Set Of Optimal Parameter Values Research Articles

Time-Delay Dynamic Model and Cost-Effectiveness Analysis of Major Emergent Infectious Diseases with Transportation-Related Infection and Entry-Exit Screening

We analyze a time-delayed SIQR model that considers transportation-related infection and entry–exit screening. This model aims to determine the measures for preventing and controlling major emergent infectious diseases and the associated costs. We calculate the basic reproduction number (R0) and prove that the disease-free equilibrium is locally and globally asymptotically stable. We collect COVID-19 infection data from two regions in the United States in 2020 for data fitting, obtain a set of optimal parameter values, and find that transportation-related infection rates increase the basic reproduction number, enhancing the impact on disease spread. Entry–exit screening effectively suppresses the spread of disease by reducing the basic reproduction number. Furthermore, we investigate the influence of the incubation period on disease and find that a shorter incubation period results in a shorter duration but a larger scale of infection and that the peaks are reduced. We conduct a sensitivity analysis of the R0 and propose three measures to prevent the spread of new infectious diseases based on the most sensitive parameters: wearing masks, implementing urban closures, and administering medication to sick but not yet hospitalized patients promptly. In the case of COVID-19, optimal control effectively controls the development and deterioration of the disease. Finally, several control measures are compared through cost-effectiveness analysis, and the results show that wearing masks is the most cost-effective measure.

SCO2 power cycle/reverse osmosis distillation system for water-electricity cogeneration in nuclear powered ships and submarines

As global concerns about climate change and environmental impacts continue to grow, there is a strong demand for cleaner, greener, and more efficient propulsion solutions in the maritime industry. By leveraging nuclear energy, which offers low carbon emissions and high efficiency, naval vessels can significantly reduce their environmental footprints and contribute to global efforts to combat climate change. Additionally, the ability to produce electricity and freshwater simultaneously addresses the critical need for self-sufficiency during long missions, particularly in remote or resource-constrained areas where access to freshwater may be limited. This study explores a novel approach to address water and energy supply challenges in naval applications through a nuclear-driven supercritical carbon dioxide (sCO2) power cycle–Reverse Osmosis (RO) cogeneration system. The aim is to assess the feasibility and benefits of this system for meeting water and electricity demands in maritime operations and enhancing the sustainability, efficiency, and self-sufficiency of naval vessels with nuclear energy and advanced technologies. A parametric study has been performed to understand the effects of important parameters such as the maximum and minimum cycle temperature and pressure, seawater inlet conditions, pressure vessel design, and membrane properties on the system performance. Moreover, an optimization study has been performed to determine the best possible combination of these parameters to achieve the highest system efficiency. For the determined optimal set of parameter values, it is possible to have an sCO2 power cycle with a cycle efficiency of 40.5 %, an electrical power output of 121.6 MW and an RO system with an efficiency of 29.4 %, a recovery ratio of 29.3 %, a specific fuel consumption (SEC) of 2.29 kW·h/m3, and a freshwater production rate of 1531.6 m3/d by utilizing 145.9 kW electrical power. The positive outcomes of this study indicate that the system has the potential to be a viable and effective solution for addressing water and energy supply challenges in naval operations.

An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3

Abstract. Physical processes within geoscientific models are sometimes described by simplified schemes known as parameterisations. The values of the parameters within these schemes can be poorly constrained by theory or observation. Uncertainty in the parameter values translates into uncertainty in the outputs of the models. Proper quantification of the uncertainty in model predictions therefore requires a systematic approach for sampling parameter space. In this study, we develop a simple and efficient approach to identify regions of multi-dimensional parameter space that are consistent with observations. Using the Parallel Ice Sheet Model to simulate the present-day state of the Antarctic Ice Sheet, we find that co-dependencies between parameters preclude any simple identification of a single optimal set of parameter values. Approaches such as large ensemble modelling are therefore required in order to generate model predictions that incorporate proper quantification of the uncertainty arising from the parameterisation of physical processes.

