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Results for “Optimism”

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At least 37 records · Page 2Linked to original sources

PubMed2000

Whole-herd optimization with the Cornell Net Carbohydrate and Protein System. I. Predicting feed biological values for diet optimization with linear programming.

We developed a diet optimizer for least-cost diet formulation with the Cornell Net Carbohydrate and Protein System (CNCPS) using linear programming. The CNCPS model is intrinsically nonlinear, and feed biological values vary with animal and feed characteristics. To allow linear diet optimization, we first used the CNCPS model to generate biological values to characterize the energy and protein content of each feed for the specific group for which the diet was being formulated. The biological values used were metabolizable energy (Mcal/kg), metabolizable protein [(% dry matter (DM)], passage rate (%/h), bacteria yield efficiencies (g/g), and degradation rate of the carbohydrate B2 fraction (%/h). In addition, the ruminal balances for nitrogen and peptides were included in the optimizer to optimize ruminal degradation of fiber. The objective function was to minimize diet cost subject to animal requirement and feed availability constraints. The animal constraints were set by requirements for DM intake (kg/d), metabolizable energy (Mcal/kg), metabolizable protein (%DM), and effective neutral detergent fiber (%DM) for a given level of production. Data from a dairy farm were used to evaluate this linear diet optimizer. Across all classes of dairy cattle, the CNCPS 4.0 model typically obtained a solution in less than six iterations that met the requirements with nearly 100% accuracy. We conclude this linear optimizer can be used to accurately formulate least-cost diets with the CNCPS model.

Animals↗

PubMed1992

[Comparative evaluation of cardiovascular function in patients after myocardial infarction during exercise test with the optimal and sub-optimal frequency of pedalling].

Cardiovascular function was studied in 59 patients with myocardial infarction during their bicycle ergometer exercise performance at optimal and +sub-optimal pedalling rates. The total performance at the optimal rate was found to average 1.48 times more than that at the +sub-optimal rate. The efficiency of cardiovascular performance was achieved by a less increase in heart rate and systolic pressure. ECG changes resulting in exercise test cessation were observed in 7 and 30 cases at the optimal and +sub-optimal rates, respectively. It is concluded that physical training that fails to impose patients' rhythm and exercise capacity are more physiological and maximally safe.

Adult↗

PubMed1989

Epidemiological study on dental fluorosis and dental caries prevalence in communities with negligible, optimal, and above-optimal fluoride concentrations in drinking water supplies.

The prevalence of dental caries and dental fluorosis was assessed in 5,107 children (2,669 boys and 2,438 girls) aged 6 to 16 years who were lifelong residents of Shenkang Hsiang. The drinking water in the various villages in this area contains negligible, optimal, and above-optimal concentrations of natural fluoride. The prevalence of caries in the optimal fluoride areas was 10.7% lower than in the negligible fluoride area. In the above-optimal fluoride areas, even greater protection from caries was evident. Caries protection was compromised, however, in children with severe fluorosis. The effect may result from food, debris, or plaque being entrapped in the hypoplastic defects of severely fluorosed enamel. Data from children residing in the optimal and above-optimal fluoride areas show that older children are affected more by fluorosis than younger children, according to the Tooth Surface Index of Fluorosis. The relevant variables associated with secular changes in the prevalence of fluorosis are explored. Of the 14 communities studied, chyuarnshing, Dahturng, and Haaiweei, according to the Community Fluorosis Index, represent a medium fluorosis problem, while Dihngsing and Shehgun represent a slight public health fluorosis problem. The remaining communities have no public health fluorosis problem.

Adolescent↗

PubMed2002

Effect of experimental error on the efficiency of different optimization methods for bioprocess media optimization.

Four optimization methods (Simplex, Rosenbrock, iterative factorial experimental design (IFED) and genetic algorithms) for the optimization of the biotechnological media composition under conditions where the measured quantities are subjected to the experimental error were compared. The computer simulations were performed on some of the selected two- to six- parameter biotechnological models. The optimization process was modified in such a way that the experimental error was considered. The results show that the optimization efficiency increases when this new termination criteria is implemented. In addition, the method efficiency becomes independent of the experimental error. In general, Simplex and Rosenbrock methods need fewer experiments and their distribution of necessary experiments is narrower than for IFED and genetic algorithms. The increase of model parameters that need to be optimized results in a decrease in the method efficiency and in an increase of the average number of required experiments. The results were further verified on the cultivation of Saccharomyces cerevisiae and were found to be in good agreement with the results obtained from the computer simulations.

Journal Article↗

PubMed2001

FILO (field interaction ligand optimization): a simplex strategy for searching the optimal ligand interaction field in drug design.

