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Sample records for optimized molecular model

  1. Hybrid Metaheuristic Approach for Nonlocal Optimization of Molecular Systems.

    Science.gov (United States)

    Dresselhaus, Thomas; Yang, Jack; Kumbhar, Sadhana; Waller, Mark P

    2013-04-09

    Accurate modeling of molecular systems requires a good knowledge of the structure; therefore, conformation searching/optimization is a routine necessity in computational chemistry. Here we present a hybrid metaheuristic optimization (HMO) algorithm, which combines ant colony optimization (ACO) and particle swarm optimization (PSO) for the optimization of molecular systems. The HMO implementation meta-optimizes the parameters of the ACO algorithm on-the-fly by the coupled PSO algorithm. The ACO parameters were optimized on a set of small difluorinated polyenes where the parameters exhibited small variance as the size of the molecule increased. The HMO algorithm was validated by searching for the closed form of around 100 molecular balances. Compared to the gradient-based optimized molecular balance structures, the HMO algorithm was able to find low-energy conformations with a 87% success rate. Finally, the computational effort for generating low-energy conformation(s) for the phenylalanyl-glycyl-glycine tripeptide was approximately 60 CPU hours with the ACO algorithm, in comparison to 4 CPU years required for an exhaustive brute-force calculation.

  2. Optimized theory for simple and molecular fluids.

    Science.gov (United States)

    Marucho, M; Montgomery Pettitt, B

    2007-03-28

    An optimized closure approximation for both simple and molecular fluids is presented. A smooth interpolation between Perkus-Yevick and hypernetted chain closures is optimized by minimizing the free energy self-consistently with respect to the interpolation parameter(s). The molecular version is derived from a refinement of the method for simple fluids. In doing so, a method is proposed which appropriately couples an optimized closure with the variant of the diagrammatically proper integral equation recently introduced by this laboratory [K. M. Dyer et al., J. Chem. Phys. 123, 204512 (2005)]. The simplicity of the expressions involved in this proposed theory has allowed the authors to obtain an analytic expression for the approximate excess chemical potential. This is shown to be an efficient tool to estimate, from first principles, the numerical value of the interpolation parameters defining the aforementioned closure. As a preliminary test, representative models for simple fluids and homonuclear diatomic Lennard-Jones fluids were analyzed, obtaining site-site correlation functions in excellent agreement with simulation data.

  3. A New Optimization Model for Computer-Aided Molecular Design Problems

    DEFF Research Database (Denmark)

    Zhang, Lei; Cignitti, Stefano; Gani, Rafiqul

    Computer-Aided Molecular Design (CAMD) is a method to design molecules with desired properties. That is, through CAMD, it is possible to generate molecules that match a specified set of target properties. CAMD has attracted much attention in recent years due to its ability to design novel as well...... with structure information considered due to the increased size of the mathematical problem and number of alternatives. Thus, decomposition-based approach is proposed to solve the problem. In this approach, only first-order groups are considered in the first step to obtain the building block of the designed...... molecule, then the property model is refined with second-order groups based on the results of the first step. However, this may result in the possibility of an optimal solution being excluded. Samudra and Sahinidis [4] used property relaxation method in the first step to avoid this situation...

  4. Kernel optimization for short-range molecular dynamics

    Science.gov (United States)

    Hu, Changjun; Wang, Xianmeng; Li, Jianjiang; He, Xinfu; Li, Shigang; Feng, Yangde; Yang, Shaofeng; Bai, He

    2017-02-01

    To optimize short-range force computations in Molecular Dynamics (MD) simulations, multi-threading and SIMD optimizations are presented in this paper. With respect to multi-threading optimization, a Partition-and-Separate-Calculation (PSC) method is designed to avoid write conflicts caused by using Newton's third law. Serial bottlenecks are eliminated with no additional memory usage. The method is implemented by using the OpenMP model. Furthermore, the PSC method is employed on Intel Xeon Phi coprocessors in both native and offload models. We also evaluate the performance of the PSC method under different thread affinities on the MIC architecture. In the SIMD execution, we explain the performance influence in the PSC method, considering the "if-clause" of the cutoff radius check. The experiment results show that our PSC method is relatively more efficient compared to some traditional methods. In double precision, our 256-bit SIMD implementation is about 3 times faster than the scalar version.

  5. A new decomposition-based computer-aided molecular/mixture design methodology for the design of optimal solvents and solvent mixtures

    DEFF Research Database (Denmark)

    Karunanithi, A.T.; Achenie, L.E.K.; Gani, Rafiqul

    2005-01-01

    This paper presents a novel computer-aided molecular/mixture design (CAMD) methodology for the design of optimal solvents and solvent mixtures. The molecular/mixture design problem is formulated as a mixed integer nonlinear programming (MINLP) model in which a performance objective is to be optim......This paper presents a novel computer-aided molecular/mixture design (CAMD) methodology for the design of optimal solvents and solvent mixtures. The molecular/mixture design problem is formulated as a mixed integer nonlinear programming (MINLP) model in which a performance objective...... is to be optimized subject to structural, property, and process constraints. The general molecular/mixture design problem is divided into two parts. For optimal single-compound design, the first part is solved. For mixture design, the single-compound design is first carried out to identify candidates...... and then the second part is solved to determine the optimal mixture. The decomposition of the CAMD MINLP model into relatively easy to solve subproblems is essentially a partitioning of the constraints from the original set. This approach is illustrated through two case studies. The first case study involves...

  6. First principles molecular dynamics without self-consistent field optimization

    International Nuclear Information System (INIS)

    Souvatzis, Petros; Niklasson, Anders M. N.

    2014-01-01

    We present a first principles molecular dynamics approach that is based on time-reversible extended Lagrangian Born-Oppenheimer molecular dynamics [A. M. N. Niklasson, Phys. Rev. Lett. 100, 123004 (2008)] in the limit of vanishing self-consistent field optimization. The optimization-free dynamics keeps the computational cost to a minimum and typically provides molecular trajectories that closely follow the exact Born-Oppenheimer potential energy surface. Only one single diagonalization and Hamiltonian (or Fockian) construction are required in each integration time step. The proposed dynamics is derived for a general free-energy potential surface valid at finite electronic temperatures within hybrid density functional theory. Even in the event of irregular functional behavior that may cause a dynamical instability, the optimization-free limit represents a natural starting guess for force calculations that may require a more elaborate iterative electronic ground state optimization. Our optimization-free dynamics thus represents a flexible theoretical framework for a broad and general class of ab initio molecular dynamics simulations

  7. Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids

    Science.gov (United States)

    Rybinska, Anna; Sosnowska, Anita; Barycki, Maciej; Puzyn, Tomasz

    2016-02-01

    Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids' (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities ({{Q}}_{{EXT}}2 = 0.87). However, PM7-based model has comparable values of quality parameters ({{Q}}_{{EXT}}2 = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.

  8. Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

    Directory of Open Access Journals (Sweden)

    Renata De Paris

    2015-01-01

    Full Text Available Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

  9. FROM ATOMISTIC TO SYSTEMATIC COARSE-GRAINED MODELS FOR MOLECULAR SYSTEMS

    KAUST Repository

    Harmandaris, Vagelis; Kalligiannaki, Evangelia; Katsoulakis, Markos; Plechac, Petr

    2017-01-01

    The development of systematic (rigorous) coarse-grained mesoscopic models for complex molecular systems is an intense research area. Here we first give an overview of methods for obtaining optimal parametrized coarse-grained models, starting from

  10. Numerical computation of molecular integrals via optimized (vectorized) FORTRAN code

    International Nuclear Information System (INIS)

    Scott, T.C.; Grant, I.P.; Saunders, V.R.

    1997-01-01

    The calculation of molecular properties based on quantum mechanics is an area of fundamental research whose horizons have always been determined by the power of state-of-the-art computers. A computational bottleneck is the numerical calculation of the required molecular integrals to sufficient precision. Herein, we present a method for the rapid numerical evaluation of molecular integrals using optimized FORTRAN code generated by Maple. The method is based on the exploitation of common intermediates and the optimization can be adjusted to both serial and vectorized computations. (orig.)

  11. FROM ATOMISTIC TO SYSTEMATIC COARSE-GRAINED MODELS FOR MOLECULAR SYSTEMS

    KAUST Repository

    Harmandaris, Vagelis

    2017-10-03

    The development of systematic (rigorous) coarse-grained mesoscopic models for complex molecular systems is an intense research area. Here we first give an overview of methods for obtaining optimal parametrized coarse-grained models, starting from detailed atomistic representation for high dimensional molecular systems. Different methods are described based on (a) structural properties (inverse Boltzmann approaches), (b) forces (force matching), and (c) path-space information (relative entropy). Next, we present a detailed investigation concerning the application of these methods in systems under equilibrium and non-equilibrium conditions. Finally, we present results from the application of these methods to model molecular systems.

  12. Fletcher-Reeves based Particle Swarm Optimization for prediction of molecular structure.

    Science.gov (United States)

    Agrawal, Shikha; Silakari, Sanjay

    2014-04-01

    The determination of the most stable conformers of a molecule can be formulated as a global optimization problem. Knowing the stable conformers of a molecule is important because it allows us to understand its properties and behavior based on its structure. The most stable conformation is that involving the global minimum of potential energy. The problem of finding this global minimum is highly complex, and is computationally difficult because of the number of local minima, which grows exponentially with molecular size. In this paper, we propose a hybrid approach combining Particle Swarm Optimization (PSO) and the Fletcher-Reeves algorithm to minimize the potential energy function. The proposed hybrid algorithm is applied to a simplified molecular potential energy function in problems with up to 100 degrees of freedom and also to a realistic potential energy function modeling a pseudoethane molecule. The computational results for both the cases show that the proposed method performs significantly better than the other algorithms. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

    Science.gov (United States)

    Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

    2015-10-06

    In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

  14. An optimized intermolecular force field for hydrogen-bonded organic molecular crystals using atomic multipole electrostatics

    International Nuclear Information System (INIS)

    Pyzer-Knapp, Edward O.; Thompson, Hugh P. G.; Day, Graeme M.

    2016-01-01

    An empirically parameterized intermolecular force field is developed for crystal structure modelling and prediction. The model is optimized for use with an atomic multipole description of electrostatic interactions. We present a re-parameterization of a popular intermolecular force field for describing intermolecular interactions in the organic solid state. Specifically we optimize the performance of the exp-6 force field when used in conjunction with atomic multipole electrostatics. We also parameterize force fields that are optimized for use with multipoles derived from polarized molecular electron densities, to account for induction effects in molecular crystals. Parameterization is performed against a set of 186 experimentally determined, low-temperature crystal structures and 53 measured sublimation enthalpies of hydrogen-bonding organic molecules. The resulting force fields are tested on a validation set of 129 crystal structures and show improved reproduction of the structures and lattice energies of a range of organic molecular crystals compared with the original force field with atomic partial charge electrostatics. Unit-cell dimensions of the validation set are typically reproduced to within 3% with the re-parameterized force fields. Lattice energies, which were all included during parameterization, are systematically underestimated when compared with measured sublimation enthalpies, with mean absolute errors of between 7.4 and 9.0%

  15. Molecular models of zinc phthalocyanines: semi-empirical molecular orbital computations and physicochemical properties studied by molecular mechanics simulations

    International Nuclear Information System (INIS)

    Gantchev, Tsvetan G.; van Lier, Johan E.; Hunting, Darel J.

    2005-01-01

    To build 3D-molecular models of Zinc-phthalocyanines (ZnPc) and to study their diverse chemical and photosensitization properties, we performed quantum mechanical molecular orbital (MO) semi-empirical (AM1) computations of the ground, excited singlet and triplet states as well as free radical (ionic) species. RHF and UHF (open shell) geometry optimizations led to near-perfect symmetrical ZnPc. Predicted ionization potentials (IP), electron affinities (EA) and lowest electronic transitions of ZnPc are in good agreement with the published experimental and theoretical data. The computation-derived D 4h /D 2h -symmetry 3D-structures of ground and excited states and free radicals of ZnPc, together with the frontier orbital energies and Mulliken electron population analysis enabled us to build robust molecular models. These models were used to predict important chemical-reactivity entities such as global electronegativity (χ), hardness (η) and local softness based on Fukui-functions analysis. Examples of molecular mechanics (MM) applications of the 3D-molecular models are presented as approaches to evaluate solvation free energy (ΔG 0 ) solv and to estimate ground- and excited- state oxidation/reduction potentials as well as intermolecular interactions and stability of ground and excited state dimers (exciplexes) and radical ion-pairs

  16. Optimal time points sampling in pathway modelling.

    Science.gov (United States)

    Hu, Shiyan

    2004-01-01

    Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

  17. Symbol interval optimization for molecular communication with drift.

    Science.gov (United States)

    Kim, Na-Rae; Eckford, Andrew W; Chae, Chan-Byoung

    2014-09-01

    In this paper, we propose a symbol interval optimization algorithm in molecular communication with drift. Proper symbol intervals are important in practical communication systems since information needs to be sent as fast as possible with low error rates. There is a trade-off, however, between symbol intervals and inter-symbol interference (ISI) from Brownian motion. Thus, we find proper symbol interval values considering the ISI inside two kinds of blood vessels, and also suggest no ISI system for strong drift models. Finally, an isomer-based molecule shift keying (IMoSK) is applied to calculate achievable data transmission rates (achievable rates, hereafter). Normalized achievable rates are also obtained and compared in one-symbol ISI and no ISI systems.

  18. Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

    Science.gov (United States)

    Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

    2017-12-26

    Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

  19. Optimization modeling with spreadsheets

    CERN Document Server

    Baker, Kenneth R

    2015-01-01

    An accessible introduction to optimization analysis using spreadsheets Updated and revised, Optimization Modeling with Spreadsheets, Third Edition emphasizes model building skills in optimization analysis. By emphasizing both spreadsheet modeling and optimization tools in the freely available Microsoft® Office Excel® Solver, the book illustrates how to find solutions to real-world optimization problems without needing additional specialized software. The Third Edition includes many practical applications of optimization models as well as a systematic framework that il

  20. System geometry optimization for molecular breast tomosynthesis with focusing multi-pinhole collimators

    Science.gov (United States)

    van Roosmalen, Jarno; Beekman, Freek J.; Goorden, Marlies C.

    2018-01-01

    Imaging of 99mTc-labelled tracers is gaining popularity for detecting breast tumours. Recently, we proposed a novel design for molecular breast tomosynthesis (MBT) based on two sliding focusing multi-pinhole collimators that scan a modestly compressed breast. Simulation studies indicate that MBT has the potential to improve the tumour-to-background contrast-to-noise ratio significantly over state-of-the-art planar molecular breast imaging. The aim of the present paper is to optimize the collimator-detector geometry of MBT. Using analytical models, we first optimized sensitivity at different fixed system resolutions (ranging from 5 to 12 mm) by tuning the pinhole diameters and the distance between breast and detector for a whole series of automatically generated multi-pinhole designs. We evaluated both MBT with a conventional continuous crystal detector with 3.2 mm intrinsic resolution and with a pixelated detector with 1.6 mm pixels. Subsequently, full system simulations of a breast phantom containing several lesions were performed for the optimized geometry at each system resolution for both types of detector. From these simulations, we found that tumour-to-background contrast-to-noise ratio was highest for systems in the 7 mm-10 mm system resolution range over which it hardly varied. No significant differences between the two detector types were found.

  1. Discovery of novel inhibitors of Mycobacterium tuberculosis MurG: homology modelling, structure based pharmacophore, molecular docking, and molecular dynamics simulations.

    Science.gov (United States)

    Saxena, Shalini; Abdullah, Maaged; Sriram, Dharmarajan; Guruprasad, Lalitha

    2017-10-17

    MurG (Rv2153c) is a key player in the biosynthesis of the peptidoglycan layer in Mycobacterium tuberculosis (Mtb). This work is an attempt to highlight the structural and functional relationship of Mtb MurG, the three-dimensional (3D) structure of protein was constructed by homology modelling using Discovery Studio 3.5 software. The quality and consistency of generated model was assessed by PROCHECK, ProSA and ERRAT. Later, the model was optimized by molecular dynamics (MD) simulations and the optimized model complex with substrate Uridine-diphosphate-N-acetylglucosamine (UD1) facilitated us to employ structure-based virtual screening approach to obtain new hits from Asinex database using energy-optimized pharmacophore modelling (e-pharmacophore). The pharmacophore model was validated using enrichment calculations, and finally, validated model was employed for high-throughput virtual screening and molecular docking to identify novel Mtb MurG inhibitors. This study led to the identification of 10 potential compounds with good fitness, docking score, which make important interactions with the protein active site. The 25 ns MD simulations of three potential lead compounds with protein confirmed that the structure was stable and make several non-bonding interactions with amino acids, such as Leu290, Met310 and Asn167. Hence, we concluded that the identified compounds may act as new leads for the design of Mtb MurG inhibitors.

  2. Media optimization for elevated molecular weight and mass production of pigment-free pullulan.

    Science.gov (United States)

    Yu, Xiaoliu; Wang, Yulei; Wei, Gongyuan; Dong, Yingying

    2012-07-01

    In this study, an Aureobasidium pullulans SZU 1001 mutant, deficient in pigment production, was screened by complex UV and γ-ray mutagenesis. Medium composition optimization for increased pullulan molecular weight and production was conducted using this mutant. Six nutrients: yeast extract, (NH4)2SO4, K2HPO4, NaCl, MgSO4·7H2O and CaCl2 were detected within pullulan production in flasks. It is shown that NaCl and K2HPO4 have significant influences on molecular weight of pullulan, while yeast extract and (NH4)2SO4 significantly affect pullulan yield. To achieve a higher molecular weight and more efficient pullulan production, a response surface methodology approach was introduced to predict an optimal nutrient combination. A molecular weight of 5.74 × 10(6) and pullulan yield on glucose of 51.30% were obtained under batch pullulan fermentation with the optimized media, which increased molecular weight and pullulan production by 97.25% and 11.04%, respectively compared with the control media. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Exact and Optimal Quantum Mechanics/Molecular Mechanics Boundaries.

    Science.gov (United States)

    Sun, Qiming; Chan, Garnet Kin-Lic

    2014-09-09

    Motivated by recent work in density matrix embedding theory, we define exact link orbitals that capture all quantum mechanical (QM) effects across arbitrary quantum mechanics/molecular mechanics (QM/MM) boundaries. Exact link orbitals are rigorously defined from the full QM solution, and their number is equal to the number of orbitals in the primary QM region. Truncating the exact set yields a smaller set of link orbitals optimal with respect to reproducing the primary region density matrix. We use the optimal link orbitals to obtain insight into the limits of QM/MM boundary treatments. We further analyze the popular general hybrid orbital (GHO) QM/MM boundary across a test suite of molecules. We find that GHOs are often good proxies for the most important optimal link orbital, although there is little detailed correlation between the detailed GHO composition and optimal link orbital valence weights. The optimal theory shows that anions and cations cannot be described by a single link orbital. However, expanding to include the second most important optimal link orbital in the boundary recovers an accurate description. The second optimal link orbital takes the chemically intuitive form of a donor or acceptor orbital for charge redistribution, suggesting that optimal link orbitals can be used as interpretative tools for electron transfer. We further find that two optimal link orbitals are also sufficient for boundaries that cut across double bonds. Finally, we suggest how to construct "approximately" optimal link orbitals for practical QM/MM calculations.

  4. In silico modelling and molecular dynamics simulation studies of thiazolidine based PTP1B inhibitors.

    Science.gov (United States)

    Mahapatra, Manoj Kumar; Bera, Krishnendu; Singh, Durg Vijay; Kumar, Rajnish; Kumar, Manoj

    2018-04-01

    Protein tyrosine phosphatase 1B (PTP1B) has been identified as a negative regulator of insulin and leptin signalling pathway; hence, it can be considered as a new therapeutic target of intervention for the treatment of type 2 diabetes. Inhibition of this molecular target takes care of both diabetes and obesity, i.e. diabestiy. In order to get more information on identification and optimization of lead, pharmacophore modelling, atom-based 3D QSAR, docking and molecular dynamics studies were carried out on a set of ligands containing thiazolidine scaffold. A six-point pharmacophore model consisting of three hydrogen bond acceptor (A), one negative ionic (N) and two aromatic rings (R) with discrete geometries as pharmacophoric features were developed for a predictive 3D QSAR model. The probable binding conformation of the ligands within the active site was studied through molecular docking. The molecular interactions and the structural features responsible for PTP1B inhibition and selectivity were further supplemented by molecular dynamics simulation study for a time scale of 30 ns. The present investigation has identified some of the indispensible structural features of thiazolidine analogues which can further be explored to optimize PTP1B inhibitors.

  5. Optimal separable bases and molecular collisions

    International Nuclear Information System (INIS)

    Poirier, L.W.

    1997-12-01

    A new methodology is proposed for the efficient determination of Green's functions and eigenstates for quantum systems of two or more dimensions. For a given Hamiltonian, the best possible separable approximation is obtained from the set of all Hilbert space operators. It is shown that this determination itself, as well as the solution of the resultant approximation, are problems of reduced dimensionality for most systems of physical interest. Moreover, the approximate eigenstates constitute the optimal separable basis, in the sense of self-consistent field theory. These distorted waves give rise to a Born series with optimized convergence properties. Analytical results are presented for an application of the method to the two-dimensional shifted harmonic oscillator system. The primary interest however, is quantum reactive scattering in molecular systems. For numerical calculations, the use of distorted waves corresponds to numerical preconditioning. The new methodology therefore gives rise to an optimized preconditioning scheme for the efficient calculation of reactive and inelastic scattering amplitudes, especially at intermediate energies. This scheme is particularly suited to discrete variable representations (DVR's) and iterative sparse matrix methods commonly employed in such calculations. State to state and cumulative reactive scattering results obtained via the optimized preconditioner are presented for the two-dimensional collinear H + H 2 → H 2 + H system. Computational time and memory requirements for this system are drastically reduced in comparison with other methods, and results are obtained for previously prohibitive energy regimes

  6. Optimization and benchmarking of a perturbative Metropolis Monte Carlo quantum mechanics/molecular mechanics program.

    Science.gov (United States)

    Feldt, Jonas; Miranda, Sebastião; Pratas, Frederico; Roma, Nuno; Tomás, Pedro; Mata, Ricardo A

    2017-12-28

    In this work, we present an optimized perturbative quantum mechanics/molecular mechanics (QM/MM) method for use in Metropolis Monte Carlo simulations. The model adopted is particularly tailored for the simulation of molecular systems in solution but can be readily extended to other applications, such as catalysis in enzymatic environments. The electrostatic coupling between the QM and MM systems is simplified by applying perturbation theory to estimate the energy changes caused by a movement in the MM system. This approximation, together with the effective use of GPU acceleration, leads to a negligible added computational cost for the sampling of the environment. Benchmark calculations are carried out to evaluate the impact of the approximations applied and the overall computational performance.

  7. Molecular Modeling

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 9; Issue 5. Molecular Modeling: A Powerful Tool for Drug Design and Molecular Docking. Rama Rao Nadendla. General Article Volume 9 Issue 5 May 2004 pp 51-60. Fulltext. Click here to view fulltext PDF. Permanent link:

  8. Design of optimal laser pulses to control molecular rovibrational ...

    Indian Academy of Sciences (India)

    Optimal control theory in combination with time-dependent quantum dynamics is employed to design laser ... area of quantum computing,22 use of molecular states of various systems ..... frequency) and ωv=0, j=1→v=1, j=0, along with small con- tributions from ... discussed here for three rovibrational excitation pro- cesses.

  9. Optimal control theory for quantum-classical systems: Ehrenfest molecular dynamics based on time-dependent density-functional theory

    International Nuclear Information System (INIS)

    Castro, A; Gross, E K U

    2014-01-01

    We derive the fundamental equations of an optimal control theory for systems containing both quantum electrons and classical ions. The system is modeled with Ehrenfest dynamics, a non-adiabatic variant of molecular dynamics. The general formulation, that needs the fully correlated many-electron wavefunction, can be simplified by making use of time-dependent density-functional theory. In this case, the optimal control equations require some modifications that we will provide. The abstract general formulation is complemented with the simple example of the H 2 + molecule in the presence of a laser field. (paper)

  10. Assessment of Molecular Modeling & Simulation

    Energy Technology Data Exchange (ETDEWEB)

    None

    2002-01-03

    This report reviews the development and applications of molecular and materials modeling in Europe and Japan in comparison to those in the United States. Topics covered include computational quantum chemistry, molecular simulations by molecular dynamics and Monte Carlo methods, mesoscale modeling of material domains, molecular-structure/macroscale property correlations like QSARs and QSPRs, and related information technologies like informatics and special-purpose molecular-modeling computers. The panel's findings include the following: The United States leads this field in many scientific areas. However, Canada has particular strengths in DFT methods and homogeneous catalysis; Europe in heterogeneous catalysis, mesoscale, and materials modeling; and Japan in materials modeling and special-purpose computing. Major government-industry initiatives are underway in Europe and Japan, notably in multi-scale materials modeling and in development of chemistry-capable ab-initio molecular dynamics codes.

  11. Optimized acoustic biochip integrated with microfluidics for biomarkers detection in molecular diagnostics.

    Science.gov (United States)

    Papadakis, G; Friedt, J M; Eck, M; Rabus, D; Jobst, G; Gizeli, E

    2017-09-01

    The development of integrated platforms incorporating an acoustic device as the detection element requires addressing simultaneously several challenges of technological and scientific nature. The present work was focused on the design of a microfluidic module, which, combined with a dual or array type Love wave acoustic chip could be applied to biomedical applications and molecular diagnostics. Based on a systematic study we optimized the mechanics of the flow cell attachment and the sealing material so that fluidic interfacing/encapsulation would impose minimal losses to the acoustic wave. We have also investigated combinations of operating frequencies with waveguide materials and thicknesses for maximum sensitivity during the detection of protein and DNA biomarkers. Within our investigations neutravidin was used as a model protein biomarker and unpurified PCR amplified Salmonella DNA as the model genetic target. Our results clearly indicate the need for experimental verification of the optimum engineering and analytical parameters, in order to develop commercially viable systems for integrated analysis. The good reproducibility of the signal together with the ability of the array biochip to detect multiple samples hold promise for the future use of the integrated system in a Lab-on-a-Chip platform for application to molecular diagnostics.

  12. Parallel low-memory quasi-Newton optimization algorithm for molecular structure

    Czech Academy of Sciences Publication Activity Database

    Klemsa, Jakub; Řezáč, Jan

    2013-01-01

    Roč. 584, Oct 1 (2013), s. 10-13 ISSN 0009-2614 R&D Projects: GA ČR GP13-01214P Institutional support: RVO:61388963 Keywords : geometry optimization * parallelization * molecular graph Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 1.991, year: 2013

  13. Molecular modeling

    Directory of Open Access Journals (Sweden)

    Aarti Sharma

    2009-01-01

    Full Text Available The use of computational chemistry in the development of novel pharmaceuticals is becoming an increasingly important tool. In the past, drugs were simply screened for effectiveness. The recent advances in computing power and the exponential growth of the knowledge of protein structures have made it possible for organic compounds to be tailored to decrease the harmful side effects and increase the potency. This article provides a detailed description of the techniques employed in molecular modeling. Molecular modeling is a rapidly developing discipline, and has been supported by the dramatic improvements in computer hardware and software in recent years.

  14. Molecular Modelling

    Directory of Open Access Journals (Sweden)

    Aarti Sharma

    2009-12-01

    Full Text Available

    The use of computational chemistry in the development of novel pharmaceuticals is becoming an increasingly important
    tool. In the past, drugs were simply screened for effectiveness. The recent advances in computing power and
    the exponential growth of the knowledge of protein structures have made it possible for organic compounds to tailored to
    decrease harmful side effects and increase the potency. This article provides a detailed description of the techniques
    employed in molecular modeling. Molecular modelling is a rapidly developing discipline, and has been supported from
    the dramatic improvements in computer hardware and software in recent years.

  15. Application of Numerical Optimization Methods to Perform Molecular Docking on Graphics Processing Units

    Directory of Open Access Journals (Sweden)

    M. A. Farkov

    2014-01-01

    Full Text Available An analysis of numerical optimization methods for solving a problem of molecular docking has been performed. Some additional requirements for optimization methods according to GPU architecture features were specified. A promising method for implementation on GPU was selected. Its implementation was described and performance and accuracy tests were performed.

  16. Glioblastoma, a brief review of history, molecular genetics, animal models and novel therapeutic strategies.

    Science.gov (United States)

    Agnihotri, Sameer; Burrell, Kelly E; Wolf, Amparo; Jalali, Sharzhad; Hawkins, Cynthia; Rutka, James T; Zadeh, Gelareh

    2013-02-01

    Glioblastoma (GBM) is the most common and lethal primary brain tumor. Over the past few years tremendous genomic and proteomic characterization along with robust animal models of GBM have provided invaluable data that show that "GBM", although histologically indistinguishable from one another, are comprised of molecularly heterogenous diseases. In addition, robust pre-clinical models and a better understanding of the core pathways disrupted in GBM are providing a renewed optimism for novel strategies targeting these devastating tumors. Here, we summarize a brief history of the disease, our current molecular knowledge, lessons from animal models and emerging concepts of angiogenesis, invasion, and metabolism in GBM that may lend themselves to therapeutic targeting.

  17. Risk modelling in portfolio optimization

    Science.gov (United States)

    Lam, W. H.; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi

    2013-09-01

    Risk management is very important in portfolio optimization. The mean-variance model has been used in portfolio optimization to minimize the investment risk. The objective of the mean-variance model is to minimize the portfolio risk and achieve the target rate of return. Variance is used as risk measure in the mean-variance model. The purpose of this study is to compare the portfolio composition as well as performance between the optimal portfolio of mean-variance model and equally weighted portfolio. Equally weighted portfolio means the proportions that are invested in each asset are equal. The results show that the portfolio composition of the mean-variance optimal portfolio and equally weighted portfolio are different. Besides that, the mean-variance optimal portfolio gives better performance because it gives higher performance ratio than the equally weighted portfolio.

  18. Magnetohydrodynamic Models of Molecular Tornadoes

    Science.gov (United States)

    Au, Kelvin; Fiege, Jason D.

    2017-07-01

    Recent observations near the Galactic Center (GC) have found several molecular filaments displaying striking helically wound morphology that are collectively known as molecular tornadoes. We investigate the equilibrium structure of these molecular tornadoes by formulating a magnetohydrodynamic model of a rotating, helically magnetized filament. A special analytical solution is derived where centrifugal forces balance exactly with toroidal magnetic stress. From the physics of torsional Alfvén waves we derive a constraint that links the toroidal flux-to-mass ratio and the pitch angle of the helical field to the rotation laws, which we find to be an important component in describing the molecular tornado structure. The models are compared to the Ostriker solution for isothermal, nonmagnetic, nonrotating filaments. We find that neither the analytic model nor the Alfvén wave model suffer from the unphysical density inversions noted by other authors. A Monte Carlo exploration of our parameter space is constrained by observational measurements of the Pigtail Molecular Cloud, the Double Helix Nebula, and the GC Molecular Tornado. Observable properties such as the velocity dispersion, filament radius, linear mass, and surface pressure can be used to derive three dimensionless constraints for our dimensionless models of these three objects. A virial analysis of these constrained models is studied for these three molecular tornadoes. We find that self-gravity is relatively unimportant, whereas magnetic fields and external pressure play a dominant role in the confinement and equilibrium radial structure of these objects.

  19. Magnetohydrodynamic Models of Molecular Tornadoes

    Energy Technology Data Exchange (ETDEWEB)

    Au, Kelvin; Fiege, Jason D., E-mail: fiege@physics.umanitoba.ca [Department of Physics and Astronomy, University of Manitoba Winnipeg, MB R3T 2N2 (Canada)

    2017-07-10

    Recent observations near the Galactic Center (GC) have found several molecular filaments displaying striking helically wound morphology that are collectively known as molecular tornadoes. We investigate the equilibrium structure of these molecular tornadoes by formulating a magnetohydrodynamic model of a rotating, helically magnetized filament. A special analytical solution is derived where centrifugal forces balance exactly with toroidal magnetic stress. From the physics of torsional Alfvén waves we derive a constraint that links the toroidal flux-to-mass ratio and the pitch angle of the helical field to the rotation laws, which we find to be an important component in describing the molecular tornado structure. The models are compared to the Ostriker solution for isothermal, nonmagnetic, nonrotating filaments. We find that neither the analytic model nor the Alfvén wave model suffer from the unphysical density inversions noted by other authors. A Monte Carlo exploration of our parameter space is constrained by observational measurements of the Pigtail Molecular Cloud, the Double Helix Nebula, and the GC Molecular Tornado. Observable properties such as the velocity dispersion, filament radius, linear mass, and surface pressure can be used to derive three dimensionless constraints for our dimensionless models of these three objects. A virial analysis of these constrained models is studied for these three molecular tornadoes. We find that self-gravity is relatively unimportant, whereas magnetic fields and external pressure play a dominant role in the confinement and equilibrium radial structure of these objects.

  20. Structure activity relationships of quinoxalin-2-one derivatives as platelet-derived growth factor-beta receptor (PDGFbeta R) inhibitors, derived from molecular modeling.

    Science.gov (United States)

    Mori, Yoshikazu; Hirokawa, Takatsugu; Aoki, Katsuyuki; Satomi, Hisanori; Takeda, Shuichi; Aburada, Masaki; Miyamoto, Ken-ichi

    2008-05-01

    We previously reported a quinoxalin-2-one compound (Compound 1) that had inhibitory activity equivalent to existing platelet-derived growth factor-beta receptor (PDGFbeta R) inhibitors. Lead optimization of Compound 1 to increase its activity and selectivity, using structural information regarding PDGFbeta R-ligand interactions, is urgently needed. Here we present models of the PDGFbeta R kinase domain complexed with quinoxalin-2-one derivatives. The models were constructed using comparative modeling, molecular dynamics (MD) and ligand docking. In particular, conformations derived from MD, and ligand binding site information presented by alpha-spheres in the pre-docking processing, allowed us to identify optimal protein structures for docking of target ligands. By carrying out molecular modeling and MD of PDGFbeta R in its inactive state, we obtained two structural models having good Compound 1 binding potentials. In order to distinguish the optimal candidate, we evaluated the structural activity relationships (SAR) between the ligand-binding free energies and inhibitory activity values (IC50 values) for available quinoxalin-2-one derivatives. Consequently, a final model with a high SAR was identified. This model included a molecular interaction between the hydrophobic pocket behind the ATP binding site and the substitution region of the quinoxalin-2-one derivatives. These findings should prove useful in lead optimization of quinoxalin-2-one derivatives as PDGFb R inhibitors.

  1. Application of Molecular Modeling to Urokinase Inhibitors Development

    Directory of Open Access Journals (Sweden)

    V. B. Sulimov

    2014-01-01

    Full Text Available Urokinase-type plasminogen activator (uPA plays an important role in the regulation of diverse physiologic and pathologic processes. Experimental research has shown that elevated uPA expression is associated with cancer progression, metastasis, and shortened survival in patients, whereas suppression of proteolytic activity of uPA leads to evident decrease of metastasis. Therefore, uPA has been considered as a promising molecular target for development of anticancer drugs. The present study sets out to develop the new selective uPA inhibitors using computer-aided structural based drug design methods. Investigation involves the following stages: computer modeling of the protein active site, development and validation of computer molecular modeling methods: docking (SOL program, postprocessing (DISCORE program, direct generalized docking (FLM program, and the application of the quantum chemical calculations (MOPAC package, search of uPA inhibitors among molecules from databases of ready-made compounds to find new uPA inhibitors, and design of new chemical structures and their optimization and experimental examination. On the basis of known uPA inhibitors and modeling results, 18 new compounds have been designed, calculated using programs mentioned above, synthesized, and tested in vitro. Eight of them display inhibitory activity and two of them display activity about 10 μM.

  2. Subthreshold SPICE Model Optimization

    Science.gov (United States)

    Lum, Gregory; Au, Henry; Neff, Joseph; Bozeman, Eric; Kamin, Nick; Shimabukuro, Randy

    2011-04-01

    The first step in integrated circuit design is the simulation of said design in software to verify proper functionally and design requirements. Properties of the process are provided by fabrication foundries in the form of SPICE models. These SPICE models contain the electrical data and physical properties of the basic circuit elements. A limitation of these models is that the data collected by the foundry only accurately model the saturation region. This is fine for most users, but when operating devices in the subthreshold region they are inadequate for accurate simulation results. This is why optimizing the current SPICE models to characterize the subthreshold region is so important. In order to accurately simulate this region of operation, MOSFETs of varying widths and lengths are fabricated and the electrical test data is collected. From the data collected the parameters of the model files are optimized through parameter extraction rather than curve fitting. With the completed optimized models the circuit designer is able to simulate circuit designs for the sub threshold region accurately.

  3. Multiscale modeling of complex molecular structure and dynamics with MBN Explorer

    CERN Document Server

    Solov’yov, Ilia A; Solov’yov, Andrey V

    2017-01-01

    This book introduces readers to MesoBioNano (MBN) Explorer – a multi-purpose software package designed to model molecular systems at various levels of size and complexity. In addition, it presents a specially designed multi-task toolkit and interface – the MBN Studio – which enables the set-up of input files, controls the simulations, and supports the subsequent visualization and analysis of the results obtained. The book subsequently provides a systematic description of the capabilities of this universal and powerful software package within the framework of computational molecular science, and guides readers through its applications in numerous areas of research in bio- and chemical physics and material science – ranging from the nano- to the meso-scale. MBN Explorer is particularly suited to computing the system’s energy, to optimizing molecular structure, and to exploring the various facets of molecular and random walk dynamics. The package allows the use of a broad variety of interatomic potenti...

  4. De Novo generation of molecular structures using optimization to select graphs on a given lattice

    DEFF Research Database (Denmark)

    Bywater, R.P.; Poulsen, Thomas Agersten; Røgen, Peter

    2004-01-01

    A recurrent problem in organic chemistry is the generation of new molecular structures that conform to some predetermined set of structural constraints that are imposed in an endeavor to build certain required properties into the newly generated structure. An example of this is the pharmacophore...... model, used in medicinal chemistry to guide de novo design or selection of suitable structures from compound databases. We propose here a method that efficiently links up a selected number of required atom positions while at the same time directing the emergent molecular skeleton to avoid forbidden...... positions. The linkage process takes place on a lattice whose unit step length and overall geometry is designed to match typical architectures of organic molecules. We use an optimization method to select from the many different graphs possible. The approach is demonstrated in an example where crystal...

  5. Prediction of Sliding Friction Coefficient Based on a Novel Hybrid Molecular-Mechanical Model.

    Science.gov (United States)

    Zhang, Xiaogang; Zhang, Yali; Wang, Jianmei; Sheng, Chenxing; Li, Zhixiong

    2018-08-01

    Sliding friction is a complex phenomenon which arises from the mechanical and molecular interactions of asperities when examined in a microscale. To reveal and further understand the effects of micro scaled mechanical and molecular components of friction coefficient on overall frictional behavior, a hybrid molecular-mechanical model is developed to investigate the effects of main factors, including different loads and surface roughness values, on the sliding friction coefficient in a boundary lubrication condition. Numerical modelling was conducted using a deterministic contact model and based on the molecular-mechanical theory of friction. In the contact model, with given external loads and surface topographies, the pressure distribution, real contact area, and elastic/plastic deformation of each single asperity contact were calculated. Then asperity friction coefficient was predicted by the sum of mechanical and molecular components of friction coefficient. The mechanical component was mainly determined by the contact width and elastic/plastic deformation, and the molecular component was estimated as a function of the contact area and interfacial shear stress. Numerical results were compared with experimental results and a good agreement was obtained. The model was then used to predict friction coefficients in different operating and surface conditions. Numerical results explain why applied load has a minimum effect on the friction coefficients. They also provide insight into the effect of surface roughness on the mechanical and molecular components of friction coefficients. It is revealed that the mechanical component dominates the friction coefficient when the surface roughness is large (Rq > 0.2 μm), while the friction coefficient is mainly determined by the molecular component when the surface is relatively smooth (Rq < 0.2 μm). Furthermore, optimal roughness values for minimizing the friction coefficient are recommended.

  6. On Atomistic Models for Molecular Oxygen

    DEFF Research Database (Denmark)

    Javanainen, Matti; Vattulainen, Ilpo; Monticelli, Luca

    2017-01-01

    Molecular oxygen (O2) is key to all life on earth, as it is constantly cycled via photosynthesis and cellular respiration. Substantial scientific effort has been devoted to understanding every part of this cycle. Classical molecular dynamics (MD) simulations have been used to study some of the key...... processes involved in cellular respiration: O2 permeation through alveolar monolayers and cellular membranes, its binding to hemoglobin during transport in the bloodstream, as well as its transport along optimal pathways toward its reduction sites in proteins. Moreover, MD simulations can help interpret...

  7. Statistical modeling/optimization and process intensification of microwave-assisted acidified oil esterification

    International Nuclear Information System (INIS)

    Ma, Lingling; Lv, Enmin; Du, Lixiong; Lu, Jie; Ding, Jincheng

    2016-01-01

    Highlights: • Microwave irradiation was employed for the esterification of acidified oil. • Optimization and modeling of the process was performed by RSM and ANN. • Both models have reliable prediction abilities but the ANN was superior over the RSM. • Membrane vapor permeation and in-situ dehydration were used to shift the equilibrium. • Two dehydration approaches improved the FFAs conversion rate by 20.0% approximately. - Abstract: The esterification of acidified oil with ethanol under microwave radiation was modeled and optimized using response surface methodology (RSM) and artificial neural network (ANN). The impacts of mass ratio of ethanol to acidified oil, catalyst loading, microwave power and reaction time are evaluated by Box-Behnken design (BBD) of RSM and multi-layer perceptron (MLP) of ANN. RSM combined with BBD shows the optimal conditions as catalyst loading of 5.85 g, mass ratio of ethanol to acidified oil of 0.35 (20.0 g acidified oil), microwave power of 328 W and reaction time of 98.0 min with the free fatty acids (FFAs) conversion of 78.57%. Both of the models are fitted well with the experimental data, however, ANN exhibits better prediction accuracy than RSM based on the statistical analyses. Furthermore, membrane vapor permeation and in-situ molecular sieve dehydration were investigated to enhance the esterification under the optimized conditions.

  8. Optimization and transferability of non-electrostatic repulsion in the polarizable density embedding model

    DEFF Research Database (Denmark)

    Hrsak, Dalibor; Olsen, Jógvan Magnus Haugaard; Kongsted, Jacob

    2017-01-01

    Embedding techniques in combination with response theory represent a successful approach to calculate molecular properties and excited states in large molecular systems such as solutions and proteins. Recently, the polarizable embedding model has been extended by introducing explicit electronic...... densities of the molecules in the nearest environment, resulting in the polarizable density embedding (PDE) model. This improvement provides a better description of the intermolecular interactions at short distances. However, the electronic densities of the environment molecules are calculated in isolation...... interaction energies calculated on the basis of full quantum-mechanical calculations. The obtained optimal factors are used in PDE calculations of various ground- and excited-state properties of molecules embedded in solvents described as polarizable environments. © 2017 Wiley Periodicals, Inc....

  9. Fast optimization of statistical potentials for structurally constrained phylogenetic models

    Directory of Open Access Journals (Sweden)

    Rodrigue Nicolas

    2009-09-01

    Full Text Available Abstract Background Statistical approaches for protein design are relevant in the field of molecular evolutionary studies. In recent years, new, so-called structurally constrained (SC models of protein-coding sequence evolution have been proposed, which use statistical potentials to assess sequence-structure compatibility. In a previous work, we defined a statistical framework for optimizing knowledge-based potentials especially suited to SC models. Our method used the maximum likelihood principle and provided what we call the joint potentials. However, the method required numerical estimations by the use of computationally heavy Markov Chain Monte Carlo sampling algorithms. Results Here, we develop an alternative optimization procedure, based on a leave-one-out argument coupled to fast gradient descent algorithms. We assess that the leave-one-out potential yields very similar results to the joint approach developed previously, both in terms of the resulting potential parameters, and by Bayes factor evaluation in a phylogenetic context. On the other hand, the leave-one-out approach results in a considerable computational benefit (up to a 1,000 fold decrease in computational time for the optimization procedure. Conclusion Due to its computational speed, the optimization method we propose offers an attractive alternative for the design and empirical evaluation of alternative forms of potentials, using large data sets and high-dimensional parameterizations.

  10. Molecular and multiscale modeling: review on the theories and applications in chemical engineering

    International Nuclear Information System (INIS)

    Morales M, Giovanni; Martinez R, Ramiro

    2010-01-01

    We call molecular modeling to the application of suitable laws in the analysis of phenomena occurred at scales less than those accounted for by the macroscopic world. Such different scales (including micro-, meso- and macro scales), can be linked and integrated in order to improve understanding and predictions of complex physical chemistry phenomena, thus originating a global or multi scale analysis. A considerable amount of chemical engineering phenomena are complex due to the interrelation among these different realms of length and time. Multi scale modeling rises as an alternative for an outstanding mathematical and conceptual representation of such phenomena. This adequate representation may help to design and optimize chemical and petrochemical processes from a microscopic point of view. Herein we present a brief introduction to both molecular and multi scale modeling methods. We also comment and examine opportunities for applying the different levels of modeling to the analysis of industrial problems. The fundamental mathematical machinery of the molecular modelling theories is presented in order to motivate the study of these new engineering tools. Finally, we show a classification of different strategies for applying multilevel analysis, illustrating various examples of each methodology.

  11. Open source molecular modeling.

    Science.gov (United States)

    Pirhadi, Somayeh; Sunseri, Jocelyn; Koes, David Ryan

    2016-09-01

    The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. An updated online version of this catalog can be found at https://opensourcemolecularmodeling.github.io. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

  12. Systematic and efficient side chain optimization for molecular docking using a cheapest-path procedure.

    Science.gov (United States)

    Schumann, Marcel; Armen, Roger S

    2013-05-30

    Molecular docking of small-molecules is an important procedure for computer-aided drug design. Modeling receptor side chain flexibility is often important or even crucial, as it allows the receptor to adopt new conformations as induced by ligand binding. However, the accurate and efficient incorporation of receptor side chain flexibility has proven to be a challenge due to the huge computational complexity required to adequately address this problem. Here we describe a new docking approach with a very fast, graph-based optimization algorithm for assignment of the near-optimal set of residue rotamers. We extensively validate our approach using the 40 DUD target benchmarks commonly used to assess virtual screening performance and demonstrate a large improvement using the developed side chain optimization over rigid receptor docking (average ROC AUC of 0.693 vs. 0.623). Compared to numerous benchmarks, the overall performance is better than nearly all other commonly used procedures. Furthermore, we provide a detailed analysis of the level of receptor flexibility observed in docking results for different classes of residues and elucidate potential avenues for further improvement. Copyright © 2013 Wiley Periodicals, Inc.

  13. Higher-Order Extended Lagrangian Born-Oppenheimer Molecular Dynamics for Classical Polarizable Models.

    Science.gov (United States)

    Albaugh, Alex; Head-Gordon, Teresa; Niklasson, Anders M N

    2018-02-13

    Generalized extended Lagrangian Born-Oppenheimer molecular dynamics (XLBOMD) methods provide a framework for fast iteration-free simulations of models that normally require expensive electronic ground state optimizations prior to the force evaluations at every time step. XLBOMD uses dynamically driven auxiliary degrees of freedom that fluctuate about a variationally optimized ground state of an approximate "shadow" potential which approximates the true reference potential. While the requirements for such shadow potentials are well understood, constructing such potentials in practice has previously been ad hoc, and in this work, we present a systematic development of XLBOMD shadow potentials that match the reference potential to any order. We also introduce a framework for combining friction-like dissipation for the auxiliary degrees of freedom with general-order integration, a combination that was not previously possible. These developments are demonstrated with a simple fluctuating charge model and point induced dipole polarization models.

  14. Optimization in engineering models and algorithms

    CERN Document Server

    Sioshansi, Ramteen

    2017-01-01

    This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...

  15. Molecular Modeling of Adsorption of 5-Aminosalicylic Acid in the Halloysite Nanotube

    Directory of Open Access Journals (Sweden)

    Ana Borrego-Sánchez

    2018-02-01

    Full Text Available Halloysite nanotubes are becoming interesting materials for drug delivery. The knowledge of surface interactions is important for optimizing this application. The aim of this work is to perform a computational study of the interaction between 5-aminosalicylic acid (5-ASA drug and halloysite nanotubes for the development of modified drug delivery systems. The optimization of this nanotube and the adsorption of different conformers of the 5-ASA drug on the internal surface of halloysite in the presence and absence of water were performed using quantum mechanical calculations by using Density Functional Theory (DFT and methods based on atomistic force fields for molecular modeling, respectively.

  16. Molecular models of alginic acid: Interactions with calcium ions and calcite surfaces

    Science.gov (United States)

    Perry, Thomas D.; Cygan, Randall T.; Mitchell, Ralph

    2006-07-01

    Cation binding by polysaccharides is observed in many environments and is important for predictive environmental modeling, and numerous industrial and food technology applications. The complexities of these cation-organic interactions are well suited for predictive molecular modeling and the analysis of conformation and configuration of polysaccharides and their influence on cation binding. In this study, alginic acid was chosen as a model polymer system and representative disaccharide and polysaccharide subunits were developed. Molecular dynamics simulation of the torsion angles of the ether linkage between various monomeric subunits identified local and global energy minima for selected disaccharides. The simulations indicate stable disaccharide configurations and a common global energy minimum for all disaccharide models at Φ = 274 ± 7°, Ψ = 227 ± 5°, where Φ and Ψ are the torsion angles about the ether linkage. The ability of disaccharide subunits to bind calcium ions and to associate with the (101¯4) surface of calcite was also investigated. Molecular models of disaccharide interactions with calcite provide binding energy differences for conformations that are related to the proximity and residence densities of the electron-donating moieties with calcium ions on the calcite surface, which are controlled, in part, by the torsion of the ether linkage between monosaccharide units. Dynamically optimized configurations for polymer alginate models with calcium ions were also derived.

