Different Sets Of Input Parameters Research Articles

Reweighting Monte Carlo predictions and automated fragmentation variations in Pythia 8

This work reports on a method for uncertainty estimation in simulated collider-event predictions. The method is based on a Monte Carlo-veto algorithm, and extends previous work on uncertainty estimates in parton showers by including uncertainty estimates for the Lund string-fragmentation model. This method is advantageous from the perspective of simulation costs: a single ensemble of generated events can be reinterpreted as though it was obtained using a different set of input parameters, where each event now is accompanied with a corresponding weight. This allows for a robust exploration of the uncertainties arising from the choice of input model parameters, without the need to rerun full simulation pipelines for each input parameter choice. Such explorations are important when determining the sensitivities of precision physics measurements. Accompanying code is available at https://gitlab.com/uchep/mlhad-weights-validation.

Hybrid parameters for fluid identification using an enhanced quantum neural network in a tight reservoir

This paper proposes a fluid classifier for a tight reservoir using a quantum neural network (QNN). It is difficult to identify the fluid in tight reservoirs, and the manual interpretation of logging data, which is an important means to identify the fluid properties, has the disadvantages of a low recognition rate and non-intelligence, and an intelligent algorithm can better identify the fluid. For tight reservoirs, the logging response characteristics of different fluid properties and the sensitivity and relevance of well log parameter and rock physics parameters to fluid identification are analyzed, and different sets of input parameters for fluid identification are constructed. On the basis of quantum neural networks, a new method for combining sample quantum state descriptions, sensitivity analysis of input parameters, and wavelet activation functions for optimization is proposed. The results of identifying the dry layer, gas layer, and gas–water co-layer in the tight reservoir in the Sichuan Basin of China show that different input parameters and activation functions affect recognition performance. The proposed quantum neural network based on hybrid parameters and a wavelet activation function has higher fluid identification accuracy than the original quantum neural network model, indicating that this method is effective and warrants promotion and application.

Методичні підходи до оцінювання соціально-економічної резильєнтності систем

The article analyzes the methodological approaches available in the scientific discussion to assess the socioeconomic resilience of systems. The study is based on a review of the literature, including the analysis of scientific articles and works on resilience, socioeconomic components of this conception and indexing of resilience. Approaches to assessing socioeconomic resilience offered by research foundations and international organizations, such as the Peace Foundation, the United Nations Food and Agriculture Organization, the Rockefeller Foundation, FM Global, and the Institute for the Study of the Future-Oriented Economy, are analyzed. It is stated that in the scientific literature, as well as in the research materials of analytical organizations, socioeconomic resilience is considered as a multifaceted concept and is assessed mainly by the index method. At this, different researchers use different sets of input parameters to calculate resistance indices, but the stages of their formation remain very similar. The advantages and disadvantages of using the index method in assessing the socioeconomic resilience of the system are shown. In particular, these are: 1) advantages: easy comparison of different quantities that are preliminarily converted into a single scale, which is especially useful when the data have different units of measurement or scales; the ability to present data clearly, which provides an easier communication with others; the ability to track changes over time, compare the results of different systems using standardized parameters; 2) disadvantages: indices do not always reflect all aspects or details that may be important for analysis (they reflect a general idea, but may not take into account specific factors or changes in data); indices are relative indicators, so their values do not provide specific information about absolute values or quantities, which can complicate the assessment of the real volume of changes; if the composition of the index changes (for example, components are added or removed), this can affect its value and comparability with previous data; probability of distortion (indices can be distorted if the data on the basis of which they are calculated is not representative or if the selected criteria do not correspond to real conditions). Taking into view the multifaceted nature of the conception of resilience, it is proposed to assess socioeconomic resilience in a more comprehensive manner. In particular, the expediency of conducting (along with the calculation of the resilience index) an analysis of a set of additional parameters – resistance, adaptability, depth of shock, recovery period and speed, etc., is indicated. Also, content analysis was used to analyze methodological approaches to assessing the socioeconomic resilience of systems proposed by scholars, researchers, international foundations and organizations.