A method (FILO, Field Interaction Ligand Optimization) for obtaining the optimal molecular interaction field was developed on the basis of the Simplex optimization procedure applied to a matrix of interaction energies obtained by performing a GRID computation on a suitable data set. The FILO procedure was tested on a set of nine HIV-1 protease inhibitors with known crystal structures. The results of FILO consist of the optimal molecular interaction field of a putative new ligand with optimal binding affinity. The final FILO model yields R2 and R2(CV) values of 0.993 and 0.936, respectively, and finds eight negative and four positive interaction nodes for the OH probe taken as an example. The eight H bonding interactions pointed out by FILO identified well the binding site AA-residues Gly A27, Asp A29, water 501, Gly B48 and Asp A25 of HIV-1 protease.

Drug Design↗

PubMed1997

Theory of force transducer design optimization for die wall stress measurement during tablet compaction: optimization and validation of split-web die using finite element analysis.

PURPOSE: 1) To illustrate how computer aided engineering stress analysis can be used to improve the transducer design process for tablet press instrumentation; 2) to use these optimal design procedures for the geometric optimization of a cylindrical, segmented, and a novel split-web die design. Discussion includes the selection of optimal die wall thickness, segment cutting angle, strain gage placement, Wheatstone bridge configuration, and the influence of tablet height and position within the die on signal output. METHODS: Stress analysis was done with a finite element analysis (FEA) software package running on a personal computer. RESULTS: For the segmented die, the admissible range of die wall thicknesses depends upon cutting angle; the signal output is non-linear because the stress distribution in the die wall is influenced by tablet height and position within the die. For the split-web die, the optimal configuration consists of a 1/8 in. sensing web with a strain gage located at the peak of the sensing-web arch. This prototype had a linear calibration curve (r2 = 0.999) with no hysteresis. Radial versus axial stress transmission curves for: starch and sodium chloride were consistent with literature data. CONCLUSIONS: Finite element analysis (FEA) is a useful numerical tool for the systematic optimization of tablet press instrumentation. By enclosing the sensing web of a three layered die design in a cylinder, the split-web design can be directly mounted without modification of the die table.

Drug Design↗

PubMed2001

Highly optimized tolerance in epidemic models incorporating local optimization and regrowth.

In the context of a coupled map model of population dynamics, which includes the rapid spread of fatal epidemics, we investigate the consequences of two new features in highly optimized tolerance (HOT), a mechanism which describes how complexity arises in systems which are optimized for robust performance in the presence of a harsh external environment. Specifically, we (1) contrast global and local optimization criteria and (2) investigate the effects of time dependent regrowth. We find that both local and global optimization lead to HOT states, which may differ in their specific layouts, but share many qualitative features. Time dependent regrowth leads to HOT states which deviate from the optimal configurations in the corresponding static models in order to protect the system from slow (or impossible) regrowth which follows the largest losses and extinctions. While the associated map can exhibit complex, chaotic solutions, HOT states are confined to relatively simple dynamical regimes.

Journal Article↗

PubMed2006

Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems.

In this correspondence, an approach based on coevolutionary particle swarm optimization to solve constrained optimization problems formulated as min-max problems is presented. In standard or canonical particle swarm optimization (PSO), a uniform probability distribution is used to generate random numbers for the accelerating coefficients of the local and global terms. We propose a Gaussian probability distribution to generate the accelerating coefficients of PSO. Two populations of PSO using Gaussian distribution are used on the optimization algorithm that is tested on a suite of well-known benchmark constrained optimization problems. Results have been compared with the canonical PSO (constriction factor) and with a coevolutionary genetic algorithm. Simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness.

Letter↗

PubMed1987

The combined effect of sub-optimal temperature and sub-optimal pH on growth and toxin formation from spores of Clostridium botulinum.

Low-acid foods (pH greater than or equal to 4.5) are not sufficiently acidic to prevent growth of Clostridium botulinum in otherwise optimal conditions. The combination of sub-optimal pH and sub-optimal temperature may, however, result in a very significant reduction in the risk of growth of this bacterium compared with the risk in optimal conditions. The combined effect of incubation temperatures of 12 degrees and 16 degrees C and pH values between 5.2 and 5.5 on growth and toxin production from spores of Cl. botulinum during incubation for 28 d has been investigated. Growth and formation of toxin (type B) were detected only in medium at pH 5.5 and incubated at 16 degrees C, corresponding to a probability of growth from a single spore within 14 d of 1.6 x 10(-5). The probability of growth in 28 d in the remaining conditions was less than 9 x 10(-6). After transfer of inoculated media from 12 degrees to 30 degrees C growth occurred at pH 5.2-5.5 within 19 d. After transfer of inoculated media from 12 degrees to 20 degrees C growth occurred at pH 5.5 and 5.4 but not at pH 5.3 or 5.2 in 40 d. Growth at pH 5.2-5.5 was accompanied by formation of toxin, in most cases of types A or B. In addition to the effect of sub-optimal temperature and pH, chelation of divalent metal ions by citrate may have contributed to inhibition.

