Optimal Patent Life in a Variety-Expansion Growth Model
Lin, Hwan C.
2013-01-01
This paper presents more channels through which the optimal patent life is determined in a R&D-based endogenous growth model that permits growth of new varieties of consumer goods over time. Its modeling features include an endogenous hazard rate facing incumbent monopolists, the prevalence of research congestion, and the aggregate welfare importance of product differentiation. As a result, a patent’s effective life is endogenized and less than its legal life. The model is calibrated to a glo...
Optimal Operational Monetary Policy Rules in an Endogenous Growth Model: a calibrated analysis
Arato, Hiroki
2009-01-01
This paper constructs an endogenous growth New Keynesian model and considers growth and welfare effect of Taylor-type (operational) monetary policy rules. The Ramsey equilibrium and optimal operational monetary policy rule is also computed. In the calibrated model, the Ramseyoptimal volatility of inflation rate is smaller than that in standard exogenous growth New Keynesian model with physical capital accumulation. Optimal operational monetary policy rule makes nominal interest rate respond s...
Numerical Simulation of a Tumor Growth Dynamics Model Using Particle Swarm Optimization.
Wang, Zhijun; Wang, Qing
Tumor cell growth models involve high-dimensional parameter spaces that require computationally tractable methods to solve. To address a proposed tumor growth dynamics mathematical model, an instance of the particle swarm optimization method was implemented to speed up the search process in the multi-dimensional parameter space to find optimal parameter values that fit experimental data from mice cancel cells. The fitness function, which measures the difference between calculated results and experimental data, was minimized in the numerical simulation process. The results and search efficiency of the particle swarm optimization method were compared to those from other evolutional methods such as genetic algorithms.
Creative Destruction and Optimal Patent Life in a Variety-Expanding Growth Model
Lin, Hwan C.
2013-01-01
This paper presents more channels through which the optimal patent life is determined in a R&D-based endogenous growth model that permits growth of new varieties of consumer goods over time. Its modeling features include an endogenous hazard rate facing incumbent monopolists, the prevalence of research congestion, and the aggregate welfare importance of product differentiation. As a result, a patent’s effective life is endogenized and less than its legal life. The model is calibrated to a glo...
Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions
Bole, Brian; Goebel, Kai; Vachtsevanos, George
2012-01-01
A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.
Optimal Product Variety, Scale Effects and Growth
de Groot, H.L.F.; Nahuis, R.
1997-01-01
We analyze the social optimality of growth and product variety in a model of endogenous growth. The model contains two sectors, one assembly sector producing a homogenous consumption good, and one intermediate goods sector producing a differentiated input used in the assembly sector. Growth results
Optimal Application Timing of Pest Control Tactics in Nonautonomous Pest Growth Model
Zhang, Shujuan; Liang, Juhua; Tang, Sanyi
2014-01-01
Considering the effects of the living environment on growth of populations, it is unrealistic to assume that the growth rates of predator and prey are all constants in the models with integrated pest management (IPM) strategies. Therefore, a nonautonomous predator-prey system with impulsive effect is developed and investigated in the present work. In order to determine the optimal application timing of IPM tactics, the threshold value which guarantees the stability of pest-free periodic solut...
Behavior and sensitivity of an optimal tree diameter growth model under data uncertainty
Don C. Bragg
2005-01-01
Using loblolly pine, shortleaf pine, white oak, and northern red oak as examples, this paper considers the behavior of potential relative increment (PRI) models of optimal tree diameter growth under data uncertainity. Recommendations on intial sample size and the PRI iteractive curve fitting process are provided. Combining different state inventories prior to PRI model...
Artificial Plant Root System Growth for Distributed Optimization: Models and Emergent Behaviors
Directory of Open Access Journals (Sweden)
Su Weixing
2016-01-01
Full Text Available Plant root foraging exhibits complex behaviors analogous to those of animals, including the adaptability to continuous changes in soil environments. In this work, we adapt the optimality principles in the study of plant root foraging behavior to create one possible bio-inspired optimization framework for solving complex engineering problems. This provides us with novel models of plant root foraging behavior and with new methods for global optimization. This framework is instantiated as a new search paradigm, which combines the root tip growth, branching, random walk, and death. We perform a comprehensive simulation to demonstrate that the proposed model accurately reflects the characteristics of natural plant root systems. In order to be able to climb the noise-filled gradients of nutrients in soil, the foraging behaviors of root systems are social and cooperative, and analogous to animal foraging behaviors.
Optimal control of nutrition restricted dynamics model of Microalgae biomass growth model
Ratianingsih, R.; Azim; Nacong, N.; Resnawati; Mardlijah; Widodo, B.
2017-12-01
The biomass of the microalgae is very potential to be proposed as an alternative renewable energy resources because it could be extracted into lipid. Afterward, the lipid could be processed to get the biodiesel or bioethanol. The extraction of the biomass on lipid synthesis process is very important to be studied because the process just gives some amount of lipid. A mathematical model of restricted microalgae biomass growth just gives 1/3 proportion of lipid with respect to the biomass in the synthesis process. An optimal control is designed to raise the ratio between the number of lipid formation and the microalgae biomass to be used in synthesis process. The minimum/ Pontryagin maximum principle is used to get the optimal lipid production. The simulation shows that the optimal lipid formation could be reach by simultaneously controlling the carbon dioxide, in the respiration and photosynthesis the process, and intake nutrition rates of liquid waste and urea substrate. The production of controlled microalgae lipid could be increase 6.5 times comparing to the uncontrolled one.
Xue, Yan
The optimal growth and its relationship with the forecast skill of the Zebiak and Cane model are studied using a simple statistical model best fit to the original nonlinear model and local linear tangent models about idealized climatic states (the mean background and ENSO cycles in a long model run), and the actual forecast states, including two sets of runs using two different initialization procedures. The seasonally varying Markov model best fit to a suite of 3-year forecasts in a reduced EOF space (18 EOFs) fits the original nonlinear model reasonably well and has comparable or better forecast skill. The initial error growth in a linear evolution operator A is governed by the eigenvalues of A^{T}A, and the square roots of eigenvalues and eigenvectors of A^{T}A are named singular values and singular vectors. One dominant growing singular vector is found, and the optimal 6 month growth rate is largest for a (boreal) spring start and smallest for a fall start. Most of the variation in the optimal growth rate of the two forecasts is seasonal, attributable to the seasonal variations in the mean background, except that in the cold events it is substantially suppressed. It is found that the mean background (zero anomaly) is the most unstable state, and the "forecast IC states" are more unstable than the "coupled model states". One dominant growing singular vector is found, characterized by north-south and east -west dipoles, convergent winds on the equator in the eastern Pacific and a deepened thermocline in the whole equatorial belt. This singular vector is insensitive to initial time and optimization time, but its final pattern is a strong function of initial states. The ENSO system is inherently unpredictable for the dominant singular vector can amplify 5-fold to 24-fold in 6 months and evolve into the large scales characteristic of ENSO. However, the inherent ENSO predictability is only a secondary factor, while the mismatches between the model and data is a
Mehrian, Mohammad; Guyot, Yann; Papantoniou, Ioannis; Olofsson, Simon; Sonnaert, Maarten; Misener, Ruth; Geris, Liesbet
2018-03-01
In regenerative medicine, computer models describing bioreactor processes can assist in designing optimal process conditions leading to robust and economically viable products. In this study, we started from a (3D) mechanistic model describing the growth of neotissue, comprised of cells, and extracellular matrix, in a perfusion bioreactor set-up influenced by the scaffold geometry, flow-induced shear stress, and a number of metabolic factors. Subsequently, we applied model reduction by reformulating the problem from a set of partial differential equations into a set of ordinary differential equations. Comparing the reduced model results to the mechanistic model results and to dedicated experimental results assesses the reduction step quality. The obtained homogenized model is 10 5 fold faster than the 3D version, allowing the application of rigorous optimization techniques. Bayesian optimization was applied to find the medium refreshment regime in terms of frequency and percentage of medium replaced that would maximize neotissue growth kinetics during 21 days of culture. The simulation results indicated that maximum neotissue growth will occur for a high frequency and medium replacement percentage, a finding that is corroborated by reports in the literature. This study demonstrates an in silico strategy for bioprocess optimization paying particular attention to the reduction of the associated computational cost. © 2017 Wiley Periodicals, Inc.
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Poul S. Larsen
2016-07-01
Full Text Available A recently developed BioEnergetic Growth (BEG model for blue mussels (Mytilus edulis, valid for juvenile mussels, has been further developed to an ‘extended model’ and an alternative ‘ad hoc BEG model’ valid for post-metamorphic mussels, where the latter accounts for changing ambient chl a concentration. It was used to predict the growth of M. edulis on optimally thinned farm-ropes in Great Belt (Denmark, from newly settled post-metamorphic mussels of an initial shell size of 0.8 mm to marketable juvenile 30–35 mm ‘mini-mussels’. Such mussels will presumably in the near future be introduced as a new Danish, smaller-sized consumer product. Field data for actual growth (from Day 0 = 14 June 2011 showed that size of ‘mini-mussel’ was reached on Day 109 (Oct 1 and length 38 mm on Day 178 (Dec 9 while the corresponding predictions using the extended model were Day 121 (Oct 13 and Day 159 (Nov 20. Similar results were obtained by use of the ad hoc BEG model which also demonstrated the sensitivity of growth prediction to levels of chl a concentration, but less to temperature. The results suggest that it is possible (when the conditions are optimal, i.e., no intraspecific competition ensured by sufficient thinning to produce ‘mini-mussels’ in Great Belt during one season, but not the usual marketable 45-mm mussels. We suggest that the prediction model may be used as a practical instrument to evaluate to what degree the actual growth of mussels on farm ropes due to intraspecific competition may deviate from the potential (optimal growth under specified chl a and temperature conditions, and this implies that the effect of thinning to optimize the individual growth by eliminating intraspecific competition can be rationally evaluated.
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Fereydoun Naghibi
2016-12-01
Full Text Available This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA using the artificial bee colony (ABC optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was
DEFF Research Database (Denmark)
Larsen, Poul Scheel; Riisgård, Hans
2016-01-01
as a new Danish, smaller-sized consumer product. Field data for actual growth (from Day 0 = 14 June 2011) showed that size of ‘mini-mussel’ was reached on Day 109 (Oct 1) and length 38 mm on Day 178 (Dec 9) while the corresponding predictions using the extended model were Day 121 (Oct 13) and Day 159 (Nov...... 20). Similar results were obtained by use of the ad hoc BEG model which also demonstrated the sensitivity of growth prediction to levels of chl a concentration, but less to temperature. The results suggest that it is possible (when the conditions are optimal, i.e., no intraspecific competition...... competition may deviate from the potential (optimal) growth under specified chl a and temperature conditions, and this implies that the effect of thinning to optimize the individual growth by eliminating intraspecific competition can be rationally evaluated....
Casey, F P; Baird, D; Feng, Q; Gutenkunst, R N; Waterfall, J J; Myers, C R; Brown, K S; Cerione, R A; Sethna, J P
2007-05-01
We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (beta -Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system.
Simulated annealing algorithm for optimal capital growth
Luo, Yong; Zhu, Bo; Tang, Yong
2014-08-01
We investigate the problem of dynamic optimal capital growth of a portfolio. A general framework that one strives to maximize the expected logarithm utility of long term growth rate was developed. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of applying simulated annealing optimized algorithm to optimize the capital growth of a given portfolio. Empirical results with real financial data indicate that the approach is inspiring for capital growth portfolio.
Zhang, Wenting; Wang, Haijun; Han, Fengxiang; Gao, Juan; Nguyen, Thuminh; Chen, Yarong; Huang, Bo; Zhan, F Benjamin; Zhou, Lequn; Hong, Song
2014-11-01
Urban growth is an unavoidable process caused by economic development and population growth. Traditional urban growth models represent the future urban growth pattern by repeating the historical urban growth regulations, which can lead to a lot of environmental problems. The Yangtze watershed is the largest and the most prosperous economic area in China, and it has been suffering from rapid urban growth from the 1970s. With the built-up area increasing from 23,238 to 31,054 km(2) during the period from 1980 to 2005, the watershed has suffered from serious nonpoint source (NPS) pollution problems, which have been mainly caused by the rapid urban growth. To protect the environment and at the same time maintain the economic development, a multiobjective optimization (MOP) is proposed to tradeoff the multiple objectives during the urban growth process of the Yangtze watershed. In particular, the four objectives of minimization of NPS pollution, maximization of GDP value, minimization of the spatial incompatibility between the land uses, and minimization of the cost of land-use change are considered by the MOP approach. Conventionally, a genetic algorithm (GA) is employed to search the Pareto solution set. In our MOP approach, a two-dimensional GA, rather than the traditional one-dimensional GA, is employed to assist with the search for the spatial optimization solution, where the land-use cells in the two-dimensional space act as genes in the GA. Furthermore, to confirm the superiority of the MOP approach over the traditional prediction approaches, a widely used urban growth prediction model, cellular automata (CA), is also carried out to allow a comparison with the Pareto solution of MOP. The results indicate that the MOP approach can make a tradeoff between the multiple objectives and can achieve an optimal urban growth pattern for Yangtze watershed, while the CA prediction model just represents the historical urban growth pattern as the future growth pattern
Xu, Li-Jian; Liu, Yuan-Shuai; Zhou, Li-Gang; Wu, Jian-Yong
2011-09-01
Beauvericin (BEA) is a cyclic hexadepsipeptide mycotoxin with notable phytotoxic and insecticidal activities. Fusarium redolens Dzf2 is a highly BEA-producing fungus isolated from a medicinal plant. The aim of the current study was to develop a simple and valid kinetic model for F. redolens Dzf2 mycelial growth and the optimal fed-batch operation for efficient BEA production. A modified Monod model with substrate (glucose) and product (BEA) inhibition was constructed based on the culture characteristics of F. redolens Dzf2 mycelia in a liquid medium. Model parameters were derived by simulation of the experimental data from batch culture. The model fitted closely with the experimental data over 20-50 g l(-1) glucose concentration range in batch fermentation. The kinetic model together with the stoichiometric relationships for biomass, substrate and product was applied to predict the optimal feeding scheme for fed-batch fermentation, leading to 54% higher BEA yield (299 mg l(-1)) than in the batch culture (194 mg l(-1)). The modified Monod model incorporating substrate and product inhibition was proven adequate for describing the growth kinetics of F. redolens Dzf2 mycelial culture at suitable but not excessive initial glucose levels in batch and fed-batch cultures.
Value function in economic growth model
Bagno, Alexander; Tarasyev, Alexandr A.; Tarasyev, Alexander M.
2017-11-01
Properties of the value function are examined in an infinite horizon optimal control problem with an unlimited integrand index appearing in the quality functional with a discount factor. Optimal control problems of such type describe solutions in models of economic growth. Necessary and sufficient conditions are derived to ensure that the value function satisfies the infinitesimal stability properties. It is proved that value function coincides with the minimax solution of the Hamilton-Jacobi equation. Description of the growth asymptotic behavior for the value function is provided for the logarithmic, power and exponential quality functionals and an example is given to illustrate construction of the value function in economic growth models.
Transaction fees and optimal rebalancing in the growth-optimal portfolio
Feng, Yu; Medo, Matúš; Zhang, Liang; Zhang, Yi-Cheng
2011-05-01
The growth-optimal portfolio optimization strategy pioneered by Kelly is based on constant portfolio rebalancing which makes it sensitive to transaction fees. We examine the effect of fees on an example of a risky asset with a binary return distribution and show that the fees may give rise to an optimal period of portfolio rebalancing. The optimal period is found analytically in the case of lognormal returns. This result is consequently generalized and numerically verified for broad return distributions and returns generated by a GARCH process. Finally we study the case when investment is rebalanced only partially and show that this strategy can improve the investment long-term growth rate more than optimization of the rebalancing period.
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Timo Pukkala
2015-03-01
Full Text Available Background Decisions on forest management are made under risk and uncertainty because the stand development cannot be predicted exactly and future timber prices are unknown. Deterministic calculations may lead to biased advice on optimal forest management. The study optimized continuous cover management of boreal forest in a situation where tree growth, regeneration, and timber prices include uncertainty. Methods Both anticipatory and adaptive optimization approaches were used. The adaptive approach optimized the reservation price function instead of fixed cutting years. The future prices of different timber assortments were described by cross-correlated auto-regressive models. The high variation around ingrowth model was simulated using a model that describes the cross- and autocorrelations of the regeneration results of different species and years. Tree growth was predicted with individual tree models, the predictions of which were adjusted on the basis of a climate-induced growth trend, which was stochastic. Residuals of the deterministic diameter growth model were also simulated. They consisted of random tree factors and cross- and autocorrelated temporal terms. Results Of the analyzed factors, timber price caused most uncertainty in the calculation of the net present value of a certain management schedule. Ingrowth and climate trend were less significant sources of risk and uncertainty than tree growth. Stochastic anticipatory optimization led to more diverse post-cutting stand structures than obtained in deterministic optimization. Cutting interval was shorter when risk and uncertainty were included in the analyses. Conclusions Adaptive optimization and management led to 6%–14% higher net present values than obtained in management that was based on anticipatory optimization. Increasing risk aversion of the forest landowner led to earlier cuttings in a mature stand. The effect of risk attitude on optimization results was small.
Optimal growth entails risky localization in population dynamics
Gueudré, Thomas; Martin, David G.
2018-03-01
Essential to each other, growth and exploration are jointly observed in alive and inanimate entities, such as animals, cells or goods. But how the environment's structural and temporal properties weights in this balance remains elusive. We analyze a model of stochastic growth with time correlations and diffusive dynamics that sheds light on the way populations grow and spread over general networks. This model suggests natural explanations of empirical facts in econo-physics or ecology, such as the risk-return trade-off and the Zipf law. We conclude that optimal growth leads to a localized population distribution, but such risky position can be mitigated through the space geometry. These results have broad applicability and are subsequently illustrated over an empirical study of financial data.
Tax Evasion in a Model of Endogenous Growth
Been-Lon Chen
2003-01-01
This paper integrates tax evasion into a standard AK growth model with public capital. In the model, the government optimizes the tax rate, while individuals optimize tax evasion. It studies tax rate, tax evasion and economic growth, and compares them with otherwise identical economies except those without tax evasion. It inquires into the effects of three government policies on tax rate, tax evasion, and economic growth. It finds that an increase in both unit cost of tax evasion and punishme...
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
DEFF Research Database (Denmark)
Kaur, Bipjeet; Owusu, Robert K. A.
2015-01-01
of the crop growth process based on information on soil quality, field seeding, spraying/fertilization and environmental information in general. Finally, references to software tools, which could form the basis for an open source platform for a planning and monitoring system for optimal crop growth......, such that the crop yield is optimized with respect to several parameters (e.g. high end user value and minimum environmental impact), thus obtaining a sustainable production. The growth process optimization is based on information, including sensor based measurements with sensor quality monitoring, from previous......This work in progress is a contribution to crop growth systems for planning and monitoring of farm activities and practices by farmers. The work outlines the initial findings related to modelling, simulation and visualization techniques for crop growth, specifically targeting the barley crop...
Optimal distributed control of a diffuse interface model of tumor growth
Colli, Pierluigi; Gilardi, Gianni; Rocca, Elisabetta; Sprekels, Jürgen
2017-06-01
In this paper, a distributed optimal control problem is studied for a diffuse interface model of tumor growth which was proposed by Hawkins-Daruud et al in Hawkins-Daruud et al (2011 Int. J. Numer. Math. Biomed. Eng. 28 3-24). The model consists of a Cahn-Hilliard equation for the tumor cell fraction φ coupled to a reaction-diffusion equation for a function σ representing the nutrient-rich extracellular water volume fraction. The distributed control u monitors as a right-hand side of the equation for σ and can be interpreted as a nutrient supply or a medication, while the cost function, which is of standard tracking type, is meant to keep the tumor cell fraction under control during the evolution. We show that the control-to-state operator is Fréchet differentiable between appropriate Banach spaces and derive the first-order necessary optimality conditions in terms of a variational inequality involving the adjoint state variables. The financial support of the FP7-IDEAS-ERC-StG #256872 (EntroPhase) and of the project Fondazione Cariplo-Regione Lombardia MEGAsTAR ‘Matematica d’Eccellenza in biologia ed ingegneria come accelleratore di una nuona strateGia per l’ATtRattività dell’ateneo pavese’ is gratefully acknowledged. The paper also benefited from the support of the MIUR-PRIN Grant 2015PA5MP7 ‘Calculus of Variations’ for PC and GG, and the GNAMPA (Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni) of INdAM (Istituto Nazionale di Alta Matematica) for PC, GG and ER.
Optimal growth when environmental quality is a research asset
DEFF Research Database (Denmark)
Groth, Christian; Ricci, Francesco
2011-01-01
We advance an original assumption whereby a good state of the environment positively affects labor productivity in R&D such that deteriorating environmental quality negatively impacts R&D. We study the implications of this assumption for the optimal solution in an R&D-based model of growth, where......, we find that it is optimal to re-allocate employment to R&D in line with productivity changes. If environmental quality recovers only partially from pollution, R&D effort optimally begins above its long-run level, then progressively declines to a minimum and eventually increases to its steady...
Directory of Open Access Journals (Sweden)
Lawrence David M
2005-05-01
Full Text Available Abstract Background The appropriateness of an individual's intra uterine growth is now considered an important determinant of both short and long term outcomes, yet currently used measures have several shortcomings. This study demonstrates a method of assessing appropriateness of intrauterine growth based on the estimation of each individual's optimal newborn dimensions from routinely available perinatal data. Appropriateness of growth can then be inferred from the ratio of the value of the observed dimension to that of the optimal dimension. Methods Fractional polynomial regression models including terms for non-pathological determinants of fetal size (gestational duration, fetal gender and maternal height, age and parity were used to predict birth weight, birth length and head circumference from a population without any major risk factors for sub-optimal intra-uterine growth. This population was selected from a total population of all singleton, Caucasian births in Western Australia 1998–2002. Births were excluded if the pregnancy was exposed to factors known to influence fetal growth pathologically. The values predicted by these models were treated as the optimal values, given infant gender, gestational age, maternal height, parity, and age. Results The selected sample (N = 62,746 comprised 60.5% of the total Caucasian singleton birth cohort. Equations are presented that predict optimal birth weight, birth length and head circumference given gestational duration, fetal gender, maternal height, age and parity. The best fitting models explained 40.5% of variance for birth weight, 32.2% for birth length, and 25.2% for head circumference at birth. Conclusion Proportion of optimal birth weight (length or head circumference provides a method of assessing appropriateness of intrauterine growth that is less dependent on the health of the reference population or the quality of their morphometric data than is percentile position on a birth weight
Stochastic models for tumoral growth
Escudero, Carlos
2006-02-01
Strong experimental evidence has indicated that tumor growth belongs to the molecular beam epitaxy universality class. This type of growth is characterized by the constraint of cell proliferation to the tumor border and the surface diffusion of cells at the growing edge. Tumor growth is thus conceived as a competition for space between the tumor and the host, and cell diffusion at the tumor border is an optimal strategy adopted for minimizing the pressure and helping tumor development. Two stochastic partial differential equations are reported in this paper in order to correctly model the physical properties of tumoral growth in (1+1) and (2+1) dimensions. The advantage of these models is that they reproduce the correct geometry of the tumor and are defined in terms of polar variables. An analysis of these models allows us to quantitatively estimate the response of the tumor to an unfavorable perturbation during growth.
Transaction fees and optimal rebalancing in the growth-optimal portfolio
Yu Feng; Matus Medo; Liang Zhang; Yi-Cheng Zhang
2010-01-01
The growth-optimal portfolio optimization strategy pioneered by Kelly is based on constant portfolio rebalancing which makes it sensitive to transaction fees. We examine the effect of fees on an example of a risky asset with a binary return distribution and show that the fees may give rise to an optimal period of portfolio rebalancing. The optimal period is found analytically in the case of lognormal returns. This result is consequently generalized and numerically verified for broad return di...
Optimal Growth in Hypersonic Boundary Layers
Paredes, Pedro; Choudhari, Meelan M.; Li, Fei; Chang, Chau-Lyan
2016-01-01
The linear form of the parabolized linear stability equations is used in a variational approach to extend the previous body of results for the optimal, nonmodal disturbance growth in boundary-layer flows. This paper investigates the optimal growth characteristics in the hypersonic Mach number regime without any high-enthalpy effects. The influence of wall cooling is studied, with particular emphasis on the role of the initial disturbance location and the value of the spanwise wave number that leads to the maximum energy growth up to a specified location. Unlike previous predictions that used a basic state obtained from a self-similar solution to the boundary-layer equations, mean flow solutions based on the full Navier-Stokes equations are used in select cases to help account for the viscous- inviscid interaction near the leading edge of the plate and for the weak shock wave emanating from that region. Using the full Navier-Stokes mean flow is shown to result in further reduction with Mach number in the magnitude of optimal growth relative to the predictions based on the self-similar approximation to the base flow.
Naghibi, Fereydoun; Delavar, Mahmoud Reza; Pijanowski, Bryan
2016-12-14
Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.
Bridging process-based and empirical approaches to modeling tree growth
Harry T. Valentine; Annikki Makela; Annikki Makela
2005-01-01
The gulf between process-based and empirical approaches to modeling tree growth may be bridged, in part, by the use of a common model. To this end, we have formulated a process-based model of tree growth that can be fitted and applied in an empirical mode. The growth model is grounded in pipe model theory and an optimal control model of crown development. Together, the...
Emergence of robust growth laws from optimal regulation of ribosome synthesis.
Scott, Matthew; Klumpp, Stefan; Mateescu, Eduard M; Hwa, Terence
2014-08-22
Bacteria must constantly adapt their growth to changes in nutrient availability; yet despite large-scale changes in protein expression associated with sensing, adaptation, and processing different environmental nutrients, simple growth laws connect the ribosome abundance and the growth rate. Here, we investigate the origin of these growth laws by analyzing the features of ribosomal regulation that coordinate proteome-wide expression changes with cell growth in a variety of nutrient conditions in the model organism Escherichia coli. We identify supply-driven feedforward activation of ribosomal protein synthesis as the key regulatory motif maximizing amino acid flux, and autonomously guiding a cell to achieve optimal growth in different environments. The growth laws emerge naturally from the robust regulatory strategy underlying growth rate control, irrespective of the details of the molecular implementation. The study highlights the interplay between phenomenological modeling and molecular mechanisms in uncovering fundamental operating constraints, with implications for endogenous and synthetic design of microorganisms. © 2014 The Authors. Published under the terms of the CC BY 4.0 license.
The old age security hypothesis and optimal population growth.
Bental, B
1989-03-01
The application of the Samuelson-Diamond overlapping generations framework to the old age security hypothesis indicates that government intervention schemes can influence the relationship between population growth and capital accumulation. The most direct means of optimizing population growth is through taxes or subsidies that relate to the intergenerational transfer of wealth. A pay-as-you-go social security scheme, in which payment is predicated on the number of children the receiver has and is financed by taxes levied on the working population, emerges as the most likely intervention to produce the optimal steady state equilibrium. This system is able to correct any distortions the private sector may build into it. In contrast, a child support system, in which the government subsidizes or taxes workers according to their family size, can guarantee the optimal capital:labor ratio but not the optimal population growth rate. Thus, if the government seeks to decrease the population growth rate, the appropriate intervention is to levy a lump-sum social-security tax on workers and transfer the revenues to the old; the direction should be reversed if the goal is to increase population growth. Another alternative, a lump sum social security system, can guarantee optimal population growth but not a desirable capital:labor ratio. Finally, the introduction of money as a valued commodity into an economy with a high capital:labor ratio will also serve to decrease the population growth rate and solve the intergenerational transfer problem through the private sector without any need for government intervention.
Optimization of growth medium and fermentation conditions for ...
African Journals Online (AJOL)
A sequential optimization approach based on statistical experimental designs was employed to optimize growth medium and fermentation conditions, in order to improve the antibiotic activity of Xenorhabdus nematophila TB. Tryptone soyptone broth (TSB) was chosen as the original medium for optimization. Glucose and ...
International Nuclear Information System (INIS)
Duncan, T.; Pasik Duncan, B.; Stettner, L.
2011-01-01
A continuous time long run growth optimal or optimal logarithmic utility portfolio with proportional transaction costs consisting of a fixed proportional cost and a cost proportional to the volume of transaction is considered. The asset prices are modeled as exponent of diffusion with jumps whose parameters depend on a finite state Markov process of economic factors. An obligatory portfolio diversification is introduced, accordingly to which it is required to invest at least a fixed small portion of our wealth in each asset.
Samanta Koruri, Sharmistha; Chowdhury, Ranjana; Bhattacharya, Pinaki
2015-09-01
The present investigation deals with the optimization of cell growth rate of the candidate probiotic Pediococcus acidilactici in the presence of the specific prebiotic inulin. Three independent variables viz. concentration of inulin, concentration of glucose and pH have been selected for optimization study using response surface methodology. Theoretical analysis indicates that the maximum cell growth rate occurs at pH 7, 20 g/dm(3) concentration of inulin and 20 g/dm(3) concentration of glucose. Validation of these values has been done through a set of programmed experiments. Studies on cell dynamics in the presence of different concentrations of inulin have also been carried out to identify any limitation on the initial inulin concentration. Results clearly indicate that cell growth is enhanced with the increase in inulin concentration. However, there is a critical value of the prebiotic concentration (20 g/dm(3) inulin) beyond which the cell growth is inhibited. A summative type growth model has been proposed to explain the growth behaviour of P. acidilactici in the presence of the dual substrate, i.e. glucose and inulin. While growth on glucose follows Monod model, Haldane-type substrate-inhibited growth model holds good for growth on inulin. Intrinsic kinetic parameters for all the model equations have been determined experimentally.
Optimizing growth and conidia production of Cercospora medicaginis
Directory of Open Access Journals (Sweden)
Naceur DJEBALI
2010-09-01
Full Text Available The fungus Cercospora medicaginis is pathogenic to annual and perennial Medicago species. It grows slowly and produces only few conidia. To test the pathogenicity or virulence of C. medicaginis and to breed resistant lines of Medicago truncatula we optimized in vitro the growth and the conidium production of four isolates of C. medicaginis derived from M. truncatula and M. polymorpha. Of the eight media tested, that with wheat bran juice (WBJ yielded optimal growth and conidium production with most strains. The optimum growth temperature on WBJ medium was 25–30°C. Growth and conidia production were better in conditions of alternating light and darkness than with constant darkness. The best growth in the liquid WBJ medium occurred at pH 6–7, but the greatest number of conidia in that medium was obtained at pH 8–9.
Modelling the interaction between flooding events and economic growth
Directory of Open Access Journals (Sweden)
J. Grames
2015-06-01
Full Text Available Socio-hydrology describes the interaction between the socio-economy and water. Recent models analyze the interplay of community risk-coping culture, flooding damage and economic growth (Di Baldassarre et al., 2013; Viglione et al., 2014. These models descriptively explain the feedbacks between socio-economic development and natural disasters like floods. Contrary to these descriptive models, our approach develops an optimization model, where the intertemporal decision of an economic agent interacts with the hydrological system. In order to build this first economic growth model describing the interaction between the consumption and investment decisions of an economic agent and the occurrence of flooding events, we transform an existing descriptive stochastic model into an optimal deterministic model. The intermediate step is to formulate and simulate a descriptive deterministic model. We develop a periodic water function to approximate the former discrete stochastic time series of rainfall events. Due to the non-autonomous exogenous periodic rainfall function the long-term path of consumption and investment will be periodic.
Root Growth Optimizer with Self-Similar Propagation
Directory of Open Access Journals (Sweden)
Xiaoxian He
2015-01-01
Full Text Available Most nature-inspired algorithms simulate intelligent behaviors of animals and insects that can move spontaneously and independently. The survival wisdom of plants, as another species of biology, has been neglected to some extent even though they have evolved for a longer period of time. This paper presents a new plant-inspired algorithm which is called root growth optimizer (RGO. RGO simulates the iterative growth behaviors of plant roots to optimize continuous space search. In growing process, main roots and lateral roots, classified by fitness values, implement different strategies. Main roots carry out exploitation tasks by self-similar propagation in relatively nutrient-rich areas, while lateral roots explore other places to seek for better chance. Inhibition mechanism of plant hormones is applied to main roots in case of explosive propagation in some local optimal areas. Once resources in a location are exhausted, roots would shrink away from infertile conditions to preserve their activity. In order to validate optimization effect of the algorithm, twelve benchmark functions, including eight classic functions and four CEC2005 test functions, are tested in the experiments. We compared RGO with other existing evolutionary algorithms including artificial bee colony, particle swarm optimizer, and differential evolution algorithm. The experimental results show that RGO outperforms other algorithms on most benchmark functions.
Reactor scale modeling of multi-walled carbon nanotube growth
International Nuclear Information System (INIS)
Lombardo, Jeffrey J.; Chiu, Wilson K.S.
2011-01-01
As the mechanisms of carbon nanotube (CNT) growth becomes known, it becomes important to understand how to implement this knowledge into reactor scale models to optimize CNT growth. In past work, we have reported fundamental mechanisms and competing deposition regimes that dictate single wall carbon nanotube growth. In this study, we will further explore the growth of carbon nanotubes with multiple walls. A tube flow chemical vapor deposition reactor is simulated using the commercial software package COMSOL, and considered the growth of single- and multi-walled carbon nanotubes. It was found that the limiting reaction processes for multi-walled carbon nanotubes change at different temperatures than the single walled carbon nanotubes and it was shown that the reactions directly governing CNT growth are a limiting process over certain parameters. This work shows that the optimum conditions for CNT growth are dependent on temperature, chemical concentration, and the number of nanotube walls. Optimal reactor conditions have been identified as defined by (1) a critical inlet methane concentration that results in hydrogen abstraction limited versus hydrocarbon adsorption limited reaction kinetic regime, and (2) activation energy of reaction for a given reactor temperature and inlet methane concentration. Successful optimization of a CNT growth processes requires taking all of those variables into account.
Stochastic growth logistic model with aftereffect for batch fermentation process
Energy Technology Data Exchange (ETDEWEB)
Rosli, Norhayati; Ayoubi, Tawfiqullah [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah; Rahman, Haliza Abdul [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia); Salleh, Madihah Md [Department of Biotechnology Industry, Faculty of Biosciences and Bioengineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)
2014-06-19
In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.
Stochastic growth logistic model with aftereffect for batch fermentation process
Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah; Rahman, Haliza Abdul; Salleh, Madihah Md
2014-06-01
In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.
Stochastic growth logistic model with aftereffect for batch fermentation process
International Nuclear Information System (INIS)
Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah; Rahman, Haliza Abdul; Salleh, Madihah Md
2014-01-01
In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits
Graphene growth process modeling: a physical-statistical approach
Wu, Jian; Huang, Qiang
2014-09-01
As a zero-band semiconductor, graphene is an attractive material for a wide variety of applications such as optoelectronics. Among various techniques developed for graphene synthesis, chemical vapor deposition on copper foils shows high potential for producing few-layer and large-area graphene. Since fabrication of high-quality graphene sheets requires the understanding of growth mechanisms, and methods of characterization and control of grain size of graphene flakes, analytical modeling of graphene growth process is therefore essential for controlled fabrication. The graphene growth process starts with randomly nucleated islands that gradually develop into complex shapes, grow in size, and eventually connect together to cover the copper foil. To model this complex process, we develop a physical-statistical approach under the assumption of self-similarity during graphene growth. The growth kinetics is uncovered by separating island shapes from area growth rate. We propose to characterize the area growth velocity using a confined exponential model, which not only has clear physical explanation, but also fits the real data well. For the shape modeling, we develop a parametric shape model which can be well explained by the angular-dependent growth rate. This work can provide useful information for the control and optimization of graphene growth process on Cu foil.
Optimal Resource Management in a Stochastic Schaefer Model
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.
An application of Pontrjagin's principle to the study of the optimal growth of population
International Nuclear Information System (INIS)
Spinadel, V. de
1976-01-01
This paper examines the consequences of an optimal control of population growth and allows to derive criteria referring to the economic basis of expenditure on population control and to obtain optimal paths for a model in which such a control is possible. By means of very simple assumptions one can reduce the problem to a two-state variable control problem and, in consequence, apply Pontrjagin's maximum principle to solve it. (author)
Specification Search for Identifying the Correct Mean Trajectory in Polynomial Latent Growth Models
Kim, Minjung; Kwok, Oi-Man; Yoon, Myeongsun; Willson, Victor; Lai, Mark H. C.