Energy distribution modelling in the weld zone for various electron beam current values in COMSOL Multiphysics

The presented work is devoted to the study of the effect of various values of the electron beam current on the distribution of the energy cross section of the weld when conducting the technological process of electron-beam welding of thin-walled structures. The work is used by the simulation system of COMSOL Multiphysics, in which mathematical models of energy distribution proposed by the authors are implemented with electron beam welding. Imitation modelling was carried out for two materials widely used in the aerospace industry: titan alloy BT14 and AMG aluminum alloy. The values of the parameters for which the simulation was carried out were selected as a result of the predetermined process of optimizing the technological mode of electron beam welding for the construction under study. In the process of the research, a change in the value of the electron beam current is carried out both in less and to the large side. The article presents visualization for the three values of the parameter under study for each material, which makes it possible to judge the effectiveness of the previously formed set of optimal parameter values.

Optimized cellular automaton for stand delineation

Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments. This is the case for instance in the Lidar-based and multi-source forest inventories of Finland where the interpretation units are 16 m × 16 m grid cells. Using these cells as simulation units in forest planning would lead to very large planning problems. This difficulty could be alleviated by aggregating the grid cells into larger homogeneous segments before planning calculations. This study developed a cellular automaton (CA) for aggregating grid cells into larger calculation units, which in this study were called stands. The criteria used in stand delineation were the shape and size of the stands, and homogeneity of stand attributes within the stand. The stand attributes were: main site type (upland or peatland forest), site fertility, mean tree diameter, mean tree height and stand basal area. In the CA, each cell was joined to one of its adjacent stands for several iterations, until the cells formed a compact layout of homogeneous stands. The CA had several parameters. Due to high number possible parameter combinations, particle swarm optimization was used to find the optimal set of parameter values. Parameter optimization aimed at minimizing within-stand variation and maximizing between-stand variation in stand attributes. When the CA was optimized without any restrictions for its parameters, the resulting stand delineation consisted of small and irregular stands. A clean layout of larger and compact stands was obtained when the CA parameters were optimized with constrained parameter values and so that the layout was penalized as a function of the number of small stands (< 0.1 ha). However, there was within-stand variation in fertility class due to small-scale variation in the data. The stands delineated by the CA explained 66–87% of variation in stand basal area, mean tree height and mean diameter, and 41–92% of variation in the fertility class of the site. It was concluded that the CA developed in this study is a flexible new tool, which could be immediately used in forest planning.

Optimising rigid motion compensation for small animal brain PET imaging

Motion compensation (MC) in PET brain imaging of awake small animals is attracting increased attention in preclinical studies since it avoids the confounding effects of anaesthesia and enables behavioural tests during the scan. A popular MC technique is to use multiple external cameras to track the motion of the animal’s head, which is assumed to be represented by the motion of a marker attached to its forehead. In this study we have explored several methods to improve the experimental setup and the reconstruction procedures of this method: optimising the camera-marker separation; improving the temporal synchronisation between the motion tracker measurements and the list-mode stream; post-acquisition smoothing and interpolation of the motion data; and list-mode reconstruction with appropriately selected subsets. These techniques have been tested and verified on measurements of a moving resolution phantom and brain scans of an awake rat. The proposed techniques improved the reconstructed spatial resolution of the phantom by 27% and of the rat brain by 14%. We suggest a set of optimal parameter values to use for awake animal PET studies and discuss the relative significance of each parameter choice.

Using Foursquare place data for estimating building block use

Information about the land use of built-up areas is required for the comprehensive planning and management of cities. However, due to the high cost of the land use surveys, land use data is out-dated or not available for many cities. Therefore, we propose the reuse of up-to-date and low-cost place data from social media applications for land use mapping purposes. As main case study, we used Foursquare place data for estimating nonresidential building block use in the city of Amsterdam. Based on the Foursquare place categories, we estimated the use of 9827 building blocks, and we compared the classification results with a reference building block use dataset. Our evaluation metric is the kappa coefficient, which determines if the classification results are significantly better than a random guess result. Using the optimal set of parameter values, we achieved the highest kappa coefficient values for the land use categories “ hotels, restaurants and cafes” (0.76) and “ retail” (0.65). The lowest kappa coefficients were found for the land use categories “ industries” and “ storage and unclear”. We have also applied the methodology in another case study area, the city of Varese in Italy, where we had similar accuracy results. We therefore conclude that Foursquare place data can be trusted only for the estimation of particular land use categories.

