Values Of Model Parameters Research Articles

Modeling the timing and extent of glaciations over southeastern Tibet during the last glacial stage

Deciphering the variations of past glaciations over southeastern Tibet which holds the largest mass of contemporary temperate glaciers over Asia helps to advance our understanding of the ice-climate interactions and how glaciers may respond to future climate change. Using an ice sheet model forced by the reconstructed climatic change during the past 88 ka, we investigate the timing and extent of glaciations over southeastern Tibet and explore the potential influences of model parameters and horizontal resolution. Our results illustrate a long-term decreasing trend in the glacierized area during the past 88 ka, suggesting progressively restricted glaciations over southeastern Tibet. Apart from the long-term trend, there are multiple glacier advance events on millennial timescales. The maximum extent of glaciation occurs at late MIS 4 (∼58 ka) and early MIS 3 (∼51 ka), with the minimum glacier extent predicted in the late Holocene (∼4 ka). The evolution of glaciations during the past 88 ka is dominated by the change in air temperature in our simulations in which temperature and precipitation serve as the climatic forcing. Regarding modeling uncertainty, we show that the pattern of glacier evolution is independent of the values of model parameters and horizontal resolution used, but the amplitude of glacier extent is significantly affected, especially for model parameters. Compared with glacial geological evidence, the timing of the modeled widespread glaciations broadly matches with the ages of known moraines, but the mapped glacier bounds across specific mountain ranges are generally underestimated. The adoption of a spatially uniform climatic forcing and the related uncertainty may contribute to the model-data discrepancy. Our results aid in the establishment of a testable link between climate change and glacier response during the last glacial stage and have important implications for glacier behavior in the future.

Life Prediction Method of Electromagnetic Contactor Based on Fusion of Improved Particle Filter and Degradation Model

Electromagnetic contactors are the key components of high-speed trains. A reasonable evaluation of the remaining life of the contactors can optimize the replacement cycle of the contactors and ensure the safe operation of the train. Aiming at the problem of remaining life prediction of contactors, a contactor life prediction method based on the fusion of improved particle filter algorithm and performance degradation model is proposed. Firstly, on the basis of analyzing the failure mechanism of the contactor and establishing its electromagnetic and dynamic simulation model, a performance degradation model based on overtravel time is established. Then design the contactor life test to obtain its full life cycle degradation data. Next, the adaptive genetic algorithm is used to optimize the resampling process of the particle filter algorithm. Finally, the optimal parameter values of the performance degradation model are obtained by the improved particle filter algorithm, and then the prediction of the remaining life of the contactor is realized. The prediction results show that the method can effectively predict the remaining life of the contactor, and the prediction accuracy meets the actual engineering needs, which provides an important reference for the operation and maintenance of the electromagnetic contactor for high-speed trains.

Method for the Iterative Refinement of Parameter Values in Analytical Models of Microelectronic Devices Based on Integrated MOS Transistors

Method for the Iterative Refinement of Parameter Values in Analytical Models of Microelectronic Devices Based on Integrated MOS Transistors

Optimization of antitumor radiotherapy fractionation via mathematical modeling with account of 4 R’s of radiobiology

A spatially-distributed continuous mathematical model of solid tumor growth and treatment by fractionated radiotherapy is presented. The model explicitly accounts for the factors, widely referred to as 4 R’s of radiobiology, which influence the efficacy of radiotherapy fractionation protocols: tumor cell repopulation, their redistribution in cell cycle, reoxygenation and repair of sublethal damage of both tumor and normal tissues. With the use of special algorithm the fractionation protocols that provide increased tumor control probability, compared to standard clinical protocol, are found for various physiologically-based values of model parameters under the constraints of fixed overall normal tissue damage and maximum admissible fractional dose. In particular, it is shown that significant gain in treatment efficacy can be achieved for tumors of low malignancy by the use of protracted hyperfractionated protocols. The optimized non-uniform protocols are characterized by gradual escalation of fractional doses in their last parts, which start after the levels of oxygen and nutrients significantly elevate throughout the tumor and accelerated tumor proliferation manifests itself, which is a well-known experimental phenomenon.

