Selection Of Parameter Values Research Articles

A super-Gaussian Poisson-Boltzmann model for electrostatic free energy calculation: smooth dielectric distribution for protein cavities and in both water and vacuum states.

Calculations of electrostatic potential and solvation free energy of macromolecules are essential for understanding the mechanism of many biological processes. In the classical implicit solvent Poisson-Boltzmann (PB) model, the macromolecule and water are modeled as two-dielectric media with a sharp border. However, the dielectric property of interior cavities and ion-channels is difficult to model realistically in a two-dielectric setting. In fact, the detection of water molecules in a protein cavity remains to be an experimental challenge. This introduces an uncertainty, which affects the subsequent solvation free energy calculation. In order to compensate this uncertainty, a novel super-Gaussian dielectric PB model is introduced in this work, which devices an inhomogeneous dielectric distribution to represent the compactness of atoms and characterizes empty cavities via a gap dielectric value. Moreover, the minimal molecular surface level set function is adopted so that the dielectric profile remains to be smooth when the protein is transferred from water phase to vacuum. An important feature of this new model is that as the order of super-Gaussian function approaches the infinity, the dielectric distribution reduces to a piecewise constant of the two-dielectric model. Mathematically, an effective dielectric constant analysis is introduced in this work to benchmark the dielectric model and select optimal parameter values. Computationally, a pseudo-time alternative direction implicit (ADI) algorithm is utilized for solving the super-Gaussian PB equation, which is found to be unconditionally stable in a smooth dielectric setting. Solvation free energy calculation of a Kirkwood sphere and various proteins is carried out to validate the super-Gaussian model and ADI algorithm. One macromolecule with both water filled and empty cavities is employed to demonstrate how the cavity uncertainty in protein structure can be bypassed through dielectric modeling in biomolecular electrostatic analysis.

Calibration and Global Sensitivity Analysis for a Salinity Model Used in Evaluating Fields Irrigated with Treated Wastewater in the Salinas Valley

Treated wastewater irrigation began two decades ago in the Salinas Valley of California and provides a unique opportunity to evaluate the long-term effects of this strategy on soil salinization. We used data from a long-term field experiment that included application of a range of blended water salinity on vegetables, strawberries and artichoke crops using surface and pressurized irrigation systems to calibrate and validate a root zone salinity model. We first applied the method of Morris to screen model parameters that have negligible influence on the output (soil‐water electrical conductivity (ECsw)), and then the variance-based method of Sobol to select parameter values and complete model calibration and validation. While model simulations successfully captured long-term trends in soil salinity, model predictions underestimated ECsw for high ECsw samples. The model prediction error for the validation case ranged from 2.6% to 39%. The degree of soil salinization due to continuous application of water with electrical conductivity (ECw) of 0.57 dS/m to 1.76 dS/m depends on multiple factors; ECw and actual crop evapotranspiration had a positive effect on ECsw, while rainfall amounts and fallow had a negative effect. A 50-year simulation indicated that soil water equilibrium (ECsw ≤ 2dS/m, the initial ECsw) was reached after 8 to 14 years for vegetable crops irrigated with ECw of 0.95 to 1.76. Annual salt output loads for the 50-year simulation with runoff was a magnitude greater (from 305 to 1028 kg/ha/year) than that in deep percolation (up to 64 kg/ha/year). However, for all sites throughout the 50-year simulation, seasonal root zone salinity (saturated paste extract) did not exceed thresholds for salt tolerance for the selected crop rotations for the range of blended applied water salinities.

Clustering algorithms: A comparative approach.

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While many classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 9 well-known clustering methods available in the R language assuming normally distributed data. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach tended to present particularly good performance. We also found that the default configuration of the adopted implementations was not always accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.

Signal Detection in Generalized Gaussian Distribution Noise With Nakagami Fading Channel

A fractional order moments-based detector is proposed for the detection of weak signals in additive impulsive noise environment assumed as generalized Gaussian distribution with properly selected parameter values. The asymptotic detection performance is derived and compared with some traditional detectors optimized for operations in Gaussian noise with Nakagami fading communication channels. The analytical and computer simulation results of the fractional order moment-based detector are shown for signal detection with fading channels in the impulsive noise.