Performance analysis of non-traditional algorithmic parameters in machining operation

Non-traditional algorithms are the realistic models that are used for solving many complex engineering optimization problems. Machining is one of the most important and widely used manufacturing processes which rely on optimization. Optimization problems may be either constrained or unconstrained in nature. The complication factor with constrained optimization is that there is a possible existence of one or more complex constraints. These constraints must be incorporated into the problem otherwise solution is unacceptable from a practical standpoint. Non-traditional algorithms are the best way for solving the constrained optimization problems. The advantages of non-traditional techniques are its tolerant of imprecision, uncertainty, and meta-heuristics. In this work, three different non-traditional algorithms such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) are used in multi-pass turning machining operation to identify the set of optimal parameter values for minimizing unit production cost. The mathematical model is taken from the literature (Chen and Tsai, Int J Prod Res 34:2803–2825, 1996 and it is used for the evaluation of algorithmic parameters. The main aim of this work is to analyze the effect of algorithmic parameters of non-traditional technique in turning operation. The analyses performed are (i) sensitivity analysis in GA, PSO, and SA, (ii) analysis to find the best reproduction method in GA (iii) analysis to analyze the performance of GA, PSO, and SA by varying algorithmic parameters, and (iv) analysis to compare the results of the algorithms with the literature, and the best one is proposed.

Modelling the evolution of the Antarctic ice sheet since the last interglacial

Abstract. We present the effects of changing two sliding parameters, a deformational velocity parameter and two bedrock deflection parameters on the evolution of the Antarctic ice sheet over the period from the last interglacial until the present. These sensitivity experiments have been conducted by running the dynamic ice model ANICE forward in time. The temporal climatological forcing is established by interpolating between two temporal climate states created with a regional climate model. The interpolation is done in such a way that both temperature and surface mass balance follow the European Project for Ice Coring in Antarctica (EPICA) Dome C ice-core proxy record for temperature. We have determined an optimal set of parameter values, for which a realistic grounding-line retreat history and present-day ice sheet can be simulated; the simulation with this set of parameter values is defined as the reference simulation. An increase of sliding with respect to this reference simulation leads to a decrease of the Antarctic ice volume due to enhanced ice velocities on mainly the West Antarctic ice sheet. The effect of changing the deformational velocity parameter mainly yields a change in east Antarctic ice volume. Furthermore, we have found a minimum in the Antarctic ice volume during the mid-Holocene, in accordance with observations. This is a robust feature in our model results, where the strength and the timing of this minimum are both dependent on the investigated parameters. More sliding and a slower responding bedrock lead to a stronger minimum which emerges at an earlier time. From the model results, we conclude that the Antarctic ice sheet has contributed 10.7 ± 1.3 m of eustatic sea level to the global ocean from the last glacial maximum (about 16 ka for the Antarctic ice sheet) until the present.

Fatty acid methyl ester production from acid oil using silica sulfuric acid: Process optimization and reaction kinetics

AbstractConversion of high free fatty acids (FFA) containing acid oil (AO) to fatty acid methyl esters (FAME) using silica sulfuric acid (SSA) as a solid acid catalyst was investigated. Process parameters such as reaction temperature, reaction time, catalyst loading, and methanol to oil molar ratio were optimized using the Taguchi orthogonal array method. Maximum FFA conversion (97.16 %) was achieved under the optimal set of parameter values viz. 70°C, 4 mass % catalyst loading, and 1: 15 oil to methanol molar ratio after 90 min. SSA was reused three times successfully without a significant loss in activity. Biodiesel produced from AO met the international biodiesel standards. Determination of kinetic parameters proved that the experimental results fit the pseudo first order kinetic law.