Slow Tourism Project to Redistribute Tourist Demand in Lazio: Cost-Benefit Analysis Using Monte Carlo Simulation

This paper examines the potential financial advantages and disadvantages of a project called Feudo Turistico, which proposes the establishment of a slow tourism destination between Rome and Fiuggi. The objective of the project is to reduce the concentration of tourist demand in the city centers of Rome and Fiuggi and instead distribute it across the Lazio region by providing a local cultural and active tourism experience with new services that are currently unavailable in the region. To assess the project’s feasibility, a cost-benefit analysis is conducted using Monte Carlo Simulation with 10,000 trials. This method allows for obtaining the net present value, internal rate of return, and discounted payback period for each simulated combination of input variables and then exploring the location and dispersion of these indicators. A simple random sampling technique is employed to simulate different sets of input parameters using a factor range between pessimistic and optimistic scenarios, where minimum and maximum values are determined based on existing literature, historical data in statistical reports of research institutions, and calculations derived from this data. Furthermore, two different settings are considered regarding the initial distribution of input parameters, and their outcomes are compared. The results and distribution of net present value, internal rate of return, and discounted payback period indicate that the Feudo Turistico project possesses a financially self-sufficient business model, generating cash flows that can repay the initial investment within a few years with a high probability. Keywords: slow tourism, cost-benefit analysis, discounted cash flow, Monte Carlo Simulation

Quantification of the contribution ratio of relevant input parameters on DEM-based granular flow simulations

Granular flow is affected by multiple parameters, which makes its study challenging. The discrete element method (DEM) is widely employed to simulate granular flow in consideration of particle motion, particularly when the effects of related parameters and particle shape on flow characteristics are being studied. In this study, different combinations of four input parameters (spring coefficient, friction angle between elements, coefficient of restitution, and bottom friction) were first obtained with the help of the Latin hypercube sampling method. Then, a series of simulations were performed using DEM with different sets of input parameters in consideration of different particle shapes and contact models. Radial basis function (RBF) interpolation was then employed to construct a response surface of run-out distance. Monte Carlo simulations were also conducted to obtain the contribution ratio of each input parameter. The result revealed that the bottom friction has a significant influence on the run-out distance, while friction angle between elements and spring coefficient account for a small proportion in the contribution ratio. Moreover, it was confirmed that the coefficient of restitution has a considerable contribution ratio in the front part of elements. The results also revealed that the influence of the particle shape and contact model on the contribution ratio was not as important in comparison.

Dimensional optimization of a two-body Wave energy converter using response surface methodology

This paper proposes a dimensional optimization procedure for a laboratory scale two-body point absorber wave energy converter (WEC) using the design of experiment (DoE) methodology. Response surface methodology (RSM) is utilized to estimate a second order polynomial function correlating the average absorbed power, as the objective function, to five geometric parameters. Optimum values of parameters correspond to the peak of the surface fitted to the absorbed powers calculated for different sets of input parameters selected by the Box-Behnken design (BBD). The sensitivity of the objective function with respect to each parameter is investigated. The WEC is assumed to operate under regular waves in a specified range of frequency, 0.5–1 Hz. The amplitude and complex-conjugate controls are applied to keep the power take-off (PTO) system in optimum conditions. ANSYS-AQWA is used to calculate hydrodynamic parameters of the WEC required to solve the equations governing the absorbed power.

Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System

This study investigates chromium removal onto modified maghemite nanoparticles in batch experiments based on a central composite design. The effect of modified maghemite nanoparticles on the adsorptive removal of chromium was quantitatively elucidated by fitting the experimental data using artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modeling approaches. The ANN and ANFIS models, relating the inputs, i.e., pH, adsorbent dose, and initial chromium concentration to the output, i.e., chromium removal efficiency (RE), were developed by comparing the predicted value with that of the experimental values. The RE of chromium ranged from 49.58% to 92.72% under the influence of varying pH (i.e., 2.6–9.4) and adsorbent dose, i.e., 0.8 g/L to 9.2 g/L. The developed ANN model fits the experimental data exceptionally well with correlation coefficients of 1.000 and 0.997 for training and testing, respectively. In addition, the Pearson’s Chi-square measure (χ2) of 0.0004 and 0.0673 for the ANN and ANFIS models, respectively, indicated the superiority of ANN over ANFIS. However, a small discrepancy in the predictability of the ANFIS model was observed owing to the fuzzy rule-based complexity and overtraining of data. Thus, the developed models can be used for the online prediction of RE onto synthesized maghemite nanoparticles with different sets of input parameters and it can also predict the operational errors in the system.

