dominated scattering, especially copepods and gas-bearing siphonophores

Recent developments and applications of hyperspectral imaging for rapid detection of mycotoxins and mycotoxigenic fungi in food products.

PubMed

Xing, Fuguo; Yao, Haibo; Liu, Yang; Dai, Xiaofeng; Brown, Robert L; Bhatnagar, Deepak

2017-08-28

Mycotoxins are the foremost naturally occurring contaminants of food products such as corn, peanuts, tree nuts, and wheat. As the secondary metabolites, mycotoxins are mainly synthesized by many species of the genera Aspergillus, Fusarium and Penicillium, and are considered highly toxic and carcinogenic to humans and animals. Most mycotoxins are detected and quantified by analytical chemistry-based methods. While mycotoxigenic fungi are usually identified and quantified by biological methods. However, these methods are time-consuming, laborious, costly, and inconsistent because of the variability of the grain-sampling process. It is desirable to develop rapid, non-destructive and efficient methods that objectively measure and evaluate mycotoxins and mycotoxigenic fungi in food. In recent years, some spectroscopy-based technologies such as hyperspectral imaging (HSI), Raman spectroscopy, and Fourier transform infrared spectroscopy have been extensively investigated for their potential use as tools for the detection, classification, and sorting of mycotoxins and toxigenic fungal contaminants in food. HSI integrates both spatial and spectral information for every pixel in an image, making it suitable for rapid detection of large quantities of samples and more heterogeneous samples and for in-line sorting in the food industry. In order to track the latest research developments in HSI, this paper gives a brief overview of the theories and fundamentals behind the technology and discusses its applications in the field of rapid detection and sorting of mycotoxins and toxigenic fungi in food products. Additionally, advantages and disadvantages of HSI are compared, and its potential use in commercial applications is reported.

Acute immobilisation facilitates premotor preparatory activity for the non-restrained hand when facing grasp affordances.

PubMed

Kühn, Simone; Werner, Anika; Lindenberger, Ulman; Verrel, Julius

2014-05-15

Use and non-use of body parts during goal-directed action are major forces driving reorganisation of neural processing. We investigated changes in functional brain activity resulting from acute short-term immobilisation of the dominant right hand. Informed by the concept of object affordances, we predicted that the presence or absence of a limb restraint would influence the perception of graspable objects in a laterally specific way. Twenty-three participants underwent fMRI scanning during a passive object-viewing task before the intervention as well as with and without wearing an orthosis. The right dorsal premotor cortex and the left cerebellum were more strongly activated when the handle of an object was oriented towards the left hand while the right hand was immobilised compared with a situation where the hand was not immobilised. The cluster in the premotor cortex showing an interaction between condition (with restraint, without restraint) and stimulus action side (right vs. left) overlapped with the general task vs. baseline contrast prior to the intervention, confirming its functional significance for the task. These results show that acute immobilisation of the dominant right hand leads to rapid changes of the perceived affordance of objects. We conclude that changes in action requirements lead to almost instantaneous changes in functional activation patterns, which in turn may trigger structural cortical plasticity. Copyright © 2014. Published by Elsevier Inc.

The Size Distribution Of Cluster Galaxies

NASA Astrophysics Data System (ADS)

Kuchner, U.; Ziegler, B.; Bamford, S.; Verdugo, M.; Haeussler, B.

2017-06-01

We establish a sample of 560 spectroscopically confirmed cluster members of MACS J1206.2- 0847 at z = 0.45 and utilize multi-wavelength and multi-component Sersic profile fitting to provide luminosities and sizes for the key structural components bulge and disk. While the difference between field and cluster galaxy properties are mostly due to a preference for cluster members to be early-type (quiescent, bulge-dominated), we see evidence for an outer disk fading and a sharp rise in the number of red disks with smaller effective radii at the tidally active cluster region around R200. Even though red disks are already virialized according to their velocity distribution, they are clearly not part of the old population found in the innermost region; they represent an important population of transitional objects in clusters.

Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks

NASA Astrophysics Data System (ADS)

Niknam, Taher; Kavousifard, Abdollah; Tabatabaei, Sajad; Aghaei, Jamshid

2011-10-01

In this paper a new multiobjective modified honey bee mating optimization (MHBMO) algorithm is presented to investigate the distribution feeder reconfiguration (DFR) problem considering renewable energy sources (RESs) (photovoltaics, fuel cell and wind energy) connected to the distribution network. The objective functions of the problem to be minimized are the electrical active power losses, the voltage deviations, the total electrical energy costs and the total emissions of RESs and substations. During the optimization process, the proposed algorithm finds a set of non-dominated (Pareto) optimal solutions which are stored in an external memory called repository. Since the objective functions investigated are not the same, a fuzzy clustering algorithm is utilized to handle the size of the repository in the specified limits. Moreover, a fuzzy-based decision maker is adopted to select the 'best' compromised solution among the non-dominated optimal solutions of multiobjective optimization problem. In order to see the feasibility and effectiveness of the proposed algorithm, two standard distribution test systems are used as case studies.