Autoimmune control of lesion growth in CNS with minimal damage

Lesions in central nervous system (CNS) and their growth leads to debilitating diseases like Multiple Sclerosis (MS), Alzheimer’s etc. We developed a model earlier [1, 2] which shows how the lesion growth can be arrested through a beneficial auto-immune mechanism. We compared some of the dynamical patterns in the model with different facets of MS. The success of the approach depends on a set of control parameters and their phase space was shown to have a smooth manifold separating the uncontrolled lesion growth region from the controlled. Here we show that an optimal set of parameter values exist in the model which minimizes system damage while, at once, achieving control of lesion growth.

화음탐색법을 이용한 교통망 링크 통행비용함수 정산기법 개발

본 연구에서는 최근 개발된 화음 탐색법을 이용해 교통망 통행배정 모형의 통행비용 함수의 하나인 BPR 함수의 계수들을 추정하였다. 기존 연구에서는 교통량과 통행시간 자료를 실측해 이를 통계적으로 분석해 계수를 추정하는 방법과 관측교통량과 통행배정 교통량을 일치시키는 계수값을 찾는 것을 목표로 통행배정 모형과 최적화 기법을 결합시킨 방법을 이용하여왔다. 이중 대형 교통망의 계수 정산에 자주 이용되어온 최적화 기법은 관측 통행패턴을 최대한 근접하게 재현하는 계수를 추정할 수 있다는 장점이 있으나 그 수학적 성질과 추정 계수값에 대한 수학적 검토가 충분히 이루어지지 못했다. 본 연구에서는 이러한 문제 인식아래 최근 개발된 전역 탐색 기법인 화음탐색법 기반의 교통망 비용함수 정산 방법을 개발하였다. 화음탐색법은 2000년대 초반 개발된 이후 다양한 분야에서 기존에 사용되던 전역탐색기법들에 비해 우수한 성질을 입증하여 왔으나 교통분야에는 그 적용 예가 거의 없었다. 본 연구는 화음탐색법의 개념을 설명하고 이를 이용해 개발된 정산 알고리즘을 기존 연구에서 사용된 점진증가법 및 황금율법과 성능 비교하였다. 화음탐색법 기반 정산기법은 기존 기법들에 비해 관측 통행패턴을 보다 근접하게 재현할 수 있는 비용함수 계수값들을 찾을 수 있는 것으로 나타났다. 또, 관측 교통량 기반 계수추정법은 BPR식의 <TEX>${\beta}$</TEX>값 추정에는 적합하지만 초기속도나 <TEX>${\alpha}$</TEX>값 정산을 위해서는 통행 속도나 시간과 같은 추가 자료가 필요한 것으로 판단된다. Some previous studies adopted a method statistically based on the observed traffic volumes and travel times to estimate the parameters. Others tried to find an optimal set of parameters to minimize the gap between the observed and estimated traffic volumes using, for instance, a combined optimization model with a traffic assignment model. The latter is frequently used in a large-scale network that has a capability to find a set of optimal parameter values, but its appropriateness has never been demonstrated. Thus, we developed a methodology to estimate a set of parameter values of BPR(Bureau of Public Road) function using Harmony Search (HS) method. HS was developed in early 2000, and is a global search method proven to be superior to other global search methods (e.g. Genetic Algorithm or Tabu search). However, it has rarely been adopted in transportation research arena yet. The HS based transportation network calibration algorithm developed in this study is tested using a grid network, and its outcomes are compared to those from incremental method (Incre) and Golden Section (GS) method. It is found that the HS algorithm outperforms Incre and GS for copying the given observed link traffic counts, and it is also pointed out that the popular optimal network calibration techniques based on an objective function of traffic volume replication are lacking the capability to find appropriate free flow travel speed and <TEX>${\alpha}$</TEX> value.

