Combinations Of Parameter Values Research Articles

Generating Artificial Reverberation via Genetic Algorithms for Real-Time Applications.

We introduce a Virtual Studio Technology (VST) 2 audio effect plugin that performs convolution reverb using synthetic Room Impulse Responses (RIRs) generated via a Genetic Algorithm (GA). The parameters of the plugin include some of those defined under the ISO 3382-1 standard (e.g., reverberation time, early decay time, and clarity), which are used to determine the fitness values of potential RIRs so that the user has some control over the shape of the resulting RIRs. In the GA, these RIRs are initially generated via a custom Gaussian noise method, and then evolve via truncation selection, random weighted average crossover, and mutation via Gaussian multiplication in order to produce RIRs that resemble real-world, recorded ones. Binaural Room Impulse Responses (BRIRs) can also be generated by assigning two different RIRs to the left and right stereo channels. With the proposed audio effect, new RIRs that represent virtual rooms, some of which may even be impossible to replicate in the physical world, can be generated and stored. Objective evaluation of the GA shows that contradictory combinations of parameter values will produce RIRs with low fitness. Additionally, through subjective evaluation, it was determined that RIRs generated by the GA were still perceptually distinguishable from similar real-world RIRs, but the perceptual differences were reduced when longer execution times were used for generating the RIRs or the unprocessed audio signals were comprised of only speech.

Using multiple objective calibrations to explore uncertainty in extreme event modeling

Deterministic watershed models are often used to estimate the probable maximum flood (PMF). An approach for investigating the uncertainty in extreme flood modeling is proposed. Using different calibration objectives, several automatic calibrations of the University of British Columbia watershed model (UBCWM) are conducted and the resulting collection of optimal combinations of parameter values are used to simulate the extreme event. An application to the Coquitlam River Watershed above Coquitlam Dam in southwestern British Columbia shows that the variability among the PMF estimates is relatively small in comparison with the potential uncertainties in estimating extreme events, with coefficient of variation values for peak flow, event volume, and time to peak of 4%, 1%, and 1%, respectively. The PMF-based simulations are relatively insensitive to the different measures of calibration performances.

On the Calibration of an Autonomous Model of the Biological Population of the Tundra Lemming

An autonomous model of a phenomenological type was designed for a biological population of lemmings in complex studies of tundra communities. The population dynamics is described in the model by a difference equation, which relates the population sizes observed in 2 consecutive years and depends on three parameters of biological and ecological nature. A combination of parameter values included in the equation determines a class of one-dimensional unimodal mappings of a dynamical system. Bifurcation properties, asymptotics, and stability of trajectories were studied both analytically and numerically in the class. The problem of model identification is the main focus. The method of identification sets was proposed for calibrating the model. The method is based on the approximation and visualization of small-dimensional projections of a multidimensional graph of the error function specified in a space of three environmental and two population parameters. An example model identification was performed using data on a tundra lemming population of the Taimyr Peninsula. Two biological and ecological parameters were shown to allow a stable location distribution in this case.

Impact of Carrier Concentration and Carrier Lifetime on MgZnO/CdSeTe/CdTe Solar Cells

The performance impact of multiple parameters related to the use of MgZnO (MZO) as the emitter for bilayer CdSeTe/CdTe solar cells has been investigated in detail through numerical simulations. Such a comprehensive study is particularly important, because while cell fabrication using MZO has been highly successful in some cases, it has been less so in others, and it has not been clear which combinations of parameter values are most effective. The parameters considered here include the recombination velocity at the emitter/absorber interface, bulk recombination lifetime, and the carrier concentrations of the emitter and absorber. The ranges chosen for the simulation parameters are those most likely to be found experimentally. The primary finding is that independent of the interfacial recombination velocity and bulk recombination lifetime, the MZO carrier density should be $>$ 1017 cm–3 and in any case greater than that of the absorber to reduce interface recombination. At the same time, a shallow dopant the order of 50 meV or less should reduce Shockley-Read-Hall recombination in the bulk and enable $V_{\text{OC}} >$ 1 V.