  17. Optimizing Likelihood Models for Particle Trajectory Segmentation in Multi-State Systems.

    Science.gov (United States)

    Young, Dylan Christopher; Scrimgeour, Jan

    2018-06-19

    Particle tracking offers significant insight into the molecular mechanics that govern the behav- ior of living cells. The analysis of molecular trajectories that transition between different motive states, such as diffusive, driven and tethered modes, is of considerable importance, with even single trajectories containing significant amounts of information about a molecule's environment and its interactions with cellular structures. Hidden Markov models (HMM) have been widely adopted to perform the segmentation of such complex tracks. In this paper, we show that extensive analysis of hidden Markov model outputs using data derived from multi-state Brownian dynamics simulations can be used both for the optimization of the likelihood models used to describe the states of the system and for characterization of the technique's failure mechanisms. This analysis was made pos- sible by the implementation of parallelized adaptive direct search algorithm on a Nvidia graphics processing unit. This approach provides critical information for the visualization of HMM failure and successful design of particle tracking experiments where trajectories contain multiple mobile states. © 2018 IOP Publishing Ltd.

  18. Optimization of a pulsed-field gel electrophoresis for molecular typing of Proteus mirabilis

    Directory of Open Access Journals (Sweden)

    Alper Karagöz

    2013-09-01

    Full Text Available Objective: For the detection of outbreaks caused byProteus mirabilis, strains clonal relations are determinedmethods as “pulsed-field gel electrophoresis (PFGE”.The aim of this study was optimization of a pulsed-fieldgel electrophoresis for molecular typing of P. mirabilis.Methods: In this study, PFGE’ protocol is optimized foruse in molecular typing of P. mirabilis. Phylogenetic analyzesof strains were evaluated with Bionumerics softwaresystem (version 6.01; Applied Maths, Sint-Martens-Latem, Belgium.Results: This protocol compared with Gram-negativebacteria PFGE protocols, NotI enzyme is suitable for thisbacterium. Electrophoresis conditions should be revealedas; - block 1: initial pulse duration 1 sec, ending pulseduration 30 sec, striking angle 120°, the current 6 V/cm2,temperature 14°C, time 8 hours; - block 2: initial pulseduration 30 sec, ending pulse duration 70 sec, strikingangle 120°, the current 6 V/cm2, temperature 14°C, time16 hours; - TBE, pH=8.4.Conclusion: P. mirabilis strains were typed by PFGE andBionumerics analysis program were determined clonal relationships.The procedure was simple, reproducible andsuitable for these bacteria. Also it was evaluated, becauseof reducing time, the solution volumes and enzymes canbe economically. Outbreaks of nosocomial infections dueto bacteria studied assessment and the potential to provideuseful information about the degree of prevalence.This optimized protocol is allowed different centers’ PFGEresults to compare with other laboratories results. J ClinExp Invest 2013; 4 (3: 306-312Key words: Proteus mirabilis, molecular typing, pulsedfieldgel electrophoresis.

  19. Portfolio optimization with mean-variance model

    Science.gov (United States)

    Hoe, Lam Weng; Siew, Lam Weng

    2016-06-01

    Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.

  20. Optimized molecular reconstruction procedure combining hybrid reverse Monte Carlo and molecular dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Bousige, Colin; Boţan, Alexandru; Coasne, Benoît, E-mail: coasne@mit.edu [Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); UMI 3466 CNRS-MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Ulm, Franz-Josef [Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Pellenq, Roland J.-M. [Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); UMI 3466 CNRS-MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); CINaM, CNRS/Aix Marseille Université, Campus de Luminy, 13288 Marseille Cedex 09 (France)

    2015-03-21

    We report an efficient atom-scale reconstruction method that consists of combining the Hybrid Reverse Monte Carlo algorithm (HRMC) with Molecular Dynamics (MD) in the framework of a simulated annealing technique. In the spirit of the experimentally constrained molecular relaxation technique [Biswas et al., Phys. Rev. B 69, 195207 (2004)], this modified procedure offers a refined strategy in the field of reconstruction techniques, with special interest for heterogeneous and disordered solids such as amorphous porous materials. While the HRMC method generates physical structures, thanks to the use of energy penalties, the combination with MD makes the method at least one order of magnitude faster than HRMC simulations to obtain structures of similar quality. Furthermore, in order to ensure the transferability of this technique, we provide rational arguments to select the various input parameters such as the relative weight ω of the energy penalty with respect to the structure optimization. By applying the method to disordered porous carbons, we show that adsorption properties provide data to test the global texture of the reconstructed sample but are only weakly sensitive to the presence of defects. In contrast, the vibrational properties such as the phonon density of states are found to be very sensitive to the local structure of the sample.

  1. Potentialities and limits of QSPR and molecular modeling in the design of the extraction solvents used in hydrometallurgy

    International Nuclear Information System (INIS)

    Cote, G.; Chagnes, A.

    2010-01-01

    Due to new challenges, new extraction solvents based on innovative extractants are needed in hydrometallurgy for specific tasks. Thus, the aim of the present paper is to discuss the potentialities and limits of QSPR and molecular modeling for identifying new extractants. QSPR methods may have useful applications in such a complex problem as the design of ligands for metal separation. Nevertheless, the degree of reliability of the predictions is still limited and, in the present state of the art, these techniques are likely more useful for optimization within a given family of extractants than to build in silico new reagents. The molecular modeling techniques provide binding energies between target metals and given ligands, as well as optimized chemical structures of the formed complexes. Thus, in principle, the information which can be deduced from the molecular modeling computations is richer than that provided by QSPR methods. Nevertheless, an effort should be made to establish more tangible links between the calculated binding energies and the physical parameters used by the hydrometallurgists, such as the complexation constants in aqueous phase (β MAn ) or better the extraction constants (K ex ). (author)

  2. Venturi scrubber modelling and optimization

    Energy Technology Data Exchange (ETDEWEB)

    Viswanathan, S [National Univ., La Jolla, CA (United States). School of Engineering and Technology; Ananthanarayanan, N.V. [National Univ. of Singapore (Singapore). Dept. of Chemical and Environmental Engineering; Azzopardi, B.J. [Nottingham Univ., Nottingham (United Kingdom). Dept. of Chemical Engineering

    2005-04-01

    This study presented a method to maintain the efficiency of venturi scrubbers in removing fine particulates during gas clean operations while minimizing pressure drop. Venturi scrubbers meet stringent emission standards. In order to choose the optimal method for predicting pressure drop, 4 established models were compared for their accuracy of prediction and simplicity in application. The enhanced algorithm optimizes Pease-Anthony type venturi scrubber performance by predicting the minimum pressure drop required to achieve the desired collection efficiency. This was accomplished by optimizing the key operating and design parameters such as liquid-to-gas ratio, throat gas velocity, number of nozzles, nozzle diameter and throat aspect ratio. Two of the 4 established models were expanded by providing an empirical algorithm to better predict pressure drop in the venturi throat. Model results were validated with experimental data. The optimization algorithm considers the non-uniformity in liquid distribution. It can be applied to cylindrical and rectangular Pease-Anthony type scrubbers. It offers an effective, systematic and accurate method to optimize the performance of new and existing scrubbers. 54 refs., 5 figs.

  3. Handbook on modelling for discrete optimization

    CERN Document Server

    Pitsoulis, Leonidas; Williams, H

    2006-01-01

    The primary objective underlying the Handbook on Modelling for Discrete Optimization is to demonstrate and detail the pervasive nature of Discrete Optimization. While its applications cut across an incredibly wide range of activities, many of the applications are only known to specialists. It is the aim of this handbook to correct this. It has long been recognized that "modelling" is a critically important mathematical activity in designing algorithms for solving these discrete optimization problems. Nevertheless solving the resultant models is also often far from straightforward. In recent years it has become possible to solve many large-scale discrete optimization problems. However, some problems remain a challenge, even though advances in mathematical methods, hardware, and software technology have pushed the frontiers forward. This handbook couples the difficult, critical-thinking aspects of mathematical modeling with the hot area of discrete optimization. It will be done in an academic handbook treatment...

  4. Optimization Models for Petroleum Field Exploitation

    Energy Technology Data Exchange (ETDEWEB)

    Jonsbraaten, Tore Wiig

    1998-12-31

    This thesis presents and discusses various models for optimal development of a petroleum field. The objective of these optimization models is to maximize, under many uncertain parameters, the project`s expected net present value. First, an overview of petroleum field optimization is given from the point of view of operations research. Reservoir equations for a simple reservoir system are derived and discretized and included in optimization models. Linear programming models for optimizing production decisions are discussed and extended to mixed integer programming models where decisions concerning platform, wells and production strategy are optimized. Then, optimal development decisions under uncertain oil prices are discussed. The uncertain oil price is estimated by a finite set of price scenarios with associated probabilities. The problem is one of stochastic mixed integer programming, and the solution approach is to use a scenario and policy aggregation technique developed by Rockafellar and Wets although this technique was developed for continuous variables. Stochastic optimization problems with focus on problems with decision dependent information discoveries are also discussed. A class of ``manageable`` problems is identified and an implicit enumeration algorithm for finding optimal decision policy is proposed. Problems involving uncertain reservoir properties but with a known initial probability distribution over possible reservoir realizations are discussed. Finally, a section on Nash-equilibrium and bargaining in an oil reservoir management game discusses the pool problem arising when two lease owners have access to the same underlying oil reservoir. Because the oil tends to migrate, both lease owners have incentive to drain oil from the competitors part of the reservoir. The discussion is based on a numerical example. 107 refs., 31 figs., 14 tabs.

  5. Model Risk in Portfolio Optimization

    Directory of Open Access Journals (Sweden)

    David Stefanovits

    2014-08-01

    Full Text Available We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have observations drawn from a normal variance mixture model. This model allows for heavy tails, tail dependence and leptokurtosis of marginals. The results show that mean-variance optimization is seriously compromised by model uncertainty, in particular, for non-Gaussian data and small sample sizes. To mitigate these shortcomings, we propose a method to adjust the sample covariance matrix in order to reduce model risk.

  6. An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

    Science.gov (United States)

    Utama, D. N.; Ani, N.; Iqbal, M. M.

    2018-03-01

    Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

  7. Porphyrin-sensitized solar cells: systematic molecular optimization, coadsorption and cosensitization.

    Science.gov (United States)

    Song, Heli; Liu, Qingyun; Xie, Yongshu

    2018-02-15

    As a promising low-cost solar energy conversion technique, dye-sensitized solar cells have undergone spectacular development since 1991. For practical applications, improvement of power conversion efficiency has always been one of the major research topics. Porphyrins are outstanding sensitizers endowed with strong sunlight harvesting ability in the visible region and multiple reaction sites available for functionalization. However, judicious molecular design in consideration of light-harvest, energy levels, operational dynamics, adsorption geometry and suppression of back reactions is specifically required for achieving excellent photovoltaic performance. This feature article highlights some of the recently developed porphyrin sensitizers, especially focusing on the systematic dye structure optimization approach in combination with coadsorption and cosensitization methods in pursuing higher efficiencies. Herein, we expect to provide more insights into the structure-performance correlation and molecular engineering strategies in a stepwise manner.

  8. Optimization of ISSR Markers for Molecular DNA Fingerprinting in Aquilaria sp

    International Nuclear Information System (INIS)

    Azhar Mohamad; Muhammad Hanif Azhari; Siti Norhayati Ismail; Parween, K.S.A.S.

    2013-01-01

    Aquilaria sp. belongs to the Thymelaeaceae family and well distributed to Asia region. The species is a multipurpose use from root to shoot and becoming an economic important crop, which generates wide interest in understanding the genetic diversity of the species. Understanding of the effectiveness in differentiating DNA-based markers is an important step towards plant germplasm characterization and evaluation. It is becoming a prerequisite for more effective application of molecular marker techniques in breeding and mapping programs. Polymerase Chain Reaction (PCR)-based approaches are in demanding as its simplicity and requirement for only small quantities of sample genomic DNA. Inter-simple sequence repeats (ISRR) requires no prior genomic information as anchor template in producing multi-loci markers of tandem repeats for polymorphic patterns by PCR amplification which becoming a key of advantageous of ISSR primers. ISSR markers have shown rapid, simple, reproducible and inexpensive means in molecular taxonomy, conservation breeding and genetic diversity analysis. The ISSR for marker applications are essential to facilitate management, conservation and genetic improvement programs towards improvement of standard resin quality for perfume and or pharmaceutical industries. In this paper, a total of 100 ISSR primers were optimized by using Aquilaria malaccensis. Primers optimization resulted, 38 ISSR primers affirmative for the polymorphism evaluation study, which encountered both from specific and degenerate ISSR primers. Marker derived from ISSR profiling is a powerful method for identification and molecular classification of Aquilaria sp from species to accessions and further will useful in identifying any mutant lines derived from nature and/or mutagenesis activities. (author)

  9. Optimization Modeling with Spreadsheets

    CERN Document Server

    Baker, Kenneth R

    2011-01-01

    This introductory book on optimization (mathematical programming) includes coverage on linear programming, nonlinear programming, integer programming and heuristic programming; as well as an emphasis on model building using Excel and Solver.  The emphasis on model building (rather than algorithms) is one of the features that makes this book distinctive. Most books devote more space to algorithmic details than to formulation principles. These days, however, it is not necessary to know a great deal about algorithms in order to apply optimization tools, especially when relying on the sp

  10. 3D Printing of Molecular Models

    Science.gov (United States)

    Gardner, Adam; Olson, Arthur

    2016-01-01

    Physical molecular models have played a valuable role in our understanding of the invisible nano-scale world. We discuss 3D printing and its use in producing models of the molecules of life. Complex biomolecular models, produced from 3D printed parts, can demonstrate characteristics of molecular structure and function, such as viral self-assembly,…

  11. Quantifying and Visualizing Uncertainties in Molecular Models

    OpenAIRE

    Rasheed, Muhibur; Clement, Nathan; Bhowmick, Abhishek; Bajaj, Chandrajit

    2015-01-01

    Computational molecular modeling and visualization has seen significant progress in recent years with sev- eral molecular modeling and visualization software systems in use today. Nevertheless the molecular biology community lacks techniques and tools for the rigorous analysis, quantification and visualization of the associated errors in molecular structure and its associated properties. This paper attempts at filling this vacuum with the introduction of a systematic statistical framework whe...

  12. Modeling and optimization of laser cutting operations

    Directory of Open Access Journals (Sweden)

    Gadallah Mohamed Hassan

    2015-01-01

    Full Text Available Laser beam cutting is one important nontraditional machining process. This paper optimizes the parameters of laser beam cutting parameters of stainless steel (316L considering the effect of input parameters such as power, oxygen pressure, frequency and cutting speed. Statistical design of experiments is carried in three different levels and process responses such as average kerf taper (Ta, surface roughness (Ra and heat affected zones are measured accordingly. A response surface model is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L27OA are employed to search for an optimal combination to achieve desired process yield. Response Surface Models (RSMs are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective optimization problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA and optimized using Matlab developed environment. Optimum solutions are compared with Taguchi Methodology results. As such, practicing engineers have means to model, analyze and optimize nontraditional machining processes. Validation experiments are carried to verify the developed models with success.

  13. Single-site Lennard-Jones models via polynomial chaos surrogates of Monte Carlo molecular simulation

    KAUST Repository

    Kadoura, Ahmad Salim

    2016-06-01

    In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jones (LJ) particles. The main advantage of such surrogates, once generated, is the capability of accurately computing the needed thermodynamic quantities in a few seconds, thus efficiently replacing the computationally expensive MC molecular simulations. Benefiting from the tremendous computational time reduction, the PC surrogates were used to conduct large-scale optimization in order to propose single-site LJ models for several simple molecules. Experimental data, a set of supercritical isotherms, and part of the two-phase envelope, of several pure components were used for tuning the LJ parameters (ε, σ). Based on the conducted optimization, excellent fit was obtained for different noble gases (Ar, Kr, and Xe) and other small molecules (CH4, N2, and CO). On the other hand, due to the simplicity of the LJ model used, dramatic deviations between simulation and experimental data were observed, especially in the two-phase region, for more complex molecules such as CO2 and C2 H6.

  14. Deep Generative Models for Molecular Science

    DEFF Research Database (Denmark)

    Jørgensen, Peter Bjørn; Schmidt, Mikkel Nørgaard; Winther, Ole

    2018-01-01

    Generative deep machine learning models now rival traditional quantum-mechanical computations in predicting properties of new structures, and they come with a significantly lower computational cost, opening new avenues in computational molecular science. In the last few years, a variety of deep...... generative models have been proposed for modeling molecules, which differ in both their model structure and choice of input features. We review these recent advances within deep generative models for predicting molecular properties, with particular focus on models based on the probabilistic autoencoder (or...

  15. Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

    Science.gov (United States)

    Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

    2015-04-01

    Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. Visual prosthesis wireless energy transfer system optimal modeling.

    Science.gov (United States)

    Li, Xueping; Yang, Yuan; Gao, Yong

    2014-01-16

    Wireless energy transfer system is an effective way to solve the visual prosthesis energy supply problems, theoretical modeling of the system is the prerequisite to do optimal energy transfer system design. On the basis of the ideal model of the wireless energy transfer system, according to visual prosthesis application condition, the system modeling is optimized. During the optimal modeling, taking planar spiral coils as the coupling devices between energy transmitter and receiver, the effect of the parasitic capacitance of the transfer coil is considered, and especially the concept of biological capacitance is proposed to consider the influence of biological tissue on the energy transfer efficiency, resulting in the optimal modeling's more accuracy for the actual application. The simulation data of the optimal model in this paper is compared with that of the previous ideal model, the results show that under high frequency condition, the parasitic capacitance of inductance and biological capacitance considered in the optimal model could have great impact on the wireless energy transfer system. The further comparison with the experimental data verifies the validity and accuracy of the optimal model proposed in this paper. The optimal model proposed in this paper has a higher theoretical guiding significance for the wireless energy transfer system's further research, and provide a more precise model reference for solving the power supply problem in visual prosthesis clinical application.

  17. A combined experimental and mathematical approach for molecular-based optimization of irinotecan circadian delivery.

    Directory of Open Access Journals (Sweden)

    Annabelle Ballesta

    2011-09-01

    Full Text Available Circadian timing largely modifies efficacy and toxicity of many anticancer drugs. Recent findings suggest that optimal circadian delivery patterns depend on the patient genetic background. We present here a combined experimental and mathematical approach for the design of chronomodulated administration schedules tailored to the patient molecular profile. As a proof of concept we optimized exposure of Caco-2 colon cancer cells to irinotecan (CPT11, a cytotoxic drug approved for the treatment of colorectal cancer. CPT11 was bioactivated into SN38 and its efflux was mediated by ATP-Binding-Cassette (ABC transporters in Caco-2 cells. After cell synchronization with a serum shock defining Circadian Time (CT 0, circadian rhythms with a period of 26 h 50 (SD 63 min were observed in the mRNA expression of clock genes REV-ERBα, PER2, BMAL1, the drug target topoisomerase 1 (TOP1, the activation enzyme carboxylesterase 2 (CES2, the deactivation enzyme UDP-glucuronosyltransferase 1, polypeptide A1 (UGT1A1, and efflux transporters ABCB1, ABCC1, ABCC2 and ABCG2. DNA-bound TOP1 protein amount in presence of CPT11, a marker of the drug PD, also displayed circadian variations. A mathematical model of CPT11 molecular pharmacokinetics-pharmacodynamics (PK-PD was designed and fitted to experimental data. It predicted that CPT11 bioactivation was the main determinant of CPT11 PD circadian rhythm. We then adopted the therapeutics strategy of maximizing efficacy in non-synchronized cells, considered as cancer cells, under a constraint of maximum toxicity in synchronized cells, representing healthy ones. We considered exposure schemes in the form of an initial concentration of CPT11 given at a particular CT, over a duration ranging from 1 to 27 h. For any dose of CPT11, optimal exposure durations varied from 3h40 to 7h10. Optimal schemes started between CT2h10 and CT2h30, a time interval corresponding to 1h30 to 1h50 before the nadir of CPT11 bioactivation rhythm in

  18. An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty

    Directory of Open Access Journals (Sweden)

    Feng Zhou

    2015-11-01

    Full Text Available An indirect simulation-optimization model framework with enhanced computational efficiency and risk-based decision-making capability was developed to determine optimal total maximum daily load (TMDL allocation under uncertainty. To convert the traditional direct simulation-optimization model into our indirect equivalent model framework, we proposed a two-step strategy: (1 application of interval regression equations derived by a Bayesian recursive regression tree (BRRT v2 algorithm, which approximates the original hydrodynamic and water-quality simulation models and accurately quantifies the inherent nonlinear relationship between nutrient load reductions and the credible interval of algal biomass with a given confidence interval; and (2 incorporation of the calibrated interval regression equations into an uncertain optimization framework, which is further converted to our indirect equivalent framework by the enhanced-interval linear programming (EILP method and provides approximate-optimal solutions at various risk levels. The proposed strategy was applied to the Swift Creek Reservoir’s nutrient TMDL allocation (Chesterfield County, VA to identify the minimum nutrient load allocations required from eight sub-watersheds to ensure compliance with user-specified chlorophyll criteria. Our results indicated that the BRRT-EILP model could identify critical sub-watersheds faster than the traditional one and requires lower reduction of nutrient loadings compared to traditional stochastic simulation and trial-and-error (TAE approaches. This suggests that our proposed framework performs better in optimal TMDL development compared to the traditional simulation-optimization models and provides extreme and non-extreme tradeoff analysis under uncertainty for risk-based decision making.

  19. Following an Optimal Batch Bioreactor Operations Model

    DEFF Research Database (Denmark)

    Ibarra-Junquera, V.; Jørgensen, Sten Bay; Virgen-Ortíz, J.J.

    2012-01-01

    The problem of following an optimal batch operation model for a bioreactor in the presence of uncertainties is studied. The optimal batch bioreactor operation model (OBBOM) refers to the bioreactor trajectory for nominal cultivation to be optimal. A multiple-variable dynamic optimization of fed...... as the master system which includes the optimal cultivation trajectory for the feed flow rate and the substrate concentration. The “real” bioreactor, the one with unknown dynamics and perturbations, is considered as the slave system. Finally, the controller is designed such that the real bioreactor...

  20. Topology of classical molecular optimal control landscapes for multi-target objectives

    Energy Technology Data Exchange (ETDEWEB)

    Joe-Wong, Carlee [Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544-1000 (United States); Ho, Tak-San; Rabitz, Herschel, E-mail: hrabitz@princeton.edu [Department of Chemistry, Princeton University, Princeton, New Jersey 08544-1009 (United States); Wu, Rebing [Department of Automation, Tsinghua University, Beijing (China)

    2015-04-21

    This paper considers laser-driven optimal control of an ensemble of non-interacting molecules whose dynamics lie in classical phase space. The molecules evolve independently under control to distinct final states. We consider a control landscape defined in terms of multi-target (MT) molecular states and analyze the landscape as a functional of the control field. The topology of the MT control landscape is assessed through its gradient and Hessian with respect to the control. Under particular assumptions, the MT control landscape is found to be free of traps that could hinder reaching the objective. The Hessian associated with an optimal control field is shown to have finite rank, indicating an inherent degree of robustness to control noise. Both the absence of traps and rank of the Hessian are shown to be analogous to the situation of specifying multiple targets for an ensemble of quantum states. Numerical simulations are presented to illustrate the classical landscape principles and further characterize the system behavior as the control field is optimized.

  1. Dynamic optimization deterministic and stochastic models

    CERN Document Server

    Hinderer, Karl; Stieglitz, Michael

    2016-01-01

    This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.

  2. Intelligent structural optimization: Concept, Model and Methods

    International Nuclear Information System (INIS)

    Lu, Dagang; Wang, Guangyuan; Peng, Zhang

    2002-01-01

    Structural optimization has many characteristics of Soft Design, and so, it is necessary to apply the experience of human experts to solving the uncertain and multidisciplinary optimization problems in large-scale and complex engineering systems. With the development of artificial intelligence (AI) and computational intelligence (CI), the theory of structural optimization is now developing into the direction of intelligent optimization. In this paper, a concept of Intelligent Structural Optimization (ISO) is proposed. And then, a design process model of ISO is put forward in which each design sub-process model are discussed. Finally, the design methods of ISO are presented

  3. Single-site Lennard-Jones models via polynomial chaos surrogates of Monte Carlo molecular simulation

    Energy Technology Data Exchange (ETDEWEB)

    Kadoura, Ahmad, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Sun, Shuyu, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa [Computational Transport Phenomena Laboratory, The Earth Sciences and Engineering Department, The Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900 (Saudi Arabia); Siripatana, Adil, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Hoteit, Ibrahim, E-mail: ibrahim.hoteit@kaust.edu.sa [Earth Fluid Modeling and Predicting Group, The Earth Sciences and Engineering Department, The Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900 (Saudi Arabia); Knio, Omar, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa [Uncertainty Quantification Center, The Applied Mathematics and Computational Science Department, The Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900 (Saudi Arabia)

    2016-06-07

    In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jones (LJ) particles. The main advantage of such surrogates, once generated, is the capability of accurately computing the needed thermodynamic quantities in a few seconds, thus efficiently replacing the computationally expensive MC molecular simulations. Benefiting from the tremendous computational time reduction, the PC surrogates were used to conduct large-scale optimization in order to propose single-site LJ models for several simple molecules. Experimental data, a set of supercritical isotherms, and part of the two-phase envelope, of several pure components were used for tuning the LJ parameters (ε, σ). Based on the conducted optimization, excellent fit was obtained for different noble gases (Ar, Kr, and Xe) and other small molecules (CH{sub 4}, N{sub 2}, and CO). On the other hand, due to the simplicity of the LJ model used, dramatic deviations between simulation and experimental data were observed, especially in the two-phase region, for more complex molecules such as CO{sub 2} and C{sub 2} H{sub 6}.

  4. Pyomo optimization modeling in Python

    CERN Document Server

    Hart, William E; Watson, Jean-Paul; Woodruff, David L; Hackebeil, Gabriel A; Nicholson, Bethany L; Siirola, John D

    2017-01-01

    This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo’s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package fo...

  5. Optimizing ultrasound molecular imaging of secreted frizzled related protein 2 expression in angiosarcoma.

    Directory of Open Access Journals (Sweden)

    James K Tsuruta

    Full Text Available Secreted frizzled related protein 2 (SFRP2 is a tumor endothelial marker expressed in angiosarcoma. Previously, we showed ultrasound molecular imaging with SFRP2-targeted contrast increased average video pixel intensity (VI of angiosarcoma vessels by 2.2 ± 0.6 VI versus streptavidin contrast. We hypothesized that redesigning our contrast agents would increase imaging performance. Improved molecular imaging reagents were created by combining NeutrAvidin™-functionalized microbubbles with biotinylated SFRP2 or IgY control antibodies. When angiosarcoma tumors in nude mice reached 8 mm, time-intensity, antibody loading, and microbubble dose experiments optimized molecular imaging. 10 minutes after injection, the control-subtracted time-intensity curve (TIC for SFRP2-targeted contrast reached a maximum, after subtracting the contribution of free-flowing contrast. SFRP2 antibody-targeted VI was greater when contrast was formulated with 10-fold molar excess of maleimide-activated NeutrAvidin™ versus 3-fold (4.5 ± 0.18 vs. 0.32 ± 0.15, VI ± SEM, 5 x 106 dose, p < 0.001. Tumor vasculature returned greater average video pixel intensity using 5 x 107 versus 5 x 106 microbubbles (21.2 ± 2.5 vs. 4.5 ± 0.18, p = 0.0011. Specificity for tumor vasculature was confirmed by low VI for SFRP2-targeted, and control contrast in peri-tumoral vasculature (3.2 ± 0.52 vs. 1.6 ± 0.71, p = 0.92. After optimization, average video pixel intensity of tumor vasculature was 14.2 ± 3.0 VI units higher with SFRP2-targeted contrast versus IgY-targeted control (22.1 ± 2.5 vs. 7.9 ± 1.6, p < 0.001. After log decompression, 14.2 ΔVI was equal to ~70% higher signal, in arbitray acoustic units (AU, for SFRP2 versus IgY. This provided ~18- fold higher acoustic signal enhancement than provided previously by 2.2 ΔVI. Basing our targeted contrast on NeutrAvidin™-functionalized microbubbles, using IgY antibodies for our control contrast, and optimizing our imaging protocol

  6. The cognitive life of mechanical molecular models.

    Science.gov (United States)

    Charbonneau, Mathieu

    2013-12-01

    The use of physical models of molecular structures as research tools has been central to the development of biochemistry and molecular biology. Intriguingly, it has received little attention from scholars of science. In this paper, I argue that these physical models are not mere three-dimensional representations but that they are in fact very special research tools: they are cognitive augmentations. Despite the fact that they are external props, these models serve as cognitive tools that augment and extend the modeler's cognitive capacities and performance in molecular modeling tasks. This cognitive enhancement is obtained because of the way the modeler interacts with these models, the models' materiality contributing to the solving of the molecule's structure. Furthermore, I argue that these material models and their component parts were designed, built and used specifically to serve as cognitive facilitators and cognitive augmentations. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. Surrogate-Based Optimization of Biogeochemical Transport Models

    Science.gov (United States)

    Prieß, Malte; Slawig, Thomas

    2010-09-01

    First approaches towards a surrogate-based optimization method for a one-dimensional marine biogeochemical model of NPZD type are presented. The model, developed by Oschlies and Garcon [1], simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. A key issue is to minimize the misfit between the model output and given observational data. Our aim is to reduce the overall optimization cost avoiding expensive function and derivative evaluations by using a surrogate model replacing the high-fidelity model in focus. This in particular becomes important for more complex three-dimensional models. We analyse a coarsening in the discretization of the model equations as one way to create such a surrogate. Here the numerical stability crucially depends upon the discrete stepsize in time and space and the biochemical terms. We show that for given model parameters the level of grid coarsening can be choosen accordingly yielding a stable and satisfactory surrogate. As one example of a surrogate-based optimization method we present results of the Aggressive Space Mapping technique (developed by John W. Bandler [2, 3]) applied to the optimization of this one-dimensional biogeochemical transport model.

  8. Optimization of Polyplex Formation between DNA Oligonucleotide and Poly(l-Lysine): Experimental Study and Modeling Approach

    Science.gov (United States)

    Vasiliu, Tudor; Cojocaru, Corneliu; Rotaru, Alexandru; Pricope, Gabriela; Pinteala, Mariana; Clima, Lilia

    2017-01-01

    The polyplexes formed by nucleic acids and polycations have received a great attention owing to their potential application in gene therapy. In our study, we report experimental results and modeling outcomes regarding the optimization of polyplex formation between the double-stranded DNA (dsDNA) and poly(l-Lysine) (PLL). The quantification of the binding efficiency during polyplex formation was performed by processing of the images captured from the gel electrophoresis assays. The design of experiments (DoE) and response surface methodology (RSM) were employed to investigate the coupling effect of key factors (pH and N/P ratio) affecting the binding efficiency. According to the experimental observations and response surface analysis, the N/P ratio showed a major influence on binding efficiency compared to pH. Model-based optimization calculations along with the experimental confirmation runs unveiled the maximal binding efficiency (99.4%) achieved at pH 5.4 and N/P ratio 125. To support the experimental data and reveal insights of molecular mechanism responsible for the polyplex formation between dsDNA and PLL, molecular dynamics simulations were performed at pH 5.4 and 7.4. PMID:28629130

  9. Optimization of Polyplex Formation between DNA Oligonucleotide and Poly(ʟ-Lysine): Experimental Study and Modeling Approach.

    Science.gov (United States)

    Vasiliu, Tudor; Cojocaru, Corneliu; Rotaru, Alexandru; Pricope, Gabriela; Pinteala, Mariana; Clima, Lilia

    2017-06-17

    The polyplexes formed by nucleic acids and polycations have received a great attention owing to their potential application in gene therapy. In our study, we report experimental results and modeling outcomes regarding the optimization of polyplex formation between the double-stranded DNA (dsDNA) and poly(ʟ-Lysine) (PLL). The quantification of the binding efficiency during polyplex formation was performed by processing of the images captured from the gel electrophoresis assays. The design of experiments (DoE) and response surface methodology (RSM) were employed to investigate the coupling effect of key factors (pH and N/P ratio) affecting the binding efficiency. According to the experimental observations and response surface analysis, the N/P ratio showed a major influence on binding efficiency compared to pH. Model-based optimization calculations along with the experimental confirmation runs unveiled the maximal binding efficiency (99.4%) achieved at pH 5.4 and N/P ratio 125. To support the experimental data and reveal insights of molecular mechanism responsible for the polyplex formation between dsDNA and PLL, molecular dynamics simulations were performed at pH 5.4 and 7.4.

  10. Medulloblastoma: Molecular Genetics and Animal Models

    Directory of Open Access Journals (Sweden)

    Corey Raffel

    2004-07-01

    Full Text Available Medulloblastoma is a primary brain tumor found in the cerebellum of children. The tumor occurs in association with two inherited cancer syndromes: Turcot syndrome and Gorlin syndrome. Insights into the molecular biology of the tumor have come from looking at alterations in the genes altered in these syndromes, PTC and APC, respectively. Murine models of medulloblastoma have been constructed based on these alterations. Additional murine models that, while mimicking the appearance of the human tumor, seem unrelated to the human tumor's molecular alterations have been made. In this review, the clinical picture, origin, molecular biology, murine models of medulloblastoma are discussed. Although a great deal has been discovered about this tumor, the genetic alterations responsible for tumor development in a majority of patients have yet to be described.

  11. A n-vector model for charge transport in molecular semiconductors.

    Science.gov (United States)

    Jackson, Nicholas E; Kohlstedt, Kevin L; Chen, Lin X; Ratner, Mark A

    2016-11-28

    We develop a lattice model utilizing coarse-grained molecular sites to study charge transport in molecular semiconducting materials. The model bridges atomistic descriptions and structureless lattice models by mapping molecular structure onto sets of spatial vectors isomorphic with spin vectors in a classical n-vector Heisenberg model. Specifically, this model incorporates molecular topology-dependent orientational and intermolecular coupling preferences, including the direct inclusion of spatially correlated transfer integrals and site energy disorder. This model contains the essential physics required to explicitly simulate the interplay of molecular topology and correlated structural disorder, and their effect on charge transport. As a demonstration of its utility, we apply this model to analyze the effects of long-range orientational correlations, molecular topology, and intermolecular interaction strength on charge motion in bulk molecular semiconductors.

  12. Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.

    Science.gov (United States)

    Aprasoff, Jonathan; Donchin, Opher

    2012-04-01

    Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.

  13. Vector-model-supported approach in prostate plan optimization

    International Nuclear Information System (INIS)

    Liu, Eva Sau Fan; Wu, Vincent Wing Cheung; Harris, Benjamin; Lehman, Margot; Pryor, David; Chan, Lawrence Wing Chi

    2017-01-01

    Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration

  14. Vector-model-supported approach in prostate plan optimization

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Eva Sau Fan [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Wu, Vincent Wing Cheung [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Harris, Benjamin [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Lehman, Margot; Pryor, David [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); School of Medicine, University of Queensland (Australia); Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong)

    2017-07-01

    Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration

  15. Review: Optimization methods for groundwater modeling and management

    Science.gov (United States)

    Yeh, William W.-G.

    2015-09-01

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

  16. Equilibrium star formation in a constant Q disc: model optimization and initial tests

    Science.gov (United States)

    Zheng, Zheng; Meurer, Gerhardt R.; Heckman, Timothy M.; Thilker, David A.; Zwaan, Martin A.

    2013-10-01

    We develop a model for the distribution of the interstellar medium (ISM) and star formation in galaxies based on recent studies that indicate that galactic discs stabilize to a constant stability parameter, which we combine with prescriptions of how the phases of the ISM are determined and for the star formation law (SFL). The model predicts the gas surface mass density and star formation intensity of a galaxy given its rotation curve, stellar surface mass density and the gas velocity dispersion. This model is tested on radial profiles of neutral and molecular ISM surface mass density and star formation intensity of 12 galaxies selected from the H I Nearby Galaxy Survey sample. Our tests focus on intermediate radii (0.3 to 1 times the optical radius) because there are insufficient data to test the outer discs and the fits are less accurate in detail in the centre. Nevertheless, the model produces reasonable agreement with the ISM mass and star formation rate integrated over the central region in all but one case. To optimize the model, we evaluate four recipes for the stability parameter, three recipes for apportioning the ISM into molecular and neutral components, and eight versions of the SFL. We find no clear-cut best prescription for the two-fluid (gas and stars) stability parameter Q2f and therefore for simplicity, we use the Wang and Silk approximation (QWS). We found that an empirical scaling between the molecular-to-neutral ISM ratio (Rmol) and the stellar surface mass density proposed by Leroy et al. works marginally better than the other two prescriptions for this ratio in predicting the ISM profiles, and noticeably better in predicting the star formation intensity from the ISM profiles produced by our model with the SFLs we tested. Thus, in the context of our modelled ISM profiles, the linear molecular SFL and the two-component SFL work better than the other prescriptions we tested. We incorporate these relations into our `constant Q disc' model.

  17. Parameter optimization for surface flux transport models

    Science.gov (United States)

    Whitbread, T.; Yeates, A. R.; Muñoz-Jaramillo, A.; Petrie, G. J. D.

    2017-11-01

    Accurate prediction of solar activity calls for precise calibration of solar cycle models. Consequently we aim to find optimal parameters for models which describe the physical processes on the solar surface, which in turn act as proxies for what occurs in the interior and provide source terms for coronal models. We use a genetic algorithm to optimize surface flux transport models using National Solar Observatory (NSO) magnetogram data for Solar Cycle 23. This is applied to both a 1D model that inserts new magnetic flux in the form of idealized bipolar magnetic regions, and also to a 2D model that assimilates specific shapes of real active regions. The genetic algorithm searches for parameter sets (meridional flow speed and profile, supergranular diffusivity, initial magnetic field, and radial decay time) that produce the best fit between observed and simulated butterfly diagrams, weighted by a latitude-dependent error structure which reflects uncertainty in observations. Due to the easily adaptable nature of the 2D model, the optimization process is repeated for Cycles 21, 22, and 24 in order to analyse cycle-to-cycle variation of the optimal solution. We find that the ranges and optimal solutions for the various regimes are in reasonable agreement with results from the literature, both theoretical and observational. The optimal meridional flow profiles for each regime are almost entirely within observational bounds determined by magnetic feature tracking, with the 2D model being able to accommodate the mean observed profile more successfully. Differences between models appear to be important in deciding values for the diffusive and decay terms. In like fashion, differences in the behaviours of different solar cycles lead to contrasts in parameters defining the meridional flow and initial field strength.

  18. Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

    Directory of Open Access Journals (Sweden)

    Adel Taieb

    2017-01-01

    Full Text Available This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO Fuzzy Optimal Model Predictive Control (FOMPC using the Adaptive Particle Swarm Optimization (APSO algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR and Tank system, where the proposed approach provides better performances compared with other methods.

  19. A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge

    2014-01-01

    Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily

  20. Statistical models for optimizing mineral exploration

    International Nuclear Information System (INIS)

    Wignall, T.K.; DeGeoffroy, J.

    1987-01-01

    The primary purpose of mineral exploration is to discover ore deposits. The emphasis of this volume is on the mathematical and computational aspects of optimizing mineral exploration. The seven chapters that make up the main body of the book are devoted to the description and application of various types of computerized geomathematical models. These chapters include: (1) the optimal selection of ore deposit types and regions of search, as well as prospecting selected areas, (2) designing airborne and ground field programs for the optimal coverage of prospecting areas, and (3) delineating and evaluating exploration targets within prospecting areas by means of statistical modeling. Many of these statistical programs are innovative and are designed to be useful for mineral exploration modeling. Examples of geomathematical models are applied to exploring for six main types of base and precious metal deposits, as well as other mineral resources (such as bauxite and uranium)

  1. Mathematical modeling and optimization of complex structures

    CERN Document Server

    Repin, Sergey; Tuovinen, Tero

    2016-01-01

    This volume contains selected papers in three closely related areas: mathematical modeling in mechanics, numerical analysis, and optimization methods. The papers are based upon talks presented  on the International Conference for Mathematical Modeling and Optimization in Mechanics, held in Jyväskylä, Finland, March 6-7, 2014 dedicated to Prof. N. Banichuk on the occasion of his 70th birthday. The articles are written by well-known scientists working in computational mechanics and in optimization of complicated technical models. Also, the volume contains papers discussing the historical development, the state of the art, new ideas, and open problems arising in  modern continuum mechanics and applied optimization problems. Several papers are concerned with mathematical problems in numerical analysis, which are also closely related to important mechanical models. The main topics treated include:  * Computer simulation methods in mechanics, physics, and biology;  * Variational problems and methods; minimiz...

  2. Molecular identification of potential denitrifying bacteria and use of D-optimal mixture experimental design for the optimization of denitrification process.

    Science.gov (United States)

    Ben Taheur, Fadia; Fdhila, Kais; Elabed, Hamouda; Bouguerra, Amel; Kouidhi, Bochra; Bakhrouf, Amina; Chaieb, Kamel

    2016-04-01

    Three bacterial strains (TE1, TD3 and FB2) were isolated from date palm (degla), pistachio and barley. The presence of nitrate reductase (narG) and nitrite reductase (nirS and nirK) genes in the selected strains was detected by PCR technique. Molecular identification based on 16S rDNA sequencing method was applied to identify positive strains. In addition, the D-optimal mixture experimental design was used to optimize the optimal formulation of probiotic bacteria for denitrification process. Strains harboring denitrification genes were identified as: TE1, Agrococcus sp LN828197; TD3, Cronobacter sakazakii LN828198 and FB2, Pedicoccus pentosaceus LN828199. PCR results revealed that all strains carried the nirS gene. However only C. sakazakii LN828198 and Agrococcus sp LN828197 harbored the nirK and the narG genes respectively. Moreover, the studied bacteria were able to form biofilm on abiotic surfaces with different degree. Process optimization showed that the most significant reduction of nitrate was 100% with 14.98% of COD consumption and 5.57 mg/l nitrite accumulation. Meanwhile, the response values were optimized and showed that the most optimal combination was 78.79% of C. sakazakii LN828198 (curve value), 21.21% of P. pentosaceus LN828199 (curve value) and absence (0%) of Agrococcus sp LN828197 (curve value). Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Structural Probing and Molecular Modeling of the A₃ Adenosine Receptor: A Focus on Agonist Binding.

    Science.gov (United States)

    Ciancetta, Antonella; Jacobson, Kenneth A

    2017-03-11

    Adenosine is an endogenous modulator exerting its functions through the activation of four adenosine receptor (AR) subtypes, termed A₁, A 2A , A 2B and A₃, which belong to the G protein-coupled receptor (GPCR) superfamily. The human A₃AR (hA₃AR) subtype is implicated in several cytoprotective functions. Therefore, hA₃AR modulators, and in particular agonists, are sought for their potential application as anti-inflammatory, anticancer, and cardioprotective agents. Structure-based molecular modeling techniques have been applied over the years to rationalize the structure-activity relationships (SARs) of newly emerged A₃AR ligands, guide the subsequent lead optimization, and interpret site-directed mutagenesis (SDM) data from a molecular perspective. In this review, we showcase selected modeling-based and guided strategies that were applied to elucidate the binding of agonists to the A₃AR and discuss the challenges associated with an accurate prediction of the receptor extracellular vestibule through homology modeling from the available X-ray templates.

  4. Process optimization of friction stir welding based on thermal models

    DEFF Research Database (Denmark)

    Larsen, Anders Astrup

    2010-01-01

    This thesis investigates how to apply optimization methods to numerical models of a friction stir welding process. The work is intended as a proof-of-concept using different methods that are applicable to models of high complexity, possibly with high computational cost, and without the possibility...... information of the high-fidelity model. The optimization schemes are applied to stationary thermal models of differing complexity of the friction stir welding process. The optimization problems considered are based on optimizing the temperature field in the workpiece by finding optimal translational speed....... Also an optimization problem based on a microstructure model is solved, allowing the hardness distribution in the plate to be optimized. The use of purely thermal models represents a simplification of the real process; nonetheless, it shows the applicability of the optimization methods considered...

  5. Mathematical model of highways network optimization

    Science.gov (United States)

    Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.

    2017-12-01

    The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.

  6. Modeling and optimization of HVAC energy consumption

    Energy Technology Data Exchange (ETDEWEB)

    Kusiak, Andrew; Li, Mingyang; Tang, Fan [Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, IA 52242 - 1527 (United States)

    2010-10-15

    A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and a set of uncontrollable parameters. The multiple-linear perceptron (MLP) ensemble outperforms other models tested in this research, and therefore it is selected to model a chiller, a pump, a fan, and a reheat device. These four models are integrated into an energy optimization model with two decision variables, the setpoint of the supply air temperature and the static pressure in the air handling unit. The model is solved with a particle swarm optimization algorithm. The optimization results have demonstrated the total energy consumed by the heating, ventilation, and air-conditioning system is reduced by over 7%. (author)

  7. Optimal Hedging with the Vector Autoregressive Model

    NARCIS (Netherlands)

    L. Gatarek (Lukasz); S.G. Johansen (Soren)

    2014-01-01

    markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be

  8. Molecular modeling and multiscaling issues for electronic material applications

    CERN Document Server

    Iwamoto, Nancy; Yuen, Matthew; Fan, Haibo

    Volume 1 : Molecular Modeling and Multiscaling Issues for Electronic Material Applications provides a snapshot on the progression of molecular modeling in the electronics industry and how molecular modeling is currently being used to understand material performance to solve relevant issues in this field. This book is intended to introduce the reader to the evolving role of molecular modeling, especially seen through the eyes of the IEEE community involved in material modeling for electronic applications.  Part I presents  the role that quantum mechanics can play in performance prediction, such as properties dependent upon electronic structure, but also shows examples how molecular models may be used in performance diagnostics, especially when chemistry is part of the performance issue.  Part II gives examples of large-scale atomistic methods in material failure and shows several examples of transitioning between grain boundary simulations (on the atomistic level)and large-scale models including an example ...

  9. Rethinking exchange market models as optimization algorithms

    Science.gov (United States)

    Luquini, Evandro; Omar, Nizam

    2018-02-01

    The exchange market model has mainly been used to study the inequality problem. Although the human society inequality problem is very important, the exchange market models dynamics until stationary state and its capability of ranking individuals is interesting in itself. This study considers the hypothesis that the exchange market model could be understood as an optimization procedure. We present herein the implications for algorithmic optimization and also the possibility of a new family of exchange market models

  10. Pareto-Optimal Model Selection via SPRINT-Race.

    Science.gov (United States)

    Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

    2018-02-01

    In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.

  11. A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design-Part I. Model development

    Energy Technology Data Exchange (ETDEWEB)

    He, L., E-mail: li.he@ryerson.ca [Department of Civil Engineering, Faculty of Engineering, Architecture and Science, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3 (Canada); Huang, G.H. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); College of Urban Environmental Sciences, Peking University, Beijing 100871 (China); Lu, H.W. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada)

    2010-04-15

    Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the 'true' ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes.

  12. Optimal velocity difference model for a car-following theory

    International Nuclear Information System (INIS)

    Peng, G.H.; Cai, X.H.; Liu, C.Q.; Cao, B.F.; Tuo, M.X.

    2011-01-01

    In this Letter, we present a new optimal velocity difference model for a car-following theory based on the full velocity difference model. The linear stability condition of the new model is obtained by using the linear stability theory. The unrealistically high deceleration does not appear in OVDM. Numerical simulation of traffic dynamics shows that the new model can avoid the disadvantage of negative velocity occurred at small sensitivity coefficient λ in full velocity difference model by adjusting the coefficient of the optimal velocity difference, which shows that collision can disappear in the improved model. -- Highlights: → A new optimal velocity difference car-following model is proposed. → The effects of the optimal velocity difference on the stability of traffic flow have been explored. → The starting and braking process were carried out through simulation. → The effects of the optimal velocity difference can avoid the disadvantage of negative velocity.

  13. A model for optimizing the production of pharmaceutical products

    Directory of Open Access Journals (Sweden)

    Nevena Gospodinova

    2017-05-01

    Full Text Available The problem associated with the optimal production planning is especially relevant in modern industrial enterprises. The most commonly used optimality criteria in this context are: maximizing the total profit; minimizing the cost per unit of production; maximizing the capacity utilization; minimizing the total production costs. This article aims to explore the possibility for optimizing the production of pharmaceutical products through the construction of a mathematical model that can be viewed in two ways – as a single-product model and a multi-product model. As an optimality criterion it is set the minimization of the cost per unit of production for a given planning period. The author proposes an analytical method for solving the nonlinear optimization problem. An optimal production plan of Tylosin tartrate is found using the single-product model.