Parameter sensitivity analysis and prediction error in field-scale NO3-N modelling

The hydrologic and nitrate (NO3-N) leaching dynamics of a maize field were respectively modelled with DRAINMOD and DRAINMOD-N. Experimental data of a 3-year period were available for model calibration and evaluation. Data from the first two years were used for model calibration whilst data from the remaining year were used for an initial evaluation. Data collected before the 3-year experiment, during a 23-year period, were used for further “backward” (in time) evaluation. The hydrologic module was calibrated through a trial and error approach. The NO3-N leaching module was calibrated and evaluated with a Monte Carlo simulations based approach. Nine parameters describing the leaching process were studied. In total, 10,000 parameter sets were tried out. The analysis revealed an acceptable prediction of the observed drainage and NO3-N leaching time series throughout both the 3-year experimental period as well as the prior 23-year “backward” evaluation period. Nevertheless, the analysis revealed that no single set of optimal parameter values could be identified. It was found that the model performance is only sensitive to the rate of denitrification. Narrow NO3-N prediction intervals were obtained, even in the longer 23-year (“backward”) evaluation period. Apparently, the behavioural DRAINMOD-N simulations were able to acceptably reproduce the limited to moderate NO3-N leaching fluctuations that occur in the modelled system.

Cutoffs and k-mers: implications from a transcriptome study in allopolyploid plants

BackgroundTranscriptome analysis is increasingly being used to study the evolutionary origins and ecology of non-model plants. One issue for both transcriptome assembly and differential gene expression analyses is the common occurrence in plants of hybridisation and whole genome duplication (WGD) and hybridization resulting in allopolyploidy. The divergence of duplicated genes following WGD creates near identical homeologues that can be problematic for de novo assembly and also reference based assembly protocols that use short reads (35 - 100 bp).ResultsHere we report a successful strategy for the assembly of two transcriptomes made using 75 bp Illumina reads from Pachycladon fastigiatum and Pachycladon cheesemanii. Both are allopolyploid plant species (2n = 20) that originated in the New Zealand Alps about 0.8 million years ago. In a systematic analysis of 19 different coverage cutoffs and 20 different k-mer sizes we showed that i) none of the genes could be assembled across all of the parameter space ii) assembly of each gene required an optimal set of parameter values and iii) these parameter values could be explained in part by different gene expression levels and different degrees of similarity between genes.ConclusionsTo obtain optimal transcriptome assemblies for allopolyploid plants, k-mer size and k-mer coverage need to be considered simultaneously across a broad parameter space. This is important for assembling a maximum number of full length ESTs and for avoiding chimeric assemblies of homeologous and paralogous gene copies.

Performance analysis and optimization of best-effort service in IEEE 802.16 networks

The IEEE 802.16-based WiMAX technology has great potential for the fourth-generation mobile networks. Some of its service classes use the contention-based broadcast polling mechanism to request resources. In this paper, we investigate the performance experienced by these services when the network is unsaturated. In particular, we model each subscriber station as an M/G/1 queue where the service time is determined by the parameters of the network configuration and the binary exponential backoff contention resolution algorithm. We develop a fixed-point analysis to derive analytical expressions for network throughput and packet access delay. The accuracy of the analytical model is validated by comparing it with simulation over a wide range of operating conditions. The implications of various different parameter configurations on the performance are investigated using the analytical model. Moreover, we show that the model can be degenerated to the saturated condition. The utility of both the unsaturated and saturated models is further demonstrated by finding the optimal set of parameter values that maximize the network throughput. Copyright © 2011 John Wiley & Sons, Ltd.

Resonancelike phenomena in the mobility of a chain of nonlinear coupled oscillators in a two-dimensional periodic potential

We study the Langevin dynamics of a two-dimensional discrete oscillator chain absorbed on a periodic substrate and subjected to an external localized point force. Going beyond the commonly used harmonic bead-spring model, we consider a nonlinear Morse interaction between the next-nearest neighbors. We focus interest on the activation of directed motion instigated by thermal fluctuations and the localized point force. In this context the local transition states are identified and the corresponding activation energies are calculated. It is found that the transport of the chain in point force direction is determined by stepwise escapes of a single unit or segments of the chain due to the existence of multiple locally stable attractors. The nonvanishing net current of the chain is quantitatively assessed by the value of the mobility of the center of mass. It turns out that the latter as a function of the ratio of the competing length scales of the system, that is the period of the substrate potential and the equilibrium distance between two chain units, shows a resonance behavior. More precisely there exists a set of optimal parameter values maximizing the mobility. Interestingly, the phenomenon of negative resistance is found, i.e., the mobility possesses a minimum at a finite value of the strength of the thermal fluctuations for a given overcritical external driving force.