Robust machine learning models of carbon dioxide trapping indexes at geological storage sites

Geological carbon dioxide (CO2) storage (GCS) in saline aquifers has been recognized as a promising way to slow down global CO2 emissions. The residual and solubility trapping efficiency of CO2 in saline aquifers play a crucial role in monitoring CO2sequestration performance. Due to this fact, the goal of this paper is to determine the effectiveness of three robust machine learning (ML) models — a general regression neural network (GRNN) model and two multilayer perceptron (MLP) models respectively optimized with the Levenberg-Marquardt algorithm (LMA) and Bayesian Regularization (BR) — in predicting the residual trapping index (RTI) and solubility trapping index (STI) of CO2 in saline aquifers. A comprehensive and wide-ranging dataset was compiled from the literature, including over 1,910 simulation samples from numerous CO2 field models. The predicted results revealed that all the proposed ML techniques have an excellent agreement with simulation data. In addition, the error analyses and a comparison of statistical indicators indicated that the GRNN model was more accurate than the MLP-LMA and MLP-BR models as well as two ML models developed in previous studies. The GRNN model exhibited overall coefficient of determination (R2) values of 0.9995 and 0.9998 and average absolute relative error (ARE) percentages of 0.7413% and 0.2950% for RTI and STI, respectively.Furthermore, a trend analysis confirmed the robustness of the GRNN model, as predicted and simulated RTI and STI exhibited strong overlap under four different sets of input parameters. Moreover, the Williams plot reveals that the validity of GRNN model was affirmed, and only a small suspected data was detected from the collected database. Therefore, the GRNN model proposed in this study could serve as a template for evaluating the feasibility of future GCS projects in saline aquifers. Lastly, the findings of this study can help better understanding the promising of machine learning techniques for predicting CO2 trapping efficiency in geological storage sites.

Prediction of Tensile Strength of 3D Printed Bronze PLA Part Using Response Surface Modelling

Background - 3D printing is a dynamic process with many process parameters influencing the product, including the type of the material; it is often difficult to understand the combined influence of these parameters. Purpose - The tensile strength of 3D printed parts is important for the functionality of components. The effects of process parameters on tensile strength must therefore be examined. The objective of this study is to develop a response surface model (RSM) to predict the final quality of a 3D printed bronze part from a different set of input parameters. Methods - The tensile test specimen was built in a Makerbot 3D printer with bronze polylactic acid (PLA) material. The three controllable input parameters were; thickness of layers, number of shells, and infill density. The three levels of layer thickness were 0.1mm, 0.2mm and 0.3mm. The number of shells was 2, 3 and 4. The infill densities were 20%, 30% and 40%. A tensile experiment tested the strength of the specimens. RSM is a statistical approach for modelling and analyzing how different variables affect the response of interest, and for optimizing it. Results - The result obtained shows that the specimen with a high layer thickness of 0.3mm and infill density of 40% is the best among all the other parameters. Finally, the regression equation produced was used for random values of layer thickness, the number of shells, and infill density, to see whether the values obtained from the tests fall into the range of experimental data. Conclusion - Infill density and layer thickness are the two criteria that significantly influence the tensile property. The number of shells has the least influence on the tensile property. However, the best tensile strength is the part printed with higher infill density, a greater number of shells, and higher layer thickness.

Predictionof Tensile Strength of 3D PrintedBronze PLA Part Using Response Surface Modelling.

Background - 3D printing is a dynamic process with many process parameters influencing the product, including the type of the material; it is often difficult to understand the combined influence of these parameters. Purpose - The tensile strength of 3D printed parts is important for the functionality of components. The effects of process parameters on tensile strength must therefore be examined. The objective of this study is to develop a response surface model (RSM) to predict the final quality of a 3D printed bronze part from a different set of input parameters. Methods - The tensile test specimen was built in aMakerbot3D printer with bronze polylactic acid (PLA) material. The three controllable input parameters were; thickness of layers, number of shells, and infill density. The three levels of layer thickness were 0.1mm, 0.2mm and 0.3mm. The number of shells was 2, 3 and 4. The infill densities were 20%, 30% and 40%. A tensile experiment tested the strength of the specimens. RSM is a statistical approach for modelling andanalyzinghow different variables affect the response of interest, and for optimizing it. Results- The result obtained shows that the specimen with a high layer thickness of 0.3mm and infill density of 40% is the best among all the other parameters. Finally, the regression equation produced was used for random values of layer thickness, the number of shells, and infill density, to see whether the values obtained from the tests fall into the range of experimental data. Conclusion - Infill density and layer thickness are the two criteria that significantly influence the tensile property. The number of shells has the least influence on the tensile property. However, the best tensile strength is the part printed with higher infill density, a greater number of shells, and higher layer thickness.