  14. Optimal laser control of ultrafast photodissociation of I2- in water: Mixed quantum/classical molecular dynamics simulation

    International Nuclear Information System (INIS)

    Nishiyama, Yoshikazu; Kato, Tsuyoshi; Ohtsuki, Yukiyoshi; Fujimura, Yuichi

    2004-01-01

    A linearized optimal control method in combination with mixed quantum/classical molecular dynamics simulation is used for numerically investigating the possibility of controlling photodissociation wave packets of I 2 - in water. Optimal pulses are designed using an ensemble of photodissociation samples, aiming at the creation of localized dissociation wave packets. Numerical results clearly show the effectiveness of the control although the control achievement is reduced with an increase in the internuclear distance associated with a target region. We introduce effective optimal pulses that are designed using a statistically averaged effective dissociation potential, and show that they semiquantitatively reproduce the control achievements calculated by using optimal pulses. The control mechanisms are interpreted from the time- and frequency-resolved spectra of the effective optimal pulses

  15. Fuzzy Simulation-Optimization Model for Waste Load Allocation

    Directory of Open Access Journals (Sweden)

    Motahhare Saadatpour

    2006-01-01

    Full Text Available This paper present simulation-optimization models for waste load allocation from multiple point sources which include uncertainty due to vagueness of the parameters and goals. This model employs fuzzy sets with appropriate membership functions to deal with uncertainties due to vagueness. The fuzzy waste load allocation model (FWLAM incorporate QUAL2E as a water quality simulation model and Genetic Algorithm (GA as an optimization tool to find the optimal combination of the fraction removal level to the dischargers and pollution control agency (PCA. Penalty functions are employed to control the violations in the system.  The results demonstrate that the goal of PCA to achieve the best water quality and the goal of the dischargers to use the full assimilative capacity of the river have not been satisfied completely and a compromise solution between these goals is provided. This fuzzy optimization model with genetic algorithm has been used for a hypothetical problem. Results demonstrate a very suitable convergence of proposed optimization algorithm to the global optima.

  16. Triplet-Based Codon Organization Optimizes the Impact of Synonymous Mutation on Nucleic Acid Molecular Dynamics.

    Science.gov (United States)

    Babbitt, Gregory A; Coppola, Erin E; Mortensen, Jamie S; Ekeren, Patrick X; Viola, Cosmo; Goldblatt, Dallan; Hudson, André O

    2018-02-01

    Since the elucidation of the genetic code almost 50 years ago, many nonrandom aspects of its codon organization remain only partly resolved. Here, we investigate the recent hypothesis of 'dual-use' codons which proposes that in addition to allowing adjustment of codon optimization to tRNA abundance, the degeneracy in the triplet-based genetic code also multiplexes information regarding DNA's helical shape and protein-binding dynamics while avoiding interference with other protein-level characteristics determined by amino acid properties. How such structural optimization of the code within eukaryotic chromatin could have arisen from an RNA world is a mystery, but would imply some preadaptation in an RNA context. We analyzed synonymous (protein-silent) and nonsynonymous (protein-altering) mutational impacts on molecular dynamics in 13823 identically degenerate alternative codon reorganizations, defined by codon transitions in 7680 GPU-accelerated molecular dynamic simulations of implicitly and explicitly solvated double-stranded aRNA and bDNA structures. When compared to all possible alternative codon assignments, the standard genetic code minimized the impact of synonymous mutations on the random atomic fluctuations and correlations of carbon backbone vector trajectories while facilitating the specific movements that contribute to DNA polymer flexibility. This trend was notably stronger in the context of RNA supporting the idea that dual-use codon optimization and informational multiplexing in DNA resulted from the preadaptation of the RNA duplex to resist changes to thermostability. The nonrandom and divergent molecular dynamics of synonymous mutations also imply that the triplet-based code may have resulted from adaptive functional expansion enabling a primordial doublet code to multiplex gene regulatory information via the shape and charge of the minor groove.

  17. Molecular modeling: An open invitation for applied mathematics

    Science.gov (United States)

    Mezey, Paul G.

    2013-10-01

    Molecular modeling methods provide a very wide range of challenges for innovative mathematical and computational techniques, where often high dimensionality, large sets of data, and complicated interrelations imply a multitude of iterative approximations. The physical and chemical basis of these methodologies involves quantum mechanics with several non-intuitive aspects, where classical interpretation and classical analogies are often misleading or outright wrong. Hence, instead of the everyday, common sense approaches which work so well in engineering, in molecular modeling one often needs to rely on rather abstract mathematical constraints and conditions, again emphasizing the high level of reliance on applied mathematics. Yet, the interdisciplinary aspects of the field of molecular modeling also generates some inertia and perhaps too conservative reliance on tried and tested methodologies, that is at least partially caused by the less than up-to-date involvement in the newest developments in applied mathematics. It is expected that as more applied mathematicians take up the challenge of employing the latest advances of their field in molecular modeling, important breakthroughs may follow. In this presentation some of the current challenges of molecular modeling are discussed.

  18. Multi-level molecular modelling for plasma medicine

    International Nuclear Information System (INIS)

    Bogaerts, Annemie; Khosravian, Narjes; Van der Paal, Jonas; Verlackt, Christof C W; Yusupov, Maksudbek; Kamaraj, Balu; Neyts, Erik C

    2016-01-01

    Modelling at the molecular or atomic scale can be very useful for obtaining a better insight in plasma medicine. This paper gives an overview of different atomic/molecular scale modelling approaches that can be used to study the direct interaction of plasma species with biomolecules or the consequences of these interactions for the biomolecules on a somewhat longer time-scale. These approaches include density functional theory (DFT), density functional based tight binding (DFTB), classical reactive and non-reactive molecular dynamics (MD) and united-atom or coarse-grained MD, as well as hybrid quantum mechanics/molecular mechanics (QM/MM) methods. Specific examples will be given for three important types of biomolecules, present in human cells, i.e. proteins, DNA and phospholipids found in the cell membrane. The results show that each of these modelling approaches has its specific strengths and limitations, and is particularly useful for certain applications. A multi-level approach is therefore most suitable for obtaining a global picture of the plasma–biomolecule interactions. (paper)

  19. Hierarchical Swarm Model: A New Approach to Optimization

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2010-01-01

    Full Text Available This paper presents a novel optimization model called hierarchical swarm optimization (HSO, which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS2O, based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS2O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.

  20. A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design--part I. Model development.

    Science.gov (United States)

    He, L; Huang, G H; Lu, H W

    2010-04-15

    Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the "true" ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes. 2009 Elsevier B.V. All rights reserved.

  1. Multipurpose optimization models for high level waste vitrification

    International Nuclear Information System (INIS)

    Hoza, M.

    1994-08-01

    Optimal Waste Loading (OWL) models have been developed as multipurpose tools for high-level waste studies for the Tank Waste Remediation Program at Hanford. Using nonlinear programming techniques, these models maximize the waste loading of the vitrified waste and optimize the glass formers composition such that the glass produced has the appropriate properties within the melter, and the resultant vitrified waste form meets the requirements for disposal. The OWL model can be used for a single waste stream or for blended streams. The models can determine optimal continuous blends or optimal discrete blends of a number of different wastes. The OWL models have been used to identify the most restrictive constraints, to evaluate prospective waste pretreatment methods, to formulate and evaluate blending strategies, and to determine the impacts of variability in the wastes. The OWL models will be used to aid in the design of frits and the maximize the waste in the glass for High-Level Waste (HLW) vitrification

  2. Integrated multiscale modeling of molecular computing devices

    International Nuclear Information System (INIS)

    Cummings, Peter T; Leng Yongsheng

    2005-01-01

    Molecular electronics, in which single organic molecules are designed to perform the functions of transistors, diodes, switches and other circuit elements used in current siliconbased microelecronics, is drawing wide interest as a potential replacement technology for conventional silicon-based lithographically etched microelectronic devices. In addition to their nanoscopic scale, the additional advantage of molecular electronics devices compared to silicon-based lithographically etched devices is the promise of being able to produce them cheaply on an industrial scale using wet chemistry methods (i.e., self-assembly from solution). The design of molecular electronics devices, and the processes to make them on an industrial scale, will require a thorough theoretical understanding of the molecular and higher level processes involved. Hence, the development of modeling techniques for molecular electronics devices is a high priority from both a basic science point of view (to understand the experimental studies in this field) and from an applied nanotechnology (manufacturing) point of view. Modeling molecular electronics devices requires computational methods at all length scales - electronic structure methods for calculating electron transport through organic molecules bonded to inorganic surfaces, molecular simulation methods for determining the structure of self-assembled films of organic molecules on inorganic surfaces, mesoscale methods to understand and predict the formation of mesoscale patterns on surfaces (including interconnect architecture), and macroscopic scale methods (including finite element methods) for simulating the behavior of molecular electronic circuit elements in a larger integrated device. Here we describe a large Department of Energy project involving six universities and one national laboratory aimed at developing integrated multiscale methods for modeling molecular electronics devices. The project is funded equally by the Office of Basic

  3. Enhanced index tracking modelling in portfolio optimization

    Science.gov (United States)

    Lam, W. S.; Hj. Jaaman, Saiful Hafizah; Ismail, Hamizun bin

    2013-09-01

    Enhanced index tracking is a popular form of passive fund management in stock market. It is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the risk. Enhanced index tracking aims to generate excess return over the return achieved by the index without purchasing all of the stocks that make up the index by establishing an optimal portfolio. The objective of this study is to determine the optimal portfolio composition and performance by using weighted model in enhanced index tracking. Weighted model focuses on the trade-off between the excess return and the risk. The results of this study show that the optimal portfolio for the weighted model is able to outperform the Malaysia market index which is Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.

  4. Modeling investor optimism with fuzzy connectives

    NARCIS (Netherlands)

    Lovric, M.; Almeida, R.J.; Kaymak, U.; Spronk, J.; Carvalho, J.P.; Dubois, D.; Kaymak, U.; Sousa, J.M.C.

    2009-01-01

    Optimism or pessimism of investors is one of the important characteristics that determine the investment behavior in financial markets. In this paper, we propose a model of investor optimism based on a fuzzy connective. The advantage of the proposed approach is that the influence of different levels

  5. Pavement maintenance optimization model using Markov Decision Processes

    Science.gov (United States)

    Mandiartha, P.; Duffield, C. F.; Razelan, I. S. b. M.; Ismail, A. b. H.

    2017-09-01

    This paper presents an optimization model for selection of pavement maintenance intervention using a theory of Markov Decision Processes (MDP). There are some particular characteristics of the MDP developed in this paper which distinguish it from other similar studies or optimization models intended for pavement maintenance policy development. These unique characteristics include a direct inclusion of constraints into the formulation of MDP, the use of an average cost method of MDP, and the policy development process based on the dual linear programming solution. The limited information or discussions that are available on these matters in terms of stochastic based optimization model in road network management motivates this study. This paper uses a data set acquired from road authorities of state of Victoria, Australia, to test the model and recommends steps in the computation of MDP based stochastic optimization model, leading to the development of optimum pavement maintenance policy.

  6. Maintenance Optimization of High Voltage Substation Model

    Directory of Open Access Journals (Sweden)

    Radim Bris

    2008-01-01

    Full Text Available The real system from practice is selected for optimization purpose in this paper. We describe the real scheme of a high voltage (HV substation in different work states. Model scheme of the HV substation 22 kV is demonstrated within the paper. The scheme serves as input model scheme for the maintenance optimization. The input reliability and cost parameters of all components are given: the preventive and corrective maintenance costs, the actual maintenance period (being optimized, the failure rate and mean time to repair - MTTR.

  7. DockingShop: A Tool for Interactive Molecular Docking

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Ting-Cheng; Max, Nelson L.; Ding, Jinhui; Bethel, E. Wes; Crivelli, Silvia N.

    2005-04-24

    Given two independently determined molecular structures, the molecular docking problem predicts the bound association, or best fit between them, while allowing for conformational changes of the individual molecules during construction of a molecular complex. Docking Shop is an integrated environment that permits interactive molecular docking by navigating a ligand or protein to an estimated binding site of a receptor with real-time graphical feedback of scoring factors as visual guides. Our program can be used to create initial configurations for a protein docking prediction process. Its output--the structure of aprotein-ligand or protein-protein complex--may serve as an input for aprotein docking algorithm, or an optimization process. This tool provides molecular graphics interfaces for structure modeling, interactive manipulation, navigation, optimization, and dynamic visualization to aid users steer the prediction process using their biological knowledge.

  8. Hierarchical models and iterative optimization of hybrid systems

    Energy Technology Data Exchange (ETDEWEB)

    Rasina, Irina V. [Ailamazyan Program Systems Institute, Russian Academy of Sciences, Peter One str. 4a, Pereslavl-Zalessky, 152021 (Russian Federation); Baturina, Olga V. [Trapeznikov Control Sciences Institute, Russian Academy of Sciences, Profsoyuznaya str. 65, 117997, Moscow (Russian Federation); Nasatueva, Soelma N. [Buryat State University, Smolina str.24a, Ulan-Ude, 670000 (Russian Federation)

    2016-06-08

    A class of hybrid control systems on the base of two-level discrete-continuous model is considered. The concept of this model was proposed and developed in preceding works as a concretization of the general multi-step system with related optimality conditions. A new iterative optimization procedure for such systems is developed on the base of localization of the global optimality conditions via contraction the control set.

  9. Modeling and Optimizing Antennas for Rotational Spectroscopy Applications

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-12-01

    Full Text Available In the paper, dielectric and metallic lenses are modeled and optimized in order to enhance the gain of a horn antenna in the frequency range from 60 GHz to 100 GHz. Properties of designed lenses are compared and discussed. The structures are modeled in CST Microwave Studio and optimized by Particle Swarm Optimization (PSO in order to get required antenna parameters.

  10. Modeling the intermolecular interactions: molecular structure of N-3-hydroxyphenyl-4-methoxybenzamide.

    Science.gov (United States)

    Karabulut, Sedat; Namli, Hilmi; Kurtaran, Raif; Yildirim, Leyla Tatar; Leszczynski, Jerzy

    2014-03-01

    The title compound, N-3-hydroxyphenyl-4-methoxybenzamide (3) was prepared by the acylation reaction of 3-aminophenol (1) and 4-metoxybenzoylchloride (2) in THF and characterized by ¹H NMR, ¹³C NMR and elemental analysis. Molecular structure of the crystal was determined by single crystal X-ray diffraction and DFT calculations. 3 crystallizes in monoclinic P2₁/c space group. The influence of intermolecular interactions (dimerization and crystal packing) on molecular geometry has been evaluated by calculations performed for three different models; monomer (3), dimer (4) and dimer with added unit cell contacts (5). Molecular structure of 3, 4 and 5 was optimized by applying B3LYP method with 6-31G+(d,p) basis set in gas phase and compared with X-ray crystallographic data including bond lengths, bond angles and selected dihedral angles. It has been concluded that although the crystal packing and dimerization have a minor effect on bond lengths and angles, however, these interactions are important for the dihedral angles and the rotational conformation of aromatic rings. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. A study on an optimal movement model

    Energy Technology Data Exchange (ETDEWEB)

    Feng Jianfeng [COGS, Sussex University, Brighton BN1 9QH, UK (United Kingdom); Zhang, Kewei [SMS, Sussex University, Brighton BN1 9QH (United Kingdom); Luo Yousong [Department of Mathematics and Statistics, RMIT University, GOP Box 2476V, Melbourne, Vic 3001 (Australia)

    2003-07-11

    We present an analytical and rigorous study on a TOPS (task optimization in the presence of signal-dependent noise) model with a hold-on or an end-point control. Optimal control signals are rigorously obtained, which enables us to investigate various issues about the model including its trajectories, velocities, control signals, variances and the dependence of these quantities on various model parameters. With the hold-on control, we find that the optimal control can be implemented with an almost 'nil' hold-on period. The optimal control signal is a linear combination of two sub-control signals. One of the sub-control signals is positive and the other is negative. With the end-point control, the end-point variance is dramatically reduced, in comparison with the hold-on control. However, the velocity is not symmetric (bell shape). Finally, we point out that the velocity with a hold-on control takes the bell shape only within a limited parameter region.

  12. Clinical dosimetry in molecular radiotherapy: protocol optimization and clinical implementation

    International Nuclear Information System (INIS)

    Ferrer, Ludovic

    2011-01-01

    Molecular radiotherapy (mrt) consists in destructing tumour targets by radiolabelled vectors. This nuclear medicine specialty is being considered with increasing interest for example via the success achieved in the treatment of non-Hodgkin lymphomas by radioimmunotherapy. One of the keys of mrt optimization relies on the personalising of absorbed doses delivered to the patient: This is required to ascertain that irradiation is focused on tumour cells while keeping surrounding healthy tissue irradiation at an acceptable - non-toxic - level. Radiation dose evaluation in mrt requires in one hand, the spatial and temporal localization of injected radioactive sources by scintigraphic imaging, and on a second hand, the knowledge of the emitted radiation propagating media, given by CT imaging. Global accuracy relies on the accuracy of each of the steps that contribute to clinical dosimetry. There is no reference, standardized dosimetric protocol to date. Due to heterogeneous implementations, evaluation of the accuracy of the absorbed dose is a difficult task. In this thesis, we developed and evaluated different dosimetric approaches that allow us to find a relationship between the absorbed dose to the bone marrow and haematological toxicity. Besides, we built a scientific project, called DosiTest, which aims at evaluating the impact of the various step that contribute to the realization of a dosimetric study, by means of a virtual multicentric comparison based on Monte-Carlo modelling. (author) [fr

  13. QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES.

    Science.gov (United States)

    Toropov, A A; Toropova, A P; Raska, I

    2008-04-01

    Simplified molecular input line entry system (SMILES) has been utilized in constructing quantitative structure-property relationships (QSPR) for octanol/water partition coefficient of vitamins and organic compounds of different classes by optimal descriptors. Statistical characteristics of the best model (vitamins) are the following: n=17, R(2)=0.9841, s=0.634, F=931 (training set); n=7, R(2)=0.9928, s=0.773, F=690 (test set). Using this approach for modeling octanol/water partition coefficient for a set of organic compounds gives a model that is statistically characterized by n=69, R(2)=0.9872, s=0.156, F=5184 (training set) and n=70, R(2)=0.9841, s=0.179, F=4195 (test set).

  14. A Framework for the Optimization of Discrete-Event Simulation Models

    Science.gov (United States)

    Joshi, B. D.; Unal, R.; White, N. H.; Morris, W. D.

    1996-01-01

    With the growing use of computer modeling and simulation, in all aspects of engineering, the scope of traditional optimization has to be extended to include simulation models. Some unique aspects have to be addressed while optimizing via stochastic simulation models. The optimization procedure has to explicitly account for the randomness inherent in the stochastic measures predicted by the model. This paper outlines a general purpose framework for optimization of terminating discrete-event simulation models. The methodology combines a chance constraint approach for problem formulation, together with standard statistical estimation and analyses techniques. The applicability of the optimization framework is illustrated by minimizing the operation and support resources of a launch vehicle, through a simulation model.

  15. Limiting assumptions in molecular modeling: electrostatics.

    Science.gov (United States)

    Marshall, Garland R

    2013-02-01

    Molecular mechanics attempts to represent intermolecular interactions in terms of classical physics. Initial efforts assumed a point charge located at the atom center and coulombic interactions. It is been recognized over multiple decades that simply representing electrostatics with a charge on each atom failed to reproduce the electrostatic potential surrounding a molecule as estimated by quantum mechanics. Molecular orbitals are not spherically symmetrical, an implicit assumption of monopole electrostatics. This perspective reviews recent evidence that requires use of multipole electrostatics and polarizability in molecular modeling.

  16. Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-07-01

    Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

  17. Optimizing a gap conductance model applicable to VVER-1000 thermal–hydraulic model

    International Nuclear Information System (INIS)

    Rahgoshay, M.; Hashemi-Tilehnoee, M.

    2012-01-01

    Highlights: ► Two known conductance models for application in VVER-1000 thermal–hydraulic code are examined. ► An optimized gap conductance model is developed which can predict the gap conductance in good agreement with FSAR data. ► The licensed thermal–hydraulic code is coupled with the gap conductance model predictor externally. -- Abstract: The modeling of gap conductance for application in VVER-1000 thermal–hydraulic codes is addressed. Two known models, namely CALZA-BINI and RELAP5 gap conductance models, are examined. By externally linking of gap conductance models and COBRA-EN thermal hydraulic code, the acceptable range of each model is specified. The result of each gap conductance model versus linear heat rate has been compared with FSAR data. A linear heat rate of about 9 kW/m is the boundary for optimization process. Since each gap conductance model has its advantages and limitation, the optimized gap conductance model can predict the gap conductance better than each of the two other models individually.

  18. Theoretical molecular biophysics

    CERN Document Server

    Scherer, Philipp O J

    2017-01-01

    This book gives an introduction to molecular biophysics. It starts from material properties at equilibrium related to polymers, dielectrics and membranes. Electronic spectra are developed for the understanding of elementary dynamic processes in photosynthesis including proton transfer and dynamics of molecular motors. Since the molecular structures of functional groups of bio-systems were resolved, it has become feasible to develop a theory based on the quantum theory and statistical physics with emphasis on the specifics of the high complexity of bio-systems. This introduction to molecular aspects of the field focuses on solvable models. Elementary biological processes provide as special challenge the presence of partial disorder in the structure which does not destroy the basic reproducibility of the processes. Apparently the elementary molecular processes are organized in a way to optimize the efficiency. Learning from nature by means exploring the relation between structure and function may even help to b...

  19. Optimization model for the design of distributed wastewater treatment networks

    Directory of Open Access Journals (Sweden)

    Ibrić Nidret

    2012-01-01

    Full Text Available In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal. The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.

  20. Empty tracks optimization based on Z-Map model

    Science.gov (United States)

    Liu, Le; Yan, Guangrong; Wang, Zaijun; Zang, Genao

    2017-12-01

    For parts with many features, there are more empty tracks during machining. If these tracks are not optimized, the machining efficiency will be seriously affected. In this paper, the characteristics of the empty tracks are studied in detail. Combining with the existing optimization algorithm, a new tracks optimization method based on Z-Map model is proposed. In this method, the tool tracks are divided into the unit processing section, and then the Z-Map model simulation technique is used to analyze the order constraint between the unit segments. The empty stroke optimization problem is transformed into the TSP with sequential constraints, and then through the genetic algorithm solves the established TSP problem. This kind of optimization method can not only optimize the simple structural parts, but also optimize the complex structural parts, so as to effectively plan the empty tracks and greatly improve the processing efficiency.

  1. A Parallel Biological Optimization Algorithm to Solve the Unbalanced Assignment Problem Based on DNA Molecular Computing.

    Science.gov (United States)

    Wang, Zhaocai; Pu, Jun; Cao, Liling; Tan, Jian

    2015-10-23

    The unbalanced assignment problem (UAP) is to optimally resolve the problem of assigning n jobs to m individuals (m applied mathematics, having numerous real life applications. In this paper, we present a new parallel DNA algorithm for solving the unbalanced assignment problem using DNA molecular operations. We reasonably design flexible-length DNA strands representing different jobs and individuals, take appropriate steps, and get the solutions of the UAP in the proper length range and O(mn) time. We extend the application of DNA molecular operations and simultaneity to simplify the complexity of the computation.

  2. Exploring RNA structure by integrative molecular modelling

    DEFF Research Database (Denmark)

    Masquida, Benoît; Beckert, Bertrand; Jossinet, Fabrice

    2010-01-01

    RNA molecular modelling is adequate to rapidly tackle the structure of RNA molecules. With new structured RNAs constituting a central class of cellular regulators discovered every year, the need for swift and reliable modelling methods is more crucial than ever. The pragmatic method based...... on interactive all-atom molecular modelling relies on the observation that specific structural motifs are recurrently found in RNA sequences. Once identified by a combination of comparative sequence analysis and biochemical data, the motifs composing the secondary structure of a given RNA can be extruded...

  3. SYNTHESIS AND CHARACTERIZATION OF NEW STABILIZERS WITH OPTIMAL MOLECULAR WEIGHT

    Institute of Scientific and Technical Information of China (English)

    Jiang-qing Pan

    2001-01-01

    Over 2 × l08 tons of polymers are produced every year, and a large portion of polymers faces the degradation problem. There are many effective methods to protect polymers against degradation and the addition of stabilizers to polymer remains the most convenient and effective way of enhancing polymer life and performance. In this article, a series of effective stabilizers with optimal molecular weight (MW), including common, monomeric and polymeric stabilizers (antioxidant and light stabilizer) were synthesized using isocyanation, controlled isocyanation, hydrosilylation, epoxide addition, macroreaction of stabilizing functional compounds and polymerization of monomeric stabilizers. The sructure and performance of these new stabilizers were characterized by using IR, NMR, MS, UV-spectra, XPS and elemental analysis. The current development of stabilizer synthesis was also reviewed.``

  4. Optimal treatment interruptions control of TB transmission model

    Science.gov (United States)

    Nainggolan, Jonner; Suparwati, Titik; Kawuwung, Westy B.

    2018-03-01

    A tuberculosis model which incorporates treatment interruptions of infectives is established. Optimal control of individuals infected with active TB is given in the model. It is obtained that the control reproduction numbers is smaller than the reproduction number, this means treatment controls could optimize the decrease in the spread of active TB. For this model, controls on treatment of infection individuals to reduce the actively infected individual populations, by application the Pontryagins Maximum Principle for optimal control. The result further emphasized the importance of controlling disease relapse in reducing the number of actively infected and treatment interruptions individuals with tuberculosis.

  5. Modeling, simulation and optimization of bipedal walking

    CERN Document Server

    Berns, Karsten

    2013-01-01

    The model-based investigation of motions of anthropomorphic systems is an important interdisciplinary research topic involving specialists from many fields such as Robotics, Biomechanics, Physiology, Orthopedics, Psychology, Neurosciences, Sports, Computer Graphics and Applied Mathematics. This book presents a study of basic locomotion forms such as walking and running is of particular interest due to the high demand on dynamic coordination, actuator efficiency and balance control. Mathematical models and numerical simulation and optimization techniques are explained, in combination with experimental data, which can help to better understand the basic underlying mechanisms of these motions and to improve them. Example topics treated in this book are Modeling techniques for anthropomorphic bipedal walking systems Optimized walking motions for different objective functions Identification of objective functions from measurements Simulation and optimization approaches for humanoid robots Biologically inspired con...

  6. Computer models for optimizing radiation therapy

    International Nuclear Information System (INIS)

    Duechting, W.

    1998-01-01

    The aim of this contribution is to outline how methods of system analysis, control therapy and modelling can be applied to simulate normal and malignant cell growth and to optimize cancer treatment as for instance radiation therapy. Based on biological observations and cell kinetic data, several types of models have been developed describing the growth of tumor spheroids and the cell renewal of normal tissue. The irradiation model is represented by the so-called linear-quadratic model describing the survival fraction as a function of the dose. Based thereon, numerous simulation runs for different treatment schemes can be performed. Thus, it is possible to study the radiation effect on tumor and normal tissue separately. Finally, this method enables a computer-assisted recommendation for an optimal patient-specific treatment schedule prior to clinical therapy. (orig.) [de

  7. Modeling and optimization of LCD optical performance

    CERN Document Server

    Yakovlev, Dmitry A; Kwok, Hoi-Sing

    2015-01-01

    The aim of this book is to present the theoretical foundations of modeling the optical characteristics of liquid crystal displays, critically reviewing modern modeling methods and examining areas of applicability. The modern matrix formalisms of optics of anisotropic stratified media, most convenient for solving problems of numerical modeling and optimization of LCD, will be considered in detail. The benefits of combined use of the matrix methods will be shown, which generally provides the best compromise between physical adequacy and accuracy with computational efficiency and optimization fac

  8. Automatized Parameterization of DFTB Using Particle Swarm Optimization.

    Science.gov (United States)

    Chou, Chien-Pin; Nishimura, Yoshifumi; Fan, Chin-Chai; Mazur, Grzegorz; Irle, Stephan; Witek, Henryk A

    2016-01-12

    We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented.

  9. Modeling and simulations for molecular scale hydrodynamics of the moving contact line in immiscible two-phase flows

    KAUST Repository

    Qian, Tiezheng

    2009-10-29

    This paper starts with an introduction to the Onsager principle of minimum energy dissipation which governs the optimal paths of deviation and restoration to equilibrium. Then there is a review of the variational approach to moving contact line hydrodynamics. To demonstrate the validity of our continuum hydrodynamic model, numerical results from model calculations and molecular dynamics simulations are presented for immiscible Couette and Poiseuille flows past homogeneous solid surfaces, with remarkable overall agreement. Our continuum model is also used to study the contact line motion on surfaces patterned with stripes of different contact angles (i.e. surfaces of varying wettability). Continuum calculations predict the stick-slip motion for contact lines moving along these patterned surfaces, in quantitative agreement with molecular dynamics simulation results. This periodic motion is tunable through pattern period (geometry) and contrast in wetting property (chemistry). The consequence of stick-slip contact line motion on energy dissipation is discussed. © 2009 IOP Publishing Ltd.

  10. Monte Carlo Optimization of Crystal Configuration for Pixelated Molecular SPECT Scanners

    Energy Technology Data Exchange (ETDEWEB)

    Mahani, Hojjat [Radiation Application Research School, Nuclear Science and Technology Research Institute, Tehran (Iran, Islamic Republic of); Research Center for Molecular and Cellular Imaging, Tehran University of Medical Science, Tehran (Iran, Islamic Republic of); Raisali, Gholamreza [Radiation Application Research School, Nuclear Science and Technology Research Institute, Tehran (Iran, Islamic Republic of); Kamali-Asl, Alireza [Radiation Medicine Department, Shahid Beheshti University, Tehran (Iran, Islamic Republic of); Ay, Mohammad Reza, E-mail: mohammadreza_ay@sina.tums.ac.ir [Research Center for Molecular and Cellular Imaging, Tehran University of Medical Science, Tehran (Iran, Islamic Republic of); Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Science, Tehran (Iran, Islamic Republic of)

    2017-02-01

    Resolution-sensitivity-PDA tradeoff is the most challenging problem in design and optimization of pixelated preclinical SPECT scanners. In this work, we addressed such a challenge from a crystal point-of-view by looking for an optimal pixelated scintillator using GATE Monte Carlo simulation. Various crystal configurations have been investigated and the influence of different pixel sizes, pixel gaps, and three scintillators on tomographic resolution, sensitivity, and PDA of the camera were evaluated. The crystal configuration was then optimized using two objective functions: the weighted-sum and the figure-of-merit methods. The CsI(Na) reveals the highest sensitivity of the order of 43.47 cps/MBq in comparison to the NaI(Tl) and the YAP(Ce), for a 1.5×1.5 mm{sup 2} pixel size and 0.1 mm gap. The results show that the spatial resolution, in terms of FWHM, improves from 3.38 to 2.21 mm while the sensitivity simultaneously deteriorates from 42.39 cps/MBq to 27.81 cps/MBq when pixel size varies from 2×2 mm{sup 2} to 0.5×0.5 mm{sup 2} for a 0.2 mm gap, respectively. The PDA worsens from 0.91 to 0.42 when pixel size decreases from 0.5×0.5 mm{sup 2} to 1×1 mm{sup 2} for a 0.2 mm gap at 15° incident-angle. The two objective functions agree that the 1.5×1.5 mm{sup 2} pixel size and 0.1 mm Epoxy gap CsI(Na) configuration provides the best compromise for small-animal imaging, using the HiReSPECT scanner. Our study highlights that crystal configuration can significantly affect the performance of the camera, and thereby Monte Carlo optimization of pixelated detectors is mandatory in order to achieve an optimal quality tomogram. - Highlights: • We optimized pixelated crystal configuration for the purpose of molecular SPECT imaging. • The weighted-sum and the figure-of-merit methods were used in order to search for an optimal crystal configuration. • The higher the pixel size, the poorer the resolution and simultaneously the higher the sensitivity and the PDA. • The

  11. Monte Carlo Optimization of Crystal Configuration for Pixelated Molecular SPECT Scanners

    International Nuclear Information System (INIS)

    Mahani, Hojjat; Raisali, Gholamreza; Kamali-Asl, Alireza; Ay, Mohammad Reza

    2017-01-01

    Resolution-sensitivity-PDA tradeoff is the most challenging problem in design and optimization of pixelated preclinical SPECT scanners. In this work, we addressed such a challenge from a crystal point-of-view by looking for an optimal pixelated scintillator using GATE Monte Carlo simulation. Various crystal configurations have been investigated and the influence of different pixel sizes, pixel gaps, and three scintillators on tomographic resolution, sensitivity, and PDA of the camera were evaluated. The crystal configuration was then optimized using two objective functions: the weighted-sum and the figure-of-merit methods. The CsI(Na) reveals the highest sensitivity of the order of 43.47 cps/MBq in comparison to the NaI(Tl) and the YAP(Ce), for a 1.5×1.5 mm"2 pixel size and 0.1 mm gap. The results show that the spatial resolution, in terms of FWHM, improves from 3.38 to 2.21 mm while the sensitivity simultaneously deteriorates from 42.39 cps/MBq to 27.81 cps/MBq when pixel size varies from 2×2 mm"2 to 0.5×0.5 mm"2 for a 0.2 mm gap, respectively. The PDA worsens from 0.91 to 0.42 when pixel size decreases from 0.5×0.5 mm"2 to 1×1 mm"2 for a 0.2 mm gap at 15° incident-angle. The two objective functions agree that the 1.5×1.5 mm"2 pixel size and 0.1 mm Epoxy gap CsI(Na) configuration provides the best compromise for small-animal imaging, using the HiReSPECT scanner. Our study highlights that crystal configuration can significantly affect the performance of the camera, and thereby Monte Carlo optimization of pixelated detectors is mandatory in order to achieve an optimal quality tomogram. - Highlights: • We optimized pixelated crystal configuration for the purpose of molecular SPECT imaging. • The weighted-sum and the figure-of-merit methods were used in order to search for an optimal crystal configuration. • The higher the pixel size, the poorer the resolution and simultaneously the higher the sensitivity and the PDA. • The higher the pixel gap, the

  12. Optimal hedging with the cointegrated vector autoregressive model

    DEFF Research Database (Denmark)

    Gatarek, Lukasz; Johansen, Søren

    We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated with the...

  13. Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models

    International Nuclear Information System (INIS)

    Esposito, Emilio Xavier; Hopfinger, Anton J.; Shao, Chi-Yu; Su, Bo-Han; Chen, Sing-Zuo; Tseng, Yufeng Jane

    2015-01-01

    Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted from the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. - Highlights: • Proposed toxicity mechanism of action for decorated nanotubes complexes • Discussion of the key molecular features for each endpoint's mechanism of action • Unique mechanisms of action for each of the six biological systems • Hypothesized mechanisms of action based on QSAR/QNAR predictive models

  14. Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models

    Energy Technology Data Exchange (ETDEWEB)

    Esposito, Emilio Xavier, E-mail: emilio@exeResearch.com [exeResearch, LLC, 32 University Drive, East Lansing, MI 48823 (United States); The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, IL 60045 (United States); Hopfinger, Anton J., E-mail: hopfingr@gmail.com [The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, IL 60045 (United States); College of Pharmacy MSC09 5360, 1 University of New Mexico, Albuquerque, NM, 87131 (United States); Shao, Chi-Yu [Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Sec. 4, Roosevelt Road, Taipei 106, Taiwan (China); Su, Bo-Han [Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Sec. 4, Roosevelt Road, Taipei 106, Taiwan (China); Chen, Sing-Zuo [Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Sec. 4, Roosevelt Road, Taipei 106, Taiwan (China); Tseng, Yufeng Jane, E-mail: yjtseng@csie.ntu.edu.tw [Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Sec. 4, Roosevelt Road, Taipei 106, Taiwan (China); Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Sec. 4, Roosevelt Road, Taipei 106, Taiwan (China)

    2015-10-01

    Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted from the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. - Highlights: • Proposed toxicity mechanism of action for decorated nanotubes complexes • Discussion of the key molecular features for each endpoint's mechanism of action • Unique mechanisms of action for each of the six biological systems • Hypothesized mechanisms of action based on QSAR/QNAR predictive models.

  15. Modeling of molecular weight and molecular weight distribution in slurry polymerization of propylene by Ziegler-Natta catalysts

    International Nuclear Information System (INIS)

    Khorasani, R.; Pourmahdian, S.

    2007-01-01

    The Precise prediction of polypropylene synthesized by heterogeneous Ziegler-Natta catalysts needs good knowledge of parameters affecting on polymerization. molecular weight and molecular weight distribution are among important characteristics of a polymer determining physical-mechanical properties. broadening of molecular weight distribution is an important and well known characteristic of polypropylene synthesized by heterogeneous Ziegler-Natta catalysts, So it is important to understand the origin of broad molecular weight. Two main factors in broadening molecular weight, namely mass transfer resistances and multiplicity of active sites, are discussed in this paper and a model including these factors is presented. Then we calculate molecular weight and molecular weight distribution by the model and compare our results with

  16. A consistent transported PDF model for treating differential molecular diffusion

    Science.gov (United States)

    Wang, Haifeng; Zhang, Pei

    2016-11-01

    Differential molecular diffusion is a fundamentally significant phenomenon in all multi-component turbulent reacting or non-reacting flows caused by the different rates of molecular diffusion of energy and species concentrations. In the transported probability density function (PDF) method, the differential molecular diffusion can be treated by using a mean drift model developed by McDermott and Pope. This model correctly accounts for the differential molecular diffusion in the scalar mean transport and yields a correct DNS limit of the scalar variance production. The model, however, misses the molecular diffusion term in the scalar variance transport equation, which yields an inconsistent prediction of the scalar variance in the transported PDF method. In this work, a new model is introduced to remedy this problem that can yield a consistent scalar variance prediction. The model formulation along with its numerical implementation is discussed, and the model validation is conducted in a turbulent mixing layer problem.

  17. Molecular level biodegradation of phenol and its derivatives through dmp operon of Pseudomonas putida: A bio-molecular modeling and docking analysis.

    Science.gov (United States)

    Ray, Sujay; Banerjee, Arundhati

    2015-10-01

    Participation of Pseudomonas putida-derived methyl phenol (dmp) operon and DmpR protein in the biodegradation of phenol or other harmful, organic, toxic pollutants was investigated at a molecular level. Documentation documents that P. putida has DmpR protein which positively regulates dmp operon in the presence of inducers; like phenols. From the operon, phenol hydroxylase encoded by dmpN gene, participates in degrading phenols after dmp operon is expressed. For the purpose, the 3-D models of the four domains from DmpR protein and of the DNA sequences from the two Upstream Activation Sequences (UAS) present at the promoter region of the operon were demonstrated using discrete molecular modeling techniques. The best modeled structures satisfying their stereo-chemical properties were selected in each of the cases. To stabilize the individual structures, energy optimization was performed. In the presence of inducers, probable interactions among domains and then the two independent DNA structures with the fourth domain were perused by manifold molecular docking simulations. The complex structures were made to be stable by minimizing their overall energy. Responsible amino acid residues, nucleotide bases and binding patterns for the biodegradation, were examined. In the presence of the inducers, the biodegradation process is initiated by the interaction of phe50 from the first protein domain with the inducers. Only after the interaction of the last domain with the DNA sequences individually, the operon is expressed. This novel residue level study is paramount for initiating transcription in the operon; thereby leading to expression of phenol hydroxylase followed by phenol biodegradation. Copyright © 2015. Published by Elsevier B.V.

  18. Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models.

    Science.gov (United States)

    Esposito, Emilio Xavier; Hopfinger, Anton J; Shao, Chi-Yu; Su, Bo-Han; Chen, Sing-Zuo; Tseng, Yufeng Jane

    2015-10-01

    Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted from the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Testing the molecular clock using mechanistic models of fossil preservation and molecular evolution.

    Science.gov (United States)

    Warnock, Rachel C M; Yang, Ziheng; Donoghue, Philip C J

    2017-06-28

    Molecular sequence data provide information about relative times only, and fossil-based age constraints are the ultimate source of information about absolute times in molecular clock dating analyses. Thus, fossil calibrations are critical to molecular clock dating, but competing methods are difficult to evaluate empirically because the true evolutionary time scale is never known. Here, we combine mechanistic models of fossil preservation and sequence evolution in simulations to evaluate different approaches to constructing fossil calibrations and their impact on Bayesian molecular clock dating, and the relative impact of fossil versus molecular sampling. We show that divergence time estimation is impacted by the model of fossil preservation, sampling intensity and tree shape. The addition of sequence data may improve molecular clock estimates, but accuracy and precision is dominated by the quality of the fossil calibrations. Posterior means and medians are poor representatives of true divergence times; posterior intervals provide a much more accurate estimate of divergence times, though they may be wide and often do not have high coverage probability. Our results highlight the importance of increased fossil sampling and improved statistical approaches to generating calibrations, which should incorporate the non-uniform nature of ecological and temporal fossil species distributions. © 2017 The Authors.

  20. Optimization and evaluation of probabilistic-logic sequence models

    DEFF Research Database (Denmark)

    Christiansen, Henning; Lassen, Ole Torp

    to, in principle, Turing complete languages. In general, such models are computationally far to complex for direct use, so optimization by pruning and approximation are needed. % The first steps are made towards a methodology for optimizing such models by approximations using auxiliary models......Analysis of biological sequence data demands more and more sophisticated and fine-grained models, but these in turn introduce hard computational problems. A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and context-free languages...

  1. MARKOV CHAIN PORTFOLIO LIQUIDITY OPTIMIZATION MODEL

    Directory of Open Access Journals (Sweden)

    Eder Oliveira Abensur

    2014-05-01

    Full Text Available The international financial crisis of September 2008 and May 2010 showed the importance of liquidity as an attribute to be considered in portfolio decisions. This study proposes an optimization model based on available public data, using Markov chain and Genetic Algorithms concepts as it considers the classic duality of risk versus return and incorporating liquidity costs. The work intends to propose a multi-criterion non-linear optimization model using liquidity based on a Markov chain. The non-linear model was tested using Genetic Algorithms with twenty five Brazilian stocks from 2007 to 2009. The results suggest that this is an innovative development methodology and useful for developing an efficient and realistic financial portfolio, as it considers many attributes such as risk, return and liquidity.

  2. Property Analysis of Exfoliated Graphite Nanoplatelets Modified Asphalt Model Using Molecular Dynamics (MD Method

    Directory of Open Access Journals (Sweden)

    Hui Yao

    2017-01-01

    Full Text Available This Molecular Dynamics (MD simulation paper presents a physical property comparison study between exfoliated graphite nanoplatelets (xGNP modified and control asphalt models, including density, glass transition temperature, viscosity and thermal conductivity. The three-component control asphalt model consists of asphaltenes, aromatics, and saturates based on previous references. The xGNP asphalt model was built by incorporating an xGNP and control asphalt model and controlling mass ratios to represent the laboratory prepared samples. The Amber Cornell Extension Force Field (ACEFF was used with assigned molecular electro-static potential (ESP charge from NWChem analysis. After optimization and ensemble relaxation, the properties of the control and xGNP modified asphalt models were computed and analyzed using the MD method. The MD simulated results have a similar trend as the test results. The property analysis showed that: (1 the density of the xGNP modified model is higher than that of the control model; (2 the glass transition temperature of the xGNP modified model is closer to the laboratory data of the Strategic Highway Research Program (SHRP asphalt binders than that of the control model; (3 the viscosities of the xGNP modified model at different temperatures are higher than those of the control model, and it coincides with the trend in the laboratory data; (4 the thermal conductivities of the xGNP modified asphalt model are higher than those of the control asphalt model at different temperatures, and it is consistent with the trend in the laboratory data.

  3. Computational modeling on the recognition of the HRE motif by HIF-1: molecular docking and molecular dynamics studies.

    Science.gov (United States)

    Sokkar, Pandian; Sathis, Vani; Ramachandran, Murugesan

    2012-05-01

    Hypoxia inducible factor-1 (HIF-1) is a bHLH-family transcription factor that controls genes involved in glycolysis, angiogenesis, migration, as well as invasion factors that are important for tumor progression and metastasis. HIF-1, a heterodimer of HIF-1α and HIF-1β, binds to the hypoxia responsive element (HRE) present in the promoter regions of hypoxia responsive genes, such as vascular endothelial growth factor (VEGF). Neither the structure of free HIF-1 nor that of its complex with HRE is available. Computational modeling of the transcription factor-DNA complex has always been challenging due to their inherent flexibility and large conformational space. The present study aims to model the interaction between the DNA-binding domain of HIF-1 and HRE. Experiments showed that rigid macromolecular docking programs (HEX and GRAMM-X) failed to predict the optimal dimerization of individually modeled HIF-1 subunits. Hence, the HIF-1 heterodimer was modeled based on the phosphate system positive regulatory protein (PHO4) homodimer. The duplex VEGF-DNA segment containing HRE with flanking nucleotides was modeled in the B form and equilibrated via molecular dynamics (MD) simulation. A rigid docking approach was used to predict the crude binding mode of HIF-1 dimer with HRE, in which the putative contacts were found to be present. An MD simulation (5 ns) of the HIF-1-HRE complex in explicit water was performed to account for its flexibility and to optimize its interactions. All of the conserved amino acid residues were found to play roles in the recognition of HRE. The present work, which sheds light on the recognition of HRE by HIF-1, could be beneficial in the design of peptide or small molecule therapeutics that can mimic HIF-1 and bind with the HRE sequence.

  4. AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening

    Directory of Open Access Journals (Sweden)

    Pajeva Ilza

    2008-10-01

    Full Text Available Abstract Background Virtual or in silico ligand screening combined with other computational methods is one of the most promising methods to search for new lead compounds, thereby greatly assisting the drug discovery process. Despite considerable progresses made in virtual screening methodologies, available computer programs do not easily address problems such as: structural optimization of compounds in a screening library, receptor flexibility/induced-fit, and accurate prediction of protein-ligand interactions. It has been shown that structural optimization of chemical compounds and that post-docking optimization in multi-step structure-based virtual screening approaches help to further improve the overall efficiency of the methods. To address some of these points, we developed the program AMMOS for refining both, the 3D structures of the small molecules present in chemical libraries and the predicted receptor-ligand complexes through allowing partial to full atom flexibility through molecular mechanics optimization. Results The program AMMOS carries out an automatic procedure that allows for the structural refinement of compound collections and energy minimization of protein-ligand complexes using the open source program AMMP. The performance of our package was evaluated by comparing the structures of small chemical entities minimized by AMMOS with those minimized with the Tripos and MMFF94s force fields. Next, AMMOS was used for full flexible minimization of protein-ligands complexes obtained from a mutli-step virtual screening. Enrichment studies of the selected pre-docked complexes containing 60% of the initially added inhibitors were carried out with or without final AMMOS minimization on two protein targets having different binding pocket properties. AMMOS was able to improve the enrichment after the pre-docking stage with 40 to 60% of the initially added active compounds found in the top 3% to 5% of the entire compound collection

  5. Hybridization of tensor-optimized and high-momentum antisymmetrized molecular dynamics for light nuclei with bare interaction

    Science.gov (United States)

    Lyu, Mengjiao; Isaka, Masahiro; Myo, Takayuki; Toki, Hiroshi; Ikeda, Kiyomi; Horiuchi, Hisashi; Suhara, Tadahiro; Yamada, Taiichi

    2018-01-01

    Many-body correlations play an essential role in the ab initio description of nuclei with nuclear bare interactions. We propose a new framework to describe light nuclei by the hybridization of the tensor-optimized antisymmetrized molecular dynamics (TOAMD) and the high-momentum AMD (HM-AMD), which we call "HM-TOAMD." In this framework, we describe the many-body correlations in terms of not only the correlation functions in TOAMD, but also the high-momentum pairs in the AMD wave function. With the bare nucleon-nucleon interaction AV8^', we sufficiently reproduce the energy and radius of the {^3}H nucleus in HM-TOAMD. The effects of tensor force and short-range repulsion in the bare interaction are nicely described in this new framework. We also discuss the convergence in calculation and flexibility of the model space for this new method.

  6. Optimal Model-Based Control in HVAC Systems

    DEFF Research Database (Denmark)

    Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik

    2008-01-01

    is developed. Then the optimal control structure is designed and implemented. The HVAC system is splitted into two subsystems. By selecting the right set-points and appropriate cost functions for each subsystem controller the optimal control strategy is respected to gaurantee the minimum thermal and electrical......This paper presents optimal model-based control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to- air heat exchanger. First dynamic model of the HVAC system...... energy consumption. Finally, the controller is applied to control the mentioned HVAC system and the results show that the expected goals are fulfilled....

  7. Analysis and optimization of a camber morphing wing model

    Directory of Open Access Journals (Sweden)

    Bing Li

    2016-09-01

    Full Text Available This article proposes a camber morphing wing model that can continuously change its camber. A mathematical model is proposed and a kinematic simulation is performed to verify the wing’s ability to change camber. An aerodynamic model is used to test its aerodynamic characteristics. Some important aerodynamic analyses are performed. A comparative analysis is conducted to explore the relationships between aerodynamic parameters, the rotation angle of the trailing edge, and the angle of attack. An improved artificial fish swarm optimization algorithm is proposed, referred to as the weighted adaptive artificial fish-swarm with embedded Hooke–Jeeves search method. Some comparison tests are used to test the performance of the improved optimization algorithm. Finally, the proposed optimization algorithm is used to optimize the proposed camber morphing wing model.

  8. Modeling and energy efficiency optimization of belt conveyors

    International Nuclear Information System (INIS)

    Zhang, Shirong; Xia, Xiaohua

    2011-01-01

    Highlights: → We take optimization approach to improve operation efficiency of belt conveyors. → An analytical energy model, originating from ISO 5048, is proposed. → Then an off-line and an on-line parameter estimation schemes are investigated. → In a case study, six optimization problems are formulated with solutions in simulation. - Abstract: The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment and operation levels. Specifically, variable speed control, an equipment level intervention, is recommended to improve operation efficiency of belt conveyors. However, the current implementations mostly focus on lower level control loops without operational considerations at the system level. This paper intends to take a model based optimization approach to improve the efficiency of belt conveyors at the operational level. An analytical energy model, originating from ISO 5048, is firstly proposed, which lumps all the parameters into four coefficients. Subsequently, both an off-line and an on-line parameter estimation schemes are applied to identify the new energy model, respectively. Simulation results are presented for the estimates of the four coefficients. Finally, optimization is done to achieve the best operation efficiency of belt conveyors under various constraints. Six optimization problems of a typical belt conveyor system are formulated, respectively, with solutions in simulation for a case study.

  9. Structural Probing and Molecular Modeling of the A3 Adenosine Receptor: A Focus on Agonist Binding

    Science.gov (United States)

    Ciancetta, Antonella; Jacobson, Kenneth A.