2016-01-01
This study investigated the optimal strategy for model specification search under the latent growth modeling (LGM) framework, specifically on searching for the correct polynomial mean or average growth model when there is no a priori hypothesized model in the absence of theory. In this simulation study, the effectiveness of different starting…
Gompertzian stochastic model with delay effect to cervical cancer growth
International Nuclear Information System (INIS)
Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti; Bahar, Arifah
2015-01-01
In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits
Gompertzian stochastic model with delay effect to cervical cancer growth
Energy Technology Data Exchange (ETDEWEB)
Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor and UTM Centre for Industrial and Applied Mathematics (UTM-CIAM), Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)
2015-02-03
In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits.
Optimal Conditions for the Mycelial Growth of Coprinus comatus Strains
Lee, Yun-Hae; Liu, Jun-Jie; Ju, Young-Cheol
2009-01-01
The principal objective of this study was to acquire basic data regarding the mycelial growth characteristics for the artificial cultivation of Coprinus comatus. 12 URP primers were employed to evaluate the genetic relationships of C. comatus, and the results were divided into three groups. Among six kinds of mushroom media, MYP medium was selected as the most favorable culture medium for C. comatus. The optimal temperature and pH ranges for the mycelial growth of C. comatus were 23~26℃ and pH 6~8, respectively. The carbon and nitrogen sources for optimal mycelial growth were sucrose and tryptone, respectively. PMID:23983517
Cheirsilp, B; Shimizu, H; Shioya, S
2001-12-01
A mathematical model for kefiran production by Lactobacillus kefiranofaciens was established, in which the effects of pH, substrate and product on cell growth, exopolysaccharide formation and substrate assimilation were considered. The model gave a good representation both of the formation of exopolysaccharides (which are not only attached to cells but also released into the medium) and of the time courses of the production of galactose and glucose in the medium (which are produced and consumed by the cells). Since pH and both lactose and lactic acid concentrations differently affected production and growth activity, the model included the effects of pH and the concentrations of lactose and lactic acid. Based on the mathematical model, an optimal pH profile for the maximum production of kefiran in batch culture was obtained. In this study, a simplified optimization method was developed, in which the optimal pH profile was determined at a particular final fermentation time. This was based on the principle that, at a certain time, switching from the maximum specific growth rate to the critical one (which yields the maximum specific production rate) results in maximum production. Maximum kefiran production was obtained, which was 20% higher than that obtained in the constant-pH control fermentation. A genetic algorithm (GA) was also applied to obtain the optimal pH profile; and it was found that practically the same solution was obtained using the GA.
Optimization modeling with spreadsheets
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
Pozzobon, Victor; Perre, Patrick
2018-01-21
This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.
R.M. Solow Adjusted Model of Economic Growth
Directory of Open Access Journals (Sweden)
Ion Gh. Rosca
2007-05-01
Full Text Available Besides the models of M. Keynes, R.F. Harrod, E. Domar, D. Romer, Ramsey-Cass-Koopmans etc., the R.M. Solow model is part of the category which characterizes the economic growth. The paper proposes the study of the R.M. Solow adjusted model of economic growth, while the adjustment consisting in the model adaptation to the Romanian economic characteristics. The article is the first one from a three paper series dedicated to the macroeconomic modelling theme, using the R.M. Solow model, such as: “Measurement of the economic growth and extensions of the R.M. Solow adjusted model” and “Evolution scenarios at the Romanian economy level using the R.M. Solow adjusted model”. The analysis part of the model is based on the study of the equilibrium to the continuous case with some interpretations of the discreet one, by using the state diagram. The optimization problem at the economic level is also used; it is built up of a specified number of representative consumers and firms in order to reveal the interaction between these elements.
Estimating the optimal growth-maximising public debt threshold for ...
African Journals Online (AJOL)
This paper attempts to estimate an optimal growth-maximising public debt threshold for Zimbabwe. The public debt threshold is estimated by assessing the relationship between public debt and economic growth. The analysis is undertaken to determine the tipping point beyond which increases in public debt adversely affect ...
Modelling and computing the peaks of carbon emission with balanced growth
International Nuclear Information System (INIS)
Chang, Shuhua; Wang, Xinyu; Wang, Zheng
2016-01-01
Highlights: • We use a more practical utility function to quantify the society’s welfare. • A so-called discontinuous Galerkin method is proposed to solve the ordinary differential equation satisfied by the consumption. • The theoretical results of the discontinuous Galerkin method are obtained. • We establish a Markov model to forecast the energy mix and the industrial structure. - Abstract: In this paper, we assume that under the balanced and optimal economic growth path, the economic growth rate is equal to the consumption growth rate, from which we can obtain the ordinary differential equation governing the consumption level by solving an optimal control problem. Then, a novel numerical method, namely a so-called discontinuous Galerkin method, is applied to solve the ordinary differential equation. The error estimation and the superconvergence estimation of this method are also performed. The model’s mechanism, which makes our assumption coherent, is that once the energy intensity is given, the economic growth is determined, followed by the GDP, the energy demand and the emissions. By applying this model to China, we obtain the conclusion that under the balanced and optimal economic growth path the CO_2 emission will reach its peak in 2030 in China, which is consistent with the U.S.-China Joint Announcement on Climate Change and with other previous scientific results.
Konovalova, Irina; Berkovich, Yuliy A.; Smolyanina, Svetlana; Erokhin, Alexei; Yakovleva, Olga; Lapach, Sergij; Radchenko, Stanislav; Znamenskii, Artem; Tarakanov, Ivan
2016-07-01
The efficiency of the photoautotrophic element as part of bio-engineering life-support systems is determined substantially by lighting regime. The artificial light regime optimization complexity results from the wide range of plant physiological functions controlled by light: trophic, informative, biosynthetical, etc. An average photosynthetic photon flux density (PPFD), light spectral composition and pulsed light effects on the crop growth and plant physiological status were studied in the multivariate experiment, including 16 independent experiments in 3 replicates. Chinese cabbage plants (Brassica chinensis L.), cultivar Vesnianka, were grown during 24 days in a climatic chamber under white and red light-emitting diodes (LEDs): photoperiod 24 h, PPFD from 260 to 500 µM/(m ^{2}*s), red light share in the spectrum varying from 33% to 73%, pulsed (pulse period from 30 to 501 µs) and non-pulsed lighting. The regressions of plant photosynthetic and biochemical indexes as well as the crop specific productivity in response to the selected parameters of lighting regime were calculated. Developed models of crop net photosynthesis and dark respiration revealed the most intense gas exchange area corresponding to PPFD level 450 - 500 µM/(m ^{2}*s) with red light share in the spectrum about 60% and the pulse length 30 µs with a pulse period from 300 to 400 µs. Shoot dry weight increased monotonically in response to the increasing PPFD and changed depending on the pulse period under stabilized PPFD level. An increase in ascorbic acid content in the shoot biomass was revealed when increasing red light share in spectrum from 33% to 73%. The lighting regime optimization criterion (Q) was designed for the vitamin space greenhouse as the maximum of a crop yield square on its ascorbic acid concentration, divided by the light energy consumption. The regression model of optimization criterion was constructed based on the experimental data. The analysis of the model made it
Miyauchi, T.; Machimura, T.
2013-12-01
In the simulation using an ecosystem process model, the adjustment of parameters is indispensable for improving the accuracy of prediction. This procedure, however, requires much time and effort for approaching the simulation results to the measurements on models consisting of various ecosystem processes. In this study, we tried to apply a general purpose optimization tool in the parameter optimization of an ecosystem model, and examined its validity by comparing the simulated and measured biomass growth of a woody plantation. A biometric survey of tree biomass growth was performed in 2009 in an 11-year old Eucommia ulmoides plantation in Henan Province, China. Climate of the site was dry temperate. Leaf, above- and below-ground woody biomass were measured from three cut trees and converted into carbon mass per area by measured carbon contents and stem density. Yearly woody biomass growth of the plantation was calculated according to allometric relationships determined by tree ring analysis of seven cut trees. We used Biome-BGC (Thornton, 2002) to reproduce biomass growth of the plantation. Air temperature and humidity from 1981 to 2010 was used as input climate condition. The plant functional type was deciduous broadleaf, and non-optimizing parameters were left default. 11-year long normal simulations were performed following a spin-up run. In order to select optimizing parameters, we analyzed the sensitivity of leaf, above- and below-ground woody biomass to eco-physiological parameters. Following the selection, optimization of parameters was performed by using the Dakota optimizer. Dakota is an optimizer developed by Sandia National Laboratories for providing a systematic and rapid means to obtain optimal designs using simulation based models. As the object function, we calculated the sum of relative errors between simulated and measured leaf, above- and below-ground woody carbon at each of eleven years. In an alternative run, errors at the last year (at the
Modeling and control of greenhouse crop growth
Rodríguez, Francisco; Guzmán, José Luis; Ramírez-Arias, Armando
2015-01-01
A discussion of challenges related to the modeling and control of greenhouse crop growth, this book presents state-of-the-art answers to those challenges. The authors model the subsystems involved in successful greenhouse control using different techniques and show how the models obtained can be exploited for simulation or control design; they suggest ideas for the development of physical and/or black-box models for this purpose. Strategies for the control of climate- and irrigation-related variables are brought forward. The uses of PID control and feedforward compensators, both widely used in commercial tools, are summarized. The benefits of advanced control techniques—event-based, robust, and predictive control, for example—are used to improve on the performance of those basic methods. A hierarchical control architecture is developed governed by a high-level multiobjective optimization approach rather than traditional constrained optimization and artificial intelligence techniques. Reference trajector...
Combined optimization model for sustainable energization strategy
Abtew, Mohammed Seid
Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.
Self-similar measures in multi-sector endogenous growth models
International Nuclear Information System (INIS)
La Torre, Davide; Marsiglio, Simone; Mendivil, Franklin; Privileggi, Fabio
2015-01-01
We analyze two types of stochastic discrete time multi-sector endogenous growth models, namely a basic Uzawa–Lucas (1965, 1988) model and an extended three-sector version as in La Torre and Marsiglio (2010). As in the case of sustained growth the optimal dynamics of the state variables are not stationary, we focus on the dynamics of the capital ratio variables, and we show that, through appropriate log-transformations, they can be converted into affine iterated function systems converging to an invariant distribution supported on some (possibly fractal) compact set. This proves that also the steady state of endogenous growth models—i.e., the stochastic balanced growth path equilibrium—might have a fractal nature. We also provide some sufficient conditions under which the associated self-similar measures turn out to be either singular or absolutely continuous (for the three-sector model we only consider the singularity).
Optimization of A(2)O BNR processes using ASM and EAWAG Bio-P models: model performance.
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.
Modeling and optimization of algae growth
Thornton, Anthony Richard; Weinhart, Thomas; Bokhove, Onno; Zhang, Bowen; van der Sar, Dick M.; Kumar, Kundan; Pisarenco, Maxim; Rudnaya, Maria; Savcenco, Valeriu; Rademacher, Jens; Zijlstra, Julia; Szabelska, Alicja; Zyprych, Joanna; van der Schans, Martin; Timperio, Vincent; Veerman, Frits
2010-01-01
The wastewater from greenhouses has a high amount of mineral contamination and an environmentally-friendly method of removal is to use algae to clean this runoff water. The algae consume the minerals as part of their growth process. In addition to cleaning the water, the created algal bio-mass has a
Prasad, Archana; Prakash, Om; Mehrotra, Shakti; Khan, Feroz; Mathur, Ajay Kumar; Mathur, Archana
2017-01-01
An artificial neural network (ANN)-based modelling approach is used to determine the synergistic effect of five major components of growth medium (Mg, Cu, Zn, nitrate and sucrose) on improved in vitro biomass yield in multiple shoot cultures of Centella asiatica. The back propagation neural network (BPNN) was employed to predict optimal biomass accumulation in terms of growth index over a defined culture duration of 35 days. The four variable concentrations of five media components, i.e. MgSO 4 (0, 0.75, 1.5, 3.0 mM), ZnSO 4 (0, 15, 30, 60 μM), CuSO 4 (0, 0.05, 0.1, 0.2 μM), NO 3 (20, 30, 40, 60 mM) and sucrose (1, 3, 5, 7 %, w/v) were taken as inputs for the ANN model. The designed model was evaluated by performing three different sets of validation experiments that indicated a greater similarity between the target and predicted dataset. The results of the modelling experiment suggested that 1.5 mM Mg, 30 μM Zn, 0.1 μM Cu, 40 mM NO 3 and 6 % (w/v) sucrose were the respective optimal concentrations of the tested medium components for achieving maximum growth index of 1654.46 with high centelloside yield (62.37 mg DW/culture) in the cultured multiple shoots. This study can facilitate the generation of higher biomass of uniform, clean, good quality C. asiatica herb that can efficiently be utilized by pharmaceutical industries.
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.
Spending Natural Resource Revenues in an Altruistic Growth Model
DEFF Research Database (Denmark)
Frederiksen, Elisabeth Hermann
This paper examines how revenues from a natural resource interact with growth and welfare in an overlapping generations model with altruism. The revenues are allocated between public productive services and direct transfers to members of society by spending policies. We analyze how these policies...... influence the dynamics, and how the dynamics are influenced by the abundance of the revenue. Abundant revenues may harm growth, but growth and welfare can be oppositely affected. We also provide the socially optimal policy. Overall, the analysis suggests that variation in the strength of altruism...
Directory of Open Access Journals (Sweden)
Arjan P Palstra
Full Text Available BACKGROUND: Zebrafish has been largely accepted as a vertebrate multidisciplinary model but its usefulness as a model for exercise physiology has been hampered by the scarce knowledge on its swimming economy, optimal swimming speeds and cost of transport. Therefore, we have performed individual and group-wise swimming experiments to quantify swimming economy and to demonstrate the exercise effects on growth in adult zebrafish. METHODOLOGY/PRINCIPAL FINDINGS: Individual zebrafish (n = 10 were able to swim at a critical swimming speed (U(crit of 0.548±0.007 m s(-1 or 18.0 standard body lengths (BL s(-1. The optimal swimming speed (U(opt at which energetic efficiency is highest was 0.396±0.019 m s(-1 (13.0 BL s(-1 corresponding to 72.26±0.29% of U(crit. The cost of transport at optimal swimming speed (COT(opt was 25.23±4.03 µmol g(-1 m(-1. A group-wise experiment was conducted with zebrafish (n = 83 swimming at U(opt for 6 h day(-1 for 5 days week(-1 for 4 weeks vs. zebrafish (n = 84 that rested during this period. Swimming zebrafish increased their total body length by 5.6% and body weight by 41.1% as compared to resting fish. For the first time, a highly significant exercise-induced growth is demonstrated in adult zebrafish. Expression analysis of a set of muscle growth marker genes revealed clear regulatory roles in relation to swimming-enhanced growth for genes such as growth hormone receptor b (ghrb, insulin-like growth factor 1 receptor a (igf1ra, troponin C (stnnc, slow myosin heavy chain 1 (smyhc1, troponin I2 (tnni2, myosin heavy polypeptide 2 (myhz2 and myostatin (mstnb. CONCLUSIONS/SIGNIFICANCE: From the results of our study we can conclude that zebrafish can be used as an exercise model for enhanced growth, with implications in basic, biomedical and applied sciences, such as aquaculture.
Optimal foraging and diel vertical migration in a life history model
DEFF Research Database (Denmark)
Sainmont, Julie; Andersen, Ken Haste; Visser, Andre
in the model, unsuited for life history modeling. We propose a method based on optimal foraging theory to take into account the emergent feeding rates as a function of the copepod metabolic cost, latitude, time and predation. We predict that copepods will balance their growth rate and mortality, playing a safe...
Optimal energy growth in a stably stratified shear flow
Jose, Sharath; Roy, Anubhab; Bale, Rahul; Iyer, Krithika; Govindarajan, Rama
2018-02-01
Transient growth of perturbations by a linear non-modal evolution is studied here in a stably stratified bounded Couette flow. The density stratification is linear. Classical inviscid stability theory states that a parallel shear flow is stable to exponentially growing disturbances if the Richardson number (Ri) is greater than 1/4 everywhere in the flow. Experiments and numerical simulations at higher Ri show however that algebraically growing disturbances can lead to transient amplification. The complexity of a stably stratified shear flow stems from its ability to combine this transient amplification with propagating internal gravity waves (IGWs). The optimal perturbations associated with maximum energy amplification are numerically obtained at intermediate Reynolds numbers. It is shown that in this wall-bounded flow, the three-dimensional optimal perturbations are oblique, unlike in unstratified flow. A partitioning of energy into kinetic and potential helps in understanding the exchange of energies and how it modifies the transient growth. We show that the apportionment between potential and kinetic energy depends, in an interesting manner, on the Richardson number, and on time, as the transient growth proceeds from an optimal perturbation. The oft-quoted stabilizing role of stratification is also probed in the non-diffusive limit in the context of disturbance energy amplification.
Repeated Microneedle Stimulation Induces Enhanced Hair Growth in a Murine Model.
Kim, Yoon Seob; Jeong, Kwan Ho; Kim, Jung Eun; Woo, Young Jun; Kim, Beom Joon; Kang, Hoon
2016-10-01
Microneedle is a method that creates transdermal microchannels across the stratum corneum barrier layer of skin. No previous study showed a therapeutic effect of microneedle itself on hair growth by wounding. The aim of this study is to investigate the effect of repeated microwound formed by microneedle on hair growth and hair growth-related genes in a murine model. A disk microneedle roller was applied to each group of mice five times a week for three weeks. First, to identify the optimal length and cycle, microneedles of lengths of 0.15 mm, 0.25 mm, 0.5 mm, and 1 mm and cycles of 3, 6, 10, and 13 cycles were applied. Second, the effect of hair growth and hair-growth-related genes such as Wnt3a, β-catenin, vascular endothelial growth factor (VEGF), and Wnt10b was observed using optimized microneedle. Outcomes were observed using visual inspection, real-time polymerase chain reaction, and immunohistochemistry. We found that the optimal length and cycle of microneedle treatment on hair growth was 0.25 mm/10 cycles and 0.5 mm/10 cycles. Repeated microneedle stimulation promoted hair growth, and it also induced the enhanced expression of Wnt3a, β-catenin, VEGF, and Wnt10b. Our study provides evidence that microneedle stimulation can induce hair growth via activation of the Wnt/β-catenin pathway and VEGF. Combined with the drug delivery effect, we believe that microneedle stimulation could lead to new approaches for alopecia.
Optimal Pricing and Advertising Policies for New Product Oligopoly Models
Gerald L. Thompson; Jinn-Tsair Teng
1984-01-01
In this paper our previous work on monopoly and oligopoly new product models is extended by the addition of pricing as well as advertising control variables. These models contain Bass's demand growth model, and the Vidale-Wolfe and Ozga advertising models, as well as the production learning curve model and an exponential demand function. The problem of characterizing an optimal pricing and advertising policy over time is an important question in the field of marketing as well as in the areas ...
Model-aided optimization of delta-endotoxin-formation in continuous culture systems
Energy Technology Data Exchange (ETDEWEB)
Schulz, V; Schorcht, R; Ignatenko, Yu N; Sakharova, Z V; Khovrychev, M P
1985-01-01
A mathematical model of growth, sporulation and delta-endotoxin-formation of bac. thuringiensis is given. The results of model-aided optimization of steady-state continuous culture systems indicate that the productivity in the one-stage system is 1.9% higher and in the two-stage system is 18.5% higher than in the batch process.
Wu, Yin-Hu; Li, Xin; Yu, Yin; Hu, Hong-Ying; Zhang, Tian-Yuan; Li, Feng-Min
2013-09-01
Microalgal growth is the key to the coupled system of wastewater treatment and microalgal biomass production. In this study, Monod model, Droop model and Steele model were incorporated to obtain an integrated growth model describing the combined effects of nitrogen, phosphorus and light intensity on the growth rate of Scenedesmus sp. LX1. The model parameters were obtained via fitting experimental data to these classical models. Furthermore, the biomass production of Scenedesmus sp. LX1 in open pond under nutrient level of secondary effluent was analyzed based on the integrated model, predicting a maximal microalgal biomass production rate about 20 g m(-2) d(-1). In order to optimize the biomass production of open pond the microalgal biomass concentration, light intensity on the surface of open pond, total depth of culture medium and hydraulic retention time should be 500 g m(-3), 16,000 lx, 0.2 m and 5.2 d in the conditions of this study, respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.
Modelling the interaction between flooding events and economic growth
Grames, Johanna; Grass, Dieter; Prskawetz, Alexia; Blöschl, Günther
2015-04-01
Socio-hydrology describes the interaction between the socio-economy, water and population dynamics. Recent models analyze the interplay of community risk-coping culture, flooding damage and economic growth (Di Baldassarre, 2013, Viglione, 2014). These models descriptively explain the feedbacks between socio-economic development and natural disasters like floods. Contrary to these descriptive models, our approach develops an optimization model, where the intertemporal decision of an economic agent interacts with the hydrological system. This is the first economic growth model describing the interaction between the consumption and investment decisions of an economic agent and the occurrence of flooding events: Investments in defense capital can avoid floods even when the water level is high, but on the other hand such investment competes with investment in productive capital and hence may reduce the level of consumption. When floods occur, the flood damage therefore depends on the existing defense capital. The aim is to find an optimal tradeoff between investments in productive versus defense capital such as to optimize the stream of consumption in the long-term. We assume a non-autonomous exogenous periodic rainfall function (Yevjevich et.al. 1990, Zakaria 2001) which implies that the long-term equilibrium will be periodic . With our model we aim to derive mechanisms that allow consumption smoothing in the long term, and at the same time allow for optimal investment in flood defense to maximize economic output. We choose an aggregate welfare function that depends on the consumption level of the society as the objective function. I.e. we assume a social planer with perfect foresight that maximizes the aggregate welfare function. Within our model framework we can also study whether the path and level of defense capital (that protects people from floods) is related to the time preference rate of the social planner. Our model also allows to investigate how the frequency
Lu, Yi
2016-01-01
To model students' math growth trajectory, three conventional growth curve models and three growth mixture models are applied to the Early Childhood Longitudinal Study Kindergarten-Fifth grade (ECLS K-5) dataset in this study. The results of conventional growth curve model show gender differences on math IRT scores. When holding socio-economic…
Human fetal growth is constrained below optimal for perinatal survival
Vasak, B.; Koenen, S. V.; Koster, M. P. H.; Hukkelhoven, C. W. P. M.; Franx, A.; Hanson, M. A.; Visser, GHA
ObjectiveThe use of fetal growth charts assumes that the optimal size at birth is at the 50(th) birth-weight centile, but interaction between maternal constraints on fetal growth and the risks associated with small and large fetal size at birth may indicate that this assumption is not valid for
Web malware spread modelling and optimal control strategies
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.
Integrating Growth Stage Deficit Irrigation into a Process Based Crop Model
Lopez, Jose R.; Winter, Jonathan M.; Elliott, Joshua; Ruane, Alex C.; Porter, Cheryl; Hoogenboom, Gerrit
2017-01-01
Current rates of agricultural water use are unsustainable in many regions, creating an urgent need to identify improved irrigation strategies for water limited areas. Crop models can be used to quantify plant water requirements, predict the impact of water shortages on yield, and calculate water productivity (WP) to link water availability and crop yields for economic analyses. Many simulations of crop growth and development, especially in regional and global assessments, rely on automatic irrigation algorithms to estimate irrigation dates and amounts. However, these algorithms are not well suited for water limited regions because they have simplistic irrigation rules, such as a single soil-moisture based threshold, and assume unlimited water. To address this constraint, a new modeling framework to simulate agricultural production in water limited areas was developed. The framework consists of a new automatic irrigation algorithm for the simulation of growth stage based deficit irrigation under limited seasonal water availability; and optimization of growth stage specific parameters. The new automatic irrigation algorithm was used to simulate maize and soybean in Gainesville, Florida, and first used to evaluate the sensitivity of maize and soybean simulations to irrigation at different growth stages and then to test the hypothesis that water productivity calculated using simplistic irrigation rules underestimates WP. In the first experiment, the effect of irrigating at specific growth stages on yield and irrigation water use efficiency (IWUE) in maize and soybean was evaluated. In the reproductive stages, IWUE tended to be higher than in the vegetative stages (e.g. IWUE was 18% higher than the well watered treatment when irrigating only during R3 in soybean), and when rainfall events were less frequent. In the second experiment, water productivity (WP) was significantly greater with optimized irrigation schedules compared to non-optimized irrigation schedules in
Government technology push in agribusiness: a model of endogenous growth
Directory of Open Access Journals (Sweden)
Francisco Venegas Martínez
2008-10-01
Full Text Available This paper develops a model of endogenous growth where the government acts as a promoting agent to boost technology in agribusiness. In the framework of a monetary economy, the optimal level of government spending to enhance technology in the agricultural industry is characterized. Moreover the impact of such a spending on economic welfare is assessed. Finally, a number of agro-oriented policies to increase growth in the sector are established.
Validation of mathematical model for CZ process using small-scale laboratory crystal growth furnace
Bergfelds, Kristaps; Sabanskis, Andrejs; Virbulis, Janis
2018-05-01
The present material is focused on the modelling of small-scale laboratory NaCl-RbCl crystal growth furnace. First steps towards fully transient simulations are taken in the form of stationary simulations that deal with the optimization of material properties to match the model to experimental conditions. For this purpose, simulation software primarily used for the modelling of industrial-scale silicon crystal growth process was successfully applied. Finally, transient simulations of the crystal growth are presented, giving a sufficient agreement to experimental results.
An optimal control model for reducing and trading of carbon emissions
Guo, Huaying; Liang, Jin
2016-03-01
A stochastic optimal control model of reducing and trading for carbon emissions is established in this paper. With considerations of reducing the carbon emission growth and the price of the allowances in the market, an optimal policy is searched to have the minimum total costs to achieve the agreement of emission reduction targets. The model turns to a two-dimension HJB equation problem. By the methods of reducing dimension and Cole-Hopf transformation, a semi-closed form solution of the corresponding HJB problem under some assumptions is obtained. For more general cases, the numerical calculations, analysis and comparisons are presented.
Filin, I
2009-06-01
Using diffusion processes, I model stochastic individual growth, given exogenous hazards and starvation risk. By maximizing survival to final size, optimal life histories (e.g. switching size for habitat/dietary shift) are determined by two ratios: mean growth rate over growth variance (diffusion coefficient) and mortality rate over mean growth rate; all are size dependent. For example, switching size decreases with either ratio, if both are positive. I provide examples and compare with previous work on risk-sensitive foraging and the energy-predation trade-off. I then decompose individual size into reversibly and irreversibly growing components, e.g. reserves and structure. I provide a general expression for optimal structural growth, when reserves grow stochastically. I conclude that increased growth variance of reserves delays structural growth (raises threshold size for its commencement) but may eventually lead to larger structures. The effect depends on whether the structural trait is related to foraging or defence. Implications for population dynamics are discussed.
Mahmoudzadeh Akherat, S. M. Javid; Boghosian, Michael; Cassel, Kevin; Hammes, Mary
2015-11-01
End-stage-renal disease patients depend on successful long-term hemodialysis via vascular access, commonly facilitated via a Brachiocephalic Fistula (BCF). The primary cause of BCF failure is Cephalic Arch Stenosis (CAS). It is believed that low Wall Shear Stress (WSS) regions, which occur because of the high flow rates through the natural bend in the cephalic vein, create hemodynamic circumstances that trigger the onset and development of Intimal Hyperplasia (IH) and subsequent CAS. IH is hypothesized to be a natural effort to reshape the vessel, aiming to bring the WSS values back to a physiologically acceptable range. We seek to explore the correlation between regions of low WSS and subsequent IH and CAS in patient-specific geometries. By utilizing a shape optimization framework, a method is proposed to predict cardiovascular adaptation that could potentially be an alternative to vascular growth and remodeling. Based on an objective functional that seeks to alter the vessel shape in such a way as to readjust the WSS to be within the normal physiological range, CFD and shape optimization are then coupled to investigate whether the optimal shape evolution is correlated with actual patient-specific geometries thereafter. Supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (R01 DK90769).
A MODEL OF ECONOMIC GROWTH WITH PUBLIC FINANCE: DYNAMICS AND ANALYTIC SOLUTION
Directory of Open Access Journals (Sweden)
Oliviero Antonio Carboni
2013-01-01
Full Text Available This paper studies the equilibrium dynamics of a growth model with public finance where two different allocations of public resources are considered. The model simultaneously determines the optimal shares of consumption, capital accumulation, taxes and composition of the two different public expenditures which maximize a representative household's lifetime utilities in a centralized economy. The analysis supplies a closed form solution. Moreover, with one restriction on the parameters ( we fully determine the solutions path for all variables of the model and determine the conditions for balanced growth.
Population Modeling Approach to Optimize Crop Harvest Strategy. The Case of Field Tomato.
Tran, Dinh T; Hertog, Maarten L A T M; Tran, Thi L H; Quyen, Nguyen T; Van de Poel, Bram; Mata, Clara I; Nicolaï, Bart M
2017-01-01
In this study, the aim is to develop a population model based approach to optimize fruit harvesting strategies with regard to fruit quality and its derived economic value. This approach was applied to the case of tomato fruit harvesting under Vietnamese conditions. Fruit growth and development of tomato (cv. "Savior") was monitored in terms of fruit size and color during both the Vietnamese winter and summer growing seasons. A kinetic tomato fruit growth model was applied to quantify biological fruit-to-fruit variation in terms of their physiological maturation. This model was successfully calibrated. Finally, the model was extended to translate the fruit-to-fruit variation at harvest into the economic value of the harvested crop. It can be concluded that a model based approach to the optimization of harvest date and harvest frequency with regard to economic value of the crop as such is feasible. This approach allows growers to optimize their harvesting strategy by harvesting the crop at more uniform maturity stages meeting the stringent retail demands for homogeneous high quality product. The total farm profit would still depend on the impact a change in harvesting strategy might have on related expenditures. This model based harvest optimisation approach can be easily transferred to other fruit and vegetable crops improving homogeneity of the postharvest product streams.
Stochastic models for tumoral growth
Escudero, Carlos
2006-01-01
Strong experimental evidence has indicated that tumor growth belongs to the molecular beam epitaxy universality class. This type of growth is characterized by the constraint of cell proliferation to the tumor border, and surface diffusion of cells at the growing edge. Tumor growth is thus conceived as a competition for space between the tumor and the host, and cell diffusion at the tumor border is an optimal strategy adopted for minimizing the pressure and helping tumor development. Two stoch...
Klamt, Steffen; Müller, Stefan; Regensburger, Georg; Zanghellini, Jürgen
2018-02-07
The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small
Directory of Open Access Journals (Sweden)
Alexander Mikhajlovich Tarasyev
2014-09-01
Full Text Available In this paper, we develop an economic growth model taking into account two factors of production: fixed capital and labor force, to study the dynamics of GDP growth. The dependence of the output of these factors is described by a production function of the exponential type. Within the framework of the optimal control theory, the optimization problem for investment levels is being solved to maximize the integral index of consumption. We study the qualitative properties of optimal trajectories as solutions of the Hamiltonian systems arising in Pontryagin’s maximum principle. The sensitivity analysis of the equilibrium solutions of the economic system is implemented with respect to the elasticity coefficients of the production function, the depreciation rate of the capital, and the discount factor, and growth trends are indicated. The econometric analysis of the model parameters is provided basing on real data for the Russian economy. In accordance with the results of the regression analysis, the projection of economic development is constructed in conditions of the applicability of the economic growth model.
Optimal tax rate and economic growth. Evidence from Nigeria and South Africa
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Olufemi Muibi SAIBU
2015-05-01
Full Text Available The recent economic crisis had made developing countries to look inward for financial resources to finance development. The readily alternative is the tax revenues however, the possible adverse direct and indirect effects of tax on productivity and work efforts as well as on aggregate consumption had make some African countries (especially Nigeria and South Africa reluctant in implementing far reaching tax policy reform. This paper examines optimal tax burden and real output growth Nigeria and South Africa, two of the top four economies in Africa. The paper empirically determined what should be the optimal tax rate for Nigeria and South Africa-the two leading economies in Africa. The paper found that nonlinearity hypothesis in the effects of tax in the case of South Africa is rejected while a significant nonlinear relationship is found in the case of Nigeria. The results suggest that the growth-maximizing tax rate is about 15% of per capita GDP for South Africa and 30% for Nigeria. At that tax rate, the economic growth rate would be around 6% and 8% instead of the actual mean growth rate of 2.84% and 4.51% for South Africa and Nigeria respectively. The paper concluded the current tax burden in the two countries may be sub-optimal and may hurt long term sustainable growth process in the two countries
Perry, Russell W.; Plumb, John M.; Huntington, Charles
2015-01-01
To estimate the parameters that govern mass- and temperature-dependent growth, we conducted a meta-analysis of existing growth data from juvenile Chinook Salmon Oncorhynchus tshawytscha that were fed an ad libitum ration of a pelleted diet. Although the growth of juvenile Chinook Salmon has been well studied, research has focused on a single population, a narrow range of fish sizes, or a narrow range of temperatures. Therefore, we incorporated the Ratkowsky model for temperature-dependent growth into an allometric growth model; this model was then fitted to growth data from 11 data sources representing nine populations of juvenile Chinook Salmon. The model fit the growth data well, explaining 98% of the variation in final mass. The estimated allometric mass exponent (b) was 0.338 (SE = 0.025), similar to estimates reported for other salmonids. This estimate of b will be particularly useful for estimating mass-standardized growth rates of juvenile Chinook Salmon. In addition, the lower thermal limit, optimal temperature, and upper thermal limit for growth were estimated to be 1.8°C (SE = 0.63°C), 19.0°C (SE = 0.27°C), and 24.9°C (SE = 0.02°C), respectively. By taking a meta-analytical approach, we were able to provide a growth model that is applicable across populations of juvenile Chinook Salmon receiving an ad libitum ration of a pelleted diet.
The Research of Economic Growth and Optimal Public Financial Expenditure%经济增长与最优财政支出规模研究
Institute of Scientific and Technical Information of China (English)
马树才; 孙长清
2005-01-01
The size of fiscal expenditure reflect the degree that government intervene in market, if government intervene in market too much, the action of government will lower economic growth. In order to choose optimal size of fiscal expenditure, the paper developed a endogenous growth model including government, and estimated the optimal size of fiscal expenditure in china from experience, the period is from 1978 to 2000. The conclusion is the optimal internal budget size of fiscal expenditure is 21.2% (fiscal expenditure relative to GDP), the total optimal size of fiscal expenditure is 24% (the total fiscal expenditure relative to GDP).
International Nuclear Information System (INIS)
McAneney, H; O'Rourke, S F C
2007-01-01
The standard linear-quadratic survival model for radiotherapy is used to investigate different schedules of radiation treatment planning to study how these may be affected by different tumour repopulation kinetics between treatments. The laws for tumour cell repopulation include the logistic and Gompertz models and this extends the work of Wheldon et al (1977 Br. J. Radiol. 50 681), which was concerned with the case of exponential re-growth between treatments. Here we also consider the restricted exponential model. This has been successfully used by Panetta and Adam (1995 Math. Comput. Modelling 22 67) in the case of chemotherapy treatment planning.Treatment schedules investigated include standard fractionation of daily treatments, weekday treatments, accelerated fractionation, optimized uniform schedules and variation of the dosage and α/β ratio, where α and β are radiobiological parameters for the tumour tissue concerned. Parameters for these treatment strategies are extracted from the literature on advanced head and neck cancer, prostate cancer, as well as radiosensitive parameters. Standardized treatment protocols are also considered. Calculations based on the present analysis indicate that even with growth laws scaled to mimic initial growth, such that growth mechanisms are comparable, variation in survival fraction to orders of magnitude emerged. Calculations show that the logistic and exponential models yield similar results in tumour eradication. By comparison the Gompertz model calculations indicate that tumours described by this law result in a significantly poorer prognosis for tumour eradication than either the exponential or logistic models. The present study also shows that the faster the tumour growth rate and the higher the repair capacity of the cell line, the greater the variation in outcome of the survival fraction. Gaps in treatment, planned or unplanned, also accentuate the differences of the survival fraction given alternative growth
Optimizing growth conditions for electroless deposition of Au films ...
Indian Academy of Sciences (India)
Unknown
Optimizing growth conditions for electroless deposition of Au films on. Si(111) substrates. BHUVANA and G U KULKARNI*. Chemistry and Physics of Materials Unit and DST Unit on Nanoscience, Jawaharlal Nehru Centre for. Advanced Scientific Research, Jakkur PO, Bangalore 560 064, India. MS received 24 March 2006.