Efficiency increasing by development of district heating systems operational modes using graphical analysis

The tasks of analyzing, developing and justifying of the district heating systems (DHS) operation modes arise at different stages of their life cycle. The article provides a brief description of the tasks, steps and methods for calculating and analyzing DHS modes, which are implementing as subtasks of the "ANGARA-HN" software. Special attention is paying to the automation of analysis processes. The developed computational and graphical software tools for parameters analyzing of the DHS operating modes consists of the means: analyzing of the initial information, calculating the values of the mode parameters and analyzing results of the calculation. Various tools allow you to visualize the distribution of selected parameters values on the graphical city plan, highlight bottlenecks, building pressure distribution graphs. The developed software designed to automate the workplaces of regimes group engineers at operational enterprises or for designer workers, when developing and justifying development schemes for urban DHS. Tools for analysis and graphical visualization significantly increase the efficiency of work by reducing the time of analysis, development and justification of operating conditions, as well as improve the quality of the results.

The capacitated modular single-allocation hub location problem with possibilities of hubs disruptions: modeling and a solution algorithm

The hub location problem (HLP) concerns the location of hub nodes and allocation of other non-hub nodes to hubs in the network. Hubs are particular facilities that serve as mediators through the aggregation, consolidation, classification and distribution of network flows from origin to destination. Most models use constant penalty parameters to calculate the aggregation and distribution cost in the objective function. The selected parameter values can significantly affect the number of hubs and their locations in the network. Furthermore, most studies on HLP assume that network hubs are always operational but every established hub has the potential to fail during use. In this paper, we propose the capacitated modular single allocation hub location problem with possibilities of hub disruptions, whereby a low number of capacitated transportation vehicles is used instead of constant parameters. The problem is modeled as a two-stage stochastic program, and a metaheuristic algorithm based on the adaptive large neighbourhood search is proposed. The computational experiment results demonstrate the high efficiency of the proposed solution method.

Fractal Behavior of a Ternary 4-Point Rational Interpolation Subdivision Scheme

In this paper, a ternary 4-point rational interpolation subdivision scheme is presented, and the necessary and sufficient conditions of the continuity are analyzed. The generalization incorporates existing schemes as special cases: Hassan–Ivrissimtzis’s scheme, Siddiqi–Rehan’s scheme, and Siddiqi–Ahmad’s scheme. Furthermore, the fractal behavior of the scheme is investigated and analyzed, and the range of the parameter of the fractal curve is the neighborhood of the singular point of the rational scheme. When the fractal curve and surface are reconstructed, it is convenient for the selection of parameter values.

An overview of the sediment acoustic models and their experimental validations

The Biot theory of acoustic wave propagation in porous media was first adapted to marine sediments by Stoll (1969). To describe the response of a slightly inelastic skeletal frame over a wide frequency range, Stoll introduced the notion of constant complex bulk and shear moduli with small imaginary parts. Stoll’s model predicts f1 frequency-dependence of attenuation at low frequencies and nearly f2 and f1/2 frequency-dependence at frequencies where maximum velocity dispersion occurs. Although the theory well reproduces the velocity and attenuation measurements in marine sediments, the assumption of constant complex moduli slightly violates the causality (Turgut, 1990). The more recent Grain-Shearing (GS) and Viscous Grain-Shearing (VGS) models (Buckingham, 2000 and 2007) are causal and the VGS model also predicts a frequency-dependence of attenuation like that of the Stoll model. With the selection of proper parameter values, both models predict compressional and shear-wave dispersions that are in agreement with those of previous in-situ and laboratory measurements. In addition, the Stoll model predicts the existence of slow compressional waves that have been observed in synthetic porous media (Plona, 1980) but not in natural marine sediments. Several reflection and in-sediment transmission experiments are discussed to facilitate the detection of slow compressional waves in marine sediments. [Work supported by ONR.]

Parallel non-dominated sorting genetic algorithm-II-based image encryption technique

ABSTRACTChaotic system requires parameters to generate random sequences. Recent studies show that the improper selection of parameter values make secret keys generated from chaotic system vulnerable. Meta-heuristic techniques have been introduced in the area of image encryption to improve the selection of chaotic system parameters. But, these techniques suffer from poor computational speed. To overcome this issue, in this paper, a parallel Non-Dominated Sorting Genetic Algorithm (NSGA-II)-based intertwining logistic map is proposed to encrypt the images. To implement NSGA-II in parallel fashion, master–slave environment is designed. Initially, the execution time analysis of NSGA-II is done to determine the computationally expensive operations. Thereafter, NSGA-II operators are divided into master and slave jobs. The Message Passing Interface (MPI) is used for intercommunication between master and slave nodes. The simulation results show that the parallel proposed technique provides a significant improvement in computational speed as compared to the existing techniques.