    2017-01-01

    Adenosine is an endogenous modulator exerting its functions through the activation of four adenosine receptor (AR) subtypes, termed A1, A2A, A2B and A3, which belong to the G protein-coupled receptor (GPCR) superfamily. The human A3AR (hA3AR) subtype is implicated in several cytoprotective functions. Therefore, hA3AR modulators, and in particular agonists, are sought for their potential application as anti-inflammatory, anticancer, and cardioprotective agents. Structure-based molecular modeling techniques have been applied over the years to rationalize the structure-activity relationships (SARs) of newly emerged A3AR ligands, guide the subsequent lead optimization, and interpret site-directed mutagenesis (SDM) data from a molecular perspective. In this review, we showcase selected modeling-based and guided strategies that were applied to elucidate the binding of agonists to the A3AR and discuss the challenges associated with an accurate prediction of the receptor extracellular vestibule through homology modeling from the available X-ray templates. PMID:28287473

  10. Structural Probing and Molecular Modeling of the A3 Adenosine Receptor: A Focus on Agonist Binding

    Directory of Open Access Journals (Sweden)

    Antonella Ciancetta

    2017-03-01

    Full Text Available Adenosine is an endogenous modulator exerting its functions through the activation of four adenosine receptor (AR subtypes, termed A1, A2A, A2B and A3, which belong to the G protein-coupled receptor (GPCR superfamily. The human A3AR (hA3AR subtype is implicated in several cytoprotective functions. Therefore, hA3AR modulators, and in particular agonists, are sought for their potential application as anti-inflammatory, anticancer, and cardioprotective agents. Structure-based molecular modeling techniques have been applied over the years to rationalize the structure–activity relationships (SARs of newly emerged A3AR ligands, guide the subsequent lead optimization, and interpret site-directed mutagenesis (SDM data from a molecular perspective. In this review, we showcase selected modeling-based and guided strategies that were applied to elucidate the binding of agonists to the A3AR and discuss the challenges associated with an accurate prediction of the receptor extracellular vestibule through homology modeling from the available X-ray templates.

  11. Model reduction for dynamic real-time optimization of chemical processes

    NARCIS (Netherlands)

    Van den Berg, J.

    2005-01-01

    The value of models in process industries becomes apparent in practice and literature where numerous successful applications are reported. Process models are being used for optimal plant design, simulation studies, for off-line and online process optimization. For online optimization applications

  12. Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm.

    Science.gov (United States)

    Li, Bai; Lin, Mu; Liu, Qiao; Li, Ya; Zhou, Changjun

    2015-10-01

    Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

  13. Constructing Molecular Models with Low-Cost Toy Beads

    Science.gov (United States)

    Ng, Pun-hon; Wong, Siu-ling; Mak, Se-yuen

    2012-01-01

    In teaching the science of the nano world, ball-and-stick molecular models are frequently used as 3D representations of molecules. Unlike a chemical formula, a molecular model allows us to visualise the 3D shape of the molecule and the relative positions of its atoms, the bonds between atoms and why a pair of mirror isomers with the same atoms,…

  14. Optimization of DNA Sensor Model Based Nanostructured Graphene Using Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Hediyeh Karimi

    2013-01-01

    Full Text Available It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.

  15. Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

    Energy Technology Data Exchange (ETDEWEB)

    Bellary, Sayed Ahmed Imran; Samad, Abdus [Indian Institute of Technology Madras, Chennai (India); Husain, Afzal [Sultan Qaboos University, Al-Khoudh (Oman)

    2014-12-15

    The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

  16. Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

    International Nuclear Information System (INIS)

    Bellary, Sayed Ahmed Imran; Samad, Abdus; Husain, Afzal

    2014-01-01

    The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

  17. Molecular Simulation towards Efficient and Representative Subsurface Reservoirs Modeling

    KAUST Repository

    Kadoura, Ahmad Salim

    2016-01-01

    This dissertation focuses on the application of Monte Carlo (MC) molecular simulation and Molecular Dynamics (MD) in modeling thermodynamics and flow of subsurface reservoir fluids. At first, MC molecular simulation is proposed as a promising method

  18. Efficient Iris Localization via Optimization Model

    Directory of Open Access Journals (Sweden)

    Qi Wang

    2017-01-01

    Full Text Available Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

  19. Models and Methods for Structural Topology Optimization with Discrete Design Variables

    DEFF Research Database (Denmark)

    Stolpe, Mathias

    in the conceptual design phase to find innovative designs. The strength of topology optimization is the capability of determining both the optimal shape and the topology of the structure. In some cases also the optimal material properties can be determined. Optimal structural design problems are modeled...... such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used in the conceptual design phase to find innovative designs. The strength of topology optimization is the capability of determining both the optimal......Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used...

  20. Adaptive surrogate model based multiobjective optimization for coastal aquifer management

    Science.gov (United States)

    Song, Jian; Yang, Yun; Wu, Jianfeng; Wu, Jichun; Sun, Xiaomin; Lin, Jin

    2018-06-01

    In this study, a novel surrogate model assisted multiobjective memetic algorithm (SMOMA) is developed for optimal pumping strategies of large-scale coastal groundwater problems. The proposed SMOMA integrates an efficient data-driven surrogate model with an improved non-dominated sorted genetic algorithm-II (NSGAII) that employs a local search operator to accelerate its convergence in optimization. The surrogate model based on Kernel Extreme Learning Machine (KELM) is developed and evaluated as an approximate simulator to generate the patterns of regional groundwater flow and salinity levels in coastal aquifers for reducing huge computational burden. The KELM model is adaptively trained during evolutionary search to satisfy desired fidelity level of surrogate so that it inhibits error accumulation of forecasting and results in correctly converging to true Pareto-optimal front. The proposed methodology is then applied to a large-scale coastal aquifer management in Baldwin County, Alabama. Objectives of minimizing the saltwater mass increase and maximizing the total pumping rate in the coastal aquifers are considered. The optimal solutions achieved by the proposed adaptive surrogate model are compared against those solutions obtained from one-shot surrogate model and original simulation model. The adaptive surrogate model does not only improve the prediction accuracy of Pareto-optimal solutions compared with those by the one-shot surrogate model, but also maintains the equivalent quality of Pareto-optimal solutions compared with those by NSGAII coupled with original simulation model, while retaining the advantage of surrogate models in reducing computational burden up to 94% of time-saving. This study shows that the proposed methodology is a computationally efficient and promising tool for multiobjective optimizations of coastal aquifer managements.

  1. Molecular dynamics modeling of polymer flammability

    International Nuclear Information System (INIS)

    Nyden, M.R.; Brown, J.E.; Lomakin, S.M.

    1992-01-01

    Molecular dynamic simulations were used to identify factors which promote char formation during the thermal degradation of polymers. Computer movies based on these simulations, indicate that cross-linked model polymers tend to undergo further cross-linking when burned, eventually forming a high molecular weight, thermally stable char. This paper reports that the prediction was confirmed by char yield measurements made on γ and e - -irradiated polyethylene and chemically cross-linked poly(methyl methacrylate)

  2. Constrained optimization via simulation models for new product innovation

    Science.gov (United States)

    Pujowidianto, Nugroho A.

    2017-11-01

    We consider the problem of constrained optimization where the decision makers aim to optimize the primary performance measure while constraining the secondary performance measures. This paper provides a brief overview of stochastically constrained optimization via discrete event simulation. Most review papers tend to be methodology-based. This review attempts to be problem-based as decision makers may have already decided on the problem formulation. We consider constrained optimization models as there are usually constraints on secondary performance measures as trade-off in new product development. It starts by laying out different possible methods and the reasons using constrained optimization via simulation models. It is then followed by the review of different simulation optimization approach to address constrained optimization depending on the number of decision variables, the type of constraints, and the risk preferences of the decision makers in handling uncertainties.

  3. Research on the decision-making model of land-use spatial optimization

    Science.gov (United States)

    He, Jianhua; Yu, Yan; Liu, Yanfang; Liang, Fei; Cai, Yuqiu

    2009-10-01

    Using the optimization result of landscape pattern and land use structure optimization as constraints of CA simulation results, a decision-making model of land use spatial optimization is established coupled the landscape pattern model with cellular automata to realize the land use quantitative and spatial optimization simultaneously. And Huangpi district is taken as a case study to verify the rationality of the model.

  4. A model of optimal voluntary muscular control.

    Science.gov (United States)

    FitzHugh, R

    1977-07-19

    In the absence of detailed knowledge of how the CNS controls a muscle through its motor fibers, a reasonable hypothesis is that of optimal control. This hypothesis is studied using a simplified mathematical model of a single muscle, based on A.V. Hill's equations, with series elastic element omitted, and with the motor signal represented by a single input variable. Two cost functions were used. The first was total energy expended by the muscle (work plus heat). If the load is a constant force, with no inertia, Hill's optimal velocity of shortening results. If the load includes a mass, analysis by optimal control theory shows that the motor signal to the muscle consists of three phases: (1) maximal stimulation to accelerate the mass to the optimal velocity as quickly as possible, (2) an intermediate level of stimulation to hold the velocity at its optimal value, once reached, and (3) zero stimulation, to permit the mass to slow down, as quickly as possible, to zero velocity at the specified distance shortened. If the latter distance is too small, or the mass too large, the optimal velocity is not reached, and phase (2) is absent. For lengthening, there is no optimal velocity; there are only two phases, zero stimulation followed by maximal stimulation. The second cost function was total time. The optimal control for shortening consists of only phases (1) and (3) above, and is identical to the minimal energy control whenever phase (2) is absent from the latter. Generalization of this model to include viscous loads and a series elastic element are discussed.

  5. Statistical molecular design of balanced compound libraries for QSAR modeling.

    Science.gov (United States)

    Linusson, A; Elofsson, M; Andersson, I E; Dahlgren, M K

    2010-01-01

    A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.

  6. Optimality principle for the coupled chemical reactions of ATP synthesis and its molecular interpretation

    Science.gov (United States)

    Nath, Sunil

    2018-05-01

    Metabolic energy obtained from the coupled chemical reactions of oxidative phosphorylation (OX PHOS) is harnessed in the form of ATP by cells. We experimentally measured thermodynamic forces and fluxes during ATP synthesis, and calculated the thermodynamic efficiency, η and the rate of free energy dissipation, Φ. We show that the OX PHOS system is tuned such that the coupled nonequilibrium processes operate at optimal η. This state does not coincide with the state of minimum Φ but is compatible with maximum Φ under the imposed constraints. Conditions that must hold for species concentration in order to satisfy the principle of optimal efficiency are derived analytically and a molecular explanation based on Nath's torsional mechanism of energy transduction and ATP synthesis is suggested. Differences of the proposed principle with Prigogine's principle are discussed.

  7. An optimization model to agroindustrial sector in antioquia (Colombia, South America)

    Science.gov (United States)

    Fernandez, J.

    2015-06-01

    This paper develops a proposal of a general optimization model for the flower industry, which is defined by using discrete simulation and nonlinear optimization, whose mathematical models have been solved by using ProModel simulation tools and Gams optimization. It defines the operations that constitute the production and marketing of the sector, statistically validated data taken directly from each operation through field work, the discrete simulation model of the operations and the linear optimization model of the entire industry chain are raised. The model is solved with the tools described above and presents the results validated in a case study.

  8. The independent molecular interaction sites model. Pt. 1

    International Nuclear Information System (INIS)

    Naumann, K.H.; Lippert, E.

    1981-01-01

    A new reference system for the treatment of molecular fluids within the framework of thermodynamic perturbation theory is presented. The basic ingredient of our approach is a potential transformation which allows us to view molecular liquids and gases as mixtures of formally independent molecular interaction sites (IMIS model). Some relations between out method and the RAM theory are discussed. (orig.)

  9. Biotechnology and genetic engineering in the new drug development. Part III. Biocatalysis, metabolic engineering and molecular modelling.

    Science.gov (United States)

    Stryjewska, Agnieszka; Kiepura, Katarzyna; Librowski, Tadeusz; Lochyński, Stanisław

    2013-01-01

    Industrial biotechnology has been defined as the use and application of biotechnology for the sustainable processing and production of chemicals, materials and fuels. It makes use of biocatalysts such as microbial communities, whole-cell microorganisms or purified enzymes. In the review these processes are described. Drug design is an iterative process which begins when a chemist identifies a compound that displays an interesting biological profile and ends when both the activity profile and the chemical synthesis of the new chemical entity are optimized. Traditional approaches to drug discovery rely on a stepwise synthesis and screening program for large numbers of compounds to optimize activity profiles. Over the past ten to twenty years, scientists have used computer models of new chemical entities to help define activity profiles, geometries and relativities. This article introduces inter alia the concepts of molecular modelling and contains references for further reading.

  10. Development of optimized dosimetric models for HDR brachytherapy

    International Nuclear Information System (INIS)

    Thayalan, K.; Jagadeesan, M.

    2003-01-01

    High dose rate brachytherapy (HDRB) systems are in clinical use for more than four decades particularly in cervical cancer. Optimization is the method to produce dose distribution which assures that doses are not compromised at the treatment sites whilst reducing the risk of overdosing critical organs. Hence HDRB optimization begins with the desired dose distribution and requires the calculations of the relative weighting factors for each dwell position with out changing the source activity. The optimization for Ca. uterine cervix treatment is simply duplication of the dose distribution used for Low dose rate (LDR) applications. In the present work, two optimized dosimetric models were proposed and studied thoroughly, to suit the local clinical conditions. These models are named as HDR-C and HDR-D, where C and D represent configuration and distance respectively. These models duplicate exactly the LDR pear shaped dose distribution, which is a golden standard. The validity of these models is tested in different clinical situations and in actual patients (n=92). These models: HDR-C and HDR-D reduce bladder dose by 11.11% and 10% and rectal dose by 8% and 7% respectively. The treatment time is also reduced by 12-14%. In a busy hospital setup, these models find a place to cater large number of patients, while addressing individual patients geometry. (author)

  11. Procedural Optimization Models for Multiobjective Flexible JSSP

    Directory of Open Access Journals (Sweden)

    Elena Simona NICOARA

    2013-01-01

    Full Text Available The most challenging issues related to manufacturing efficiency occur if the jobs to be sched-uled are structurally different, if these jobs allow flexible routings on the equipments and mul-tiple objectives are required. This framework, called Multi-objective Flexible Job Shop Scheduling Problems (MOFJSSP, applicable to many real processes, has been less reported in the literature than the JSSP framework, which has been extensively formalized, modeled and analyzed from many perspectives. The MOFJSSP lie, as many other NP-hard problems, in a tedious place where the vast optimization theory meets the real world context. The paper brings to discussion the most optimization models suited to MOFJSSP and analyzes in detail the genetic algorithms and agent-based models as the most appropriate procedural models.

  12. Thermodynamic metrics and optimal paths.

    Science.gov (United States)

    Sivak, David A; Crooks, Gavin E

    2012-05-11

    A fundamental problem in modern thermodynamics is how a molecular-scale machine performs useful work, while operating away from thermal equilibrium without excessive dissipation. To this end, we derive a friction tensor that induces a Riemannian manifold on the space of thermodynamic states. Within the linear-response regime, this metric structure controls the dissipation of finite-time transformations, and bestows optimal protocols with many useful properties. We discuss the connection to the existing thermodynamic length formalism, and demonstrate the utility of this metric by solving for optimal control parameter protocols in a simple nonequilibrium model.

  13. Optimizing Biorefinery Design and Operations via Linear Programming Models

    Energy Technology Data Exchange (ETDEWEB)

    Talmadge, Michael; Batan, Liaw; Lamers, Patrick; Hartley, Damon; Biddy, Mary; Tao, Ling; Tan, Eric

    2017-03-28

    The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for

  14. An optimization strategy for a biokinetic model of inhaled radionuclides

    International Nuclear Information System (INIS)

    Shyr, L.J.; Griffith, W.C.; Boecker, B.B.

    1991-01-01

    Models for material disposition and dosimetry involve predictions of the biokinetics of the material among compartments representing organs and tissues in the body. Because of a lack of human data for most toxicants, many of the basic data are derived by modeling the results obtained from studies using laboratory animals. Such a biomathematical model is usually developed by adjusting the model parameters to make the model predictions match the measured retention and excretion data visually. The fitting process can be very time-consuming for a complicated model, and visual model selections may be subjective and easily biased by the scale or the data used. Due to the development of computerized optimization methods, manual fitting could benefit from an automated process. However, for a complicated model, an automated process without an optimization strategy will not be efficient, and may not produce fruitful results. In this paper, procedures for, and implementation of, an optimization strategy for a complicated mathematical model is demonstrated by optimizing a biokinetic model for 144Ce in fused aluminosilicate particles inhaled by beagle dogs. The optimized results using SimuSolv were compared to manual fitting results obtained previously using the model simulation software GASP. Also, statistical criteria provided by SimuSolv, such as likelihood function values, were used to help or verify visual model selections

  15. Cost optimization model and its heuristic genetic algorithms

    International Nuclear Information System (INIS)

    Liu Wei; Wang Yongqing; Guo Jilin

    1999-01-01

    Interest and escalation are large quantity in proportion to the cost of nuclear power plant construction. In order to optimize the cost, the mathematics model of cost optimization for nuclear power plant construction was proposed, which takes the maximum net present value as the optimization goal. The model is based on the activity networks of the project and is an NP problem. A heuristic genetic algorithms (HGAs) for the model was introduced. In the algorithms, a solution is represented with a string of numbers each of which denotes the priority of each activity for assigned resources. The HGAs with this encoding method can overcome the difficulty which is harder to get feasible solutions when using the traditional GAs to solve the model. The critical path of the activity networks is figured out with the concept of predecessor matrix. An example was computed with the HGAP programmed in C language. The results indicate that the model is suitable for the objectiveness, the algorithms is effective to solve the model

  16. Optimization of spectral printer modeling based on a modified cellular Yule-Nielsen spectral Neugebauer model.

    Science.gov (United States)

    Liu, Qiang; Wan, Xiaoxia; Xie, Dehong

    2014-06-01

    The study presented here optimizes several steps in the spectral printer modeling workflow based on a cellular Yule-Nielsen spectral Neugebauer (CYNSN) model. First, a printer subdividing method was developed that reduces the number of sub-models while maintaining the maximum device gamut. Second, the forward spectral prediction accuracy of the CYNSN model for each subspace of the printer was improved using back propagation artificial neural network (BPANN) estimated n values. Third, a sequential gamut judging method, which clearly reduced the complexity of the optimal sub-model and cell searching process during printer backward modeling, was proposed. After that, we further modified the use of the modeling color metric and comprehensively improved the spectral and perceptual accuracy of the spectral printer model. The experimental results show that the proposed optimization approaches provide obvious improvements in aspects of the modeling accuracy or efficiency for each of the corresponding steps, and an overall improvement of the optimized spectral printer modeling workflow was also demonstrated.

  17. Model-based dynamic control and optimization of gas networks

    Energy Technology Data Exchange (ETDEWEB)

    Hofsten, Kai

    2001-07-01

    This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum

  18. Molecular modeling of inorganic compounds

    National Research Council Canada - National Science Library

    Comba, Peter; Hambley, Trevor W; Martin, Bodo

    2009-01-01

    ... mechanics to inorganic and coordination compounds. Initially, simple metal complexes were modeled, but recently the field has been extended to include organometallic compounds, catalysis and the interaction of metal ions with biological macromolecules. The application of molecular mechanics to coordination compounds is complicated by the numbe...

  19. Computer modeling for optimal placement of gloveboxes

    International Nuclear Information System (INIS)

    Hench, K.W.; Olivas, J.D.; Finch, P.R.

    1997-08-01

    Reduction of the nuclear weapons stockpile and the general downsizing of the nuclear weapons complex has presented challenges for Los Alamos. One is to design an optimized fabrication facility to manufacture nuclear weapon primary components (pits) in an environment of intense regulation and shrinking budgets. Historically, the location of gloveboxes in a processing area has been determined without benefit of industrial engineering studies to ascertain the optimal arrangement. The opportunity exists for substantial cost savings and increased process efficiency through careful study and optimization of the proposed layout by constructing a computer model of the fabrication process. This paper presents an integrative two- stage approach to modeling the casting operation for pit fabrication. The first stage uses a mathematical technique for the formulation of the facility layout problem; the solution procedure uses an evolutionary heuristic technique. The best solutions to the layout problem are used as input to the second stage - a computer simulation model that assesses the impact of competing layouts on operational performance. The focus of the simulation model is to determine the layout that minimizes personnel radiation exposures and nuclear material movement, and maximizes the utilization of capacity for finished units

  20. Computer modeling for optimal placement of gloveboxes

    Energy Technology Data Exchange (ETDEWEB)

    Hench, K.W.; Olivas, J.D. [Los Alamos National Lab., NM (United States); Finch, P.R. [New Mexico State Univ., Las Cruces, NM (United States)

    1997-08-01

    Reduction of the nuclear weapons stockpile and the general downsizing of the nuclear weapons complex has presented challenges for Los Alamos. One is to design an optimized fabrication facility to manufacture nuclear weapon primary components (pits) in an environment of intense regulation and shrinking budgets. Historically, the location of gloveboxes in a processing area has been determined without benefit of industrial engineering studies to ascertain the optimal arrangement. The opportunity exists for substantial cost savings and increased process efficiency through careful study and optimization of the proposed layout by constructing a computer model of the fabrication process. This paper presents an integrative two- stage approach to modeling the casting operation for pit fabrication. The first stage uses a mathematical technique for the formulation of the facility layout problem; the solution procedure uses an evolutionary heuristic technique. The best solutions to the layout problem are used as input to the second stage - a computer simulation model that assesses the impact of competing layouts on operational performance. The focus of the simulation model is to determine the layout that minimizes personnel radiation exposures and nuclear material movement, and maximizes the utilization of capacity for finished units.

  1. AN OPTIMAL MAINTENANCE MANAGEMENT MODEL FOR AIRPORT CONCRETE PAVEMENT

    Science.gov (United States)

    Shimomura, Taizo; Fujimori, Yuji; Kaito, Kiyoyuki; Obama, Kengo; Kobayashi, Kiyoshi

    In this paper, an optimal management model is formulated for the performance-based rehabilitation/maintenance contract for airport concrete pavement, whereby two types of life cycle cost risks, i.e., ground consolidation risk and concrete depreciation risk, are explicitly considered. The non-homogenous Markov chain model is formulated to represent the deterioration processes of concrete pavement which are conditional upon the ground consolidation processes. The optimal non-homogenous Markov decision model with multiple types of risk is presented to design the optimal rehabilitation/maintenance plans. And the methodology to revise the optimal rehabilitation/maintenance plans based upon the monitoring data by the Bayesian up-to-dating rules. The validity of the methodology presented in this paper is examined based upon the case studies carried out for the H airport.

  2. Optimality models in the age of experimental evolution and genomics

    OpenAIRE

    Bull, J. J.; Wang, I.-N.

    2010-01-01

    Optimality models have been used to predict evolution of many properties of organisms. They typically neglect genetic details, whether by necessity or design. This omission is a common source of criticism, and although this limitation of optimality is widely acknowledged, it has mostly been defended rather than evaluated for its impact. Experimental adaptation of model organisms provides a new arena for testing optimality models and for simultaneously integrating genetics. First, an experimen...

  3. Optimization Models and Methods Developed at the Energy Systems Institute

    OpenAIRE

    N.I. Voropai; V.I. Zorkaltsev

    2013-01-01

    The paper presents shortly some optimization models of energy system operation and expansion that have been created at the Energy Systems Institute of the Siberian Branch of the Russian Academy of Sciences. Consideration is given to the optimization models of energy development in Russia, a software package intended for analysis of power system reliability, and model of flow distribution in hydraulic systems. A general idea of the optimization methods developed at the Energy Systems Institute...

  4. Multi-objective optimization of GENIE Earth system models.

    Science.gov (United States)

    Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

    2009-07-13

    The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

  5. Multiple Surrogate Modeling for Wire-Wrapped Fuel Assembly Optimization

    International Nuclear Information System (INIS)

    Raza, Wasim; Kim, Kwang-Yong

    2007-01-01

    In this work, shape optimization of seven pin wire wrapped fuel assembly has been carried out in conjunction with RANS analysis in order to evaluate the performances of surrogate models. Previously, Ahmad and Kim performed the flow and heat transfer analysis based on the three-dimensional RANS analysis. But numerical optimization has not been applied to the design of wire-wrapped fuel assembly, yet. Surrogate models are being widely used in multidisciplinary optimization. Queipo et al. reviewed various surrogates based models used in aerospace applications. Goel et al. developed weighted average surrogate model based on response surface approximation (RSA), radial basis neural network (RBNN) and Krigging (KRG) models. In addition to the three basic models, RSA, RBNN and KRG, the multiple surrogate model, PBA also has been employed. Two geometric design variables and a multi-objective function with a weighting factor have been considered for this problem

  6. Stochastic Robust Mathematical Programming Model for Power System Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Cong; Changhyeok, Lee; Haoyong, Chen; Mehrotra, Sanjay

    2016-01-01

    This paper presents a stochastic robust framework for two-stage power system optimization problems with uncertainty. The model optimizes the probabilistic expectation of different worst-case scenarios with ifferent uncertainty sets. A case study of unit commitment shows the effectiveness of the proposed model and algorithms.

  7. Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach

    Science.gov (United States)

    Aguilo, Miguel A.; Warner, James E.

    2017-01-01

    This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of model evaluations that are necessary to accurately quantify and propagate uncertainties. This computational complexity is greatly magnified if a high-fidelity, physics-based numerical model is used for the topology optimization calculations. Stochastic reduced order model (SROM) methods are applied here to effectively 1) alleviate the prohibitive computational cost associated with an uncertainty aware topology optimization problem; and 2) quantify and propagate the inherent uncertainties due to design imperfections. A generic SROM framework that transforms the uncertainty aware, stochastic topology optimization problem into a deterministic optimization problem that relies only on independent calls to a deterministic numerical model is presented. This approach facilitates the use of existing optimization and modeling tools to accurately solve the uncertainty aware topology optimization problems in a fraction of the computational demand required by Monte Carlo methods. Finally, an example in structural topology optimization is presented to demonstrate the effectiveness of the proposed uncertainty aware structural topology optimization approach.

  8. GEMSFITS: Code package for optimization of geochemical model parameters and inverse modeling

    International Nuclear Information System (INIS)

    Miron, George D.; Kulik, Dmitrii A.; Dmytrieva, Svitlana V.; Wagner, Thomas

    2015-01-01

    Highlights: • Tool for generating consistent parameters against various types of experiments. • Handles a large number of experimental data and parameters (is parallelized). • Has a graphical interface and can perform statistical analysis on the parameters. • Tested on fitting the standard state Gibbs free energies of aqueous Al species. • Example on fitting interaction parameters of mixing models and thermobarometry. - Abstract: GEMSFITS is a new code package for fitting internally consistent input parameters of GEM (Gibbs Energy Minimization) geochemical–thermodynamic models against various types of experimental or geochemical data, and for performing inverse modeling tasks. It consists of the gemsfit2 (parameter optimizer) and gfshell2 (graphical user interface) programs both accessing a NoSQL database, all developed with flexibility, generality, efficiency, and user friendliness in mind. The parameter optimizer gemsfit2 includes the GEMS3K chemical speciation solver ( (http://gems.web.psi.ch/GEMS3K)), which features a comprehensive suite of non-ideal activity- and equation-of-state models of solution phases (aqueous electrolyte, gas and fluid mixtures, solid solutions, (ad)sorption. The gemsfit2 code uses the robust open-source NLopt library for parameter fitting, which provides a selection between several nonlinear optimization algorithms (global, local, gradient-based), and supports large-scale parallelization. The gemsfit2 code can also perform comprehensive statistical analysis of the fitted parameters (basic statistics, sensitivity, Monte Carlo confidence intervals), thus supporting the user with powerful tools for evaluating the quality of the fits and the physical significance of the model parameters. The gfshell2 code provides menu-driven setup of optimization options (data selection, properties to fit and their constraints, measured properties to compare with computed counterparts, and statistics). The practical utility, efficiency, and

  9. On Optimal Input Design and Model Selection for Communication Channels

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL

    2013-01-01

    In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.

  10. Agent-Based Modeling in Molecular Systems Biology.

    Science.gov (United States)

    Soheilypour, Mohammad; Mofrad, Mohammad R K

    2018-06-08

    Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease. © 2018 WILEY Periodicals, Inc.

  11. A useful framework for optimal replacement models

    International Nuclear Information System (INIS)

    Aven, Terje; Dekker, Rommert

    1997-01-01

    In this note we present a general framework for optimization of replacement times. It covers a number of models, including various age and block replacement models, and allows a uniform analysis for all these models. A relation to the marginal cost concept is described

  12. Density-optimized efficiency for magneto-optical production of a stable molecular Bose-Einstein condensate

    Energy Technology Data Exchange (ETDEWEB)

    Mackie, Matt [Helsinki Institute of Physics, PL 64, FIN-00014 Helsingin yliopisto (Finland); Collin, Anssi [Helsinki Institute of Physics, PL 64, FIN-00014 Helsingin yliopisto (Finland); Suominen, Kalle-Antti [Helsinki Institute of Physics, PL 64, FIN-00014 Helsingin yliopisto (Finland); Javanainen, Juha [Department of Physics, University of Connecticut, Storrs, CT 06269-3046 (United States)

    2003-08-01

    Although photoassociation and the Feshbach resonance are feasible means in principle for creating a molecular Bose-Einstein condensate (MBEC) from an already quantum-degenerate gas of atoms, collision-induced mean-field shifts and irreversible decay place practical constraints on the efficient Raman delivery of stable molecules. Focusing on stimulated Raman adiabatic passage, we propose that the efficiency of both mechanisms for producing a stable MBEC can be improved by treating the density of the initial atom condensate as an optimization parameter.

  13. Uncertain and multi-objective programming models for crop planting structure optimization

    Directory of Open Access Journals (Sweden)

    Mo LI,Ping GUO,Liudong ZHANG,Chenglong ZHANG

    2016-03-01

    Full Text Available Crop planting structure optimization is a significant way to increase agricultural economic benefits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic profits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study, three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming (IFCCP model and an inexact fuzzy linear programming (IFLP model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimization-theory-based fuzzy linear multi-objective programming model was developed, which is capable of reflecting both uncertainties and multi-objective. In addition, a multi-objective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic benefits and the denominator representing minimum crop planting area allocation. These models better reflect actual situations, considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in Minqin County, north-west China. The advantages, the applicable conditions and the solution methods

  14. Molecular Modeling and Simulation Tools in the Development of Peptide-Based Biosensors for Mycotoxin Detection: Example of Ochratoxin

    Directory of Open Access Journals (Sweden)

    Aby A. Thyparambil

    2017-12-01

    Full Text Available Mycotoxin contamination of food and feed is now ubiquitous. Exposures to mycotoxin via contact or ingestion can potentially induce adverse health outcomes. Affordable mycotoxin-monitoring systems are highly desired but are limited by (a the reliance on technically challenging and costly molecular recognition by immuno-capture technologies; and (b the lack of predictive tools for directing the optimization of alternative molecular recognition modalities. Our group has been exploring the development of ochratoxin detection and monitoring systems using the peptide NFO4 as the molecular recognition receptor in fluorescence, electrochemical and multimodal biosensors. Using ochratoxin as the model mycotoxin, we share our perspective on addressing the technical challenges involved in biosensor fabrication, namely: (a peptide receptor design; and (b performance evaluation. Subsequently, the scope and utility of molecular modeling and simulation (MMS approaches to address the above challenges are described. Informed and enabled by phage display, the subsequent application of MMS approaches can rationally guide subsequent biomolecular engineering of peptide receptors, including bioconjugation and bioimmobilization approaches to be used in the fabrication of peptide biosensors. MMS approaches thus have the potential to reduce biosensor development cost, extend product life cycle, and facilitate multi-analyte detection of mycotoxins, each of which positively contributes to the overall affordability of mycotoxin biosensor monitoring systems.

  15. Simplified ejector model for control and optimization

    International Nuclear Information System (INIS)

    Zhu Yinhai; Cai Wenjian; Wen Changyun; Li Yanzhong

    2008-01-01

    In this paper, a simple yet effective ejector model for a real time control and optimization of an ejector system is proposed. Firstly, a fundamental model for calculation of ejector entrainment ratio at critical working conditions is derived by one-dimensional analysis and the shock circle model. Then, based on thermodynamic principles and the lumped parameter method, the fundamental ejector model is simplified to result in a hybrid ejector model. The model is very simple, which only requires two or three parameters and measurement of two variables to determine the ejector performance. Furthermore, the procedures for on line identification of the model parameters using linear and non-linear least squares methods are also presented. Compared with existing ejector models, the solution of the proposed model is much easier without coupled equations and iterative computations. Finally, the effectiveness of the proposed model is validated by published experimental data. Results show that the model is accurate and robust and gives a better match to the real performances of ejectors over the entire operating range than the existing models. This model is expected to have wide applications in real time control and optimization of ejector systems

  16. Realistic molecular model of kerogen's nanostructure.

    Science.gov (United States)

    Bousige, Colin; Ghimbeu, Camélia Matei; Vix-Guterl, Cathie; Pomerantz, Andrew E; Suleimenova, Assiya; Vaughan, Gavin; Garbarino, Gaston; Feygenson, Mikhail; Wildgruber, Christoph; Ulm, Franz-Josef; Pellenq, Roland J-M; Coasne, Benoit

    2016-05-01

    Despite kerogen's importance as the organic backbone for hydrocarbon production from source rocks such as gas shale, the interplay between kerogen's chemistry, morphology and mechanics remains unexplored. As the environmental impact of shale gas rises, identifying functional relations between its geochemical, transport, elastic and fracture properties from realistic molecular models of kerogens becomes all the more important. Here, by using a hybrid experimental-simulation method, we propose a panel of realistic molecular models of mature and immature kerogens that provide a detailed picture of kerogen's nanostructure without considering the presence of clays and other minerals in shales. We probe the models' strengths and limitations, and show that they predict essential features amenable to experimental validation, including pore distribution, vibrational density of states and stiffness. We also show that kerogen's maturation, which manifests itself as an increase in the sp(2)/sp(3) hybridization ratio, entails a crossover from plastic-to-brittle rupture mechanisms.

  17. An integrated model of water resources optimization allocation based on projection pursuit model - Grey wolf optimization method in a transboundary river basin

    Science.gov (United States)

    Yu, Sen; Lu, Hongwei

    2018-04-01

    Under the effects of global change, water crisis ranks as the top global risk in the future decade, and water conflict in transboundary river basins as well as the geostrategic competition led by it is most concerned. This study presents an innovative integrated PPMGWO model of water resources optimization allocation in a transboundary river basin, which is integrated through the projection pursuit model (PPM) and Grey wolf optimization (GWO) method. This study uses the Songhua River basin and 25 control units as examples, adopting the PPMGWO model proposed in this study to allocate the water quantity. Using water consumption in all control units in the Songhua River basin in 2015 as reference to compare with optimization allocation results of firefly algorithm (FA) and Particle Swarm Optimization (PSO) algorithms as well as the PPMGWO model, results indicate that the average difference between corresponding allocation results and reference values are 0.195 bil m3, 0.151 bil m3, and 0.085 bil m3, respectively. Obviously, the average difference of the PPMGWO model is the lowest and its optimization allocation result is closer to reality, which further confirms the reasonability, feasibility, and accuracy of the PPMGWO model. And then the PPMGWO model is adopted to simulate allocation of available water quantity in Songhua River basin in 2018, 2020, and 2030. The simulation results show water quantity which could be allocated in all controls demonstrates an overall increasing trend with reasonable and equal exploitation and utilization of water resources in the Songhua River basin in future. In addition, this study has a certain reference value and application meaning to comprehensive management and water resources allocation in other transboundary river basins.

  18. Space engineering modeling and optimization with case studies

    CERN Document Server

    Pintér, János

    2016-01-01

    This book presents a selection of advanced case studies that cover a substantial range of issues and real-world challenges and applications in space engineering. Vital mathematical modeling, optimization methodologies and numerical solution aspects of each application case study are presented in detail, with discussions of a range of advanced model development and solution techniques and tools. Space engineering challenges are discussed in the following contexts: •Advanced Space Vehicle Design •Computation of Optimal Low Thrust Transfers •Indirect Optimization of Spacecraft Trajectories •Resource-Constrained Scheduling, •Packing Problems in Space •Design of Complex Interplanetary Trajectories •Satellite Constellation Image Acquisition •Re-entry Test Vehicle Configuration Selection •Collision Risk Assessment on Perturbed Orbits •Optimal Robust Design of Hybrid Rocket Engines •Nonlinear Regression Analysis in Space Engineering< •Regression-Based Sensitivity Analysis and Robust Design ...

  19. Small business development for molecular diagnostics.

    Science.gov (United States)

    Anagostou, Anthanasia; Liotta, Lance A

    2012-01-01

    Molecular profiling, which is the application of molecular diagnostics technology to tissue and blood -specimens, is an integral element in the new era of molecular medicine and individualized therapy. Molecular diagnostics is a fertile ground for small business development because it can generate products that meet immediate demands in the health-care sector: (a) Detection of disease risk, or early-stage disease, with a higher specificity and sensitivity compared to previous testing methods, and (b) "Companion diagnostics" for stratifying patients to receive a treatment choice optimized to their individual disease. This chapter reviews the promise and challenges of business development in this field. Guidelines are provided for the creation of a business model and the generation of a marketing plan around a candidate molecular diagnostic product. Steps to commercialization are outlined using existing molecular diagnostics companies as learning examples.

  20. Fuzzy multiobjective models for optimal operation of a hydropower system

    Science.gov (United States)

    Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

    2013-06-01

    Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

  1. Modeling and Optimization : Theory and Applications Conference

    CERN Document Server

    Terlaky, Tamás

    2017-01-01

    This volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 17-19, 2016. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, health, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.

  2. Modeling and Optimization : Theory and Applications Conference

    CERN Document Server

    Terlaky, Tamás

    2015-01-01

    This volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 13-15, 2014. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, healthcare, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.

  3. Optimal control of information epidemics modeled as Maki Thompson rumors

    Science.gov (United States)

    Kandhway, Kundan; Kuri, Joy

    2014-12-01

    We model the spread of information in a homogeneously mixed population using the Maki Thompson rumor model. We formulate an optimal control problem, from the perspective of single campaigner, to maximize the spread of information when the campaign budget is fixed. Control signals, such as advertising in the mass media, attempt to convert ignorants and stiflers into spreaders. We show the existence of a solution to the optimal control problem when the campaigning incurs non-linear costs under the isoperimetric budget constraint. The solution employs Pontryagin's Minimum Principle and a modified version of forward backward sweep technique for numerical computation to accommodate the isoperimetric budget constraint. The techniques developed in this paper are general and can be applied to similar optimal control problems in other areas. We have allowed the spreading rate of the information epidemic to vary over the campaign duration to model practical situations when the interest level of the population in the subject of the campaign changes with time. The shape of the optimal control signal is studied for different model parameters and spreading rate profiles. We have also studied the variation of the optimal campaigning costs with respect to various model parameters. Results indicate that, for some model parameters, significant improvements can be achieved by the optimal strategy compared to the static control strategy. The static strategy respects the same budget constraint as the optimal strategy and has a constant value throughout the campaign horizon. This work finds application in election and social awareness campaigns, product advertising, movie promotion and crowdfunding campaigns.

  4. Groundwater Pollution Source Identification using Linked ANN-Optimization Model

    Science.gov (United States)

    Ayaz, Md; Srivastava, Rajesh; Jain, Ashu

    2014-05-01

    Groundwater is the principal source of drinking water in several parts of the world. Contamination of groundwater has become a serious health and environmental problem today. Human activities including industrial and agricultural activities are generally responsible for this contamination. Identification of groundwater pollution source is a major step in groundwater pollution remediation. Complete knowledge of pollution source in terms of its source characteristics is essential to adopt an effective remediation strategy. Groundwater pollution source is said to be identified completely when the source characteristics - location, strength and release period - are known. Identification of unknown groundwater pollution source is an ill-posed inverse problem. It becomes more difficult for real field conditions, when the lag time between the first reading at observation well and the time at which the source becomes active is not known. We developed a linked ANN-Optimization model for complete identification of an unknown groundwater pollution source. The model comprises two parts- an optimization model and an ANN model. Decision variables of linked ANN-Optimization model contain source location and release period of pollution source. An objective function is formulated using the spatial and temporal data of observed and simulated concentrations, and then minimized to identify the pollution source parameters. In the formulation of the objective function, we require the lag time which is not known. An ANN model with one hidden layer is trained using Levenberg-Marquardt algorithm to find the lag time. Different combinations of source locations and release periods are used as inputs and lag time is obtained as the output. Performance of the proposed model is evaluated for two and three dimensional case with error-free and erroneous data. Erroneous data was generated by adding uniformly distributed random error (error level 0-10%) to the analytically computed concentration

  5. Robust and fast nonlinear optimization of diffusion MRI microstructure models.

    Science.gov (United States)

    Harms, R L; Fritz, F J; Tobisch, A; Goebel, R; Roebroeck, A

    2017-07-15

    Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of

  6. Optimality models in the age of experimental evolution and genomics.

    Science.gov (United States)

    Bull, J J; Wang, I-N

    2010-09-01

    Optimality models have been used to predict evolution of many properties of organisms. They typically neglect genetic details, whether by necessity or design. This omission is a common source of criticism, and although this limitation of optimality is widely acknowledged, it has mostly been defended rather than evaluated for its impact. Experimental adaptation of model organisms provides a new arena for testing optimality models and for simultaneously integrating genetics. First, an experimental context with a well-researched organism allows dissection of the evolutionary process to identify causes of model failure--whether the model is wrong about genetics or selection. Second, optimality models provide a meaningful context for the process and mechanics of evolution, and thus may be used to elicit realistic genetic bases of adaptation--an especially useful augmentation to well-researched genetic systems. A few studies of microbes have begun to pioneer this new direction. Incompatibility between the assumed and actual genetics has been demonstrated to be the cause of model failure in some cases. More interestingly, evolution at the phenotypic level has sometimes matched prediction even though the adaptive mutations defy mechanisms established by decades of classic genetic studies. Integration of experimental evolutionary tests with genetics heralds a new wave for optimality models and their extensions that does not merely emphasize the forces driving evolution.

  7. Modeling of biological intelligence for SCM system optimization.

    Science.gov (United States)

    Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  8. Modeling of Biological Intelligence for SCM System Optimization

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  9. Modeling of Biological Intelligence for SCM System Optimization

    Science.gov (United States)

    Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

  10. Fracture of Carbon Nanotube - Amorphous Carbon Composites: Molecular Modeling

    Science.gov (United States)

    Jensen, Benjamin D.; Wise, Kristopher E.; Odegard, Gregory M.

    2015-01-01

    Carbon nanotubes (CNTs) are promising candidates for use as reinforcements in next generation structural composite materials because of their extremely high specific stiffness and strength. They cannot, however, be viewed as simple replacements for carbon fibers because there are key differences between these materials in areas such as handling, processing, and matrix design. It is impossible to know for certain that CNT composites will represent a significant advance over carbon fiber composites before these various factors have been optimized, which is an extremely costly and time intensive process. This work attempts to place an upper bound on CNT composite mechanical properties by performing molecular dynamics simulations on idealized model systems with a reactive forcefield that permits modeling of both elastic deformations and fracture. Amorphous carbon (AC) was chosen for the matrix material in this work because of its structural simplicity and physical compatibility with the CNT fillers. It is also much stiffer and stronger than typical engineering polymer matrices. Three different arrangements of CNTs in the simulation cell have been investigated: a single-wall nanotube (SWNT) array, a multi-wall nanotube (MWNT) array, and a SWNT bundle system. The SWNT and MWNT array systems are clearly idealizations, but the SWNT bundle system is a step closer to real systems in which individual tubes aggregate into large assemblies. The effect of chemical crosslinking on composite properties is modeled by adding bonds between the CNTs and AC. The balance between weakening the CNTs and improving fiber-matrix load transfer is explored by systematically varying the extent of crosslinking. It is, of course, impossible to capture the full range of deformation and fracture processes that occur in real materials with even the largest atomistic molecular dynamics simulations. With this limitation in mind, the simulation results reported here provide a plausible upper limit on

  11. Group Elevator Peak Scheduling Based on Robust Optimization Model

    Directory of Open Access Journals (Sweden)

    ZHANG, J.

    2013-08-01

    Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.

  12. Asymptotic behaviour of optimal fraction-rational series of the perturbation theory at description of molecular rotational spectra

    International Nuclear Information System (INIS)

    Burenin, A.V.

    1994-01-01

    A possibility is shown of substantial expansion of the choice of asymptotic behaviour of optimal fraction-rational series of the perturbation theory on description of molecular rotational spectra. The expansion permits to hope for substantial improvement of results of using the conception of effective rotational hamiltonian in a fraction-rational form on the description of highly perturbed vibrational states

  13. Gradient models in molecular biophysics: progress, challenges, opportunities

    Science.gov (United States)

    Bardhan, Jaydeep P.

    2013-12-01

    In the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g., molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding nonlocal dielectric response. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain, and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost 40 years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The review concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics.

  14. Reduced order modeling in topology optimization of vibroacoustic problems

    DEFF Research Database (Denmark)

    Creixell Mediante, Ester; Jensen, Jakob Søndergaard; Brunskog, Jonas

    2017-01-01

    complex 3D parts. The optimization process can therefore become highly time consuming due to the need to solve a large system of equations at each iteration. Projection-based parametric Model Order Reduction (pMOR) methods have successfully been applied for reducing the computational cost of material......There is an interest in introducing topology optimization techniques in the design process of structural-acoustic systems. In topology optimization, the design space must be finely meshed in order to obtain an accurate design, which results in large numbers of degrees of freedom when designing...... or size optimization in large vibroacoustic models; however, new challenges are encountered when dealing with topology optimization. Since a design parameter per element is considered, the total number of design variables becomes very large; this poses a challenge to most existing pMOR techniques, which...

  15. Optlang: An algebraic modeling language for mathematical optimization

    DEFF Research Database (Denmark)

    Jensen, Kristian; Cardoso, Joao; Sonnenschein, Nikolaus

    2016-01-01

    Optlang is a Python package implementing a modeling language for solving mathematical optimization problems, i.e., maximizing or minimizing an objective function over a set of variables subject to a number of constraints. It provides a common native Python interface to a series of optimization...

  16. Aerodynamic modelling and optimization of axial fans

    Energy Technology Data Exchange (ETDEWEB)

    Noertoft Soerensen, Dan

    1998-01-01

    A numerically efficient mathematical model for the aerodynamics of low speed axial fans of the arbitrary vortex flow type has been developed. The model is based on a blade-element principle, whereby the rotor is divided into a number of annular stream tubes. For each of these stream tubes relations for velocity, pressure and radial position are derived from the conservation laws for mass, tangential momentum and energy. The equations are solved using the Newton-Raphson methods, and solutions converged to machine accuracy are found at small computing costs. The model has been validated against published measurements on various fan configurations, comprising two rotor-only fan stages, a counter-rotating fan unit and a stator-rotor stator stage. Comparisons of local and integrated properties show that the computed results agree well with the measurements. Optimizations have been performed to maximize the mean value of fan efficiency in a design interval of flow rates, thus designing a fan which operates well over a range of different flow conditions. The optimization scheme was used to investigate the dependence of maximum efficiency on 1: the number of blades, 2: the width of the design interval and 3: the hub radius. The degree of freedom in the choice of design variable and constraints, combined with the design interval concept, provides a valuable design-tool for axial fans. To further investigate the use of design optimization, a model for the vortex shedding noise from the trailing edge of the blades has been incorporated into the optimization scheme. The noise emission from the blades was minimized in a flow rate design point. Optimizations were performed to investigate the dependence of the noise on 1: the number of blades, 2: a constraint imposed on efficiency and 3: the hub radius. The investigations showed, that a significant reduction of noise could be achieved, at the expense of a small reduction in fan efficiency. (EG) 66 refs.

  17. Integrated production planning and control: A multi-objective optimization model

    Directory of Open Access Journals (Sweden)

    Cheng Wang

    2013-09-01

    Full Text Available Purpose: Production planning and control has crucial impact on the production and business activities of enterprise. Enterprise Resource Planning (ERP is the most popular resources planning and management system, however there are some shortcomings and deficiencies in the production planning and control because its core component is still the Material Requirements Planning (MRP. For the defects of ERP system, many local improvement and optimization schemes have been proposed, and improve the feasibility and practicality of the plan in some extent, but study considering the whole planning system optimization in the multiple performance management objectives and achieving better application performance is less. The purpose of this paper is to propose a multi-objective production planning optimization model Based on the point of view of the integration of production planning and control, in order to achieve optimization and control of enterprise manufacturing management. Design/methodology/approach: On the analysis of ERP planning system’s defects and disadvantages, and related research and literature, a multi-objective production planning optimization model is proposed, in addition to net demand and capacity, multiple performance management objectives, such as on-time delivery, production balance, inventory, overtime production, are considered incorporating into the examination scope of the model, so that the manufacturing process could be management and controlled Optimally between multiple objectives. The validity and practicability of the model will be verified by the instance in the last part of the paper. Findings: The main finding is that production planning management of manufacturing enterprise considers not only the capacity and materials, but also a variety of performance management objectives in the production process, and building a multi-objective optimization model can effectively optimize the management and control of enterprise

  18. A stochastic discrete optimization model for designing container terminal facilities

    Science.gov (United States)

    Zukhruf, Febri; Frazila, Russ Bona; Burhani, Jzolanda Tsavalista

    2017-11-01

    As uncertainty essentially affect the total transportation cost, it remains important in the container terminal that incorporates several modes and transshipments process. This paper then presents a stochastic discrete optimization model for designing the container terminal, which involves the decision of facilities improvement action. The container terminal operation model is constructed by accounting the variation of demand and facilities performance. In addition, for illustrating the conflicting issue that practically raises in the terminal operation, the model also takes into account the possible increment delay of facilities due to the increasing number of equipment, especially the container truck. Those variations expectantly reflect the uncertainty issue in the container terminal operation. A Monte Carlo simulation is invoked to propagate the variations by following the observed distribution. The problem is constructed within the framework of the combinatorial optimization problem for investigating the optimal decision of facilities improvement. A new variant of glow-worm swarm optimization (GSO) is thus proposed for solving the optimization, which is rarely explored in the transportation field. The model applicability is tested by considering the actual characteristics of the container terminal.

