Number Of Effective Parameters Research Articles

Optimizing rock fragmentation in open-pit mines through fuzzy intelligent prediction method

Blasting is one of the most important steps in mining operation and it directly affects final results (extraction ore body and costs). Various parameters such as rock mass and explosive properties, and blast geometry influence blasting results. A number of effective parameters in fragmentation should be taken into account to design a suitable blasting pattern, reduce the secondary costs and minimize the adverse effects such as flyrock, back break and ground vibration. Fuzzy theory is a widely used technique in many engineering subjects in which there exist concepts of quality and uncertainly. In this study, the information obtained from blasting operation in B anomaly Sangan Iron Mines have been used. In this model, the blasting pattern parameters such as burden, spacing, hole depth, stemming, charging length, ratio of (K/B), number of rows, specific charge and charge per delay ratio were considered as the input parameters in fuzzy model. Then, the results of fuzzy model were compared with statistical models. Finally, the results of the two models produced from mine blasting operation were compared and evaluated with real values. The correlation coefficient index for two models were 97.8% and 72.19%, and the RMSE were 2.613 and 9.18, respectively.

Evaluation of Predictive Ability of Bayesian Regularized Neural Network Using Cholesky Factorization of Genetic Relationship Matrices for Additive and Non-additive Genetic Effects

This study aimed to explore the effects of additive and non-additive genetic effects on the prediction of complex traits using Bayesian regularized artificial neural network (BRANN). The data sets were simulated for two hypothetical pedigrees with five different fractions of total genetic variance accounted by additive, additive x additive, and additive x additive x additive genetic effects. A feed forward artificial neural network (ANN) with Bayesian regularization (BR) was used to assess the performance of different nonlinear ANNs and compare their predictive ability with those from linear models under different genetic architectures of phenotypic traits. Effective number of parameters and sum of squares error (SSE) in test data sets were used to evaluate the performance of ANNs. Distribution of weights and correlation between observed and predicted values in the test data set were used to evaluate the predictive ability. There were clear and significant improvements in terms of the predictive ability of linear (equivalent Bayesian ridge regression) and nonlinear models when the proportion of additive genetic variance in total genetic variance ( ) increased. On the other hand, nonlinear models outperformed the linear models across different genetic architectures. The weights for the linear models were larger and more variable than for the nonlinear network, and presented leptokurtic distributions, indicating strong shrinkage towards 0. In conclusion, our results showed that: a) inclusion of non-additive effects did not improve the prediction ability compared to purely additive models, b) The predictive ability of BRANN architectures with nonlinear activation function were substantially larger than the linear models for the scenarios considered.

Empirical evaluation of fully Bayesian information criteria for mixture IRT models using NUTS

This study is to evaluate the performance of fully Bayesian information criteria, namely, LOO, WAIC and WBIC in terms of the accuracy in determining the number of latent classes of a mixture IRT model while comparing it to the conventional model via non-random walk MCMC algorithms and to further compare their performance with conventional information criteria including AIC, BIC, CAIC, SABIC, and DIC. Monte Carlo simulations were carried out to evaluate these criteria under different situations. The results indicate that AIC, BIC, and their related CAIC and SABIC tend to select the simpler model and are not recommended when the actual data involve multiple latent classes. For the three fully Bayesian measures, WBIC can be used for detecting the number of latent classes for tests with at least 30 items, while WAIC and LOO are suggested to be used together with their effective number of parameters in choosing the correct number of latent classes.

A Short Overview of Soft Computing Techniques in Tunnel Construction

Tunnel construction is a complex technology, with a huge number of effective parameters, which cannot be accurately analyzed/designed using empirical or theoretical methods. With the rapid development of computer technologies, Soft Computing (SC) approaches have been widely used in tunnel construction. Typically, the two common tunneling methods, blasting and mechanical excavation (e.g., tunnel boring machine, shield, pipe jacking method), have been used in conjunction with some SC techniques to solve specific problems and have shown a good fit. On this basis, this paper first summarizes the current research on the application of SC techniques in the field of tunnel construction methods. For example, in the case of blasting, the application of SC techniques is focusing on the environmental problems induced by blasting, such as the prediction of peak particle velocity and over-break. As for mechanical tunnel construction, the SC techniques were used to analyze the boring characteristics of the machine, such as the estimation of penetration rate and advance rate. Additionally, an important aspect for the application of SC techniques is the identification of the influencing factors for each of the study subjects, i.e. the necessary input parameters for the SC. Finally, this paper elaborates on the working process of the supervised learning models, highlights the points that need to be taken care of in each step, and points out that the SC technique, which is synergistic with the physical process, is more useful to explain the actual phenomenon.