Novel local restart strategies with hyper-populated ant colonies for dynamic optimization problems

The emergence of novel metaheuristic algorithms and the impracticality of exact algorithms led to the increased application of stochastic optimization techniques to solve combinatorial optimization problems. Determination of population size, stopping criteria, selection of optimal parameter values, getting out of the local optima and most importantly the interplay between various parameters are yet to be addressed. In this work, the significance of population size and how it interplays with other parameters in determining the effective convergence of the system in both static and dynamic scenarios for travelling salesman problem (TSP) are explained. This work utilizes a more complex variant of introducing dynamism in TSP, by swapping existing nodes with new nodes. This work proposes novel local restart strategies for efficient search space reset during node replacements in dynamic TSP. The proposed local restart strategy in combination with hyper-populated ant systems is found to outperform existing state-of-the-art solutions on benchmark datasets including Oliver30, Eilon51 and KROA100 from TSPLIB.

Compensation for Traveling Wave Delay Through Selection of Dendritic Delays Using Spike-Timing-Dependent Plasticity in a Model of the Auditory Brainstem

Asynchrony among synaptic inputs may prevent a neuron from responding to behaviorally relevant sensory stimuli. For example, “octopus cells” are monaural neurons in the auditory brainstem of mammals that receive input from auditory nerve fibers (ANFs) representing a broad band of sound frequencies. Octopus cells are known to respond with finely timed action potentials at the onset of sounds despite the fact that due to the traveling wave delay in the cochlea, synaptic input from the auditory nerve is temporally diffuse. This paper provides a proof of principle that the octopus cells' dendritic delay may provide compensation for this input asynchrony, and that synaptic weights may be adjusted by a spike-timing dependent plasticity (STDP) learning rule. This paper used a leaky integrate and fire model of an octopus cell modified to include a “rate threshold,” a property that is known to create the appropriate onset response in octopus cells. Repeated audio click stimuli were passed to a realistic auditory nerve model which provided the synaptic input to the octopus cell model. A genetic algorithm was used to find the parameters of the STDP learning rule that reproduced the microscopically observed synaptic connectivity. With these selected parameter values it was shown that the STDP learning rule was capable of adjusting the values of a large number of input synaptic weights, creating a configuration that compensated the traveling wave delay of the cochlea.

MCMC methods for inference in a mathematical model of pulmonary circulation

This study performs parameter inference in a partial differential equations system of pulmonary circulation. We use a fluid dynamics network model that takes selected parameter values and mimics the behaviour of the pulmonary haemodynamics under normal physiological and pathological conditions. This is of medical interest as it enables tracking the progression of pulmonary hypertension. We show how we make the fluids model tractable by reducing the parameter dimension from a 55D to a 5D problem. The Delayed Rejection Adaptive Metropolis algorithm, coupled with constraint non‐linear optimization, is successfully used to learn the parameter values and quantify the uncertainty in the parameter estimates. To accommodate for different magnitudes of the parameter values, we introduce an improved parameter scaling technique in the Delayed Rejection Adaptive Metropolis algorithm. Formal convergence diagnostics are employed to check for convergence of the Markov chains. Additionally, we perform model selection using different information criteria, including Watanabe Akaike Information Criteria.

Use of the Minimax Regret Approach for Robust Selection of Rainfall-Runoff Model Parameter Values Considering Multiple Events and Multiple Performance Indices

When hydrological simulation models are calibrated, the optimized parameter set based on a single event often does not show good accuracy for other events, even using the same performance measure. Therefore, this study improved the robust parameter set (ROPS) selection method for hydrological simulation models to consider multiple rainfall events by combining the Minimax Regret Approach (MRA) with Pareto optimums. A multi-event objective function, which is the linear combination of three weighting values and three Nash-Sutcliffe coefficients, was used and individually solved using a genetic algorithm. All available 63 multi-objective functions with weighting values based on three events were determined and then, the Pareto optimum was derived. These optimized parameter sets were considered as the ROPS candidates for the final selection. This study used two approaches. First, Nash-Sutcliffe efficiencies (NSEs) for the additional three rainfall events were used to identify the ROPS. Second, four performance indices, the NSE, the Peak Flow Error (PFE), the Root Mean Square Error (RMSE) and the Percent Bias (PBIAS), were used for the other three events. From the results, it can be concluded that the optimized parameter set from the best weighted multi-objective function using multiple events can simulate most rainfall events well with relatively high precision for both the NSE only and the four performance indices. Furthermore, it can be extended to combine multiple events from multiple sites.

