Combinations Of Parameter Values Research Articles

Vertical electrothermal actuator with separated metal and nitride structural layers

We report on a novel low-power, large-displacement, vertical electrothermal actuator made of multiple materials, primarily silicon nitride, polysilicon, and nickel. The architecture of separated metal and nitride layers bypasses the limitation imposed by conventional bimorph actuators. A vertical tip displacement of 20 µm can be achieved at a driving current as low as 1.06 mA. The corresponding power consumption is 16.29 mW. Devices with different combinations of parameter values are characterized and compared. At the end of the work, we compare our device with several selections from the archives.

Experimental characterization of two-axis MEMS scanners with hidden radial vertical combdrive actuators and cross-bar spring structures

In this paper, we perform the experimental characterization of two-axis MEMS scanners driven by radial vertical combdrive actuators. The dc scan ranges are limited by the pull-in effect. Each scanner utilizes a cross-bar spring structure to achieve two rotational degrees of freedom (DOFs) without employing any gimbal. Both the actuators and torsion springs are hidden underneath the mirror to obtain a small form factor. The devices are fabricated by a five-layer polysilicon surface micromachining process (SUMMiT-V). Devices with different combinations of parameter values are experimentally characterized and compared.

Threshold Autoregressive Individuals Control Charts

We investigate historically troublesome threshold autoregressive (TAR) model specification in the context of epidemiologic surveillance with meta-models of log likelihood arising from reasonable combinations of TAR parameter values. In this research, a system for intelligently specifying TAR with influenza activity data has lead to a valuable new model-based chart: The threshold autoregressive individuals control (TARIC) chart.

Controlling protein molecular dynamics: How to accelerate folding while preserving the native state

The dynamics of peptides and proteins generated by classical molecular dynamics (MD) is described by using a Markov model. The model is built by clustering the trajectory into conformational states and estimating transition probabilities between the states. Assuming that it is possible to influence the dynamics of the system by varying simulation parameters, we show how to use the Markov model to determine the parameter values that preserve the folded state of the protein and at the same time, reduce the folding time in the simulation. We investigate this by applying the method to two systems. The first system is an imaginary peptide described by given transition probabilities with a total folding time of 1 micros. We find that only small changes in the transition probabilities are needed to accelerate (or decelerate) the folding. This implies that folding times for slowly folding peptides and proteins calculated using MD cannot be meaningfully compared to experimental results. The second system is a four residue peptide valine-proline-alanine-leucine in water. We control the dynamics of the transitions by varying the temperature and the atom masses. The simulation results show that it is possible to find the combinations of parameter values that accelerate the dynamics and at the same time preserve the native state of the peptide. A method for accelerating larger systems without performing simulations for the whole folding process is outlined.

The solution of convection–diffusion equations for solute transport to plant roots

To help develop models of solute uptake that are computationally efficient and suitable for up-scaling to whole root systems, we provide three alternative analytical solutions of equations for transport to absorbing roots by convection and diffusion, and we compare their performance with a numerical solution over the range of plant and soil conditions found in practice. We point out an important pitfall in commonly used methods to solve convection–diffusion equations numerically and show how it can be avoided. We identify a simple analytical solution that is valid for all realistic combinations of parameter values, and show that for most purposes it performs as well as a complete, but more complex, analytical solution allowing fully for both convection and diffusion. We conclude that this solution is suitable for use in general solute uptake models.

Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics

A stochastic individual-based model (IBM) of mosquitofish population dynamics in experimental ponds was constructed in order to increase, virtually, the number of replicates of control populations in an ecotoxicology trial, and thus to increase the statistical power of the experiments. In this context, great importance had to be paid to model calibration as this conditions the use of the model as a reference for statistical comparisons. Accordingly, model calibration required that both mean behaviour and variability behaviour of the model were in accordance with real data. Currently, identifying parameter values from observed data is still an open issue for IBMs, especially when the parameter space is large. Our model included 41 parameters: 30 driving the model expectancy and 11 driving the model variability. Under these conditions, the use of “Latin hypercube” sampling would most probably have “missed” some important combinations of parameter values. Therefore, complete factorial design was preferred. Unfortunately, due to the constraints of the computational capacity, cost-acceptable “complete designs” were limited to no more than nine parameters, the calibration question becoming a parameter selection question. In this study, successive “complete designs” were conducted with different sets of parameters and different parameter values, in order to progressively narrow the parameter space. For each “complete design”, the selection of a maximum of nine parameters and their respective n values was carefully guided by sensitivity analysis. Sensitivity analysis was decisive in selecting parameters that were both influential and likely to have strong interactions. According to this strategy, the model of mosquitofish population dynamics was calibrated on real data from two different years of experiments, and validated on real data from another independent year. This model includes two categories of agents; fish and their living environment. Fish agents have four main processes: growth, survival, puberty and reproduction. The outputs of the model are the length frequency distribution of the population and the 16 scalar variables describing the fish populations. In this study, the length frequency distribution was parameterized by 10 scalars in order to be able to perform calibration. The recently suggested notion of “probabilistic distribution of the distributions” was also applied to our case study, and was shown to be very promising for comparing length frequency distributions (as such).