Botulinum Toxins↗

PubMed2001

Selection of an optimal neural network architecture for computer-aided detection of microcalcifications--comparison of automated optimization techniques.

Many computer-aided diagnosis (CAD) systems use neural networks (NNs) for either detection or classification of abnormalities. Currently, most NNs are "optimized" by manual search in a very limited parameter space. In this work, we evaluated the use of automated optimization methods for selecting an optimal convolution neural network (CNN) architecture. Three automated methods, the steepest descent (SD), the simulated annealing (SA), and the genetic algorithm (GA), were compared. We used as an example the CNN that classifies true and false microcalcifications detected on digitized mammograms by a prescreening algorithm. Four parameters of the CNN architecture were considered for optimization, the numbers of node groups and the filter kernel sizes in the first and second hidden layers, resulting in a search space of 432 possible architectures. The area Az under the receiver operating characteristic (ROC) curve was used to design a cost function. The SA experiments were conducted with four different annealing schedules. Three different parent selection methods were compared for the GA experiments. An available data set was split into two groups with approximately equal number of samples. By using the two groups alternately for training and testing, two different cost surfaces were evaluated. For the first cost surface, the SD method was trapped in a local minimum 91% (392/432) of the time. The SA using the Boltzman schedule selected the best architecture after evaluating, on average, 167 architectures. The GA achieved its best performance with linearly scaled roulette-wheel parent selection; however, it evaluated 391 different architectures, on average, to find the best one. The second cost surface contained no local minimum. For this surface, a simple SD algorithm could quickly find the global minimum, but the SA with the very fast reannealing schedule was still the most efficient. The same SA scheme, however, was trapped in a local minimum on the first cost surface. Our CNN study demonstrated that, if optimization is to be performed on a cost surface whose characteristics are not known a priori, it is advisable that a moderately fast algorithm such as a SA using a Boltzman cooling schedule be used to conduct an efficient and thorough search, which may offer a better chance of reaching the global minimum.

Algorithms↗

PubMed2002

Optimization of operating conditions in a high-shear mixer using dem model: determination of optimal fill level.

For the purpose of evaluating optimal fill level of starting materials in a high-shear mixer, discrete element method (DEM) simulation was conducted to visualize kinetic status between particles. The simulation results obtained by changing fill levels were used to determine solid fraction of particles, particle velocity, particle velocity vector, and kinetic energy and discuss the flow pattern. Optimal fill level was obtained from the information on these matters. It was pointed out that understanding the kinetic energy between particles in an agitating vessel was effective in determining the optimal fill level. Granulation experiment was conducted to validate the optimal fill level obtained by the simulation, confirming the good agreement between these two results. It was pointed out that determination of kinetic energy between particles through the simulation was effective in obtaining an index of the kinetic status of particles. Further, it was confirmed that the simulation could provide more information than conventional granulation experiments could provide and also helpful in optimizing the operating conditions.

Drug Compounding↗

PubMed1986

Prevalence of dental caries and dental fluorosis in areas with negligible, optimal, and above-optimal fluoride concentrations in drinking water.

The prevalence of dental caries and dental fluorosis was assessed in 1,123 children aged 8 to 16 years who were lifelong residents of areas with negligible, optimal, and above-optimal concentrations of natural fluoride in drinking water. Caries prevalence in the optimal fluoride area was 38.1% lower than it was in the negligible fluoride area, and, in the higher-than-optimal fluoride areas, even greater caries protection was evident. Caries protection was compromised in children with severe fluorosis. Findings do not support the contention that definite increases in the prevalence of fluorosis are occurring in communities with negligible and optimal water-fluoride concentrations because of increased total fluoride consumption from various sources.

Adolescent↗

PubMed2006

Optimization of nutrient medium containing agricultural wastes for xylanase production by Aspergillus niger B03 using optimal composite experimental design.

The xylanase biosynthesis is induced by its substrate - xylan. The high xylan content in some of the wastes like corn cobs and wheat bran makes them an accessible and cheap source of inducers. Nutrient medium for xylanase biosynthesis in submerged cultivation of Aspergillus niger B03 has been optimized. The optimization process was analyzed using optimal composite experimental design and response surface methodology. The predicted by the regression model optimum components of nutrient medium are as follows (g/l): (NH(4))(2)HPO(4) 2.6, urea 0.9, corn cobs 24.0, wheat bran 14.6 and malt sprout 6.0. Five parallel experiments have been carried out, at definite, optimum components concentrations of the nutrient medium, and a mean value of the activity Y=996.30 U/ml has been obtained. The xylanase activity, obtained with the optimized nutrient medium is 33% higher than the activity, achieved with the basic medium.