  19. Two-Dimensional Model of Scrolled Packings of Molecular Nanoribbons

    Science.gov (United States)

    Savin, A. V.; Mazo, M. A.

    2018-04-01

    A simplified model of the in-plane molecular chain, allowing the description of folded and scrolled packings of molecular nanoribbons of different structures, is proposed. Using this model, possible steady states of single-layer nanoribbons scrolls of graphene, graphane, fluorographene, and fluorographane (graphene hydrogenated on the one side and fluorinated on the other side) are obtained. Their stability is demonstrated and their energy is calculated as a function of the nanoribbon length. It is shown that the scrolled packing is the most energetically favorable nanoribbon conformation at long lengths. The existences of scrolled packings for fluorographene nanoribbons and the existence of two different scroll types corresponding to left- and right-hand Archimedean spirals for fluorographane nanoribbons in the chain model are shown for the first time. The simplicity of the proposed model makes it possible to consider the dynamics of scrolls of rather long molecular nanoribbons at long enough time intervals.

  20. Molecular systematics of terraranas (Anura: Brachycephaloidea) with an assessment of the effects of alignment and optimality criteria.

    Science.gov (United States)

    Padial, José M; Grant, Taran; Frost, Darrel R

    2014-06-26

    Brachycephaloidea is a monophyletic group of frogs with more than 1000 species distributed throughout the New World tropics, subtropics, and Andean regions. Recently, the group has been the target of multiple molecular phylogenetic analyses, resulting in extensive changes in its taxonomy. Here, we test previous hypotheses of phylogenetic relationships for the group by combining available molecular evidence (sequences of 22 genes representing 431 ingroup and 25 outgroup terminals) and performing a tree-alignment analysis under the parsimony optimality criterion using the program POY. To elucidate the effects of alignment and optimality criterion on phylogenetic inferences, we also used the program MAFFT to obtain a similarity-alignment for analysis under both parsimony and maximum likelihood using the programs TNT and GARLI, respectively. Although all three analytical approaches agreed on numerous points, there was also extensive disagreement. Tree-alignment under parsimony supported the monophyly of the ingroup and the sister group relationship of the monophyletic marsupial frogs (Hemiphractidae), while maximum likelihood and parsimony analyses of the MAFFT similarity-alignment did not. All three methods differed with respect to the position of Ceuthomantis smaragdinus (Ceuthomantidae), with tree-alignment using parsimony recovering this species as the sister of Pristimantis + Yunganastes. All analyses rejected the monophyly of Strabomantidae and Strabomantinae as originally defined, and the tree-alignment analysis under parsimony further rejected the recently redefined Craugastoridae and Pristimantinae. Despite the greater emphasis in the systematics literature placed on the choice of optimality criterion for evaluating trees than on the choice of method for aligning DNA sequences, we found that the topological differences attributable to the alignment method were as great as those caused by the optimality criterion. Further, the optimal tree-alignment indicates

  1. Adaptive stimulus optimization and model-based experiments for sensory systems neuroscience

    Directory of Open Access Journals (Sweden)

    Christopher eDiMattina

    2013-06-01

    Full Text Available In this paper we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical textit{optimal stimulus} paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the textit{iso-response} paradigm which finds sets of stimuli giving rise to constant responses, and the textit{system identification} paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of nonlinear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify nonlinear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and towards a new paradigm of real-time model estimation and comparison.

  2. Optimal harvesting for a predator-prey agent-based model using difference equations.

    Science.gov (United States)

    Oremland, Matthew; Laubenbacher, Reinhard

    2015-03-01

    In this paper, a method known as Pareto optimization is applied in the solution of a multi-objective optimization problem. The system in question is an agent-based model (ABM) wherein global dynamics emerge from local interactions. A system of discrete mathematical equations is formulated in order to capture the dynamics of the ABM; while the original model is built up analytically from the rules of the model, the paper shows how minor changes to the ABM rule set can have a substantial effect on model dynamics. To address this issue, we introduce parameters into the equation model that track such changes. The equation model is amenable to mathematical theory—we show how stability analysis can be performed and validated using ABM data. We then reduce the equation model to a simpler version and implement changes to allow controls from the ABM to be tested using the equations. Cohen's weighted κ is proposed as a measure of similarity between the equation model and the ABM, particularly with respect to the optimization problem. The reduced equation model is used to solve a multi-objective optimization problem via a technique known as Pareto optimization, a heuristic evolutionary algorithm. Results show that the equation model is a good fit for ABM data; Pareto optimization provides a suite of solutions to the multi-objective optimization problem that can be implemented directly in the ABM.

  3. Molecular modeling of nucleic Acid structure: electrostatics and solvation.

    Science.gov (United States)

    Bergonzo, Christina; Galindo-Murillo, Rodrigo; Cheatham, Thomas E

    2014-12-19

    This unit presents an overview of computer simulation techniques as applied to nucleic acid systems, ranging from simple in vacuo molecular modeling techniques to more complete all-atom molecular dynamics treatments that include an explicit representation of the environment. The third in a series of four units, this unit focuses on critical issues in solvation and the treatment of electrostatics. UNITS 7.5 & 7.8 introduced the modeling of nucleic acid structure at the molecular level. This included a discussion of how to generate an initial model, how to evaluate the utility or reliability of a given model, and ultimately how to manipulate this model to better understand its structure, dynamics, and interactions. Subject to an appropriate representation of the energy, such as a specifically parameterized empirical force field, the techniques of minimization and Monte Carlo simulation, as well as molecular dynamics (MD) methods, were introduced as a way of sampling conformational space for a better understanding of the relevance of a given model. This discussion highlighted the major limitations with modeling in general. When sampling conformational space effectively, difficult issues are encountered, such as multiple minima or conformational sampling problems, and accurately representing the underlying energy of interaction. In order to provide a realistic model of the underlying energetics for nucleic acids in their native environments, it is crucial to include some representation of solvation (by water) and also to properly treat the electrostatic interactions. These subjects are discussed in detail in this unit. Copyright © 2014 John Wiley & Sons, Inc.

  4. An optimal control model of crop thinning in viticulture

    OpenAIRE

    Schamel Guenter H.; Schubert Stefan F.

    2016-01-01

    We develop an economic model of cluster thinning in viticulture to control for grape quantity harvested and grape quality, applying a simple optimal control model with the aim to raise grape quality and related economic profits. The model maximizes vineyard owner profits and allows to discuss two relevant scenarios using a phase diagram analysis: (1) when the initial grape quantity is sufficiently small, thinning grapes will not be optimal and (2) when the initial grape quantity is high enoug...

  5. Protein structure modeling and refinement by global optimization in CASP12.

    Science.gov (United States)

    Hong, Seung Hwan; Joung, InSuk; Flores-Canales, Jose C; Manavalan, Balachandran; Cheng, Qianyi; Heo, Seungryong; Kim, Jong Yun; Lee, Sun Young; Nam, Mikyung; Joo, Keehyoung; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung

    2018-03-01

    For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded. © 2017 Wiley Periodicals, Inc.

  6. Fuzzy Stochastic Optimization Theory, Models and Applications

    CERN Document Server

    Wang, Shuming

    2012-01-01

    Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies.   The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins...

  7. Optimal inventory management and order book modeling

    KAUST Repository

    Baradel, Nicolas

    2018-02-16

    We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) firms, and institutional brokers (IB). Given a prior dynamic of the order book, similar to the one considered in the Queue-Reactive models [14, 20, 21], the MM and the HFT define their trading strategy by optimizing the expected utility of terminal wealth, while the IB has a prescheduled task to sell or buy many shares of the considered asset. We derive the variational partial differential equations that characterize the value functions of the MM and HFT and explain how almost optimal control can be deduced from them. We then provide a first illustration of the interactions that can take place between these different market participants by simulating the dynamic of an order book in which each of them plays his own (optimal) strategy.

  8. A system-theory-based model for monthly river runoff forecasting: model calibration and optimization

    Directory of Open Access Journals (Sweden)

    Wu Jianhua

    2014-03-01

    Full Text Available River runoff is not only a crucial part of the global water cycle, but it is also an important source for hydropower and an essential element of water balance. This study presents a system-theory-based model for river runoff forecasting taking the Hailiutu River as a case study. The forecasting model, designed for the Hailiutu watershed, was calibrated and verified by long-term precipitation observation data and groundwater exploitation data from the study area. Additionally, frequency analysis, taken as an optimization technique, was applied to improve prediction accuracy. Following model optimization, the overall relative prediction errors are below 10%. The system-theory-based prediction model is applicable to river runoff forecasting, and following optimization by frequency analysis, the prediction error is acceptable.

  9. Computer-Based Molecular Modelling: Finnish School Teachers' Experiences and Views

    Science.gov (United States)

    Aksela, Maija; Lundell, Jan

    2008-01-01

    Modern computer-based molecular modelling opens up new possibilities for chemistry teaching at different levels. This article presents a case study seeking insight into Finnish school teachers' use of computer-based molecular modelling in teaching chemistry, into the different working and teaching methods used, and their opinions about necessary…

  10. Ground Vehicle System Integration (GVSI) and Design Optimization Model

    National Research Council Canada - National Science Library

    Horton, William

    1996-01-01

    This report documents the Ground Vehicle System Integration (GVSI) and Design Optimization Model GVSI is a top-level analysis tool designed to support engineering tradeoff studies and vehicle design optimization efforts...

  11. A Parallel Biological Optimization Algorithm to Solve the Unbalanced Assignment Problem Based on DNA Molecular Computing

    Directory of Open Access Journals (Sweden)

    Zhaocai Wang

    2015-10-01

    Full Text Available The unbalanced assignment problem (UAP is to optimally resolve the problem of assigning n jobs to m individuals (m < n, such that minimum cost or maximum profit obtained. It is a vitally important Non-deterministic Polynomial (NP complete problem in operation management and applied mathematics, having numerous real life applications. In this paper, we present a new parallel DNA algorithm for solving the unbalanced assignment problem using DNA molecular operations. We reasonably design flexible-length DNA strands representing different jobs and individuals, take appropriate steps, and get the solutions of the UAP in the proper length range and O(mn time. We extend the application of DNA molecular operations and simultaneity to simplify the complexity of the computation.

  12. Molecular models and simulations of layered materials

    International Nuclear Information System (INIS)

    Kalinichev, Andrey G.; Cygan, Randall Timothy; Heinz, Hendrik; Greathouse, Jeffery A.

    2008-01-01

    The micro- to nano-sized nature of layered materials, particularly characteristic of naturally occurring clay minerals, limits our ability to fully interrogate their atomic dispositions and crystal structures. The low symmetry, multicomponent compositions, defects, and disorder phenomena of clays and related phases necessitate the use of molecular models and modern simulation methods. Computational chemistry tools based on classical force fields and quantum-chemical methods of electronic structure calculations provide a practical approach to evaluate structure and dynamics of the materials on an atomic scale. Combined with classical energy minimization, molecular dynamics, and Monte Carlo techniques, quantum methods provide accurate models of layered materials such as clay minerals, layered double hydroxides, and clay-polymer nanocomposites

  13. Gradient Models in Molecular Biophysics: Progress, Challenges, Opportunities.

    Science.gov (United States)

    Bardhan, Jaydeep P

    2013-12-01

    In the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g. molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding features such as nonlocal dielectric response, and nonlinearities resulting from dielectric saturation. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost forty years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The paper concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics.

  14. Gradient Models in Molecular Biophysics: Progress, Challenges, Opportunities

    Science.gov (United States)

    Bardhan, Jaydeep P.

    2014-01-01

    In the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g. molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding features such as nonlocal dielectric response, and nonlinearities resulting from dielectric saturation. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost forty years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The paper concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics. PMID:25505358

  15. Experimental design, modeling and optimization of polyplex formation between DNA oligonucleotides and branched polyethylenimine.

    Science.gov (United States)

    Clima, Lilia; Ursu, Elena L; Cojocaru, Corneliu; Rotaru, Alexandru; Barboiu, Mihail; Pinteala, Mariana

    2015-09-28

    The complexes formed by DNA and polycations have received great attention owing to their potential application in gene therapy. In this study, the binding efficiency between double-stranded oligonucleotides (dsDNA) and branched polyethylenimine (B-PEI) has been quantified by processing of the images captured from the gel electrophoresis assays. The central composite experimental design has been employed to investigate the effects of controllable factors on the binding efficiency. On the basis of experimental data and the response surface methodology, a multivariate regression model has been constructed and statistically validated. The model has enabled us to predict the binding efficiency depending on experimental factors, such as concentrations of dsDNA and B-PEI as well as the initial pH of solution. The optimization of the binding process has been performed using simplex and gradient methods. The optimal conditions determined for polyplex formation have yielded a maximal binding efficiency close to 100%. In order to reveal the mechanism of complex formation at the atomic-scale, a molecular dynamic simulation has been carried out. According to the computation results, B-PEI amine hydrogen atoms have interacted with oxygen atoms from dsDNA phosphate groups. These interactions have led to the formation of hydrogen bonds between macromolecules, stabilizing the polyplex structure.

  16. Reduced order modeling and parameter identification of a building energy system model through an optimization routine

    International Nuclear Information System (INIS)

    Harish, V.S.K.V.; Kumar, Arun

    2016-01-01

    Highlights: • A BES model based on 1st principles is developed and solved numerically. • Parameters of lumped capacitance model are fitted using the proposed optimization routine. • Validations are showed for different types of building construction elements. • Step response excitations for outdoor air temperature and relative humidity are analyzed. - Abstract: Different control techniques together with intelligent building technology (Building Automation Systems) are used to improve energy efficiency of buildings. In almost all control projects, it is crucial to have building energy models with high computational efficiency in order to design and tune the controllers and simulate their performance. In this paper, a set of partial differential equations are formulated accounting for energy flow within the building space. These equations are then solved as conventional finite difference equations using Crank–Nicholson scheme. Such a model of a higher order is regarded as a benchmark model. An optimization algorithm has been developed, depicted through a flowchart, which minimizes the sum squared error between the step responses of the numerical and the optimal model. Optimal model of the construction element is nothing but a RC-network model with the values of Rs and Cs estimated using the non-linear time invariant constrained optimization routine. The model is validated with comparing the step responses with other two RC-network models whose parameter values are selected based on a certain criteria. Validations are showed for different types of building construction elements viz., low, medium and heavy thermal capacity elements. Simulation results show that the optimal model closely follow the step responses of the numerical model as compared to the responses of other two models.

  17. An optimization model for metabolic pathways.

    Science.gov (United States)

    Planes, F J; Beasley, J E

    2009-10-15

    Different mathematical methods have emerged in the post-genomic era to determine metabolic pathways. These methods can be divided into stoichiometric methods and path finding methods. In this paper we detail a novel optimization model, based upon integer linear programming, to determine metabolic pathways. Our model links reaction stoichiometry with path finding in a single approach. We test the ability of our model to determine 40 annotated Escherichia coli metabolic pathways. We show that our model is able to determine 36 of these 40 pathways in a computationally effective manner.

  18. A restraint molecular dynamics and simulated annealing approach for protein homology modeling utilizing mean angles

    Directory of Open Access Journals (Sweden)

    Maurer Till

    2005-04-01

    Full Text Available Abstract Background We have developed the program PERMOL for semi-automated homology modeling of proteins. It is based on restrained molecular dynamics using a simulated annealing protocol in torsion angle space. As main restraints defining the optimal local geometry of the structure weighted mean dihedral angles and their standard deviations are used which are calculated with an algorithm described earlier by Döker et al. (1999, BBRC, 257, 348–350. The overall long-range contacts are established via a small number of distance restraints between atoms involved in hydrogen bonds and backbone atoms of conserved residues. Employing the restraints generated by PERMOL three-dimensional structures are obtained using standard molecular dynamics programs such as DYANA or CNS. Results To test this modeling approach it has been used for predicting the structure of the histidine-containing phosphocarrier protein HPr from E. coli and the structure of the human peroxisome proliferator activated receptor γ (Ppar γ. The divergence between the modeled HPr and the previously determined X-ray structure was comparable to the divergence between the X-ray structure and the published NMR structure. The modeled structure of Ppar γ was also very close to the previously solved X-ray structure with an RMSD of 0.262 nm for the backbone atoms. Conclusion In summary, we present a new method for homology modeling capable of producing high-quality structure models. An advantage of the method is that it can be used in combination with incomplete NMR data to obtain reasonable structure models in accordance with the experimental data.

  19. Portfolio optimization by using linear programing models based on genetic algorithm

    Science.gov (United States)

    Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

    2018-01-01

    In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

  20. CREATION OF OPTIMIZATION MODEL OF STEAM BOILER RECUPERATIVE AIR HEATER

    Directory of Open Access Journals (Sweden)

    N. B. Carnickiy

    2006-01-01

    Full Text Available The paper proposes to use a mathematical modeling as one of the ways intended to improve quality of recuperative air heater design (RAH without significant additional costs, connected with the change of design materials or fuel type. The described conceptual mathematical AHP optimization model of RAH consists of optimized and constant parameters, technical limitations and optimality criteria.The paper considers a methodology for search of design and regime parameters of an air heater which is based on the methods of multi-criteria optimization. Conclusions for expediency of the given approach usage are made in the paper.

  1. Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things.

    Directory of Open Access Journals (Sweden)

    Murat Kuscu

    Full Text Available We consider a microfluidic molecular communication (MC system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA. However, analytical models are key for the information and communication technology (ICT, as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics.

  2. Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things.

    Science.gov (United States)

    Kuscu, Murat; Akan, Ozgur B

    2018-01-01

    We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics.

  3. Modelling, simulating and optimizing boiler heating surfaces and evaporator circuits

    DEFF Research Database (Denmark)

    Sørensen, K.; Condra, T.; Houbak, Niels

    2003-01-01

    A model for optimizing the dynamic performance of boiler have been developed. Design variables related to the size of the boiler and its dynamic performance have been defined. The object function to be optimized takes the weight of the boiler and its dynamic capability into account. As constraints...... for the optimization a dynamic model for the boiler is applied. Furthermore a function for the value of the dynamic performance is included in the model. The dynamic models for simulating boiler performance consists of a model for the flue gas side, a model for the evaporator circuit and a model for the drum....... The dynamic model has been developed for the purpose of determining boiler material temperatures and heat transfer from the flue gas side to the water-/steam side in order to simulate the circulation in the evaporator circuit and hereby the water level fluctuations in the drum. The dynamic model has been...

  4. A Novel Parametric Modeling Method and Optimal Design for Savonius Wind Turbines

    Directory of Open Access Journals (Sweden)

    Baoshou Zhang

    2017-03-01

    Full Text Available Under the inspiration of polar coordinates, a novel parametric modeling and optimization method for Savonius wind turbines was proposed to obtain the highest power output, in which a quadratic polynomial curve was bent to describe a blade. Only two design parameters are needed for the shape-complicated blade. Therefore, this novel method reduces sampling scale. A series of transient simulations was run to get the optimal performance coefficient (power coefficient C p for different modified turbines based on computational fluid dynamics (CFD method. Then, a global response surface model and a more precise local response surface model were created according to Kriging Method. These models defined the relationship between optimization objective Cp and design parameters. Particle swarm optimization (PSO algorithm was applied to find the optimal design based on these response surface models. Finally, the optimal Savonius blade shaped like a “hook” was obtained. Cm (torque coefficient, Cp and flow structure were compared for the optimal design and the classical design. The results demonstrate that the optimal Savonius turbine has excellent comprehensive performance. The power coefficient Cp is significantly increased from 0.247 to 0.262 (6% higher. The weight of the optimal blade is reduced by 17.9%.

  5. Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization.

    Directory of Open Access Journals (Sweden)

    Devaraj Jayachandran

    Full Text Available 6-Mercaptopurine (6-MP is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN through enzymatic reaction involving thiopurine methyltransferase (TPMT. Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach.

  6. Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

    Science.gov (United States)

    Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami

    2015-01-01

    6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP’s widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient’s ability to metabolize the drug instead of the traditional standard-dose-for-all approach. PMID:26226448

  7. Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine

    Directory of Open Access Journals (Sweden)

    Bambang Wahono

    2014-01-01

    Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.

  8. Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region.

    Science.gov (United States)

    Yang, Qidong; Zuo, Hongchao; Li, Weidong

    2016-01-01

    Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.

  9. Modelling and Optimizing Mathematics Learning in Children

    Science.gov (United States)

    Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Baschera, Gian-Marco; Kohn, Juliane; Kucian, Karin; von Aster, Michael; Gross, Markus

    2013-01-01

    This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic…

  10. Desiccant wheel thermal performance modeling for indoor humidity optimal control

    International Nuclear Information System (INIS)

    Wang, Nan; Zhang, Jiangfeng; Xia, Xiaohua

    2013-01-01

    Highlights: • An optimal humidity control model is formulated to control the indoor humidity. • MPC strategy is used to implement the optimal operation solution. • Practical applications of the MPC strategy is illustrated by the case study. - Abstract: Thermal comfort is an important concern in the energy efficiency improvement of commercial buildings. Thermal comfort research focuses mostly on temperature control, but humidity control is an important aspect to maintain indoor comfort too. In this paper, an optimal humidity control model (OHCM) is presented. Model predictive control (MPC) strategy is applied to implement the optimal operation of the desiccant wheel during working hours of a commercial building. The OHCM is revised to apply the MPC strategy. A case is studied to illustrate the practical applications of the MPC strategy

  11. Existence and characterization of optimal control in mathematics model of diabetics population

    Science.gov (United States)

    Permatasari, A. H.; Tjahjana, R. H.; Udjiani, T.

    2018-03-01

    Diabetes is a chronic disease with a huge burden affecting individuals and the whole society. In this paper, we constructed the optimal control mathematical model by applying a strategy to control the development of diabetic population. The constructed mathematical model considers the dynamics of disabled people due to diabetes. Moreover, an optimal control approach is proposed in order to reduce the burden of pre-diabetes. Implementation of control is done by preventing the pre-diabetes develop into diabetics with and without complications. The existence of optimal control and characterization of optimal control is discussed in this paper. Optimal control is characterized by applying the Pontryagin minimum principle. The results indicate that there is an optimal control in optimization problem in mathematics model of diabetic population. The effect of the optimal control variable (prevention) is strongly affected by the number of healthy people.

  12. Profile-driven regression for modeling and runtime optimization of mobile networks

    DEFF Research Database (Denmark)

    McClary, Dan; Syrotiuk, Violet; Kulahci, Murat

    2010-01-01

    Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization...... of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike...

  13. Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models.

    Science.gov (United States)

    Pozo, Carlos; Marín-Sanguino, Alberto; Alves, Rui; Guillén-Gosálbez, Gonzalo; Jiménez, Laureano; Sorribas, Albert

    2011-08-25

    Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

  14. Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

    Directory of Open Access Journals (Sweden)

    Sorribas Albert

    2011-08-01

    Full Text Available Abstract Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

  15. A tool for efficient, model-independent management optimization under uncertainty

    Science.gov (United States)

    White, Jeremy; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.

    2018-01-01

    To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

  16. Multiobjective optimization of an extremal evolution model

    International Nuclear Information System (INIS)

    Elettreby, M.F.

    2004-09-01

    We propose a two-dimensional model for a co-evolving ecosystem that generalizes the extremal coupled map lattice model. The model takes into account the concept of multiobjective optimization. We find that the system self-organizes into a critical state. The distributions of the distances between subsequent mutations as well as the distribution of avalanches sizes follow power law. (author)

  17. Size-exclusion chromatography (HPLC-SEC) technique optimization by simplex method to estimate molecular weight distribution of agave fructans.

    Science.gov (United States)

    Moreno-Vilet, Lorena; Bostyn, Stéphane; Flores-Montaño, Jose-Luis; Camacho-Ruiz, Rosa-María

    2017-12-15

    Agave fructans are increasingly important in food industry and nutrition sciences as a potential ingredient of functional food, thus practical analysis tools to characterize them are needed. In view of the importance of the molecular weight on the functional properties of agave fructans, this study has the purpose to optimize a method to determine their molecular weight distribution by HPLC-SEC for industrial application. The optimization was carried out using a simplex method. The optimum conditions obtained were at column temperature of 61.7°C using tri-distilled water without salt, adjusted pH of 5.4 and a flow rate of 0.36mL/min. The exclusion range is from 1 to 49 of polymerization degree (180-7966Da). This proposed method represents an accurate and fast alternative to standard methods involving multiple-detection or hydrolysis of fructans. The industrial applications of this technique might be for quality control, study of fractionation processes and determination of purity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Modeling of ultrafast THz interactions in molecular crystals

    DEFF Research Database (Denmark)

    Pedersen, Pernille Klarskov; Clark, Stewart J.; Jepsen, Peter Uhd

    2014-01-01

    In this paper we present a numerical study of terahertz pulses interacting with crystals of cesium iodide. We model the molecular dynamics of the cesium iodide crystals with the Density Functional Theory software CASTEP, where ultrafast terahertz pulses are implemented to the CASTEP software...... to interact with molecular crystals. We investigate the molecular dynamics of cesium iodide crystals when interacting with realistic terahertz pulses of field strengths from 0 to 50 MV/cm. We find nonlinearities in the response of the CsI crystals at field strengths higher than 10 MV/cm....

  19. Modeling molecular boiling points using computed interaction energies.

    Science.gov (United States)

    Peterangelo, Stephen C; Seybold, Paul G

    2017-12-20

    The noncovalent van der Waals interactions between molecules in liquids are typically described in textbooks as occurring between the total molecular dipoles (permanent, induced, or transient) of the molecules. This notion was tested by examining the boiling points of 67 halogenated hydrocarbon liquids using quantum chemically calculated molecular dipole moments, ionization potentials, and polarizabilities obtained from semi-empirical (AM1 and PM3) and ab initio Hartree-Fock [HF 6-31G(d), HF 6-311G(d,p)], and density functional theory [B3LYP/6-311G(d,p)] methods. The calculated interaction energies and an empirical measure of hydrogen bonding were employed to model the boiling points of the halocarbons. It was found that only terms related to London dispersion energies and hydrogen bonding proved significant in the regression analyses, and the performances of the models generally improved at higher levels of quantum chemical computation. An empirical estimate for the molecular polarizabilities was also tested, and the best models for the boiling points were obtained using either this empirical polarizability itself or the polarizabilities calculated at the B3LYP/6-311G(d,p) level, along with the hydrogen-bonding parameter. The results suggest that the cohesive forces are more appropriately described as resulting from highly localized interactions rather than interactions between the global molecular dipoles.

  20. Discounted cost model for condition-based maintenance optimization

    International Nuclear Information System (INIS)

    Weide, J.A.M. van der; Pandey, M.D.; Noortwijk, J.M. van

    2010-01-01

    This paper presents methods to evaluate the reliability and optimize the maintenance of engineering systems that are damaged by shocks or transients arriving randomly in time and overall degradation is modeled as a cumulative stochastic point process. The paper presents a conceptually clear and comprehensive derivation of formulas for computing the discounted cost associated with a maintenance policy combining both condition-based and age-based criteria for preventive maintenance. The proposed discounted cost model provides a more realistic basis for optimizing the maintenance policies than those based on the asymptotic, non-discounted cost rate criterion.

  1. Model averaging, optimal inference and habit formation

    Directory of Open Access Journals (Sweden)

    Thomas H B FitzGerald

    2014-06-01

    Full Text Available Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge – that of determining which model or models of their environment are the best for guiding behaviour. Bayesian model averaging – which says that an agent should weight the predictions of different models according to their evidence – provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent’s behaviour should show an equivalent balance. We hypothesise that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realisable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behaviour. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded Bayesian inference, focussing particularly upon the relationship between goal-directed and habitual behaviour.

  2. Optimal moment determination in POME-copula based hydrometeorological dependence modelling

    Science.gov (United States)

    Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

    2017-07-01

    Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

  3. Tangible Models and Haptic Representations Aid Learning of Molecular Biology Concepts

    Science.gov (United States)

    Johannes, Kristen; Powers, Jacklyn; Couper, Lisa; Silberglitt, Matt; Davenport, Jodi

    2016-01-01

    Can novel 3D models help students develop a deeper understanding of core concepts in molecular biology? We adapted 3D molecular models, developed by scientists, for use in high school science classrooms. The models accurately represent the structural and functional properties of complex DNA and Virus molecules, and provide visual and haptic…

  4. Optimization Research of Generation Investment Based on Linear Programming Model

    Science.gov (United States)

    Wu, Juan; Ge, Xueqian

    Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

  5. Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction.

    Science.gov (United States)

    Ahmed, Shiek S S J; Ramakrishnan, V

    2012-01-01

    Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties. The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction. The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy

  6. Research on NC laser combined cutting optimization model of sheet metal parts

    Science.gov (United States)

    Wu, Z. Y.; Zhang, Y. L.; Li, L.; Wu, L. H.; Liu, N. B.

    2017-09-01

    The optimization problem for NC laser combined cutting of sheet metal parts was taken as the research object in this paper. The problem included two contents: combined packing optimization and combined cutting path optimization. In the problem of combined packing optimization, the method of “genetic algorithm + gravity center NFP + geometric transformation” was used to optimize the packing of sheet metal parts. In the problem of combined cutting path optimization, the mathematical model of cutting path optimization was established based on the parts cutting constraint rules of internal contour priority and cross cutting. The model played an important role in the optimization calculation of NC laser combined cutting.

  7. A Fuzzy Optimization Model for High-Speed Railway Timetable Rescheduling

    Directory of Open Access Journals (Sweden)

    Li Wang

    2012-01-01

    Full Text Available A fuzzy optimization model based on improved symmetric tolerance approach is introduced, which allows for rescheduling high-speed railway timetable under unexpected interferences. The model nests different parameters of the soft constraints with uncertainty margin to describe their importance to the optimization purpose and treats the objective in the same manner. Thus a new optimal instrument is expected to achieve a new timetable subject to little slack of constraints. The section between Nanjing and Shanghai, which is the busiest, of Beijing-Shanghai high-speed rail line in China is used as the simulated measurement. The fuzzy optimization model provides an accurate approximation on train running time and headway time, and hence the results suggest that the number of seriously impacted trains and total delay time can be reduced significantly subject to little cost and risk.

  8. An optimal control model of crop thinning in viticulture

    Directory of Open Access Journals (Sweden)

    Schamel Guenter H.

    2016-01-01

    Full Text Available We develop an economic model of cluster thinning in viticulture to control for grape quantity harvested and grape quality, applying a simple optimal control model with the aim to raise grape quality and related economic profits. The model maximizes vineyard owner profits and allows to discuss two relevant scenarios using a phase diagram analysis: (1 when the initial grape quantity is sufficiently small, thinning grapes will not be optimal and (2 when the initial grape quantity is high enough, it is optimal to thin grapes from the beginning of the relevant planning horizon and to reduce the quantity over time until the stock of grapes arrives at its optimum. Depending on the model's parameters, the “stopping time” for thinning grapes is reached sooner or later. After the stopping time, grape quantity evolves solely according to natural decay. The results relate to observed dynamics in viticulture and for other horticultural crops.

  9. Method to determine the optimal constitutive model from spherical indentation tests

    Directory of Open Access Journals (Sweden)

    Tairui Zhang

    2018-03-01

    Full Text Available The limitation of current indentation theories was investigated and a method to determine the optimal constitutive model through spherical indentation tests was proposed. Two constitutive models, the Power-law and the Linear-law, were used in Finite Element (FE calculations, and then a set of indentation governing equations was established for each model. The load-depth data from the normal indentation depth was used to fit the best parameters in each constitutive model while the data from the further loading part was compared with those from FE calculations, and the model that better predicted the further deformation was considered the optimal one. Moreover, a Yang’s modulus calculation model which took the previous plastic deformation and the phenomenon of pile-up (or sink-in into consideration was also proposed to revise the original Sneddon-Pharr-Oliver model. The indentation results on six materials, 304, 321, SA508, SA533, 15CrMoR, and Fv520B, were compared with tensile ones, which validated the reliability of the revised E calculation model and the optimal constitutive model determination method in this study. Keywords: Optimal constitutive model, Spherical indentation test, Finite Element calculations, Yang’s modulus

  10. GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS

    International Nuclear Information System (INIS)

    Rogers, Adam; Fiege, Jason D.

    2011-01-01

    Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image χ 2 and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest χ 2 is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.

  11. Viscoelasticity in Polymers: Phenomenological to Molecular Mathematical Modelling

    National Research Council Canada - National Science Library

    Banks, H. T; Luke, N. S

    2006-01-01

    We report on two recent advances in the modelling of viscoelastic polymers: (i) a new constitutive model which combines the virtual stick-slip continuum "molecular-based" ideas of Johnson and Stacer with the Rouse bead chain ideas; (ii...

  12. Molecular modeling of protein materials: case study of elastin

    International Nuclear Information System (INIS)

    Tarakanova, Anna; Buehler, Markus J

    2013-01-01

    Molecular modeling of protein materials is a quickly growing area of research that has produced numerous contributions in fields ranging from structural engineering to medicine and biology. We review here the history and methods commonly employed in molecular modeling of protein materials, emphasizing the advantages for using modeling as a complement to experimental work. We then consider a case study of the protein elastin, a critically important ‘mechanical protein’ to exemplify the approach in an area where molecular modeling has made a significant impact. We outline the progression of computational modeling studies that have considerably enhanced our understanding of this important protein which endows elasticity and recoil to the tissues it is found in, including the skin, lungs, arteries and the heart. A vast collection of literature has been directed at studying the structure and function of this protein for over half a century, the first molecular dynamics study of elastin being reported in the 1980s. We review the pivotal computational works that have considerably enhanced our fundamental understanding of elastin's atomistic structure and its extraordinary qualities—focusing on two in particular: elastin's superb elasticity and the inverse temperature transition—the remarkable ability of elastin to take on a more structured conformation at higher temperatures, suggesting its effectiveness as a biomolecular switch. Our hope is to showcase these methods as both complementary and enriching to experimental approaches that have thus far dominated the study of most protein-based materials. (topical review)

  13. Prospective molecular profiling of canine cancers provides a clinically relevant comparative model for evaluating personalized medicine (PMed trials.

    Directory of Open Access Journals (Sweden)

    Melissa Paoloni

    Full Text Available Molecularly-guided trials (i.e. PMed now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting.A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77% successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days. Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type.Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (<1 week. Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical

  14. A fermionic molecular dynamics technique to model nuclear matter

    International Nuclear Information System (INIS)

    Vantournhout, K.; Jachowicz, N.; Ryckebusch, J.

    2009-01-01

    Full text: At sub-nuclear densities of about 10 14 g/cm 3 , nuclear matter arranges itself in a variety of complex shapes. This can be the case in the crust of neutron stars and in core-collapse supernovae. These slab like and rod like structures, designated as nuclear pasta, have been modelled with classical molecular dynamics techniques. We present a technique, based on fermionic molecular dynamics, to model nuclear matter at sub-nuclear densities in a semi classical framework. The dynamical evolution of an antisymmetric ground state is described making the assumption of periodic boundary conditions. Adding the concepts of antisymmetry, spin and probability distributions to classical molecular dynamics, brings the dynamical description of nuclear matter to a quantum mechanical level. Applications of this model vary from investigation of macroscopic observables and the equation of state to the study of fundamental interactions on the microscopic structure of the matter. (author)

  15. Energy savings by optimization of drying processes through use of models and effective control; Energibesparelser ved optimering af toerreprocesser gennem anvendelse af modeller og effektiv regulering

    Energy Technology Data Exchange (ETDEWEB)

    Didriksen, H.; Sandvig Nielsen, J.; Weel Hansen, M.

    2001-06-01

    The aim of the project is to present a procedure to optimize existing drying processes. The optimization deals with energy consumption, capacity utilization and product quality. Other factors can also be included in the optimization, e.g. minimization of volume of discharged air. The optimization of existing drying processes will use calculation tool based on a mathematical simulation model for the process to calculate the most optimum operation situation on the basis of given conditions. In the project mathematical models have been developed precisely with this aim. The calculation tools have been developed with a user interface so that the tools can be used by technical staff in industrial companies and by consultants. The project also illustrates control of drying processes. Based on the developed models, the effect of using different types of control strategies by means of model simulations is illustrated. Three types of drying processes are treated: drum dryers, disc dryers and drying chambers. The work with the development of the simulation models has been very central in the project, as these have to be the basis for the optimization of the processes. The work is based on a large amount of information from academical literature and knowledge and experience about modelling thermal processes at dk-TEKNIK. The models constitute the core in the simulation programmes. The models describe the most important physical effects in connection with mass and energy transfer and transport under the drying for the three treated drying technologies. (EHS)

  16. SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

    International Nuclear Information System (INIS)

    Zhou, Z; Folkert, M; Wang, J

    2016-01-01

    Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

  17. SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

  18. Aggregate meta-models for evolutionary multiobjective and many-objective optimization

    Czech Academy of Sciences Publication Activity Database

    Pilát, Martin; Neruda, Roman

    Roč. 116, 20 September (2013), s. 392-402 ISSN 0925-2312 R&D Projects: GA ČR GAP202/11/1368 Institutional support: RVO:67985807 Keywords : evolutionary algorithms * multiobjective optimization * many-objective optimization * surrogate models * meta-models * memetic algorithm Subject RIV: IN - Informatics, Computer Science Impact factor: 2.005, year: 2013

  19. Electron and Phonon Transport in Molecular Junctions

    DEFF Research Database (Denmark)

    Li, Qian

    Molecular electronics provide the possibility to investigate electron and phonon transport at the smallest imaginable scale, where quantum effects can be investigated and exploited directly in the design. In this thesis, we study both electron transport and phonon transport in molecular junctions....... The system we are interested in here are π-stacked molecules connected with two semi-infinite leads. π-stacked aromatic rings, connected via π-π electronic coupling, provides a rather soft mechanical bridge while maintaining high electronic conductivity. We investigate electron transport...... transmission at the Fermi energy. We propose and analyze a way of using π   stacking to design molecular junctions to control heat transport. We develop a simple model system to identify optimal parameter regimes and then use density functional theory (DFT) to extract model parameters for a number of specific...

  20. A tutorial on fundamental model structures for railway timetable optimization

    DEFF Research Database (Denmark)

    Harrod, Steven

    2012-01-01

    This guide explains the role of railway timetables relative to all other railway scheduling activities, and then presents four fundamental timetable formulations suitable for optimization. Timetabling models may be classified according to whether they explicitly model the track structure, and whe......This guide explains the role of railway timetables relative to all other railway scheduling activities, and then presents four fundamental timetable formulations suitable for optimization. Timetabling models may be classified according to whether they explicitly model the track structure...

  1. Topology optimization of a pseudo 3D thermofluid heat sink model

    DEFF Research Database (Denmark)

    Haertel, Jan H. K.; Engelbrecht, Kurt; Lazarov, Boyan S.

    2018-01-01

    sink and a fixed heat production rate in the base plate. Optimized designs are presented and the resulting fin geometry is discussed from a thermal engineering point of view and compared to fin shapes resulting from a pressure drop minimization objective. Parametric studies are conducted to analyze......This paper investigates the application of density-based topology optimization to the design of air-cooled forced convection heat sinks. To reduce the computational burden that is associated with a full 3D optimization, a pseudo 3D optimization model comprising a 2D modeled conducting metal base...... layer and a thermally coupled 2D modeled thermofluid design layer is used. Symmetry conditions perpendicular to the flow direction are applied to generate periodic heat sink designs. The optimization objective is to minimize the heat sink heat transfer resistance for a fixed pressure drop over the heat...

  2. Optimal model-free prediction from multivariate time series

    Science.gov (United States)

    Runge, Jakob; Donner, Reik V.; Kurths, Jürgen

    2015-05-01

    Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.

  3. Extended Lagrangian formulation of charge-constrained tight-binding molecular dynamics.

    Science.gov (United States)

    Cawkwell, M J; Coe, J D; Yadav, S K; Liu, X-Y; Niklasson, A M N

    2015-06-09

    The extended Lagrangian Born-Oppenheimer molecular dynamics formalism [Niklasson, Phys. Rev. Lett., 2008, 100, 123004] has been applied to a tight-binding model under the constraint of local charge neutrality to yield microcanonical trajectories with both precise, long-term energy conservation and a reduced number of self-consistent field optimizations at each time step. The extended Lagrangian molecular dynamics formalism restores time reversal symmetry in the propagation of the electronic degrees of freedom, and it enables the efficient and accurate self-consistent optimization of the chemical potential and atomwise potential energy shifts in the on-site elements of the tight-binding Hamiltonian that are required when enforcing local charge neutrality. These capabilities are illustrated with microcanonical molecular dynamics simulations of a small metallic cluster using an sd-valent tight-binding model for titanium. The effects of weak dissipation on the propagation of the auxiliary degrees of freedom for the chemical potential and on-site Hamiltonian matrix elements that is used to counteract the accumulation of numerical noise during trajectories was also investigated.

  4. Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization

    Directory of Open Access Journals (Sweden)

    Hsu Hui-Chi

    2011-04-01

    Full Text Available Abstract Background Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization. Methods Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied. Results We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was

  5. Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization

    International Nuclear Information System (INIS)

    Kao, Kuo-Jang; Chang, Kai-Ming; Hsu, Hui-Chi; Huang, Andrew T

    2011-01-01

    Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization. Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied. We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was consistent with a unique subset of luminal A intrinsic

  6. Optimized numerical annular flow dryout model using the drift-flux model in tube geometry

    International Nuclear Information System (INIS)

    Chun, Ji Han; Lee, Un Chul

    2008-01-01

    Many experimental analyses for annular film dryouts, which is one of the Critical Heat Flux (CHF) mechanisms, have been performed because of their importance. Numerical approaches must also be developed in order to assess the results from experiments and to perform pre-tests before experiments. Various thermal-hydraulic codes, such as RELAP, COBRATF, MARS, etc., have been used in the assessment of the results of dryout experiments and in experimental pre-tests. These thermal-hydraulic codes are general tools intended for the analysis of various phenomena that could appear in nuclear power plants, and many models applying these codes are unnecessarily complex for the focused analysis of dryout phenomena alone. In this study, a numerical model was developed for annular film dryout using the drift-flux model from uniform heated tube geometry. Several candidates of models that strongly affect dryout, such as the entrainment model, deposition model, and the criterion for the dryout point model, were tested as candidates for inclusion in an optimized annular film dryout model. The optimized model was developed by adopting the best combination of these candidate models, as determined through comparison with experimental data. This optimized model showed reasonable results, which were better than those of MARS code

  7. Behavioral optimization models for multicriteria portfolio selection

    Directory of Open Access Journals (Sweden)

    Mehlawat Mukesh Kumar

    2013-01-01

    Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.

  8. Portfolio optimization for index tracking modelling in Malaysia stock market

    Science.gov (United States)

    Siew, Lam Weng; Jaaman, Saiful Hafizah; Ismail, Hamizun

    2016-06-01

    Index tracking is an investment strategy in portfolio management which aims to construct an optimal portfolio to generate similar mean return with the stock market index mean return without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using the optimization model which adopts regression approach in tracking the benchmark stock market index return. In this study, the data consists of weekly price of stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2013. The results of this study show that the optimal portfolio is able to track FBMKLCI Index at minimum tracking error of 1.0027% with 0.0290% excess mean return over the mean return of FBMKLCI Index. The significance of this study is to construct the optimal portfolio using optimization model which adopts regression approach in tracking the stock market index without purchasing all index components.

  9. A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

    Science.gov (United States)

    Tong, Cao; Gong, Haili

    2018-03-01

    This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.

  10. Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm

    International Nuclear Information System (INIS)

    Wang, Xinli; Cai, Wenjian; Lu, Jiangang; Sun, Youxian; Zhao, Lei

    2015-01-01

    This study presents a model-based optimization strategy for an actual chiller driven dehumidifier of liquid desiccant dehumidification system operating with lithium chloride solution. By analyzing the characteristics of the components, energy predictive models for the components in the dehumidifier are developed. To minimize the energy usage while maintaining the outlet air conditions at the pre-specified set-points, an optimization problem is formulated with an objective function, the constraints of mechanical limitations and components interactions. Model-based optimization strategy using genetic algorithm is proposed to obtain the optimal set-points for desiccant solution temperature and flow rate, to minimize the energy usage in the dehumidifier. Experimental studies on an actual system are carried out to compare energy consumption between the proposed optimization and the conventional strategies. The results demonstrate that energy consumption using the proposed optimization strategy can be reduced by 12.2% in the dehumidifier operation. - Highlights: • Present a model-based optimization strategy for energy saving in LDDS. • Energy predictive models for components in dehumidifier are developed. • The Optimization strategy are applied and tested in an actual LDDS. • Optimization strategy can achieve energy savings by 12% during operation

  11. Modeling and optimization of potable water network

    Energy Technology Data Exchange (ETDEWEB)

    Djebedjian, B.; Rayan, M.A. [Mansoura Univ., El-Mansoura (Egypt); Herrick, A. [Suez Canal Authority, Ismailia (Egypt)

    2000-07-01

    Software was developed in order to optimize the design of water distribution systems and pipe networks. While satisfying all the constraints imposed such as pipe diameter and nodal pressure, it was based on a mathematical model treating looped networks. The optimum network configuration and cost are determined considering parameters like pipe diameter, flow rate, corresponding pressure and hydraulic losses. It must be understood that minimum cost is relative to the different objective functions selected. The determination of the proper objective function often depends on the operating policies of a particular company. The solution for the optimization technique was obtained by using a non-linear technique. To solve the optimal design of network, the model was derived using the sequential unconstrained minimization technique (SUMT) of Fiacco and McCormick, which decreased the number of iterations required. The pipe diameters initially assumed were successively adjusted to correspond to the existing commercial pipe diameters. The technique was then applied to a two-loop network without pumps or valves. Fed by gravity, it comprised eight pipes, 1000 m long each. The first evaluation of the method proved satisfactory. As with other methods, it failed to find the global optimum. In the future, research efforts will be directed to the optimization of networks with pumps and reservoirs. 24 refs., 3 tabs., 1 fig.

  12. [Multi-mathematical modelings for compatibility optimization of Jiangzhi granules].

    Science.gov (United States)

    Yang, Ming; Zhang, Li; Ge, Yingli; Lu, Yanliu; Ji, Guang

    2011-12-01

    To investigate into the method of "multi activity index evaluation and combination optimized of mult-component" for Chinese herbal formulas. According to the scheme of uniform experimental design, efficacy experiment, multi index evaluation, least absolute shrinkage, selection operator (LASSO) modeling, evolutionary optimization algorithm, validation experiment, we optimized the combination of Jiangzhi granules based on the activity indexes of blood serum ALT, ALT, AST, TG, TC, HDL, LDL and TG level of liver tissues, ratio of liver tissue to body. Analytic hierarchy process (AHP) combining with criteria importance through intercriteria correlation (CRITIC) for multi activity index evaluation was more reasonable and objective, it reflected the information of activity index's order and objective sample data. LASSO algorithm modeling could accurately reflect the relationship between different combination of Jiangzhi granule and the activity comprehensive indexes. The optimized combination of Jiangzhi granule showed better values of the activity comprehensive indexed than the original formula after the validation experiment. AHP combining with CRITIC can be used for multi activity index evaluation and LASSO algorithm, it is suitable for combination optimized of Chinese herbal formulas.

  13. Modeling and optimization of cloud-ready and content-oriented networks

    CERN Document Server

    Walkowiak, Krzysztof

    2016-01-01

    This book focuses on modeling and optimization of cloud-ready and content-oriented networks in the context of different layers and accounts for specific constraints following from protocols and technologies used in a particular layer. It addresses a wide range of additional constraints important in contemporary networks, including various types of network flows, survivability issues, multi-layer networking, and resource location. The book presents recent existing and new results in a comprehensive and cohesive way. The contents of the book are organized in five chapters, which are mostly self-contained. Chapter 1 briefly presents information on cloud computing and content-oriented services, and introduces basic notions and concepts of network modeling and optimization. Chapter 2 covers various optimization problems that arise in the context of connection-oriented networks. Chapter 3 focuses on modeling and optimization of Elastic Optical Networks. Chapter 4 is devoted to overlay networks. The book concludes w...

  14. Learning optimal quantum models is NP-hard

    Science.gov (United States)

    Stark, Cyril J.

    2018-02-01

    Physical modeling translates measured data into a physical model. Physical modeling is a major objective in physics and is generally regarded as a creative process. How good are computers at solving this task? Here, we show that in the absence of physical heuristics, the inference of optimal quantum models cannot be computed efficiently (unless P=NP ). This result illuminates rigorous limits to the extent to which computers can be used to further our understanding of nature.

  15. Solving Optimal Control Problem of Monodomain Model Using Hybrid Conjugate Gradient Methods

    Directory of Open Access Journals (Sweden)

    Kin Wei Ng

    2012-01-01

    Full Text Available We present the numerical solutions for the PDE-constrained optimization problem arising in cardiac electrophysiology, that is, the optimal control problem of monodomain model. The optimal control problem of monodomain model is a nonlinear optimization problem that is constrained by the monodomain model. The monodomain model consists of a parabolic partial differential equation coupled to a system of nonlinear ordinary differential equations, which has been widely used for simulating cardiac electrical activity. Our control objective is to dampen the excitation wavefront using optimal applied extracellular current. Two hybrid conjugate gradient methods are employed for computing the optimal applied extracellular current, namely, the Hestenes-Stiefel-Dai-Yuan (HS-DY method and the Liu-Storey-Conjugate-Descent (LS-CD method. Our experiment results show that the excitation wavefronts are successfully dampened out when these methods are used. Our experiment results also show that the hybrid conjugate gradient methods are superior to the classical conjugate gradient methods when Armijo line search is used.