Finite Element Analysis of Spinal Cord Injury in the Rat

A three-dimensional (3D) finite element model (FEM) that simulates the Impactor weight-drop experimental model of traumatic spinal cord injury (SCI) was developed. The model consists of the rat spinal cord, with distinct element sets for the gray and white matter, the cerebrospinal fluid (CSF), the dura mater, a rigid rat spinal column, and a rigid impactor. Loading conditions were taken from the average impact velocities determined from previous parallel weight-drop experiments employing a 2.5-mm-diameter, 10-g rod dropped from either 12.5 or 25 mm. The mechanical properties were calibrated by comparing the predicted displacement of the spinal cord at the impact site to that measured experimentally. Parametric studies were performed to determine the sensitivity of the model to the relevant material properties, loading conditions, and essential boundary conditions, and it was determined that the shear modulus had the greatest influence on spinal cord displacement. Additional simulations were performed where gray and white matter were prescribed different material properties. These simulations generated similar drop trajectories to the homogeneous model, but the stress and strain distributions better matched patterns of acute albumin extravasation across the blood-spinal cord barrier following weight-drop SCI, as judged by a logit analysis. A final simulation was performed where the impact site was shifted laterally by 0.35 mm. The off-center impact had little effect on the rod trajectory, but caused marked shifts in the location of stress and strain contours. Different combinations of parameter values could reproduce the impactor trajectory, which suggests that another experimental measure of the tissue response is required for validation. The FEM can be a valuable tool for understanding the injury biomechanics associated with experimental SCI to identify areas for improvement in animal models and future research to identify thresholds for injury.

Non-adaptive and adaptive hybrid approaches for enhancing water quality management

Using optimization to help solve groundwater management problems cost-effectively is becoming increasingly important. Hybrid optimization approaches, that combine two or more optimization algorithms, will become valuable and common tools for addressing complex nonlinear hydrologic problems. Hybrid heuristic optimizers have capabilities far beyond those of a simple genetic algorithm (SGA), and are continuously improving. SGAs having only parent selection, crossover, and mutation are inefficient and rarely used for optimizing contaminant transport management. Even an advanced genetic algorithm (AGA) that includes elitism (to emphasize using the best strategies as parents) and healing (to help assure optimal strategy feasibility) is undesirably inefficient. Much more efficient than an AGA is the presented hybrid (AGCT), which adds comprehensive tabu search (TS) features to an AGA. TS mechanisms (TS probability, tabu list size, search coarseness and solution space size, and a TS threshold value) force the optimizer to search portions of the solution space that yield superior pumping strategies, and to avoid reproducing similar or inferior strategies. An AGCT characteristic is that TS control parameters are unchanging during optimization. However, TS parameter values that are ideal for optimization commencement can be undesirable when nearing assumed global optimality. The second presented hybrid, termed global converger (GC), is significantly better than the AGCT. GC includes AGCT plus feedback-driven auto-adaptive control that dynamically changes TS parameters during run-time. Before comparing AGCT and GC, we empirically derived scaled dimensionless TS control parameter guidelines by evaluating 50 sets of parameter values for a hypothetical optimization problem. For the hypothetical area, AGCT optimized both well locations and pumping rates. The parameters are useful starting values because using trial-and-error to identify an ideal combination of control parameter values for a new optimization problem can be time consuming. For comparison, AGA, AGCT, and GC are applied to optimize pumping rates for assumed well locations of a complex large-scale contaminant transport and remediation optimization problem at Blaine Naval Ammunition Depot (NAD). Both hybrid approaches converged more closely to the optimal solution than the non-hybrid AGA. GC averaged 18.79% better convergence than AGCT, and 31.9% than AGA, within the same computation time (12.5 days). AGCT averaged 13.1% better convergence than AGA. The GC can significantly reduce the burden of employing computationally intensive hydrologic simulation models within a limited time period and for real-world optimization problems. Although demonstrated for a groundwater quality problem, it is also applicable to other arenas, such as managing salt water intrusion and surface water contaminant loading.