Agriculture↗

PubMed2003

Simulation results for on-line optimization of a batch bioreactor using nonlinear filtering and optimal control.

The computation of optimal control profiles for batch bioreactors is based on the use of simple and empirical dynamic models. Since these models present some level of uncertainty, the difference between the model dynamics and the reactor dynamics can have significant effects in the reliability of the calculated profile. To develop near optimal control trajectories considering this drawback, we propose to calculate successive control profiles on a moving time horizon using a mathematical model in which the kinetic parameters are estimated by an observer. The desired objective is to generate a near optimal control trajectory adapted to the "running" fermentation. This idea results in a nonlinear estimator plus an optimizer arrangement that so far has not been applied to batch fermentors. Numerical simulations are performed on xanthan-gum batch fermentations and reasonably good results are obtained.

Algorithms↗

PubMed2003

Effect of varying optimization parameters on optimization by guided evolutionary simulated annealing (GESA) using a tablet film coat as an example formulation.

The purpose of this study was to investigate the effect of varying optimization parameters on the proposed optimum of a tablet coating formulation requiring minimization of crack velocity and maximization of film opacity. An artificial neural network (ANN) comprising six input and two output nodes separated by a single hidden layer of five nodes was trained using 100 pseudo-randomly distributed records and optimized by guided evolutionary simulated annealing (GESA). GESA was unable to identify a formulation that satisfied both a crack velocity of 0 ms(-1) and a film opacity of 100% due to conflict centred on the response of the properties to variation in pigment particle size. Constraining film thickness exacerbated the property conflict. By adjusting property weights (i.e. the relative importance of each property), GESA was able to propose formulations that were either crack resistant or that were fully opaque. Reducing the stringency of the performance criteria (crack velocity >0 ms(-1), film opacity <100%) enabled GESA to propose optima that met or exceeded the looser targets. Under these conditions, starting GESA from different locations within model space resulted in the proposal of different optima. Therefore, application of loose targets resulted in the identification of an optimal zone within which all formulations satisfied these less stringent performance criteria. It is concluded that application of the most stringent performance criteria and selection of appropriate property weights is necessary for unequivocal identification of the true optimum. A strategy for optimization experiments is proposed.

Chemistry, Pharmaceutical↗

PubMed2006

Optimization of self-microemulsifying drug delivery systems (SMEDDS) using a D-optimal design and the desirability function.

D-optimal design and the desirability function were applied to optimize a self-microemulsifying drug delivery system (SMEDDS). The optimized key parameters were the following: 1) particle size of the dispersed emulsion, 2) solubility of the drug in the vehicle, and 3) the vehicle compatibility with the hard gelatin capsule. Three formulation variables, PEG200, a surfactant mixture, and an oil mixture, were included in the experimental design. The results of the mathematical analysis of the data demonstrated significant interactions among the formulation variables, and the desirability function was demonstrated to be a powerful tool to predict the optimal formulation for the explored system.

Capsules↗

PubMed1995

The influence of optimization target selection on the structure of arterial tree models generated by constrained constructive optimization.

The computational method of constrained constructive optimization was used to generate complex arterial model trees by optimization with respect to a target function. Changing the target function also changes the tree structure obtained. For a parameterized family of target functions a series of trees was created, showing visually striking differences in structure that can also be quantified by appropriately chosen numerical indexes. Blood transport path length, pressure profile, and an index for relative segment orientation show clear dependencies on the optimization target, and the nature of changes can be explained on theoretical grounds. The main goal was to display, quantify, and explain the structural changes induced by different optimization target functions.

Animals↗

PubMed2004

Optimal healers: igniting the spark and fanning the flame. Training academic medical faculty in optimal healing.

Optimal healing environments (OHEs) require optimal healers to keep them flourishing. Academic medical faculty are in a unique position, through their involvement in education, research, and leadership, to create and perpetuate OHEs. Means by which they might do so using a motivational interviewing model as a framework are discussed. A four-part process to facilitate faculty involvement is described: (1) means of assessing interest in creating OHEs; (2) methods for moving interested individuals beyond a merely intellectual interest to a deeper level of commitment; (3) ways of providing optimal healers with the tools they will need to successfully create OHEs; and (4) perpetuation of OHEs through support networks and educational methods. Resources and examples which can guide the creation of an optimal healing curriculum are provided.

Attitude of Health Personnel↗