G-EMME/2: Automatic Calibration Tool of the EMME/2 Transit Assignment Using Genetic Algorithms

This research presents an automatic procedure for calibrating transit-assignment model parameters. The calibration process targets the optimal set of parameter values by ensuring that the assignment output volumes match ridership volumes obtained from on-board surveys. Due to the combinatorial nature of the problem of interest, the proposed calibration process is automated using genetic algorithm techniques to find the best values for parameters through minimizing a “misfit” function. This study presents the new G-EMME/2 tool, which is an automatic calibration tool designed to find the optimal set of values for the transit-assignment model parameters implemented in the EMME/2 transportation planning software. The G-EMME/2 tool was applied to the Toronto transit network, and the five EMME/2 aggregate transit-assignment model parameters were estimated. The results are very encouraging. This research is an attempt to help automate the tedious process of calibrating transit assignment models.

Optimization of a quasi-zero-stiffness isolator

The frequency range over which a mount can isolate a mass from a vibrating base (or vice versa) is often limited by the mount stiffness required to support the weight of the mass. This compromise can be made more favourable by employing non-linear mounts with a softening spring characteristic such that small excursions about the static equilibrium position result in small dynamic spring forces and a correspondingly low natural frequency. This paper concerns the force-displacement characteristic of a so-called quasi-zero-stiffness (QZS) mechanism which is characterised by an appreciable static stiffness but very small (theoretically zero) dynamic stiffness. The mechanism studied comprises a vertical spring acting in parallel with two further springs which, when inclined at an appropriate angle to the vertical, produce a cancelling negative stiffness effect. Analysis of the system shows that a QZS characteristic can be obtained if the system's parameters (angle of inclination and ratio of spring stiffnesses) are opportunely chosen. By introducing the additional criterion that the displacement of the system be largest without exceeding a desired (low) value of stiffness an optimal set of parameter values is derived. Under sufficiently large displacements the stiffness of the QZS mechanism can eventually exceed that of the simple mass-spring system and criteria for this detrimental scenario to arise are presented.

Calibration of conceptual rainfall–runoff models using global optimisation methods with hydrologic process-based parameter constraints

Summary The major difficulty associated with the use of conceptual rainfall–runoff (CRR) models in hydrology is their calibration since most of these models involve a large number of parameters. CRR model calibration is a global optimisation problem since its main objective is to find a set of optimal model parameter values that provides a best fit between observed and estimated flow hydrographs. However, even when using the superior search capabilities of modern global optimisation methods (GOM), the search for a set of optimal parameter values within an inflated multi-dimensional search space can result in an inefficient search and may lead to inaccurate parameter estimates. In addition, in most CRR model calibration studies to-date, no explicit constraint in the search procedure was used that could ensure the physical consistency of the estimated parameters. Improvements in parameter estimates could be achieved if the search space could be reduced through the incorporation of constraints that describe the logical interactions between the rainfall and runoff processes. A methodology is proposed herein for formulating constraints to improve the probability of success of calibration methods. More specifically, inequalities relating CRR model parameters with the available hydrologic data were developed and incorporated into a GOM to reduce the search space. The shuffled complex evolution (SCE) GOM showed that this approach resulted in significant improvements in the estimation of CRR model parameters for the case of synthetic streamflow data without errors as well as for data with heteroscesdastic errors. Furthermore, the suggested constrained SCE-based calibration procedure could provide CRR model parameter estimates that are consistent with the physically plausible interactions between the rainfall and runoff processes under consideration.

Quantum hypervirial theorems

The quantum mechanical hypervirial theorems (HVT) are given by the expectation values of a commutator between the virial operator W and the system Hamiltonian H. We propose application of the HVT to testing and improving approximate solutions of the Schroedinger equations. This is especially relevant for scattering states, where simple testing criteria are not readily available. The HVT, with judicious choices for W, can provide a criterion to test the accuracy of approximate solutions, both for the bound excited states and scattering states, and also ways to determine an optimal set of parameter values as the wave function improves.