Directory of Open Access Journals (Sweden)
Edward Schram
Full Text Available Dover sole (Solea solea is an obligate ectotherm with a natural thermal habitat ranging from approximately 5 to 27°C. Thermal optima for growth lie in the range of 20 to 25°C. More precise information on thermal optima for growth is needed for cost-effective Dover sole aquaculture. The main objective of this study was to determine the optimal growth temperature of juvenile Dover sole (Solea solea and in addition to test the hypothesis that the final preferendum equals the optimal growth temperature. Temperature preference was measured in a circular preference chamber for Dover sole acclimated to 18, 22 and 28°C. Optimal growth temperature was measured by rearing Dover sole at 19, 22, 25 and 28°C. The optimal growth temperature resulting from this growth experiment was 22.7°C for Dover sole with a size between 30 to 50 g. The temperature preferred by juvenile Dover sole increases with acclimation temperature and exceeds the optimal temperature for growth. A final preferendum could not be detected. Although a confounding effect of behavioural fever on temperature preference could not be entirely excluded, thermal preference and thermal optima for physiological processes seem to be unrelated in Dover sole.
Energy Technology Data Exchange (ETDEWEB)
Denysenko, I; Azarenkov, N A, E-mail: idenysenko@yahoo.com [School of Physics and Technology, V N Karazin Kharkiv National University, 4 Svobody sq., 61077 Kharkiv (Ukraine)
2011-05-04
Results on modelling of the plasma-assisted growth of vertically aligned carbon nanostructures and of the energy exchange between the plasma and the growing nanostructures are reviewed. Growth of carbon nanofibres and single-walled carbon nanotubes is considered. Focus is made on studies that use the models based on mass balance equations for species, which are adsorbed on catalyst nanoparticles or walls of the nanostructures. It is shown that the models can be effectively used for the study and optimization of nanostructure growth in plasma-enhanced chemical vapour deposition. The results from these models are in good agreement with the available experimental data on the growth of nanostructures. It is discussed how input parameters for the models may be obtained.
Lattice Gas Model Based Optimization of Plasma-Surface Processes for GaN-Based Compound Growth
Nonokawa, Kiyohide; Suzuki, Takuma; Kitamori, Kazutaka; Sawada, Takayuki
2001-10-01
Progress of the epitaxial growth technique for GaN-based compounds makes these materials attractive for applications in high temperature/high-power electronic devices as well as in short-wavelength optoelectronic devices. For MBE growth of GaN epilayer, atomic nitrogen is usually supplied from ECR-plasma while atomic Ga is supplied from conventional K-cell. To grow high-quality epilayer, fundamental knowledge of the detailed atomic process, such as adsorption, surface migration, incorporation, desorption and so forth, is required. We have studied the influence of growth conditions on the flatness of the growth front surface and the growth rate using Monte Carlo simulation based on the lattice gas model. Under the fixed Ga flux condition, the lower the nitrogen flux and/or the higher the growth temperature, the better the flatness of the front surface at the sacrifice of the growth rate of the epilayer. When the nitrogen flux is increased, the growth rate reaches saturation value determined from the Ga flux. At a fixed growth temperature, increasing of nitrogen to Ga flux ratio results in rough surface owing to 3-dimensional island formation. Other characteristics of MBE-GaN growth using ECR-plasma can be well reproduced.
Optimization of Phenotyping Assays for the Model Monocot Setaria viridis.
Acharya, Biswa R; Roy Choudhury, Swarup; Estelle, Aiden B; Vijayakumar, Anitha; Zhu, Chuanmei; Hovis, Laryssa; Pandey, Sona
2017-01-01
Setaria viridis (green foxtail) is an important model plant for the study of C4 photosynthesis in panicoid grasses, and is fast emerging as a system of choice for the study of plant development, domestication, abiotic stress responses and evolution. Basic research findings in Setaria are expected to advance research not only in this species and its close relative S. italica (foxtail millet), but also in other panicoid grasses, many of which are important food or bioenergy crops. Here we report on the standardization of multiple growth and development assays for S. viridis under controlled conditions, and in response to several phytohormones and abiotic stresses. We optimized these assays at three different stages of the plant's life: seed germination and post-germination growth using agar plate-based assays, early seedling growth and development using germination pouch-based assays, and adult plant growth and development under environmentally controlled growth chambers and greenhouses. These assays will be useful for the community to perform large scale phenotyping analyses, mutant screens, comparative physiological analysis, and functional characterization of novel genes of Setaria or other related agricultural crops. Precise description of various growth conditions, effective treatment conditions and description of the resultant phenotypes will help expand the use of S. viridis as an effective model system.
Optimization of Phenotyping Assays for the Model Monocot Setaria viridis
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Biswa R. Acharya
2017-12-01
Full Text Available Setaria viridis (green foxtail is an important model plant for the study of C4 photosynthesis in panicoid grasses, and is fast emerging as a system of choice for the study of plant development, domestication, abiotic stress responses and evolution. Basic research findings in Setaria are expected to advance research not only in this species and its close relative S. italica (foxtail millet, but also in other panicoid grasses, many of which are important food or bioenergy crops. Here we report on the standardization of multiple growth and development assays for S. viridis under controlled conditions, and in response to several phytohormones and abiotic stresses. We optimized these assays at three different stages of the plant’s life: seed germination and post-germination growth using agar plate-based assays, early seedling growth and development using germination pouch-based assays, and adult plant growth and development under environmentally controlled growth chambers and greenhouses. These assays will be useful for the community to perform large scale phenotyping analyses, mutant screens, comparative physiological analysis, and functional characterization of novel genes of Setaria or other related agricultural crops. Precise description of various growth conditions, effective treatment conditions and description of the resultant phenotypes will help expand the use of S. viridis as an effective model system.
Risk modelling in portfolio optimization
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.
Hoeben, B.A.W.; Molkenboer-Kuenen, J.D.M.; Oyen, W.J.G.; Peeters, W.J.M.; Kaanders, J.H.A.M.; Bussink, J.; Boerman, O.C.
2011-01-01
Noninvasive imaging of the epidermal growth factor receptor (EGFR) in head-and-neck squamous cell carcinoma could be of value to select patients for EGFR-targeted therapy. We assessed dose optimization of (111) Indium-DTPA-cetuximab ((111) In-cetuximab) for EGFR imaging in a head-and-neck squamous
A model for optimal offspring size in fish, including live-bearing and parental effects.
Jørgensen, Christian; Auer, Sonya K; Reznick, David N
2011-05-01
Since Smith and Fretwell's seminal article in 1974 on the optimal offspring size, most theory has assumed a trade-off between offspring number and offspring fitness, where larger offspring have better survival or fitness, but with diminishing returns. In this article, we use two ubiquitous biological mechanisms to derive the shape of this trade-off: the offspring's growth rate combined with its size-dependent mortality (predation). For a large parameter region, we obtain the same sigmoid relationship between offspring size and offspring survival as Smith and Fretwell, but we also identify parameter regions where the optimal offspring size is as small or as large as possible. With increasing growth rate, the optimal offspring size is smaller. We then integrate our model with strategies of parental care. Egg guarding that reduces egg mortality favors smaller or larger offspring, depending on how mortality scales with size. For live-bearers, the survival of offspring to birth is a function of maternal survival; if the mother's survival increases with her size, then the model predicts that larger mothers should produce larger offspring. When using parameters for Trinidadian guppies Poecilia reticulata, differences in both growth and size-dependent predation are required to predict observed differences in offspring size between wild populations from high- and low-predation environments.
E. Gregory McPherson; Paula J. Peper
2012-01-01
This paper describes three long-term tree growth studies conducted to evaluate tree performance because repeated measurements of the same trees produce critical data for growth model calibration and validation. Several empirical and process-based approaches to modeling tree growth are reviewed. Modeling is more advanced in the fields of forestry and...
Optimization of multi-environment trials for genomic selection based on crop models.
Rincent, R; Kuhn, E; Monod, H; Oury, F-X; Rousset, M; Allard, V; Le Gouis, J
2017-08-01
We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.
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
A Stochastic Growth Model with Income Tax Evasion: Implications for Australia
Ratbek Dzhumashev; Emin Gahramanov
2009-01-01
In this paper we develop a stochastic endogenous growth model augmented with income tax evasion. Our model avoids some existing discrepancies between empirical evidence and theoretical predictions of traditional tax evasion models. Further, we show that: i) productive government expenditures play an important role in affecting economy's tax evasion rate; ii) the average marginal income tax rate in Australia come close to the optimal; and iii) the phenomenon of tax evasion is not an excuse for...
Sustainable growth of EU economies and Baltic context: Characteristics and modelling
Directory of Open Access Journals (Sweden)
Girts Karnitis
2017-05-01
Full Text Available The united general growth strategy for all EU Member States, a common economic and political vision as well as location in the same geographic region provides a necessary basis for the benchmarking modelling of economies. The main objective of this study is determination of the functional regularities and drivers of the growth of EU economies and the context of the Baltic States in line with the general trend of the EU, as well as development of the growth model, which can be used for sustainable planning and prediction. Analysis of several regularly published analytical indexes suggests a thesis on innovation as the real basic driving force for EU economies and outlines Innovation Performance Index, which have a very strong compliance with the economic growth of particular country. At the same time study of the data set and methodology of the Index indicates space for further optimization. By use of several linear regression tools the growth model was created. It is based on three hard independent statistical indicators (predictors only; of course, these indicators is a peak of a complex pyramid. Despite of the simplicity of the model, the long-term correlation of fitted values with the real GDP per capita is extremely strong 0.961 – 0.987.
Directory of Open Access Journals (Sweden)
Zhong SHI
2015-02-01
Full Text Available Although epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs have been widely used in non-small cell lung cancer (NSCLC patients, it is still controversial about how to combine EGFR-TKI with chemotherapy and other targeted drugs. We have made a summary on the current therapeutic models of EGFR-TKI combined with chemotherapy/bevacizumab in this review and aimed to find the optimal therapeutic strategy for NSCLC patients with EGFR mutation.
Smooth Solutions to Optimal Investment Models with Stochastic Volatilities and Portfolio Constraints
International Nuclear Information System (INIS)
Pham, H.
2002-01-01
This paper deals with an extension of Merton's optimal investment problem to a multidimensional model with stochastic volatility and portfolio constraints. The classical dynamic programming approach leads to a characterization of the value function as a viscosity solution of the highly nonlinear associated Bellman equation. A logarithmic transformation expresses the value function in terms of the solution to a semilinear parabolic equation with quadratic growth on the derivative term. Using a stochastic control representation and some approximations, we prove the existence of a smooth solution to this semilinear equation. An optimal portfolio is shown to exist, and is expressed in terms of the classical solution to this semilinear equation. This reduction is useful for studying numerical schemes for both the value function and the optimal portfolio. We illustrate our results with several examples of stochastic volatility models popular in the financial literature
Mathematical modeling of liver metastases tumour growth and control with radiotherapy
International Nuclear Information System (INIS)
Campbell, Adrienne; Sivakumaran, Thiru; Wong, Eugene; Davidson, Melanie; Lock, Michael
2008-01-01
Generating an optimized radiation treatment plan requires understanding the factors affecting tumour control. Mathematical models of tumour dynamics may help in future studies of factors predicting tumour sensitivity to radiotherapy. In this study, a time-dependent differential model, incorporating biological cancer markers, is presented to describe pre-treatment tumour growth, response to radiation, and recurrence. The model uses Gompertzian-Exponential growth to model pre-treatment tumour growth. The effect of radiotherapy is handled by a realistic cell-kill term that includes a volume-dependent change in tumour sensitivity. Post-treatment, a Gompertzian, accelerated, delayed repopulation is employed. As proof of concept, we examined the fit of the model's prediction using various liver enzyme levels as markers of metastatic liver tumour growth in a liver cancer patient. A tumour clonogen population model was formulated. Each enzyme was coupled to the same tumour population, and served as surrogates of the tumour. This dynamical model was solved numerically and compared to the measured enzyme levels. By minimizing the mean-squared error of the model enzyme predictions, we determined the following tumour model parameters: growth rate prior to treatment was 0.52% per day; the fractional radiation cell kill for the prescribed dose (60 Gy in 15 fractions) was 42% per day, and the tumour repopulation rate was 2.9% per day. These preliminary results provided the basis to test the model in a larger series of patients, to apply biological markers for improving the efficacy of radiotherapy by determining the underlying tumour dynamics.
LENUS (Irish Health Repository)
Unterscheider, Julia
2013-04-01
The objective of the Prospective Observational Trial to Optimize Pediatric Health in Intrauterine Growth Restriction (IUGR) (PORTO Study), a national prospective observational multicenter study, was to evaluate which sonographic findings were associated with perinatal morbidity and mortality in pregnancies affected by growth restriction, originally defined as estimated fetal weight (EFW) <10th centile.
Subthreshold SPICE Model Optimization
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.
Modelling the interaction between flooding events and economic growth
Grames, Johanna; Fürnkranz-Prskawetz, Alexia; Grass, Dieter; Viglione, Alberto; Blöschl, Günter
2016-04-01
Recently socio-hydrology models have been proposed to analyze the interplay of community risk-coping culture, flooding damage and economic growth. These models descriptively explain the feedbacks between socio-economic development and natural disasters such as floods. Complementary to these descriptive models, we develop a dynamic optimization model, where the inter-temporal decision of an economic agent interacts with the hydrological system. This interdisciplinary approach matches with the goals of Panta Rhei i.e. to understand feedbacks between hydrology and society. It enables new perspectives but also shows limitations of each discipline. Young scientists need mentors from various scientific backgrounds to learn their different research approaches and how to best combine them such that interdisciplinary scientific work is also accepted by different science communities. In our socio-hydrology model we apply a macro-economic decision framework to a long-term flood-scenario. We assume a standard macro-economic growth model where agents derive utility from consumption and output depends on physical capital that can be accumulated through investment. To this framework we add the occurrence of flooding events which will destroy part of the capital. We identify two specific periodic long term solutions and denote them rich and poor economies. Whereas rich economies can afford to invest in flood defense and therefore avoid flood damage and develop high living standards, poor economies prefer consumption instead of investing in flood defense capital and end up facing flood damages every time the water level rises. Nevertheless, they manage to sustain at least a low level of physical capital. We identify optimal investment strategies and compare simulations with more frequent and more intense high water level events.
Directory of Open Access Journals (Sweden)
Lim, C. H.
2007-01-01
Full Text Available Production of Lactobacillus salivarius i 24, a probiotic strain for chicken, was studied in batch fermentation using 500 mL Erlenmeyer flask. Response surface method (RSM was used to optimize the medium for efficient cultivation of the bacterium. The factors investigated were yeast extract, glucose and initial culture pH. A polynomial regression model with cubic and quartic terms was used for the analysis of the experimental data. Estimated optimal conditions of the factors for growth of L. salivarius i 24 were; 3.32 % (w/v glucose, 4.31 % (w/v yeast extract and initial culture pH of 6.10.
Huang, Bing; Han, Lijuan; Li, Shaomei; Yan, Chunyan
2015-01-01
Angelica sinensis (Oliv.) Diels is an important traditional Chinese medicine, and the medicinal position is its root. This perennial herb grows vigorously only in specific areas and the environment. Tissue culture induction of callus and plant regeneration is an important and effective way to obtain large scale cultures of A. sinensis. The objective was to optimize the inductive, subculture conditions, and growth kinetics of A. sinensis. Tissue culture conditions for A. sinensis were optimized using leaves and petioles (types I and II) as explants source. Murashige and Skoog (MS) and H media supplemented with 30 g/L sucrose, 7.5 g/L agar, and varying concentrations of plant growth regulators were used for callus induction. In addition, four different basal media supplemented with 1.0 mg/L 2,4-dichlorophenoxy acetic acid (2,4-D), 0.2 mg/L 6-benzyladenine (BA) and 30 g/L sucrose were optimized for callus subculture. Finally, growth kinetics of A. sinensis cultured on different subculture media was investigated based on callus properties, including fresh weight, dry weight, medium pH, callus relative fresh weight growth, callus relative growth rate (CRGR), and sucrose content. MS medium supplemented with 5 mg/L α-naphthaleneacetic acid, 0.5 mg/L BA, 0.7 mg/L 2,4-D, 30 g/L sucrose and 7.5 g/L agar resulted in optimal callus induction in A. sinensis while petiole I was found as the best plant organ for callus induction. The B5 medium supplemented with 1.0 mg/L 2,4-D, 0.2 mg/L BA and 30 g/L sucrose displayed the best results in A. sinensis callus subculture assays. The optimized conditions could be one of the most potent methods for large-scale tissue culture of A. sinensis.
Directory of Open Access Journals (Sweden)
Anna-Sophia eWahl
2014-06-01
Full Text Available After stroke the central nervous system reveals a spectrum of intrinsic capacities to react as a highly dynamic system which can change the properties of its circuits, form new contacts, erase others, and remap related cortical and spinal cord regions. This plasticity can lead to a surprising degree of spontaneous recovery. It includes the activation of neuronal molecular mechanisms of growth and of extrinsic growth promoting factors and guidance signals in the tissue. Rehabilitative training and pharmacological interventions may modify and boost these neuronal processes, but almost nothing is known on the optimal timing of the different processes and therapeutic interventions and on their detailed interactions. Finding optimal rehabilitation paradigms requires an optimal orchestration of the internal processes of re‐organization and the therapeutic interventions in accordance with defined plastic time windows.In this review we summarize the mechanisms of spontaneous plasticity after stroke and experimental interventions to enhance growth and plasticity, with an emphasis on anti‐Nogo‐A immunotherapy. We highlight critical time windows of growth and of rehabilitative training and consider different approaches of combinatorial rehabilitative schedules. Finally, we discuss potential future strategies for designing repair and rehabilitation paradigms by introducing a 3 step model: determination of the metabolic and plastic status of the brain, pharmacological enhancement of its plastic mechanisms, and stabilization of newly formed functional connections by rehabilitative training.
A mean-field game economic growth model
Gomes, Diogo A.
2016-08-05
Here, we examine a mean-field game (MFG) that models the economic growth of a population of non-cooperative, rational agents. In this MFG, agents are described by two state variables - the capital and consumer goods they own. Each agent seeks to maximize his/her utility by taking into account statistical data about the whole population. The individual actions drive the evolution of the players, and a market-clearing condition determines the relative price of capital and consumer goods. We study the existence and uniqueness of optimal strategies of the agents and develop numerical methods to compute these strategies and the equilibrium price.
NONLINEAR MODELS FOR DESCRIPTION OF CACAO FRUIT GROWTH WITH ASSUMPTION VIOLATIONS
Directory of Open Access Journals (Sweden)
JOEL AUGUSTO MUNIZ
2017-01-01
Full Text Available Cacao (Theobroma cacao L. is an important fruit in the Brazilian economy, which is mainly cultivated in the southern State of Bahia. The optimal stage for harvesting is a major factor for fruit quality and the knowledge on its growth curves can help, especially in identifying the ideal maturation stage for harvesting. Nonlinear regression models have been widely used for description of growth curves. However, several studies in this subject do not consider the residual analysis, the existence of a possible dependence between longitudinal observations, or the sample variance heterogeneity, compromising the modeling quality. The objective of this work was to compare the fit of nonlinear regression models, considering residual analysis and assumption violations, in the description of the cacao (clone Sial-105 fruit growth. The data evaluated were extracted from Brito and Silva (1983, who conducted the experiment in the Cacao Research Center, Ilheus, State of Bahia. The variables fruit length, diameter and volume as a function of fruit age were studied. The use of weighting and incorporation of residual dependencies was efficient, since the modeling became more consistent, improving the model fit. Considering the first-order autoregressive structure, when needed, leads to significant reduction in the residual standard deviation, making the estimates more reliable. The Logistic model was the most efficient for the description of the cacao fruit growth.
DEFF Research Database (Denmark)
Jónsdóttir, Kristjana Ýr; Schmiegel, Jürgen; Jensen, Eva Bjørn Vedel
2008-01-01
In the present paper, we give a condensed review, for the nonspecialist reader, of a new modelling framework for spatio-temporal processes, based on Lévy theory. We show the potential of the approach in stochastic geometry and spatial statistics by studying Lévy-based growth modelling of planar o...... objects. The growth models considered are spatio-temporal stochastic processes on the circle. As a by product, flexible new models for space–time covariance functions on the circle are provided. An application of the Lévy-based growth models to tumour growth is discussed....
Optimization in engineering models and algorithms
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 ...
Optimization of source pencil deployment based on plant growth simulation algorithm
International Nuclear Information System (INIS)
Yang Lei; Liu Yibao; Liu Yujuan
2009-01-01
A plant growth simulation algorithm was proposed for optimizing source pencil deployment for a 60 Co irradiator. A method used to evaluate the calculation results was presented with the objective function defined by relative standard deviation of the exposure rate at the reference points, and the method to transform two kinds of control variables, i.e., position coordinates x j and y j of source pencils in the source plaque, into proper integer variables was also analyzed and solved. The results show that the plant growth simulation algorithm, which possesses both random and directional search mechanism, has good global search ability and can be used conveniently. The results are affected a little by initial conditions, and improve the uniformity in the irradiation fields. It creates a dependable field for the optimization of source bars arrangement at irradiation facility. (authors)
Directory of Open Access Journals (Sweden)
Gasser Brigitte
2006-12-01
Full Text Available Abstract Background Secretion of heterologous proteins depends both on biomass concentration and on the specific product secretion rate, which in turn is not constant at varying specific growth rates. As fed batch processes usually do not maintain a steady state throughout the feed phase, it is not trivial to model and optimize such a process by mathematical means. Results We have developed a model for product accumulation in fed batch based on iterative calculation in Microsoft Excel spreadsheets, and used the Solver software to optimize the time course of the media feed in order to maximize the volumetric productivity. The optimum feed phase consisted of an exponential feed at maximum specific growth rate, followed by a phase with linearly increasing feed rate and consequently steadily decreasing specific growth rate. The latter phase could be modeled also by exact mathematical treatment by the calculus of variations, yielding the explicit shape of the growth function, however, with certain indeterminate parameters. To evaluate the latter, one needs a numerical optimum search algorithm. The explicit shape of the growth function provides additional evidence that the Excel model results in correct data. Experimental evaluation in two independent fed batch cultures resulted in a good correlation to the optimized model data, and a 2.2 fold improvement of the volumetric productivity. Conclusion The advantages of the procedure we describe here are the ease of use and the flexibility, applying software familiar to every scientist and engineer, and rapid calculation which makes predictions extremely easy, so that many options can be tested in silico quickly. Additional options like further biological and technological constraints or different functions for specific productivity and biomass yield can easily be integrated.
A hydroeconomic modeling framework for optimal integrated management of forest and water
Garcia-Prats, Alberto; del Campo, Antonio D.; Pulido-Velazquez, Manuel
2016-10-01
Forests play a determinant role in the hydrologic cycle, with water being the most important ecosystem service they provide in semiarid regions. However, this contribution is usually neither quantified nor explicitly valued. The aim of this study is to develop a novel hydroeconomic modeling framework for assessing and designing the optimal integrated forest and water management for forested catchments. The optimization model explicitly integrates changes in water yield in the stands (increase in groundwater recharge) induced by forest management and the value of the additional water provided to the system. The model determines the optimal schedule of silvicultural interventions in the stands of the catchment in order to maximize the total net benefit in the system. Canopy cover and biomass evolution over time were simulated using growth and yield allometric equations specific for the species in Mediterranean conditions. Silvicultural operation costs according to stand density and canopy cover were modeled using local cost databases. Groundwater recharge was simulated using HYDRUS, calibrated and validated with data from the experimental plots. In order to illustrate the presented modeling framework, a case study was carried out in a planted pine forest (Pinus halepensis Mill.) located in south-western Valencia province (Spain). The optimized scenario increased groundwater recharge. This novel modeling framework can be used in the design of a "payment for environmental services" scheme in which water beneficiaries could contribute to fund and promote efficient forest management operations.
Palstra, A.P.; Mes, D.; Kusters, K.; Roques, J.A.C.; Flik, G.; Kloet, K.; Blonk, R.J.W.
2015-01-01
Swimming exercise at optimal speed may optimize growth performance of yellowtail kingfish in a recirculating aquaculture system. Therefore, optimal swimming speeds (U-opt in m s(-1) or body lengths s(-1), BL s(-1)) were assessed and then applied to determine the effects of long-term forced and
Towards Sustainable Growth Business Models
Energy Technology Data Exchange (ETDEWEB)
Kamp-Roelands, N.; Balkenende, J.P.; Van Ommen, P.
2012-03-15
The Dutch Sustainable Growth Coalition (DSGC) has the following objectives: The DSGC aims to pro-actively drive sustainable growth business models along three lines: (1) Shape. DSGC member companies aim to connect economic profitability with environmental and social progress on the basis of integrated sustainable growth business models; (2) Share. DSGC member companies aim for joint advocacy of sustainable growth business models both internationally and nationally; and (3) Stimulate. DSGC member companies aim to stimulate and influence the policy debate on enabling sustainable growth - with a view to finding solutions to the environmental and social challenges we are facing. This is their first report. The vision, actions and mission of DSGC are documented in the Manifesto in Chapter 2 of this publication. Chapter 3 contains an overview of key features of an integrated sustainable growth business model and the roadmap towards such a model. In Chapter 4, project examples of DSGC members are presented, providing insight into the hands-on reality of implementing the good practices. Chapter 5 offers an overview of how the Netherlands provides an enabling environment for sustainable growth business models. Chapter 6 offers the key conclusions.
Modeling and multidimensional optimization of a tapered free electron laser
Directory of Open Access Journals (Sweden)
Y. Jiao
2012-05-01
Full Text Available Energy extraction efficiency of a free electron laser (FEL can be greatly increased using a tapered undulator and self-seeding. However, the extraction rate is limited by various effects that eventually lead to saturation of the peak intensity and power. To better understand these effects, we develop a model extending the Kroll-Morton-Rosenbluth, one-dimensional theory to include the physics of diffraction, optical guiding, and radially resolved particle trapping. The predictions of the model agree well with that of the GENESIS single-frequency numerical simulations. In particular, we discuss the evolution of the electron-radiation interaction along the tapered undulator and show that the decreasing of refractive guiding is the major cause of the efficiency reduction, particle detrapping, and then saturation of the radiation power. With this understanding, we develop a multidimensional optimization scheme based on GENESIS simulations to increase the energy extraction efficiency via an improved taper profile and variation in electron beam radius. We present optimization results for hard x-ray tapered FELs, and the dependence of the maximum extractable radiation power on various parameters of the initial electron beam, radiation field, and the undulator system. We also study the effect of the sideband growth in a tapered FEL. Such growth induces increased particle detrapping and thus decreased refractive guiding that together strongly limit the overall energy extraction efficiency.
Portfolio optimization with mean-variance model
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.
Optimization of processing parameters on the controlled growth of c-axis oriented ZnO nanorod arrays
Energy Technology Data Exchange (ETDEWEB)
Malek, M. F., E-mail: mfmalek07@gmail.com; Rusop, M., E-mail: rusop@salam.uitm.my [NANO-ElecTronic Centre (NET), Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor (Malaysia); NANO-SciTech Centre (NST), Institute of Science (IOS), Universiti Teknologi MARA - UiTM, 40450 Shah Alam, Selangor (Malaysia); Mamat, M. H., E-mail: hafiz-030@yahoo.com [NANO-ElecTronic Centre (NET), Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor (Malaysia); Musa, M. Z., E-mail: musa948@gmail.com [NANO-ElecTronic Centre (NET), Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor (Malaysia); Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM) Pulau Pinang, Jalan Permatang Pauh, 13500 Permatang Pauh, Pulau Pinang (Malaysia); Saurdi, I., E-mail: saurdy788@gmail.com; Ishak, A., E-mail: ishak@sarawak.uitm.edu.my [NANO-ElecTronic Centre (NET), Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor (Malaysia); Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM) Sarawak, Kampus Kota Samarahan, Jalan Meranek, 94300 Kota Samarahan, Sarawak (Malaysia); Alrokayan, Salman A. H., E-mail: dr.salman@alrokayan.com; Khan, Haseeb A., E-mail: khan-haseeb@yahoo.com [Chair of Targeting and Treatment of Cancer Using Nanoparticles, Deanship of Scientific Research, King Saud University (KSU), Riyadh 11451 (Saudi Arabia)
2016-07-06
Optimization of the growth time parameter was conducted to synthesize high-quality c-axis ZnO nanorod arrays. The effects of the parameter on the crystal growth and properties were systematically investigated. Our studies confirmed that the growth time influence the properties of ZnO nanorods where the crystallite size of the structures was increased at higher deposition time. Field emission scanning electron microsope analysis confirmed the morphologies structure of the ZnO nanorods. The ZnO nanostructures prepared under the optimized growth conditions showed an intense XRD peak which reveal a higher c-axis oriented ZnO nanorod arrays thus demonstrating the formation of defect free structure.
The growth response of plants to elevated CO2 under non-optimal environmental conditions
Poorter, H.; Pérez-Soba, M.
2001-01-01
Under benign environmental conditions, plant growth is generally stimulated by elevated atmospheric CO2 concentrations. When environmental conditions become sub- or supra-optimal for growth, changes in the biomass enhancement ratio (BER; total plant biomass at elevated CO2 divided by plant biomass
The Growth Trade-off between Direct and Indirect Taxes in South Africa: Evidence from a STR Model
Directory of Open Access Journals (Sweden)
Andrew Phiri
2016-09-01
Full Text Available The tax system forms the backbone to the functioning of the South African fiscal authorities and it is has been recently questioned whether alterations in the existing tax mix could promote economic growth. Using quarterly data from 1990:Q1 and 2015:Q2, this study investigated the effects of direct and indirect taxes on economic growth for South Africa using the recently developed smooth transition regression (STR model. Our findings suggest an optimal tax of 10.27 percent on the indirect tax-growth ratio, of which below this rate indirect taxes are positively related with economic growth whereas direct taxes are negatively related with growth. Above the optimal tax rate, taxation bears no significant relationship with economic growth. We therefore suggest that policymakers place a greater burden on indirect taxes and yet ensure that the contribution of indirect taxes to economic growth does not exceed the threshold of 10.27 percent.
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.
Handbook on modelling for discrete optimization
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...
Optimal design of multistage chemostats in series using different microbial growth kinetics
Energy Technology Data Exchange (ETDEWEB)
Qasim, Muhammad [Petroleum Engineering Technology, Abu Dhabi Polytechnic (United Arab Emirates)
2013-07-01
In this paper, the optimum design of multistage chemostats (CSTRs) was investigated. The optimal design was based on the minimum overall reactor volume using different volume for each chemostat. The paper investigates three different microbial growth kinetics; Monod kinetics, Contois kinetics and the Logistic equation. The total dimensionless residence time (theta Total) was set as the optimization objective function that was minimized by varying the intermediate dimensionless substrate concentration (alfa i). The effect of inlet substrate concentration (S0) to the first reactor on the optimized total dimensionless residence time was investigated at a constant conversion of 0.90. In addition, the effect of conversion on the optimized total dimensionless residence time was also investigated at constant inlet substrate concentration (S0). For each case, optimization was done using up to five chemostats in series.
Pollution and economic growth in a model of overlapping generations
Energy Technology Data Exchange (ETDEWEB)
Fisher, Eric O`N. [Department of Economics, The Ohio State University, Columbus, OH (United States); Van Marrewijk, Charles [Department of Economics, Erasmus University, Rotterdam (Netherlands)
1994-01-22
We analyze a model of overlapping generations in which clean air, a pure public consumption good, is used as a private input into production. Although production exhibits constant returns to scale, endogenous growth can occur because the economy has tWO sectors. In a laissez-faire equilibrium, there is no market for pollution rights, and firms appropriate clean air in an arbitrary manner. Growth occurs only if the marginal propensity to save is high enough and the asymptotic share of pollution in the investment sector is zero. Firms generate quasi-rents that are the value of pollution rights. These quasi-rents crowd out investment and slow economic growth. A laissez- faire equilibrium may not support Pareto optimal allocations, but a Pigouvian tax with lump-sum distribution of the resulting revenues does. Hence, a pollution lax yields a double dividend because it can increase both the static efficiency of the economy and its growth rate. 1 fig., 20 refs.
Pollution and economic growth in a model of overlapping generations
International Nuclear Information System (INIS)
Fisher, Eric O'N.; Van Marrewijk, Charles
1994-01-01
We analyze a model of overlapping generations in which clean air, a pure public consumption good, is used as a private input into production. Although production exhibits constant returns to scale, endogenous growth can occur because the economy has tWO sectors. In a laissez-faire equilibrium, there is no market for pollution rights, and firms appropriate clean air in an arbitrary manner. Growth occurs only if the marginal propensity to save is high enough and the asymptotic share of pollution in the investment sector is zero. Firms generate quasi-rents that are the value of pollution rights. These quasi-rents crowd out investment and slow economic growth. A laissez- faire equilibrium may not support Pareto optimal allocations, but a Pigouvian tax with lump-sum distribution of the resulting revenues does. Hence, a pollution lax yields a double dividend because it can increase both the static efficiency of the economy and its growth rate. 1 fig., 20 refs
Biodegradation of kerosene: Study of growth optimization and metabolic fate of P. janthinellum SDX7
Directory of Open Access Journals (Sweden)
Shamiyan R. Khan
2015-06-01
Full Text Available Penicillum janthinellum SDX7 was isolated from aged petroleum hydrocarbon-affected soil at the site of Anand, Gujarat, India, and was tested for different pH, temperature, agitation and concentrations for optimal growth of the isolate that was capable of degrading upto 95%, 63% and 58% of 1%, 3% and 5% kerosene, respectively, after a period of 16 days, at optimal growth conditions of pH 6.0, 30 °C and 180 rpm agitation. The GC/MS chromatograms revealed that then-alkane fractions are easily degraded; however, the rate might be lower for branched alkanes, n-alkylaromatics, cyclic alkanes and polynuclear aromatics. The test doses caused a concentration-dependent depletion of carbohydrates of P. janthinellum SDX7 by 3% to 80%, proteins by 4% to 81% and amino acids by 8% to 95% upto 16 days of treatment. The optimal concentration of 3% kerosene resulted in the least reduction of the metabolites of P. janthinellum such as carbohydrates, proteins and amino acids with optimal growth compared to 5% and 1% (v/v kerosene doses on the 12th and 16th day of exposure. Phenols were found to be mounted by 43% to 66% at lower and higher concentrations during the experimental period. Fungal isolate P. janthinellum SDX7 was also tested for growth on various xenobiotic compounds.
McNeish, Daniel; Dumas, Denis
2017-01-01
Recent methodological work has highlighted the promise of nonlinear growth models for addressing substantive questions in the behavioral sciences. In this article, we outline a second-order nonlinear growth model in order to measure a critical notion in development and education: potential. Here, potential is conceptualized as having three components-ability, capacity, and availability-where ability is the amount of skill a student is estimated to have at a given timepoint, capacity is the maximum amount of ability a student is predicted to be able to develop asymptotically, and availability is the difference between capacity and ability at any particular timepoint. We argue that single timepoint measures are typically insufficient for discerning information about potential, and we therefore describe a general framework that incorporates a growth model into the measurement model to capture these three components. Then, we provide an illustrative example using the public-use Early Childhood Longitudinal Study-Kindergarten data set using a Michaelis-Menten growth function (reparameterized from its common application in biochemistry) to demonstrate our proposed model as applied to measuring potential within an educational context. The advantage of this approach compared to currently utilized methods is discussed as are future directions and limitations.
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.
Product unit neural network models for predicting the growth limits of Listeria monocytogenes.
Valero, A; Hervás, C; García-Gimeno, R M; Zurera, G
2007-08-01
A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.
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.
Xylem formation can be modeled statistically as a function of primary growth and cambium activity.
Huang, Jian-Guo; Deslauriers, Annie; Rossi, Sergio
2014-08-01
Primary (budburst, foliage and shoot) growth and secondary (cambium and xylem) growth of plants play a vital role in sequestering atmospheric carbon. However, their potential relationships have never been mathematically quantified and the underlying physiological mechanisms are unclear. We monitored primary and secondary growth in Picea mariana and Abies balsamea on a weekly basis from 2010 to 2013 at four sites over an altitudinal gradient (25-900 m) in the eastern Canadian boreal forest. We determined the timings of onset and termination through the fitted functions and their first derivative. We quantified the potential relationships between primary growth and secondary growth using the mixed-effects model. We found that xylem formation of boreal conifers can be modeled as a function of cambium activity, bud phenology, and shoot and needle growth, as well as species- and site-specific factors. Our model reveals that there may be an optimal mechanism to simultaneously allocate the photosynthetic products and stored nonstructural carbon to growth of different organs at different times in the growing season. This mathematical link can bridge phenological modeling, forest ecosystem productivity and carbon cycle modeling, which will certainly contribute to an improved prediction of ecosystem productivity and carbon equilibrium. © 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.