Stomatal conductance parameters of tomatoes are regulated by reducing osmotic potential and pre-dawn leaf water potential via increasing ABA under salt stress

The opening and closing of stomata control the diffusion of CO2 and water vapor, and accurate modeling of stomatal conductance (gs) is essential for predicting global water and CO2 fluxes. The Ball-Berry and Medlyn models with key slope parameters (m or g1) have been widely used to estimate gs. However, there are few studies on quantifying the model parameter values of different tomato genotypes and analyzing their relationship with physiological traits under salt stress. Here, we fit m and g1 of tomatoes using stomatal response curves and analyzed their correlations with physiological characteristics under five genotypes and two salt treatments. m was significantly different while g1 was more stable among genotypes, and salt stress significantly reduced them. By testing differences in regression slopes or intercepts, the relationships between leaf water status, and biochemical and stomatal development are found to be uniform among tomato genotypes. Osmotic potential, pre-dawn leaf water potential and abscisic acid (ABA) are driving factors for gs model parameters. Structural equation modeling showed that ABA directly modulates model parameters, not through leaf morphology or stomatal development. Overall, this study quantified gs model parameters and identified driving factors for different tomato genotypes under salt stress, providing a practical approach to model water and carbon fluxes under salt stress.

Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers)

Brazil is one of the world’s biggest monogastric producers and exporters (of pig and broiler meat). Farmers need to improve their production planning through the reliability of animal growth forecasts. Predicting pig and broiler growth is optimizing production planning, minimizing the use of resources, and forecasting meat production. The present study aims to apply a hybrid metaheuristic algorithm (SAGAC) to find the best combination of values for the growth curve model parameters for monogastric farm animals (pigs and broilers). We propose a hybrid method to optimize the growth curve model parameters by combining two metaheuristic algorithms Simulated Annealing (SA) and Genetic Algorithm (GA), with the inclusion of a function to promote the acceleration of the convergence (GA + AC) of the results. The idea was to improve the coefficient of determination of these models to achieve better production planning and minimized costs. Two datasets with age (day) and average weight (kg) were obtained. We tested three growth curves: Gompertz, Logistic, and von Bertalanffy. After 300 performed assays, experimental data were tabulated and organized, and a descriptive analysis was completed. Results showed that the SAGAC algorithm provided better results than previous estimations, thus improving the predictive data on pig and broiler production consistency. Using SAGAC to optimize the growth parameter models for pigs and broilers led to optimizing the results with the nondeterministic polynomial time (NP-hardness) of the studied functions. All tuning of the growth curves using the proposed SAGAC method for broilers presented R2 above 99%, and the SAGAC for pigs showed R2 above 94% for the growth curve.

Novel methodological and computational techniques for uncertainty quantification in diabetes short-term management models using real data

An open problem in diabetes clinical practice is determining where and how much insulin should be administered to a person with diabetes (PwD) and how many carbohydrates they should eat to maintain blood glucose levels at healthy safe levels. Here, we propose the use of a minimal model describing the glucose dynamics of PwD. Using glucose Pwd's data, we calibrate the minimal model considering the uncertainty due to errors in glucose measurement, finding the model parameter values that best reproduce the current glucose levels. Then, all the possible combinations of insulin administration and carbohydrate intake are analysed with the aim of maintaining the glucose at safe levels during the following hours. The resulting procedure is tested with data from two real persons with scenarios of the most typical situations. We expect to apply this procedure in more complex models to help the physicians to give suitable recommendations to PwD.

Deep reinforcement learning for optimal experimental design in biology.

The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.

Expectation-based consumer purchase decisions: behavioral modeling and observations.

The online version contains supplementary material available at 10.1007/s11002-022-09650-7.

A study of the influence of rainfall datasets' spatial resolution on stream simulation in Chaliyar River Basin, India

Abstract Rainfall is a vital input to model watershed hydrology, and the availability of numerous gridded and point-observed rainfall datasets poses a major challenge to the modellers to choose the appropriate data. This study compares three gridded rainfall datasets (i.e., 1° × 1°, 0.5° × 0.5°, and 0.25° × 0.25°) and point rainfall observations of the India Meteorological Department (IMD) on the simulation of streamflow of a river basin in the southern Western Ghats (India) using the Soil and Water Assessment Tool (SWAT). The results show that the different datasets lead to different optimal model parameter values and consequent water balance components, significantly in groundwater hydrology. The 0.5° × 0.5° and 0.25° × 0.25° datasets result in comparable SWAT model performances (NSE = 0.75 and 0.70, respectively), probably due to the similarity in the rain gauge network density employed for deriving the datasets and also due to the spatial discretization threshold used for sub-watershed delineation. However, the coarser resolution data (1° × 1°) results in poor performance (NSE = 0.21). The study suggests that the choice of rainfall data depends on the spatial resolution of the data and the spatial discretization threshold while compromising the computational requirement vis-à-vis simulation accuracy.