Importations of COVID-19 into African countries and risk of onward spread

BackgroundThe emergence of a novel coronavirus (SARS-CoV-2) in Wuhan, China, at the end of 2019 has caused widespread transmission around the world. As new epicentres in Europe and America have arisen, of particular concern is the increased number of imported coronavirus disease 2019 (COVID-19) cases in Africa, where the impact of the pandemic could be more severe. We aim to estimate the number of COVID-19 cases imported from 12 major epicentres in Europe and America to each African country, as well as the probability of reaching 10,000 cases in total by the end of March, April, May, and June following viral introduction.MethodsWe used the reported number of cases imported from the 12 major epicentres in Europe and America to Singapore, as well as flight data, to estimate the number of imported cases in each African country. Under the assumption that Singapore has detected all the imported cases, the estimates for Africa were thus conservative. We then propagated the uncertainty in the imported case count estimates to simulate the onward spread of the virus, until 10,000 cases are reached or the end of June, whichever is earlier. Specifically, 1,000 simulations were run separately under four different combinations of parameter values to test the sensitivity of our results.ResultsWe estimated Morocco, Algeria, South Africa, Egypt, Tunisia, and Nigeria as having the largest number of COVID-19 cases imported from the 12 major epicentres. Based on our 1,000 simulation runs, Morocco and Algeria’s estimated probability of reaching 10,000 cases by end of March was close to 100% under all scenarios. In particular, we identified countries with less than 1,000 cases in total reported by end of June whilst the estimated probability of reaching 10,000 cases by then was higher than 50% even under the most optimistic scenario.ConclusionsOur study highlights particular countries that are likely to reach (or have reached) 10,000 cases far earlier than the reported data suggest, calling for the prioritization of resources to mitigate the further spread of the epidemic.

Dietary bioaccumulation of persistent organic pollutants in the common sole Solea solea in the context of global change. Part 2: Sensitivity of juvenile growth and contamination to toxicokinetic parameters uncertainty and environmental conditions variability in estuaries

The amount of potentially toxic chemicals in a fish depends on various environmental factors, such as temperature and feeding ecology, which can be affected by Global Changes (GC). The main objective of the present work was to study the relative influence of temperature, food quality and food availability on the growth and contamination of juveniles of common sole (Solea solea), a marine flatfish species known to be a relevant indicator of the nursery quality. It focuses on two Persistent Organic Pollutants (CB153 and L-PFOS) of legacy and emerging concern, respectively. To achieve this, we used a toxicokinetic (TK) model in which toxicant flows are mechanistically predicted using a bioenergetic model based on the Dynamic Energy Budget (DEB) theory. This modelling framework was applied to juvenile sole from the Gironde estuary (SW France) and allows accounting for the influence of environmental conditions on fish biological processes involved in toxicant fluxes. To compare their respective influence on model predictions of age, length, and contamination at puberty, we included in a global sensitivity analysis: (1) environmental variability gathered from literature for this particular estuary and (2) TK parameters (i.e. assimilation efficiency AE and elimination rate k˙e) variability and uncertainty gathered from literature about each contaminant but for different fish species and experimental conditions. Then, model predictions were confronted to fish contamination measurements from the Gironde Estuary with different combinations of TK parameter values from literature. Results highlighted a key role of diet composition on fish contamination and growth while water temperature only affected growth. It stressed the need to focus on GC impact on benthic communities and their consequences on juvenile fish diet for future work on GC scenarios. Furthermore, for both chemical, the range of variability of TK parameters from experiments led to underestimated fish contaminations. The best model fits were obtained using TK parameter values from model applications: from Mounier et al. (n.d.) for CB153 (Solea solea, experiment, AE=0.8 and k˙e=0 d−1) and from de Vos et al. (2008) for PFOS (food chain of the Western Scheldt estuary, The Netherlands, AE=0.8 and k˙e=0.8 10−2.d−1).

Passive Suppression of Panel Flutter Using a Nonlinear Energy Sink

A nonlinear energy sink (NES) is used to suppress panel flutter. A nonlinear aeroelastic model for a two-dimensional flat panel with an NES in supersonic flow is established using the Galerkin method. First-order piston aerodynamic theory is adopted to build the aerodynamic load. The effects of NES parameters on flutter boundaries of the panel are investigated using Lyapunov’s indirect method. The mechanism of the NES suppression of panel flutter is studied through energy analysis. Effects of NES parameters on aeroelastic responses of the panel are obtained, and a design technique is adopted to find a suitable combination of parameter values of the NES that suppresses the panel flutter effectively. Results show that the NES can increase or reduce the onset dynamic pressure of the panel flutter and it can reduce the aeroelastic response amplitude effectively within a certain range of dynamic pressure behind the onset dynamic pressure. The installation position of the NES depends on the direction of the airflow. The robust characteristics should be considered to find the suitable combination of parameter values of the NES.