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.
Stelian, Carmen; Velázquez, Matias; Veber, Philippe; Ahmine, Abdelmounaim; Sand, Jean-Baptiste; Buşe, Gabriel; Cabane, Hugues; Duffar, Thierry
2018-06-01
Lithium molybdate Li2MoO4 (LMO) crystals of mass ranging between 350 and 500 g are excellent candidates to build heat-scintillation cryogenic bolometers likely to be used for the detection of rare events in astroparticle physics. In this work, numerical modeling is applied in order to investigate the Czochralski growth of Li2MoO4 crystals in an inductive furnace. The numerical model was validated by comparing the numerical predictions of the crystal-melt interface shape to experimental visualization of the growth interface. Modeling was performed for two different Czochralski furnaces that use inductive heating. The simulation of the first furnace, which was used to grow Li2MoO4 crystals of 3-4 cm in diameter, reveals non-optimal heat transfer conditions for obtaining good quality crystals. The second furnace, which will be used to grow crystals of 5 cm in diameter, was numerically optimized in order to reduce the temperature gradients in the crystal and to avoid fast crystallization of the bath at the later stages of the growth process.
Optimization Modeling with Spreadsheets
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
Analysis, numerics, and optimization of algae growth
Kumar, K.; Pisarenco, M.; Rudnaya, M.; Savcenco, V.
2010-01-01
We extend the mathematical model for algae growth as described in [11] to include new effects. The roles of light, nutrients and acidity of the water body are taken into account. Important properties of the model such as existence and uniqueness of solution, as well as boundedness and positivity are
Modeling and optimization of laser cutting operations
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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.
Li, Jian; Fei, Ze-yuan; Xu, Yi-feng; Wang, Jie; Fan, Bing-feng; Ma, Xue-jin; Wang, Gang
2018-02-01
Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.
Modeling marine surface microplastic transport to assess optimal removal locations
Sherman, Peter; van Sebille, Erik
2016-01-01
Marine plastic pollution is an ever-increasing problem that demands immediate mitigation and reduction plans. Here, a model based on satellite-tracked buoy observations and scaled to a large data set of observations on microplastic from surface trawls was used to simulate the transport of plastics floating on the ocean surface from 2015 to 2025, with the goal to assess the optimal marine microplastic removal locations for two scenarios: removing the most surface microplastic and reducing the impact on ecosystems, using plankton growth as a proxy. The simulations show that the optimal removal locations are primarily located off the coast of China and in the Indonesian Archipelago for both scenarios. Our estimates show that 31% of the modeled microplastic mass can be removed by 2025 using 29 plastic collectors operating at a 45% capture efficiency from these locations, compared to only 17% when the 29 plastic collectors are moored in the North Pacific garbage patch, between Hawaii and California. The overlap of ocean surface microplastics and phytoplankton growth can be reduced by 46% at our proposed locations, while sinks in the North Pacific can only reduce the overlap by 14%. These results are an indication that oceanic plastic removal might be more effective in removing a greater microplastic mass and in reducing potential harm to marine life when closer to shore than inside the plastic accumulation zones in the centers of the gyres.
Modeling marine surface microplastic transport to assess optimal removal locations
International Nuclear Information System (INIS)
Sherman, Peter; Van Sebille, Erik
2016-01-01
Marine plastic pollution is an ever-increasing problem that demands immediate mitigation and reduction plans. Here, a model based on satellite-tracked buoy observations and scaled to a large data set of observations on microplastic from surface trawls was used to simulate the transport of plastics floating on the ocean surface from 2015 to 2025, with the goal to assess the optimal marine microplastic removal locations for two scenarios: removing the most surface microplastic and reducing the impact on ecosystems, using plankton growth as a proxy. The simulations show that the optimal removal locations are primarily located off the coast of China and in the Indonesian Archipelago for both scenarios. Our estimates show that 31% of the modeled microplastic mass can be removed by 2025 using 29 plastic collectors operating at a 45% capture efficiency from these locations, compared to only 17% when the 29 plastic collectors are moored in the North Pacific garbage patch, between Hawaii and California. The overlap of ocean surface microplastics and phytoplankton growth can be reduced by 46% at our proposed locations, while sinks in the North Pacific can only reduce the overlap by 14%. These results are an indication that oceanic plastic removal might be more effective in removing a greater microplastic mass and in reducing potential harm to marine life when closer to shore than inside the plastic accumulation zones in the centers of the gyres. (letter)
Modeling determinants of growth: evidence for a community-based target in height?
Aßmann, Christian; Hermanussen, Michael
2013-07-01
Human growth is traditionally envisaged as a target-seeking process regulated by genes, nutrition, health, and the state of an individual's social and economic environment; it is believed that under optimal physical conditions, an individual will achieve his or her full genetic potential. Using a panel data set on individual height increments, we suggest a statistical modeling approach that characterizes growth as first-order trend stationary and allows for controlling individual growth tempo via observable measures of individual maturity. A Bayesian framework and corresponding Markov-chain Monte Carlo techniques allowing for a conceptually stringent treatment of missing values are adapted for parameter estimation. The model provides evidence for the adjustment of the individual growth rate toward average height of the population. The increase in adult body height during the past 150 y has been explained by the steady improvement of living conditions that are now being considered to have reached an optimum in Western societies. The current investigation questions the notion that the traditional concept in the understanding of this target-seeking process is sufficient. We consider an additional regulator that possibly points at community-based target seeking in growth.
Auconi, Pietro; Scazzocchio, Marco; Defraia, Efisio; McNamara, James A; Franchi, Lorenzo
2014-04-01
To develop a mathematical model that adequately represented the pattern of craniofacial growth in class III subject consistently, with the goal of using this information to make growth predictions that could be amenable to longitudinal verification and clinical use. A combination of computational techniques (i.e. Fuzzy clustering and Network analysis) was applied to cephalometric data derived from 429 untreated growing female patients with class III malocclusion to visualize craniofacial growth dynamics and correlations. Four age groups of subjects were examined individually: from 7 to 9 years of age, from 10 to 12 years, from 13 to 14 years, and from 15 to 17 years. The connections between pathway components of class III craniofacial growth can be visualized from Network profiles. Fuzzy clustering analysis was able to define further growth patterns and coherences of the traditionally reported dentoskeletal characteristics of this structural imbalance. Craniofacial growth can be visualized as a biological, space-constraint-based optimization process; the prediction of individual growth trajectories depends on the rate of membership to a specific 'winner' cluster, i.e. on a specific individual growth strategy. The reliability of the information thus gained was tested to forecast craniofacial growth of 28 untreated female class III subjects followed longitudinally. The combination of Fuzzy clustering and Network algorithms allowed the development of principles for combining multiple auxological cephalometric features into a joint global model and to predict the individual risk of the facial pattern imbalance during growth.
Visual prosthesis wireless energy transfer system optimal modeling.
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.
De Lara, Michel
2006-05-01
In their 1990 paper Optimal reproductive efforts and the timing of reproduction of annual plants in randomly varying environments, Amir and Cohen considered stochastic environments consisting of i.i.d. sequences in an optimal allocation discrete-time model. We suppose here that the sequence of environmental factors is more generally described by a Markov chain. Moreover, we discuss the connection between the time interval of the discrete-time dynamic model and the ability of the plant to rebuild completely its vegetative body (from reserves). We formulate a stochastic optimization problem covering the so-called linear and logarithmic fitness (corresponding to variation within and between years), which yields optimal strategies. For "linear maximizers'', we analyse how optimal strategies depend upon the environmental variability type: constant, random stationary, random i.i.d., random monotonous. We provide general patterns in terms of targets and thresholds, including both determinate and indeterminate growth. We also provide a partial result on the comparison between ;"linear maximizers'' and "log maximizers''. Numerical simulations are provided, allowing to give a hint at the effect of different mathematical assumptions.
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.
Evaluating and optimizing horticultural regimes in space plant growth facilities
Berkovich, Y.; Chetirkin, R.; Wheeler, R.; Sager, J.
In designing innovative Space Plant Growth Facilities (SPGF) for long duration space f ightl various limitations must be addressed including onboard resources: volume, energy consumption, heat transfer and crew labor expenditure. The required accuracy in evaluating onboard resources by using the equivalent mass methodology and applying it to the design of such facilities is not precise. This is due to the uncertainty of the structure and not completely understanding of the properties of all associated hardware, including the technology in these systems. We present a simple criteria of optimization for horticultural regimes in SPGF: Qmax = max [M · (EBI) 2 / (V · E · T) ], where M is the crop harvest in terms of total dry biomass in the plant growth system; EBI is the edible biomass index (harvest index), V is a volume occupied by the crop; E is the crop light energy supply during growth; T is the crop growth duration. The criterion reflects directly on the consumption of onboard resources for crop production. We analyzed the efficiency of plant crops and the environmental parameters by examining the criteria for 15 salad and 12 wheat crops from the data in the ALS database at Kennedy Space Center. Some following conclusion have been established: 1. The technology involved in growing salad crops on a cylindrical type surface provides a more meaningful Q-criterion; 2. Wheat crops were less efficient than leafy greens (salad crops) when examining resource utilization; 3. By increasing light intensity of the crop the efficiency of the resource utilization could decrease. Using the existing databases and Q-criteria we have found that the criteria can be used in optimizing design and horticultural regimes in the SPGF.
Directory of Open Access Journals (Sweden)
Benoit ePallas
2013-11-01
Full Text Available The ability to assimilate C and allocate NSC (non structural carbohydrates to the most appropriate organs is crucial to maximize plant ecological or agronomic performance. Such C source and sink activities are differentially affected by environmental constraints. Under drought, plant growth is generally more sink than source limited as organ expansion or appearance rate is earlier and stronger affected than C assimilation. This favors plant survival and recovery but not always agronomic performance as NSC are stored rather than used for growth due to a modified metabolism in source and sink leaves. Such interactions between plant C and water balance are complex and plant modeling can help analyzing their impact on plant phenotype. This paper addresses the impact of trade-offs between C sink and source activities and plant production under drought, combining experimental and modeling approaches. Two contrasted monocotyledonous species (rice, oil palm were studied. Experimentally, the sink limitation of plant growth under moderate drought was confirmed as well as the modifications in NSC metabolism in source and sink organs. Under severe stress, when C source became limiting, plant NSC concentration decreased. Two plant models dedicated to oil palm and rice morphogenesis were used to perform a sensitivity analysis and further explore how to optimize C sink and source drought sensitivity to maximize plant growth. Modeling results highlighted that optimal drought sensitivity depends both on drought type and species and that modeling is a great opportunity to analyse such complex processes. Further modeling needs and more generally the challenge of using models to support complex trait breeding are discussed.
Shifts in growth strategies reflect tradeoffs in cellular economics
Molenaar, Douwe; van Berlo, Rogier; de Ridder, Dick; Teusink, Bas
2009-01-01
The growth rate-dependent regulation of cell size, ribosomal content, and metabolic efficiency follows a common pattern in unicellular organisms: with increasing growth rates, cell size and ribosomal content increase and a shift to energetically inefficient metabolism takes place. The latter two phenomena are also observed in fast growing tumour cells and cell lines. These patterns suggest a fundamental principle of design. In biology such designs can often be understood as the result of the optimization of fitness. Here we show that in basic models of self-replicating systems these patterns are the consequence of maximizing the growth rate. Whereas most models of cellular growth consider a part of physiology, for instance only metabolism, the approach presented here integrates several subsystems to a complete self-replicating system. Such models can yield fundamentally different optimal strategies. In particular, it is shown how the shift in metabolic efficiency originates from a tradeoff between investments in enzyme synthesis and metabolic yields for alternative catabolic pathways. The models elucidate how the optimization of growth by natural selection shapes growth strategies. PMID:19888218
Optimal control applied to native-invasive species competition via a PDE model
Directory of Open Access Journals (Sweden)
Wandi Ding
2012-12-01
Full Text Available We consider an optimal control problem of a system of parabolic partial differential equations modelling the competition between an invasive and a native species. The motivating example is cottonwood-salt cedar competition, where the effect of disturbance in the system (such as flooding is taken to be a control variable. Flooding being detrimental at low and high levels, and advantageous at medium levels led us to consider the quadratic growth function of the control. The objective is to maximize the native species and minimize the invasive species while minimizing the cost of implementing the control. An existence result for an optimal control is given. Numerical examples are presented to illustrate the results.
Ran, Tao; Liu, Yong; Li, Hengzhi; Tang, Shaoxun; He, Zhixiong; Munteanu, Cristian R; González-Díaz, Humberto; Tan, Zhiliang; Zhou, Chuanshe
2016-07-27
The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R(2) of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system.
Optimization of heat saving in buildings using unsteady heat transfer model
Directory of Open Access Journals (Sweden)
Dedinec Aleksandra
2015-01-01
Full Text Available Reducing the energy consumption growth rate is increasingly becoming one of the main challenges for ensuring sustainable development, particularly in the buildings as the largest end-use sector in many countries. Along this line, the aim of this paper is to analyse the possibilities for energy savings in the construction of new buildings and reconstruction of the existing ones developing a tool that, in terms of the available heating technologies and insulation, provides answer to the problem of optimal cost effective energy consumption. The tool is composed of an unsteady heat transfer model which is incorporated into a cost-effective energy saving optimization. The unsteady heat transfer model uses annual hourly meteorological data, chosen as typical for the last ten-year period, as well as thermo physical features of the layers of the building walls. The model is tested for the typical conditions in the city of Skopje, Macedonia. The results show that the most cost effective heating technology for the given conditions is the wood fired stove, followed by the inverter air-conditioner. The centralized district heating and the pellet fired stoves are the next options. The least cost effective option is the panel that uses electricity. In this paper, the optimal insulation thickness is presented for each type of heating technology.
Directory of Open Access Journals (Sweden)
Nathaphon Boonnam
2017-07-01
Full Text Available Greenhouse cultivation is easy to keep up and control important factors such as light, temperature, and humidity. Using of sensors and actuators in the greenhouse to capture different values allows for the control of the equipment, it can also be optimized for growth at optimal temperature and humidity of various crops planted. We use wireless sensor networks’ system by sending results to the cloud service, monitoring values, and devices’s controlling via smart phone. The results of this study are useful for growing crops not only in technical parts, but also in physical part; it was evaluated by questionnaire using technology acceptance model.
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...
Alaa F. Sheta; Amal Abdel-Raouf
2016-01-01
In this age of technology, building quality software is essential to competing in the business market. One of the major principles required for any quality and business software product for value fulfillment is reliability. Estimating software reliability early during the software development life cycle saves time and money as it prevents spending larger sums fixing a defective software product after deployment. The Software Reliability Growth Model (SRGM) can be used to predict the number of...
Latent Growth and Dynamic Structural Equation Models.
Grimm, Kevin J; Ram, Nilam
2018-05-07
Latent growth models make up a class of methods to study within-person change-how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.
Testing mechanistic models of growth in insects.
Maino, James L; Kearney, Michael R
2015-11-22
Insects are typified by their small size, large numbers, impressive reproductive output and rapid growth. However, insect growth is not simply rapid; rather, insects follow a qualitatively distinct trajectory to many other animals. Here we present a mechanistic growth model for insects and show that increasing specific assimilation during the growth phase can explain the near-exponential growth trajectory of insects. The presented model is tested against growth data on 50 insects, and compared against other mechanistic growth models. Unlike the other mechanistic models, our growth model predicts energy reserves per biomass to increase with age, which implies a higher production efficiency and energy density of biomass in later instars. These predictions are tested against data compiled from the literature whereby it is confirmed that insects increase their production efficiency (by 24 percentage points) and energy density (by 4 J mg(-1)) between hatching and the attainment of full size. The model suggests that insects achieve greater production efficiencies and enhanced growth rates by increasing specific assimilation and increasing energy reserves per biomass, which are less costly to maintain than structural biomass. Our findings illustrate how the explanatory and predictive power of mechanistic growth models comes from their grounding in underlying biological processes. © 2015 The Author(s).
Dynamic optimization deterministic and stochastic models
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.
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
Slininger, P J; Dien, B S; Lomont, J M; Bothast, R J; Ladisch, M R; Okos, M R
2014-08-01
Scheffersomyces (formerly Pichia) stipitis is a potential biocatalyst for converting lignocelluloses to ethanol because the yeast natively ferments xylose. An unstructured kinetic model based upon a system of linear differential equations has been formulated that describes growth and ethanol production as functions of ethanol, oxygen, and xylose concentrations for both growth and fermentation stages. The model was validated for various growth conditions including batch, cell recycle, batch with in situ ethanol removal and fed-batch. The model provides a summary of basic physiological yeast properties and is an important tool for simulating and optimizing various culture conditions and evaluating various bioreactor designs for ethanol production. © 2014 Wiley Periodicals, Inc.
Dynamic optimization in environmental economics
International Nuclear Information System (INIS)
Moser, Elke; Tragler, Gernot; Veliov, Vladimir M.; Semmler, Willi
2014-01-01
This book contains two chapters with the topics: 1. Chapter: INTERACTIONS BETWEEN ECONOMY AND CLIMATE: (a) Climate Change and Technical Progress: Impact of Informational Constraints. (b) Environmental Policy in a Dynamic Model with Heterogeneous Agents and Voting. (c) Optimal Environmental Policy in the Presence of Multiple Equilibria and Reversible Hysteresis. (d). Modeling the Dynamics of the Transition to a Green Economy. (e) One-Parameter GHG Emission Policy With R and D-Based Growth. (f) Pollution, Public Health Care, and Life Expectancy when Inequality Matters. (g) Uncertain Climate Policy and the Green Paradox. (h) Uniqueness Versus Indeterminacy in the Tragedy of the Commons - A ''Geometric'' Approach. 2. Chapter: OPTIMAL EXTRACTION OF RESOURCES: (j) Dynamic Behavior of Oil Importers and Exporters Under Uncertainty. (k) Robust Control of a Spatially Distributed Commercial Fishery. (l) On the Effect of Resource Exploitation on Growth: Domestic Innovation vs. Technological Diffusion Through Trade. (m) Forest Management and Biodiversity in Size-Structured Forests Under Climate Change. (n) Carbon Taxes and Comparison of Trading Regimes in Fossil Fuels. (o) Landowning, Status and Population Growth. (p) Optimal Harvesting of Size-Structured Biological Populations.
Chen, Yu; Dong, Fengqing; Wang, Yonghong
2016-09-01
With determined components and experimental reducibility, the chemically defined medium (CDM) and the minimal chemically defined medium (MCDM) are used in many metabolism and regulation studies. This research aimed to develop the chemically defined medium supporting high cell density growth of Bacillus coagulans, which is a promising producer of lactic acid and other bio-chemicals. In this study, a systematic methodology combining the experimental technique with flux balance analysis (FBA) was proposed to design and simplify a CDM. The single omission technique and single addition technique were employed to determine the essential and stimulatory compounds, before the optimization of their concentrations by the statistical method. In addition, to improve the growth rationally, in silico omission and addition were performed by FBA based on the construction of a medium-size metabolic model of B. coagulans 36D1. Thus, CDMs were developed to obtain considerable biomass production of at least five B. coagulans strains, in which two model strains B. coagulans 36D1 and ATCC 7050 were involved.
Modeling and Design Optimization of Variable-Speed Wind Turbine Systems
Directory of Open Access Journals (Sweden)
Ulas Eminoglu
2014-01-01
Full Text Available As a result of the increase in energy demand and government subsidies, the usage of wind turbine system (WTS has increased dramatically. Due to the higher energy production of a variable-speed WTS as compared to a fixed-speed WTS, the demand for this type of WTS has increased. In this study, a new method for the calculation of the power output of variable-speed WTSs is proposed. The proposed model is developed from the S-type curve used for population growth, and is only a function of the rated power and rated (nominal wind speed. It has the advantage of enabling the user to calculate power output without using the rotor power coefficient. Additionally, by using the developed model, a mathematical method to calculate the value of rated wind speed in terms of turbine capacity factor and the scale parameter of the Weibull distribution for a given wind site is also proposed. Design optimization studies are performed by using the particle swarm optimization (PSO and artificial bee colony (ABC algorithms, which are applied into this type of problem for the first time. Different sites such as Northern and Mediterranean sites of Europe have been studied. Analyses for various parameters are also presented in order to evaluate the effect of rated wind speed on the design parameters and produced energy cost. Results show that proposed models are reliable and very useful for modeling and optimization of WTSs design by taking into account the wind potential of the region. Results also show that the PSO algorithm has better performance than the ABC algorithm for this type of problem.
Pyomo optimization modeling in Python
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...
Stygar, A H; Kristensen, A R; Makulska, J
2014-08-01
The aim of this study was to provide farmers an efficient tool for supporting optimal decisions in the beef heifer rearing process. The complexity of beef heifer management prompted the development of a model including decisions on the feeding level during prepuberty (age optimal rearing strategy was found by maximizing the total discounted net revenues from the predicted future productivity of the Polish Limousine heifers defined as the cumulative BW of calves born from a cow calved until the age of 5 yr, standardized on the 210th day of age. According to the modeled optimal policy, heifers fed during the whole rearing period at the ADG of 810 g/d and generally weaned after the maximum suckling period of 9 mo should already be bred at the age of 13.2 mo and BW constituting 55.6% of the average mature BW. Based on the optimal strategy, 52% of all heifers conceived from May to July and calved from February to April. This optimal rearing pattern resulted in an average net return of EUR 311.6 per pregnant heifer. It was found that the economic efficiency of beef operations can be improved by applying different herd management practices to those currently used in Poland. Breeding at 55.6% of the average mature BW, after a shorter and less expensive rearing period, resulted in an increase in the average net return per heifer by almost 18% compared to the conventional system, in which heifers were bred after attaining 65% of the mature BW. Extension of the rearing period by 2.5 mo (breeding at the age 15.7 mo), due to a prepubertal growth rate lowered by 200 g, reduced the average net return per heifer by 6.2% compared to the results obtained under the basic model assumptions. In the future, the model may also be extended to investigate additional aspects of the beef heifer development, such as the environmental impacts of various heifer management decisions.
A model of optimal protein allocation during phototrophic growth
Czech Academy of Sciences Publication Activity Database
Faizi, M.; Zavřel, Tomáš; Loureiro, C.; Červený, Jan; Steuer, Ralf
2018-01-01
Roč. 166, apr (2018), s. 26-36 ISSN 0303-2647 R&D Projects: GA MŠk(CZ) LO1415; GA ČR(CZ) GA15-17367S; GA MŠk(CZ) LM2015055 Institutional support: RVO:86652079 Keywords : Cellular protein economy * Cyanobacteria * Microbial growth laws * Photosynthesis * Resource allocation * Systems biology Subject RIV: EI - Biotechnology ; Bionics OBOR OECD: Environmental biotechnology Impact factor: 1.652, year: 2016
Mohammad Aghaei; Amin Asadollahi; Elham Vahedi; Mahdi Pirooz
2013-01-01
To maintain and achieve optimal growth, development and to be more competitive, organizations need a comprehensive and coherent plan compatible with their objectives and goals which is called strategic planning. This research aims to analyse strategically “Etka Chain Stores” and to propose optimal strategies by using SWOT model and based on fuzzy logic. The scope of this research is limited to “Etka Chain stores in Tehran”. As instrumentation, a questioner, consisting of 138 questions, was us...
Mazzoleni, Stefano; Landi, Carmine; Cartenì, Fabrizio; de Alteriis, Elisabetta; Giannino, Francesco; Paciello, Lucia; Parascandola, Palma
2015-07-30
Microbial population dynamics in bioreactors depend on both nutrients availability and changes in the growth environment. Research is still ongoing on the optimization of bioreactor yields focusing on the increase of the maximum achievable cell density. A new process-based model is proposed to describe the aerobic growth of Saccharomyces cerevisiae cultured on glucose as carbon and energy source. The model considers the main metabolic routes of glucose assimilation (fermentation to ethanol and respiration) and the occurrence of inhibition due to the accumulation of both ethanol and other self-produced toxic compounds in the medium. Model simulations reproduced data from classic and new experiments of yeast growth in batch and fed-batch cultures. Model and experimental results showed that the growth decline observed in prolonged fed-batch cultures had to be ascribed to self-produced inhibitory compounds other than ethanol. The presented results clarify the dynamics of microbial growth under different feeding conditions and highlight the relevance of the negative feedback by self-produced inhibitory compounds on the maximum cell densities achieved in a bioreactor.
Rayleigh-Taylor instability under curved substrates: An optimal transient growth analysis
Balestra, Gioele; Brun, P.-T.; Gallaire, François
2016-12-01
We investigate the stability of thin viscous films coated on the inside of a horizontal cylindrical substrate. In such a case, gravity acts both as a stabilizing force through the progressive drainage of the film and as a destabilizing force prone to form droplets via the Rayleigh-Taylor instability. The drainage solution, derived from lubrication equations, is found asymptotically stable with respect to infinitesimally small perturbations, although in reality, droplets often form. To resolve this paradox, we perform an optimal transient growth analysis for the first-order perturbations of the liquid's interface, generalizing the results of Trinh et al. [Phys. Fluids 26, 051704 (2014), 10.1063/1.4876476]. We find that the system displays a linear transient growth potential that gives rise to two different scenarios depending on the value of the Bond number (prescribing the relative importance of gravity and surface tension forces). At low Bond numbers, the optimal perturbation of the interface does not generate droplets. In contrast, for higher Bond numbers, perturbations on the upper hemicircle yield gains large enough to potentially form droplets. The gain increases exponentially with the Bond number. In particular, depending on the amplitude of the initial perturbation, we find a critical Bond number above which the short-time linear growth is sufficient to trigger the nonlinear effects required to form dripping droplets. We conclude that the transition to droplets detaching from the substrate is noise and perturbation dependent.
Surrogate-Based Optimization of Biogeochemical Transport Models
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.
Layered growth model and epitaxial growth structures for SiCAlN alloys
International Nuclear Information System (INIS)
Liu Zhaoqing; Ni Jun; Su Xiaoao; Dai Zhenhong
2009-01-01
Epitaxial growth structures for (SiC) 1-x (AlN) x alloys are studied using a layered growth model. First-principle calculations are used to determine the parameters in the layered growth model. The phase diagrams of epitaxial growth are given. There is a rich variety of the new metastable polytype structures at x=1/6 ,1/5 ,1/4 ,1/3 , and 1/2 in the layered growth phase diagrams. We have also calculated the electronic properties of the short periodical SiCAlN alloys predicted by our layered growth model. The results show that various ordered structures of (SiC) 1-x (AlN) x alloys with the band gaps over a wide range are possible to be synthesized by epitaxial growth.
Spot-shadowing optimization to mitigate damage growth in a high-energy-laser amplifier chain.
Bahk, Seung-Whan; Zuegel, Jonathan D; Fienup, James R; Widmayer, C Clay; Heebner, John
2008-12-10
A spot-shadowing technique to mitigate damage growth in a high-energy laser is studied. Its goal is to minimize the energy loss and undesirable hot spots in intermediate planes of the laser. A nonlinear optimization algorithm solves for the complex fields required to mitigate damage growth in the National Ignition Facility amplifier chain. The method is generally applicable to any large fusion laser.
A model for predicting the growth of Eucalyptus globulus seedling stands in Bolivia
Energy Technology Data Exchange (ETDEWEB)
Guzman, G.; Morales, M.; Pukkala, T.; Miguel, S. de
2012-11-01
Eucalyptus globulus is one of the most planted species in the Inter-Andean Valleys of Bolivia, where growing conditions are different from most places where eucalyptus have been studied. This prevents a straightforward utilization of models fitted elsewhere. In this study a distance-independent individual-tree growth model for E. globulus plantations in Bolivia was developed based on data from 67 permanent sample plots. The model consists of sub-models for dominant height, tree diameter increment, height-diameter relationship and survival. According to model-based simulations, the mean annual increment with the optimal rotation length is about 13 m3 ha{sup -}1 yr{sup -}1 on medium-quality sites and 18 m{sup 3} ha{sup -}1 yr -1 on the best sites. A suitable rotation length for maximizing wood production is approximately 30 years on medium sites and 20 years on the most productive sites. The developed models provide valuable information for further studies on optimizing the management and evaluating alternative management regimes for the species. (Author) 22 refs.
System dynamic modelling of industrial growth and landscape ecology in China.
Xu, Jian; Kang, Jian; Shao, Long; Zhao, Tianyu
2015-09-15
With the rapid development of large industrial corridors in China, the landscape ecology of the country is currently being affected. Therefore, in this study, a system dynamic model with multi-dimensional nonlinear dynamic prediction function that considers industrial growth and landscape ecology is developed and verified to allow for more sustainable development. Firstly, relationships between industrial development and landscape ecology in China are examined, and five subsystems are then established: industry, population, urban economy, environment and landscape ecology. The main influencing factors are then examined for each subsystem to establish flow charts connecting those factors. Consequently, by connecting the subsystems, an overall industry growth and landscape ecology model is established. Using actual data and landscape index calculated based on GIS of the Ha-Da-Qi industrial corridor, a typical industrial corridor in China, over the period 2005-2009, the model is validated in terms of historical behaviour, logical structure and future prediction, where for 84.8% of the factors, the error rate of the model is less than 5%, the mean error rate of all factors is 2.96% and the error of the simulation test for the landscape ecology subsystem is less than 2%. Moreover, a model application has been made to consider the changes in landscape indices under four industrial development modes, and the optimal industrial growth plan has been examined for landscape ecological protection through the simulation prediction results over 2015-2020. Copyright © 2015 Elsevier Ltd. All rights reserved.
Three essays on multi-level optimization models and applications
Rahdar, Mohammad
The general form of a multi-level mathematical programming problem is a set of nested optimization problems, in which each level controls a series of decision variables independently. However, the value of decision variables may also impact the objective function of other levels. A two-level model is called a bilevel model and can be considered as a Stackelberg game with a leader and a follower. The leader anticipates the response of the follower and optimizes its objective function, and then the follower reacts to the leader's action. The multi-level decision-making model has many real-world applications such as government decisions, energy policies, market economy, network design, etc. However, there is a lack of capable algorithms to solve medium and large scale these types of problems. The dissertation is devoted to both theoretical research and applications of multi-level mathematical programming models, which consists of three parts, each in a paper format. The first part studies the renewable energy portfolio under two major renewable energy policies. The potential competition for biomass for the growth of the renewable energy portfolio in the United States and other interactions between two policies over the next twenty years are investigated. This problem mainly has two levels of decision makers: the government/policy makers and biofuel producers/electricity generators/farmers. We focus on the lower-level problem to predict the amount of capacity expansions, fuel production, and power generation. In the second part, we address uncertainty over demand and lead time in a multi-stage mathematical programming problem. We propose a two-stage tri-level optimization model in the concept of rolling horizon approach to reducing the dimensionality of the multi-stage problem. In the third part of the dissertation, we introduce a new branch and bound algorithm to solve bilevel linear programming problems. The total time is reduced by solving a smaller relaxation
Stochastic ontogenetic growth model
West, B. J.; West, D.
2012-02-01
An ontogenetic growth model (OGM) for a thermodynamically closed system is generalized to satisfy both the first and second law of thermodynamics. The hypothesized stochastic ontogenetic growth model (SOGM) is shown to entail the interspecies allometry relation by explicitly averaging the basal metabolic rate and the total body mass over the steady-state probability density for the total body mass (TBM). This is the first derivation of the interspecies metabolic allometric relation from a dynamical model and the asymptotic steady-state distribution of the TBM is fit to data and shown to be inverse power law.
Bacterial Quorum Sensing Stabilizes Cooperation by Optimizing Growth Strategies.
Bruger, Eric L; Waters, Christopher M
2016-11-15
Communication has been suggested as a mechanism to stabilize cooperation. In bacteria, chemical communication, termed quorum sensing (QS), has been hypothesized to fill this role, and extracellular public goods are often induced by QS at high cell densities. Here we show, with the bacterium Vibrio harveyi, that QS provides strong resistance against invasion of a QS defector strain by maximizing the cellular growth rate at low cell densities while achieving maximum productivity through protease upregulation at high cell densities. In contrast, QS mutants that act as defectors or unconditional cooperators maximize either the growth rate or the growth yield, respectively, and thus are less fit than the wild-type QS strain. Our findings provide experimental evidence that regulation mediated by microbial communication can optimize growth strategies and stabilize cooperative phenotypes by preventing defector invasion, even under well-mixed conditions. This effect is due to a combination of responsiveness to environmental conditions provided by QS, lowering of competitive costs when QS is not induced, and pleiotropic constraints imposed on defectors that do not perform QS. Cooperation is a fundamental problem for evolutionary biology to explain. Conditional participation through phenotypic plasticity driven by communication is a potential solution to this dilemma. Thus, among bacteria, QS has been proposed to be a proximate stabilizing mechanism for cooperative behaviors. Here, we empirically demonstrate that QS in V. harveyi prevents cheating and subsequent invasion by nonproducing defectors by maximizing the growth rate at low cell densities and the growth yield at high cell densities, whereas an unconditional cooperator is rapidly driven to extinction by defectors. Our findings provide experimental evidence that QS regulation prevents the invasion of cooperative populations by QS defectors even under unstructured conditions, and they strongly support the role of
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.
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.
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
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
Review: Optimization methods for groundwater modeling and management
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.
The optimal atmospheric CO2 concentration for the growth of winter wheat (Triticum aestivum).
Xu, Ming
2015-07-20
This study examined the optimal atmospheric CO2 concentration of the CO2 fertilization effect on the growth of winter wheat with growth chambers where the CO2 concentration was controlled at 400, 600, 800, 1000, and 1200 ppm respectively. I found that initial increase in atmospheric CO2 concentration dramatically enhanced winter wheat growth through the CO2 fertilization effect. However, this CO2 fertilization effect was substantially compromised with further increase in CO2 concentration, demonstrating an optimal CO2 concentration of 889.6, 909.4, and 894.2 ppm for aboveground, belowground, and total biomass, respectively, and 967.8 ppm for leaf photosynthesis. Also, high CO2 concentrations exceeding the optima not only reduced leaf stomatal density, length and conductance, but also changed the spatial distribution pattern of stomata on leaves. In addition, high CO2 concentration also decreased the maximum carboxylation rate (Vc(max)) and the maximum electron transport rate (J(max)) of leaf photosynthesis. However, the high CO2 concentration had little effect on leaf length and plant height. The optimal CO2 fertilization effect found in this study can be used as an indicator in selecting and breeding new wheat strains in adapting to future high atmospheric CO2 concentrations and climate change. Copyright © 2015. Published by Elsevier GmbH.
Parameter optimization for surface flux transport models
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.
DEFF Research Database (Denmark)
Ahmad, Amais; Zachariasen, Camilla; Christiansen, Lasse Engbo
2016-01-01
using a mathematical model to simulate the competitive growth of E. coli strains in a pig intestine under specified plasma concentration profiles of ampicillin. Results : In vitro growth results demonstrated that the resistant strains did not carry a fitness cost for their resistance, and that the most...... susceptible strains were more affected by increasing concentrations of antibiotics that the rest of the strains. The modeling revealed that short treatment duration resulted in lower levels of resistance and that dosing frequency did not substantially influence the growth of resistant strains. Resistance...... with ampicillin resistance in E. coli. Besides dosing factors, epidemiological factors (such as number of competing strains and bacterial excretion) influenced resistance development and need to be considered further in relation to optimal treatment strategies. The modeling approach used in the study is generic...
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.
Wang, S. Y.; Ho, C. C.; Chang, L. C.
2017-12-01
The public use water in Hsinchu are mainly supplied from Baoshan Reservoir, Second Baoshan Reservoir, Yongheshan Reservoir and Longen Weir. However, the increasing water demand, caused by development of the Hsinchu Science and Industrial Park, results in supply stable water getting more difficult. For stabilize water supply in Hsinchu, the study applies long-term and short-term plans to fulfill the water shortage. Developing an efficient methodology to define a cost-effective action portfolio is an important task. Hence, the study develops a novel decision model, the Stochastic Programming with Recourse Decision Model (SPRDM), to estimate a cost-effective action portfolio. The first-stage of SPRDM determine the long-term action portfolio and the portfolio accompany recourse information (the probability for water shortage event). The second-stage of SPRDM optimize the cost-effective action portfolio in response to the recourse information. In order to consider the uncertainty of reservoir sediment and demand growth, the study set 9 scenarios comprise optimistic, most likely, and pessimistic reservoir sediment and demand growth. The results show the optimal action portfolio consist of FengTain Lake and Panlon Weir, Hsinchu Desalination Plant, Domestic and Industrial Water long-term plans, and Emergency Backup Well, Irrigation Water Transference, Preliminary Water Rationing, Advanced Water Rationing and Water Transport from Other Districts short-term plans. The minimum expected cost of optimal action portfolio is NT$1.1002 billion. The results can be used as a reference for decision making because the results have considered the uncertainty of varied hydrology, reservoir sediment, and water demand growth.