Optimization of Logging Interpretation Parameters Based on Committee Machine

Aiming at the problems of low precision and low applicability of the selection method of macroscopic capture cross-section parameters of rock skeleton, formation water, oil and gas, and mud in the volume model of remaining oil saturation logging, this study proposes a method for optimizing the capture section parameters based on the committee machine regression model. To begin, we select well sections from well logging data in a reasonable manner, allocate well sections with different component parameters in the same proportion, and construct sample datasets. The basic experts are then selected as multiple regression models, particle swarm optimization, and robust regression methods to train and learn the input parameters. By combining multiple experts, the regression committee machine improves the overall performance of the intelligent model. Finally, the genetic algorithm is used as a combiner to determine the contribution of each basic expert network in the final output, and the optimized parameters are obtained, which are then fed into the volume model to calculate the remaining oil saturation of the newly developed production wells, guiding the perforation and development work. The model is used to evaluate the remaining oil in the X oilfield, and the calculated water saturation matches the oil test results, proving the model’s accuracy and availability. The use of real-world data demonstrates that this method can effectively characterize the four parameter values in the volume model and provide reliable geophysical technical support for the evaluation of remaining oil.

Application of a risk-averse objective function for scheduling surgeries

Traditional objective functions for scheduling surgeries, such as maximising utilisation of operating rooms, maximising throughput of surgeries, or minimising cost, can lead to inequitable outcomes for patients in terms of the time waiting for their operation. Using a simple mixed integer program and historical data from a surgical centre we compare two objective functions (a risk neutral and a risk averse objective) intended to reduce the lateness of the latest-performed operations. Further, the effects of other model parameters on the surgical schedules are compared. By applying the model to a case study at a surgical centre, it is demonstrated that, with the appropriate values of the other model parameters, the risk neutral objective can achieve similar schedules to the risk averse objective, and results in problems that are easier to solve.

Price-Rising Competition: a Higher Market Price When a Monopoly Faces a Small Entrant

The UK retail electricity market revealed (i) the co-occurrence between a declining market concentration and an increasing price and (ii) price differentiation between incumbents and small suppliers. We construct an infinite sequential game in which a monopoly faces a small entrant and find an equilibrium where both players are not motivated to deviate. However, this equilibrium imposes a condition on the values of model parameters. If the condition is unmet, the interactions between the two players will be unending. Nonetheless, we suggest that two states where at least one player is not motivated to deviate could be a place to settle, but the choice depends on which player is more concerned about market stability. Besides, the two observations are found in equilibrium and two states. Finally, we argue that the finding is jointly contributed by four features: small entrant with a lower marginal cost and constrained capacity, switching costs, and barriers to entry. If any feature is relaxed, the entry of the small firm will not lead to a rising market price.

Analysis of cosmic aspects through string-inspired teleparallel gravity models

We consider a generic string inspired gravitational theory in the framework of teleparallel gravity whose stringy action based on a particular function which depends on the scalar field, its kinetic energy, the boundary and torsion terms as well as the Gauss-Bonnet terms We examine some cosmographic parameters like the equation of state parameter (ω tot), plane behavior and their stability with the help of squared speed of sound parameter (v2 s ). Under the choice of some specific values of model parameters, the equation of state parameter ω tot indicates the range of cosmological constant and quintessence region of the evolving universe for the past, present and near future times while phantom phase in the far future times. The phase plane expresses the super accelerated phase (the freezing region) whereas gives the stability of each underlying model in the realm of accelerated universe.

Evaluation and projections of the East Asian summer monsoon in a perturbed parameter ensemble

The East Asian summer monsoon (EASM) is a dominant driver of East Asian climate, with variations in its strength potentially impacting the livelihoods of millions of people. Understanding, predicting, and assessing uncertainties in these variations are therefore important area of research. Here, we present a study of the projected twenty-first century changes in the EASM using a ‘perturbed parameter ensemble’ (PPE) of HadGEM3-GC3.05 coupled climate models, which samples uncertainties arising from differences in model parameter values. We show that the performance of PPE members for leading order EASM metrics is comparable to CMIP5 and CMIP6 models in many respects. But the PPE also exposes model biases which exist for almost all parameter combinations. These ‘structural’ biases are found mainly to affect metrics for the low-level circulation. We also show that future changes in regional circulation and precipitation are projected consistently across the PPE members. A more detailed moisture budget analysis of the precipitation changes in a region covering the Yangtze River valley shows that the spread of these changes is mainly due to spread in dynamic responses. We also perform parameter sensitivity analyses and find that a parameter controlling the amplitude of deep-level entrainment is the main driver of spread in the PPE’s representation of the EASM circulation. Finally, we discuss how the information provided by the PPE may be used in practice, considering the plausibility of the models, and giving examples of ways to sub-select ensemble members to capture the diversity in the moisture budget changes.

Gaussian Multiuser Wiretap Channels in the Presence of a Jammer-Aided Eavesdropper.