  16. Optimizing refiner operation with statistical modelling

    Energy Technology Data Exchange (ETDEWEB)

    Broderick, G [Noranda Research Centre, Pointe Claire, PQ (Canada)

    1997-02-01

    The impact of refining conditions on the energy efficiency of the process and on the handsheet quality of a chemi-mechanical pulp was studied as part of a series of pilot scale refining trials. Statistical models of refiner performance were constructed from these results and non-linear optimization of process conditions were conducted. Optimization results indicated that increasing the ratio of specific energy applied in the first stage led to a reduction of some 15 per cent in the total energy requirement. The strategy can also be used to obtain significant increases in pulp quality for a given energy input. 20 refs., 6 tabs.

  17. An Optimization Model and Modified Harmony Search Algorithm for Microgrid Planning with ESS

    Directory of Open Access Journals (Sweden)

    Yang Jiao

    2017-01-01

    Full Text Available To solve problems such as the high cost of microgrids (MGs, balance between supply and demand, stability of system operation, and optimizing the MG planning model, the energy storage system (ESS and harmony search algorithm (HSA are proposed. First, the conventional MG planning optimization model is constructed and the constraint conditions are defined: the supply and demand balance and reserve requirements. Second, an ESS is integrated into the optimal model of MG planning. The model with an ESS can solve and identify parameters such as the optimal power, optimal capacity, and optimal installation year. Third, the convergence speed and robustness of the ESS are optimized and improved. A case study comprising three different cases concludes the paper. The results show that the modified HSA (MHSA can effectively improve the stability and economy of MG operation with an ESS.

  18. Optimization of Operations Resources via Discrete Event Simulation Modeling

    Science.gov (United States)

    Joshi, B.; Morris, D.; White, N.; Unal, R.

    1996-01-01

    The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistical methods. However, in a simulation environment that is characterized by search spaces of unknown topology and stochastic measures, these optimization approaches often prove inadequate. In this paper, we have explored the applicability of genetic algorithms to the simulation domain. Genetic algorithms provide a robust search strategy that does not require continuity and differentiability of the problem domain. The genetic algorithm successfully minimized the operation and support activities for a space vehicle, through a discrete event simulation model. The practical issues associated with simulation optimization, such as stochastic variables and constraints, were also taken into consideration.

  19. Molecular Treatment of Nano-Kaolinite Generations.

    Science.gov (United States)

    Táborosi, Attila; Szilagyi, Robert K; Zsirka, Balázs; Fónagy, Orsolya; Horváth, Erzsébet; Kristóf, János

    2018-06-18

    A procedure is developed for defining a compositionally and structurally realistic, atomic-scale description of exfoliated clay nanoparticles from the kaolinite family of phylloaluminosilicates. By use of coordination chemical principles, chemical environments within a nanoparticle can be separated into inner, outer, and peripheral spheres. The edges of the molecular models of nanoparticles were protonated in a validated manner to achieve charge neutrality. Structural optimizations using semiempirical methods (NDDO Hamiltonians and DFTB formalism) and ab initio density functionals with a saturated basis set revealed previously overlooked molecular origins of morphological changes as a result of exfoliation. While the use of semiempirical methods is desirable for the treatment of nanoparticles composed of tens of thousands of atoms, the structural accuracy is rather modest in comparison to DFT methods. We report a comparative survey of our infrared data for untreated crystalline and various exfoliated states of kaolinite and halloysite. Given the limited availability of experimental techniques for providing direct structural information about nano-kaolinite, the vibrational spectra can be considered as an essential tool for validating structural models. The comparison of experimental and calculated stretching and bending frequencies further justified the use of the preferred level of theory. Overall, an optimal molecular model of the defect-free, ideal nano-kaolinite can be composed with respect to stationary structure and curvature of the potential energy surface using the PW91/SVP level of theory with empirical dispersion correction (PW91+D) and polarizable continuum solvation model (PCM) without the need for a scaled quantum chemical force field. This validated theoretical approach is essential in order to follow the formation of exfoliated clays and their surface reactivity that is experimentally unattainable.

  20. Low Complexity Models to improve Incomplete Sensitivities for Shape Optimization

    Science.gov (United States)

    Stanciu, Mugurel; Mohammadi, Bijan; Moreau, Stéphane

    2003-01-01

    The present global platform for simulation and design of multi-model configurations treat shape optimization problems in aerodynamics. Flow solvers are coupled with optimization algorithms based on CAD-free and CAD-connected frameworks. Newton methods together with incomplete expressions of gradients are used. Such incomplete sensitivities are improved using reduced models based on physical assumptions. The validity and the application of this approach in real-life problems are presented. The numerical examples concern shape optimization for an airfoil, a business jet and a car engine cooling axial fan.

  1. Nuclear-fuel-cycle optimization: methods and modelling techniques

    International Nuclear Information System (INIS)

    Silvennoinen, P.

    1982-01-01

    This book present methods applicable to analyzing fuel-cycle logistics and optimization as well as in evaluating the economics of different reactor strategies. After an introduction to the phases of a fuel cycle, uranium cost trends are assessed in a global perspective. Subsequent chapters deal with the fuel-cycle problems faced by a power utility. The fuel-cycle models cover the entire cycle from the supply of uranium to the disposition of spent fuel. The chapter headings are: Nuclear Fuel Cycle, Uranium Supply and Demand, Basic Model of the LWR (light water reactor) Fuel Cycle, Resolution of Uncertainties, Assessment of Proliferation Risks, Multigoal Optimization, Generalized Fuel-Cycle Models, Reactor Strategy Calculations, and Interface with Energy Strategies. 47 references, 34 figures, 25 tables

  2. Decision Support Model for Optimal Management of Coastal Gate

    Science.gov (United States)

    Ditthakit, Pakorn; Chittaladakorn, Suwatana

    2010-05-01

    The coastal areas are intensely settled by human beings owing to their fertility of natural resources. However, at present those areas are facing with water scarcity problems: inadequate water and poor water quality as a result of saltwater intrusion and inappropriate land-use management. To solve these problems, several measures have been exploited. The coastal gate construction is a structural measure widely performed in several countries. This manner requires the plan for suitably operating coastal gates. Coastal gate operation is a complicated task and usually concerns with the management of multiple purposes, which are generally conflicted one another. This paper delineates the methodology and used theories for developing decision support modeling for coastal gate operation scheduling. The developed model was based on coupling simulation and optimization model. The weighting optimization technique based on Differential Evolution (DE) was selected herein for solving multiple objective problems. The hydrodynamic and water quality models were repeatedly invoked during searching the optimal gate operations. In addition, two forecasting models:- Auto Regressive model (AR model) and Harmonic Analysis model (HA model) were applied for forecasting water levels and tide levels, respectively. To demonstrate the applicability of the developed model, it was applied to plan the operations for hypothetical system of Pak Phanang coastal gate system, located in Nakhon Si Thammarat province, southern part of Thailand. It was found that the proposed model could satisfyingly assist decision-makers for operating coastal gates under various environmental, ecological and hydraulic conditions.

  3. The emerging role of cloud computing in molecular modelling.

    Science.gov (United States)

    Ebejer, Jean-Paul; Fulle, Simone; Morris, Garrett M; Finn, Paul W

    2013-07-01

    There is a growing recognition of the importance of cloud computing for large-scale and data-intensive applications. The distinguishing features of cloud computing and their relationship to other distributed computing paradigms are described, as are the strengths and weaknesses of the approach. We review the use made to date of cloud computing for molecular modelling projects and the availability of front ends for molecular modelling applications. Although the use of cloud computing technologies for molecular modelling is still in its infancy, we demonstrate its potential by presenting several case studies. Rapid growth can be expected as more applications become available and costs continue to fall; cloud computing can make a major contribution not just in terms of the availability of on-demand computing power, but could also spur innovation in the development of novel approaches that utilize that capacity in more effective ways. Copyright © 2013 Elsevier Inc. All rights reserved.

  4. Optimization of A(2)O BNR processes using ASM and EAWAG Bio-P models: model performance.

    Science.gov (United States)

    El Shorbagy, Walid E; Radif, Nawras N; Droste, Ronald L

    2013-12-01

    This paper presents the performance of an optimization model for a biological nutrient removal (BNR) system using the anaerobic-anoxic-oxic (A(2)O) process. The formulated model simulates removal of organics, nitrogen, and phosphorus using a reduced International Water Association (IWA) Activated Sludge Model #3 (ASM3) model and a Swiss Federal Institute for Environmental Science and Technology (EAWAG) Bio-P module. Optimal sizing is attained considering capital and operational costs. Process performance is evaluated against the effect of influent conditions, effluent limits, and selected parameters of various optimal solutions with the following results: an increase of influent temperature from 10 degrees C to 25 degrees C decreases the annual cost by about 8.5%, an increase of influent flow from 500 to 2500 m(3)/h triples the annual cost, the A(2)O BNR system is more sensitive to variations in influent ammonia than phosphorus concentration and the maximum growth rate of autotrophic biomass was the most sensitive kinetic parameter in the optimization model.

  5. Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.

    Science.gov (United States)

    Patri, Jean-François; Diard, Julien; Perrier, Pascal

    2015-12-01

    The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.

  6. Theoretical model for calculation of molecular stopping power

    International Nuclear Information System (INIS)

    Xu, Y.J.

    1984-01-01

    A modified local plasma model based on the work of Linhard-Winther, Bethe, Brown, and Walske is established. The Gordon-Kim's molecular charged density model is employed to obtain a formula to evaluate the stopping power of many useful molecular systems. The stopping power of H 2 and He gas was calculated for incident proton energy ranging from 100 KeV to 2.5 MeV. The stopping power of O 2 , N 2 , and water vapor was also calculated for incident proton energy ranging from 40 keV to 2.5 MeV. Good agreement with experimental data was obtained. A discussion of molecular effects leading to departure from Bragg's rule is presented. The equipartition rule and the effect of nuclear momentum recoiling in stopping power are also discussed in the appendix. The calculation procedure presented hopefully can easily be extended to include the most useful organic systems such as the molecules composed of carbon, nitrogen, hydrogen and oxygen which are useful in radiation protection field

  7. Introductory Molecular Orbital Theory: An Honors General Chemistry Computational Lab as Implemented Using Three-Dimensional Modeling Software

    Science.gov (United States)

    Ruddick, Kristie R.; Parrill, Abby L.; Petersen, Richard L.

    2012-01-01

    In this study, a computational molecular orbital theory experiment was implemented in a first-semester honors general chemistry course. Students used the GAMESS (General Atomic and Molecular Electronic Structure System) quantum mechanical software (as implemented in ChemBio3D) to optimize the geometry for various small molecules. Extended Huckel…

  8. Spherical convolutions and their application in molecular modelling

    DEFF Research Database (Denmark)

    Boomsma, Wouter; Frellsen, Jes

    2017-01-01

    Convolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried over...... to other problem domains almost unaltered. Unfortunately, this convenience does not trivially extend to data in non-euclidean spaces, such as spherical data. In this paper, we introduce two strategies for conducting convolutions on the sphere, using either a spherical-polar grid or a grid based...... of spherical convolutions in the context of molecular modelling, by considering structural environments within proteins. We show that the models are capable of learning non-trivial functions in these molecular environments, and that our spherical convolutions generally outperform standard 3D convolutions...

  9. Global Optimization Ensemble Model for Classification Methods

    Science.gov (United States)

    Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

    2014-01-01

    Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

  10. Global Optimization Ensemble Model for Classification Methods

    Directory of Open Access Journals (Sweden)

    Hina Anwar

    2014-01-01

    Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

  11. Determining the Optimal Number of Core Needle Biopsy Passes for Molecular Diagnostics.

    Science.gov (United States)

    Hoang, Nam S; Ge, Benjamin H; Pan, Lorraine Y; Ozawa, Michael G; Kong, Christina S; Louie, John D; Shah, Rajesh P

    2018-03-01

    The number of core biopsy passes required for adequate next-generation sequencing is impacted by needle cut, needle gauge, and the type of tissue involved. This study evaluates diagnostic adequacy of core needle lung biopsies based on number of passes and provides guidelines for other tissues based on simulated biopsies in ex vivo porcine organ tissues. The rate of diagnostic adequacy for pathology and molecular testing from lung biopsy procedures was measured for eight operators pre-implementation (September 2012-October 2013) and post-implementation (December 2013-April 2014) of a standard protocol using 20-gauge side-cut needles for ten core biopsy passes at a single academic hospital. Biopsy pass volume was then estimated in ex vivo porcine muscle, liver, and kidney using side-cut devices at 16, 18, and 20 gauge and end-cut devices at 16 and 18 gauge to estimate minimum number of passes required for adequate molecular testing. Molecular diagnostic adequacy increased from 69% (pre-implementation period) to 92% (post-implementation period) (p < 0.001) for lung biopsies. In porcine models, both 16-gauge end-cut and side-cut devices require one pass to reach the validated volume threshold to ensure 99% adequacy for molecular characterization, while 18- and 20-gauge devices require 2-5 passes depending on needle cut and tissue type. Use of 20-gauge side-cut core biopsy needles requires a significant number of passes to ensure diagnostic adequacy for molecular testing across all tissue types. To ensure diagnostic adequacy for molecular testing, 16- and 18-gauge needles require markedly fewer passes.

  12. All-Polymer Solar Cell Performance Optimized via Systematic Molecular Weight Tuning of Both Donor and Acceptor Polymers.

    Science.gov (United States)

    Zhou, Nanjia; Dudnik, Alexander S; Li, Ting I N G; Manley, Eric F; Aldrich, Thomas J; Guo, Peijun; Liao, Hsueh-Chung; Chen, Zhihua; Chen, Lin X; Chang, Robert P H; Facchetti, Antonio; Olvera de la Cruz, Monica; Marks, Tobin J

    2016-02-03

    The influence of the number-average molecular weight (Mn) on the blend film morphology and photovoltaic performance of all-polymer solar cells (APSCs) fabricated with the donor polymer poly[5-(2-hexyldodecyl)-1,3-thieno[3,4-c]pyrrole-4,6-dione-alt-5,5-(2,5-bis(3-dodecylthiophen-2-yl)thiophene)] (PTPD3T) and acceptor polymer poly{[N,N'-bis(2-octyldodecyl)naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5'-(2,2'-bithiophene)} (P(NDI2OD-T2); N2200) is systematically investigated. The Mn effect analysis of both PTPD3T and N2200 is enabled by implementing a polymerization strategy which produces conjugated polymers with tunable Mns. Experimental and coarse-grain modeling results reveal that systematic Mn variation greatly influences both intrachain and interchain interactions and ultimately the degree of phase separation and morphology evolution. Specifically, increasing Mn for both polymers shrinks blend film domain sizes and enhances donor-acceptor polymer-polymer interfacial areas, affording increased short-circuit current densities (Jsc). However, the greater disorder and intermixed feature proliferation accompanying increasing Mn promotes charge carrier recombination, reducing cell fill factors (FF). The optimized photoactive layers exhibit well-balanced exciton dissociation and charge transport characteristics, ultimately providing solar cells with a 2-fold PCE enhancement versus devices with nonoptimal Mns. Overall, it is shown that proper and precise tuning of both donor and acceptor polymer Mns is critical for optimizing APSC performance. In contrast to reports where maximum power conversion efficiencies (PCEs) are achieved for the highest Mns, the present two-dimensional Mn optimization matrix strategy locates a PCE "sweet spot" at intermediate Mns of both donor and acceptor polymers. This study provides synthetic methodologies to predictably access conjugated polymers with desired Mn and highlights the importance of optimizing Mn for both polymer

  13. Correlations in state space can cause sub-optimal adaptation of optimal feedback control models

    OpenAIRE

    Aprasoff, Jonathan; Donchin, Opher

    2011-01-01

    Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedb...

  14. Optimal modeling and forecasting of the energy consumption and production in China

    International Nuclear Information System (INIS)

    Xiong, Ping-ping; Dang, Yao-guo; Yao, Tian-xiang; Wang, Zheng-xin

    2014-01-01

    Energy is of fundamental importance to a nation's economy. Accurate prediction of the energy consumption and production in China can play a guiding role in making the energy consumption plan, and facilitate timely and effective decision making of energy policy. This article proposes a novel GM (gray model) (1,1) model based on optimizing initial condition according to the principle of new information priority. The optimized model and five other GM (1,1) models are applied in the modeling of China's energy consumption and production. Both the simulation and prediction accuracy of the models are compared and analyzed. We obtain the result that the optimized model has higher prediction accuracy than the other five models. Therefore, the presented optimized model is further utilized to predict China's energy consumption and production from 2013 to 2017. The result indicates that China's energy consumption and production will keep increasing and the gap between the energy production and consumption will also be increasing. Finally, we predict Iran's and Argentina's energy consumption to further prove the effectiveness of the proposed model. - Highlights: • We proposed a novel GM (1,1) model based on optimizing initial condition. • The prediction accuracy of the proposed model is better than the other models. • We used the proposed model to predict China's energy consumption and production. • The proposed model can be used to predict other countries' energy consumption

  15. Optimization models and techniques for implementation and pricing of electricity markets

    International Nuclear Information System (INIS)

    Madrigal Martinez, M.

    2001-01-01

    The operation and planning of vertically integrated electric power systems can be optimized using models that simulate solutions to problems. As the electric power industry is going through a period of restructuring, there is a need for new optimization tools. This thesis describes the importance of optimization tools and presents techniques for implementing them. It also presents methods for pricing primary electricity markets. Three modeling groups are studied. The first considers a simplified continuous and discrete model for power pool auctions. The second considers the unit commitment problem, and the third makes use of a new type of linear network-constrained clearing system model for daily markets for power and spinning reserve. The newly proposed model considers bids for supply and demand and bilateral contracts. It is a direct current model for the transmission network

  16. Multiple time step molecular dynamics in the optimized isokinetic ensemble steered with the molecular theory of solvation: Accelerating with advanced extrapolation of effective solvation forces

    International Nuclear Information System (INIS)

    Omelyan, Igor; Kovalenko, Andriy

    2013-01-01

    We develop efficient handling of solvation forces in the multiscale method of multiple time step molecular dynamics (MTS-MD) of a biomolecule steered by the solvation free energy (effective solvation forces) obtained from the 3D-RISM-KH molecular theory of solvation (three-dimensional reference interaction site model complemented with the Kovalenko-Hirata closure approximation). To reduce the computational expenses, we calculate the effective solvation forces acting on the biomolecule by using advanced solvation force extrapolation (ASFE) at inner time steps while converging the 3D-RISM-KH integral equations only at large outer time steps. The idea of ASFE consists in developing a discrete non-Eckart rotational transformation of atomic coordinates that minimizes the distances between the atomic positions of the biomolecule at different time moments. The effective solvation forces for the biomolecule in a current conformation at an inner time step are then extrapolated in the transformed subspace of those at outer time steps by using a modified least square fit approach applied to a relatively small number of the best force-coordinate pairs. The latter are selected from an extended set collecting the effective solvation forces obtained from 3D-RISM-KH at outer time steps over a broad time interval. The MTS-MD integration with effective solvation forces obtained by converging 3D-RISM-KH at outer time steps and applying ASFE at inner time steps is stabilized by employing the optimized isokinetic Nosé-Hoover chain (OIN) ensemble. Compared to the previous extrapolation schemes used in combination with the Langevin thermostat, the ASFE approach substantially improves the accuracy of evaluation of effective solvation forces and in combination with the OIN thermostat enables a dramatic increase of outer time steps. We demonstrate on a fully flexible model of alanine dipeptide in aqueous solution that the MTS-MD/OIN/ASFE/3D-RISM-KH multiscale method of molecular dynamics

  17. Synthesis, molecular modeling and biological evaluation of PSB as targeted antibiotics.

    Science.gov (United States)

    Cheng, Kui; Zheng, Qing-Zhong; Hou, Jin; Zhou, Yang; Liu, Chang-Hong; Zhao, Jing; Zhu, Hai-Liang

    2010-04-01

    We described here the design, synthesis, molecular modeling, and biological evaluation of a series of peptide and Schiff bases (PSB) small molecules, inhibitors of Escherichia coli beta-Ketoacyl-acyl carrier protein synthase III (ecKAS III). The initial lead compound was reported by us previously, we continued to carry out structure-activity relationship studies and optimize the lead structure to potent inhibitors in this research. The results demonstrated that both N-(2-(3,5-dichloro-2-hydroxybenzylideneamino)propyl)-2-hydroxybenzamide (1f) and 2-hydroxy-N-(2-(2-hydroxy-5-iodobenzylideneamino)propyl)-4-methylbenzamide (3e) posses good ecKAS III inhibitory activity and well binding affinities by bonding Gly152/Gly209 of ecKAS III and fit into the mouth of the substrate tunnel, and can be as potential antibiotics agent, displaying minimal inhibitory concentration values in the range 0.20-3.13microg/mL and 0.39-3.13microg/mL against various bacteria. Copyright 2010 Elsevier Ltd. All rights reserved.

  18. Multi-objective optimization for generating a weighted multi-model ensemble

    Science.gov (United States)

    Lee, H.

    2017-12-01

    Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

  19. A trust region approach with multivariate Padé model for optimal circuit design

    Science.gov (United States)

    Abdel-Malek, Hany L.; Ebid, Shaimaa E. K.; Mohamed, Ahmed S. A.

    2017-11-01

    Since the optimization process requires a significant number of consecutive function evaluations, it is recommended to replace the function by an easily evaluated approximation model during the optimization process. The model suggested in this article is based on a multivariate Padé approximation. This model is constructed using data points of ?, where ? is the number of parameters. The model is updated over a sequence of trust regions. This model avoids the slow convergence of linear models of ? and has features of quadratic models that need interpolation data points of ?. The proposed approach is tested by applying it to several benchmark problems. Yield optimization using such a direct method is applied to some practical circuit examples. Minimax solution leads to a suitable initial point to carry out the yield optimization process. The yield is optimized by the proposed derivative-free method for active and passive filter examples.

  20. Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

    Directory of Open Access Journals (Sweden)

    Sie Long Kek

    2015-01-01

    Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.

  1. Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

    Science.gov (United States)

    Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

    2017-01-01

    Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

  2. Optimal foraging in marine ecosystem models: selectivity, profitability and switching

    DEFF Research Database (Denmark)

    Visser, Andre W.; Fiksen, Ø.

    2013-01-01

    ecological mechanics and evolutionary logic as a solution to diet selection in ecosystem models. When a predator can consume a range of prey items it has to choose which foraging mode to use, which prey to ignore and which ones to pursue, and animals are known to be particularly skilled in adapting...... to the preference functions commonly used in models today. Indeed, depending on prey class resolution, optimal foraging can yield feeding rates that are considerably different from the ‘switching functions’ often applied in marine ecosystem models. Dietary inclusion is dictated by two optimality choices: 1...... by letting predators maximize energy intake or more properly, some measure of fitness where predation risk and cost are also included. An optimal foraging or fitness maximizing approach will give marine ecosystem models a sound principle to determine trophic interactions...

  3. Optimization of single photon detection model based on GM-APD

    Science.gov (United States)

    Chen, Yu; Yang, Yi; Hao, Peiyu

    2017-11-01

    One hundred kilometers high precision laser ranging hopes the detector has very strong detection ability for very weak light. At present, Geiger-Mode of Avalanche Photodiode has more use. It has high sensitivity and high photoelectric conversion efficiency. Selecting and designing the detector parameters according to the system index is of great importance to the improvement of photon detection efficiency. Design optimization requires a good model. In this paper, we research the existing Poisson distribution model, and consider the important detector parameters of dark count rate, dead time, quantum efficiency and so on. We improve the optimization of detection model, select the appropriate parameters to achieve optimal photon detection efficiency. The simulation is carried out by using Matlab and compared with the actual test results. The rationality of the model is verified. It has certain reference value in engineering applications.

  4. Collective properties of evolving molecular quasispecies

    Directory of Open Access Journals (Sweden)

    Manrubia Susanna C

    2007-07-01

    Full Text Available Abstract Background RNA molecules, through their dual appearance as sequence and structure, represent a suitable model to study evolutionary properties of quasispecies. The essential ingredient in this model is the differentiation between genotype (molecular sequences which are affected by mutation and phenotype (molecular structure, affected by selection. This framework allows a quantitative analysis of organizational properties of quasispecies as they adapt to different environments, such as their robustness, the effect of the degeneration of the sequence space, or the adaptation under different mutation rates and the error threshold associated. Results We describe and analyze the structural properties of molecular quasispecies adapting to different environments both during the transient time before adaptation takes place and in the asymptotic state, once optimization has occurred. We observe a minimum in the adaptation time at values of the mutation rate relatively far from the phenotypic error threshold. Through the definition of a consensus structure, it is shown that the quasispecies retains relevant structural information in a distributed fashion even above the error threshold. This structural robustness depends on the precise shape of the secondary structure used as target of selection. Experimental results available for natural RNA populations are in qualitative agreement with our observations. Conclusion Adaptation time of molecular quasispecies to a given environment is optimized at values of the mutation rate well below the phenotypic error threshold. The optimal value results from a trade-off between diversity generation and fixation of advantageous mutants. The critical value of the mutation rate is a function not only of the sequence length, but also of the specific properties of the environment, in this case the selection pressure and the shape of the secondary structure used as target phenotype. Certain functional motifs of RNA

  5. Heat transfer optimization of SCO2 porous flow based on Brinkman model

    Directory of Open Access Journals (Sweden)

    Lin David T.W.

    2016-01-01

    Full Text Available The purpose of this study is to obtain the optimal operating condition in order to find the maximum supercritical CO2 heat extraction in the enhanced geothermal system (EGS. In this study, the heat transfer model conjugated with the Brinkman model is used to evaluate the thermal behavior in the reservoir of the EGS. This numerical model is validated by experiment. Optimization is processed based on the Nelder-Mead approach. The optimal operating conditions are proposed with different pressure, porosity. This study will build the optimal platform of heat source of geothermal power plant.

  6. Optimization and mathematical modeling in computer architecture

    CERN Document Server

    Sankaralingam, Karu; Nowatzki, Tony

    2013-01-01

    In this book we give an overview of modeling techniques used to describe computer systems to mathematical optimization tools. We give a brief introduction to various classes of mathematical optimization frameworks with special focus on mixed integer linear programming which provides a good balance between solver time and expressiveness. We present four detailed case studies -- instruction set customization, data center resource management, spatial architecture scheduling, and resource allocation in tiled architectures -- showing how MILP can be used and quantifying by how much it outperforms t

  7. Modeling, Analysis, and Optimization Issues for Large Space Structures

    Science.gov (United States)

    Pinson, L. D. (Compiler); Amos, A. K. (Compiler); Venkayya, V. B. (Compiler)

    1983-01-01

    Topics concerning the modeling, analysis, and optimization of large space structures are discussed including structure-control interaction, structural and structural dynamics modeling, thermal analysis, testing, and design.

  8. Sparse optimization for inverse problems in atmospheric modelling

    Czech Academy of Sciences Publication Activity Database

    Adam, Lukáš; Branda, Martin

    2016-01-01

    Roč. 79, č. 3 (2016), s. 256-266 ISSN 1364-8152 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Inverse modelling * Sparse optimization * Integer optimization * Least squares * European tracer experiment * Free Matlab codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.404, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0457037.pdf

  9. Deterministic operations research models and methods in linear optimization

    CERN Document Server

    Rader, David J

    2013-01-01

    Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear

  10. Molecular Dynamics Simulations of Kinetic Models for Chiral Dominance in Soft Condensed Matter

    DEFF Research Database (Denmark)

    Toxvaerd, Søren

    2001-01-01

    Molecular dynamics simulation, models for isomerization kinetics, origin of biomolecular chirality......Molecular dynamics simulation, models for isomerization kinetics, origin of biomolecular chirality...

  11. Observational Constraints for Modeling Diffuse Molecular Clouds

    Science.gov (United States)

    Federman, S. R.

    2014-02-01

    Ground-based and space-borne observations of diffuse molecular clouds suggest a number of areas where further improvements to modeling efforts is warranted. I will highlight those that have the widest applicability. The range in CO fractionation caused by selective isotope photodissociation, in particular the large 12C16O/13C16O ratios observed toward stars in Ophiuchus, is not reproduced well by current models. Our ongoing laboratory measurements of oscillator strengths and predissociation rates for Rydberg transitions in CO isotopologues may help clarify the situtation. The CH+ abundance continues to draw attention. Small scale structure seen toward ζ Per may provide additional constraints on the possible synthesis routes. The connection between results from optical transitions and those from radio and sub-millimeter wave transitions requires further effort. A study of OH+ and OH toward background stars reveals that these species favor different environments. This brings to focus the need to model each cloud along the line of sight separately, and to allow the physical conditions to vary within an individual cloud, in order to gain further insight into the chemistry. Now that an extensive set of data on molecular excitation is available, the models should seek to reproduce these data to place further constraints on the modeling results.

  12. Variational methods in molecular modeling

    CERN Document Server

    2017-01-01

    This book presents tutorial overviews for many applications of variational methods to molecular modeling. Topics discussed include the Gibbs-Bogoliubov-Feynman variational principle, square-gradient models, classical density functional theories, self-consistent-field theories, phase-field methods, Ginzburg-Landau and Helfrich-type phenomenological models, dynamical density functional theory, and variational Monte Carlo methods. Illustrative examples are given to facilitate understanding of the basic concepts and quantitative prediction of the properties and rich behavior of diverse many-body systems ranging from inhomogeneous fluids, electrolytes and ionic liquids in micropores, colloidal dispersions, liquid crystals, polymer blends, lipid membranes, microemulsions, magnetic materials and high-temperature superconductors. All chapters are written by leading experts in the field and illustrated with tutorial examples for their practical applications to specific subjects. With emphasis placed on physical unders...

  13. Solving bi-level optimization problems in engineering design using kriging models

    Science.gov (United States)

    Xia, Yi; Liu, Xiaojie; Du, Gang

    2018-05-01

    Stackelberg game-theoretic approaches are applied extensively in engineering design to handle distributed collaboration decisions. Bi-level genetic algorithms (BLGAs) and response surfaces have been used to solve the corresponding bi-level programming models. However, the computational costs for BLGAs often increase rapidly with the complexity of lower-level programs, and optimal solution functions sometimes cannot be approximated by response surfaces. This article proposes a new method, namely the optimal solution function approximation by kriging model (OSFAKM), in which kriging models are used to approximate the optimal solution functions. A detailed example demonstrates that OSFAKM can obtain better solutions than BLGAs and response surface-based methods, and at the same time reduce the workload of computation remarkably. Five benchmark problems and a case study of the optimal design of a thin-walled pressure vessel are also presented to illustrate the feasibility and potential of the proposed method for bi-level optimization in engineering design.

  14. Communication: Calculation of interatomic forces and optimization of molecular geometry with auxiliary-field quantum Monte Carlo

    Science.gov (United States)

    Motta, Mario; Zhang, Shiwei

    2018-05-01

    We propose an algorithm for accurate, systematic, and scalable computation of interatomic forces within the auxiliary-field quantum Monte Carlo (AFQMC) method. The algorithm relies on the Hellmann-Feynman theorem and incorporates Pulay corrections in the presence of atomic orbital basis sets. We benchmark the method for small molecules by comparing the computed forces with the derivatives of the AFQMC potential energy surface and by direct comparison with other quantum chemistry methods. We then perform geometry optimizations using the steepest descent algorithm in larger molecules. With realistic basis sets, we obtain equilibrium geometries in agreement, within statistical error bars, with experimental values. The increase in computational cost for computing forces in this approach is only a small prefactor over that of calculating the total energy. This paves the way for a general and efficient approach for geometry optimization and molecular dynamics within AFQMC.

  15. In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data

    Science.gov (United States)

    Yi, Yeon-Sook

    2017-01-01

    This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model…

  16. Progresses in optimization strategy for radiolabeled molecular probes targeting integrin αvβ3

    International Nuclear Information System (INIS)

    Chen Haojun; Wu Hua

    2012-01-01

    Tumor angiogenesis is critical in the growth, invasion and metastasis of malignant tumors. The integrins, which express on many types of tumor cells and activated vascular endothelial cells, play an important role in regulation of the tumor angiogenesis. RGD peptide, which contains Arg-Gly-Asp sequence, binds specifically to integrin α v β 3 . Therefore, the radiolabeled RGD peptides may have broad application prospects in radionuclide imaging and therapy. Major research interests include the selection of radionuclides, modification and improvement of RGD structures. In this article, we give a review on research progresses in optimization strategy for radiolabeled molecular probes targeting integrin α v β 3 . (authors)

  17. Applied probability models with optimization applications

    CERN Document Server

    Ross, Sheldon M

    1992-01-01

    Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.

  18. Melting behavior of a model molecular crystalline GeI4

    International Nuclear Information System (INIS)

    Fuchizaki, Kazuhiro; Asano, Yuta

    2015-01-01

    A model molecular crystalline GeI 4 was examined using molecular dynamics simulation. The model was constructed in such a way that rigid tetrahedral molecules interact with each other via Lennard-Jones potentials whose centers are located at the vertices of a tetrahedron. Because no other interaction that can “soften” the intermolecular interaction was introduced, the melting curve of the model crystalline material does not exhibit the anomaly that was found for the real substance. However, the current investigation is useful in that it could settle the upper bound of pressure below which the model can predict properties of the molecular liquid. Moreover, singularity-free nature of the melting curve allowed us to analytically treat the melting curve in the light of the Kumari-Dass-Kechin equation. As a result, we could definitely conclude that the well-known Simon equation for the melting curve is merely an approximate expression. The condition for the validity of Simon’s equation was identified. (author)

  19. Hedging Rules for Water Supply Reservoir Based on the Model of Simulation and Optimization

    Directory of Open Access Journals (Sweden)

    Yi Ji

    2016-06-01

    Full Text Available This study proposes a hedging rule model which is composed of a two-period reservior operation model considering the damage depth and hedging rule parameter optimization model. The former solves hedging rules based on a given poriod’s water supply weighting factor and carryover storage target, while the latter optimization model is used to optimize the weighting factor and carryover storage target based on the hedging rules. The coupling model gives the optimal poriod’s water supply weighting factor and carryover storage target to guide release. The conclusions achieved from this study as follows: (1 the water supply weighting factor and carryover storage target have a direct impact on the three elements of the hedging rule; (2 parameters can guide reservoirs to supply water reasonably after optimization of the simulation and optimization model; and (3 in order to verify the utility of the hedging rule, the Heiquan reservoir is used as a case study and particle swarm optimization algorithm with a simulation model is adopted for optimizing the parameter. The results show that the proposed hedging rule can improve the operation performances of the water supply reservoir.

  20. Optimization of large-scale heterogeneous system-of-systems models.

    Energy Technology Data Exchange (ETDEWEB)

    Parekh, Ojas; Watson, Jean-Paul; Phillips, Cynthia Ann; Siirola, John; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Lee, Herbert K. H. (University of California, Santa Cruz, Santa Cruz, CA); Hart, William Eugene; Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Woodruff, David L. (University of California, Davis, Davis, CA)

    2012-01-01

    Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.

  1. Quantum Mechanics/Molecular Mechanics Modeling of Drug Metabolism

    DEFF Research Database (Denmark)

    Lonsdale, Richard; Fort, Rachel M; Rydberg, Patrik

    2016-01-01

    )-mexiletine in CYP1A2 with hybrid quantum mechanics/molecular mechanics (QM/MM) methods, providing a more detailed and realistic model. Multiple reaction barriers have been calculated at the QM(B3LYP-D)/MM(CHARMM27) level for the direct N-oxidation and H-abstraction/rebound mechanisms. Our calculated barriers......The mechanism of cytochrome P450(CYP)-catalyzed hydroxylation of primary amines is currently unclear and is relevant to drug metabolism; previous small model calculations have suggested two possible mechanisms: direct N-oxidation and H-abstraction/rebound. We have modeled the N-hydroxylation of (R...... indicate that the direct N-oxidation mechanism is preferred and proceeds via the doublet spin state of Compound I. Molecular dynamics simulations indicate that the presence of an ordered water molecule in the active site assists in the binding of mexiletine in the active site...

  2. Model-Based Optimization of Velocity Strategy for Lightweight Electric Racing Cars

    Directory of Open Access Journals (Sweden)

    Mirosław Targosz

    2018-01-01

    Full Text Available The article presents a method for optimizing driving strategies aimed at minimizing energy consumption while driving. The method was developed for the needs of an electric powered racing vehicle built for the purposes of the Shell Eco-marathon (SEM, the most famous and largest race of energy efficient vehicles. Model-based optimization was used to determine the driving strategy. The numerical model was elaborated in Simulink environment, which includes both the electric vehicle model and the environment, i.e., the race track as well as the vehicle environment and the atmospheric conditions. The vehicle model itself includes vehicle dynamic model, numerical model describing issues concerning resistance of rolling tire, resistance of the propulsion system, aerodynamic phenomena, model of the electric motor, and control system. For the purpose of identifying design and functional features of individual subassemblies and components, numerical and stand tests were carried out. The model itself was tested on the research tracks to tune the model and determine the calculation parameters. The evolutionary algorithms, which are available in the MATLAB Global Optimization Toolbox, were used for optimization. In the race conditions, the model was verified during SEM races in Rotterdam where the race vehicle scored the result consistent with the results of simulation calculations. In the following years, the experience gathered by the team gave us the vice Championship in the SEM 2016 in London.

  3. Runtime Optimizations for Tree-Based Machine Learning Models

    NARCIS (Netherlands)

    N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

    2014-01-01

    htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

  4. Developments in model-based optimization and control distributed control and industrial applications

    CERN Document Server

    Grancharova, Alexandra; Pereira, Fernando

    2015-01-01

    This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...

  5. Optimal Strategy and Business Models

    DEFF Research Database (Denmark)

    Johnson, Peter; Foss, Nicolai Juul

    2016-01-01

    This study picks up on earlier suggestions that control theory may further the study of strategy. Strategy can be formally interpreted as an idealized path optimizing heterogeneous resource deployment to produce maximum financial gain. Using standard matrix methods to describe the firm Hamiltonia...... variable of firm path, suggesting in turn that the firm's business model is the codification of the application of investment resources used to control the strategic path of value realization....

  6. Study on network traffic forecast model of SVR optimized by GAFSA

    International Nuclear Information System (INIS)

    Liu, Yuan; Wang, RuiXue

    2016-01-01

    There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.

  7. MODELLING, SIMULATING AND OPTIMIZING BOILERS

    DEFF Research Database (Denmark)

    Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels

    2004-01-01

    In the present work a framework for optimizing the design of boilers for dynamic operation has been developed. A cost function to be minimized during the optimization has been formulated and for the present design variables related to the Boiler Volume and the Boiler load Gradient (i.e. ring rate...... on the boiler) have been dened. Furthermore a number of constraints related to: minimum and maximum boiler load gradient, minimum boiler size, Shrinking and Swelling and Steam Space Load have been dened. For dening the constraints related to the required boiler volume a dynamic model for simulating the boiler...... performance has been developed. Outputs from the simulations are shrinking and swelling of water level in the drum during for example a start-up of the boiler, these gures combined with the requirements with respect to allowable water level uctuations in the drum denes the requirements with respect to drum...

  8. Diamond-like nanoparticles influence on flavonoids transport: molecular modelling

    Science.gov (United States)

    Plastun, Inna L.; Agandeeva, Ksenia E.; Bokarev, Andrey N.; Zenkin, Nikita S.

    2017-03-01

    Intermolecular interaction of diamond-like nanoparticles and flavonoids is investigated by numerical simulation. Using molecular modelling by the density functional theory method, we analyze hydrogen bonds formation and their influence on IR - spectra and structure of molecular complex which is formed due to interaction between flavonoids and nanodiamonds surrounded with carboxylic groups. Enriched adamantane (1,3,5,7 - adamantanetetracarboxylic acid) is used as an example of diamond-like nanoparticles. Intermolecular forces and structure of hydrogen bonds are investigated. IR - spectra and structure parameters of quercetin - adamantanetetracarboxylic acid molecular complex are obtained by numerical simulation using the Gaussian software complex. Received data coincide well with experimental results. Intermolecular interactions and hydrogen bonding structure in the obtained molecular complex are examined. Possibilities of flavonoids interaction with DNA at the molecular level are also considered.

  9. Innovative supply chain optimization models with multiple uncertainty factors

    DEFF Research Database (Denmark)

    Choi, Tsan Ming; Govindan, Kannan; Li, Xiang

    2017-01-01

    Uncertainty is an inherent factor that affects all dimensions of supply chain activities. In today’s business environment, initiatives to deal with one specific type of uncertainty might not be effective since other types of uncertainty factors and disruptions may be present. These factors relate...... to supply chain competition and coordination. Thus, to achieve a more efficient and effective supply chain requires the deployment of innovative optimization models and novel methods. This preface provides a concise review of critical research issues regarding innovative supply chain optimization models...

  10. Energy-saving management modelling and optimization for lead-acid battery formation process

    Science.gov (United States)

    Wang, T.; Chen, Z.; Xu, J. Y.; Wang, F. Y.; Liu, H. M.

    2017-11-01

    In this context, a typical lead-acid battery producing process is introduced. Based on the formation process, an efficiency management method is proposed. An optimization model with the objective to minimize the formation electricity cost in a single period is established. This optimization model considers several related constraints, together with two influencing factors including the transformation efficiency of IGBT charge-and-discharge machine and the time-of-use price. An example simulation is shown using PSO algorithm to solve this mathematic model, and the proposed optimization strategy is proved to be effective and learnable for energy-saving and efficiency optimization in battery producing industries.

  11. Optimization of hybrid model on hajj travel

    Science.gov (United States)

    Cahyandari, R.; Ariany, R. L.; Sukono

    2018-03-01

    Hajj travel insurance is an insurance product offered by the insurance company in preparing funds to perform the pilgrimage. This insurance product helps would-be pilgrims to set aside a fund of saving hajj with regularly, but also provides funds of profit sharing (mudharabah) and insurance protection. Scheme of insurance product fund management is largely using the hybrid model, which is the fund from would-be pilgrims will be divided into three account management, that is personal account, tabarru’, and ujrah. Scheme of hybrid model on hajj travel insurance was already discussed at the earlier paper with titled “The Hybrid Model Algorithm on Sharia Insurance”, taking the example case of Mitra Mabrur Plus product from Bumiputera company. On these advanced paper, will be made the previous optimization model design, with partition of benefit the tabarru’ account. Benefits such as compensation for 40 critical illness which initially only for participants of insurance only, on optimization is intended for participants of the insurance and his heir, also to benefit the hospital bills. Meanwhile, the benefits of death benefit is given if the participant is fixed die.

  12. Dynamic optimal control of homeostasis: an integrative system approach for modeling of the central nitrogen metabolism in Saccharomyces cerevisiae.

    Science.gov (United States)

    van Riel, N A; Giuseppin, M L; Verrips, C T

    2000-01-01

    The theory of dynamic optimal metabolic control (DOMC), as developed by Giuseppin and Van Riel (Metab. Eng., 2000), is applied to model the central nitrogen metabolism (CNM) in Saccharomyces cerevisiae. The CNM represents a typical system encountered in advanced metabolic engineering. The CNM is the source of the cellular amino acids and proteins, including flavors and potentially valuable biomolecules; therefore, it is also of industrial interest. In the DOMC approach the cell is regarded as an optimally controlled system. Given the metabolic genotype, the cell faces a control problem to maintain an optimal flux distribution in a changing environment. The regulation is based on strategies and balances feedback control of homeostasis and feedforward regulation for adaptation. The DOMC approach is an integrative, holistic approach, not based on mechanistic descriptions and (therefore) not biased by the variation present in biochemical and molecular biological data. It is an effective tool to structure the rapidly increasing amount of data on the function of genes and pathways. The DOMC model is used successfully to predict the responses of pulses of ammonia and glutamine to nitrogen-limited continuous cultures of a wild-type strain and a glutamine synthetase-negative mutant. The simulation results are validated with experimental data.

  13. Crucial optimization steps in getting premier quality of Aquilaria malaccensis genomic DNA for molecular activities

    International Nuclear Information System (INIS)

    Muhammad Hanif Azhari; Azhar Mohamad

    2013-01-01

    Gaharu resin is derived from Aquilaria sp. or Agar wood tree in a tropical ecosystem. In Malaysia the Aquilaria species especially A. malaccensis in danger of extinction in the wild due to illegal logging as its resin is highly used for the production of greatly valued incense throughout Asia. A significant tool in fingerprinting the species is through molecular activities of Polymerase Chain Reaction (PCR) application on which requires total genomic DNA as a starting material. This paper, described optimizations of both fresh and dried samples derived from A. malaccensis for genomic DNA. Three main parameters for the optimization were temperature (60, 65, and 70 degree Celsius), incubation period (30, 60, 90 minutes) and concentration of CTAB (1 %, 3 %, 5 %). The experimental design in these work resulted a total of 46 combinations of the parameters in which 0.5 g samples was used in each combination. Nano-drop spectrometer was used in detecting the quantitative genomic DNA at ng/ μl. In fresh samples, incubation temperatures at 65 degree Celsius for 60 minutes in 3 % were yielded 723.2 ng/ μl genomic DNA. Whereas, for dried samples, incubation temperature at 70 degree Celsius for 90 minutes in 5 % CTAB were yielded 70.2 ng/ μl of genomic DNA. Spectrometer reading at OD280/ 260 was 1.9 for both type of samples. The isolated genomic DNA is useful for the molecular activities to identify specific plants between the same species or among the Aquilaria species. (author)

  14. Multi-scale modelling of supercapacitors: From molecular simulations to a transmission line model

    Science.gov (United States)

    Pean, C.; Rotenberg, B.; Simon, P.; Salanne, M.

    2016-09-01

    We perform molecular dynamics simulations of a typical nanoporous-carbon based supercapacitor. The organic electrolyte consists in 1-ethyl-3-methylimidazolium and hexafluorophosphate ions dissolved in acetonitrile. We simulate systems at equilibrium, for various applied voltages. This allows us to determine the relevant thermodynamic (capacitance) and transport (in-pore resistivities) properties. These quantities are then injected in a transmission line model for testing its ability to predict the charging properties of the device. The results from this macroscopic model are in good agreement with non-equilibrium molecular dynamics simulations, which validates its use for interpreting electrochemical impedance experiments.

  15. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Science.gov (United States)

    Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

    2016-01-01

    Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  16. An improved model for the oPtImal Measurement Probes Allocation tool

    International Nuclear Information System (INIS)

    Sterle, C.; Neto, A.C.; De Tommasi, G.

    2015-01-01

    Highlights: • The problem of optimally allocating the probes of a diagnostic system is tackled. • The problem is decomposed in two consecutive optimization problems. • Two original ILP models are proposed and sequentially solved to optimality. • The proposed ILP models improve and extend the previous work present in literature. • Real size instances have been optimally solved with very low computation time. - Abstract: The oPtImal Measurement Probes Allocation (PIMPA) tool has been recently proposed in [1] to maximize the reliability of a tokamak diagnostic system against the failure of one or more of the processing nodes. PIMPA is based on the solution of integer linear programming (ILP) problems, and it minimizes the effect of the failure of a data acquisition component. The first formulation of the PIMPA model did not support the concept of individual slots. This work presents an improved ILP model that addresses the above mentioned problem, by taking into account all the individual probes.

  17. An improved model for the oPtImal Measurement Probes Allocation tool

    Energy Technology Data Exchange (ETDEWEB)

    Sterle, C., E-mail: claudio.sterle@unina.it [Consorzio CREATE/Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy); Neto, A.C. [Fusion for Energy, 08019 Barcelona (Spain); De Tommasi, G. [Consorzio CREATE/Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy)

    2015-10-15

    Highlights: • The problem of optimally allocating the probes of a diagnostic system is tackled. • The problem is decomposed in two consecutive optimization problems. • Two original ILP models are proposed and sequentially solved to optimality. • The proposed ILP models improve and extend the previous work present in literature. • Real size instances have been optimally solved with very low computation time. - Abstract: The oPtImal Measurement Probes Allocation (PIMPA) tool has been recently proposed in [1] to maximize the reliability of a tokamak diagnostic system against the failure of one or more of the processing nodes. PIMPA is based on the solution of integer linear programming (ILP) problems, and it minimizes the effect of the failure of a data acquisition component. The first formulation of the PIMPA model did not support the concept of individual slots. This work presents an improved ILP model that addresses the above mentioned problem, by taking into account all the individual probes.

  18. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Directory of Open Access Journals (Sweden)

    Nadia Said

    Full Text Available Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  19. Parametric modeling and stagger angle optimization of an axial flow fan

    International Nuclear Information System (INIS)

    Li, M X; Zhang, C H; Liu, Y; Zheng, S Y

    2013-01-01

    Axial flow fans are widely used in every field of social production. Improving their efficiency is a sustained and urgent demand of domestic industry. The optimization of stagger angle is an important method to improve fan performance. Parametric modeling and calculation process automation are realized in this paper to improve optimization efficiency. Geometric modeling and mesh division are parameterized based on GAMBIT. Parameter setting and flow field calculation are completed in the batch mode of FLUENT. A control program is developed in Visual C++ to dominate the data exchange of mentioned software. It also extracts calculation results for optimization algorithm module (provided by Matlab) to generate directive optimization control parameters, which as feedback are transferred upwards to modeling module. The center line of the blade airfoil, based on CLARK y profile, is constructed by non-constant circulation and triangle discharge method. Stagger angles of six airfoil sections are optimized, to reduce the influence of inlet shock loss as well as gas leak in blade tip clearance and hub resistance at blade root. Finally an optimal solution is obtained, which meets the total pressure requirement under given conditions and improves total pressure efficiency by about 6%

  20. In Silico Exploration of 1,7-Diazacarbazole Analogs as Checkpoint Kinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study, and Molecular Dynamics Simulations

    Directory of Open Access Journals (Sweden)

    Xiaodong Gao

    2016-05-01

    Full Text Available Checkpoint kinase 1 (Chk1 is an important serine/threonine kinase with a self-protection function. The combination of Chk1 inhibitors and anti-cancer drugs can enhance the selectivity of tumor therapy. In this work, a set of 1,7-diazacarbazole analogs were identified as potent Chk1 inhibitors through a series of computer-aided drug design processes, including three-dimensional quantitative structure–activity relationship (3D-QSAR modeling, molecular docking, and molecular dynamics simulations. The optimal QSAR models showed significant cross-validated correlation q2 values (0.531, 0.726, fitted correlation r2 coefficients (higher than 0.90, and standard error of prediction (less than 0.250. These results suggested that the developed models possess good predictive ability. Moreover, molecular docking and molecular dynamics simulations were applied to highlight the important interactions between the ligand and the Chk1 receptor protein. This study shows that hydrogen bonding and electrostatic forces are key interactions that confer bioactivity.

