dependent parameter
Recently Published Documents


TOTAL DOCUMENTS

218
(FIVE YEARS 52)

H-INDEX

23
(FIVE YEARS 4)

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Beulah M ◽  
MR Sudhir ◽  
Shenen Chen ◽  
Sasha Rai ◽  
Deekshith Jain

Numerous works are reported in literature regarding the enhancement of compressive strength of fly ash-GGBS geopolymer combinations with addition of alkali activators of varying concentrations. However, a limited study has been chronicled, revealing the specific role of alkali or alkaline earth contributed by the fly ash-GGBS combinations on the compressive strength development. It is well known that the strength of a geopolymer is dependent on gel formation from Al/Si ratio, Ca/Si ratio, and Ca/(Si + Al) ratio but their exact role when cured for various extended periods is unknown as yet. In the present study, alkali concentration in a fly ash-GGBS geopolymer combination was varied from 6 M to 12 M with increments of two mol in six different fly ash-GGBS combinations with a minimum of 20 percent and a maximum of 70 percent GGBS. The correlation coefficients between compressive strength and Al/Si, Ca/Si, and Ca/(Si + Al) ratios exhibited values higher than 0.95 taken individually. Multiple linear regression analysis with compressive strength (as dependent parameter) and individual values of Al/Si, Ca/Si, and Ca/(Si + Al) ratios (as independent parameters) was effectuated. It was observed that, depending on the composition, the compressive strength circumstantiated a changeover from Ca/Si to Ca/(Si + Al) ratio in the intermediate composition range. Such a detailed analysis is considered supportive of developing a suitable composition which will provide the optimum compressive strength of the combination.


2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Tailong Xiao ◽  
Jianping Fan ◽  
Guihua Zeng

AbstractParameter estimation is a pivotal task, where quantum technologies can enhance precision greatly. We investigate the time-dependent parameter estimation based on deep reinforcement learning, where the noise-free and noisy bounds of parameter estimation are derived from a geometrical perspective. We propose a physical-inspired linear time-correlated control ansatz and a general well-defined reward function integrated with the derived bounds to accelerate the network training for fast generating quantum control signals. In the light of the proposed scheme, we validate the performance of time-dependent and time-independent parameter estimation under noise-free and noisy dynamics. In particular, we evaluate the transferability of the scheme when the parameter has a shift from the true parameter. The simulation showcases the robustness and sample efficiency of the scheme and achieves the state-of-the-art performance. Our work highlights the universality and global optimality of deep reinforcement learning over conventional methods in practical parameter estimation of quantum sensing.


Author(s):  
Joydeep Das ◽  
Arjun Sil

The reinforced concrete (RC) bridges deteriorate essentially due to strength loss induced by aging of the structure, extreme weathering conditions, and unplanned increased service loads. However, these load variations and aging factors equally could compromise structural reliability, and service life for continuous satisfactory operation of service bridges for future performance. A reasonable model of bridge strength and applied loads becomes the basis of accurate prediction of bridge functionality. Hence, time-dependent reliability approaches could be used efficiently to gain a reliable understanding of issues facing by the bridges in the study area for appropriate solutions. In this paper, the reliability of bridges under harsh conditions studied using time-variant and time-invariant reliability models in which both load and resistance considered as a time-dependent parameter. A combination of condition rating (CR) and time-dependent load employed to attain accurate insights about the degradation of structural resistance of the existing bridges. The result shows the significant impact of aging as well as traffic loads influence in the service life of both national highways (NH) and rural road service bridges. These observations might be used to adopt appropriate planning strategies as well as rational decisions to ensure the safety of the bridges for future operation.


2021 ◽  
Vol 9 ◽  
Author(s):  
Basma Souayeh ◽  
Essam Yasin ◽  
Mir Waqas Alam ◽  
Syed Ghazanfar Hussain

The main objective of current communication is to present a mathematical model and numerical simulation for momentum and heat transference characteristics of Maxwell nanofluid flow over a stretching sheet. Further, magnetic dipole, non-uniform heat source/sink, and chemical reaction effects are considered. By using well-known similarity transformation, formulated flow equations are modelled into OD equations. Numerical solutions of the governing flow equations are attained by utilizing the shooting method consolidated with the fourth-order Runge-Kutta with shooting system. Graphical results are deliberated and scrutinized for the consequence of different parameters on fluid characteristics. Results reveal that the temperature profile accelerates for diverse values of space dependent parameter, but it shows opposite behaviour for escalated integrity of temperature dependent parameter.


Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1560
Author(s):  
Lina Jaurigue ◽  
Elizabeth Robertson ◽  
Janik Wolters ◽  
Kathy Lüdge

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.


Author(s):  
Lina Jaurigue ◽  
Elizabeth Robertson ◽  
Janik Wolters ◽  
Kathy Lüdge

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.


2021 ◽  
Vol 1203 (3) ◽  
pp. 032032
Author(s):  
Malik M Barakathullah ◽  
Elias Jakobus Willemse ◽  
Bige Tunçer ◽  
Roland Bouffanais

Abstract Predicting the temporal evolution of the demography and the residents’ spatial movements would immensely aid the estate development and urban planning. The evolution of population in three townships of Singapore is simulated at neighbourhood scale using a novel agent-based probabilistic approach with inputs from large-scale survey and statistical data. The demographic changes due to age-dependent rates of death and fertility are studied by considering the inter-ethnic marriages that has a varying probability depending on the ethnicities of the male and female partners. The predicted changes in the age and household compositions and family types have been found to reflect the population trends in Singapore over the past years. The decline in family types that contain children and the structure of age composition over years underline the issue of prevailing low fertility rates. The strategies for incorporating the population relocation to consider the long-term spatial movement are also discussed. In Singapore’s context, we consider in the relocation model an added complexity of ethnic quota for the residential units developed by public housing board. The ethnicity dependent parameter coupled with other parameters that represent the number of children in a household besides their size, the household income, the proximity of children’s schools, and the places of employment could play a strong role in predicting the spatial evolution of the residents. These predictions can be used by the urban planners and policy makers to improve the quality of life in Singapore.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Youngjin Hwang ◽  
Soobin Kwak ◽  
Junseok Kim

In this study, we propose a time-dependent susceptible-unidentified infected-confirmed (tSUC) epidemic mathematical model for the COVID-19 pandemic, which has a time-dependent transmission parameter. Using the tSUC model with real confirmed data, we can estimate the number of unidentified infected cases. We can perform a long-time epidemic analysis from the beginning to the current pandemic of COVID-19 using the time-dependent parameter. To verify the performance of the proposed model, we present several numerical experiments. The computational test results confirm the usefulness of the proposed model in the analysis of the COVID-19 pandemic.


2021 ◽  
Vol 63 ◽  
pp. 178-202
Author(s):  
Piyapoom Nonsoong ◽  
Khamron Mekchay ◽  
Sanae Rujivan

We present an analytical option pricing formula for the European options, in which the price dynamics of a risky asset follows a mean-reverting process with a time-dependent parameter. The process can be adapted to describe a seasonal variation in price such as in agricultural commodity markets. An analytical solution is derived based on the solution of a partial differential equation, which shows that a European option price can be decomposed into two terms: the payoff of the option at the initial time and the time-integral over the lifetime of the option driven by a time-dependent parameter. Finally, results obtained from the formula have been compared with Monte Carlo simulations and a Black–Scholes-type formula under various kinds of long-run mean functions, and some examples of option price behaviours have been provided. doi:10.1017/S1446181121000262  


2021 ◽  
Author(s):  
Mainak Saha ◽  
Manab Mallik

The last two decades have witnessed a large volume of research revolving around structure-property correlation in carbon-based nanocomposites, synthesized by several methods. In the simplest of terms, the electronic properties of these nanomaterials, which form the present context of discussion, vary mainly as a function of three parameters, out of which two are process parameters (viz. (i) the kind of reinforcement and (ii) method of synthesis), and one is a structure-dependent parameter. The structure-dependent parameter is highly influenced by the two process parameters and plays a vital role in determining the ionic and electronic transport phenomenon in these materials. In other words, the interaction between electrons and the equilibrium 0-D (point) defects, along with different types of 2-D interfaces, plays a crucial role in the understanding of electronic properties, apart from physical and chemical properties of these materials. The present chapter provides a brief overview of the state-of-the-art on research along with detailed discussions on some recent developments in understanding electronic properties of some conventional carbon-based nanocomposites (synthesized by different techniques) based on the structure-property correlation in these materials. Finally, some of the significant challenges in this field have been addressed from both industrial and fundamental viewpoints.


Sign in / Sign up

Export Citation Format

Share Document