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
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)
A dynamic optimization model of the diel vertical distribution of a pelagic planktivorous fish
Rosland, Rune; Giske, Jarl
A stochastic dynamic optimization model for the diel depth distribution of juveniles and adults of the mesopelagic planktivore Maurolicus muelleri (Gmelin) is developed and used for a winter situation. Observations from Masfjorden, western Norway, reveal differences in vertical distribution, growth and mortality between juveniles and adults in January. Juveniles stay within the upper 100m with high feeding rates, while adults stay within the 100-150m zone with very low feeding rates during the diel cycle. The difference in depth profitability is assumed to be caused by age-dependent processes, and are calculated from a mechanistic model for visual feeding. The environment is described as a set of habitats represented by discrete depth intervals along the vertical axis, differing with respect to light intensity, food abundance, predation risk and temperature. The short time interval (24h) allows fitness to be linearly related to growth (feeding), assuming that growth increases the future reproductive output of the fish. Optimal depth position is calculated from balancing feeding opportunity against mortality risk, where the fitness reward gained by feeding is weighted against the danger of being killed by a predator. A basic run is established, and the model is validated by comparing predictions and observations. The sensitivity for different parameter values is also tested. The modelled vertical distributions and feeding patterns of juvenile and adult fish correspond well with the observations, and the assumption of age differences in mortality-feeding trade-offs seems adequate to explain the different depth profitability of the two age groups. The results indicate a preference for crepuscular feeding activity of the juveniles, and the vertical distribution of zooplankton seems to be the most important environmental factor regulating the adult depth position during the winter months in Masfjorden.
Mathematical modeling and optimization of complex structures
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...
Model parameter-related optimal perturbations and their contributions to El Niño prediction errors
Tao, Ling-Jiang; Gao, Chuan; Zhang, Rong-Hua
2018-04-01
Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling ({α _τ } ), and the other involves the thermocline effect on the SST ({α _{Te}} ). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the {α _τ } component is mainly concentrated in the central equatorial Pacific, and the {α _{Te}} component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.
Grain nucleation and growth during phase transformations
DEFF Research Database (Denmark)
Offerman, S.E.; Dijk, N.H. van; Sietsma, J.
2002-01-01
of individual grains. Our measurements show that the activation energy for grain nucleation is at least two orders of magnitude smaller than that predicted by thermodynamic models. The observed growth curves of the newly formed grains confirm the parabolic growth model but also show three fundamentally...... different types of growth. Insight into the grain nucleation and growth mechanisms during phase transformations contributes to the development of materials with optimal mechanical properties....
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...
Directory of Open Access Journals (Sweden)
Faranak Noori
2016-07-01
Full Text Available Background and Objective: Lactobacillus plantarum is one of the probiotics species used in functional food products. These bacteria or their purified bacteriocins are used as biological preservatives in the food industry. The first step in production of an array of probiotic products is optimizing production in fermentors. This study aimed to examine factors affecting the in vitro growth optimization of Lactobacillus plantarum T5JQ301796.1 in a lab scale fermentor.Materials and Methods: Following 24 hours of anaerobic culture of the lactobacillus at 37°C, the pre-culture was ready and was inoculated to a 5 liter fermentor at 37°C and stirred at 40 rpm. Then factors affecting lactobacillus growth including carbon and nitrogen sources and pH were studied. The results were interpreted using response surface methodology (RSM, and optimal conditions for the equipment were determined.Results and Conclusion: For optimal growth of Lactobacillus plantarum T5JQ301796.1 in lab scale fermentor, the optimal conditions were 25.96 gl-1 of glucose, 1.82% of yeast extract, pH of 7.26, and stirring at 40 rpm at optimum temperature between 37-40°C. In this condition, maximum viable cell in the batch fermentation was 1.25×1010 CFU ml-1. Application of central composite design for the growth optimization of this bacterium led to maximum viable cells equal to 1.25×1010 CFU ml-1. So the mentioned features can lead to optimum industrial scale production and usage of this probiotic strain in probiotic products.Conflict of interest: The authors declare that there is no conflict of interest.
Bacterial growth laws reflect the evolutionary importance of energy efficiency.
Maitra, Arijit; Dill, Ken A
2015-01-13
We are interested in the balance of energy and protein synthesis in bacterial growth. How has evolution optimized this balance? We describe an analytical model that leverages extensive literature data on growth laws to infer the underlying fitness landscape and to draw inferences about what evolution has optimized in Escherichia coli. Is E. coli optimized for growth speed, energy efficiency, or some other property? Experimental data show that at its replication speed limit, E. coli produces about four mass equivalents of nonribosomal proteins for every mass equivalent of ribosomes. This ratio can be explained if the cell's fitness function is the the energy efficiency of cells under fast growth conditions, indicating a tradeoff between the high energy costs of ribosomes under fast growth and the high energy costs of turning over nonribosomal proteins under slow growth. This model gives insight into some of the complex nonlinear relationships between energy utilization and ribosomal and nonribosomal production as a function of cell growth conditions.
Optimizing Patient Management and Adherence for Children Receiving Growth Hormone.
Acerini, Carlo L; Wac, Katarzyna; Bang, Peter; Lehwalder, Dagmar
2017-01-01
Poor adherence with growth hormone (GH) therapy has been associated with worse clinical outcomes, which in children relates specifically to their linear growth and loss of quality of life. The "360° GH in Europe" meeting, held in Lisbon, Portugal, in June 2016 and funded by Merck KGaA (Germany), examined many aspects of GH diseases. The three sessions, entitled " Short Stature Diagnosis and Referral ," " Optimizing Patient Management ," and " Managing Transition ," each benefited from three guest speaker presentations, followed by an open discussion and are reported as a manuscript, authored by the speakers. Reported here is a summary of the proceedings of the second session, which reviewed the determinants of GH therapy response, factors affecting GH therapy adherence and the development of innovative technologies to improve GH treatment in children. Response to GH therapy varies widely, particularly in regard to the underlying diagnosis, although there is little consensus on the definition of a poor response. If the growth response is seen to be less than expected, the possible reasons should be discussed with patients and their parents, including compliance with the therapy regimen. Understanding and addressing the multiple factors that influence adherence, in order to optimize GH therapy, requires a multi-disciplinary approach. Because therapy continues over many years, various healthcare professionals will be involved at different periods of the patient's journey. The role of the injection device for GH therapy, frequent monitoring of response, and patient support are all important for maintaining adherence. New injection devices are incorporating electronic technologies for automated monitoring and recording of clinically relevant information on injections. Study results are indicating that such devices can at least maintain GH adherence; however, acceptance of novel devices needs to be assessed and there remains an on-going need for innovations.
Optimizing Patient Management and Adherence for Children Receiving Growth Hormone
Directory of Open Access Journals (Sweden)
Carlo L. Acerini
2017-11-01
Full Text Available Poor adherence with growth hormone (GH therapy has been associated with worse clinical outcomes, which in children relates specifically to their linear growth and loss of quality of life. The “360° GH in Europe” meeting, held in Lisbon, Portugal, in June 2016 and funded by Merck KGaA (Germany, examined many aspects of GH diseases. The three sessions, entitled “Short Stature Diagnosis and Referral,” “Optimizing Patient Management,” and “Managing Transition,” each benefited from three guest speaker presentations, followed by an open discussion and are reported as a manuscript, authored by the speakers. Reported here is a summary of the proceedings of the second session, which reviewed the determinants of GH therapy response, factors affecting GH therapy adherence and the development of innovative technologies to improve GH treatment in children. Response to GH therapy varies widely, particularly in regard to the underlying diagnosis, although there is little consensus on the definition of a poor response. If the growth response is seen to be less than expected, the possible reasons should be discussed with patients and their parents, including compliance with the therapy regimen. Understanding and addressing the multiple factors that influence adherence, in order to optimize GH therapy, requires a multi-disciplinary approach. Because therapy continues over many years, various healthcare professionals will be involved at different periods of the patient’s journey. The role of the injection device for GH therapy, frequent monitoring of response, and patient support are all important for maintaining adherence. New injection devices are incorporating electronic technologies for automated monitoring and recording of clinically relevant information on injections. Study results are indicating that such devices can at least maintain GH adherence; however, acceptance of novel devices needs to be assessed and there remains an on
Mathematical model of highways network optimization
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.
Yuan, Zhihui; Ruan, Jujun; Li, Yaying; Qiu, Rongliang
2018-04-10
Bioleaching is a green recycling technology for recovering precious metals from waste printed circuit boards (WPCBs). However, this technology requires increasing cyanide production to obtain desirable recovery efficiency. Luria-Bertani medium (LB medium, containing tryptone 10 g/L, yeast extract 5 g/L, NaCl 10 g/L) was commonly used in bioleaching of precious metal. In this study, results showed that LB medium did not produce highest yield of cyanide. Under optimal culture conditions (25 °C, pH 7.5), the maximum cyanide yield of the optimized medium (containing tryptone 6 g/L and yeast extract 5 g/L) was 1.5 times as high as that of LB medium. In addition, kinetics and relationship of cell growth and cyanide production was studied. Data of cell growth fitted logistics model well. Allometric model was demonstrated effective in describing relationship between cell growth and cyanide production. By inserting logistics equation into allometric equation, we got a novel hybrid equation containing five parameters. Kinetic data for cyanide production were well fitted to the new model. Model parameters reflected both cell growth and cyanide production process. Copyright © 2018 Elsevier B.V. All rights reserved.
On a Versatile Stochastic Growth Model
Directory of Open Access Journals (Sweden)
Samiur Arif
2012-06-01
Full Text Available Growth phenomena are ubiquitous and pervasive not only in biology and the medical sciences, but also in economics, marketing and the computer and social sciences. We introduce a three-parameter version of the classic pure-birth process growth model when suitably instantiated, can be used to model growth phenomena in many seemingly unrelated application domains. We point out that the model is computationally attractive since it admits of conceptually simple, closed form solutions for the time-dependent probabilities.
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)
Viscoelastic model of tungsten 'fuzz' growth
International Nuclear Information System (INIS)
Krasheninnikov, S I
2011-01-01
A viscoelastic model of fuzz growth is presented. The model describes the main features of tungsten fuzz observed in experiments. It gives estimates of fuzz growth rate and temperature range close to experimental ones.
Optimal Hedging with the Vector Autoregressive Model
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
Validation of a predictive model for the growth of chalk yeasts on bread.
Burgain, Anaïs; Bensoussan, Maurice; Dantigny, Philippe
2015-07-02
The present study focused on the effects of temperature, T, and water activity, aw, on the growth of Hyphopichia burtonii, Pichia anomala, and Saccharomycopsis fibuligera on Sabouraud Agar Medium. Cardinal values were estimated by means of cardinal models with inflection. All the yeasts were xerophilic, and they exhibited growth at 0.85 aw. The combined effects of T, aw, and pH on the growth of these species were described by the gamma-concept and validated on bread in the range of 15-25 °C, 0.91-0.97 aw, and pH 4.6-6.8. The optimum growth rates on bread were 2.88, 0.259, and 1.06 mm/day for H. burtonii, P. anomala, and S. fibuligera, respectively. The optimal growth rate of S. fibuligera on bread was about 2 fold that obtained on Sabouraud. Due to reproduction by budding, P. anomala exhibited low growth on Sabouraud and bread. However, this species is of major concern in the baker's industry because of the production of ethyl acetate in bread. Copyright © 2015 Elsevier B.V. All rights reserved.
Optimal Investment Control of Macroeconomic Systems
Institute of Scientific and Technical Information of China (English)
ZHAO Ke-jie; LIU Chuan-zhe
2006-01-01
Economic growth is always accompanied by economic fluctuation. The target of macroeconomic control is to keep a basic balance of economic growth, accelerate the optimization of economic structures and to lead a rapid, sustainable and healthy development of national economies, in order to propel society forward. In order to realize the above goal, investment control must be regarded as the most important policy for economic stability. Readjustment and control of investment includes not only control of aggregate investment, but also structural control which depends on economic-technology relationships between various industries of a national economy. On the basis of the theory of a generalized system, an optimal investment control model for government has been developed. In order to provide a scientific basis for government to formulate a macroeconomic control policy, the model investigates the balance of total supply and aggregate demand through an adjustment in investment decisions realizes a sustainable and stable growth of the national economy. The optimal investment decision function proposed by this study has a unique and specific expression, high regulating precision and computable characteristics.
Dynamic optimization in environmental economics
Energy Technology Data Exchange (ETDEWEB)
Moser, Elke; Tragler, Gernot; Veliov, Vladimir M. (eds.) [Vienna Univ. of Technology (Austria). Inst. of Mathematical Methods in Economics; Semmler, Willi [The New School for Social Research, New York, NY (United States). Dept. of Economics
2014-11-01
This book contains two chapters with the topics: 1. Chapter: INTERACTIONS BETWEEN ECONOMY AND CLIMATE: (a) Climate Change and Technical Progress: Impact of Informational Constraints. (b) Environmental Policy in a Dynamic Model with Heterogeneous Agents and Voting. (c) Optimal Environmental Policy in the Presence of Multiple Equilibria and Reversible Hysteresis. (d). Modeling the Dynamics of the Transition to a Green Economy. (e) One-Parameter GHG Emission Policy With R and D-Based Growth. (f) Pollution, Public Health Care, and Life Expectancy when Inequality Matters. (g) Uncertain Climate Policy and the Green Paradox. (h) Uniqueness Versus Indeterminacy in the Tragedy of the Commons - A ''Geometric'' Approach. 2. Chapter: OPTIMAL EXTRACTION OF RESOURCES: (j) Dynamic Behavior of Oil Importers and Exporters Under Uncertainty. (k) Robust Control of a Spatially Distributed Commercial Fishery. (l) On the Effect of Resource Exploitation on Growth: Domestic Innovation vs. Technological Diffusion Through Trade. (m) Forest Management and Biodiversity in Size-Structured Forests Under Climate Change. (n) Carbon Taxes and Comparison of Trading Regimes in Fossil Fuels. (o) Landowning, Status and Population Growth. (p) Optimal Harvesting of Size-Structured Biological Populations.
Mathematical modeling of microbial growth in milk
Directory of Open Access Journals (Sweden)
Jhony Tiago Teleken
2011-12-01
Full Text Available A mathematical model to predict microbial growth in milk was developed and analyzed. The model consists of a system of two differential equations of first order. The equations are based on physical hypotheses of population growth. The model was applied to five different sets of data of microbial growth in dairy products selected from Combase, which is the most important database in the area with thousands of datasets from around the world, and the results showed a good fit. In addition, the model provides equations for the evaluation of the maximum specific growth rate and the duration of the lag phase which may provide useful information about microbial growth.
Palstra, Arjan P; Mes, Daan; Kusters, Kasper; Roques, Jonathan A C; Flik, Gert; Kloet, Kees; Blonk, Robbert J W
2014-01-01
Swimming exercise at optimal speed may optimize growth performance of yellowtail kingfish in a recirculating aquaculture system. Therefore, optimal swimming speeds (U opt in m s(-1) or body lengths s(-1), BL s(-1)) were assessed and then applied to determine the effects of long-term forced and sustained swimming at U opt on growth performance of juvenile yellowtail kingfish. U opt was quantified in Blazka-type swim-tunnels for 145, 206, and 311 mm juveniles resulting in values of: (1) 0.70 m s(-1) or 4.83 BL s(-1), (2) 0.82 m s(-1) or 3.25 BL s(-1), and (3) 0.85 m s(-1) or 2.73 BL s(-1). Combined with literature data from larger fish, a relation of U opt (BL s(-1)) = 234.07(BL)(-0.779) (R (2) = 0.9909) was established for this species. Yellowtail kingfish, either forced to perform sustained swimming exercise at an optimal speed of 2.46 BL s(-1) ("swimmers") or allowed to perform spontaneous activity at low water flow ("resters") in a newly designed 3600 L oval flume (with flow created by an impeller driven by an electric motor), were then compared. At the start of the experiment, ten fish were sampled representing the initial condition. After 18 days, swimmers (n = 23) showed a 92% greater increase in BL and 46% greater increase in BW as compared to resters (n = 23). As both groups were fed equal rations, feed conversion ratio (FCR) for swimmers was 1.21 vs. 1.74 for resters. Doppler ultrasound imaging showed a statistically significant higher blood flow (31%) in the ventral aorta of swimmers vs. resters (44 ± 3 vs. 34 ± 3 mL min(-1), respectively, under anesthesia). Thus, growth performance can be rapidly improved by optimal swimming, without larger feed investments.
Barman, Sukanta; Menon, Krishnakumar S. R.
2018-04-01
We present here a detailed growth optimization of CoO thin film on Ag(001) involving the effects of different growth parameters on the electronic structure. A well-ordered stoichiometric growth of 5 ML CoO film has been observed at 473 K substrate temperature and 1 × 10-6 mbar oxygen partial pressure. The growth at lower substrate temperature and oxygen partial pressure show non-stoichiometric impurity phases which have been investigated further to correlate the growth parameters with surface electronic structure. The coverage dependent valence band electronic structure of the films grown at optimized condition reveals the presence of interfacial states near the Fermi edge (EF) for lower film coverages. Presence of interfacial states in the stoichiometric films rules out their defect-induced origin. We argue that this is an intrinsic feature of transition metal monoxides like NiO, CoO, MnO in the low coverage regime.
Directory of Open Access Journals (Sweden)
Alì Soltani
2013-06-01
Full Text Available The simulation of urban growth can be considered as a useful way for analyzing the complex process of urban physical evolution. The aim of this study is to model and simulate the complex patterns of land use change by utilizing remote sensing and artificial intelligence techniques in the fast growing city of Mahabad, north-west of Iran which encountered with several environmental subsequences. The key subject is how to allocate optimized weight into effective parameters upon urban growth and subsequently achieving an improved simulation. Artificial Neural Networks (ANN algorithm was used to allocate the weight via an iteration approach. In this way, weight allocation was carried out by the ANN training accomplishing through time-series satellite images representing urban growth process. Cellular Automata (CA was used as the principal motor of the model and then ANN applied to find suitable scale of parameters and relations between potential factors affecting urban growth. The general accuracy of the suggested model and obtained Fuzzy Kappa Coefficient confirms achieving better results than classic CA models in simulating nonlinear urban evolution process.
Rethinking exchange market models as optimization algorithms
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
Growth or reproduction: emergence of an evolutionary optimal strategy
International Nuclear Information System (INIS)
Grilli, J; Suweis, S; Maritan, A
2013-01-01
Modern ecology has re-emphasized the need for a quantitative understanding of the original ‘survival of the fittest theme’ based on analysis of the intricate trade-offs between competing evolutionary strategies that characterize the evolution of life. This is key to the understanding of species coexistence and ecosystem diversity under the omnipresent constraint of limited resources. In this work we propose an agent-based model replicating a community of interacting individuals, e.g. plants in a forest, where all are competing for the same finite amount of resources and each competitor is characterized by a specific growth–reproduction strategy. We show that such an evolution dynamics drives the system towards a stationary state characterized by an emergent optimal strategy, which in turn depends on the amount of available resources the ecosystem can rely on. We find that the share of resources used by individuals is power-law distributed with an exponent directly related to the optimal strategy. The model can be further generalized to devise optimal strategies in social and economical interacting systems dynamics. (paper)
Pareto-Optimal Model Selection via SPRINT-Race.
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.
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.
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.
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.
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.
Targeting nominal income growth or inflation?
DEFF Research Database (Denmark)
Jensen, Henrik
2002-01-01
Within a simple New Keynesian model emphasizing forward-looking behavior of private agents, I evaluate optimal nominal income growth targeting versus optimal inflation targeting. When the economy is mainly subject to shocks that do not involve monetary policy trade-offs for society, inflation...
Stochastic process corrosion growth models for pipeline reliability
International Nuclear Information System (INIS)
Bazán, Felipe Alexander Vargas; Beck, André Teófilo
2013-01-01
Highlights: •Novel non-linear stochastic process corrosion growth model is proposed. •Corrosion rate modeled as random Poisson pulses. •Time to corrosion initiation and inherent time-variability properly represented. •Continuous corrosion growth histories obtained. •Model is shown to precisely fit actual corrosion data at two time points. -- Abstract: Linear random variable corrosion models are extensively employed in reliability analysis of pipelines. However, linear models grossly neglect well-known characteristics of the corrosion process. Herein, a non-linear model is proposed, where corrosion rate is represented as a Poisson square wave process. The resulting model represents inherent time-variability of corrosion growth, produces continuous growth and leads to mean growth at less-than-one power of time. Different corrosion models are adjusted to the same set of actual corrosion data for two inspections. The proposed non-linear random process corrosion growth model leads to the best fit to the data, while better representing problem physics
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.
Directory of Open Access Journals (Sweden)
Ariyanti Saleh
2017-04-01
Full Text Available Introduction: The first five years of age of a child is a critical time that will affect the child growth development process. Any untreated disorders may impair the process that subsequently influences quality of life of the child in the future. Therefore, it is imperative for a mother to optimize the growth development process. This study aimed to identify the effectiveness of health education with modelling approach on mother's knowledge, practice ability and maternal confidence of infant (0-6 months growth and development. Method: A quasy eksperimental pre-post with control group design was used. The intervention given was health education with modelling approach related to lactation management and infant growth development stimulation. The research was conducted in Maros Regency wiht 81 samples (41 in the treatment group and 40 in the control group. Result: The wilcoxon test reveals that there was a signi fi cant difference between treatment and control group, accordingly, knowledge (p = 0.00, p = 0.01, practice ability (p = 0.00, p = 0,006 and maternal confidence (p = 0.03, p = 0.03. In addition, from mann whitney test, between the two group, the data obtained are: knowledge (p = 0,950, practice ability (p = 0.00 and maternal con fi dence (p = 0,061. Discussion: Health education with modelling approach conducting by nurse was effective in increasing knowledge, practice ability, maternal confidence breastfeeding and baby stimulation, which was in turn can optimize baby growth and development. That is why, community health nurses role should be increase by making community health nursing program as one of primary public health centre program.
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.
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
Don C. Bragg
2002-01-01
This article is an introduction to the computer software used by the Potential Relative Increment (PRI) approach to optimal tree diameter growth modeling. These DOS programs extract qualified tree and plot data from the Eastwide Forest Inventory Data Base (EFIDB), calculate relative tree increment, sort for the highest relative increments by diameter class, and...
Nonconvex Model of Material Growth: Mathematical Theory
Ganghoffer, J. F.; Plotnikov, P. I.; Sokolowski, J.
2018-06-01
The model of volumetric material growth is introduced in the framework of finite elasticity. The new results obtained for the model are presented with complete proofs. The state variables include the deformations, temperature and the growth factor matrix function. The existence of global in time solutions for the quasistatic deformations boundary value problem coupled with the energy balance and the evolution of the growth factor is shown. The mathematical results can be applied to a wide class of growth models in mechanics and biology.
Enhanced index tracking modelling in portfolio optimization
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.
Structure optimization by heuristic algorithm in a coarse-grained off-lattice model
International Nuclear Information System (INIS)
Jing-Fa, Liu
2009-01-01
A heuristic algorithm is presented for a three-dimensional off-lattice AB model consisting of hydrophobic (A) and hydrophilic (B) residues in Fibonacci sequences. By incorporating extra energy contributions into the original potential function, we convert the constrained optimization problem of AB model into an unconstrained optimization problem which can be solved by the gradient method. After the gradient minimization leads to the basins of the local energy minima, the heuristic off-trap strategy and subsequent neighborhood search mechanism are then proposed to get out of local minima and search for the lower-energy configurations. Furthermore, in order to improve the efficiency of the proposed algorithm, we apply the improved version called the new PERM with importance sampling (nPERMis) of the chain-growth algorithm, pruned-enriched-Rosenbluth method (PERM), to face-centered-cubic (FCC)-lattice to produce the initial configurations. The numerical results show that the proposed methods are very promising for finding the ground states of proteins. In several cases, we found the ground state energies are lower than the best values reported in the present literature
Growth Characterization and Optimization of Cyanobacterial Isolates from the Arabian Gulf
Siller Rodriguez, Luis F.
2013-12-01
Photoautotrophic organisms have been highlighted as carbon capture and conversion platforms for sustainable production of agricultural and chemicals in KSA. Previously two cyanobacterial strains, Geitlerinema spp. CT7801 and CT7802, were isolated from an industrial brine outfall site in the Eastern Province of the Kingdom of Saudi Arabia. Initial characterization of their growth characteristics showed growth at high temperature (38 ºC) and high salinity ( > 60 PSU), making them potentially good candidates for industrial applications. In this study, quantitative growth assays were performed using standardized methods developed for the analysis of Red Sea photosynthetic microorganisms supported by microscopic observations, optimal growth media preference assays, CO2 concentration effect, photoperiod effect, mixotrophic and heterotrophic growth tests. Data was recorded for absorbance (600 and 750 nm wave lenght), dry cell weight (DCW), colorimetric observations, and chlorophyll a content. Both CT7801 and CT7802 exhibited a clear preference for Walne\\'s Red Sea medium. An analysis on media composition highlights B and Fe as growth enhancers, as well as a base requirement of seawater. Tests on the effect of supplied concentration of CO2 showed that air enhanced with 1 % v/v CO2 allows approximately 2-fold increase in DCW for Geitlerinema spp. CT7802. Photoperiod tests showed that continuous light is disadvantageous for phototrophic growth of Geitlerinema spp. CT7801 and CT7802. Results for mixotrophic and heterotrophic growth of Geitlerinema spp. CT7801 and CT7802 revealed their ability to metabolize glycerol. Analysis on the complete genome of CT7802 identified three key enzymes, glycerol kinase, glycerol-3-phosphate dehydrogenase and triosephosphate isomerase, which may catalyze the glycerol metabolic pathway in the strain. Utilization of glycerol, a residue of the biodiesel industry, might provide a sustainable alternative for growth of Geitlerinema sp. CT7802.
Modeling investor optimism with fuzzy connectives
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
Pavement maintenance optimization model using Markov Decision Processes
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.
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.
Directory of Open Access Journals (Sweden)
Dejun Yang
Full Text Available ABSTRACT Simulations for root growth, crop growth, and N uptake in agro-hydrological models are of significant concern to researchers. SWMS_2D is one of the most widely used physical hydrologically related models. This model solves equations that govern soil-water movement by the finite element method, and has a public access source code. Incorporating key agricultural components into the SWMS_2D model is of practical importance, especially for modeling some critical cereal crops such as winter wheat. We added root growth, crop growth, and N uptake modules into SWMS_2D. The root growth model had two sub-models, one for root penetration and the other for root length distribution. The crop growth model used was adapted from EU-ROTATE_N, linked to the N uptake model. Soil-water limitation, nitrogen limitation, and temperature effects were all considered in dry-weight modeling. Field experiments for winter wheat in Bouwing, the Netherlands, in 1983-1984 were selected for validation. Good agreements were achieved between simulations and measurements, including soil water content at different depths, normalized root length distribution, dry weight and nitrogen uptake. This indicated that the proposed new modules used in the SWMS_2D model are robust and reliable. In the future, more rigorous validation should be carried out, ideally under 2D situations, and attention should be paid to improve some modules, including the module simulating soil N mineralization.
Optimal Inspection Planning for Fatigue Damage of Offshore Structures
DEFF Research Database (Denmark)
Madsen, H.O.; Sørensen, John Dalsgaard; Olesen, R.
1990-01-01
A formulation of optimal design, inspection and maintenance against damage caused by fatigue crack growth is formulated. A stochastic model for fatigue crack growth based on linear elastic fracture mechanics Is applied. Failure is defined by crack growth beyond a critical crack size. The failure ...
Testing R&D-Based Endogenous Growth Models
DEFF Research Database (Denmark)
Kruse-Andersen, Peter Kjær
2017-01-01
R&D-based growth models are tested using US data for the period 1953-2014. A general growth model is developed which nests the model varieties of interest. The model implies a cointegrating relationship between multifactor productivity, research intensity, and employment. This relationship...... is estimated using cointegrated VAR models. The results provide evidence against the widely used fully endogenous variety and in favor of the semi-endogenous variety. Forecasts based on the empirical estimates suggest that the slowdown in US productivity growth will continue. Particularly, the annual long...
In vitro placental model optimization for nanoparticle transport studies
Directory of Open Access Journals (Sweden)
Cartwright L
2012-01-01
Full Text Available Laura Cartwright1, Marie Sønnegaard Poulsen2, Hanne Mørck Nielsen3, Giulio Pojana4, Lisbeth E Knudsen2, Margaret Saunders1, Erik Rytting2,51Bristol Initiative for Research of Child Health (BIRCH, Biophysics Research Unit, St Michael's Hospital, UH Bristol NHS Foundation Trust, Bristol, UK; 2University of Copenhagen, Faculty of Health Sciences, Department of Public Health, 3University of Copenhagen, Faculty of Pharmaceutical Sciences, Department of Pharmaceutics and Analytical Chemistry, Copenhagen, Denmark; 4Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari Venice, Venice, Italy; 5Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, Texas, USABackground: Advances in biomedical nanotechnology raise hopes in patient populations but may also raise questions regarding biodistribution and biocompatibility, especially during pregnancy. Special consideration must be given to the placenta as a biological barrier because a pregnant woman's exposure to nanoparticles could have significant effects on the fetus developing in the womb. Therefore, the purpose of this study is to optimize an in vitro model for characterizing the transport of nanoparticles across human placental trophoblast cells.Methods: The growth of BeWo (clone b30 human placental choriocarcinoma cells for nanoparticle transport studies was characterized in terms of optimized Transwell® insert type and pore size, the investigation of barrier properties by transmission electron microscopy, tight junction staining, transepithelial electrical resistance, and fluorescein sodium transport. Following the determination of nontoxic concentrations of fluorescent polystyrene nanoparticles, the cellular uptake and transport of 50 nm and 100 nm diameter particles was measured using the in vitro BeWo cell model.Results: Particle size measurements, fluorescence readings, and confocal microscopy indicated both cellular uptake of
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.
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.
Temperature impacts on economic growth warrant stringent mitigation policy
Moore, Frances C.; Diaz, Delavane B.
2015-02-01
Integrated assessment models compare the costs of greenhouse gas mitigation with damages from climate change to evaluate the social welfare implications of climate policy proposals and inform optimal emissions reduction trajectories. However, these models have been criticized for lacking a strong empirical basis for their damage functions, which do little to alter assumptions of sustained gross domestic product (GDP) growth, even under extreme temperature scenarios. We implement empirical estimates of temperature effects on GDP growth rates in the DICE model through two pathways, total factor productivity growth and capital depreciation. This damage specification, even under optimistic adaptation assumptions, substantially slows GDP growth in poor regions but has more modest effects in rich countries. Optimal climate policy in this model stabilizes global temperature change below 2 °C by eliminating emissions in the near future and implies a social cost of carbon several times larger than previous estimates. A sensitivity analysis shows that the magnitude of climate change impacts on economic growth, the rate of adaptation, and the dynamic interaction between damages and GDP are three critical uncertainties requiring further research. In particular, optimal mitigation rates are much lower if countries become less sensitive to climate change impacts as they develop, making this a major source of uncertainty and an important subject for future research.
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.
A Framework for the Optimization of Discrete-Event Simulation Models
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.
DEFF Research Database (Denmark)
Deepagoda Thuduwe Kankanamge Kelum, Chamindu; Jones, Scott B.; Tuller, Markus
2014-01-01
utilization to conserve energy and to limit transport costs, native materials mined on Moon or Mars are of primary interest for plant growth media in a future outpost, while terrestrial porous substrates with optimal growth media characteristics will be useful for onboard plant growth during space missions....... Due to limited experimental opportunities and prohibitive costs, liquid and gas behavior in porous substrates under reduced gravity conditions has been less studied and hence remains poorly understood. Based on ground-based measurements, this study examined water retention, oxygen diffusivity and air...... that estimates the gas percolation threshold based on the pore size distribution. The model successfully captured measured data for all investigated media and demonstrated the implications of the poorly-understood shift in gas percolation threshold with improved gas percolation in reduced gravity. Finally, using...
Kinetic Model of Growth of Arthropoda Populations
Ershov, Yu. A.; Kuznetsov, M. A.
2018-05-01
Kinetic equations were derived for calculating the growth of crustacean populations ( Crustacea) based on the biological growth model suggested earlier using shrimp ( Caridea) populations as an example. The development cycle of successive stages for populations can be represented in the form of quasi-chemical equations. The kinetic equations that describe the development cycle of crustaceans allow quantitative prediction of the development of populations depending on conditions. In contrast to extrapolation-simulation models, in the developed kinetic model of biological growth the kinetic parameters are the experimental characteristics of population growth. Verification and parametric identification of the developed model on the basis of the experimental data showed agreement with experiment within the error of the measurement technique.
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.
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.
Differential model of macroeconomic growth with endogenic cyclicity
Directory of Open Access Journals (Sweden)
Mikhail I. Geraskin
2017-09-01
Full Text Available Objective to elaborate a mathematical model of economic growth taking into account the cyclical nature of macroeconomic dynamics with the model parameters based on the Russian economy statistics. Methods economic and mathematical modeling system analysis regression factor analysis econometric time series analysis. Results the article states that under unstable economic growth in Russia forecasting of strategic prospects of the Russian economy is one of the topical directions of scientific studies. Furthermore construction of predictive models should be based on multiple factors taking into account such basic concepts as the neoKeynesian HarrodDomar model Ramsey ndash Cass ndash Koopmans model S. V. Dubovskiyrsquos concept as well as the neoclassical growth model by R. Solow. They served as the basis for developing a multifactor differential economic growth model which is a modification of the neoclassical growth model by R. Solow taking into account the laborsaving and capitalsaving forms of scientifictechnical progress and the Keynesian concept of investment. The model parameters are determined based on the dynamics of actual GDP employment fixed assets and investments in fixed assets for 19652016 in Russia on the basis of official statistics. The generalized model showed the presence of longwave fluctuations that are not detected during the individual periods modeling. The cyclical nature of macroeconomic dynamics with a period of 54 years was found which corresponds to the parameters of long waves by N. D. Kondratiev. Basing on the model the macroeconomic growth forecast was generated which shows that after 2020 the increase of scientifictechnical progress will be negative. Scientific novelty a model is proposed of the scientifictechnical progress indicator showing the growth rate of the capital productivity ratio to the saving rate a differential model of macroeconomic growth is obtained which endogenously takes cyclicity into account
Lemercier, Aurélien; Viel, Quentin; Brandel, Clément; Cartigny, Yohann; Dargent, Eric; Petit, Samuel; Coquerel, Gérard
2017-08-01
Since more and more pharmaceutical substances are developed as amorphous forms, it is nowadays of major relevance to get insights into the nucleation and growth mechanisms from supercooled melts (SCM). A step-by-step approach of recrystallization from a SCM is presented here, designed to elucidate the impact of various experimental parameters. Using the bronchodilator agent Diprophylline (DPL) as a model compound, it is shown that optimal conditions for informative observations of the crystallization behaviour from supercooled racemic DPL require to place samples between two cover slides with a maximum sample thickness of 20 μm, and to monitor recrystallization during an annealing step of 30 min at 70 °C, i.e. about 33 °C above the temperature of glass transition. In these optimized conditions, it could be established that DPL crystallization proceeds in two steps: spontaneous nucleation and growth of large and well-faceted particles of a new crystal form (primary crystals: PC) and subsequent crystallization of a previously known form (RII) that develops from specific surfaces of PC. The formation of PC particles therefore constitutes the key-step of the crystallization events and is shown to be favoured by at least 2.33 wt% of the major chemical impurity, Theophylline.
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.
Empty tracks optimization based on Z-Map model
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.