This paper considers secure communication in the presence of an eavesdropper and a malicious jammer. The jammer is assumed to be oblivious of the communication signals emitted by the legitimate transmitter(s) but can employ any jamming strategy subject to a given power constraint and shares her jamming signal with the eavesdropper. Four such models are considered: (i) the Gaussian point-to-point wiretap channel; (ii) the Gaussian multiple-access wiretap channel; (iii) the Gaussian broadcast wiretap channel; and (iv) the Gaussian symmetric interference wiretap channel. The use of pre-shared randomness between the legitimate users is not allowed in our models. Inner and outer bounds are derived for these four models. For (i), the secrecy capacity is obtained. For (ii) and (iv) under a degraded setup, the optimal secrecy sum-rate is characterized. Finally, for (iii), ranges of model parameter values for which the inner and outer bounds coincide are identified.

Statistical Learning of Nonlinear Stochastic Differential Equations from Nonstationary Time Series using Variational Clustering

Parameter estimation for non-stationary stochastic differential equations (SDE) with an arbitrary non-linear drift and non-linear diffusion is accomplished in combination with a non-parametric clustering methodology. Such a model-based clustering approach includes a quadratic programming (QP) problem with equality and inequality constraints. We couple the QP problem to a closed-form likelihood function approach based on suitable Hermite-expansions to approximate the parameter values of the SDE model. The classification problem provides a smooth indicator function, which enables us to recover the underlying temporal parameter modulation of the one-dimensional SDE. As shown by the numerical examples, the clustering approach recovers a hidden functional relationship between the SDE model parameters and an additional auxiliary process. The study builds upon this functional relationship to develop closed-form, non-stationary, data-driven stochastic models for multiscale dynamical systems in real-world applications.

Variance-based sensitivity analysis of tuberculosis transmission models.

Mathematical models are widely used to provide evidence to inform policies for tuberculosis (TB) control. These models contain many sources of input uncertainty including the choice of model structure, parameter values and input data. Quantifying the role of these different sources of input uncertainty on the model outputs is important for understanding model dynamics and improving evidence for policy making. In this paper, we applied the Sobol sensitivity analysis method to a TB transmission model used to simulate the effects of a hypothetical population-wide screening strategy. We demonstrated how the method can be used to quantify the importance of both model parameters and model structure and how the analysis can be conducted on groups of inputs. Uncertainty in the model outputs was dominated by uncertainty in the intervention parameters. The important inputs were context dependent, depending on the setting, time horizon and outcome measure considered. In particular, the choice of model structure had an increasing effect on output uncertainty in high TB incidence settings. Grouping inputs identified the same influential inputs. Wider use of the Sobol method could inform ongoing development of infectious disease models and improve the use of modelling evidence in decision making.

Signal Modeling with IG Noise and Parameter Estimation Based on RJMCMC

Piecewise constant (PC) is a stochastic model that can be applied in various fields such as engineering and ecology. The stochastic model contains a noise. The accuracy of the stochastic model in modeling a signal is influenced by the type of noise. This paper aims to propose inverse-gamma noise in the PC model and the procedure for estimating the model parameters. The model parameters are estimated using the Bayes approach. Model parameters have a variable dimension space so that the Bayesian estimator cannot be determined analytically. Therefore, the Bayesian estimator is calculated using the reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm. The performance of the RJMCMC algorithm is validated using data synthesis. The finding is a new PC model in which the noise has an inverse-gamma distribution. In addition, this paper also proposes a parameter estimation procedure for the model based on an RJMCMC. The simulation study shows that the model parameter estimators generated by this algorithm are close to the model parameter values. This paper concludes that inverse gamma noise can be used as an alternative noise in the PC model. The RJMCMC is categorized as a valid algorithm and can estimate the PC model parameters where the noise has an inverse-gamma distribution. The novelty in this paper is the development of a new stochastic model and the procedure for estimating the model parameters. In application, the findings in this paper have the potential to improve the suitability of the stochastic model to the signal.

Prediction of 6 Personality Characters (HEXACO) from Social Media using Random Forest Classifier and Particle Swarm Optimization

The importance of personality assessment is required in determining whether an individual can support a job, or which position is suitable and in accordance with his personality. The personality model that is often used is the Myer Brigs Type Indicator, but the model is considered inappropriate for use because the model is too dichotomous in defining one's personality. Unlike the HEXACO personality model, the model provides a numerical assessment of each personality trait. In determining an individual's personality, it can be done by utilizing one's writing using the classification algorithm model. In the test, the data collected previously was classified according to its target to get the results in the form of personality classifications. Testing is done using the PSO algorithm so that the results obtained have a high level of accuracy, equal to 0.9 (90%). The use of PSO algorithm can help to get high accuracy results in the prediction process because the search for parameter values in the prediction model is done automatically.