Consensus Classification Using Non-Optimized Classifiers.

Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.

Influence of morphological variations on cervical spine segmental responses from inertial loading

ABSTRACTObjectives: The objective of this study was to investigate the influence of morphological variations in osteoligamentous lower cervical spinal segment responses under postero-anterior inertial loading.Methods: A parametric finite element model of the C5–C6 spinal segment was used to generate models. Variations in the vertebral body and facet depth (anteroposterior), posterior process length, intervertebral disc height, facet articular process height and slope, segment orientation ranging from lordotic to straight, and segment size were parameterized. These variations included male–female differences. A Latin hypercube sampling method was used to select parameter values for model generation. Forces and moments associated with the inertial loading were applied to the generated model segments. The 7 parameters were grouped as local or global depending on the number of spinal components involved in the shape variation. Four output responses representing overall segmental and soft tissue responses were analyzed for each model variation: response angle of the segment, anterior longitudinal ligament stretch, anterior capsular ligament stretch, and facet joint compression in the posterior region. Pearson's correlation coefficient was used to compute the correlations of these output responses with morphological variations.Results: Fifty models were generated from the parameterized model using a Latin hypercube sampling technique. Variation in response angle among the models was 4° and was most influenced by change in the combined dimension of vertebral body and facet depth, followed by size of the segment. The maximum anterior longitudinal ligament stretch varied between 0.1 and 0.3 and was strongly influenced by the change in the segment orientation. The anterior facet joint region sustained tension, whereas the posterior region sustained compression. For the anterior capsular ligament stretch, the most influential global variation was segment orientation, whereas the most influential local variations were the facet height and facet angle parameters. In the case of posterior facet joint compression, segment orientation was again most influential, whereas among the local variations, the facet angle had the most influence.Conclusion: Shape variations in the intervertebral disc influenced segmental rotation and ligament responses; however, the influence of shape variations in the facet joint was confined to capsular ligament responses. Response angle was most influenced by the vertebral body depth variations, explaining greater segmental rotations in female spines. Straighter spine segments sustained greater posterior facet joint compression, which may offer an explanation for the higher incidence of whiplash-associated disorders among females, who exhibit a straighter cervical spine. The anterior longitudinal ligament stretch was also greater in straighter segments. These findings indicate that the morphological features specific to the anatomy of the female cervical spine may predispose it to injury under inertial loading.

Domain-growth-induced patterning for reaction-diffusion systems with linear cross-diffusion

In this article we present, for the first time, domain-growth induced pattern formation for reaction-diffusion systems with linear cross-diffusion on evolving domains and surfaces. Our major contribution is that by selecting parameter values from spaces induced by domain and surface evolution, patterns emerge only when domain growth is present. Such patterns do not exist in the absence of domain and surface evolution. In order to compute these domain-induced parameter spaces, linear stability theory is employed to establish the necessary conditions for domain-growth induced cross-diffusion-driven instability for reaction-diffusion systems with linear cross-diffusion. Model reaction-kinetic parameter values are then identified from parameter spaces induced by domain-growth only; these exist outside the classical standard Turing space on stationary domains and surfaces. To exhibit these patterns we employ the finite element method for solving reaction-diffusion systems with cross-diffusion on continuously evolving domains and surfaces.

Thermodynamic designing of the small-scale gas turbine engine family with common core

The paper describes the method of selecting the working process parameters of a family of small-scale gas turbine engines (GTE) with common core. As an example, the thermodynamic design of a family of small-scale gas turbine engines (SGTE) with common core was carried out. The engine family includes a small-scale turbojet engine (STJE) and a gas turbine plant (GTP), which electric generator is driven by power turbine. The selection of rational values for the working process parameters of STJE and GTP was carried out in CAE system ASTRA on the basis of nonlinear optimization of these parameters, taking into account functional and parametric constraints. The quantitative results of deterioration in the performance of the engines of the family with common core are obtained in comparison with the engines with the optimum core for each type. However, the advanced creation of a common core can reduce the cost and timing of the engine creation, ensure its higher reliability (due to the development of the base common core) and reduce the cost of its production. The method of selecting the parameters of the working process of the GTE family with common core presents the solution to more complex problems, such as the possibility of developing a family consisting of five engines: a turbojet engine, turbofan engine, turbofan engine with a complex cycle, GTE with power turbine (GTE-PT), GTE-PT with recovery.