Carbon Sequestration and Optimal Climate Policy
International Nuclear Information System (INIS)
Grimaud, Andre; Rouge, Luc
2009-01-01
We present an endogenous growth model in which the use of a non-renewable natural resource generates carbon-dioxide emissions that can be partly sequestered. This approach breaks with the systematic link between resource use and pollution emission. The accumulated stock of remaining emissions has a negative impact on household utility and corporate productivity. While sequestration quickens the optimal extraction rate, it can also generate higher emissions in the short run. It also has an adverse effect on economic growth. We study the impact of a carbon tax: the level of the tax has an effect in our model, its optimal level is positive, and it can be interpreted ex post as a decreasing ad valorem tax on the resource
Momen, Seyed Bahman; Siadat, Seyed Davar; Akbari, Neda; Ranjbar, Bijan; Khajeh, Khosro
2016-06-01
Haemophilus influenzae type b (Hib) is the leading cause of bacterial meningitis, otitis media, pneumonia, cellulitis, bacteremia, and septic arthritis in infants and young children. The Hib capsule contains the major virulence factor, and is composed of polyribosyl ribitol phosphate (PRP) that can induce immune system response. Vaccines consisting of Hib capsular polysaccharide (PRP) conjugated to a carrier protein are effective in the prevention of the infections. However, due to costly processes in PRP production, these vaccines are too expensive. To enhance biomass, in this research we focused on optimizing Hib growth with respect to physical factors such as pH, temperature, and agitation by using a response surface methodology (RSM). We employed a central composite design (CCD) and a response surface methodology to determine the optimum cultivation conditions for growth and biomass production of H. influenzae type b. The treatment factors investigated were initial pH, agitation, and temperature, using shaking flasks. After Hib cultivation and determination of dry biomass, analysis of experimental data was performed by the RSM-CCD. The model showed that temperature and pH had an interactive effect on Hib biomass production. The dry biomass produced in shaking flasks was about 5470 mg/L, which was under an initial pH of 8.5, at 250 rpm and 35° C. We found CCD and RSM very effective in optimizing Hib culture conditions, and Hib biomass production was greatly influenced by pH and incubation temperature. Therefore, optimization of the growth factors to maximize Hib production can lead to 1) an increase in bacterial biomass and PRP productions, 2) lower vaccine prices, 3) vaccination of more susceptible populations, and 4) lower risk of Hib infections.
Directory of Open Access Journals (Sweden)
Arjan P. Palstra
2015-01-01
Full Text Available Swimming exercise at optimal speed may optimize growth performance of yellowtail kingfish in a recirculating aquaculture system. Therefore, optimal swimming speeds (Uopt in m s-1 or body lengths s-1, BL s-1 were assessed and then applied to determine the effects of long-term forced and sustained swimming at Uopt on growth performance of juvenile yellowtail kingfish. Uopt was quantified in Blazka-type swim-tunnels for 145 mm, 206 mm and 311 mm juveniles resulting in values of: 1 0.70 m s-1 or 4.83 BL s-1, 2 0.82 m s-1 or 3.25 BL s-1 and 3 0.85 m s-1 or 2.73 BL s-1. Combined with literature data from larger fish, a relation of Uopt (BL s-1 = 234.07(BL-0.779 (R2= 0.9909 was established for this species. Yellowtail kingfish, either forced to perform sustained swimming exercise at an optimal speed of 2.46 BL s-1 (‘swimmers’ or allowed to perform spontaneous activity at low water flow (‘resters’ in a newly designed 3,600 L oval flume (with flow created by an impeller driven by an electric motor, were then compared. At the start of the experiment, ten fish were sampled representing the initial condition. After 18 days, swimmers (n= 23 showed a 92% greater increase in BL and 46% greater increase in BW as compared to resters (n= 23. As both groups were fed equal rations, feed conversion ratio (FCR for swimmers was 1.21 vs. 1.74 for resters. Doppler ultrasound imaging showed a statistically significant higher blood flow (31% in the ventral aorta of swimmers vs. resters (44 ± 3 mL min-1 vs. 34 ± 3 mL min-1, respectively, under anesthesia. Thus growth performance can be rapidly improved by optimal swimming, without larger feed investments.
Testing linear growth rate formulas of non-scale endogenous growth models
Ziesemer, Thomas
2017-01-01
Endogenous growth theory has produced formulas for steady-state growth rates of income per capita which are linear in the growth rate of the population. Depending on the details of the models, slopes and intercepts are positive, zero or negative. Empirical tests have taken over the assumption of
Larsen, Poul S.; Filgueira, Ramón; Riisgård, Hans Ulrik
2014-04-01
Prediction of somatic growth of blue mussels, Mytilus edulis, based on the data from 2 field-growth studies of mussels in suspended net-bags in Danish waters was made by 3 models: the bioenergetic growth (BEG), the dynamic energy budget (DEB), and the scope for growth (SFG). Here, the standard BEG model has been expanded to include the temperature dependence of filtration rate and respiration and an ad hoc modification to ensure a smooth transition to zero ingestion as chlorophyll a (chl a) concentration approaches zero, both guided by published data. The first 21-day field study was conducted at nearly constant environmental conditions with a mean chl a concentration of C = 2.7 μg L- 1, and the observed monotonous growth in the dry weight of soft parts was best predicted by DEB while BEG and SFG models produced lower growth. The second 165-day field study was affected by large variations in chl a and temperature, and the observed growth varied accordingly, but nevertheless, DEB and SFG predicted monotonous growth in good agreement with the mean pattern while BEG mimicked the field data in response to observed changes in chl a concentration and temperature. The general features of the models were that DEB produced the best average predictions, SFG mostly underestimated growth, whereas only BEG was sensitive to variations in chl a concentration and temperature. DEB and SFG models rely on the calibration of the half-saturation coefficient to optimize the food ingestion function term to that of observed growth, and BEG is independent of observed actual growth as its predictions solely rely on the time history of the local chl a concentration and temperature.
When Environmental Policy is Superfluous: Growth and Polluting Resources
International Nuclear Information System (INIS)
Schou, Poul
2002-01-01
In a research-driven endogenous growth model, a non-renewable resource gives rise to pollution. Consumption may either grow or decline along the optimal balanced growth path, hut the (flow) pollution level necessarily diminishes continuously. Any positive balanced growth path is sustainable. Utility may improve, even though consumption declines. Although positive growth is optimal, the market economy may nevertheless result in permanently declining consumption possibilities. At the same time, a growth-enhancing government policy may improve long-run environmental conditions. The pollution externality does not distort the decisions of the market economy, so that a specific environmental policy is superfluous
Optimal treatment interruptions control of TB transmission model
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.
Directory of Open Access Journals (Sweden)
BUDIMAN
2012-01-01
Full Text Available Budiman, Arisoesilaningsih E. 2012. Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis. Biodiversitas 13: 18-22. The aims of this research was to determine the multiple regression models of vegetative and corm growth of Amorphophallus muelleri Blume in some age variations and habitat conditions of agroforestry in East Java. Descriptive exploratory research method was conducted by systematic random sampling at five agroforestries on four plantations in East Java: Saradan, Bojonegoro, Nganjuk and Blitar. In each agroforestry, we observed A. muelleri vegetative and corm growth on four growing age (1, 2, 3 and 4 years old respectively as well as environmental variables such as altitude, vegetation, climate and soil conditions. Data were analyzed using descriptive statistics to compare A. muelleri habitat in five agroforestries. Meanwhile, the influence and contribution of each environmental variable to the growth of A. muelleri vegetative and corm were determined using multiple regression analysis of SPSS 17.0. The multiple regression models of A. muelleri vegetative and corm growth were generated based on some characteristics of agroforestries and age showed high validity with R2 = 88-99%. Regression model showed that age, monthly temperatures, percentage of radiation and soil calcium (Ca content either simultaneously or partially determined the growth of A. muelleri vegetative and corm. Based on these models, the A. muelleri corm reached the optimal growth after four years of cultivation and they will be ready to be harvested. Additionally, the soil Ca content should reach 25.3 me.hg-1 as Sugihwaras agroforestry, with the maximal radiation of 60%.
Modeling, simulation and optimization of bipedal walking
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...
Optimal control of an invasive species using a reaction-diffusion model and linear programming
Bonneau, Mathieu; Johnson, Fred A.; Smith, Brian J.; Romagosa, Christina M.; Martin, Julien; Mazzotti, Frank J.
2017-01-01
Managing an invasive species is particularly challenging as little is generally known about the species’ biological characteristics in its new habitat. In practice, removal of individuals often starts before the species is studied to provide the information that will later improve control. Therefore, the locations and the amount of control have to be determined in the face of great uncertainty about the species characteristics and with a limited amount of resources. We propose framing spatial control as a linear programming optimization problem. This formulation, paired with a discrete reaction-diffusion model, permits calculation of an optimal control strategy that minimizes the remaining number of invaders for a fixed cost or that minimizes the control cost for containment or protecting specific areas from invasion. We propose computing the optimal strategy for a range of possible model parameters, representing current uncertainty on the possible invasion scenarios. Then, a best strategy can be identified depending on the risk attitude of the decision-maker. We use this framework to study the spatial control of the Argentine black and white tegus (Salvator merianae) in South Florida. There is uncertainty about tegu demography and we considered several combinations of model parameters, exhibiting various dynamics of invasion. For a fixed one-year budget, we show that the risk-averse strategy, which optimizes the worst-case scenario of tegus’ dynamics, and the risk-neutral strategy, which optimizes the expected scenario, both concentrated control close to the point of introduction. A risk-seeking strategy, which optimizes the best-case scenario, focuses more on models where eradication of the species in a cell is possible and consists of spreading control as much as possible. For the establishment of a containment area, assuming an exponential growth we show that with current control methods it might not be possible to implement such a strategy for some of the
Modeling and optimization of LCD optical performance
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
A Stand-Class Growth and Yield Model for Mexico’s Northern Temperate, Mixed and Multiaged Forests
Directory of Open Access Journals (Sweden)
José Návar
2014-12-01
Full Text Available The aim of this research was to develop a stand-class growth and yield model based on the diameter growth dynamics of Pinus spp. and Quercus spp. of Mexico’s mixed temperate forests. Using a total of 2663 temporary, circular-sampling plots of 1000 m2 each, nine Weibull distribution techniques of parameter estimation were fitted to the diameter structures of pines and oaks. Statistical equations using stand attributes and the first three moments of the diameter distribution predicted and recovered the Weibull parameters. Using nearly 1200 and 100 harvested trees for pines and oaks, respectively, I developed the total height versus diameter at breast height relationship by fitting three non-linear functions. The Newnham model predicted stem taper and numerical integration was done to estimate merchantable timber volume for all trees in the stand for each diameter class. The independence of the diameter structures of pines and oaks was tested by regressing the Weibull parameters and projecting diameter structures. The model predicts diameter distributions transition from exponential (J inverse, logarithmic to well-balanced distributions with increasing mean stand diameter at breast height. Pine diameter distributions transition faster and the model predicts independent growth rates between pines and oaks. The stand-class growth and yield model must be completed with the diameter-age relationship for oaks in order to carry a full optimization procedure to find stand density and genera composition to maximize forest growth.
International Nuclear Information System (INIS)
Grassberger, C; Paganetti, H
2016-01-01
Purpose: To develop a model that includes the process of resistance development into the treatment optimization process for schedules that include targeted therapies. Further, to validate the approach using clinical data and to apply the model to assess the optimal induction period with targeted agents before curative treatment with chemo-radiation in stage III lung cancer. Methods: Growth of the tumor and its subpopulations is modeled by Gompertzian growth dynamics, resistance induction as a stochastic process. Chemotherapy induced cell kill is modeled by log-cell kill dynamics, targeted agents similarly but restricted to the sensitive population. Radiation induced cell kill is assumed to follow the linear-quadratic model. The validation patient data consist of a cohort of lung cancer patients treated with tyrosine kinase inhibitors that had longitudinal imaging data available. Results: The resistance induction model was successfully validated using clinical trial data from 49 patients treated with targeted agents. The observed recurrence kinetics, with tumors progressing from 1.4–63 months, result in tumor growth equaling a median volume doubling time of 92 days [34–248] and a median fraction of pre-existing resistance of 0.035 [0–0.22], in agreement with previous clinical studies. The model revealed widely varying optimal time points for the use of curative therapy, reaching from ∼1m to >6m depending on the patient’s growth rate and amount of pre-existing resistance. This demonstrates the importance of patient-specific treatment schedules when targeted agents are incorporated into the treatment. Conclusion: We developed a model including evolutionary dynamics of resistant sub-populations with traditional chemotherapy and radiation cell kill models. Fitting to clinical data yielded patient specific growth rates and resistant fraction in agreement with previous studies. Further application of the model demonstrated how proper timing of chemo
Energy Technology Data Exchange (ETDEWEB)
Grassberger, C; Paganetti, H [Massachusetts General Hospital, Boston, MA (United States)
2016-06-15
Purpose: To develop a model that includes the process of resistance development into the treatment optimization process for schedules that include targeted therapies. Further, to validate the approach using clinical data and to apply the model to assess the optimal induction period with targeted agents before curative treatment with chemo-radiation in stage III lung cancer. Methods: Growth of the tumor and its subpopulations is modeled by Gompertzian growth dynamics, resistance induction as a stochastic process. Chemotherapy induced cell kill is modeled by log-cell kill dynamics, targeted agents similarly but restricted to the sensitive population. Radiation induced cell kill is assumed to follow the linear-quadratic model. The validation patient data consist of a cohort of lung cancer patients treated with tyrosine kinase inhibitors that had longitudinal imaging data available. Results: The resistance induction model was successfully validated using clinical trial data from 49 patients treated with targeted agents. The observed recurrence kinetics, with tumors progressing from 1.4–63 months, result in tumor growth equaling a median volume doubling time of 92 days [34–248] and a median fraction of pre-existing resistance of 0.035 [0–0.22], in agreement with previous clinical studies. The model revealed widely varying optimal time points for the use of curative therapy, reaching from ∼1m to >6m depending on the patient’s growth rate and amount of pre-existing resistance. This demonstrates the importance of patient-specific treatment schedules when targeted agents are incorporated into the treatment. Conclusion: We developed a model including evolutionary dynamics of resistant sub-populations with traditional chemotherapy and radiation cell kill models. Fitting to clinical data yielded patient specific growth rates and resistant fraction in agreement with previous studies. Further application of the model demonstrated how proper timing of chemo
Optimal time points sampling in pathway modelling.
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.
Baroukh, Caroline; Muñoz-Tamayo, Rafael; Steyer, Jean-Philippe; Bernard, Olivier
2014-01-01
Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.
Directory of Open Access Journals (Sweden)
Caroline Baroukh
Full Text Available Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.
Performance comparison of renewable incentive schemes using optimal control
International Nuclear Information System (INIS)
Oak, Neeraj; Lawson, Daniel; Champneys, Alan
2014-01-01
Many governments worldwide have instituted incentive schemes for renewable electricity producers in order to meet carbon emissions targets. These schemes aim to boost investment and hence growth in renewable energy industries. This paper examines four such schemes: premium feed-in tariffs, fixed feed-in tariffs, feed-in tariffs with contract for difference and the renewable obligations scheme. A generalised mathematical model of industry growth is presented and fitted with data from the UK onshore wind industry. The model responds to subsidy from each of the four incentive schemes. A utility or ‘fitness’ function that maximises installed capacity at some fixed time in the future while minimising total cost of subsidy is postulated. Using this function, the optimal strategy for provision and timing of subsidy for each scheme is calculated. Finally, a comparison of the performance of each scheme, given that they use their optimal control strategy, is presented. This model indicates that the premium feed-in tariff and renewable obligation scheme produce the joint best results. - Highlights: • Stochastic differential equation model of renewable energy industry growth and prices, using UK onshore wind data 1992–2010. • Cost of production reduces as cumulative installed capacity of wind energy increases, consistent with the theory of learning. • Studies the effect of subsidy using feed-in tariff schemes, and the ‘renewable obligations’ scheme. • We determine the optimal timing and quantity of subsidy required to maximise industry growth and minimise costs. • The premium feed-in tariff scheme and the renewable obligations scheme produce the best results under optimal control
A Growth Model of Inflation, Tax Evasion and Financial Repression
Roubini, Nouriel; Sala-i-Martin, Xavier
1992-01-01
In this paper we study the effects of policies of financial repression on long term growth and try to explain why optimizing governments might want to repress the financial sector. We also explain why inflation may be negatively related to growth, even though it does not affect growth directly. We argue that the main reason why governments repress the financial sector is that this sector is the source of "easy" resources for the public budget The source of revenue stemming from this intervent...
A day in the life of fish larvae: modeling foraging and growth using quirks.
Directory of Open Access Journals (Sweden)
Klaus B Huebert
Full Text Available This article introduces "Quirks," a generic, individual-based model synthesizing over 40 years of empirical and theoretical insights into the foraging behavior and growth physiology of marine fish larvae. In Quirks, different types of larvae are defined by a short list of their biological traits, and all foraging and growth processes (including the effects of key environmental factors are modeled following one unified set of mechanistic rules. This approach facilitates ecologically meaningful comparisons between different species and environments. We applied Quirks to model young exogenously feeding larvae of four species: 5.5-mm European anchovy (Engraulis encrasicolus, 7-mm Atlantic cod (Gadus morhua, 13-mm Atlantic herring (Clupea harengus, and 7-mm European sprat (Sprattus sprattus. Modeled growth estimates explained the majority of variability among 53 published empirical growth estimates, and displayed very little bias: 0.65% ± 1.2% d(-1 (mean ± standard error. Prey organisms of ∼ 67% the maximum ingestible prey length were optimal for all larval types, in terms of the expected ingestion per encounter. Nevertheless, the foraging rate integrated over all favorable prey sizes was highest when smaller organisms made up >95% of the prey biomass under the assumption of constant normalized size spectrum slopes. The overall effect of turbulence was consistently negative, because its detrimental influence on prey pursuit success exceeded its beneficial influence on prey encounter rate. Model sensitivity to endogenous traits and exogenous environmental factors was measured and is discussed in depth. Quirks is free software and open source code is provided.
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...
Filgueira, Ramón; Rosland, Rune; Grant, Jon
2011-11-01
Growth of Mytilus edulis was simulated using individual based models following both Scope For Growth (SFG) and Dynamic Energy Budget (DEB) approaches. These models were parameterized using independent studies and calibrated for each dataset by adjusting the half-saturation coefficient of the food ingestion function term, XK, a common parameter in both approaches related to feeding behavior. Auto-calibration was carried out using an optimization tool, which provides an objective way of tuning the model. Both approaches yielded similar performance, suggesting that although the basis for constructing the models is different, both can successfully reproduce M. edulis growth. The good performance of both models in different environments achieved by adjusting a single parameter, XK, highlights the potential of these models for (1) producing prospective analysis of mussel growth and (2) investigating mussel feeding response in different ecosystems. Finally, we emphasize that the convergence of two different modeling approaches via calibration of XK, indicates the importance of the feeding behavior and local trophic conditions for bivalve growth performance. Consequently, further investigations should be conducted to explore the relationship of XK to environmental variables and/or to the sophistication of the functional response to food availability with the final objective of creating a general model that can be applied to different ecosystems without the need for calibration.
Clarkson, Sonya M; Giannone, Richard J; Kridelbaugh, Donna M; Elkins, James G; Guss, Adam M; Michener, Joshua K
2017-09-15
The production of biofuels from lignocellulose yields a substantial lignin by-product stream that currently has few applications. Biological conversion of lignin-derived compounds into chemicals and fuels has the potential to improve the economics of lignocellulose-derived biofuels, but few microbes are able both to catabolize lignin-derived aromatic compounds and to generate valuable products. While Escherichia coli has been engineered to produce a variety of fuels and chemicals, it is incapable of catabolizing most aromatic compounds. Therefore, we engineered E. coli to catabolize protocatechuate, a common intermediate in lignin degradation, as the sole source of carbon and energy via heterologous expression of a nine-gene pathway from Pseudomonas putida KT2440. We next used experimental evolution to select for mutations that increased growth with protocatechuate more than 2-fold. Increasing the strength of a single ribosome binding site in the heterologous pathway was sufficient to recapitulate the increased growth. After optimization of the core pathway, we extended the pathway to enable catabolism of a second model compound, 4-hydroxybenzoate. These engineered strains will be useful platforms to discover, characterize, and optimize pathways for conversions of lignin-derived aromatics. IMPORTANCE Lignin is a challenging substrate for microbial catabolism due to its polymeric and heterogeneous chemical structure. Therefore, engineering microbes for improved catabolism of lignin-derived aromatic compounds will require the assembly of an entire network of catabolic reactions, including pathways from genetically intractable strains. Constructing defined pathways for aromatic compound degradation in a model host would allow rapid identification, characterization, and optimization of novel pathways. We constructed and optimized one such pathway in E. coli to enable catabolism of a model aromatic compound, protocatechuate, and then extended the pathway to a related
Use of Phytone Peptone to Optimize Growth and Cell Density of Lactobacillus reuteri.
Atilola, Olabiyi A; Gyawali, Rabin; Aljaloud, Sulaiman O; Ibrahim, Salam A
2015-08-10
The objective of this study was to determine the use of phytone peptone to optimize the growth and cell density of Lactobacillus reuteri. Four strains of L. reuteri (DSM 20016, SD 2112, CF 2-7F, and MF 2-3,) were used in this study. An overnight culture of individual strains was inoculated into fresh basal media with various protein sources (peptone, tryptone, proteose peptone #3, phytone peptone, tryptic soy broth, yeast extract, and beef extract). Samples were then mixed well and incubated at 37 °C for 15 h. Bacterial growth was monitored by measuring turbidity (optical density 610 nm) at different time intervals during the incubation period. At the end of incubation, samples were plated on de-Man Rogosa Sharpe (MRS) agar to determine the bacterial population. Our results showed that phytone peptone promoted the growth of L. reuteri ( p reuteri .
Growth curve models and statistical diagnostics
Pan, Jian-Xin
2002-01-01
Growth-curve models are generalized multivariate analysis-of-variance models. These models are especially useful for investigating growth problems on short times in economics, biology, medical research, and epidemiology. This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. The authors provide complete proofs of theorems as well as practical data sets and MATLAB code.
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...
Model of a single mode energy harvester and properties for optimal power generation
International Nuclear Information System (INIS)
Liao Yabin; Sodano, Henry A
2008-01-01
The process of acquiring the energy surrounding a system and converting it into usable electrical energy is termed power harvesting. In the last few years, the field of power harvesting has experienced significant growth due to the ever increasing desire to produce portable and wireless electronics with extended life. Current portable and wireless devices must be designed to include electrochemical batteries as the power source. The use of batteries can be troublesome due to their finite energy supply, which necessitates their periodic replacement. In the case of wireless sensors that are to be placed in remote locations, the sensor must be easily accessible or of disposable nature to allow the device to function over extended periods of time. Energy scavenging devices are designed to capture the ambient energy surrounding the electronics and covert it into usable electrical energy. The concept of power harvesting works towards developing self-powered devices that do not require replaceable power supplies. The development of energy harvesting systems is greatly facilitated by an accurate model to assist in the design of the system. This paper will describe a theoretical model of a piezoelectric based energy harvesting system that is simple to apply yet provides an accurate prediction of the power generated around a single mode of vibration. Furthermore, this model will allow optimization of system parameters to be studied such that maximal performance can be achieved. Using this model an expression for the optimal resistance and a parameter describing the energy harvesting efficiency will be presented and evaluated through numerical simulations. The second part of this paper will present an experimental validation of the model and optimal parameters
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.
Effects of Spatial Localization on Microbial Consortia Growth.
Directory of Open Access Journals (Sweden)
Michael Venters
Full Text Available Microbial consortia are commonly observed in natural and synthetic systems, and these consortia frequently result in higher biomass production relative to monocultures. The focus here is on the impact of initial spatial localization and substrate diffusivity on the growth of a model microbial consortium consisting of a producer strain that consumes glucose and produces acetate and a scavenger strain that consumes the acetate. The mathematical model is based on an individual cell model where growth is described by Monod kinetics, and substrate transport is described by a continuum-based, non-equilibrium reaction-diffusion model where convective transport is negligible (e.g., in a biofilm. The first set of results focus on a single producer cell at the center of the domain and surrounded by an initial population of scavenger cells. The impact of the initial population density and substrate diffusivity is examined. A transition is observed from the highest initial density resulting in the greatest cell growth to cell growth being independent of initial density. A high initial density minimizes diffusive transport time and is typically expected to result in the highest growth, but this expected behavior is not predicted in environments with lower diffusivity or larger length scales. When the producer cells are placed on the bottom of the domain with the scavenger cells above in a layered biofilm arrangement, a similar critical transition is observed. For the highest diffusivity values examined, a thin, dense initial scavenger layer is optimal for cell growth. However, for smaller diffusivity values, a thicker, less dense initial scavenger layer provides maximal growth. The overall conclusion is that high density clustering of members of a food chain is optimal under most common transport conditions, but under some slow transport conditions, high density clustering may not be optimal for microbial growth.
Rangjaroen, Chakrapong; Sungthong, Rungroch; Rerkasem, Benjavan; Teaumroong, Neung; Noisangiam, Rujirek; Lumyong, Saisamorn
2017-01-01
With the aim of searching for potent diazotrophic bacteria that are free of public health concerns and optimize rice cultivation, the endophytic colonization and plant growth-promoting activities of some endophytic diazotrophic bacteria isolated from rice were evaluated. Among these bacteria, the emerging diazotrophic strains of the genus Novosphingobium effectively associated with rice plant interiors and consequently promoted the growth of rice, even with the lack of a nitrogen source. Thes...
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....
Sales Growth Rate Forecasting Using Improved PSO and SVM
Directory of Open Access Journals (Sweden)
Xibin Wang
2014-01-01
Full Text Available Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO for sales growth rate forecasting. We use support vector machine (SVM as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.
Modelling asymmetric growth in crowded plant communities
DEFF Research Database (Denmark)
Damgaard, Christian
2010-01-01
A class of models that may be used to quantify the effect of size-asymmetric competition in crowded plant communities by estimating a community specific degree of size-asymmetric growth for each species in the community is suggested. The model consists of two parts: an individual size......-asymmetric growth part, where growth is assumed to be proportional to a power function of the size of the individual, and a term that reduces the relative growth rate as a decreasing function of the individual plant size and the competitive interactions from other plants in the neighbourhood....
A new model for simulating growth in fish
Directory of Open Access Journals (Sweden)
Johannes Hamre
2014-01-01
Full Text Available A real dynamic population model calculates change in population sizes independent of time. The Beverton & Holt (B&H model commonly used in fish assessment includes the von Bertalanffy growth function which has age or accumulated time as an independent variable. As a result the B&H model has to assume constant fish growth. However, growth in fish is highly variable depending on food availability and environmental conditions. We propose a new growth model where the length increment of fish living under constant conditions and unlimited food supply, decreases linearly with increasing fish length until it reaches zero at a maximal fish length. The model is independent of time and includes a term which accounts for the environmental variation. In the present study, the model was validated in zebrafish held at constant conditions. There was a good fit of the model to data on observed growth in Norwegian spring spawning herring, capelin from the Barents Sea, North Sea herring and in farmed coastal cod. Growth data from Walleye Pollock from the Eastern Bering Sea and blue whiting from the Norwegian Sea also fitted reasonably well to the model, whereas data from cod from the North Sea showed a good fit to the model only above a length of 70 cm. Cod from the Barents Sea did not grow according to the model. The last results can be explained by environmental factors and variable food availability in the time under study. The model implicates that the efficiency of energy conversion from food decreases as the individual animal approaches its maximal length and is postulated to represent a natural law of fish growth.
Modeling Population Growth and Extinction
Gordon, Sheldon P.
2009-01-01
The exponential growth model and the logistic model typically introduced in the mathematics curriculum presume that a population grows exclusively. In reality, species can also die out and more sophisticated models that take the possibility of extinction into account are needed. In this article, two extensions of the logistic model are considered,…
Modeling of Hybrid Growth Wastewater Bio-reactor
International Nuclear Information System (INIS)
EI Nashaei, S.; Garhyan, P.; Prasad, P.; Abdel Halim, H.S.; Ibrahim, G.
2004-01-01
The attached/suspended growth mixed reactors are considered one of the recently tried approaches to improve the performance of the biological treatment by increasing the volume of the accumulated biomass in terms of attached growth as well as suspended growth. Moreover, the domestic WW can be easily mixed with a high strength non-hazardous industrial wastewater and treated together in these bio-reactors if the need arises. Modeling of Hybrid hybrid growth wastewater reactor addresses the need of understanding the rational of such system in order to achieve better design and operation parameters. This paper aims at developing a heterogeneous mathematical model for hybrid growth system considering the effect of diffusion, external mass transfer, and power input to the system in a rational manner. The model will be based on distinguishing between liquid/solid phase (bio-film and bio-floc). This model would be a step ahead to the fine tuning the design of hybrid systems based on the experimental data of a pilot plant to be implemented in near future
Energy Technology Data Exchange (ETDEWEB)
Ayres, Robert U. [Department of Physical Resource Theory, Chalmers Institute, Gothenburg (Sweden); Van den Bergh, Jeroen C.J.M. [Department of Spatial Economics, Faculty of Economics and Business Administration, and Institute for Environmental Studies, Free University, De Boelelaan 1105, Amsterdam 1081 HV (Netherlands)
2005-10-05
The nature of energy and material resources in a non-optimizing growth theory framework is clarified. This involves two modifications of the conventional theory. Firstly, multiple feedback mechanisms or 'growth engines' are identified, such that the impact of the cost of production through demand on growth is accounted for. Secondly, a production function distinguishes between resource use, technical efficiency, and value creation. The resulting model is analytically solved under the condition of a constant growth rate. Given model complexity, numerical experiments are performed as well, providing relevant insights to the academic and political debates on 'environmental Kuznets curves' and 'dematerialization.'.
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.
Digital Repository Service at National Institute of Oceanography (India)
Sumitra-Vijayaraghavan; Krishnakumari, L.; Royan, J.P.
intestinalis and Ulva fasciata). Rice bran yielded best growth at an optimal level of 0.1 and 0.5 mg.ml-1 d-1 respectively for 1-4 and 4-8 d old larvae. Rice bran was also the best food for outdoor cultures. Artemia fed rice bran took shortest time to reach...
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.
Model reduction for dynamic real-time optimization of chemical processes
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
Economic Growth Models Transition
Directory of Open Access Journals (Sweden)
Coralia Angelescu
2006-03-01
Full Text Available The transitional recession in countries of Eastern Europe has been much longer than expected. The legacy and recent policy mistakes have both contributed to the slow progress. As structural reforms and gradual institution building have taken hold, the post-socialist economics have started to recover, with some leading countries building momentum toward faster growth. There is a possibility that in wider context of globalization several of these emerging market economies will be able to catch up with the more advanced industrial economies in a matter of one or two generations. Over the past few years, most candidate countries have made progress in the transition to a competitive market economy, macroeconomic stabilization and structural reform. However their income levels have remained far below those in the Member States. Measured by per capita income in purchasing power standards, there has been a very limited amount of catching up over the past fourteen years. Prior, the distinctions between Solow-Swan model and endogenous growth model. The interdependence between transition and integration are stated in this study. Finally, some measures of macroeconomic policy for sustainable growth are proposed in correlation with real macroeconomic situation of the Romanian economy. Our study would be considered the real convergence for the Romanian economy and the recommendations for the adequate policies to achieve a fast real convergence and sustainable growth.
Economic Growth Models Transition
Directory of Open Access Journals (Sweden)
Coralia Angelescu
2006-01-01
Full Text Available The transitional recession in countries of Eastern Europe has been much longer than expected. The legacy and recent policy mistakes have both contributed to the slow progress. As structural reforms and gradual institution building have taken hold, the post-socialist economics have started to recover, with some leading countries building momentum toward faster growth. There is a possibility that in wider context of globalization several of these emerging market economies will be able to catch up with the more advanced industrial economies in a matter of one or two generations. Over the past few years, most candidate countries have made progress in the transition to a competitive market economy, macroeconomic stabilization and structural reform. However their income levels have remained far below those in the Member States. Measured by per capita income in purchasing power standards, there has been a very limited amount of catching up over the past fourteen years. Prior, the distinctions between Solow-Swan model and endogenous growth model. The interdependence between transition and integration are stated in this study. Finally, some measures of macroeconomic policy for sustainable growth are proposed in correlation with real macroeconomic situation of the Romanian economy. Our study would be considered the real convergence for the Romanian economy and the recommendations for the adequate policies to achieve a fast real convergence and sustainable growth.
Recent advances in modelling creep crack growth
International Nuclear Information System (INIS)
Riedel, H.
1988-08-01
At the time of the previous International Conference on Fracture, the C* integral had long been recognized as a promising load parameter for correlating crack growth rates in creep-ductile materials. The measured crack growth rates as a function of C* and of the temperature could be understood on the basis of micromechanical models. The distinction between C*-controlled and K I -controlled creep crack growth had been clarified and first attempts had been made to describe creep crack growth in the transient regime between elastic behavior and steady-state creep. This paper describes the progress in describing transient crack growth including the effect of primary creep. The effect of crack-tip geometry changes by blunting and by crack growth on the crack-tip fields and on the validity of C* is analyzed by idealizing the growing-crack geometry by a sharp notch and using recent solutions for the notch-tip fields. A few new three-dimensional calculations of C* are cited and important theoretical points are emphasized regarding the three-dimensional fields at crack tips. Finally, creep crack growth is described by continuum-damage models for which similarity solutions can be obtained. Crack growth under small-scale creep conditions turns out to be difficult to understand. Slightly different models yield very different crack growth rates. (orig.) With 4 figs
Mathematical models of tumor growth: translating absorbed dose to tumor control probability
International Nuclear Information System (INIS)
Sgouros, G.
1996-01-01
Full text: The dose-rate in internal emitter therapy is low and time-dependent as compared to external beam radiotherapy. Once the total absorbed dose delivered to a target tissue is calculated, however, most dosimetric analyses of radiopharmaceuticals are considered complete. To translate absorbed dose estimates obtained for internal emitter therapy to biologic effect, the growth characteristics, repair capacity, and radiosensitivity of the tumor must be considered. Tumor growth may be represented by the Gompertz equation in which tumor cells increase at an exponential growth rate that is itself decreasing at an exponential rate; as the tumor increases in size, the growth rate diminishes. The empirical Gompertz expression for tumor growth may be derived from a mechanistic model in which growth is represented by a balance between tumor-cell birth and loss. The birth rate is assumed to be fixed, while the cell loss rate is time-dependent and increases with tumor size. The birth rate of the tumors may be related to their potential doubling time. Multiple biopsies of individual tumors have demonstrated a heterogeneity in the potential doubling time of tumors. By extending the mechanistic model described above to allow for sub-populations of tumor cells with different birth rates, the effect of kinetic heterogeneity within a tumor may be examined. Model simulations demonstrate that the cell kinetic parameters of a tumor are predicted to change over time and measurements obtained using a biopsy are unlikely to reflect the kinetics of the tumor throughout its growth history. A decrease in overall tumor mass, in which each sub-population is reduced in proportion to its cell number, i.e., the log-kill assumption, leads to re-growth of a tumor that has a greater proliferation rate. Therapy that is linked to the potential doubling time or to the effective proliferation rate of the tumor may lead to re-growth of a tumor that is kinetically unchanged. The simplest model of
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.
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.
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.
Organizing principles underlying microorganism's growth-robustness trade-off.
Bolli, Alessandro; Salvador, Armindo
2014-10-01
Growth Robustness Reciprocity (GRR) is an intriguing microbial manifestation: the impairment of microorganism's growth enhances their ability to resist acute stresses, and vice-versa. This is caused by regulatory interactions that determine higher expression of protection mechanisms in response to low growth rates. But because such regulatory mechanisms are species-specific, GRR must result from convergent evolution. Why does natural selection favor such an outcome? We used mathematical models of optimal cellular resource allocation to identify the general principles underlying GRR. Non-linear optimization allowed to predict allocation patterns of biosynthetic resources (ribosomes devoted to the synthesis of each cell component) that maximize growth. These models predict the down-regulation of stress defenses under high substrate availabilities and low stress levels. Under these conditions, stress tolerance ensues from growth-related damage dilution: the higher the substrate availability, the fastest the dilution of damaged proteins by newly synthesized proteins, the lower the accumulation of damaged components into the cell. In turn, under low substrate availability growth is too slow for effective damage dilution, and the expression of the defenses up to some optimal level then increases growth. As a consequence, slow-growing cells are pre-adapted to withstand acute stresses. Therefore, the observed negative correlation between growth and stress tolerance can be explained as a consequence of optimal resource allocation for maximal growth. We acknowledge fellowship SFRH/BPD/90065/2012 and grants PEst-C/SAU/LA0001/2013-2014 and FCOMP-01-0124-FEDER-020978 financed by FEDER through the "Programa Operacional Factores de Competitividade, COMPETE" and by national funds through "FCT, Fundação para a Ciência e a Tecnologia" (project PTDC/QUI-BIQ/119657/2010). Copyright © 2014. Published by Elsevier Inc.