Factors that affect the optimal amount of central control in complex systems

AbstractWhereas much of the previous research in complex systems has focused on emergent properties resulting from self‐organization of the individual agents that make up the system, this article studies one vital role of central organization. In particular, four factors are conjectured to be key in determining the optimal amount of central control. To validate this hypothesis, these factors are represented as controllable parameters in a mathematical model. For different combinations of parameter values, the optimal amount of central control is found, either analytically or by computer simulation. The model is shown to provide results that match well with the level of control found across a broad spectrum of specific complex systems. This model also provides general guidelines as to how combinations of these factors affect the desirable level of control and specific guidelines for selecting and evaluating leaders. These results indicate that all of these factors, though not exhaustive, should be considered carefully when attempting to determine the amount of control that is best for a system. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008

A multiple objective particle swarm optimization approach for inventory classification

This paper presents a particle swarm optimization approach for inventory classification problems where inventory items are classified based on a specific objective or multiple objectives, such as minimizing costs, maximizing inventory turnover ratios, and maximizing inventory correlation. In addition, this approach determines the best number of inventory classes and how items should be categorized for the desired objectives at the same time. Experiments are employed to determine the best combination of algorithm parameter values. Extensive numerical studies are conducted and results are compared to other known classification methods. The performance of the algorithm on a practical case is also presented.

Transfer batch scheduling using genetic algorithms

This paper examines scheduling in a manufacturing system with transfer batches. Transfer batches are considered as different batches although they stem from the same job. Genetic algorithms determine the size of the transfer batches for each job and the final schedule with a makespan criterion. A novelty of the genetic algorithm developed is twin chromosome encoding, the first chromosome representing the relative size (participation ratio) of each transfer batch with respect to the whole batch; and the second chromosome applying in effect a dynamic heuristic dispatching rule representation for resolving operation antagonism. New crossover and mutation operators were employed for the first chromosome and standard operators for the second. The genetic algorithms were coded in C++ for better control. A 20 job × eight machine shop was used as a test case. Results favour genetic algorithms over heuristic procedures, but the latter close the gap with an increase in the number of transfer batches. Design of Experiments was used to focus on the most promising genetic algorithm parameter value combinations.

Parentage Analysis with Few Contributing Breeders: Validation and Improvement

Validation of parental allocation using PAPA software (Duchesne P, Godbout MH, Bernatchez L. 2002. PAPA (package for the analysis of parental allocation): a computer program for simulated and real parental allocation. Mol Ecol Notes. 2:191-193.) was investigated under the assumption that only a small proportion of potential breeders contributed to the offspring sample. Inbreeding levels proved to have a large impact on allocation error rate. Consequently, simulations from artificial, unrelated parents may strongly underestimate allocation error, and so, whenever possible, simulations based on the actual parental genotypes should be run. An unexpected and interesting finding was that ambiguity (the highest likelihood is shared by several parental pairs) rates below 10% stood very close to exact allocation error rates (true proportions of wrong allocations). Hence, the ambiguity rate statistic may be viewed as a ready-made indicator of the resolution power of a specific parental allocation run and, if not exceeding 10%, used as an estimate of allocation error rate. It was found that the PAPA simulator, even with few contributing breeders, can be trusted to output reasonably accurate estimates of allocation error as long as those estimates do not exceed 15%. Indeed, most discrepancies between exact and estimated error then stood below 3%. Reproductive success variance had little impact on error estimate discrepancies within the same range. Finally, a (focal set) method was described to correct the estimated family sizes computed directly from parental allocations. Essentially, this method makes use of the detailed structure of the allocation probabilities associated with each parental pair with at least 1 allocated offspring. The allocation probabilities are expressed in matrix form, and the subsequent calculations are run based on standard matrix algebra. On average, this method provided better estimates of family sizes for each investigated combination of parameter values. As the size of offspring samples increased, the corrections improved until a plateau was finally reached. Typically, samples comprising 250, 500, and 1000 offspring would bring corrections in the order of 10-20%, 20-30%, and 30-40%, respectively.