Optimizing thermocouple\u2019s ZT through design innovation

This work demonstrates that in parallel with the one existed at high doping concentration, there also exists an optimal combination of the transport properties of a thermoelectric material at low doping concentration as the curve of the relation between electrical conductivity and doping concentration is rigidly shifted toward that direction without disturbing the Seebeck coefficient and the thermal conductivity. Based on this finding, a new thermocouple design that uses low doping legs and high doping semiconductors as the external carrier injectors surrounding the legs is developed. The analytical model developed for the new thermocouple indicated that its efficiency and power output could be more than tripled as compared to those of the original design. A single thermocouple made of Silicon semiconductors was simulated numerically using different sets of input parameters. The results showed that the density of the externally injected carriers played a significant role in enhancing the thermocouple’s efficiency and power output.

Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs

In the present study, Radial Basis Function (RBF) neural networks are applied to forecast the compressive strength and elastic modulus of Self-Compacting Concrete (SCC). To construct the models, different experimental specimens of diverse kinds of SCC are gathered from the literature. The data used in the networks are classified into two different sets of input parameters. The results revealed that the proposed RBF models can accurately forecast the properties of SCCs with low test error. Furthermore, a comparison between models with two different sets of inputs proves that the selected parameters as input variables, straightly impress the precision of the networks, in the prediction of the intended outputs.

Discussion: Determining the plasticity properties of high plastic clays: a new empirical approach [Arab J Geosci (2020) 13(11), 394

This article presents a discussion of the Arabian Journal of Geosciences, 394, Vol. 13, No. 11, which (among other things) proposed a new empirical correlation for the prediction of the plasticity index magnitude (and hence by association the plastic limit value) of fine-grained soils sampled from different depths in the Bakirkoy District of Istanbul City. As inputs, the proposed correlation employs the activity index, along with defining parameters of the semi-logarithmic flow curve obtained from the Casagrande (percussion-cup) liquid limit test. This article presents the following: (i) a discussion of the relevant literature that was not covered in the original paper; (ii) clarifications on, and observations regarding, the general approach adopted in the original investigation; (iii) concerns over whether the proposed new empirical correlation can be meaningfully recalibrated for particular clay soil types specific to other regions around the world; and (iv) suggestions for improvement of the new empirical correlation by adopting a different set of input parameters.

Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks

AbstractForecasting geomagnetic indices represents a key point to develop warning systems for the mitigation of possible effects of severe geomagnetic storms on critical ground infrastructures. Here we focus on SYM‐H index, a proxy of the axially symmetric magnetic field disturbance at low and middle latitudes on the Earth's surface. To forecast SYM‐H, we built two artificial neural network (ANN) models and trained both of them on two different sets of input parameters including interplanetary magnetic field components and magnitude and differing for the presence or not of previous SYM‐H values. These ANN models differ in architecture being based on two conceptually different neural networks: the long short‐term memory (LSTM) and the convolutional neural network (CNN). Both networks are trained, validated, and tested on a total of 42 geomagnetic storms among the most intense that occurred between 1998 and 2018. Performance comparison of the two ANN models shows that (1) both are able to well forecast SYM‐H index 1 h in advance, with an accuracy of more than 95% in terms of the coefficient of determination R2; (2) the model based on LSTM is slightly more accurate than that based on CNN when including SYM‐H index at previous steps among the inputs; and (3) the model based on CNN has interesting potentialities being more accurate than that based on LSTM when not including SYM‐H index among the inputs. Predictions made including SYM‐H index among the inputs provide a root mean squared error on average 42% lower than that of predictions made without SYM‐H.

Three-dimensional general relativistic Poynting-Robertson effect. III. Static and nonspherical quadrupolar massive source

We investigate the three-dimensional motion of a test particle in the gravitational field generated by a non-spherical compact object endowed with a mass quadrupole moment, described by the Erez-Rosen metric, and a radiation field, including the general relativistic Poynting-Robertson effect, coming from a rigidly rotating spherical emitting source located outside of the compact object. We derive the equations of motion for test particles influenced by such radiation field, recovering the two-dimensional description, and the weak-field approximation. This dynamical system admits the existence of a critical hypersurface, region where gravitational and radiation forces balance. Selected test particle orbits for different set of input parameters are displayed. The possible configurations on the critical hypersurfaces can be either latitudinal drift towards the equatorial ring or suspended orbits. We discuss about the existence of multiple hypersurface solutions through a simple method to perform the calculations. We graphically prove also that the critical hypersurfaces are stable configurations within the Lyapunov theory.