Use of Phytone Peptone to Optimize Growth and Cell Density of Lactobacillus reuteri
Directory of Open Access Journals (Sweden)
Olabiyi A. Atilola
2015-08-01
Full Text Available The objective of this study was to determine the use of phytone peptone to optimize the growth and cell density of Lactobacillus reuteri. Four strains of L. reuteri (DSM 20016, SD 2112, CF 2-7F, and MF 2-3, were used in this study. An overnight culture of individual strains was inoculated into fresh basal media with various protein sources (peptone, tryptone, proteose peptone #3, phytone peptone, tryptic soy broth, yeast extract, and beef extract. Samples were then mixed well and incubated at 37 °C for 15 h. Bacterial growth was monitored by measuring turbidity (optical density 610 nm at different time intervals during the incubation period. At the end of incubation, samples were plated on de-Man Rogosa Sharpe (MRS agar to determine the bacterial population. Our results showed that phytone peptone promoted the growth of L. reuteri (p < 0.05 by 1.4 log CFU/mL on average compared to the control samples. Therefore, phytone peptone could be included in laboratory media to enhance growth and increase the cell density of L. reuteri.
Directory of Open Access Journals (Sweden)
Caroline Baroukh
2017-06-01
Full Text Available Microalgae are promising microorganisms for the production of numerous molecules of interest, such as pigments, proteins or triglycerides that can be turned into biofuels. Heterotrophic or mixotrophic growth on fermentative wastes represents an interesting approach to achieving higher biomass concentrations, while reducing cost and improving the environmental footprint. Fermentative wastes generally consist of a blend of diverse molecules and it is thus crucial to understand microalgal metabolism in such conditions, where switching between substrates might occur. Metabolic modeling has proven to be an efficient tool for understanding metabolism and guiding the optimization of biomass or target molecule production. Here, we focused on the metabolism of Chlorella sorokiniana growing heterotrophically and mixotrophically on acetate and butyrate. The metabolism was represented by 172 metabolic reactions. The DRUM modeling framework with a mildly relaxed quasi-steady-state assumption was used to account for the switching between substrates and the presence of light. Nine experiments were used to calibrate the model and nine experiments for the validation. The model efficiently predicted the experimental data, including the transient behavior during heterotrophic, autotrophic, mixotrophic and diauxic growth. It shows that an accurate model of metabolism can now be constructed, even in dynamic conditions, with the presence of several carbon substrates. It also opens new perspectives for the heterotrophic and mixotrophic use of microalgae, especially for biofuel production from wastes.
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.
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...
Adaptive surrogate model based multiobjective optimization for coastal aquifer management
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.
Pollution externalities in a Schumpeterian growth model
Koesler, Simon
2010-01-01
This paper extends a standard Schumpeterian growth model to include an environmental dimension. Thereby, it explicitly links the pollution intensity of economic activity to technological progress. In a second step, it investigates the effect of pollution on economic growth under the assumption that pollution intensities are related to technological progress. Several conclusions emerge from the model. In equilibrium, the economy follows a balanced growth path. The effect of pollution on the ec...
Mardlijah; Jamil, Ahmad; Hanafi, Lukman; Sanjaya, Suharmadi
2017-09-01
There are so many benefit of algae. One of them is using for renewable energy and sustainable in the future. The greater growth of algae will increasing biodiesel production and the increase of algae growth is influenced by glucose, nutrients and photosynthesis process. In this paper, the optimal control problem of the growth of algae is discussed. The objective function is to maximize the concentration of dry algae while the control is the flow of carbon dioxide and the nutrition. The solution is obtained by applying the Pontryagin Maximum Principle. and the result show that the concentration of algae increased more than 15 %.
Constrained optimization via simulation models for new product innovation
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.
Zang, Xiaonan; Zhang, Xuecheng; Mu, Xiaosheng; Liu, Bin
2013-03-01
This study aimed to optimize the purification of recombinant growth hormone from Paralichthys olivaceus. Recombinant flounder growth hormone (r-fGH) was expressed by Escherichia coli in form of inclusion body or as soluble protein under different inducing conditions. The inclusion body was renatured using two recovery methods, i.e., dilution and dialysis. Thereafter, the refolded protein was purified by Glutathione Sepharase 4B affinity chromatography and r-fGH was obtained by cleavage of thrombin. For soluble products, r-fGH was directly purified from the lysates by Glutathione Sepharase 4B affinity chromatography. ELISA-receptor assay demonstrated that despite its low receptor binding activity, the r-fGH purified from refolded inclusion body had a higher yield (2.605 mg L-1) than that from soluble protein (1.964 mg L-1). Of the tested recovery methods, addition of renaturing buffer (pH 8.5) into denatured inclusion body yielded the best recovery rate (17.9%). This work provided an optimized purification method for high recovery of r-fGH, thus contributing to the application of r-fGH to aquaculture.
Mechanistic model for microbial growth on hydrocarbons
Energy Technology Data Exchange (ETDEWEB)
Mallee, F M; Blanch, H W
1977-12-01
Based on available information describing the transport and consumption of insoluble alkanes, a mechanistic model is proposed for microbial growth on hydrocarbons. The model describes the atypical growth kinetics observed, and has implications in the design of large scale equipment for single cell protein (SCP) manufacture from hydrocarbons. The model presents a framework for comparison of the previously published experimental kinetic data.
Research on the decision-making model of land-use spatial optimization
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.
A model of optimal voluntary muscular control.
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.
Bressan, Alberto; Lewicka, Marta
2018-03-01
We consider a free boundary problem for a system of PDEs, modeling the growth of a biological tissue. A morphogen, controlling volume growth, is produced by specific cells and then diffused and absorbed throughout the domain. The geometric shape of the growing tissue is determined by the instantaneous minimization of an elastic deformation energy, subject to a constraint on the volumetric growth. For an initial domain with C}^{2,α boundary, our main result establishes the local existence and uniqueness of a classical solution, up to a rigid motion.
Chong, Haining; Wang, Zhewei; Chen, Chaonan; Xu, Zemin; Wu, Ke; Wu, Lan; Xu, Bo; Ye, Hui
2018-04-01
In order to suppress dislocation generation, we develop a "three-step growth" method to heteroepitaxy low dislocation density germanium (Ge) layers on silicon with the MBE process. The method is composed of 3 growth steps: low temperature (LT) seed layer, LT-HT intermediate layer as well as high temperature (HT) epilayer, successively. Threading dislocation density (TDD) of epitaxial Ge layers is measured as low as 1.4 × 106 cm-2 by optimizing the growth parameters. The results of Raman spectrum showed that the internal strain of heteroepitaxial Ge layers is tensile and homogeneous. During the growth of LT-HT intermediate layer, TDD reduction can be obtained by lowering the temperature ramping rate, and high rate deposition maintains smooth surface morphology in Ge epilayer. A mechanism based on thermodynamics is used to explain the TDD and surface morphological dependence on temperature ramping rate and deposition rate. Furthermore, we demonstrate that the Ge layer obtained can provide an excellent platform for III-V materials integrated on Si.
An optimization model to agroindustrial sector in antioquia (Colombia, South America)
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.
Trajectories and models of individual growth
Directory of Open Access Journals (Sweden)
Arseniy Karkach
2006-11-01
Full Text Available It has long been recognized that the patterns of growth play an important role in the evolution of age trajectories of fertility and mortality (Williams, 1957. Life history studies would benefit from a better understanding of strategies and mechanisms of growth, but still no comparative research on individual growth strategies has been conducted. Growth patterns and methods have been shaped by evolution and a great variety of them are observed. Two distinct patterns - determinate and indeterminate growth - are of a special interest for these studies since they present qualitatively different outcomes of evolution. We attempt to draw together studies covering growth in plant and animal species across a wide range of phyla focusing primarily on the noted qualitative features. We also review mathematical descriptions of growth, namely empirical growth curves and growth models, and discuss the directions of future research.
Modeling error distributions of growth curve models through Bayesian methods.
Zhang, Zhiyong
2016-06-01
Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is proposed to flexibly model normal and non-normal data through the explicit specification of the error distributions. A simulation study shows when the distribution of the error is correctly specified, one can avoid the loss in the efficiency of standard error estimates. A real example on the analysis of mathematical ability growth data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 is used to show the application of the proposed methods. Instructions and code on how to conduct growth curve analysis with both normal and non-normal error distributions using the the MCMC procedure of SAS are provided.
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)
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.
On Latent Growth Models for Composites and Their Constituents.
Hancock, Gregory R; Mao, Xiulin; Kher, Hemant
2013-09-01
Over the last decade and a half, latent growth modeling has become an extremely popular and versatile technique for evaluating longitudinal change and its determinants. Most common among the models applied are those for a single measured variable over time. This model has been extended in a variety of ways, most relevant for the current work being the multidomain and the second-order latent growth models. Whereas the former allows for growth function characteristics to be modeled for multiple outcomes simultaneously, with the degree of growth characteristics' relations assessed within the model (e.g., cross-domain slope factor correlations), the latter models growth in latent outcomes, each of which has effect indicators repeated over time. But what if one has an outcome that is believed to be formative relative to its indicator variables rather than latent? In this case, where the outcome is a composite of multiple constituents, modeling change over time is less straightforward. This article provides analytical and applied details for simultaneously modeling growth in composites and their constituent elements, including a real data example using a general computer self-efficacy questionnaire.
Structural modelling of economic growth: Technological changes
Directory of Open Access Journals (Sweden)
Sukharev Oleg
2016-01-01
Full Text Available Neoclassical and Keynesian theories of economic growth assume the use of Cobb-Douglas modified functions and other aggregate econometric approaches to growth dynamics modelling. In that case explanations of economic growth are based on the logic of the used mathematical ratios often including the ideas about aggregated values change and factors change a priori. The idea of assessment of factor productivity is the fundamental one among modern theories of economic growth. Nevertheless, structural parameters of economic system, institutions and technological changes are practically not considered within known approaches, though the latter is reflected in the changing parameters of production function. At the same time, on the one hand, the ratio of structural elements determines the future value of the total productivity of the factors and, on the other hand, strongly influences the rate of economic growth and its mode of innovative dynamics. To put structural parameters of economic system into growth models with the possibility of assessment of such modes under conditions of interaction of new and old combinations is an essential step in the development of the theory of economic growth/development. It allows forming stimulation policy of economic growth proceeding from the structural ratios and relations recognized for this economic system. It is most convenient in such models to use logistic functions demonstrating the resource change for old and new combination within the economic system. The result of economy development depends on starting conditions, and on institutional parameters of velocity change of resource borrowing in favour of a new combination and creation of its own resource. Model registration of the resource is carried out through the idea of investments into new and old combinations.
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
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
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
Optimal Spatial Harvesting Strategy and Symmetry-Breaking
International Nuclear Information System (INIS)
Kurata, Kazuhiro; Shi Junping
2008-01-01
A reaction-diffusion model with logistic growth and constant effort harvesting is considered. By minimizing an intrinsic biological energy function, we obtain an optimal spatial harvesting strategy which will benefit the population the most. The symmetry properties of the optimal strategy are also discussed, and related symmetry preserving and symmetry breaking phenomena are shown with several typical examples of habitats
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.
Liu, Xiaojun; Ferguson, Richard B; Zheng, Hengbiao; Cao, Qiang; Tian, Yongchao; Cao, Weixing; Zhu, Yan
2017-03-24
The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI = ( 1 + e - 15.2829 × ( R A G D D i - 0.1944 ) ) - 1 - ( 1 + e - 11.6517 × ( R A G D D i - 1.0267 ) ) - 1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status.
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
Clark, Ryan L; McGinley, Laura L; Purdy, Hugh M; Korosh, Travis C; Reed, Jennifer L; Root, Thatcher W; Pfleger, Brian F
2018-03-27
Cyanobacteria are photosynthetic microorganisms whose metabolism can be modified through genetic engineering for production of a wide variety of molecules directly from CO 2 , light, and nutrients. Diverse molecules have been produced in small quantities by engineered cyanobacteria to demonstrate the feasibility of photosynthetic biorefineries. Consequently, there is interest in engineering these microorganisms to increase titer and productivity to meet industrial metrics. Unfortunately, differing experimental conditions and cultivation techniques confound comparisons of strains and metabolic engineering strategies. In this work, we discuss the factors governing photoautotrophic growth and demonstrate nutritionally replete conditions in which a model cyanobacterium can be grown to stationary phase with light as the sole limiting substrate. We introduce a mathematical framework for understanding the dynamics of growth and product secretion in light-limited cyanobacterial cultures. Using this framework, we demonstrate how cyanobacterial growth in differing experimental systems can be easily scaled by the volumetric photon delivery rate using the model organisms Synechococcus sp. strain PCC7002 and Synechococcus elongatus strain UTEX2973. We use this framework to predict scaled up growth and product secretion in 1L photobioreactors of two strains of Synechococcus PCC7002 engineered for production of l-lactate or L-lysine. The analytical framework developed in this work serves as a guide for future metabolic engineering studies of cyanobacteria to allow better comparison of experiments performed in different experimental systems and to further investigate the dynamics of growth and product secretion. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.
Lough, R. G.; Broughton, E. A.; Buckley, L. J.; Incze, L. S.; Pehrson Edwards, K.; Converse, R.; Aretxabaleta, A.; Werner, F. E.
2006-11-01
Cruises were conducted in spring 1999 to describe the interaction between tidal-front processes and the transport, retention, and growth of cod larvae and their prey during the seasonal transition to a stratified water-column along the southern flank of Georges Bank. All the physical and biological observations were integrated in coupled circulation-trophodynamic simulations. The three-dimensional circulation fields were modeled using data assimilation methods described in Aretxabaleta et al. [2005. Data assimilative hindcast on the Southern Flank of Georges Bank during May 1999: frontal circulation and implications. Continental Shelf Research 25, 849-874]. The individual-based model (IBM) of Lough et al. [2005. A general biophysical model of larval cod growth applied to populations on Georges Bank. Fisheries Oceanography 14, 241-262] was used to consider trophodynamic effects on the growth and survival of larval cod. Prey fields were specified for mixed and stratified water columns from field surveys and allowed to adjust in the circulation model. Encounter and ingestion rates of larvae were functions of prey concentration, larval search patterns, light, swimming speeds of predator and prey, and turbulence. Model outputs provide hourly depth-dependent estimates of growth rates, prey biomass ingested, and larval length and weight. Simulations were conducted along a 2-D transect across the tidal front, from mixed to stratified water columns, before and after a wind event. Pre-storm, observed larval cod growth rates, based on RNA-DNA analysis, were highest in the surface 20 m at the stratified and front stations. Post-storm, larval growth rates decreased 1-2% d -1 at the stratified and front stations, corresponding with a <1 °C decrease in temperature. At the mixed station, there was no apparent difference in growth rates with depth, either before or after the storm. Simulations indicate that maximum larval growth rates can occur at the tidal-mixing front due to the
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
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.
AN OPTIMAL MAINTENANCE MANAGEMENT MODEL FOR AIRPORT CONCRETE PAVEMENT
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.
Slow Growth and Optimal Approximation of Pseudoanalytic Functions on the Disk
Directory of Open Access Journals (Sweden)
Devendra Kumar
2013-07-01
Full Text Available Pseudoanalytic functions (PAF are constructed as complex combination of real-valued analytic solutions to the Stokes-Betrami System. These solutions include the generalized biaxisymmetric potentials. McCoy [10] considered the approximation of pseudoanalytic functions on the disk. Kumar et al. [9] studied the generalized order and generalized type of PAF in terms of the Fourier coefficients occurring in its local expansion and optimal approximation errors in Bernstein sense on the disk. The aim of this paper is to improve the results of McCoy [10] and Kumar et al. [9]. Our results apply satisfactorily for slow growth.
Optimality models in the age of experimental evolution and genomics
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...
Optimization Models and Methods Developed at the Energy Systems Institute
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...
Multi-objective optimization of GENIE Earth system models.
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.
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
Directory of Open Access Journals (Sweden)
Carlos Pozo
Full Text Available Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study
Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano
2012-01-01
Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the
Model uncertainty in growth empirics
Prüfer, P.
2008-01-01
This thesis applies so-called Bayesian model averaging (BMA) to three different economic questions substantially exposed to model uncertainty. Chapter 2 addresses a major issue of modern development economics: the analysis of the determinants of pro-poor growth (PPG), which seeks to combine high
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.
Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach
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.
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
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.
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
Stochastic Stability of Endogenous Growth: Theory and Applications
Boucekkine, Raouf; Pintus, Patrick; Zou, Benteng
2015-01-01
We examine the issue of stability of stochastic endogenous growth. First, stochastic stability concepts are introduced and applied to stochastic linear homogenous differen- tial equations to which several stochastic endogenous growth models reduce. Second, we apply the mathematical theory to two models, starting with the stochastic AK model. It’s shown that in this case exponential balanced paths, which characterize optimal trajectories in the absence of uncertainty, are not robust to uncerta...
Lee, Hee-Seok; Song, Hyuk-Hwan; An, Joong-Hoon; Shin, Cha-Gyun; Lee, Gung Pyo; Lee, Chan
2008-01-01
The production of the entomopathogenic and phytotoxic cyclic depsipeptide beauvericin (BEA) was studied in submerged cultures of Fusarium oxysporum KFCC 11363P isolated in Korea. The influences of various factors on mycelia growth and BEA production were examined in both complete and chemically defined culture media. The mycelia growth and BEA production were highest in Fusarium defined medium. The optimal carbon and nitrogen sources for maximizing BEA production were glucose and NaNO3, respectively. The carbon/ nitrogen ratio for maximal production of BEA was investigated using response surface methodology (RSM). Equations derived by differentiation of the RSM model revealed that the production of BEA was maximal when using 108 mM glucose and 25 mM NaNO3.
Optimal back-to-front airplane boarding
Bachmat, Eitan; Khachaturov, Vassilii; Kuperman, Ran
2013-06-01
The problem of finding an optimal back-to-front airplane boarding policy is explored, using a mathematical model that is related to the 1+1 polynuclear growth model with concave boundary conditions and to causal sets in gravity. We study all airplane configurations and boarding group sizes. Optimal boarding policies for various airplane configurations are presented. Detailed calculations are provided along with simulations that support the main conclusions of the theory. We show that the effectiveness of back-to-front policies undergoes a phase transition when passing from lightly congested airplanes to heavily congested airplanes. The phase transition also affects the nature of the optimal or near-optimal policies. Under what we consider to be realistic conditions, optimal back-to-front policies lead to a modest 8-12% improvement in boarding time over random (no policy) boarding, using two boarding groups. Having more than two groups is not effective.
Optimal back-to-front airplane boarding.
Bachmat, Eitan; Khachaturov, Vassilii; Kuperman, Ran
2013-06-01
The problem of finding an optimal back-to-front airplane boarding policy is explored, using a mathematical model that is related to the 1+1 polynuclear growth model with concave boundary conditions and to causal sets in gravity. We study all airplane configurations and boarding group sizes. Optimal boarding policies for various airplane configurations are presented. Detailed calculations are provided along with simulations that support the main conclusions of the theory. We show that the effectiveness of back-to-front policies undergoes a phase transition when passing from lightly congested airplanes to heavily congested airplanes. The phase transition also affects the nature of the optimal or near-optimal policies. Under what we consider to be realistic conditions, optimal back-to-front policies lead to a modest 8-12% improvement in boarding time over random (no policy) boarding, using two boarding groups. Having more than two groups is not effective.
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
Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes
Leite, Walter L.; Stapleton, Laura M.
2011-01-01
In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…
Energy Technology Data Exchange (ETDEWEB)
Tomaschek, Jan
2013-12-11
The transport sector is seen as one of the key factors for driving future energy consumption and greenhouse gas (GHG) emissions. Especially in developing countries, significant growth in transport demand is expected. Gauteng province, as the economic centre of South Africa and transport hub for the whole of southern Africa, is one emerging urban region that faces rapid growth. However, the province is on its way to playing a leading role for supporting ways to adapt to climate change and mitigate GHG emissions. Conversely, there is a lack of scientific research on the promising measures for GHG mitigation in the transport sector. For the rapidly growing transport sector of the province in particular, research is focused primarily on extending and structuring the road infrastructure. Moreover, it is important that the transport sector is considered as part of the whole energy system, as significant contributions to GHG emissions and the associated costs arise from energy supply, provision and conversion. This research is the first application of an integrated energy system model (i.e. the TIMES-GEECO model) for the optimization of the transport sector of Gauteng. Optimizing energy system models allows finding least-cost measures for various scenarios, by considering dependencies and interlinkages in the energy system as well as environmental constraints. To do so, the transport sector and the energy supply sector had to be incorporated into the model application in terms of the characteristics of a developing urban region, which includes all relevant transport modes, vehicle technologies, fuel options, vehicle-to-grid energy storage, the consideration of road types as well as explicit expansions of the public transport system and income-dependent travel demand modelling. Additionally, GHG mitigation options outside the provincial boundaries were incorporated to allow for mitigation at least cost and to consider regional resource availability. Moreover, in TIMES
International Nuclear Information System (INIS)
Tomaschek, Jan
2013-01-01
The transport sector is seen as one of the key factors for driving future energy consumption and greenhouse gas (GHG) emissions. Especially in developing countries, significant growth in transport demand is expected. Gauteng province, as the economic centre of South Africa and transport hub for the whole of southern Africa, is one emerging urban region that faces rapid growth. However, the province is on its way to playing a leading role for supporting ways to adapt to climate change and mitigate GHG emissions. Conversely, there is a lack of scientific research on the promising measures for GHG mitigation in the transport sector. For the rapidly growing transport sector of the province in particular, research is focused primarily on extending and structuring the road infrastructure. Moreover, it is important that the transport sector is considered as part of the whole energy system, as significant contributions to GHG emissions and the associated costs arise from energy supply, provision and conversion. This research is the first application of an integrated energy system model (i.e. the TIMES-GEECO model) for the optimization of the transport sector of Gauteng. Optimizing energy system models allows finding least-cost measures for various scenarios, by considering dependencies and interlinkages in the energy system as well as environmental constraints. To do so, the transport sector and the energy supply sector had to be incorporated into the model application in terms of the characteristics of a developing urban region, which includes all relevant transport modes, vehicle technologies, fuel options, vehicle-to-grid energy storage, the consideration of road types as well as explicit expansions of the public transport system and income-dependent travel demand modelling. Additionally, GHG mitigation options outside the provincial boundaries were incorporated to allow for mitigation at least cost and to consider regional resource availability. Moreover, in TIMES
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
Optimal control of suspended sediment distribution model of Talaga lake
Ratianingsih, R.; Resnawati, Azim, Mardlijah, Widodo, B.
2017-08-01
Talaga Lake is one of several lakes in Central Sulawesi that potentially to be managed in multi purposes scheme because of its characteristic. The scheme is addressed not only due to the lake maintenance because of its sediment but also due to the Algae farming for its biodiesel fuel. This paper governs a suspended sediment distribution model of Talaga lake. The model is derived from the two dimensional hydrodynamic shallow water equations of the mass and momentum conservation law of sediment transport. An order reduction of the model gives six equations of hyperbolic systems of the depth, two dimension directional velocities and sediment concentration while the bed elevation as the second order of turbulent diffusion and dispersion are neglected. The system is discreted and linearized such that could be solved numerically by box-Keller method for some initial and boundary condition. The solutions shows that the downstream velocity is play a role in transversal direction of stream function flow. The downstream accumulated sediment indicate that the suspended sediment and its changing should be controlled by optimizing the downstream velocity and transversal suspended sediment changing due to the ideal algae growth need.
Directory of Open Access Journals (Sweden)
Takayuki Ohara
2017-10-01
Full Text Available Plants need to avoid carbon starvation and resultant growth inhibition under fluctuating light environments to ensure optimal growth and reproduction. As diel patterns of carbon metabolism are influenced by the circadian clock, appropriate regulation of the clock is essential for plants to properly manage their carbon resources. For proper adjustment of the circadian phase, higher plants utilize environmental signals such as light or temperature and metabolic signals such as photosynthetic products; the importance of the latter as phase regulators has been recently elucidated. A mutant of Arabidopsis thaliana that is deficient in phase response to sugar has been shown, under fluctuating light conditions, to be unable to adjust starch turnover and to realize carbon homeostasis. Whereas, the effects of light entrainment on growth and survival of higher plants are well studied, the impact of phase regulation by sugar remains unknown. Here we show that endogenous sugar entrainment facilitates plant growth. We integrated two mathematical models, one describing the dynamics of carbon metabolism in A. thaliana source leaves and the other growth of sink tissues dependent on sucrose translocation from the source. The integrated model predicted that sugar-sensitive plants grow faster than sugar-insensitive plants under constant as well as changing photoperiod conditions. We found that sugar entrainment enables efficient carbon investment for growth by stabilizing sucrose supply to sink tissues. Our results highlight the importance of clock entrainment by both exogenous and endogenous signals for optimizing growth and increasing fitness.
International Nuclear Information System (INIS)
Waterman, T.E.; Takata, A.N.
1983-01-01
The IITRI Urban Fire Spread Model as well as others of similar vintage were constrained by computer size and running costs such that many approximations/generalizations were introduced to reduce program complexity and data storage requirements. Simplifications were introduced both in input data and in fire growth and spread calculations. Modern computational capabilities offer the means to introduce greater detail and to examine its practical significance on urban fire predictions. Selected portions of the model are described as presently configured, and potential modifications are discussed. A single tract model is hypothesized which permits the importance of various model details to be assessed, and, other model applications are identified
Anthun, Kjartan Sarheim; Kittelsen, Sverre Andreas Campbell; Magnussen, Jon
2017-04-01
This paper analyses productivity growth in the Norwegian hospital sector over a period of 16 years, 1999-2014. This period was characterized by a large ownership reform with subsequent hospital reorganizations and mergers. We describe how technological change, technical productivity, scale efficiency and the estimated optimal size of hospitals have evolved during this period. Hospital admissions were grouped into diagnosis-related groups using a fixed-grouper logic. Four composite outputs were defined and inputs were measured as operating costs. Productivity and efficiency were estimated with bootstrapped data envelopment analyses. Mean productivity increased by 24.6% points from 1999 to 2014, an average annual change of 1.5%. There was a substantial growth in productivity and hospital size following the ownership reform. After the reform (2003-2014), average annual growth was case mix between hospitals, and thus provides a framework for future studies. The study adds to the discussion on optimal hospital size. Copyright © 2017 Elsevier B.V. All rights reserved.
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.
Space engineering modeling and optimization with case studies
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 ...
Neo-logistic model for the growth of bacteria
Tashiro, Tohru; Yoshimura, Fujiko
2017-01-01
We propose a neo-logistic model that can describe bacterial growth data precisely. This model is not derived by modifying the logistic model formally, but by incorporating the synthesis of inducible enzymes into the logistic model indirectly. Therefore, the meaning of the parameters of the neo-logistic model becomes physically clear. The neo-logistic model can approximate bacterial growth better than models previously presented, and predict the order of the saturated number of bacteria in the...
An Optimal Lower Eigenvalue System
Directory of Open Access Journals (Sweden)
Yingfan Liu
2011-01-01
Full Text Available An optimal lower eigenvalue system is studied, and main theorems including a series of necessary and suffcient conditions concerning existence and a Lipschitz continuity result concerning stability are obtained. As applications, solvability results to some von-Neumann-type input-output inequalities, growth, and optimal growth factors, as well as Leontief-type balanced and optimal balanced growth paths, are also gotten.
DEFF Research Database (Denmark)
Ahmad, Amais; Zachariasen, Camilla; Christiansen, Lasse Engbo
2016-01-01
using a mathematical model to simulate the competitive growth of E. coli strains in a pig intestine under specified plasma concentration profiles of ampicillin. Results : In vitro growth results demonstrated that the resistant strains did not carry a fitness cost for their resistance, and that the most...... with ampicillin resistance in E. coli. Besides dosing factors, epidemiological factors (such as number of competing strains and bacterial excretion) influenced resistance development and need to be considered further in relation to optimal treatment strategies. The modeling approach used in the study is generic......Background : This study evaluated how dosing regimen for intramuscularly-administered ampicillin, composition of Escherichia coli strains with regard to ampicillin susceptibility, and excretion of bacteria from the intestine affected the level of resistance among Escherichia coli strains...
Fuzzy multiobjective models for optimal operation of a hydropower system
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.
Non-linear Growth Models in Mplus and SAS
Grimm, Kevin J.; Ram, Nilam
2013-01-01
Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling procedure NLMIXED in SAS. Using longitudinal achievement data collected as part of a study examining the effects of preschool instruction on academic gain we illustrate the procedures for fitting growth models of logistic, Gompertz, and Richards functions. Brief notes regarding the practical benefits, limitations, and choices faced in the fitting and estimation of such models are included. PMID:23882134
Modelling diameter growth, mortality and recruitment of trees in ...
African Journals Online (AJOL)
Modelling diameter growth, mortality and recruitment of trees in miombo woodlands of Tanzania. ... Individual tree diameter growth and mortality models, and area-based recruitment models were developed. ... AJOL African Journals Online.
Modeling and Optimization : Theory and Applications Conference
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.
Modeling and Optimization : Theory and Applications Conference
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.
International Nuclear Information System (INIS)
Huang, B.-N.; Hwang, M.J.; Yang, C.W.
2008-01-01
This paper separates data extending from 1971 to 2002 into the energy crisis period (1971-1980) and the post-energy crisis period (1981-2000) for 82 countries. The cross-sectional data (yearly averages) in these two periods are used to investigate the nonlinear relationships between energy consumption growth and economic growth when threshold variables are used. If threshold variables are higher than certain optimal threshold levels, there is either no significant relationship or else a significant negative relationship between energy consumption and economic growth. However, when these threshold variables are lower than certain optimal levels, there is a significant positive relationship between the two. In 48 out of the 82 countries studied, none of the four threshold variables is found to be higher than the optimal levels. It is inferred that these 48 countries should adopt a more aggressive energy policy. As for the other 34 countries, at least one threshold variable is higher than the optimal threshold level and thus these countries should adopt energy policies with varying degrees of conservation based on the number of threshold variables that are higher than the optimal threshold levels
Optimal control of information epidemics modeled as Maki Thompson rumors
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.
Forest growth modeling in the Southern Region, National Forest System
International Nuclear Information System (INIS)
Belcher, D.M.
1988-01-01
This paper discusses an attempt to combine individual tree growth models and stand level growth models currently available for the Region into one computer program. Operation of the program is explained and growth models are included
Groundwater Pollution Source Identification using Linked ANN-Optimization Model
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
United States geological survey's reserve-growth models and their implementation
Klett, T.R.
2005-01-01
The USGS has developed several mathematical models to forecast reserve growth of fields both in the United States (U.S.) and the world. The models are based on historical reserve growth patterns of fields in the U.S. The patterns of past reserve growth are extrapolated to forecast future reserve growth. Changes of individual field sizes through time are extremely variable, therefore, the reserve growth models take on a statistical approach whereby volumetric changes for populations of fields are used in the models. Field age serves as a measure of the field-development effort that is applied to promote reserve growth. At the time of the USGS World Petroleum Assessment 2000, a reserve growth model for discovered fields of the world was not available. Reserve growth forecasts, therefore, were made based on a model of historical reserve growth of fields of the U.S. To test the feasibility of such an application, reserve growth forecasts were made of 186 giant oil fields of the world (excluding the U.S. and Canada). In addition, forecasts were made for these giant oil fields subdivided into those located in and outside of Organization of Petroleum Exporting Countries (OPEC). The model provided a reserve-growth forecast that closely matched the actual reserve growth that occurred from 1981 through 1996 for the 186 fields as a whole, as well as for both OPEC and non-OPEC subdivisions, despite the differences in reserves definition among the fields of the U.S. and the rest of the world. ?? 2005 International Association for Mathematical Geology.
Energy Technology Data Exchange (ETDEWEB)
Chu, Manh-Hung; Tian, Liang; Chaker, Ahmad; Skopin, Evgenii; Cantelli, Valentina; Ouled, Toufik; Boichot, Raphaël; Crisci, Alexandre; Lay, Sabine; Richard, Marie-Ingrid; Thomas, Olivier; Deschanvres, Jean-Luc; Renevier, Hubert; Fong, Dillon; Ciatto, Gianluca
2017-03-20
ZnO thin films are interesting for applications in several technological fields, including optoelectronics and renewable energies. Nanodevice applications require controlled synthesis of ZnO structures at nanometer scale, which can be achieved via atomic layer deposition (ALD). However, the mechanisms governing the initial stages of ALD had not been addressed until very recently. Investigations into the initial nucleation and growth as well as the atomic structure of the heterointerface are crucial to optimize the ALD process and understand the structure-property relationships for ZnO. We have used a complementary suite of in situ synchrotron x-ray techniques to investigate both the structural and chemical evolution during ZnO growth by ALD on two different substrates, i.e., SiO2 and Al2O3, which led us to formulate an atomistic model of the incipient growth of ZnO. The model relies on the formation of nanoscale islands of different size and aspect ratio and consequent disorder induced in the Zn neighbors' distribution. However, endorsement of our model requires testing and discussion of possible alternative models which could account for the experimental results. In this work, we review, test, and rule out several alternative models; the results confirm our view of the atomistic mechanisms at play, which influence the overall microstructure and resulting properties of the final thin film.
Robust and fast nonlinear optimization of diffusion MRI microstructure models.
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
Optimality models in the age of experimental evolution and genomics.
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.
Román-Román, Patricia; Román-Román, Sergio; Serrano-Pérez, Juan José; Torres-Ruiz, Francisco
2016-10-21
In experimental studies on tumor growth, whenever the time evolution of the relative volume of a tumor in an untreated (control) group can be fitted by a Gompertz diffusion process there is a possibility that an antiproliferative therapy, which modifies the growth rate of the process that fits the treated group, may also affect its variability. The present paper proposes several procedures for the estimation of the time function included in the infinitesimal variance of the new process, as well as the time function affecting the growth rate (which is included in the infinitesimal mean). Also, a hypothesis testing is designed to confirm or refute the need for including such a time-dependent function in the infinitesimal variance. In order to validate and compare the proposed procedures a simulation study has been carried out. In addition, a proposal is made for a strategy aimed at finding the optimal combination of procedures for each case. A real data application concerning the effects of cisplatin on a patient-derived xenograft (PDX) tumor model showcases the advantages of this model over others that have been used in the past. Copyright © 2016 Elsevier Ltd. All rights reserved.
Modeling of biological intelligence for SCM system optimization.
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.
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.
Modeling of Biological Intelligence for SCM System Optimization
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
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.
Growth optimization for thick crack-free GaN layers on sapphire with HVPE
Energy Technology Data Exchange (ETDEWEB)
Richter, E.; Hennig, Ch.; Kissel, H.; Sonia, G.; Zeimer, U.; Weyers, M. [Ferdinand-Braun-Institut fuer Hoechstfrequenztechnik, 12489 Berlin (Germany)
2005-05-01
Conditions for optimized growth of thick GaN layers with crack-free surfaces by HVPE are reported. It was found that a 1:1 mixture of H{sub 2}/N{sub 2} as carrier gas leads to the lowest density of cracks in the surface. Crack formation also depends on the properties of the GaN/sapphire templates used. Best results have been obtained for 5 {mu}m thick GaN/sapphire templates grown by MOVPE with medium compressive strain {epsilon}{sub zz} of about 0.05%. But there is no simple dependence of the crack formation on the strain status of the starting layer indicating that the HVPE growth of GaN can itself introduce strong tensile strain. (copyright 2005 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
In silico modeling for tumor growth visualization.
Jeanquartier, Fleur; Jean-Quartier, Claire; Cemernek, David; Holzinger, Andreas
2016-08-08
Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research. We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ . In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.
Directory of Open Access Journals (Sweden)
Andrew K. Wills
2016-04-01
Full Text Available Abstract Background Regression models are widely used to link serial measures of anthropometric size or changes in size to a later outcome. Different parameterisations of these models enable one to target different questions about the effect of growth, however, their interpretation can be challenging. Our objective was to formulate and classify several sets of parameterisations by their underlying growth pattern contrast, and to discuss their utility using an expository example. Methods We describe and classify five sets of model parameterisations in accordance with their underlying growth pattern contrast (conditional growth; being bigger v being smaller; becoming bigger and staying bigger; growing faster v being bigger; becoming and staying bigger versus being bigger. The contrasts are estimated by including different sets of repeated measures of size and changes in size in a regression model. We illustrate these models in the setting of linking infant growth (measured on 6 occasions: birth, 6 weeks, 3, 6, 12 and 24 months in weight-for-height-for-age z-scores to later childhood overweight at 8y using complete cases from the Norwegian Childhood Growth study (n = 900. Results In our expository example, conditional growth during all periods, becoming bigger in any interval and staying bigger through infancy, and being bigger from birth were all associated with higher odds of later overweight. The highest odds of later overweight occurred for individuals who experienced high conditional growth or became bigger in the 3 to 6 month period and stayed bigger, and those who were bigger from birth to 24 months. Comparisons between periods and between growth patterns require large sample sizes and need to consider how to scale associations to make comparisons fair; with respect to the latter, we show one approach. Conclusion Studies interested in detrimental growth patterns may gain extra insight from reporting several sets of growth pattern
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...