Clinical interest of bone texture analysis in osteoporosis: a case control multicenter study

We demonstrate the clinical interest of bone texture analysis with a new high resolution X-ray device. We have found that the combination of BMD and texture parameter values provided a better assessment of the fracture risk than that obtainable solely by BMD measurement. Osteoporosis is characterized by BMD and trabecular bone microarchitecture. We have developed a new high-resolution X-ray device with direct digitization. The aim of this study was to demonstrate in a multicenter case control study the clinical interest of bone texture analysis with this new device. In this cross-sectional multicenter case-control population study in post-menopausal women, 159 osteoporotic fractures were compared with 219 control cases. Images were obtained on calcaneus with a direct digital X-ray device (BMA, D3A Medical Systems). Co-occurrence, run-length matrices and the fractal parameter Hmean were evaluated. BMD was measured at the lumbar spine (LS), femoral neck (FN) and total hip (TH) by DXA. The three texture parameters were significantly lower in osteoporotic fracture cases than in control cases. These differences persisted after adjustment for TH BMD. Receiver operating characteristic curves were used to compare the discriminant capacity of texture parameters and BMD measurements for fracture. The highest areas under curve (AUC) were 0.721 for TH BMD and 0.706 for Hmean (AUC THBMD vs. AUC Hmean, p = NS). We determined the threshold between high and low Hmean parameter values and then the odds ratios (OR) of fracture for low Hmean, for BMD < or =2.5 SD in the T-score and for combinations of both parameters. The OR of fracture for low H was 2.72 (95% CI, 1.36-5.4). For a FN BMD < or = -2.5 SD, the OR of 4.78 (2.19-10.43) shifted to 14.06 (4.41-44.85) adding H. These data confirmed the clinical interest of the combination of BMD and texture parameters to improve the assessment of the risk of fracture other that obtainable by the sole BMD measurement.

Air plasma spray processing and electrochemical characterization of SOFC composite cathodes

Air plasma spraying has been used to produce porous composite cathodes containing (La 0.8Sr 0.2) 0.98MnO 3− y (LSM) and yttria-stabilized zirconia (YSZ) for use in solid oxide fuel cells (SOFCs). Preliminary investigations focused on determining the range of plasma conditions under which each of the individual materials could be successfully deposited. A range of conditions was thereby determined that was suitable for the deposition of a composite cathode from pre-mixed LSM and YSZ powders. A number of composite cathodes were produced using different combinations of parameter values within the identified range according to a Uniform Design experimental grid. Coatings were then characterized for composition and microstructure using EDX and SEM. As a result of these tests, combinations of input parameter values were identified that are best suited to the production of coatings with microstructures appropriate for use in SOFC composite cathodes. A selection of coatings representative of the types of observed microstructures were then subjected to electrochemical testing to evaluate the performance of these cathodes. From these tests, it was found that, in general, the coatings that appeared to have the most suitable microstructures also had the highest electrochemical performances, provided that the deposition efficiency of both phases was sufficiently high.

Autocalibration in hydrologic modeling: Using SWAT2005 in small-scale watersheds

SWAT is a physically based model that can simulate water quality and quantity at the watershed scale. Due to many of the processes involved in the manual- or autocalibration of model parameters and the knowledge of realistic input values, calibration can become difficult. An autocalibration-sensitivity analysis procedure was embedded in SWAT version 2005 (SWAT2005) to optimize parameter processing. This embedded procedure is applied to six small-scale watersheds (subwatersheds) in the central Texas Blackland Prairie. The objective of this study is to evaluate the effectiveness of the autocalibration-sensitivity analysis procedures at small-scale watersheds (4.0–8.4 ha). Model simulations are completed using two data scenarios: (1) 1 year used for parameter calibration; (2) 5 years used for parameter calibration. The impact of manual parameter calibration versus autocalibration with manual adjustment on model simulation results is tested. The combination of autocalibration tool parameter values and manually adjusted parameters for the 2000–2004 simulation period resulted in the highest E NS and R 2 values for discharge; however, the same 5-year period yielded better overall E NS, R 2 and P-values for the simulation values that were manually adjusted. The disparity is most likely due to the limited number of parameters that are included in this version of the autocalibration tool (i.e. Nperco, Pperco, and nitrate). Overall, SWAT2005 simulated the hydrology and the water quality constituents at the subwatershed-scale more adequately when all of the available observed data were used for model simulation as evidenced by statistical measure when both the autocalibration and manually adjusted parameters were used in the simulation.

Force Field of the TMGL Ionic Liquid and the Solubility of SO2 and CO2 in the TMGL from Molecular Dynamics Simulation

An all-atom force field is developed using a combination of density functional theory calculations and OPLS force field parameter values for the 1,1,3,3-tetramethylguanidium lactate (TMG) lactic acid (LAC) ionic liquid (TMGL). The computed density of the TMGL is in good agreement with available experimental values. The internal energy components, cohesive energy density, and the self-diffusion constants are also discussed. Molecular dynamics simulations are then conducted to investigate the solubility of the SO2 and CO2 gases in the TMGL. The simulation results show strong organization of SO2 about the TMG cation and the LAC anion, especially the LAC anion, but relatively weak organization of CO2 about the cation and the anion of the TMGL, which explained well the selectivity of the TMGL toward the SO2 and CO2.