Fate and Transport Modelling of Emerging Pollutants from Watersheds to Oceans: A Review.

This chapter provides a review of the fate and transport modelling of emerging pollutants (EPs) and discusses the major research challenges. The overwhelming limitation of the past modelling studies has been the lack of data necessary for model validation, thus calling for large-scale field data sampling. The identification and understanding of fate and transport processes and their interactions of the target EPs and the corresponding selection of appropriate parameter values were also challenging. Such limitations and challenges were evidenced by the elaboration of the representative models in the field. The review also reveales that the model parameter values varied significantly with the EPs (and chemical compositions) and media of concerns. Sensitivity analysis was found to be necessary for modelling of those EPs with limited references in the literature. In comparison with traditional water pollutants, the concentrations of many EPs in water bodies are usually low and even at a trace level, leading to uncertainties or inaccuracy in measured data. This could further challenge model calibration and validation, and especially the determination of parameter values when lacking sufficient data support. How to improve the existing models to address such an issue special for EPs is an urgent task for researchers to ensure the accuracy and reliability of modelling results.

A Synthetic Nervous System Controls a Simulated Cockroach

The purpose of this work is to better understand how animals control locomotion. This knowledge can then be applied to neuromechanical design to produce more capable and adaptable robot locomotion. To test hypotheses about animal motor control, we model animals and their nervous systems with dynamical simulations, which we call synthetic nervous systems (SNS). However, one major challenge is picking parameter values that produce the intended dynamics. This paper presents a design process that solves this problem without the need for global optimization. We test this method by selecting parameter values for SimRoach2, a dynamical model of a cockroach. Each leg joint is actuated by an antagonistic pair of Hill muscles. A distributed SNS was designed based on pathways known to exist in insects, as well as hypothetical pathways that produced insect-like motion. Each joint’s controller was designed to function as a proportional-integral (PI) feedback loop and tuned with numerical optimization. Once tuned, SimRoach2 walks through a simulated environment, with several cockroach-like features. A model with such reliable low-level performance is necessary to investigate more sophisticated locomotion patterns in the future.

Parametric study on kink instabilities of twisted magnetic flux ropes in the solar atmosphere

Aims. Twisted magnetic flux ropes (MFRs) in the solar atmosphere have been researched extensively because of their close connection to many solar eruptive phenomena, such as flares, filaments, and coronal mass ejections (CMEs). In this work, we performed a set of 3D isothermal magnetohydrodynamic (MHD) numerical simulations, which use analytical twisted MFR models and study dynamical processes parametrically inside and around current-carrying twisted loops. We aim to generalize earlier findings by applying finite plasma β conditions. Methods. Inside the MFR, approximate internal equilibrium is obtained by pressure from gas and toroidal magnetic fields to maintain balance with the poloidal magnetic field. We selected parameter values to isolate best either internal or external kink instability before studying complex evolutions with mixed characteristics. We studied kink instabilities and magnetic reconnection in MFRs with low and high twists. Results. The curvature of MFRs is responsible for a tire tube force due to its internal plasma pressure, which tends to expand the MFR. The curvature effect of toroidal field inside the MFR leads to a downward movement toward the photosphere. We obtain an approximate internal equilibrium using the opposing characteristics of these two forces. A typical external kink instability totally dominates the evolution of MFR with infinite twist turns. Because of line-tied conditions and the curvature, the central MFR region loses its external equilibrium and erupts outward. We emphasize the possible role of two different kink instabilities during the MFR evolution: internal and external kink. The external kink is due to the violation of the Kruskal-Shafranov condition, while the internal kink requires a safety factor q = 1 surface inside the MFR. We show that in mixed scenarios, where both instabilities compete, complex evolutions occur owing to reconnections around and within the MFR. The S-shaped structures in current distributions appear naturally without invoking flux emergence. Magnetic reconfigurations common to eruptive MFRs and flare loop systems are found in our simulations.