Estimating Population Growth Rates and Instantaneous Population from Periodized Settlement Data

Settlement surveys that yield counts of the number of houses or rooms dated to a chronological period can be used to indicate population and to estimate rates of population growth or decline. Population estimates and population growth rates are, of course, interpretively critical for describing and explaining many aspects of social dynamics. For this sort of survey data, population growth rates can be estimated by applying an ordinary, compound interest formula to room counts, standardized by period length, for successive time periods. This article offers a more nuanced approach that simulates a continuous process of room construction and abandonment, yielding a total number of rooms occupied during a period. The modeled growth rate is the value at which the model total most closely matches the value observed in the settlement data, given the set of model parameters. The model results are sensitive to both structure use-life and the founding room count. This sensitivity does not reflect a defect of the model. Instead it points to a major problem of equifinality—a great variety of different processes could account for the observed data. Exploration of three cases shows that reviewing the model results for a range of combinations of reasonable parameter values can provide demographic insights that are more informative—and can be markedly different—than those provided by the standard formula.

Interactive Programming for Parametric CAD

AbstractParametric computer‐aided design (CAD) enables description of a family of objects, wherein each valid combination of parameter values results in a different final form. Although Graphical User Interface (GUI)‐based CAD tools are significantly more popular, GUI operations do not carry a semantic description, and are therefore brittle with respect to changes in parameter values. Programmatic interfaces, on the other hand, are more robust due to an exact specification of how the operations are applied. However, programming is unintuitive and has a steep learning curve. In this work, we link the interactivity of GUI with the robustness of programming. Inspired by programme synthesis by example, our technique synthesizes code representative of selections made by users in a GUI interface. Through experiments, we demonstrate that our technique can synthesize relevant and robust sub‐programmes in a reasonable amount of time. A user study reveals that our interface offers significant improvements over a programming‐only interface.

Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem

The multidimensional knapsack problem (MKP) is a generalization of the classical knapsack problem, a problem for allocating a resource by selecting a subset of objects that seek for the highest profit while satisfying the capacity of knapsack constraint. The MKP have many practical applications in different areas and classified as a NP-hard problem. An exact method like branch and bound and dynamic programming can solve the problem, but its time computation increases exponentially with the size of the problem. Whereas some approximation method has been developed to produce a near-optimal solution within reasonable computational times. In this paper a pigeon inspired optimization (PIO) is proposed for solving MKP. PIO is one of the metaheuristic algorithms that is classified in population-based swarm intelligent that is developed based on the behavior of the pigeon to find its home although it had gone far away from it home. In this paper, PIO implementation to solve MKP is applied to two different characteristic cases in total 10 cases. The result of the implementation of the two-best combination of parameter values for 10 cases compared to particle swarm optimization, intelligent water drop algorithm and the genetic algorithm gives satisfactory results.

Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials

Close remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and performance. In the present study, sigmoid models were fitted to multi-temporal CC and CH data obtained using RGB imagery captured with a drone for a broad set of soybean genotypes. The Gompertz and Beta functions were used to fit CC and CH data, respectively. Overall, 90.4% fits for CC and 99.4% fits for CH reached an adjusted R2 > 0.70, demonstrating good performance of the models chosen. Using these growth curves, parameters including maximum absolute growth rate, early vigor, maximum height, and senescence were calculated for a collection of soybean genotypes. This information was also used to estimate seed yield and maturity (R8 stage) (adjusted R2 = 0.51 and 0.82). Combinations of parameter values were tested to identify genotypes with interesting traits. An integrative approach of fitting a curve to a multi-temporal dataset resulted in biologically interpretable parameters that were informative for relevant traits.

Uncertainty and sensitivity analysis of the chemistry of cesium sorption in deep geological repositories