Constructing a coherent joint prior while respecting biological realism: application to marine mammal stock assessments

AbstractBrandon, J. R., Breiwick, J. M., Punt, A. E., and Wade, P. R. 2007. Constructing a coherent joint prior while respecting biological realism: application to marine mammal stock assessments. – ICES Journal of Marine Science, 64: 1085–1100. Bayesian estimation methods, employing the Sampling–Importance–Resampling algorithm, are currently used to perform stock assessments for several stocks of marine mammals, including the Bering–Chukchi–Beaufort Seas stock of bowhead whales (Balaena mysticetus) and walrus (Odobenus rosmarus rosmarus) off Greenland. However, owing to the functional relationships among parameters in deterministic age-structured population dynamics models, placing explicit priors on each life history parameter in addition to the population growth rate parameter results in an incoherent joint prior distribution (i.e. two different priors on the estimated parameters). One solution to constructing a coherent joint prior is to solve for juvenile survival analytically, using values generated from the prior distributions for the remaining parameters. However, certain combinations of model parameter values result in values for juvenile survival that are larger than adult survival, which is biologically implausible. Therefore, to respect biological realism, certain parameter values must be rejected for some or all the remaining parameters. This study investigates several alternative resampling schemes for obtaining a realistic joint prior distribution, given the constraint on survival rates. The sensitivity of assessment results is investigated for data-rich (bowhead) and data-poor (walrus) scenarios. The results based on limited data are especially sensitive to the choice of alternative resampling scheme.

Feasible region approximation: a comparison of search cone and convex hull methods

The concept of the design space encapsulates the search for optimal design configurations of engineering systems. During this search, infeasible or unsafe regions may be encountered, representing particular combinations of parameter values which cause the system to fail. These regions need to be characterized so that they can be avoided during the search for improved designs. In addition, the proximity of a design to a feasible boundary may be related to the reliability and robustness of the design in the face of variation in manufacture and use. A chemical engineering example is used to show how feasible design regions can be modelled using integer lattices and kriging methods. An algorithm using Hilbert bases is developed and used to select points on the boundary of the feasible region with arbitrary closeness so that the properties of the kriging model are such that the model remains stable and the star-shaped necessary condition is met.

Optimizing Chew and Chen's Pitch-Spelling Algorithm

Pitch-spelling algorithms attempt to compute the correct pitch names (e.g., C#4, Bb5) of the notes in a passage of tonal music, when given only the onset time, MIDI note number, and possibly the duration and voice of each note. This article reports on a study in which Chew and Chen’s (2003a, 2003b, 2005) pitch-spelling algorithm was re-implemented and then optimized by running it with a range of different parameter value combinations on a test corpus containing 195,972 notes and consisting of 216 movements from works by eight Baroque and Classical composers. The results of this evaluation cast doubt upon some of the claims made by Chew and Chen that were based on results obtained by running their algorithm on a much smaller test corpus containing only 4,462 notes and consisting of just two movements from sonatas by Beethoven and You-Di Huang’s Song of Ali-Shan. The results presented here suggest that Chew and Chen’s algorithm could be simplified in various ways without compromising its performance.

A parametrized algorithm that implements sequential, causal, and cache memory consistencies

In this paper, we present an algorithm that can be used to implement sequential, causal, or cache consistency in distributed shared memory (DSM) systems. For this purpose it includes a parameter that allows us to choose the consistency model to be implemented. If all processes run the algorithm with the same value in this parameter, the corresponding consistency is achieved. (Additionally, the algorithm tolerates that processes use certain combination of parameter values.) This characteristic allows a concrete consistency model to be chosen, but implements it with the more efficient algorithm in each case (depending of the requirements of the applications). Additionally, as far as we know, this is the first algorithm proposed that implements cache coherence. In our algorithm, all the read and write operations are executed locally when implementing causal and cache consistency (i.e., they are fast). It is known that no sequential algorithm has only fast memory operations. In our algorithm, however, all the write operations and some read operations are fast when implementing sequential consistency. The algorithm uses propagation and full replication, where the values written by a process are propagated to the rest of the processes. It works in a cyclic turn fashion, with each process of the DSM system, broadcasting one message in turn. The values written by the process are sent in the message (instead of sending one message for each write operation): However, unnecessary values are excluded. All this permits the amount of message traffic owing to the algorithm to be controlled.