Study of the (n,2n) reaction cross section on 174,176Hf isotopes

Cross section measurements of the 174Hf(n,2n)173Hf and 176Hf(n,2n)175Hf reactions have been performed at the VdG Tandem accelerator of NCSR “Demokritos” in Athens, in the neutron energy region from 8.8 to 11.5 MeV, using the activation technique. Statistical model calculations based on the Hauser - Feshbach theory have also been implemented by using the code EMPIRE-II with different sets of input parameters. The predictions were compared to the data of the present work as well as data from literature.

Processing Oxford Nanopore Long Reads Using Amazon Web Services

Studies of genomes and transcriptomes are performed using sequencers that read the sequence of nucleotide residues of genomic DNA, RNA, or complementary DNA (cDNA). The analysis consists of an experimental part (obtaining primary data) and bioinformatic processing of primary data. The bioinformatics part is performed with different sets of input parameters. The selection of the optimal values of the parameters, as a rule, requires significant computing power. The article describes a protocol for processing transcriptome data by virtual computers provided by the cloud platform Amazon Web Services (AWS) using the example of the recently emerging technology of long DNA and RNA sequences (Oxford Nanopore Technology). As a result, a virtual machine and instructions for its use have been developed, thus allowing a wide range of molecular biologists to independently process the results obtained using the "Oxford nanopore".

Proton firehose instability: An interplay of thermal core and non-thermal halo protons

Higher levels of solar wind temperatures are reported to be constrained. Microinstabilities play a key role under dilute space plasma conditions. The present study highlights the role of proton firehose instability in defining parallel temperatures of protons. Considering reality, we chose a bi-Maxwellian model for core protons, while halo protons are best modeled with kappa distribution. Taking different sets of input parameters like temperature anisotropy, plasma beta, and kappa index into account, the growth rate levels and associated domains for an unstable firehose mode are investigated.

Inverse problem instabilities in large-scale modeling of matter in extreme conditions

Our understanding of physical systems often depends on our ability to match complex computational modeling with the measured experimental outcomes. However, simulations with large parameter spaces suffer from inverse problem instabilities, where similar simulated outputs can map back to very different sets of input parameters. While of fundamental importance, such instabilities are seldom resolved due to the intractably large number of simulations required to comprehensively explore parameter space. Here, we show how Bayesian inference can be used to address inverse problem instabilities in the interpretation of x-ray emission spectroscopy and inelastic x-ray scattering diagnostics. We find that the extraction of information from measurements on the basis of agreement with simulations alone is unreliable and leads to a significant underestimation of uncertainties. We describe how to statistically quantify the effect of unstable inverse models and describe an approach to experimental design that mitigates its impact.

Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment.

Multiscale computational models of the heart are being extensively investigated for improved assessment of drug-induced torsades de pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics such as action potential duration and net charge carried by ionic currents (qNet) have been proposed as potential candidates for TdP risk stratification after being tested on small datasets. Unlike purely statistical approaches, model-derived metrics are thought to provide mechanism-based classification. In particular, qNet has been recently proposed as a surrogate metric for early afterdepolarizations (EADs), which are known to be cellular triggers of TdP. Analysis of critical model components and of the ion channels that have major impact on model-derived metrics can lead to improvements in the confidence of the prediction. In this paper, we analyze large populations of virtual drugs to systematically examine the influence of different ion channels on model-derived metrics that have been proposed for proarrhythmic risk assessment. We demonstrate via global sensitivity analysis (GSA) that model-derived metrics are most sensitive to different sets of input parameters. Similarly, important differences in sensitivity to specific channel blocks are highlighted when classifying drugs into different risk categories by either qNet or a metric directly based on simulated EADs. In particular, the higher sensitivity of qNet to the block of the late sodium channel might explain why its classification accuracy is better than that of the EAD-based metric, as shown for a small set of known drugs. Our results highlight the need for a better mechanistic interpretation of promising metrics like qNet based on a formal analysis of models. GSA should, therefore, constitute an essential component of the in silico workflow for proarrhythmic risk assessment, as an improved understanding of the structure of model-derived metrics could increase confidence in model-predicted risk.