Sorption is the most important process responsible for the retention of radionuclides or heavy metals in repositories. A major task in understanding sorption is to account for uncertainties that affect the long-term repository performance, and eventually, study their effect on the migration of radionuclides. This paper presents a novel comprehensive uncertainty and sensitivity analysis study of a detailed sorption chemistry model used in Cesium reactive transport modeling, based on the Morris and Sobol method. The analysis addresses the effects on the transport of Cesium through clay, exemplified for Cesium migration in the vicinity of a deep geological repository for nuclear waste, due to uncertainties in 1) site-specific detailed equilibrium sorption reactions constants and 2) clay pore water cations’ concentration, i.e. the pore water composition. The results show that the equilibrium sorption reaction constants on Type2 and FES sites in addition to the concentrations of the respective cations involved in these surface reactions are the most important uncertain parameters affecting the transport of Cesium. Furthermore, a classification tree is constructed to identify the combination of parameters values leading to the maximum Cesium concentrations at a specified location. Finally, a new Cesium isotherm and Kduncertainty range are constructed numerically based on our deterministic complex sorption chemistry calculations using the MCOTAC reactive transport code. These results can be used in large-scale repository performance assessment to inform the estimates for Cesium transport parameters, with values solidly based on detailed reactive transport modeling. The presented approach can be used for parameter uncertainty analysis of any complex reactive transport problem and can be applied to analyze any radionuclide of interest.

On the exact distribution of generalized Hollander-Proschan type statistics

In this article we derive the exact null distribution of a class of Hollander-Proschan type statistics for testing exponentiality against NWBUE alternatives. The exact null distribution of a previously established statistic is obtained as a special case. Critical values are tabulated for different combinations of parameter values. Performance of the tests for small sample sizes has also been studied in various alternative scenarios. We analyse two wellknown data sets in the light of our result.

Agent-based modelling reveals strategies to reduce the fitness and metastatic potential of circulating tumour cell clusters.

Metastasis—the ability of cancer cells to disperse throughout the body and establish new tumours at distant locations—is responsible for most cancer‐related deaths. Although both single and clusters of circulating tumour cells (CTCs) have been isolated from cancer patients, CTC clusters are generally associated with higher metastatic potential and worse prognosis. From an evolutionary perspective, being part of a cluster can provide cells with several benefits both in terms of survival (e.g. protection) and reproduction (group dispersal). Thus, strategies aimed at inducing cluster dissociation could decrease the metastatic potential of CTCs. However, finding agents or conditions that induce the dissociation of CTC clusters is hampered by the fact that their detection, isolation and propagation remain challenging. Here, we used a mechanistic agent‐based model to (a) investigate the response of CTC clusters of various sizes and densities to different challenges—in terms of cell survival and cluster stability, and (b) make predictions as to the combination of factors and parameter values that could decrease the fitness and metastatic potential of CTC clusters. Our model shows that the resilience and stability of CTC clusters are dependent on both their size and density. Also, CTC clusters of distinct sizes and densities respond differently to changes in resource availability, with high‐density clusters being least affected. In terms of responses to microenvironmental threats (such as drugs), increasing their intensity is, generally, least effective on high‐density clusters. Lastly, we found that combining various levels of resource availability and threat intensity can be more effective at decreasing the survival of CTC clusters than each factor alone. We suggest that the complex effects that cluster density and size showed on both the resilience and stability of the CTC clusters are likely to have significant consequences for their metastatic potential and responses to therapies.

JULES-GL7: the Global Land configuration of the Joint UK Land Environment Simulator version 7.0 and 7.2

Abstract. We present the latest global land configuration of the Joint UK Land Environment Simulator (JULES) model as used in the latest international Coupled Model Intercomparison Project (CMIP6). The configuration is defined by the combination of switches, parameter values and ancillary data, which we provide alongside a set of historical forcing data that defines the experimental setup. The configurations provided are JULES-GL7.0, the base setup used in CMIP6 and JULES-GL7.2, a subversion that includes improvements to the representation of canopy radiation and interception. These configurations are recommended for all JULES applications focused on the exchange and state of heat, water and momentum at the land surface. In addition, we provide a standardised modelling system that runs on the Natural Environment Research Council (NERC) JASMIN cluster, accessible to all JULES users. This is provided so that users can test and evaluate their own science against the standard configuration to promote community engagement in the development of land surface modelling capability through JULES. It is intended that JULES configurations should be independent of the underlying code base, and thus they will be available in the latest release of the JULES code. This means that different code releases will produce scientifically comparable results for a given configuration version. Versioning is therefore determined by the configuration as opposed to the underlying code base.