  1. Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization

    International Nuclear Information System (INIS)

    Yong, Li; Ying-Gan, Tang

    2010-01-01

    A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method

  2. Radio-over-fiber linearization with optimized genetic algorithm CPWL model.

    Science.gov (United States)

    Mateo, Carlos; Carro, Pedro L; García-Dúcar, Paloma; De Mingo, Jesús; Salinas, Íñigo

    2017-02-20

    This article proposes an optimized version of a canonical piece-wise-linear (CPWL) digital predistorter in order to enhance the linearity of a radio-over-fiber (RoF) LTE mobile fronthaul. In this work, we propose a threshold allocation optimization process carried out by a genetic algorithm (GA) in order to optimize the CPWL model (GA-CPWL). Firstly, experiments show how the CPWL model outperforms the classical memory polynomial DPD in an intensity modulation/direct detection (IM/DD) RoF link. Then, the GA-CPWL predistorter is compared with the CPWL model in several scenarios, in order to verify that the proposed DPD offers better performance in different optical transmission conditions. Experimental results reveal that with a proper threshold allocation, the GA-CPWL predistorter offers very promising outcomes.

  3. Grid computing in large pharmaceutical molecular modeling.

    Science.gov (United States)

    Claus, Brian L; Johnson, Stephen R

    2008-07-01

    Most major pharmaceutical companies have employed grid computing to expand their compute resources with the intention of minimizing additional financial expenditure. Historically, one of the issues restricting widespread utilization of the grid resources in molecular modeling is the limited set of suitable applications amenable to coarse-grained parallelization. Recent advances in grid infrastructure technology coupled with advances in application research and redesign will enable fine-grained parallel problems, such as quantum mechanics and molecular dynamics, which were previously inaccessible to the grid environment. This will enable new science as well as increase resource flexibility to load balance and schedule existing workloads.

  4. On the existence of the optimal order for wavefunction extrapolation in Born-Oppenheimer molecular dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Fang, Jun; Wang, Han, E-mail: wang-han@iapcm.ac.cn [Institute of Applied Physics and Computational Mathematics, Beijing (China); CAEP Software Center for High Performance Numerical Simulation, Beijing (China); Gao, Xingyu; Song, Haifeng [Institute of Applied Physics and Computational Mathematics, Beijing (China); CAEP Software Center for High Performance Numerical Simulation, Beijing (China); Laboratory of Computational Physics, Beijing (China)

    2016-06-28

    Wavefunction extrapolation greatly reduces the number of self-consistent field (SCF) iterations and thus the overall computational cost of Born-Oppenheimer molecular dynamics (BOMD) that is based on the Kohn–Sham density functional theory. Going against the intuition that the higher order of extrapolation possesses a better accuracy, we demonstrate, from both theoretical and numerical perspectives, that the extrapolation accuracy firstly increases and then decreases with respect to the order, and an optimal extrapolation order in terms of minimal number of SCF iterations always exists. We also prove that the optimal order tends to be larger when using larger MD time steps or more strict SCF convergence criteria. By example BOMD simulations of a solid copper system, we show that the optimal extrapolation order covers a broad range when varying the MD time step or the SCF convergence criterion. Therefore, we suggest the necessity for BOMD simulation packages to open the user interface and to provide more choices on the extrapolation order. Another factor that may influence the extrapolation accuracy is the alignment scheme that eliminates the discontinuity in the wavefunctions with respect to the atomic or cell variables. We prove the equivalence between the two existing schemes, thus the implementation of either of them does not lead to essential difference in the extrapolation accuracy.

  5. Hapten design and indirect competitive immunoassay for parathion determination: Correlation with molecular modeling and principal component analysis

    Energy Technology Data Exchange (ETDEWEB)

    Liu Yihua [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Jin Maojun [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Gui Wenjun [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Cheng Jingli [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Guo Yirong [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Zhu Guonian [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China)]. E-mail: zhugn@zju.edu.cn

    2007-05-22

    A novel procedure for parathion hapten design is described. The optimal antigen for parathion was selected after molecular modeling studies of six types of potentially immunizing haptens with the aim to identify the best mimicking target analyte. Heterologous competitive indirect enzyme-linked immunosorbent assay (ELISA) was developed after screening a battery of competitors as coating antigens. The relationship between the heterology degree of the competitor and the resulting immunoassay detectability was investigated according to the electronic similarities of the competitor haptens and the target analyte. Molecular modeling and principal component analysis were performed to understand the electronic distribution and steric parameters of the haptens at their minimum energetic levels. The results suggested that the competitors should have a high heterology to produce assays with good detectability values. An indirect competitive ELISA was finally selected for further investigation. The immunoassay had an IC{sub 50} value of 4.79 ng mL{sup -1} and a limit of detection of 0.31 ng mL{sup -1}. There was little or no cross-reactivity to similar compounds tested except for the insecticide parathion-methyl, which showed a cross-reactivity of 7.8%.

  6. Optimized molecular resolution of cross-contamination alerts in clinical mycobacteriology laboratories

    Directory of Open Access Journals (Sweden)

    de Viedma Darío

    2008-02-01

    Full Text Available Abstract Background The phenomenon of misdiagnosing tuberculosis (TB by laboratory cross-contamination when culturing Mycobacterium tuberculosis (MTB has been widely reported and it has an obvious clinical, therapeutic and social impact. The final confirmation of a cross-contamination event requires the molecular identification of the same MTB strain cultured from both the potential source of the contamination and from the false-positive candidate. The molecular tool usually applied in this context is IS6110-RFLP which takes a long time to provide an answer, usually longer than is acceptable for microbiologists and clinicians to make decisions. Our purpose in this study is to evaluate a novel PCR-based method, MIRU-VNTR as an alternative to assure a rapid and optimized analysis of cross-contamination alerts. Results MIRU-VNTR was prospectively compared with IS6110-RFLP for clarifying 19 alerts of false positivity from other laboratories. MIRU-VNTR highly correlated with IS6110-RFLP, reduced the response time by 27 days and clarified six alerts unresolved by RFLP. Additionally, MIRU-VNTR revealed complex situations such as contamination events involving polyclonal isolates and a false-positive case due to the simultaneous cross-contamination from two independent sources. Conclusion Unlike standard RFLP-based genotyping, MIRU-VNTR i could help reduce the impact of a false positive diagnosis of TB, ii increased the number of events that could be solved and iii revealed the complexity of some cross-contamination events that could not be dissected by IS6110-RFLP.

  7. A Bone-Implant Interaction Mouse Model for Evaluating Molecular Mechanism of Biomaterials/Bone Interaction.

    Science.gov (United States)

    Liu, Wenlong; Dan, Xiuli; Wang, Ting; Lu, William W; Pan, Haobo

    2016-11-01

    The development of an optimal animal model that could provide fast assessments of the interaction between bone and orthopedic implants is essential for both preclinical and theoretical researches in the design of novel biomaterials. Compared with other animal models, mice have superiority in accessing the well-developed transgenic modification techniques (e.g., cell tracing, knockoff, knockin, and so on), which serve as powerful tools in studying molecular mechanisms. In this study, we introduced the establishment of a mouse model, which was specifically tailored for the assessment of bone-implant interaction in a load-bearing bone marrow microenvironment and could potentially allow the molecular mechanism study of biomaterials by using transgenic technologies. The detailed microsurgery procedures for developing a bone defect (Φ = 0.8 mm) at the metaphysis region of the mouse femur were recorded. According to our results, the osteoconductive and osseointegrative properties of a well-studied 45S5 bioactive glass were confirmed by utilizing our mouse model, verifying the reliability of this model. The feasibility and reliability of the present model were further checked by using other materials as objects of study. Furthermore, our results indicated that this animal model provided a more homogeneous tissue-implant interacting surface than the rat at the early stage of implantation and this is quite meaningful for conducting quantitative analysis. The availability of transgenic techniques to mechanism study of biomaterials was further testified by establishing our model on Nestin-GFP transgenic mice. Intriguingly, the distribution of Nestin + cells was demonstrated to be recruited to the surface of 45S5 glass as early as 3 days postsurgery, indicating that Nestin + lineage stem cells may participate in the subsequent regeneration process. In summary, the bone-implant interaction mouse model could serve as a potential candidate to evaluate the early stage tissue

  8. Optimization of a Two-Fluid Hydrodynamic Model of Churn-Turbulent Flow

    Energy Technology Data Exchange (ETDEWEB)

    Donna Post Guillen

    2009-07-01

    A hydrodynamic model of two-phase, churn-turbulent flows is being developed using the computational multiphase fluid dynamics (CMFD) code, NPHASE-CMFD. The numerical solutions obtained by this model are compared with experimental data obtained at the TOPFLOW facility of the Institute of Safety Research at the Forschungszentrum Dresden-Rossendorf. The TOPFLOW data is a high quality experimental database of upward, co-current air-water flows in a vertical pipe suitable for validation of computational fluid dynamics (CFD) codes. A five-field CMFD model was developed for the continuous liquid phase and four bubble size groups using mechanistic closure models for the ensemble-averaged Navier-Stokes equations. Mechanistic models for the drag and non-drag interfacial forces are implemented to include the governing physics to describe the hydrodynamic forces controlling the gas distribution. The closure models provide the functional form of the interfacial forces, with user defined coefficients to adjust the force magnitude. An optimization strategy was devised for these coefficients using commercial design optimization software. This paper demonstrates an approach to optimizing CMFD model parameters using a design optimization approach. Computed radial void fraction profiles predicted by the NPHASE-CMFD code are compared to experimental data for four bubble size groups.

  9. The analysis of optimal singular controls for SEIR model of tuberculosis

    Science.gov (United States)

    Marpaung, Faridawaty; Rangkuti, Yulita M.; Sinaga, Marlina S.

    2014-12-01

    The optimally of singular control for SEIR model of Tuberculosis is analyzed. There are controls that correspond to time of the vaccination and treatment schedule. The optimally of singular control is obtained by differentiate a switching function of the model. The result shows that vaccination and treatment control are singular.

  10. Web malware spread modelling and optimal control strategies

    Science.gov (United States)

    Liu, Wanping; Zhong, Shouming

    2017-02-01

    The popularity of the Web improves the growth of web threats. Formulating mathematical models for accurate prediction of malicious propagation over networks is of great importance. The aim of this paper is to understand the propagation mechanisms of web malware and the impact of human intervention on the spread of malicious hyperlinks. Considering the characteristics of web malware, a new differential epidemic model which extends the traditional SIR model by adding another delitescent compartment is proposed to address the spreading behavior of malicious links over networks. The spreading threshold of the model system is calculated, and the dynamics of the model is theoretically analyzed. Moreover, the optimal control theory is employed to study malware immunization strategies, aiming to keep the total economic loss of security investment and infection loss as low as possible. The existence and uniqueness of the results concerning the optimality system are confirmed. Finally, numerical simulations show that the spread of malware links can be controlled effectively with proper control strategy of specific parameter choice.

  11. Sustainable logistics and transportation optimization models and algorithms

    CERN Document Server

    Gakis, Konstantinos; Pardalos, Panos

    2017-01-01

    Focused on the logistics and transportation operations within a supply chain, this book brings together the latest models, algorithms, and optimization possibilities. Logistics and transportation problems are examined within a sustainability perspective to offer a comprehensive assessment of environmental, social, ethical, and economic performance measures. Featured models, techniques, and algorithms may be used to construct policies on alternative transportation modes and technologies, green logistics, and incentives by the incorporation of environmental, economic, and social measures. Researchers, professionals, and graduate students in urban regional planning, logistics, transport systems, optimization, supply chain management, business administration, information science, mathematics, and industrial and systems engineering will find the real life and interdisciplinary issues presented in this book informative and useful.

  12. HIV Treatment and Prevention: A Simple Model to Determine Optimal Investment.

    Science.gov (United States)

    Juusola, Jessie L; Brandeau, Margaret L

    2016-04-01

    To create a simple model to help public health decision makers determine how to best invest limited resources in HIV treatment scale-up and prevention. A linear model was developed for determining the optimal mix of investment in HIV treatment and prevention, given a fixed budget. The model incorporates estimates of secondary health benefits accruing from HIV treatment and prevention and allows for diseconomies of scale in program costs and subadditive benefits from concurrent program implementation. Data sources were published literature. The target population was individuals infected with HIV or at risk of acquiring it. Illustrative examples of interventions include preexposure prophylaxis (PrEP), community-based education (CBE), and antiretroviral therapy (ART) for men who have sex with men (MSM) in the US. Outcome measures were incremental cost, quality-adjusted life-years gained, and HIV infections averted. Base case analysis indicated that it is optimal to invest in ART before PrEP and to invest in CBE before scaling up ART. Diseconomies of scale reduced the optimal investment level. Subadditivity of benefits did not affect the optimal allocation for relatively low implementation levels. The sensitivity analysis indicated that investment in ART before PrEP was optimal in all scenarios tested. Investment in ART before CBE became optimal when CBE reduced risky behavior by 4% or less. Limitations of the study are that dynamic effects are approximated with a static model. Our model provides a simple yet accurate means of determining optimal investment in HIV prevention and treatment. For MSM in the US, HIV control funds should be prioritized on inexpensive, effective programs like CBE, then on ART scale-up, with only minimal investment in PrEP. © The Author(s) 2015.

  13. Optimizing the molecular diagnosis of CDKL5 gene-related epileptic encephalopathy in boys.

    Science.gov (United States)

    Mei, Davide; Darra, Francesca; Barba, Carmen; Marini, Carla; Fontana, Elena; Chiti, Laura; Parrini, Elena; Dalla Bernardina, Bernardo; Guerrini, Renzo

    2014-11-01

    Mutations involving the cyclin-dependent kinase-like 5 (CDKL5) gene cause an early onset epileptic encephalopathy (EE) with severe neurologic impairment and a skewed 12:1 female-to-male ratio. To date, 18 mutations have been described in boys. We analyzed our cohort of boys with early onset EE to assess the diagnostic yield of our molecular approach. We studied 74 boys who presented early onset severe seizures, including infantile spasms and developmental delay, in the setting of EE, using Sanger sequencing, next-generation sequencing (NGS) and multiplex ligation-dependent probe amplification (MLPA). We identified alterations involving CDKL5 in four boys (5.4%) using NGS in one and MLPA in three. Three of four mutations were indicative of somatic mosaicism. CDKL5 gene mutations accounted for 5.4% of boys with early onset EE. Somatic mosaic mutations might be even more represented than germline mutations, probably because their less deleterious effect enhances viability of the male embryo. The molecular approach used for CDKL5 screening remarkably influences the diagnostic yield in boys. Diagnosis is optimized by Sanger sequencing combined with array-based methods or MLPA; alternatively, NGS targeted resequencing designed to also detect copy number alterations, may be performed. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

  14. Optimal Resource Management in a Stochastic Schaefer Model

    OpenAIRE

    Richard Hartman

    2008-01-01

    This paper incorporates uncertainty into the growth function of the Schaefer model for the optimal management of a biological resource. There is a critical value for the biological stock, and it is optimal to do no harvesting if the biological stock is below that critical value and to exert whatever harvesting effort is necessary to prevent the stock from rising above that critical value. The introduction of uncertainty increases the critical value of the stock.

  15. Method to determine the optimal constitutive model from spherical indentation tests

    Science.gov (United States)

    Zhang, Tairui; Wang, Shang; Wang, Weiqiang

    2018-03-01

    The limitation of current indentation theories was investigated and a method to determine the optimal constitutive model through spherical indentation tests was proposed. Two constitutive models, the Power-law and the Linear-law, were used in Finite Element (FE) calculations, and then a set of indentation governing equations was established for each model. The load-depth data from the normal indentation depth was used to fit the best parameters in each constitutive model while the data from the further loading part was compared with those from FE calculations, and the model that better predicted the further deformation was considered the optimal one. Moreover, a Yang's modulus calculation model which took the previous plastic deformation and the phenomenon of pile-up (or sink-in) into consideration was also proposed to revise the original Sneddon-Pharr-Oliver model. The indentation results on six materials, 304, 321, SA508, SA533, 15CrMoR, and Fv520B, were compared with tensile ones, which validated the reliability of the revised E calculation model and the optimal constitutive model determination method in this study.

  16. High-energy, stable and recycled molecular solar thermal storage materials using AZO/graphene hybrids by optimizing hydrogen bonds.

    Science.gov (United States)

    Luo, Wen; Feng, Yiyu; Qin, Chengqun; Li, Man; Li, Shipei; Cao, Chen; Long, Peng; Liu, Enzuo; Hu, Wenping; Yoshino, Katsumi; Feng, Wei

    2015-10-21

    An important method for establishing a high-energy, stable and recycled molecular solar heat system is by designing and preparing novel photo-isomerizable molecules with a high enthalpy and a long thermal life by controlling molecular interactions. A meta- and ortho-bis-substituted azobenzene chromophore (AZO) is covalently grafted onto reduced graphene oxide (RGO) for solar thermal storage materials. High grafting degree and close-packed molecules enable intermolecular hydrogen bonds (H-bonds) for both trans-(E) and cis-(Z) isomers of AZO on the surface of nanosheets, resulting in a dramatic increase in enthalpy and lifetime. The metastable Z-form of AZO on RGO is thermally stabilized with a half-life of 52 days by steric hindrance and intermolecular H-bonds calculated using density functional theory (DFT). The AZO-RGO fuel shows a high storage capacity of 138 Wh kg(-1) by optimizing intermolecular H-bonds with a good cycling stability for 50 cycles induced by visible light at 520 nm. Our work opens up a new method for making advanced molecular solar thermal storage materials by tuning molecular interactions on a nano-template.

  17. Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power

    DEFF Research Database (Denmark)

    Chen, Peiyuan; Siano, Pierluigi; Bak-Jensen, Birgitte

    2010-01-01

    This paper proposes a stochastic optimization algorithm that aims to minimize the expectation of the system power losses by controlling wind turbine (WT) power factors. This objective of the optimization is subject to the probability constraints of bus voltage and line current requirements....... The optimization algorithm utilizes the stochastic models of wind power generation (WPG) and load demand to take into account their stochastic variation. The stochastic model of WPG is developed on the basis of a limited autoregressive integrated moving average (LARIMA) model by introducing a crosscorrelation...... structure to the LARIMA model. The proposed stochastic optimization is carried out on a 69-bus distribution system. Simulation results confirm that, under various combinations of WPG and load demand, the system power losses are considerably reduced with the optimal setting of WT power factor as compared...

  18. A model for optimization of process integration investments under uncertainty

    International Nuclear Information System (INIS)

    Svensson, Elin; Stroemberg, Ann-Brith; Patriksson, Michael

    2011-01-01

    The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency. -- Highlights: → Stochastic programming approach to long-term planning of process integration investments. → Extensive mathematical model formulation. → Multi-stage investment decisions and scenario-based modelling of uncertain energy prices. → Results illustrate how investments made now affect later investment and operation opportunities. → Approach for evaluation of robustness with respect to variations in probability distribution.

  19. Modeling and optimization of an electric power distribution network ...

    African Journals Online (AJOL)

    Modeling and optimization of an electric power distribution network planning system using ... of the network was modelled with non-linear mathematical expressions. ... given feasible locations, re-conductoring of existing feeders in the network, ...

  20. A model for HIV/AIDS pandemic with optimal control

    Science.gov (United States)

    Sule, Amiru; Abdullah, Farah Aini

    2015-05-01

    Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is pandemic. It has affected nearly 60 million people since the detection of the disease in 1981 to date. In this paper basic deterministic HIV/AIDS model with mass action incidence function are developed. Stability analysis is carried out. And the disease free equilibrium of the basic model was found to be locally asymptotically stable whenever the threshold parameter (RO) value is less than one, and unstable otherwise. The model is extended by introducing two optimal control strategies namely, CD4 counts and treatment for the infective using optimal control theory. Numerical simulation was carried out in order to illustrate the analytic results.

  1. Cohesion between two clay lamellae: From Primitive Model to Full Molecular Simulation

    International Nuclear Information System (INIS)

    Carrier, Benoit; Vandamme, Matthieu; Pellenq, Roland; Van Damme, Henri

    2012-01-01

    Document available in extended abstract form only. The objective of this work is to investigate the range of validity of various models to describe accurately the cohesion between two charged clay lamellae. These models, in order of increasing complexity, are the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, the primitive model, the explicit solvent primitive model and the full molecular model. We aim at providing a clear picture of which physical mechanisms play a significant role for various interlayer spacings, surface charges and cationic charges. The up-scaling of the mechanical properties starting from the lamellar microstructure of a smectite is usually performed within the framework of the DLVO theory. In this case, the interaction between two charged lamellae with cations between them is the sum of the repulsive double layer electrostatic interaction and of the attractive Van der Waals interaction. However, the Primitive Model shows that concentration fluctuations of counter-ions can generate a strongly attractive ionic correlation force. The Primitive Model is a Monte-Carlo simulation of hydrated counter-ions between two infinite charges surfaces and the water is implicitly modeled by scaling all electrostatic interactions by the dielectric permittivity of bulk water. Nevertheless, for very small inter-layer spacings (1 nm), molecular simulations and experiments show that water is organized in a layered structure and does not behave like bulk water. Therefore, we investigate the role of the solvent in the cohesion of clay lamellae. For this purpose, we use a modified version of the original Primitive Model in which the solvent is modeled by point-dipoles: This model is called the Explicit Solvent Primitive Model. We consider four different systems: A Na + -montmorillonite, a Ca 2+ -montmorillonite, a Na + -vermiculite, a Ca 2+ -vermiculite. The vermiculite layers are twice as charged as the montmorillonite layers. We use a full molecular model as a

  2. Time dependent optimal switching controls in online selling models

    Energy Technology Data Exchange (ETDEWEB)

    Bradonjic, Milan [Los Alamos National Laboratory; Cohen, Albert [MICHIGAN STATE UNIV

    2010-01-01

    We present a method to incorporate dishonesty in online selling via a stochastic optimal control problem. In our framework, the seller wishes to maximize her average wealth level W at a fixed time T of her choosing. The corresponding Hamilton-Jacobi-Bellmann (HJB) equation is analyzed for a basic case. For more general models, the admissible control set is restricted to a jump process that switches between extreme values. We propose a new approach, where the optimal control problem is reduced to a multivariable optimization problem.

  3. The optimization model of the logging machinery usage in forestry practice

    Directory of Open Access Journals (Sweden)

    Jitka Janová

    2009-01-01

    Full Text Available The decision support systems commonly used in industry and economy managerial practice for optimizing the processes are based on algoritmization of the typical decision problems. In Czech forestry business, there is a lack of developed decision support systems, which could be easily used in daily practice. This stems from the fact, that the application of optimization methods is less successful in forestry decision making than in industry or economy due to inherent complexity of the forestry decision problems. There is worldwide ongoing research on optimization models applicable in forestry decision making, but the results are not globally applicable and moreover the cost of possibly arising software tools are indispensable. Especially small and medium forestry companies in Czech Republic can not afford such additional costs, although the results of optimization could positively in­fluen­ce not only the business itself but also the impact of forestry business on the environment. Hence there is a need for user friendly optimization models for forestry decision making in the area of Czech Republic, which could be easily solved in commonly available software, and whose results would be both, realistic and easily applicable in the daily decision making.The aim of this paper is to develop the optimization model for the machinery use planning in Czech logging firm in such a way, that the results can be obtained using MS EXCEL. The goal is to identify the integer number of particular machines which should be outsourced for the next period, when the total cost minimization is required. The linear programming model is designed covering the typical restrictions on available machinery and total volume of trees to be cut and transported. The model offers additional result in the form of optimal employment of particular machines. The solution procedure is described in detail and the results obtained are discussed with respect to its applicability in

  4. Simulation-Optimization Model for Seawater Intrusion Management at Pingtung Coastal Area, Taiwan

    Science.gov (United States)

    Huang, P. S.; Chiu, Y.

    2015-12-01

    In 1970's, the agriculture and aquaculture were rapidly developed at Pingtung coastal area in southern Taiwan. The groundwater aquifers were over-pumped and caused the seawater intrusion. In order to remedy the contaminated groundwater and find the best strategies of groundwater usage, a management model to search the optimal groundwater operational strategies is developed in this study. The objective function is to minimize the total amount of injection water and a set of constraints are applied to ensure the groundwater levels and concentrations are satisfied. A three-dimension density-dependent flow and transport simulation model, called SEAWAT developed by U.S. Geological Survey, is selected to simulate the phenomenon of seawater intrusion. The simulation model is well calibrated by the field measurements and replaced by the surrogate model of trained artificial neural networks (ANNs) to reduce the computational time. The ANNs are embedded in the management model to link the simulation and optimization models, and the global optimizer of differential evolution (DE) is applied for solving the management model. The optimal results show that the fully trained ANNs could substitute the original simulation model and reduce much computational time. Under appropriate setting of objective function and constraints, DE can find the optimal injection rates at predefined barriers. The concentrations at the target locations could decrease more than 50 percent within the planning horizon of 20 years. Keywords : Seawater intrusion, groundwater management, numerical model, artificial neural networks, differential evolution

  5. Optimal dimensioning model of water distribution systems | Gomes ...

    African Journals Online (AJOL)

    This study is aimed at developing a pipe-sizing model for a water distribution system. The optimal solution minimises the system's total cost, which comprises the hydraulic network capital cost, plus the capitalised cost of pumping energy. The developed model, called Lenhsnet, may also be used for economical design when ...

  6. Modeling the optimal management of spent nuclear fuel

    International Nuclear Information System (INIS)

    Nachlas, J.A.; Kurstedt, H.A. Jr.; Swindle, D.W. Jr.; Korcz, K.O.

    1977-01-01

    Recent governmental policy decisions dictate that strategies for managing spent nuclear fuel be developed. Two models are constructed to investigate the optimum residence time and the optimal inventory withdrawal policy for fuel material that presently must be stored. The mutual utility of the models is demonstrated through reference case application

  7. NLP model and stochastic multi-start optimization approach for heat exchanger networks

    International Nuclear Information System (INIS)

    Núñez-Serna, Rosa I.; Zamora, Juan M.

    2016-01-01

    Highlights: • An NLP model for the optimal design of heat exchanger networks is proposed. • The NLP model is developed from a stage-wise grid diagram representation. • A two-phase stochastic multi-start optimization methodology is utilized. • Improved network designs are obtained with different heat load distributions. • Structural changes and reductions in the number of heat exchangers are produced. - Abstract: Heat exchanger network synthesis methodologies frequently identify good network structures, which nevertheless, might be accompanied by suboptimal values of design variables. The objective of this work is to develop a nonlinear programming (NLP) model and an optimization approach that aim at identifying the best values for intermediate temperatures, sub-stream flow rate fractions, heat loads and areas for a given heat exchanger network topology. The NLP model that minimizes the total annual cost of the network is constructed based on a stage-wise grid diagram representation. To improve the possibilities of obtaining global optimal designs, a two-phase stochastic multi-start optimization algorithm is utilized for the solution of the developed model. The effectiveness of the proposed optimization approach is illustrated with the optimization of two network designs proposed in the literature for two well-known benchmark problems. Results show that from the addressed base network topologies it is possible to achieve improved network designs, with redistributions in exchanger heat loads that lead to reductions in total annual costs. The results also show that the optimization of a given network design sometimes leads to structural simplifications and reductions in the total number of heat exchangers of the network, thereby exposing alternative viable network topologies initially not anticipated.

  8. Advanced Nuclear Fuel Cycle Transitions: Optimization, Modeling Choices, and Disruptions

    Science.gov (United States)

    Carlsen, Robert W.

    Many nuclear fuel cycle simulators have evolved over time to help understan the nuclear industry/ecosystem at a macroscopic level. Cyclus is one of th first fuel cycle simulators to accommodate larger-scale analysis with it liberal open-source licensing and first-class Linux support. Cyclus also ha features that uniquely enable investigating the effects of modeling choices o fuel cycle simulators and scenarios. This work is divided into thre experiments focusing on optimization, effects of modeling choices, and fue cycle uncertainty. Effective optimization techniques are developed for automatically determinin desirable facility deployment schedules with Cyclus. A novel method fo mapping optimization variables to deployment schedules is developed. Thi allows relationships between reactor types and scenario constraints to b represented implicitly in the variable definitions enabling the usage o optimizers lacking constraint support. It also prevents wasting computationa resources evaluating infeasible deployment schedules. Deployed power capacit over time and deployment of non-reactor facilities are also included a optimization variables There are many fuel cycle simulators built with different combinations o modeling choices. Comparing results between them is often difficult. Cyclus flexibility allows comparing effects of many such modeling choices. Reacto refueling cycle synchronization and inter-facility competition among othe effects are compared in four cases each using combinations of fleet of individually modeled reactors with 1-month or 3-month time steps. There are noticeable differences in results for the different cases. The larges differences occur during periods of constrained reactor fuel availability This and similar work can help improve the quality of fuel cycle analysi generally There is significant uncertainty associated deploying new nuclear technologie such as time-frames for technology availability and the cost of buildin advanced reactors

  9. Robust Optimization Model for Production Planning Problem under Uncertainty

    Directory of Open Access Journals (Sweden)

    Pembe GÜÇLÜ

    2017-01-01

    Full Text Available Conditions of businesses change very quickly. To take into account the uncertainty engendered by changes has become almost a rule while planning. Robust optimization techniques that are methods of handling uncertainty ensure to produce less sensitive results to changing conditions. Production planning, is to decide from which product, when and how much will be produced, with a most basic definition. Modeling and solution of the Production planning problems changes depending on structure of the production processes, parameters and variables. In this paper, it is aimed to generate and apply scenario based robust optimization model for capacitated two-stage multi-product production planning problem under parameter and demand uncertainty. With this purpose, production planning problem of a textile company that operate in İzmir has been modeled and solved, then deterministic scenarios’ and robust method’s results have been compared. Robust method has provided a production plan that has higher cost but, will result close to feasible and optimal for most of the different scenarios in the future.

  10. Real-time economic optimization for a fermentation process using Model Predictive Control

    DEFF Research Database (Denmark)

    Petersen, Lars Norbert; Jørgensen, John Bagterp

    2014-01-01

    Fermentation is a widely used process in production of many foods, beverages, and pharmaceuticals. The main goal of the control system is to maximize profit of the fermentation process, and thus this is also the main goal of this paper. We present a simple dynamic model for a fermentation process...... and demonstrate its usefulness in economic optimization. The model is formulated as an index-1 differential algebraic equation (DAE), which guarantees conservation of mass and energy in discrete form. The optimization is based on recent advances within Economic Nonlinear Model Predictive Control (E......-NMPC), and also utilizes the index-1 DAE model. The E-NMPC uses the single-shooting method and the adjoint method for computation of the optimization gradients. The process constraints are relaxed to soft-constraints on the outputs. Finally we derive the analytical solution to the economic optimization problem...

  11. An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China

    Directory of Open Access Journals (Sweden)

    Zheng-Xin Wang

    2014-01-01

    Full Text Available The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and n series related, abbreviated as GDMC(1,n, performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1,n, n interpolation coefficients (taken as unknown parameters are introduced into the background values of the n variables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC(1,n model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC(1,n model. The modelling results can assist the government in developing future policies regarding high-tech industry management.

  12. Power Grid Construction Project Portfolio Optimization Based on Bi-level programming model

    Science.gov (United States)

    Zhao, Erdong; Li, Shangqi

    2017-08-01

    As the main body of power grid operation, county-level power supply enterprises undertake an important emission to guarantee the security of power grid operation and safeguard social power using order. The optimization of grid construction projects has been a key issue of power supply capacity and service level of grid enterprises. According to the actual situation of power grid construction project optimization of county-level power enterprises, on the basis of qualitative analysis of the projects, this paper builds a Bi-level programming model based on quantitative analysis. The upper layer of the model is the target restriction of the optimal portfolio; the lower layer of the model is enterprises’ financial restrictions on the size of the enterprise project portfolio. Finally, using a real example to illustrate operation proceeding and the optimization result of the model. Through qualitative analysis and quantitative analysis, the bi-level programming model improves the accuracy and normative standardization of power grid enterprises projects.

  13. Molecular Profiling to Optimize Treatment in Non-Small Cell Lung Cancer: A Review of Potential Molecular Targets for Radiation Therapy by the Translational Research Program of the Radiation Therapy Oncology Group

    International Nuclear Information System (INIS)

    Ausborn, Natalie L.; Le, Quynh Thu; Bradley, Jeffrey D.; Choy, Hak; Dicker, Adam P.; Saha, Debabrata; Simko, Jeff; Story, Michael D.; Torossian, Artour; Lu, Bo

    2012-01-01

    Therapeutic decisions in non-small cell lung cancer (NSCLC) have been mainly based on disease stage, performance status, and co-morbidities, and rarely on histological or molecular classification. Rather than applying broad treatments to unselected patients that may result in survival increase of only weeks to months, research efforts should be, and are being, focused on identifying predictive markers for molecularly targeted therapy and determining genomic signatures that predict survival and response to specific therapies. The availability of such targeted biologics requires their use to be matched to tumors of corresponding molecular vulnerability for maximum efficacy. Molecular markers such as epidermal growth factor receptor (EGFR), K-ras, vascular endothelial growth factor (VEGF), mammalian target of rapamycin (mTOR), and anaplastic lymphoma kinase (ALK) represent potential parameters guide treatment decisions. Ultimately, identifying patients who will respond to specific therapies will allow optimal efficacy with minimal toxicity, which will result in more judicious and effective application of expensive targeted therapy as the new paradigm of personalized medicine develops.

  14. A PSO–GA optimal model to estimate primary energy demand of China

    International Nuclear Information System (INIS)

    Yu Shiwei; Wei Yiming; Wang Ke

    2012-01-01

    To improve estimation efficiency for future projections, the present study has proposed a hybrid algorithm, Particle Swarm Optimization and Genetic Algorithm optimal Energy Demand Estimating (PSO–GA EDE) model, for China. The coefficients of the three forms of the model (linear, exponential, and quadratic) are optimized by PSO–GA using factors, such as GDP, population, economic structure, urbanization rate, and energy consumption structure, that affect demand. Based on 20-year historical data between 1990 and 2009, the simulation results of the proposed model have greater accuracy and reliability than other single optimization methods. Moreover, it can be used with optimal coefficients for the energy demand projections of China. The departure coefficient method is applied to get the weights of the three forms of the model to obtain a combinational prediction. The energy demand of China is going to be 4.79, 4.04, and 4.48 billion tce in 2015, and 6.91, 5.03, and 6.11 billion tce (“standard” tons coal equivalent) in 2020 under three different scenarios. Further, the projection results are compared with other estimating methods. - Highlights: ► A hybrid algorithm PSO–GA optimal energy demands estimating model for China. ► Energy demand of China is estimated by 2020 in three different scenarios. ► The projection results are compared with other estimating methods.

  15. Qualitative and Quantitative Integrated Modeling for Stochastic Simulation and Optimization

    Directory of Open Access Journals (Sweden)

    Xuefeng Yan

    2013-01-01

    Full Text Available The simulation and optimization of an actual physics system are usually constructed based on the stochastic models, which have both qualitative and quantitative characteristics inherently. Most modeling specifications and frameworks find it difficult to describe the qualitative model directly. In order to deal with the expert knowledge, uncertain reasoning, and other qualitative information, a qualitative and quantitative combined modeling specification was proposed based on a hierarchical model structure framework. The new modeling approach is based on a hierarchical model structure which includes the meta-meta model, the meta-model and the high-level model. A description logic system is defined for formal definition and verification of the new modeling specification. A stochastic defense simulation was developed to illustrate how to model the system and optimize the result. The result shows that the proposed method can describe the complex system more comprehensively, and the survival probability of the target is higher by introducing qualitative models into quantitative simulation.

  16. Quantum optimal control pathways of ozone isomerization dynamics subject to competing dissociation: A two-state one-dimensional model

    International Nuclear Information System (INIS)

    Kurosaki, Yuzuru; Ho, Tak-San; Rabitz, Herschel

    2014-01-01

    We construct a two-state one-dimensional reaction-path model for ozone open → cyclic isomerization dynamics. The model is based on the intrinsic reaction coordinate connecting the cyclic and open isomers with the O 2 + O asymptote on the ground-state 1 A ′ potential energy surface obtained with the high-level ab initio method. Using this two-state model time-dependent wave packet optimal control simulations are carried out. Two possible pathways are identified along with their respective band-limited optimal control fields; for pathway 1 the wave packet initially associated with the open isomer is first pumped into a shallow well on the excited electronic state potential curve and then driven back to the ground electronic state to form the cyclic isomer, whereas for pathway 2 the corresponding wave packet is excited directly to the primary well of the excited state potential curve. The simulations reveal that the optimal field for pathway 1 produces a final yield of nearly 100% with substantially smaller intensity than that obtained in a previous study [Y. Kurosaki, M. Artamonov, T.-S. Ho, and H. Rabitz, J. Chem. Phys. 131, 044306 (2009)] using a single-state one-dimensional model. Pathway 2, due to its strong coupling to the dissociation channel, is less effective than pathway 1. The simulations also show that nonlinear field effects due to molecular polarizability and hyperpolarizability are small for pathway 1 but could become significant for pathway 2 because much higher field intensity is involved in the latter. The results suggest that a practical control may be feasible with the aid of a few lowly excited electronic states for ozone isomerization

  17. A system-level cost-of-energy wind farm layout optimization with landowner modeling

    International Nuclear Information System (INIS)

    Chen, Le; MacDonald, Erin

    2014-01-01

    Highlights: • We model the role of landowners in determining the success of wind projects. • A cost-of-energy (COE) model with realistic landowner remittances is developed. • These models are included in a system-level wind farm layout optimization. • Basic verification indicates the optimal COE is in-line with real-world data. • Land plots crucial to a project’s success can be identified with the approach. - Abstract: This work applies an enhanced levelized wind farm cost model, including landowner remittance fees, to determine optimal turbine placements under three landowner participation scenarios and two land-plot shapes. Instead of assuming a continuous piece of land is available for the wind farm construction, as in most layout optimizations, the problem formulation represents landowner participation scenarios as a binary string variable, along with the number of turbines. The cost parameters and model are a combination of models from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory, and Windustry. The system-level cost-of-energy (COE) optimization model is also tested under two land-plot shapes: equally-sized square land plots and unequal rectangle land plots. The optimal COEs results are compared to actual COE data and found to be realistic. The results show that landowner remittances account for approximately 10% of farm operating costs across all cases. Irregular land-plot shapes are easily handled by the model. We find that larger land plots do not necessarily receive higher remittance fees. The model can help site developers identify the most crucial land plots for project success and the optimal positions of turbines, with realistic estimates of costs and profitability

  18. Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.

    Science.gov (United States)

    Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui

    2006-01-01

    This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.

  19. Models and algorithms for biomolecules and molecular networks

    CERN Document Server

    DasGupta, Bhaskar

    2016-01-01

    By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms. * Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms * Sampling techniques for estimating evolutionary rates and generating molecular structures * Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations * End-of-chapter exercises

  20. A Convex Optimization Model and Algorithm for Retinex

    Directory of Open Access Journals (Sweden)

    Qing-Nan Zhao

    2017-01-01

    Full Text Available Retinex is a theory on simulating and explaining how human visual system perceives colors under different illumination conditions. The main contribution of this paper is to put forward a new convex optimization model for Retinex. Different from existing methods, the main idea is to rewrite a multiplicative form such that the illumination variable and the reflection variable are decoupled in spatial domain. The resulting objective function involves three terms including the Tikhonov regularization of the illumination component, the total variation regularization of the reciprocal of the reflection component, and the data-fitting term among the input image, the illumination component, and the reciprocal of the reflection component. We develop an alternating direction method of multipliers (ADMM to solve the convex optimization model. Numerical experiments demonstrate the advantages of the proposed model which can decompose an image into the illumination and the reflection components.

  1. PEM fuel cell model suitable for energy optimization purposes

    International Nuclear Information System (INIS)

    Caux, S.; Hankache, W.; Fadel, M.; Hissel, D.

    2010-01-01

    Many fuel cell stack models or fuel cell system models exist. A model must be built with a main objective, sometimes for accurate electro-chemical behavior description, sometimes for optimization procedure at a system level. In this paper, based on the fundamental reactions present in a fuel cell stack, an accurate model and identification procedure is presented for future energy management in a Hybrid Electrical Vehicle (HEV). The proposed approach extracts all important state variables in such a system and based on the control of the fuel cell's gas flows and temperature, simplification arises to a simple electrical model. Assumptions verified due to the control of the stack allow simplifying the relationships within keeping accuracy in the description of a global fuel cell stack behavior from current demand to voltage. Modeled voltage and current dynamic behaviors are compared with actual measurements. The obtained accuracy is sufficient and less time-consuming (versus other previously published system-oriented models) leading to a suitable model for optimization iterative off-line algorithms.

  2. PEM fuel cell model suitable for energy optimization purposes

    Energy Technology Data Exchange (ETDEWEB)

    Caux, S.; Hankache, W.; Fadel, M. [LAPLACE/CODIASE: UMR CNRS 5213, Universite de Toulouse - INPT, UPS, - ENSEEIHT: 2 rue Camichel BP7122, 31071 Toulouse (France); CNRS, LAPLACE, F-31071 Toulouse (France); Hissel, D. [FEMTO-ST ENISYS/FCLAB, UMR CNRS 6174, University of Franche-Comte, Rue Thierry Mieg, 90010 Belfort (France)

    2010-02-15

    Many fuel cell stack models or fuel cell system models exist. A model must be built with a main objective, sometimes for accurate electro-chemical behavior description, sometimes for optimization procedure at a system level. In this paper, based on the fundamental reactions present in a fuel cell stack, an accurate model and identification procedure is presented for future energy management in a Hybrid Electrical Vehicle (HEV). The proposed approach extracts all important state variables in such a system and based on the control of the fuel cell's gas flows and temperature, simplification arises to a simple electrical model. Assumptions verified due to the control of the stack allow simplifying the relationships within keeping accuracy in the description of a global fuel cell stack behavior from current demand to voltage. Modeled voltage and current dynamic behaviors are compared with actual measurements. The obtained accuracy is sufficient and less time-consuming (versus other previously published system-oriented models) leading to a suitable model for optimization iterative off-line algorithms. (author)

  3. Bayesian semiparametric regression models to characterize molecular evolution

    Directory of Open Access Journals (Sweden)

    Datta Saheli

    2012-10-01

    Full Text Available Abstract Background Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. Results The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. Conclusions The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.

  4. OPTIMIZATION OF ATM AND BRANCH CASH OPERATIONS USING AN INTEGRATED CASH REQUIREMENT FORECASTING AND CASH OPTIMIZATION MODEL

    Directory of Open Access Journals (Sweden)

    Canser BİLİR

    2018-04-01

    Full Text Available In this study, an integrated cash requirement forecasting and cash inventory optimization model is implemented in both the branch and automated teller machine (ATM networks of a mid-sized bank in Turkey to optimize the bank’s cash supply chain. The implemented model’s objective is to minimize the idle cash levels at both branches and ATMs without decreasing the customer service level (CSL by providing the correct amount of cash at the correct location and time. To the best of our knowledge, the model is the first integrated model in the literature to be applied to both ATMs and branches simultaneously. The results demonstrated that the integrated model dramatically decreased the idle cash levels at both branches and ATMs without degrading the availability of cash and hence customer satisfaction. An in-depth analysis of the results also indicated that the results were more remarkable for branches. The results also demonstrated that the utilization of various seasonal indices plays a very critical role in the forecasting of cash requirements for a bank. Another unique feature of the study is that the model is the first to include the recycling feature of ATMs. The results demonstrated that as a result of the inclusion of the deliberate seasonal indices in the forecasting model, the integrated cash optimization models can be used to estimate the cash requirements of recycling ATMs.

  5. Building Energy Modeling and Control Methods for Optimization and Renewables Integration

    Science.gov (United States)

    Burger, Eric M.

    dynamics within a building by learning from sensor data. Control techniques encompass the application of optimal control theory, model predictive control, and convex distributed optimization to TCLs. First, we present the alternative control trajectory (ACT) representation, a novel method for the approximate optimization of non-convex discrete systems. This approach enables the optimal control of a population of non-convex agents using distributed convex optimization techniques. Second, we present a distributed convex optimization algorithm for the control of a TCL population. Experimental results demonstrate the application of this algorithm to the problem of renewable energy generation following. This dissertation contributes to the development of intelligent energy management systems for buildings by presenting a suite of novel and adaptable modeling and control techniques. Applications focus on optimizing the performance of building operations and on facilitating the integration of renewable energy resources.

  6. A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

    International Nuclear Information System (INIS)

    Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao

    2014-01-01

    Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. (paper)

  7. OPTIMAL STOCK MODELING FOR NON-FOODS RETAILER SELLING ON-CREDIT

    Directory of Open Access Journals (Sweden)

    Vladimir V. Manakhov

    2016-01-01

    Full Text Available The article is dedicated to the problem of retail stock optimization within mo-nopolistic competition market while selling non-food goods to customers on-credit. Optimization model has been developed and appropriate technique of stock volume planning has been introduced

  8. Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

    Science.gov (United States)

    Janardhanan, S.; Datta, B.

    2011-12-01

    Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of

  9. A three-dimensional topology optimization model for tooth-root morphology.

    Science.gov (United States)

    Seitz, K-F; Grabe, J; Köhne, T

    2018-02-01

    To obtain the root of a lower incisor through structural optimization, we used two methods: optimization with Solid Isotropic Material with Penalization (SIMP) and Soft-Kill Option (SKO). The optimization was carried out in combination with a finite element analysis in Abaqus/Standard. The model geometry was based on cone-beam tomography scans of 10 adult males with healthy bone-tooth interface. Our results demonstrate that the optimization method using SIMP for minimum compliance could not adequately predict the actual root shape. The SKO method, however, provided optimization results that were comparable to the natural root form and is therefore suitable to set up the basic topology of a dental root.

  10. Optimization Model for Web Based Multimodal Interactive Simulations.

    Science.gov (United States)

    Halic, Tansel; Ahn, Woojin; De, Suvranu

    2015-07-15

    This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update . In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach.

  11. Hyperopt: a Python library for model selection and hyperparameter optimization

    Science.gov (United States)

    Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.

    2015-01-01

    Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.

  12. Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

    OpenAIRE

    Zhang, Xiangsheng; Pan, Feng

    2015-01-01

    Aimed at the parameters optimization in support vector machine (SVM) for glutamate fermentation modelling, a new method is developed. It optimizes the SVM parameters via an improved particle swarm optimization (IPSO) algorithm which has better global searching ability. The algorithm includes detecting and handling the local convergence and exhibits strong ability to avoid being trapped in local minima. The material step of the method was shown. Simulation experiments demonstrate the effective...

  13. Model checking optimal finite-horizon control for probabilistic gene regulatory networks.

    Science.gov (United States)

    Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan

    2017-12-14

    Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.

  14. A Comparative Study of Successful Central Nervous System Drugs Using Molecular Modeling

    Science.gov (United States)

    Kim, Hyosub; Sulaimon, Segun; Menezes, Sandra; Son, Anne; Menezes, Warren J. C.

    2011-01-01

    Molecular modeling is a powerful tool used for three-dimensional visualization and for exploring electrostatic forces involved in drug transport. This tool enhances student understanding of structure-property relationships, as well as actively engaging them in class. Molecular modeling of several central nervous system (CNS) drugs is used to…

  15. Applications of Molecular and Materials Modeling

    Science.gov (United States)

    2002-01-01

    Chimica Industriale Molecular modeling of solvation Prof. Jacopo Tomasi http://www.dcci.unipi.it/attivita /attivita.html; http://www.dcci.unipi.it...solutions/ cases/notes/scale.html BNFL Sorption of gases in zeolites Dr. Scott L. Owens http://www.bnfl.co.uk/ BAE (British Aerospace Engineering) Rare...permeation of gases ; adhesion and interfacial interactions of siloxane networks; chemical reactivity and catalysis; environmental and cosmetics

  16. MoCha: Molecular Characterization of Unknown Pathways.

    Science.gov (United States)

    Lobo, Daniel; Hammelman, Jennifer; Levin, Michael

    2016-04-01

    Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.

  17. A molecular prognostic model predicts esophageal squamous cell carcinoma prognosis.

    Directory of Open Access Journals (Sweden)

    Hui-Hui Cao

    Full Text Available Esophageal squamous cell carcinoma (ESCC has the highest mortality rates in China. The 5-year survival rate of ESCC remains dismal despite improvements in treatments such as surgical resection and adjuvant chemoradiation, and current clinical staging approaches are limited in their ability to effectively stratify patients for treatment options. The aim of the present study, therefore, was to develop an immunohistochemistry-based prognostic model to improve clinical risk assessment for patients with ESCC.We developed a molecular prognostic model based on the combined expression of axis of epidermal growth factor receptor (EGFR, phosphorylated Specificity protein 1 (p-Sp1, and Fascin proteins. The presence of this prognostic model and associated clinical outcomes were analyzed for 130 formalin-fixed, paraffin-embedded esophageal curative resection specimens (generation dataset and validated using an independent cohort of 185 specimens (validation dataset.The expression of these three genes at the protein level was used to build a molecular prognostic model that was highly predictive of ESCC survival in both generation and validation datasets (P = 0.001. Regression analysis showed that this molecular prognostic model was strongly and independently predictive of overall survival (hazard ratio = 2.358 [95% CI, 1.391-3.996], P = 0.001 in generation dataset; hazard ratio = 1.990 [95% CI, 1.256-3.154], P = 0.003 in validation dataset. Furthermore, the predictive ability of these 3 biomarkers in combination was more robust than that of each individual biomarker.This technically simple immunohistochemistry-based molecular model accurately predicts ESCC patient survival and thus could serve as a complement to current clinical risk stratification approaches.