Growth of cortical neuronal network in vitro: Modeling and analysis
International Nuclear Information System (INIS)
Lai, P.-Y.; Jia, L. C.; Chan, C. K.
2006-01-01
We present a detailed analysis and theoretical growth models to account for recent experimental data on the growth of cortical neuronal networks in vitro [Phys. Rev. Lett. 93, 088101 (2004)]. The experimentally observed synchronized firing frequency of a well-connected neuronal network is shown to be proportional to the mean network connectivity. The growth of the network is consistent with the model of an early enhanced growth of connection, but followed by a retarded growth once the synchronized cluster is formed. Microscopic models with dominant excluded volume interactions are consistent with the observed exponential decay of the mean connection probability as a function of the mean network connectivity. The biological implications of the growth model are also discussed
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...
Balanced growth path solutions of a Boltzmann mean field game model for knowledge growth
Burger, Martin
2016-11-18
In this paper we study balanced growth path solutions of a Boltzmann mean field game model proposed by Lucas and Moll [15] to model knowledge growth in an economy. Agents can either increase their knowledge level by exchanging ideas in learning events or by producing goods with the knowledge they already have. The existence of balanced growth path solutions implies exponential growth of the overall production in time. We prove existence of balanced growth path solutions if the initial distribution of individuals with respect to their knowledge level satisfies a Pareto-tail condition. Furthermore we give first insights into the existence of such solutions if in addition to production and knowledge exchange the knowledge level evolves by geometric Brownian motion.
Modeling the effects of ozone on soybean growth and yield.
Kobayashi, K; Miller, J E; Flagler, R B; Heck, W W
1990-01-01
A simple mechanistic model was developed based on an existing growth model in order to address the mechanisms of the effects of ozone on growth and yield of soybean [Glycine max. (L.) Merr. 'Davis'] and interacting effects of other environmental stresses. The model simulates daily growth of soybean plants using environmental data including shortwave radiation, temperature, precipitation, irrigation and ozone concentration. Leaf growth, dry matter accumulation, water budget, nitrogen input and seed growth linked to senescence and abscission of leaves are described in the model. The effects of ozone are modeled as reduced photosynthate production and accelerated senescence. The model was applied to the open-top chamber experiments in which soybean plants were exposed to ozone under two levels of soil moisture regimes. After calibrating the model to the growth data and seed yield, goodness-of-fit of the model was tested. The model fitted well for top dry weight in the vegetative growth phase and also at maturity. The effect of ozone on seen yield was also described satisfactorily by the model. The simulation showed apparent interaction between the effect of ozone and soil moisture stress on the seed yield. The model revealed that further work is needed concerning the effect of ozone on the senescence process and the consequences of alteration of canopy microclimate by the open-top chambers.
Plant Growth Modeling Using L-System Approach and Its Visualization
Directory of Open Access Journals (Sweden)
Atris Suyantohadi
2011-05-01
Full Text Available The visualizationof plant growth modeling using computer simulation has rarely been conducted with Lindenmayer System (L-System approach. L-System generally has been used as framework for improving and designing realistic modeling on plant growth. It is one kind of tools for representing plant growth based on grammar sintax and mathematic formulation. This research aimed to design modeling and visualizing plant growth structure generated using L-System. The environment on modeling design used three dimension graphic on standart OpenGL format. The visualization on system design has been developed by some of L-System grammar, and the output graphic on three dimension reflected on plant growth as a virtual plant growth system. Using some of samples on grammar L-System rules for describing of the charaterictics of plant growth, the visualization of structure on plant growth has been resulted and demonstrated.
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.
R.L. Busby; S.J. Chang; P.R. Pasala; J.C.G. Goelz
2004-01-01
We developed two growth-and-yield models for thinned and unthinned plantations of slash pine (Pinus elliottii Engelm. var elliottii) and loblolly pine (P. taeda L.). The models, VB Merch-Slash and VB Merch-Lob, can be used to forecast product volumes and stand values for stands partitioned into 1-inch diameter-at...
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
A stochastic discrete optimization model for designing container terminal facilities
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.
Arisoy, Yigit Muzaffer
Manufacturing processes may significantly affect the quality of resultant surfaces and structural integrity of the metal end products. Controlling manufacturing process induced changes to the product's surface integrity may improve the fatigue life and overall reliability of the end product. The goal of this study is to model the phenomena that result in microstructural alterations and improve the surface integrity of the manufactured parts by utilizing physics-based process simulations and other computational methods. Two different (both conventional and advanced) manufacturing processes; i.e. machining of Titanium and Nickel-based alloys and selective laser melting of Nickel-based powder alloys are studied. 3D Finite Element (FE) process simulations are developed and experimental data that validates these process simulation models are generated to compare against predictions. Computational process modeling and optimization have been performed for machining induced microstructure that includes; i) predicting recrystallization and grain size using FE simulations and the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model, ii) predicting microhardness using non-linear regression models and the Random Forests method, and iii) multi-objective machining optimization for minimizing microstructural changes. Experimental analysis and computational process modeling of selective laser melting have been also conducted including; i) microstructural analysis of grain sizes and growth directions using SEM imaging and machine learning algorithms, ii) analysis of thermal imaging for spattering, heating/cooling rates and meltpool size, iii) predicting thermal field, meltpool size, and growth directions via thermal gradients using 3D FE simulations, iv) predicting localized solidification using the Phase Field method. These computational process models and predictive models, once utilized by industry to optimize process parameters, have the ultimate potential to improve performance of
Non-rigid image registration using bone growth model
DEFF Research Database (Denmark)
Bro-Nielsen, Morten; Gramkow, Claus; Kreiborg, Sven
1997-01-01
Non-rigid registration has traditionally used physical models like elasticity and fluids. These models are very seldom valid models of the difference between the registered images. This paper presents a non-rigid registration algorithm, which uses a model of bone growth as a model of the change...... between time sequence images of the human mandible. By being able to register the images, this paper at the same time contributes to the validation of the growth model, which is based on the currently available medical theories and knowledge...
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.
Optimal harvesting for a predator-prey agent-based model using difference equations.
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.
Chang, Hai-Xing; Huang, Yun; Fu, Qian; Liao, Qiang; Zhu, Xun
2016-04-01
Understanding and optimizing the microalgae growth process is an essential prerequisite for effective CO2 capture using microalgae in photobioreactors. In this study, the kinetic characteristics of microalgae Chlorella vulgaris growth in response to light intensity and dissolved inorganic carbon (DIC) concentration were investigated. The greatest values of maximum biomass concentration (Xmax) and maximum specific growth rate (μmax) were obtained as 2.303 g L(-1) and 0.078 h(-1), respectively, at a light intensity of 120 μmol m(-2) s(-1) and DIC concentration of 17 mM. Based on the results, mathematical models describing the coupled effects of light intensity and DIC concentration on microalgae growth and CO2 biofixation are proposed. The models are able to predict the temporal evolution of C. vulgaris growth and CO2 biofixation rates from lag to stationary phases. Verification experiments confirmed that the model predictions agreed well with the experimental results. Copyright © 2016 Elsevier Ltd. All rights reserved.
An optimal control model of crop thinning in viticulture
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...
Fuzzy Stochastic Optimization Theory, Models and Applications
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...
Optimal inventory management and order book modeling
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.
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.
Directory of Open Access Journals (Sweden)
Mirtha Henríquez
Full Text Available Piscirickettsia salmonis is the bacterium that causes Piscirickettsiosis, a systemic disease of salmonid fish responsible for significant economic losses within the aquaculture industry worldwide. The growth of the bacterium for vaccine formulation has been traditionally accomplished by infecting eukaryotic cell lines, a process that involves high production costs and is time-consuming. Recent research has demonstrated that it is possible to culture pure P. salmonis in a blood containing (cell-free medium. In the present work we demonstrate the growth of P. salmonis in a liquid medium free from blood and serum components, thus establishing a novel and simplified bacteriological medium. Additionally, the new media reported provides improved growth conditions for P. salmonis, where biomass concentrations of approximately 800 mg cell dry weight L(-1 were obtained, about eight times higher than those reported for the blood containing medium. A 2- level full factorial design was employed to evaluate the significance of the main medium components on cell growth and an optimal temperature range of 23-27°C was determined for the microorganism to grow in the novel liquid media. Therefore, these results represent a breakthrough regarding P. salmonis research in order to optimize pure P. salmonis growth in liquid blood and serum free medium.
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...
A mathematical model of microalgae growth in cylindrical photobioreactor
Bakeri, Noorhadila Mohd; Jamaian, Siti Suhana
2017-08-01
Microalgae are unicellular organisms, which exist individually or in chains or groups but can be utilized in many applications. Researchers have done various efforts in order to increase the growth rate of microalgae. Microalgae have a potential as an effective tool for wastewater treatment, besides as a replacement for natural fuel such as coal and biodiesel. The growth of microalgae can be estimated by using Geider model, which this model is based on photosynthesis irradiance curve (PI-curve) and focused on flat panel photobioreactor. Therefore, in this study a mathematical model for microalgae growth in cylindrical photobioreactor is proposed based on the Geider model. The light irradiance is the crucial part that affects the growth rate of microalgae. The absorbed photon flux will be determined by calculating the average light irradiance in a cylindrical system illuminated by unidirectional parallel flux and considering the cylinder as a collection of differential parallelepipeds. Results from this study showed that the specific growth rate of microalgae increases until the constant level is achieved. Therefore, the proposed mathematical model can be used to estimate the rate of microalgae growth in cylindrical photobioreactor.
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.
An optimization model for metabolic pathways.
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.
Localisation in a Growth Model with Interaction
Costa, M.; Menshikov, M.; Shcherbakov, V.; Vachkovskaia, M.
2018-05-01
This paper concerns the long term behaviour of a growth model describing a random sequential allocation of particles on a finite cycle graph. The model can be regarded as a reinforced urn model with graph-based interaction. It is motivated by cooperative sequential adsorption, where adsorption rates at a site depend on the configuration of existing particles in the neighbourhood of that site. Our main result is that, with probability one, the growth process will eventually localise either at a single site, or at a pair of neighbouring sites.
Portfolio optimization by using linear programing models based on genetic algorithm
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.
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.
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...
A global review of freshwater crayfish temperature tolerance, preference, and optimal growth
Westhoff, Jacob T.; Rosenberger, Amanda E.
2016-01-01
Conservation efforts, environmental planning, and management must account for ongoing ecosystem alteration due to a changing climate, introduced species, and shifting land use. This type of management can be facilitated by an understanding of the thermal ecology of aquatic organisms. However, information on thermal ecology for entire taxonomic groups is rarely compiled or summarized, and reviews of the science can facilitate its advancement. Crayfish are one of the most globally threatened taxa, and ongoing declines and extirpation could have serious consequences on aquatic ecosystem function due to their significant biomass and ecosystem roles. Our goal was to review the literature on thermal ecology for freshwater crayfish worldwide, with emphasis on studies that estimated temperature tolerance, temperature preference, or optimal growth. We also explored relationships between temperature metrics and species distributions. We located 56 studies containing information for at least one of those three metrics, which covered approximately 6 % of extant crayfish species worldwide. Information on one or more metrics existed for all 3 genera of Astacidae, 4 of the 12 genera of Cambaridae, and 3 of the 15 genera of Parastacidae. Investigations employed numerous methodological approaches for estimating these parameters, which restricts comparisons among and within species. The only statistically significant relationship we observed between a temperature metric and species range was a negative linear relationship between absolute latitude and optimal growth temperature. We recommend expansion of studies examining the thermal ecology of freshwater crayfish and identify and discuss methodological approaches that can improve standardization and comparability among studies.
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%.
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.
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
Directory of Open Access Journals (Sweden)
S. Benzer
2017-06-01
Full Text Available In this current publication the growth characteristics of big-scale sand smelt data were compared for population dynamics within artificial neural networks and length-weight relationships models. This study aims to describe the optimal decision of the growth model of big-scale sand smelt by artificial neural networks and length-weight relationships models at Hirfanll Dam Lake, Klrsehir, Turkey. There were a total of 1449 samples collected from Hirfanll Dam Lake between May 2015 and May 2016. Both model results were compared with each other and the results were also evaluated with MAPE (mean absolute percentage error, MSE (mean squared error and r2 (coefficient correlation data as a performance criterion. The results of the current study show that artificial neural networks is a superior estimation tool compared to length-weight relationships models of big-scale sand smelt in Hirfanll Dam Lake.
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.
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.
Growth rate in the dynamical dark energy models
International Nuclear Information System (INIS)
Avsajanishvili, Olga; Arkhipova, Natalia A.; Samushia, Lado; Kahniashvili, Tina
2014-01-01
Dark energy models with a slowly rolling cosmological scalar field provide a popular alternative to the standard, time-independent cosmological constant model. We study the simultaneous evolution of background expansion and growth in the scalar field model with the Ratra-Peebles self-interaction potential. We use recent measurements of the linear growth rate and the baryon acoustic oscillation peak positions to constrain the model parameter α that describes the steepness of the scalar field potential. (orig.)
Growth rate in the dynamical dark energy models.
Avsajanishvili, Olga; Arkhipova, Natalia A; Samushia, Lado; Kahniashvili, Tina
Dark energy models with a slowly rolling cosmological scalar field provide a popular alternative to the standard, time-independent cosmological constant model. We study the simultaneous evolution of background expansion and growth in the scalar field model with the Ratra-Peebles self-interaction potential. We use recent measurements of the linear growth rate and the baryon acoustic oscillation peak positions to constrain the model parameter [Formula: see text] that describes the steepness of the scalar field potential.
Modelling and Optimizing Mathematics Learning in Children
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…
Investigation of Mediational Processes Using Parallel Process Latent Growth Curve Modeling
Cheong, JeeWon; MacKinnon, David P.; Khoo, Siek Toon
2010-01-01
This study investigated a method to evaluate mediational processes using latent growth curve modeling. The mediator and the outcome measured across multiple time points were viewed as 2 separate parallel processes. The mediational process was defined as the independent variable influencing the growth of the mediator, which, in turn, affected the growth of the outcome. To illustrate modeling procedures, empirical data from a longitudinal drug prevention program, Adolescents Training and Learning to Avoid Steroids, were used. The program effects on the growth of the mediator and the growth of the outcome were examined first in a 2-group structural equation model. The mediational process was then modeled and tested in a parallel process latent growth curve model by relating the prevention program condition, the growth rate factor of the mediator, and the growth rate factor of the outcome. PMID:20157639
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
Studying historical occupational careers with multilevel growth models
Directory of Open Access Journals (Sweden)
Wiebke Schulz
2010-10-01
Full Text Available In this article we propose to study occupational careers with historical data by using multilevel growth models. Historical career data are often characterized by a lack of information on the timing of occupational changes and by different numbers of observations of occupations per individual. Growth models can handle these specificities, whereas standard methods, such as event history analyses can't. We illustrate the use of growth models by studying career success of men and women, using data from the Historical Sample of the Netherlands. The results show that the method is applicable to male careers, but causes trouble when analyzing female careers.
Cellular Automaton Modeling of Dendritic Growth Using a Multi-grid Method
International Nuclear Information System (INIS)
Natsume, Y; Ohsasa, K
2015-01-01
A two-dimensional cellular automaton model with a multi-grid method was developed to simulate dendritic growth. In the present model, we used a triple-grid system for temperature, solute concentration and solid fraction fields as a new approach of the multi-grid method. In order to evaluate the validity of the present model, we carried out simulations of single dendritic growth, secondary dendrite arm growth, multi-columnar dendritic growth and multi-equiaxed dendritic growth. From the results of the grid dependency from the simulation of single dendritic growth, we confirmed that the larger grid can be used in the simulation and that the computational time can be reduced dramatically. In the simulation of secondary dendrite arm growth, the results from the present model were in good agreement with the experimental data and the simulated results from a phase-field model. Thus, the present model can quantitatively simulate dendritic growth. From the simulated results of multi-columnar and multi-equiaxed dendrites, we confirmed that the present model can perform simulations under practical solidification conditions. (paper)
Nonlinear Growth Models in M"plus" and SAS
Grimm, Kevin J.; Ram, Nilam
2009-01-01
Nonlinear growth curves or growth curves that follow a specified nonlinear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this article we describe how a variety of sigmoid curves can be fit using the M"plus" structural modeling program and the nonlinear…
Optimized inorganic carbon regime for enhanced growth and lipid accumulation in Chlorella vulgaris.
Lohman, Egan J; Gardner, Robert D; Pedersen, Todd; Peyton, Brent M; Cooksey, Keith E; Gerlach, Robin
2015-01-01
Large-scale algal biofuel production has been limited, among other factors, by the availability of inorganic carbon in the culture medium at concentrations higher than achievable with atmospheric CO2. Life cycle analyses have concluded that costs associated with supplying CO2 to algal cultures are significant contributors to the overall energy consumption. A two-phase optimal growth and lipid accumulation scenario is presented, which (1) enhances the growth rate and (2) the triacylglyceride (TAG) accumulation rate in the oleaginous Chlorophyte Chlorella vulgaris strain UTEX 395, by growing the organism in the presence of low concentrations of NaHCO3 (5 mM) and controlling the pH of the system with a periodic gas sparge of 5 % CO2 (v/v). Once cultures reached the desired cell densities, which can be "fine-tuned" based on initial nutrient concentrations, cultures were switched to a lipid accumulation metabolism through the addition of 50 mM NaHCO3. This two-phase approach increased the specific growth rate of C. vulgaris by 69 % compared to cultures sparged continuously with 5 % CO2 (v/v); further, biomass productivity (g L(-1) day(-1)) was increased by 27 %. Total biodiesel potential [assessed as total fatty acid methyl ester (FAME) produced] was increased from 53.3 to 61 % (FAME biomass(-1)) under the optimized conditions; biodiesel productivity (g FAME L(-1) day(-1)) was increased by 7.7 %. A bicarbonate salt screen revealed that American Chemical Society (ACS) and industrial grade NaHCO3 induced the highest TAG accumulation (% w/w), whereas Na2CO3 did not induce significant TAG accumulation. NH4HCO3 had a negative effect on cell health presumably due to ammonia toxicity. The raw, unrefined form of trona, NaHCO3∙Na2CO3 (sodium sesquicarbonate) induced TAG accumulation, albeit to a slightly lower extent than the more refined forms of sodium bicarbonate. The strategic addition of sodium bicarbonate was found to enhance growth and lipid accumulation rates in
Endogenous economic growth, EROI, and transition towards renewable energy
Victor Court; Pierre-André Jouvet; Frédéric Lantz
2015-01-01
Due to their initial lack of emphasis on energy and natural resources, exogenous and endogenous growth models have suffered the same critic regarding the limits to economic growth imposed by finite Earth resources. Thus, various optimal control models that incorporate energy or natural resources have been developed during the last decades. However, in all these models the importance of the Energy Return On Energy Investment (EROI) has never been raised. The EROI is the ratio of the quantity o...
Existence and characterization of optimal control in mathematics model of diabetics population
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.
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...
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.
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.
A tool for efficient, model-independent management optimization under uncertainty
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.
Optimal Pricing Strategy for New Products
Trichy V. Krishnan; Frank M. Bass; Dipak C. Jain
1999-01-01
Robinson and Lakhani (1975) initiated a long research stream in marketing when they used the Bass model (1969) to develop optimal pricing path for a new product. A careful analysis of the extant literature reveals that the research predominantly suggests that the optimal price path should be largely based on the sales growth pattern. However, in the real world we rarely find new products that have such pricing pattern. We observe either a monotonically declining pricing pattern or an increase...
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)
Infinite-horizon optimal control problems in economics
International Nuclear Information System (INIS)
Aseev, Sergei M; Besov, Konstantin O; Kryazhimskii, Arkadii V
2012-01-01
This paper extends optimal control theory to a class of infinite-horizon problems that arise in studying models of optimal dynamic allocation of economic resources. In a typical problem of this sort the initial state is fixed, no constraints are imposed on the behaviour of the admissible trajectories at large times, and the objective functional is given by a discounted improper integral. We develop the method of finite-horizon approximations in a broad context and use it to derive complete versions of the Pontryagin maximum principle for such problems. We provide sufficient conditions for the normality of infinite-horizon optimal control problems and for the validity of the 'standard' limit transversality conditions with time going to infinity. As a meaningful example, we consider a new two-sector model of optimal economic growth subject to a random jump in prices. Bibliography: 53 titles.
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.
Stringent Mitigation Policy Implied By Temperature Impacts on Economic Growth
Moore, F.; Turner, D.
2014-12-01
Integrated assessment models (IAMs) compare the costs of greenhouse gas mitigation with damages from climate change in order to evaluate the social welfare implications of climate policy proposals and inform optimal emissions reduction trajectories. However, these models have been criticized for lacking a strong empirical basis for their damage functions, which do little to alter assumptions of sustained GDP growth, even under extreme temperature scenarios. We implement empirical estimates of temperature effects on GDP growth-rates in the Dynamic Integrated Climate and Economy (DICE) model via two pathways, total factor productivity (TFP) growth and capital depreciation. Even under optimistic adaptation assumptions, this damage specification implies that optimal climate policy involves the elimination of emissions in the near future, the stabilization of global temperature change below 2°C, and a social cost of carbon (SCC) an order of magnitude larger than previous estimates. A sensitivity analysis shows that the magnitude of growth effects, the rate of adaptation, and the dynamic interaction between damages from warming and GDP are three critical uncertainties and an important focus for future research.
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.
Optimal moment determination in POME-copula based hydrometeorological dependence modelling
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.
Optimal pricing and promotional effort control policies for a new product growth in segmented market
Directory of Open Access Journals (Sweden)
Jha P.C.
2015-01-01
Full Text Available Market segmentation enables the marketers to understand and serve the customers more effectively thereby improving company’s competitive position. In this paper, we study the impact of price and promotion efforts on evolution of sales intensity in segmented market to obtain the optimal price and promotion effort policies. Evolution of sales rate for each segment is developed under the assumption that marketer may choose both differentiated as well as mass market promotion effort to influence the uncaptured market potential. An optimal control model is formulated and a solution method using Maximum Principle has been discussed. The model is extended to incorporate budget constraint. Model applicability is illustrated by a numerical example. P.C. Jha, P. Manik, K. Chaudhary, R. Cambini / Optimal Pricing and Promotional 2 Since the discrete time data is available, the formulated model is discretized. For solving the discrete model, differential evolution algorithm is used.
Stochastic modelling of avascular tumour growth and therapy
International Nuclear Information System (INIS)
Sahoo, S; Sahoo, A; Shearer, S F C
2011-01-01
In this paper, a generalized stochastic model for the growth of avascular tumours is presented. This model captures the dynamical evolution of avascular tumour cell subpopulations by incorporating Gaussian white noise into the growth rate of the mitotic function. This work generalizes the deterministic model proposed by Sherratt and Chaplain (2001 J. Math. Biol. 43 291) where they formulated a tumour model in an in vivo setting, in terms of continuum densities of proliferating, quiescent and necrotic cells. Detailed simulations of our model show that the inclusion of Gaussian noise in the original model of Sherratt and Chaplain substantially distorts the overall structure of the density profiles in addition to reducing the speed of tumour growth. Within this stochastic carcinogenesis framework the action of therapy is also investigated by replacing Gaussian white noise with a therapy term. We compare a constant therapy protocol with a logarithmic time-dependent protocol. Our results predict that a logarithmic therapy is more effective than the constant therapy protocol.
Optimization Research of Generation Investment Based on Linear Programming Model
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.
Surface-bounded growth modeling applied to human mandibles
DEFF Research Database (Denmark)
Andresen, Per Rønsholt
1999-01-01
This thesis presents mathematical and computational techniques for three dimensional growth modeling applied to human mandibles. The longitudinal shape changes make the mandible a complex bone. The teeth erupt and the condylar processes change direction, from pointing predominantly backward...... of the common features. 3.model the process that moves the matched points (growth modeling). A local shape feature called crest line has shown itself to be structurally stable on mandibles. Registration of crest lines (from different mandibles) results in a sparse deformation field, which must be interpolated...... old mandible based on the 3 month old scan. When using successively more recent scans as basis for the model the error drops to 2.0 mm for the 11 years old scan. Thus, it seems reasonable to assume that the mandibular growth is linear....
Research on NC laser combined cutting optimization model of sheet metal parts
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.
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.
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.
Growth Curve Models and Applications : Indian Statistical Institute
2017-01-01
Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas. There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of app lied work, and these contributions have been externally r...
Selection, calibration, and validation of models of tumor growth.
Lima, E A B F; Oden, J T; Hormuth, D A; Yankeelov, T E; Almeida, R C
2016-11-01
This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology ( in vivo ). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory
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
A mathematical model of the growth of uterine myomas.
Chen, C Y; Ward, J P
2014-12-01
Uterine myomas or fibroids are common, benign smooth muscle tumours that can grow to 10 cm or more in diameter and are routinely removed surgically. They are typically slow- growing, well-vascularised, spherical tumours that, on a macro-scale, are a structurally uniform, hard elastic material. We present a multi-phase mathematical model of a fully vascularised myoma growing within a surrounding elastic tissue. Adopting a continuum approach, the model assumes the conservation of mass and momentum of four phases, namely cells/collagen, extracellular fluid, arterial and venous phases. The cell/collagen phase is treated as a poro-elastic material, based on a linear stress-strain relationship, and Darcy's law is applied to describe flow in the extracellular fluid and the two vascular phases. The supply of extracellular fluid is dependent on the capillary flow rate and mean capillary pressure expressed in terms of the arterial and venous pressures. Cell growth and division is limited to the myoma domain and dependent on the local stress in the material. The resulting model consists of a system of nonlinear partial differential equations with two moving boundaries. Numerical solutions of the model successfully reproduce qualitatively the clinically observed three-phase "fast-slow-fast" growth profile that is typical for myomas. The results suggest that this growth profile requires stress-induced resistance to growth by the surrounding tissue and a switch-like cell growth response to stress. Analysis of large-time solutions reveal that while there is a functioning vasculature throughout the myoma, exponential growth results, otherwise power-law growth is predicted. An extensive survey of the effect of parameters on model solutions is also presented, and in particular, the enhanced growth caused by factors such as oestrogen is predicted by the model.
Growth Kinetics and Modeling of Direct Oxynitride Growth with NO-O2 Gas Mixtures
Energy Technology Data Exchange (ETDEWEB)
Everist, Sarah; Nelson, Jerry; Sharangpani, Rahul; Smith, Paul Martin; Tay, Sing-Pin; Thakur, Randhir
1999-05-03
We have modeled growth kinetics of oxynitrides grown in NO-O_{2} gas mixtures from first principles using modified Deal-Grove equations. Retardation of oxygen diffusion through the nitrided dielectric was assumed to be the dominant growth-limiting step. The model was validated against experimentally obtained curves with good agreement. Excellent uniformity, which exceeded expected walues, was observed.
Parameter Estimates in Differential Equation Models for Population Growth
Winkel, Brian J.
2011-01-01
We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and two-species competition models. We discuss student-evolved strategies and offer "Mathematica" code for a gradient search approach. We use historical (1930s) data from microbial studies of the Russian biologist,…
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.
Mudcake growth: Model and implications
Liu, Q.; Santamarina, Carlos
2017-01-01
cementing, and to prevent partial differential sticking. We developed a robust mud cake growth model for water-based mud based on wide stress-range constitutive equations within a Lagrangian reference system to avoid non-natural moving boundary solutions
Joshi, V K; Chauhan, Arjun; Devi, Sarita; Kumar, Vikas
2015-08-01
Lactic acid fermentation of radish was conducted using various additive and growth stimulators such as salt (2 %-3 %), lactose, MgSO4 + MnSO4 and Mustard (1 %, 1.5 % and 2 %) to optimize the process. Response surface methodology (Design expert, Trial version 8.0.5.2) was applied to the experimental data for the optimization of process variables in lactic acid fermentation of radish. Out of various treatments studied, only the treatments having ground mustard had an appreciable effect on lactic acid fermentation. Both linear and quadratic terms of the variables studied had a significant effect on the responses studied. The interactions between the variables were found to contribute to the response at a significant level. The best results were obtained in the treatment with 2.5 % salt, 1.5 % lactose, 1.5 % (MgSO4 + MnSO4) and 1.5 % mustard. These optimized concentrations increased titrable acidity and LAB count, but lowered pH. The second-order polynomial regression model determined that the highest titrable acidity (1.69), lowest pH (2.49) and maximum LAB count (10 × 10(8) cfu/ml) would be obtained at these concentrations of additives. Among 30 runs conducted, run 2 has got the optimum concentration of salt- 2.5 %, lactose- 1.5 %, MgSO4 + MnSO4- 1.5 % and mustard- 1.5 % for lactic acid fermentation of radish. The values for different additives and growth stimulators optimized in this study could successfully be employed for the lactic acid fermentation of radish as a postharvest reduction tool and for product development.
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)
Energy Technology Data Exchange (ETDEWEB)
Clarkson, Sonya M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Biosciences Division; Giannone, Richard J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division; Kridelbaugh, Donna M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Biosciences Division; Elkins, James G. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Biosciences Division; Guss, Adam M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Biosciences Division; Michener, Joshua K. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Biosciences Division, BioEnergy Science Center; Vieille, Claire [Michigan State Univ., East Lansing, MI (United States)
2017-07-21
The production of biofuels from lignocellulose yields a substantial lignin by-product stream that currently has few applications. Biological conversion of lignin-derived compounds into chemicals and fuels has the potential to improve the economics of lignocellulose-derived biofuels, but few microbes are able both to catabolize lignin-derived aromatic compounds and to generate valuable products. While
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.
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.
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
A Finite Element Model for Mixed Porohyperelasticity with Transport, Swelling, and Growth.
Directory of Open Access Journals (Sweden)
Michelle Hine Armstrong
Full Text Available The purpose of this manuscript is to establish a unified theory of porohyperelasticity with transport and growth and to demonstrate the capability of this theory using a finite element model developed in MATLAB. We combine the theories of volumetric growth and mixed porohyperelasticity with transport and swelling (MPHETS to derive a new method that models growth of biological soft tissues. The conservation equations and constitutive equations are developed for both solid-only growth and solid/fluid growth. An axisymmetric finite element framework is introduced for the new theory of growing MPHETS (GMPHETS. To illustrate the capabilities of this model, several example finite element test problems are considered using model geometry and material parameters based on experimental data from a porcine coronary artery. Multiple growth laws are considered, including time-driven, concentration-driven, and stress-driven growth. Time-driven growth is compared against an exact analytical solution to validate the model. For concentration-dependent growth, changing the diffusivity (representing a change in drug fundamentally changes growth behavior. We further demonstrate that for stress-dependent, solid-only growth of an artery, growth of an MPHETS model results in a more uniform hoop stress than growth in a hyperelastic model for the same amount of growth time using the same growth law. This may have implications in the context of developing residual stresses in soft tissues under intraluminal pressure. To our knowledge, this manuscript provides the first full description of an MPHETS model with growth. The developed computational framework can be used in concert with novel in-vitro and in-vivo experimental approaches to identify the governing growth laws for various soft tissues.
A Finite Element Model for Mixed Porohyperelasticity with Transport, Swelling, and Growth.
Armstrong, Michelle Hine; Buganza Tepole, Adrián; Kuhl, Ellen; Simon, Bruce R; Vande Geest, Jonathan P
2016-01-01
The purpose of this manuscript is to establish a unified theory of porohyperelasticity with transport and growth and to demonstrate the capability of this theory using a finite element model developed in MATLAB. We combine the theories of volumetric growth and mixed porohyperelasticity with transport and swelling (MPHETS) to derive a new method that models growth of biological soft tissues. The conservation equations and constitutive equations are developed for both solid-only growth and solid/fluid growth. An axisymmetric finite element framework is introduced for the new theory of growing MPHETS (GMPHETS). To illustrate the capabilities of this model, several example finite element test problems are considered using model geometry and material parameters based on experimental data from a porcine coronary artery. Multiple growth laws are considered, including time-driven, concentration-driven, and stress-driven growth. Time-driven growth is compared against an exact analytical solution to validate the model. For concentration-dependent growth, changing the diffusivity (representing a change in drug) fundamentally changes growth behavior. We further demonstrate that for stress-dependent, solid-only growth of an artery, growth of an MPHETS model results in a more uniform hoop stress than growth in a hyperelastic model for the same amount of growth time using the same growth law. This may have implications in the context of developing residual stresses in soft tissues under intraluminal pressure. To our knowledge, this manuscript provides the first full description of an MPHETS model with growth. The developed computational framework can be used in concert with novel in-vitro and in-vivo experimental approaches to identify the governing growth laws for various soft tissues.
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...
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...
Optimal model-free prediction from multivariate time series
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.
Random-growth urban model with geographical fitness
Kii, Masanobu; Akimoto, Keigo; Doi, Kenji
2012-12-01
This paper formulates a random-growth urban model with a notion of geographical fitness. Using techniques of complex-network theory, we study our system as a type of preferential-attachment model with fitness, and we analyze its macro behavior to clarify the properties of the city-size distributions it predicts. First, restricting the geographical fitness to take positive values and using a continuum approach, we show that the city-size distributions predicted by our model asymptotically approach Pareto distributions with coefficients greater than unity. Then, allowing the geographical fitness to take negative values, we perform local coefficient analysis to show that the predicted city-size distributions can deviate from Pareto distributions, as is often observed in actual city-size distributions. As a result, the model we propose can generate a generic class of city-size distributions, including but not limited to Pareto distributions. For applications to city-population projections, our simple model requires randomness only when new cities are created, not during their subsequent growth. This property leads to smooth trajectories of city population growth, in contrast to other models using Gibrat’s law. In addition, a discrete form of our dynamical equations can be used to estimate past city populations based on present-day data; this fact allows quantitative assessment of the performance of our model. Further study is needed to determine appropriate formulas for the geographical fitness.
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
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.
Portfolio optimization for index tracking modelling in Malaysia stock market
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.
A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling
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.
Modelling the growth of a methanotrophic biofilm
DEFF Research Database (Denmark)
Arcangeli, J.-P.; Arvin, E.
1999-01-01
This article discusses the growth of methanotrophic biofilms. Several independent biofilm growths scenarios involving different inocula were examined. Biofilm growth, substrate removal and product formation were monitored throughout the experiments. Based on the oxygen consumption it was concluded...... that heterotrophs and nitrifiers co-existed with methanotrophs in the biofilm. Heterotrophic biomass grew on soluble polymers formed by the hydrolysis of dead biomass entrapped in the biofilm. Nitrifier populations developed because of the presence of ammonia in the mineral medium. Based on these experimental...... was performed on this model. It indicated that the most influential parameters were those related to the biofilm (i.e. density; solid-volume fraction; thickness). This suggests that in order to improve the model, further research regarding the biofilm structure and composition is needed....
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
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.
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.
Modelling of tomato stem diameter growth rate based on physiological responses
International Nuclear Information System (INIS)
Li, L.; Tan, J.; Lv, T.
2017-01-01
The stem diameter is an important parameter describing the growth of tomato plant during vegetative growth stage. A stem diameter growth model was developed to predict the response of plant growth under different conditions. By analyzing the diurnal variations of stem diameter in tomato (Solanum lycopersicum L.), it was found that the stem diameter measured at 3:00 am was the representative value as the daily basis of tomato stem diameter. Based on the responses of growth rate in stem diameter to light and temperature, a linear regression relationship was applied to establish the stem diameter growth rate prediction model for the vegetative growth stage in tomato and which was further validated by experiment. The root mean square error (RMSE) and relative error (RE) were used to test the correlation between measured and modeled stem diameter variations. Results showed that the model can be used in prediction for stem diameter growth rate at vegetative growth stage in tomato. (author)
[Multi-mathematical modelings for compatibility optimization of Jiangzhi granules].
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.
Modeling and optimization of cloud-ready and content-oriented networks
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...
Learning optimal quantum models is NP-hard
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.
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 c