Multiobjective Direct Policy Search Using Physically Based Operating Rules in Multireservoir Systems

This study explores the ways to introduce physical interpretability into the process of optimizing operating rules for multireservoir systems with multiple objectives. Prior studies applied the concept of direct policy search (DPS), in which the release policy is expressed as a set of parameterized functions (e.g., neural networks) that are optimized by simulating the performance of different parameter value combinations over a testing period. The problem with this approach is that the operators generally avoid adopting such artificial black-box functions for the direct real-time control of their systems, preferring simpler tools with a clear connection to the system physics. This study addresses this mismatch by replacing the black-box functions in DPS with physically based parameterized operating rules, for example by directly using target levels in dams as decision variables. This leads to results that are physically interpretable and may be more acceptable to operators. The methodology proposed in this work is applied to a network of five reservoirs and four power plants in the Nechi catchment in Colombia, with four interests involved: average energy generation, firm energy generation, flood hazard, and flow regime alteration. The release policy is expressed depending on only 12 parameters, which significantly reduces the computational complexity compared to existing approaches of multiobjective DPS. The resulting four-dimensional Pareto-approximate set offers a variety of operational strategies from which operators may choose one that corresponds best to their preferences. For demonstration purposes, one particular optimized policy is selected and its parameter values are analyzed to illustrate how the physically based operating rules can be directly interpreted by the operators.

Investigation of Light-Induced Surface Roughness in Projection Micro-Stereolithography Additive Manufacturing (PµSLA)

Abstract Parts manufactured by PµSLA have intrinsic surface roughness on the sidewalls, which is a result of two main factors, the staircase effect and diffraction of the projected light. Each micromirror in the digital light projector acts like a micro aperture. Therefore, the projected light has a Gaussian intensity profile that is reflected in the cured geometry. Proposed here is a physics-based analytical model as a function of the exposure time and the layer thickness to predict the surface roughness of the manufactured parts. The predicted results are then assessed experimentally with parts manufactured with a combination of different process parameters values. The different lay directions of surface roughness produced by this process are identified. The effect of the process parameters on surface profile in the different lay directions is investigated experimentally.

О КАЛИБРОВКЕ АВТОНОМНОЙ МОДЕЛИ ТУНДРОВОЙ БИОЛОГИЧЕСКОЙ ПОПУЛЯЦИИ ЛЕММИНГОВ

The paper presents an autonomous model of the biological population of lemmings of a phenomenological type, designed in complex studies of tundra communities. In the model, the dynamics of a population is described through a difference equation relating the population in two neighboring years that depends on three parameters of the biological and ecological genesis. The combination of parameter values included in the equation under consideration determines a class of one-dimensional unimodal mappings of a dynamical system in which the bifurcation properties, asymptotics, and stability of trajectories were analytically and numerically studied. In this paper the main focus is made on model identification criterion. The method of identification sets is proposed to be used for calibration of the model. The identification sets method is based on the approximation and visualization of small-dimensional projections of a multidimensional graph of the error function given in the space of three environmental and two population parameters. This paper describes a case study of model identification using data on the tundra lemming population on the Taimyr Peninsula. It is shown that in this case two biological and ecological parameters allow for stable location distribution.

Does Calibration Affect the Complexity of Agent-Based Models? A Multifractal Grid Analysis

We examine the complexity of financial returns generated by popular agent-based models through studying multifractal properties of such time series. Specifically, we are interested in the sensitivity of the models to their parameter settings and whether some patterns emerge in the connection between complexity and a specific type of parameter. We find that (i) herding behavior mostly boosts the model complexity as measured by multifractality, (ii) various in-built stabilizing factors increase model complexity, (iii) the role of the intensity of choice as well as the chartists' representation have rather model-specific effects, and (iv) the increasing number of agents has no statistically significant effect on the model complexity. Finally, the core feature driving the model complexity seems to be the implementation of a switching mechanism governing agents' interactions. The heterogeneous set of nine analyzed models thus offers some universal concepts that hold across their range. Our results also indicate that complex dynamics are observed not only for the benchmark parameter settings but also for other combinations of parameter values for most models. This opens new avenues for future research and specifically motivates examining the models in more detail by focusing on other dynamic properties in addition to the herein presented multifractality.

Design and optimization of energy-efficient milling processes using a geometric physically-based process simulation system

The increasing complexity of production processes, shortening product lifecycles and rising energy costs lead to an emerging need for an efficiency optimization approach including process simulations in factory adaption planning as well as in process planning. This paper presents a method for the design of milling processes by analyzing process times, energy consumption and process stability based on process simulations. By evaluating simplified virtually conducted processes, reasonable parameter value combinations can be selected efficiently and provide flexibility by generating multiple solutions. Using detailed process simulations and conducting iterative adjustments to ensure a sufficient workpiece quality, the selected parameter sets are transferred to actual NC processes.