  18. Model assisted multiobjective optimization with {lambda}-control; Multikriterielle Optimierung mit {lambda}-geregelten modellgestuetzten Evolutionsstrategien

    Energy Technology Data Exchange (ETDEWEB)

    Braun, Jan; Hoffmann, Frank; Krettek, Johannes; Bertram, Torsten [Technische Univ. Dortmund (Germany). Lehrstuhl RST

    2009-07-01

    Evolutionary algorithms require a large number of fitness evaluations in order to find an optimal solution. This property limits their application to hardware in the loop optimization or optimization of time-consuming simulations and calculations. This contribution proposes a preselection with data based models in order to reduce the number of true fitness evaluations. It extends previous approaches for model assisted scalar optimization to multiobjective problems by a proper redefinition of model quality and ?-control. The application to multiobjective benchmark optimization problems underlies the improved convergence of the model assisted evolution strategy compared to a multiobjective evolution strategy as well as the advantages of a {lambda}-controlled variant compared to a static preselection. (orig.)

  19. Multi-infill strategy for kriging models used in variable fidelity optimization

    Directory of Open Access Journals (Sweden)

    Chao SONG

    2018-03-01

    Full Text Available In this paper, a computationally efficient optimization method for aerodynamic design has been developed. The low-fidelity model and the multi-infill strategy are utilized in this approach. Low-fidelity data is employed to provide a good global trend for model prediction, and multiple sample points chosen by different infill criteria in each updating cycle are used to enhance the exploitation and exploration ability of the optimization approach. Take the advantages of low-fidelity model and the multi-infill strategy, and no initial sample for the high-fidelity model is needed. This approach is applied to an airfoil design case and a high-dimensional wing design case. It saves a large number of high-fidelity function evaluations for initial model construction. What’s more, faster reduction of an aerodynamic function is achieved, when compared to ordinary kriging using the multi-infill strategy and variable-fidelity model using single infill criterion. The results indicate that the developed approach has a promising application to efficient aerodynamic design when high-fidelity analyses are involved. Keywords: Aerodynamics, Infill criteria, Kriging models, Multi-infill, Optimization

  20. Layout optimization of DRAM cells using rigorous simulation model for NTD

    Science.gov (United States)

    Jeon, Jinhyuck; Kim, Shinyoung; Park, Chanha; Yang, Hyunjo; Yim, Donggyu; Kuechler, Bernd; Zimmermann, Rainer; Muelders, Thomas; Klostermann, Ulrich; Schmoeller, Thomas; Do, Mun-hoe; Choi, Jung-Hoe

    2014-03-01

    DRAM chip space is mainly determined by the size of the memory cell array patterns which consist of periodic memory cell features and edges of the periodic array. Resolution Enhancement Techniques (RET) are used to optimize the periodic pattern process performance. Computational Lithography such as source mask optimization (SMO) to find the optimal off axis illumination and optical proximity correction (OPC) combined with model based SRAF placement are applied to print patterns on target. For 20nm Memory Cell optimization we see challenges that demand additional tool competence for layout optimization. The first challenge is a memory core pattern of brick-wall type with a k1 of 0.28, so it allows only two spectral beams to interfere. We will show how to analytically derive the only valid geometrically limited source. Another consequence of two-beam interference limitation is a "super stable" core pattern, with the advantage of high depth of focus (DoF) but also low sensitivity to proximity corrections or changes of contact aspect ratio. This makes an array edge correction very difficult. The edge can be the most critical pattern since it forms the transition from the very stable regime of periodic patterns to non-periodic periphery, so it combines the most critical pitch and highest susceptibility to defocus. Above challenge makes the layout correction to a complex optimization task demanding a layout optimization that finds a solution with optimal process stability taking into account DoF, exposure dose latitude (EL), mask error enhancement factor (MEEF) and mask manufacturability constraints. This can only be achieved by simultaneously considering all criteria while placing and sizing SRAFs and main mask features. The second challenge is the use of a negative tone development (NTD) type resist, which has a strong resist effect and is difficult to characterize experimentally due to negative resist profile taper angles that perturb CD at bottom characterization by

  1. Optimal evolution models for quantum tomography

    International Nuclear Information System (INIS)

    Czerwiński, Artur

    2016-01-01

    The research presented in this article concerns the stroboscopic approach to quantum tomography, which is an area of science where quantum physics and linear algebra overlap. In this article we introduce the algebraic structure of the parametric-dependent quantum channels for 2-level and 3-level systems such that the generator of evolution corresponding with the Kraus operators has no degenerate eigenvalues. In such cases the index of cyclicity of the generator is equal to 1, which physically means that there exists one observable the measurement of which performed a sufficient number of times at distinct instants provides enough data to reconstruct the initial density matrix and, consequently, the trajectory of the state. The necessary conditions for the parameters and relations between them are introduced. The results presented in this paper seem to have considerable potential applications in experiments due to the fact that one can perform quantum tomography by conducting only one kind of measurement. Therefore, the analyzed evolution models can be considered optimal in the context of quantum tomography. Finally, we introduce some remarks concerning optimal evolution models in the case of n-dimensional Hilbert space. (paper)

  2. The PDB_REDO server for macromolecular structure model optimization

    Directory of Open Access Journals (Sweden)

    Robbie P. Joosten

    2014-07-01

    Full Text Available The refinement and validation of a crystallographic structure model is the last step before the coordinates and the associated data are submitted to the Protein Data Bank (PDB. The success of the refinement procedure is typically assessed by validating the models against geometrical criteria and the diffraction data, and is an important step in ensuring the quality of the PDB public archive [Read et al. (2011, Structure, 19, 1395–1412]. The PDB_REDO procedure aims for `constructive validation', aspiring to consistent and optimal refinement parameterization and pro-active model rebuilding, not only correcting errors but striving for optimal interpretation of the electron density. A web server for PDB_REDO has been implemented, allowing thorough, consistent and fully automated optimization of the refinement procedure in REFMAC and partial model rebuilding. The goal of the web server is to help practicing crystallographers to improve their model prior to submission to the PDB. For this, additional steps were implemented in the PDB_REDO pipeline, both in the refinement procedure, e.g. testing of resolution limits and k-fold cross-validation for small test sets, and as new validation criteria, e.g. the density-fit metrics implemented in EDSTATS and ligand validation as implemented in YASARA. Innovative ways to present the refinement and validation results to the user are also described, which together with auto-generated Coot scripts can guide users to subsequent model inspection and improvement. It is demonstrated that using the server can lead to substantial improvement of structure models before they are submitted to the PDB.

  3. MENENTUKAN PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL CONDITIONAL MEAN VARIANCE

    Directory of Open Access Journals (Sweden)

    I GEDE ERY NISCAHYANA

    2016-08-01

    Full Text Available When the returns of stock prices show the existence of autocorrelation and heteroscedasticity, then conditional mean variance models are suitable method to model the behavior of the stocks. In this thesis, the implementation of the conditional mean variance model to the autocorrelated and heteroscedastic return was discussed. The aim of this thesis was to assess the effect of the autocorrelated and heteroscedastic returns to the optimal solution of a portfolio. The margin of four stocks, Fortune Mate Indonesia Tbk (FMII.JK, Bank Permata Tbk (BNLI.JK, Suryamas Dutamakmur Tbk (SMDM.JK dan Semen Gresik Indonesia Tbk (SMGR.JK were estimated by GARCH(1,1 model with standard innovations following the standard normal distribution and the t-distribution.  The estimations were used to construct a portfolio. The portfolio optimal was found when the standard innovation used was t-distribution with the standard deviation of 1.4532 and the mean of 0.8023 consisting of 0.9429 (94% of FMII stock, 0.0473 (5% of  BNLI stock, 0% of SMDM stock, 1% of  SMGR stock.

  4. Optimization of Regional Geodynamic Models for Mantle Dynamics

    Science.gov (United States)

    Knepley, M.; Isaac, T.; Jadamec, M. A.

    2016-12-01

    The SubductionGenerator program is used to construct high resolution, 3D regional thermal structures for mantle convection simulations using a variety of data sources, including sea floor ages and geographically referenced 3D slab locations based on seismic observations. The initial bulk temperature field is constructed using a half-space cooling model or plate cooling model, and related smoothing functions based on a diffusion length-scale analysis. In this work, we seek to improve the 3D thermal model and test different model geometries and dynamically driven flow fields using constraints from observed seismic velocities and plate motions. Through a formal adjoint analysis, we construct the primal-dual version of the multi-objective PDE-constrained optimization problem for the plate motions and seismic misfit. We have efficient, scalable preconditioners for both the forward and adjoint problems based upon a block preconditioning strategy, and a simple gradient update is used to improve the control residual. The full optimal control problem is formulated on a nested hierarchy of grids, allowing a nonlinear multigrid method to accelerate the solution.

  5. Molecular modeling used to evaluate CYP2C9-dependent metabolism: homology modeling, molecular dynamics and docking simulations.

    Science.gov (United States)

    Mendieta-Wejebe, Jessica E; Correa-Basurto, José; García-Segovia, Erika M; Ceballos-Cancino, Gisela; Rosales-Hernández, Martha C

    2011-07-01

    Cytochrome P450 (CYP) 2C9 is the principal isoform of the CYP2C subfamily in the human liver and is involved in the oxidation of several endogenous and xenobiotic compounds, including many therapeutic drugs. The metabolism of drugs by CYP2C9 can yield either safe or toxic products, which may be related to the recognition and binding modes of the substrates to this isoform. These interactions can be studied using in silico methods such as quantum chemistry, molecular dynamics and docking simulations, which can also be useful for predicting the structure of metabolites. In these types of studies, the ligand and the protein must be tridimensional models; thus, the protein can be built by homology modeling or retrieved from the Protein Data Bank. Therefore, the current review emphasizes the importance of using in silico methods to predict the metabolism of CYP2C9 because these computational tools have allowed the description of the principal characteristics of the active site of this isoform at the molecular level and the chemical properties of its ligands.

  6. An aircraft noise pollution model for trajectory optimization

    Science.gov (United States)

    Barkana, A.; Cook, G.

    1976-01-01

    A mathematical model describing the generation of aircraft noise is developed with the ultimate purpose of reducing noise (noise-optimizing landing trajectories) in terminal areas. While the model is for a specific aircraft (Boeing 737), the methodology would be applicable to a wide variety of aircraft. The model is used to obtain a footprint on the ground inside of which the noise level is at or above 70 dB.

  7. Model-based plant-wide optimization of large-scale lignocellulosic bioethanol plants

    DEFF Research Database (Denmark)

    Prunescu, Remus Mihail; Blanke, Mogens; Jakobsen, Jon Geest

    2017-01-01

    Second generation biorefineries transform lignocellulosic biomass into chemicals with higher added value following a conversion mechanism that consists of: pretreatment, enzymatic hydrolysis, fermentation and purification. The objective of this study is to identify the optimal operational point...... with respect to maximum economic profit of a large scale biorefinery plant using a systematic model-based plantwide optimization methodology. The following key process parameters are identified as decision variables: pretreatment temperature, enzyme dosage in enzymatic hydrolysis, and yeast loading per batch...... in fermentation. The plant is treated in an integrated manner taking into account the interactions and trade-offs between the conversion steps. A sensitivity and uncertainty analysis follows at the optimal solution considering both model and feed parameters. It is found that the optimal point is more sensitive...

  8. Modeling ion sensing in molecular electronics

    International Nuclear Information System (INIS)

    Chen, Caroline J.; Smeu, Manuel; Ratner, Mark A.

    2014-01-01

    We examine the ability of molecules to sense ions by measuring the change in molecular conductance in the presence of such charged species. The detection of protons (H + ), alkali metal cations (M + ), calcium ions (Ca 2+ ), and hydronium ions (H 3 O + ) is considered. Density functional theory (DFT) is used within the Keldysh non-equilibrium Green's function framework (NEGF) to model electron transport properties of quinolinedithiol (QDT, C 9 H 7 NS 2 ), bridging Al electrodes. The geometry of the transport region is relaxed with DFT. The transport properties of the device are modeled with NEGF-DFT to determine if this device can distinguish among the M + + QDT species containing monovalent cations, where M + = H + , Li + , Na + , or K + . Because of the asymmetry of QDT in between the two electrodes, both positive and negative biases are considered. The electron transmission function and conductance properties are simulated for electrode biases in the range from −0.5 V to 0.5 V at increments of 0.1 V. Scattering state analysis is used to determine the molecular orbitals that are the main contributors to the peaks in the transmission function near the Fermi level of the electrodes, and current-voltage relationships are obtained. The results show that QDT can be used as a proton detector by measuring transport through it and can conceivably act as a pH sensor in solutions. In addition, QDT may be able to distinguish among different monovalent species. This work suggests an approach to design modern molecular electronic conductance sensors with high sensitivity and specificity using well-established quantum chemistry

  9. Molecular modelling and radiopharmaceutical design

    International Nuclear Information System (INIS)

    Neves, M.; Gano, L.; Costa, M.C.; Raminhos, H.; Rosado, M.; Fausto, R.

    2002-01-01

    Aim: Among several headings for radiopharmaceuticals (RPs) design, molecular modelling (MM) could be used for the prediction of ligands and metal-complexes structures. Using MM it is also possible to simulate molecular interactions between predicted structures and specific biomolecules. Bisphosphonates (BPs) are ligands that are able to coordinate radioactive metals, such as 153 Sm, 166 Ho, 186 Re, etc., but they are all polymeric complexes difficult to characterize. It is reported that the bone uptake does not depend on the nature of metal center, but is primarily driven by the nature of the ligand, as in the case of HEDP-M (M= 99m Tc, 186 Re, 113 Sn). So, it would be interesting to estimate the relevant molecular properties of BPs by MM, simulate their interaction with hydroxyapatite (HAP) the main bone component, and then correlate the predicted molecular parameters with experimental data obtained from HAP binding and biodistribution studies of BPs carrying radioactive metals. Materials and Methods: The molecular structures and preferred conformations of BPs differing in the length of the aliphatic chain attached to their substituted amine groups (pami-dronate, olpadronate and ibandronate) were obtained using the second-generation CVFF 950 (version 1.01) force field of Hwang et al. Simulation of the interactions between the studied BPs and HAP were performed using a Cerius-2 system of programs running on a Silicon Graphics O2 workstation. BPs- 153 Sm complexes were synthesized and characterized by ITLC. Their binding to HAP and in vivo biodistribution studies were carried out as usual described in literature. Results: A direct correlation could be established between in vitro BPs affinity towards HAP and their corresponding energies from the Coulomb interactions involving the N and P atoms of the studied BPs bound to the HAP (0,0,1) surface and the nearest Ca atoms of HAP. The BPs- 153 Sm showing the highest binding to HAP and skeletal uptake are those which

  10. Resolving the anomalous infrared spectrum of the MeCN-HCl molecular cluster using ab Initio molecular dynamics

    DEFF Research Database (Denmark)

    Bork, Nicolai Christian; Loukonen, Ville; Kjærgaard, Henrik Grum

    2014-01-01

    We present a molecular dynamics (MD) based study of the acetonitrile-hydrogen chloride molecular cluster in the gas phase, aimed at resolving the anomalous features often seen in infrared spectra of hydrogen bonded complexes. We find that the infrared spectrum obtained from the Fourier transform...... of the electric dipole moment autocorrelation function converges very slowly due to the floppy nature of the complex. Even after 55 picoseconds of simulation, significant differences in the modelled and experimental spectrum are seen, likely due to insufficient configurational sampling. Instead, we utilize the MD...... trajectory for a structural based analysis. We find that the most populated values of the N-H-Cl angle are around 162°. The global minimum energy conformation at 180.0° is essentially unpopulated. We re-model the spectrum by combining population data from the MD simulations with optimizations constraining...

  11. Inverse Optimization: A New Perspective on the Black-Litterman Model

    Science.gov (United States)

    Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch.

    2014-01-01

    The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct “BL”-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new “BL”-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views. PMID:25382873

  12. Inverse Optimization: A New Perspective on the Black-Litterman Model.

    Science.gov (United States)

    Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch

    2012-12-11

    The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct "BL"-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new "BL"-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views.

  13. RISK LOAN PORTFOLIO OPTIMIZATION MODEL BASED ON CVAR RISK MEASURE

    Directory of Open Access Journals (Sweden)

    Ming-Chang LEE

    2015-07-01

    Full Text Available In order to achieve commercial banks liquidity, safety and profitability objective requirements, loan portfolio risk analysis based optimization decisions are rational allocation of assets.  The risk analysis and asset allocation are the key technology of banking and risk management.  The aim of this paper, build a loan portfolio optimization model based on risk analysis.  Loan portfolio rate of return by using Value-at-Risk (VaR and Conditional Value-at-Risk (CVaR constraint optimization decision model reflects the bank's risk tolerance, and the potential loss of direct control of the bank.  In this paper, it analyze a general risk management model applied to portfolio problems with VaR and CVaR risk measures by using Using the Lagrangian Algorithm.  This paper solves the highly difficult problem by matrix operation method.  Therefore, the combination of this paper is easy understanding the portfolio problems with VaR and CVaR risk model is a hyperbola in mean-standard deviation space.  It is easy calculation in proposed method.

  14. An algorithm for mass matrix calculation of internally constrained molecular geometries

    International Nuclear Information System (INIS)

    Aryanpour, Masoud; Dhanda, Abhishek; Pitsch, Heinz

    2008-01-01

    Dynamic models for molecular systems require the determination of corresponding mass matrix. For constrained geometries, these computations are often not trivial but need special considerations. Here, assembling the mass matrix of internally constrained molecular structures is formulated as an optimization problem. Analytical expressions are derived for the solution of the different possible cases depending on the rank of the constraint matrix. Geometrical interpretations are further used to enhance the solution concept. As an application, we evaluate the mass matrix for a constrained molecule undergoing an electron-transfer reaction. The preexponential factor for this reaction is computed based on the harmonic model

  15. An algorithm for mass matrix calculation of internally constrained molecular geometries.

    Science.gov (United States)

    Aryanpour, Masoud; Dhanda, Abhishek; Pitsch, Heinz

    2008-01-28

    Dynamic models for molecular systems require the determination of corresponding mass matrix. For constrained geometries, these computations are often not trivial but need special considerations. Here, assembling the mass matrix of internally constrained molecular structures is formulated as an optimization problem. Analytical expressions are derived for the solution of the different possible cases depending on the rank of the constraint matrix. Geometrical interpretations are further used to enhance the solution concept. As an application, we evaluate the mass matrix for a constrained molecule undergoing an electron-transfer reaction. The preexponential factor for this reaction is computed based on the harmonic model.

  16. A multidimensional model of optimal participation of children with physical disabilities.

    Science.gov (United States)

    Kang, Lin-Ju; Palisano, Robert J; King, Gillian A; Chiarello, Lisa A

    2014-01-01

    To present a conceptual model of optimal participation in recreational and leisure activities for children with physical disabilities. The conceptualization of the model was based on review of contemporary theories and frameworks, empirical research and the authors' practice knowledge. A case scenario is used to illustrate application to practice. The model proposes that optimal participation in recreational and leisure activities involves the dynamic interaction of multiple dimensions and determinants of participation. The three dimensions of participation are physical, social and self-engagement. Determinants of participation encompass attributes of the child, family and environment. Experiences of optimal participation are hypothesized to result in long-term benefits including better quality of life, a healthier lifestyle and emotional and psychosocial well-being. Consideration of relevant child, family and environment determinants of dimensions of optimal participation should assist children, families and health care professionals to identify meaningful goals and outcomes and guide the selection and implementation of innovative therapy approaches and methods of service delivery. Implications for Rehabilitation Optimal participation is proposed to involve the dynamic interaction of physical, social and self-engagement and attributes of the child, family and environment. The model emphasizes the importance of self-perceptions and participation experiences of children with physical disabilities. Optimal participation may have a positive influence on quality of life, a healthy lifestyle and emotional and psychosocial well-being. Knowledge of child, family, and environment determinants of physical, social and self-engagement should assist children, families and professionals in identifying meaningful goals and guiding innovative therapy approaches.

  17. Influence of model errors in optimal sensor placement

    Science.gov (United States)

    Vincenzi, Loris; Simonini, Laura

    2017-02-01

    The paper investigates the role of model errors and parametric uncertainties in optimal or near optimal sensor placements for structural health monitoring (SHM) and modal testing. The near optimal set of measurement locations is obtained by the Information Entropy theory; the results of placement process considerably depend on the so-called covariance matrix of prediction error as well as on the definition of the correlation function. A constant and an exponential correlation function depending on the distance between sensors are firstly assumed; then a proposal depending on both distance and modal vectors is presented. With reference to a simple case-study, the effect of model uncertainties on results is described and the reliability and the robustness of the proposed correlation function in the case of model errors are tested with reference to 2D and 3D benchmark case studies. A measure of the quality of the obtained sensor configuration is considered through the use of independent assessment criteria. In conclusion, the results obtained by applying the proposed procedure on a real 5-spans steel footbridge are described. The proposed method also allows to better estimate higher modes when the number of sensors is greater than the number of modes of interest. In addition, the results show a smaller variation in the sensor position when uncertainties occur.

  18. Modeling Reservoir-River Networks in Support of Optimizing Seasonal-Scale Reservoir Operations

    Science.gov (United States)

    Villa, D. L.; Lowry, T. S.; Bier, A.; Barco, J.; Sun, A.

    2011-12-01

    HydroSCOPE (Hydropower Seasonal Concurrent Optimization of Power and the Environment) is a seasonal time-scale tool for scenario analysis and optimization of reservoir-river networks. Developed in MATLAB, HydroSCOPE is an object-oriented model that simulates basin-scale dynamics with an objective of optimizing reservoir operations to maximize revenue from power generation, reliability in the water supply, environmental performance, and flood control. HydroSCOPE is part of a larger toolset that is being developed through a Department of Energy multi-laboratory project. This project's goal is to provide conventional hydropower decision makers with better information to execute their day-ahead and seasonal operations and planning activities by integrating water balance and operational dynamics across a wide range of spatial and temporal scales. This presentation details the modeling approach and functionality of HydroSCOPE. HydroSCOPE consists of a river-reservoir network model and an optimization routine. The river-reservoir network model simulates the heat and water balance of river-reservoir networks for time-scales up to one year. The optimization routine software, DAKOTA (Design Analysis Kit for Optimization and Terascale Applications - dakota.sandia.gov), is seamlessly linked to the network model and is used to optimize daily volumetric releases from the reservoirs to best meet a set of user-defined constraints, such as maximizing revenue while minimizing environmental violations. The network model uses 1-D approximations for both the reservoirs and river reaches and is able to account for surface and sediment heat exchange as well as ice dynamics for both models. The reservoir model also accounts for inflow, density, and withdrawal zone mixing, and diffusive heat exchange. Routing for the river reaches is accomplished using a modified Muskingum-Cunge approach that automatically calculates the internal timestep and sub-reach lengths to match the conditions of

  19. Real-time process optimization based on grey-box neural models

    Directory of Open Access Journals (Sweden)

    F. A. Cubillos

    2007-09-01

    Full Text Available This paper investigates the feasibility of using grey-box neural models (GNM in Real Time Optimization (RTO. These models are based on a suitable combination of fundamental conservation laws and neural networks, being used in at least two different ways: to complement available phenomenological knowledge with empirical information, or to reduce dimensionality of complex rigorous physical models. We have observed that the benefits of using these simple adaptable models are counteracted by some difficulties associated with the solution of the optimization problem. Nonlinear Programming (NLP algorithms failed in finding the global optimum due to the fact that neural networks can introduce multimodal objective functions. One alternative considered to solve this problem was the use of some kind of evolutionary algorithms, like Genetic Algorithms (GA. Although these algorithms produced better results in terms of finding the appropriate region, they took long periods of time to reach the global optimum. It was found that a combination of genetic and nonlinear programming algorithms can be use to fast obtain the optimum solution. The proposed approach was applied to the Williams-Otto reactor, considering three different GNM models of increasing complexity. Results demonstrated that the use of GNM models and mixed GA/NLP optimization algorithms is a promissory approach for solving dynamic RTO problems.

  20. Oyster Creek cycle 10 nodal model parameter optimization study using PSMS

    International Nuclear Information System (INIS)

    Dougher, J.D.

    1987-01-01

    The power shape monitoring system (PSMS) is an on-line core monitoring system that uses a three-dimensional nodal code (NODE-B) to perform nodal power calculations and compute thermal margins. The PSMS contains a parameter optimization function that improves the ability of NODE-B to accurately monitor core power distributions. This functions iterates on the model normalization parameters (albedos and mixing factors) to obtain the best agreement between predicted and measured traversing in-core probe (TIP) reading on a statepoint-by-statepoint basis. Following several statepoint optimization runs, an average set of optimized normalization parameters can be determined and can be implemented into the current or subsequent cycle core model for on-line core monitoring. A statistical analysis of 19 high-power steady-state state-points throughout Oyster Creek cycle 10 operation has shown a consistently poor virgin model performance. The normalization parameters used in the cycle 10 NODE-B model were based on a cycle 8 study, which evaluated only Exxon fuel types. The introduction of General Electric (GE) fuel into cycle 10 (172 assemblies) was a significant fuel/core design change that could have altered the optimum set of normalization parameters. Based on the need to evaluate a potential change in the model normalization parameters for cycle 11 and in an attempt to account for the poor cycle 10 model performance, a parameter optimization study was performed

  1. Molecular modeling of fentanyl analogs

    Directory of Open Access Journals (Sweden)

    LJILJANA DOSEN-MICOVIC

    2004-11-01

    Full Text Available Fentanyl is a highly potent and clinically widely used narcotic analgesic. A large number of its analogs have been synthesized, some of which (sufentanil and alfentanyl are also in clinical use. Theoretical studies, in recent years, afforded a better understanding of the structure-activity relationships of this class of opiates and allowed insight into the molecular mechanism of the interactions of fentanyl analogs with their receptors. An overview of the current computational techniques for modeling fentanyl analogs, their receptors and ligand-receptor interactions is presented in this paper.

  2. Optimization model for rotor blades of horizontal axis wind turbines

    Institute of Scientific and Technical Information of China (English)

    LIU Xiong; CHEN Yan; YE Zhiquan

    2007-01-01

    This paper presents an optimization model for rotor blades of horizontal axis wind turbines. The model refers to the wind speed distribution function on the specific wind site, with an objective to satisfy the maximum annual energy output. To speed up the search process and guarantee a global optimal result, the extended compact genetic algorithm (ECGA) is used to carry out the search process.Compared with the simple genetic algorithm, ECGA runs much faster and can get more accurate results with a much smaller population size and fewer function evaluations. Using the developed optimization program, blades of a 1.3 MW stall-regulated wind turbine are designed. Compared with the existing blades, the designed blades have obviously better aerodynamic performance.

  3. Strategic Design and Optimization of Inorganic Sorbents for Cesium, Strontium and Actinides

    International Nuclear Information System (INIS)

    Maginn, Edward J.

    2009-01-01

    The primary objective of the Notre Dame component of the project was computational in nature. The goal was to provide a design tool for the synthesis of optimized sorbents for the removal of cesium, strontium and actinides from nuclear waste solutions. Molecular modeling enables us to observe and better understand the molecular level interactions that govern the selectivity of specific radionuclides in a particular sorbent. The research focused on the development and validation of a suitable and transferable model for all the cations and ion exchangers of interest, nd then subsequent simulations which determined the siting and mobility of water and cations. Speciic accomplishments include: (1) improving existing intermolecular force fields to accurately model the sorbents of interest; (2) utilizing energy-minimizations and molecular dynamics simulations for structural prediction of CST and niobium-substituted CST materials; (3) determining Na+/water positions in polyoxoniobate materials using molecular dynamics simulations; and (4) developing Hybrid Monte Carlo methods for improved structural prediction.

  4. Optimal management of ecosystem services with pollution traps : The lake model revisited

    NARCIS (Netherlands)

    de Zeeuw, Aart; Grass, Dieter; Xepapadeas, Anastasios

    2017-01-01

    In this paper, optimal management of the lake model and common-property outcomes are reconsidered when the lake model is extended with the slowly changing variable. New optimal trajectories are found that were hidden in the simplified analysis. Furthermore, it is shown that two Nash equilibria may

  5. Optimal transportation networks models and theory

    CERN Document Server

    Bernot, Marc; Morel, Jean-Michel

    2009-01-01

    The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.

  6. Optimization and Simulation Modeling of Disaster Relief Supply Chain: A Literature Review

    OpenAIRE

    Feng, Keli; Bizimana, Emmanuel; Agu, Deedee D.; Issac, Tana T.

    2012-01-01

    Recent natural and man-made disasters underscore the need of a resilient and agile disaster relief supply chain to mitigate the damages and save people’s lives. Optimization and simulation modeling have become powerful and useful tools to help decision makers tackle problems related to disaster relief supply chain. This paper reviews optimization and simulation models used in the field of disaster relief supply chain. We review the literature of the facility location optimization problems of ...

  7. Construction of molecular potential energy curves by an optimization method

    Science.gov (United States)

    Wang, J.; Blake, A. J.; McCoy, D. G.; Torop, L.

    1991-01-01

    A technique for determining the potential energy curves for diatomic molecules from measurements of diffused or continuum spectra is presented. It is based on a numerical procedure which minimizes the difference between the calculated spectra and the experimental measurements and can be used in cases where other techniques, such as the conventional RKR method, are not applicable. With the aid of suitable spectral data, the associated dipole electronic transition moments can be simultaneously obtained. The method is illustrated by modeling the "longest band" of molecular oxygen to extract the E 3Σ u- and B 3Σ u- potential curves in analytical form.

  8. Optimization of a centrifugal compressor impeller using CFD: the choice of simulation model parameters

    Science.gov (United States)

    Neverov, V. V.; Kozhukhov, Y. V.; Yablokov, A. M.; Lebedev, A. A.

    2017-08-01

    Nowadays the optimization using computational fluid dynamics (CFD) plays an important role in the design process of turbomachines. However, for the successful and productive optimization it is necessary to define a simulation model correctly and rationally. The article deals with the choice of a grid and computational domain parameters for optimization of centrifugal compressor impellers using computational fluid dynamics. Searching and applying optimal parameters of the grid model, the computational domain and solver settings allows engineers to carry out a high-accuracy modelling and to use computational capability effectively. The presented research was conducted using Numeca Fine/Turbo package with Spalart-Allmaras and Shear Stress Transport turbulence models. Two radial impellers was investigated: the high-pressure at ψT=0.71 and the low-pressure at ψT=0.43. The following parameters of the computational model were considered: the location of inlet and outlet boundaries, type of mesh topology, size of mesh and mesh parameter y+. Results of the investigation demonstrate that the choice of optimal parameters leads to the significant reduction of the computational time. Optimal parameters in comparison with non-optimal but visually similar parameters can reduce the calculation time up to 4 times. Besides, it is established that some parameters have a major impact on the result of modelling.

  9. A "Reverse-Schur" Approach to Optimization With Linear PDE Constraints: Application to Biomolecule Analysis and Design.

    Science.gov (United States)

    Bardhan, Jaydeep P; Altman, Michael D; Tidor, B; White, Jacob K

    2009-01-01

    We present a partial-differential-equation (PDE)-constrained approach for optimizing a molecule's electrostatic interactions with a target molecule. The approach, which we call reverse-Schur co-optimization, can be more than two orders of magnitude faster than the traditional approach to electrostatic optimization. The efficiency of the co-optimization approach may enhance the value of electrostatic optimization for ligand-design efforts-in such projects, it is often desirable to screen many candidate ligands for their viability, and the optimization of electrostatic interactions can improve ligand binding affinity and specificity. The theoretical basis for electrostatic optimization derives from linear-response theory, most commonly continuum models, and simple assumptions about molecular binding processes. Although the theory has been used successfully to study a wide variety of molecular binding events, its implications have not yet been fully explored, in part due to the computational expense associated with the optimization. The co-optimization algorithm achieves improved performance by solving the optimization and electrostatic simulation problems simultaneously, and is applicable to both unconstrained and constrained optimization problems. Reverse-Schur co-optimization resembles other well-known techniques for solving optimization problems with PDE constraints. Model problems as well as realistic examples validate the reverse-Schur method, and demonstrate that our technique and alternative PDE-constrained methods scale very favorably compared to the standard approach. Regularization, which ordinarily requires an explicit representation of the objective function, can be included using an approximate Hessian calculated using the new BIBEE/P (boundary-integral-based electrostatics estimation by preconditioning) method.

  10. Optimizing incomplete sample designs for item response model parameters

    NARCIS (Netherlands)

    van der Linden, Willem J.

    Several models for optimizing incomplete sample designs with respect to information on the item parameters are presented. The following cases are considered: (1) known ability parameters; (2) unknown ability parameters; (3) item sets with multiple ability scales; and (4) response models with

  11. Stochastic modelling of turbulent combustion for design optimization of gas turbine combustors

    Science.gov (United States)

    Mehanna Ismail, Mohammed Ali

    The present work covers the development and the implementation of an efficient algorithm for the design optimization of gas turbine combustors. The purpose is to explore the possibilities and indicate constructive suggestions for optimization techniques as alternative methods for designing gas turbine combustors. The algorithm is general to the extent that no constraints are imposed on the combustion phenomena or on the combustor configuration. The optimization problem is broken down into two elementary problems: the first is the optimum search algorithm, and the second is the turbulent combustion model used to determine the combustor performance parameters. These performance parameters constitute the objective and physical constraints in the optimization problem formulation. The examination of both turbulent combustion phenomena and the gas turbine design process suggests that the turbulent combustion model represents a crucial part of the optimization algorithm. The basic requirements needed for a turbulent combustion model to be successfully used in a practical optimization algorithm are discussed. In principle, the combustion model should comply with the conflicting requirements of high fidelity, robustness and computational efficiency. To that end, the problem of turbulent combustion is discussed and the current state of the art of turbulent combustion modelling is reviewed. According to this review, turbulent combustion models based on the composition PDF transport equation are found to be good candidates for application in the present context. However, these models are computationally expensive. To overcome this difficulty, two different models based on the composition PDF transport equation were developed: an improved Lagrangian Monte Carlo composition PDF algorithm and the generalized stochastic reactor model. Improvements in the Lagrangian Monte Carlo composition PDF model performance and its computational efficiency were achieved through the

  12. A Multiobjective Optimization Model in Automotive Supply Chain Networks

    Directory of Open Access Journals (Sweden)

    Abdolhossein Sadrnia

    2013-01-01

    Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.

  13. A Gas Scheduling Optimization Model for Steel Enterprises

    Directory of Open Access Journals (Sweden)

    Niu Honghai

    2017-01-01

    Full Text Available Regarding the scheduling problems of steel enterprises, this research designs the gas scheduling optimization model according to the rules and priorities. Considering different features and the process changes of the gas unit in the process of actual production, the calculation model of process state and gas consumption soft measurement together with the rules of scheduling optimization is proposed to provide the dispatchers with real-time gas using status of each process, then help them to timely schedule and reduce the gas volume fluctuations. In the meantime, operation forewarning and alarm functions are provided to avoid the abnormal situation in the scheduling, which has brought about very good application effect in the actual scheduling and ensures the safety of the gas pipe network system and the production stability.

  14. A Model of How Different Biology Experts Explain Molecular and Cellular Mechanisms

    Science.gov (United States)

    Trujillo, Caleb M.; Anderson, Trevor R.; Pelaez, Nancy J.

    2015-01-01

    Constructing explanations is an essential skill for all science learners. The goal of this project was to model the key components of expert explanation of molecular and cellular mechanisms. As such, we asked: What is an appropriate model of the components of explanation used by biology experts to explain molecular and cellular mechanisms? Do…

  15. On meeting capital requirements with a chance-constrained optimization model.

    Science.gov (United States)

    Atta Mills, Ebenezer Fiifi Emire; Yu, Bo; Gu, Lanlan

    2016-01-01

    This paper deals with a capital to risk asset ratio chance-constrained optimization model in the presence of loans, treasury bill, fixed assets and non-interest earning assets. To model the dynamics of loans, we introduce a modified CreditMetrics approach. This leads to development of a deterministic convex counterpart of capital to risk asset ratio chance constraint. We pursue the scope of analyzing our model under the worst-case scenario i.e. loan default. The theoretical model is analyzed by applying numerical procedures, in order to administer valuable insights from a financial outlook. Our results suggest that, our capital to risk asset ratio chance-constrained optimization model guarantees banks of meeting capital requirements of Basel III with a likelihood of 95 % irrespective of changes in future market value of assets.

  16. Linear versus quadratic portfolio optimization model with transaction cost

    Science.gov (United States)

    Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah

    2014-06-01

    Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.

  17. Quantitative computational models of molecular self-assembly in systems biology.

    Science.gov (United States)

    Thomas, Marcus; Schwartz, Russell

    2017-05-23

    Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally.

  18. Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

    Directory of Open Access Journals (Sweden)

    Xiangsheng Zhang

    2015-01-01

    Full Text Available Aimed at the parameters optimization in support vector machine (SVM for glutamate fermentation modelling, a new method is developed. It optimizes the SVM parameters via an improved particle swarm optimization (IPSO algorithm which has better global searching ability. The algorithm includes detecting and handling the local convergence and exhibits strong ability to avoid being trapped in local minima. The material step of the method was shown. Simulation experiments demonstrate the effectiveness of the proposed algorithm.

  19. Optimal policies of non-cross-resistant chemotherapy on Goldie and Coldman's cancer model.

    Science.gov (United States)

    Chen, Jeng-Huei; Kuo, Ya-Hui; Luh, Hsing Paul

    2013-10-01

    Mathematical models can be used to study the chemotherapy on tumor cells. Especially, in 1979, Goldie and Coldman proposed the first mathematical model to relate the drug sensitivity of tumors to their mutation rates. Many scientists have since referred to this pioneering work because of its simplicity and elegance. Its original idea has also been extended and further investigated in massive follow-up studies of cancer modeling and optimal treatment. Goldie and Coldman, together with Guaduskas, later used their model to explain why an alternating non-cross-resistant chemotherapy is optimal with a simulation approach. Subsequently in 1983, Goldie and Coldman proposed an extended stochastic based model and provided a rigorous mathematical proof to their earlier simulation work when the extended model is approximated by its quasi-approximation. However, Goldie and Coldman's analytic study of optimal treatments majorly focused on a process with symmetrical parameter settings, and presented few theoretical results for asymmetrical settings. In this paper, we recast and restate Goldie, Coldman, and Guaduskas' model as a multi-stage optimization problem. Under an asymmetrical assumption, the conditions under which a treatment policy can be optimal are derived. The proposed framework enables us to consider some optimal policies on the model analytically. In addition, Goldie, Coldman and Guaduskas' work with symmetrical settings can be treated as a special case of our framework. Based on the derived conditions, this study provides an alternative proof to Goldie and Coldman's work. In addition to the theoretical derivation, numerical results are included to justify the correctness of our work. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. Molecular modeling of directed self-assembly of block copolymers: Fundamental studies of processing conditions and evolutionary pattern design

    Science.gov (United States)

    Khaira, Gurdaman Singh

    Rapid progress in the semi-conductor industry has pushed for smaller feature sizes on integrated electronic circuits. Current photo-lithographic techniques for nanofabrication have reached their technical limit and are problematic when printing features small enough to meet future industrial requirements. "Bottom-up'' techniques, such as the directed self-assembly (DSA) of block copolymers (BCP), are the primary contenders to compliment current "top-down'' photo-lithography ones. For industrial requirements, the defect density from DSA needs to be less than 1 defect per 10 cm by 10 cm. Knowledge of both material synthesis and the thermodynamics of the self-assembly process are required before optimal operating conditions can be found to produce results adequate for industry. The work present in this thesis is divided into three chapters, each discussing various aspects of DSA as studied via a molecular model that contains the essential physics of BCP self-assembly. Though there are various types of guiding fields that can be used to direct BCPs over large wafer areas with minimum defects, this study focuses only on chemically patterned substrates. The first chapter addresses optimal pattern design by describing a framework where molecular simulations of various complexities are coupled with an advanced optimization technique to find a pattern that directs a target morphology. It demonstrates the first ever study where BCP self-assembly on a patterned substrate is optimized using a three-dimensional description of the block-copolymers. For problems pertaining to DSA, the methodology is shown to converge much faster than the traditional random search approach. The second chapter discusses the metrology of BCP thin films using TEM tomography and X-ray scattering techniques, such as CDSAXS and GISAXS. X-ray scattering has the advantage of being able to quickly probe the average structure of BCP morphologies over large wafer areas; however, deducing the BCP morphology

  1. Identification of the cognate peptide-MHC target of T cell receptors using molecular modeling and force field scoring

    DEFF Research Database (Denmark)

    Lanzarotti, Esteban; Marcatili, Paolo; Nielsen, Morten

    2018-01-01

    Interactions of T cell receptors (TCR) to peptides in complex with MHC (p:MHC) are key features that mediate cellular immune responses. While MHC binding is required for a peptide to be presented to T cells, not all MHC binders are immunogenic. The interaction of a TCR to the p:MHC complex holds...... terms. Building a benchmark of TCR:p:MHC complexes where epitopes and non-epitopes are modelled using state-of-the-art molecular modelling tools, scoring p:MHC to a given TCR using force-fields, optimized in a cross-validation setup to evaluate TCR inter atomic interactions involved with each p:MHC, we...... and model the TCR:p:MHC complex structure. In summary, we conclude that it is possible to identify the TCR cognate target among different candidate peptides by using a force-field based model, and believe this works could lay the foundation for future work within prediction of TCR:p:MHC interactions....

  2. A review on condition-based maintenance optimization models for stochastically deteriorating system

    International Nuclear Information System (INIS)

    Alaswad, Suzan; Xiang, Yisha

    2017-01-01

    Condition-based maintenance (CBM) is a maintenance strategy that collects and assesses real-time information, and recommends maintenance decisions based on the current condition of the system. In recent decades, research on CBM has been rapidly growing due to the rapid development of computer-based monitoring technologies. Research studies have proven that CBM, if planned properly, can be effective in improving equipment reliability at reduced costs. This paper presents a review of CBM literature with emphasis on mathematical modeling and optimization approaches. We focus this review on important aspects of the CBM, such as optimization criteria, inspection frequency, maintenance degree, solution methodology, etc. Since the modeling choice for the stochastic deterioration process greatly influences CBM strategy decisions, this review classifies the literature on CBM models based on the underlying deterioration processes, namely discrete- and continuous-state deterioration, and proportional hazard model. CBM models for multi-unit systems are also reviewed in this paper. This paper provides useful references for CBM management professionals and researchers working on CBM modeling and optimization. - Highlights: • A review on Condition-based maintenance (CBM) optimization models is presented. • The CBM models are classified based on the underlying deterioration processes. • Existing CBM models for both single- and multi-unit systems are reviewed. • Future essential research directions on CBM are identified.

  3. An extended continuum model considering optimal velocity change with memory and numerical tests

    Science.gov (United States)

    Qingtao, Zhai; Hongxia, Ge; Rongjun, Cheng

    2018-01-01

    In this paper, an extended continuum model of traffic flow is proposed with the consideration of optimal velocity changes with memory. The new model's stability condition and KdV-Burgers equation considering the optimal velocities change with memory are deduced through linear stability theory and nonlinear analysis, respectively. Numerical simulation is carried out to study the extended continuum model, which explores how optimal velocity changes with memory affected velocity, density and energy consumption. Numerical results show that when considering the effects of optimal velocity changes with memory, the traffic jams can be suppressed efficiently. Both the memory step and sensitivity parameters of optimal velocity changes with memory will enhance the stability of traffic flow efficiently. Furthermore, numerical results demonstrates that the effect of optimal velocity changes with memory can avoid the disadvantage of historical information, which increases the stability of traffic flow on road, and so it improve the traffic flow stability and minimize cars' energy consumptions.

  4. Modeling of nanotoxicity molecular interactions of nanomaterials with bionanomachines

    CERN Document Server

    Zhou, Ruhong

    2015-01-01

    This book provides a comprehensive overview of the fundamentals of nanotoxicity modeling and its implications for the development of novel nanomedicines. It lays out the fundamentals of nanotoxicity modeling for an array of nanomaterial systems, ranging from carbon-based nanoparticles to noble metals, metal oxides, and quantum dots. The author illustrates how molecular (classical mechanics) and atomic (quantum mechanics) modeling approaches can be applied to bolster our understanding of many important aspects of this critical nanotoxicity issue. Each chapter is organized by types of nanomaterials for practicality, making this an ideal book for senior undergraduate students, graduate students, and researchers in nanotechnology, chemistry, physics, molecular biology, and computer science. It is also of interest to academic and industry professionals who work on nanodrug delivery and related biomedical applications, and aids readers in their biocompatibility assessment efforts in the coming age of nanotechnology...

  5. Shape interior modeling and mass property optimization using ray-reps

    DEFF Research Database (Denmark)

    Wu, Jun; Kramer, Lou; Westermann, Rüdiger

    2016-01-01

    , and prove this parametrization covers the optimal interior regarding static and rotational stability criteria. This compact formulation thoroughly reduces the number of design variables compared to the general volumetric element-wise formulation. We demonstrate the effectiveness of our reduced formulation......We present a novel method for the modeling and optimization of the material distribution inside 3D shapes, such that their 3D printed replicas satisfy prescribed constraints regarding mass properties. In particular, we introduce an extension of ray-representation to shape interior modeling...

  6. Molecular Model of a Quantum Dot Beyond the Constant Interaction Approximation

    Science.gov (United States)

    Temirov, Ruslan; Green, Matthew F. B.; Friedrich, Niklas; Leinen, Philipp; Esat, Taner; Chmielniak, Pawel; Sarwar, Sidra; Rawson, Jeff; Kögerler, Paul; Wagner, Christian; Rohlfing, Michael; Tautz, F. Stefan

    2018-05-01

    We present a physically intuitive model of molecular quantum dots beyond the constant interaction approximation. It accurately describes their charging behavior and allows the extraction of important molecular properties that are otherwise experimentally inaccessible. The model is applied to data recorded with a noncontact atomic force microscope on three different molecules that act as a quantum dot when attached to the microscope tip. The results are in excellent agreement with first-principles simulations.

  7. Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization.

    Science.gov (United States)

    Kim, Seongho; Li, Lang

    2014-02-01

    The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  8. Worst-Case Investment and Reinsurance Optimization for an Insurer under Model Uncertainty

    Directory of Open Access Journals (Sweden)

    Xiangbo Meng

    2016-01-01

    Full Text Available In this paper, we study optimal investment-reinsurance strategies for an insurer who faces model uncertainty. The insurer is allowed to acquire new business and invest into a financial market which consists of one risk-free asset and one risky asset whose price process is modeled by a Geometric Brownian motion. Minimizing the expected quadratic distance of the terminal wealth to a given benchmark under the “worst-case” scenario, we obtain the closed-form expressions of optimal strategies and the corresponding value function by solving the Hamilton-Jacobi-Bellman (HJB equation. Numerical examples are presented to show the impact of model parameters on the optimal strategies.

  9. Environmental optimal control strategies based on plant canopy photosynthesis responses and greenhouse climate model

    Science.gov (United States)

    Deng, Lujuan; Xie, Songhe; Cui, Jiantao; Liu, Tao

    2006-11-01

    It is the essential goal of intelligent greenhouse environment optimal control to enhance income of cropper and energy save. There were some characteristics such as uncertainty, imprecision, nonlinear, strong coupling, bigger inertia and different time scale in greenhouse environment control system. So greenhouse environment optimal control was not easy and especially model-based optimal control method was more difficult. So the optimal control problem of plant environment in intelligent greenhouse was researched. Hierarchical greenhouse environment control system was constructed. In the first level data measuring was carried out and executive machine was controlled. Optimal setting points of climate controlled variable in greenhouse was calculated and chosen in the second level. Market analysis and planning were completed in third level. The problem of the optimal setting point was discussed in this paper. Firstly the model of plant canopy photosynthesis responses and the model of greenhouse climate model were constructed. Afterwards according to experience of the planting expert, in daytime the optimal goals were decided according to the most maximal photosynthesis rate principle. In nighttime on plant better growth conditions the optimal goals were decided by energy saving principle. Whereafter environment optimal control setting points were computed by GA. Compared the optimal result and recording data in real system, the method is reasonable and can achieve energy saving and the maximal photosynthesis rate in intelligent greenhouse

  10. Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

    Science.gov (United States)

    Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

    2012-12-01

    Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis

  11. An optimal control strategies using vaccination and fogging in dengue fever transmission model

    Science.gov (United States)

    Fitria, Irma; Winarni, Pancahayani, Sigit; Subchan

    2017-08-01

    This paper discussed regarding a model and an optimal control problem of dengue fever transmission. We classified the model as human and vector (mosquito) population classes. For the human population, there are three subclasses, such as susceptible, infected, and resistant classes. Then, for the vector population, we divided it into wiggler, susceptible, and infected vector classes. Thus, the model consists of six dynamic equations. To minimize the number of dengue fever cases, we designed two optimal control variables in the model, the giving of fogging and vaccination. The objective function of this optimal control problem is to minimize the number of infected human population, the number of vector, and the cost of the controlling efforts. By giving the fogging