Statistical analysis and mathematical modeling of modified single slope solar still

ABSTRACT Conventional single slope solar still integrated with a parabolic trough collector in addition to packaged glass ball layer was used in water desalination. Experimental work carried and data obtained were used in modeling the influential parameters affecting water desalination using principal component analysis to reduce parameters’ numbers. These parameters were then applied in constructing a Response Surface Model (RSM). System’s Performance has been predicted in terms of temperatures; saline water temperatures (Tw), glass cover temperatures (Tg), dry bulb temperature (Tdb), and wet bulb temperature (Twb) inside the conventional solar still. Along with the above temperatures, ambient air temperature (Ta), Oil inlet temperature (Toi) and solar intensity (I). The impact of these parameters can highly effect the water desalination yield. The RSM is developed to predict the impact of these parameters. Principal Component Analysis (PCA) reduced and categorized the number of effective parameters to three components containing the parameters (Tw, Twb, and Tdb), (I, Ta, and Tg) and (Ta, Toi, and I) respectively. These combinations were then tested using the two different RSM optimized models; Modified Reduced Quadratic and Two-Factor Interaction (2FI) model. These models were tested by monitoring nine statistical indices namely: RMSE, P-value, F-value, R2, R2 adj, R2 pred, BIC, PRESS, and AICc. Results obtained for this model showed minimum RMSE value and higher values of R2, R2 adj and R2 pred as well as lowest values for BIC and AICc. Concluding supremacy of (I, Ta, and Tg) Modified Reduced 2FI model over the others.

Efficient removal of Cu (II) by SnO2/MWCNTs nanocomposite by pulsed laser ablation method

In this work, SnO2 nanoparticles were synthesized and decorated multiwalled carbon nanotubes (MWCNTs) in a simple one-pot step by pulsed laser ablation of Sn plate immersed in liquid media from functionalized MWCNTs to be applicable in the water purification as adsorbent material for removal Cu (II) metal ion from aqueous solution. The crystallinity, surface morphology, the molecular interaction, the elemental analysis, and thermal analysis of the interaction between MWCNTs and SnO2 were illustrated by different techniques as XRD, SEM, TEM, EDX, FT-IR, Raman, and TGA. The results revealed that the embedding of MWCNTs with SnO2 nanoparticles helps to enhance the performance of the adsorption process. The efficiency of the adsorbent nanocomposite to uptake Cu (II) from the water was studied via a number of effective parameters (pH value, adsorbent concentration, and contact time) to inform the best condition that could be used from this composite for Cu (II) removal from the aqueous solutions.

<i>π</i>-theorem generalization of the ice-age theory

Abstract. Analyzing a dynamical system describing the global climate variations requires, in principle, exploring a large space spanned by the numerous parameters involved in this model. Dimensional analysis is traditionally employed to deal with equations governing physical phenomena to reduce the number of parameters to be explored, but it does not work well with dynamical ice-age models, because, as a rule, the number of parameters in such systems is much larger than the number of independent dimensions. Physical reasoning may, however, allow us to reduce the number of effective parameters and apply dimensional analysis in a way that is insightful. We show this with a specific ice-age model (Verbitsky et al., 2018), which is a low-order dynamical system based on ice-flow physics coupled with a linear climate feedback. In this model, the ratio of positive-to-negative feedback is effectively captured by a dimensionless number called the “V number”, which aggregates several parameters and, hence, reduces the number of governing parameters. This allows us to apply the central theorem of the dimensional analysis, the π theorem, efficiently. Specifically, we show that the relationship between the amplitude and duration of glacial cycles is governed by a property of scale invariance that does not depend on the physical nature of the underlying positive and negative feedbacks incorporated by the system. This specific example suggests a broader idea; that is, the scale invariance can be deduced as a general property of ice age dynamics if the latter are effectively governed by a single ratio between positive and negative feedbacks.

Bayesian modeling of spatiotemporal patterns of TB-HIV co-infection risk in Kenya

BackgroundTuberculosis (TB) and Human Immunodeficiency Virus (HIV) diseases are globally acknowledged as a public health challenge that exhibits adverse bidirectional relations due to the co-epidemic overlap. To understand the co-infection burden we used the case notification data to generate spatiotemporal maps that described the distribution and exposure hypotheses for further epidemiologic investigations in areas with unusual case notification levels.MethodsWe analyzed the TB and TB-HIV case notification data from the Kenya national TB control program aggregated for forty-seven counties over a seven-year period (2012–2018). Using spatiotemporal poisson regression models within the Integrated Nested Laplace Approach (INLA) paradygm, we modeled the risk of TB-HIV co-infection. Six competing models with varying space-time formulations were compared to determine the best fit model. We then assessed the geographic patterns and temporal trends of coinfection risk by mapping the posterior marginal from the best fit model.ResultsOf the total 608,312 TB case notifications, 194,129 were HIV co-infected. The proportion of TB-HIV co-infection was higher in females (39.7%) than in males (27.0%). A significant share of the co-infection was among adults aged 35 to 44 years (46.7%) and 45 to 54 years (42.1%). Based on the Bayesian Defiance Information (DIC) and the effective number of parameters (pD) comparisons, the spatiotemporal model allowing space-time interaction was the best in explaining the geographical variations in TB-HIV coinfection. The model results suggested that the risk of TB-HIV coinfection was influenced by infrastructure index (Relative risk (RR) = 5.75, Credible Interval (Cr.I) = (1.65, 19.89)) and gender ratio (RR = 5.81e−04, Cr. I = (1.06e−04, 3.18e−03). The lowest and highest temporal relative risks were in the years 2016 at 0.9 and 2012 at 1.07 respectively. The spatial pattern presented an increased co-infection risk in a number of counties. For the spatiotemporal interaction, only a few counties had a relative risk greater than 1 that varied in different years.ConclusionsWe identified elevated risk areas for TB/HIV co-infection and fluctuating temporal trends which could be because of improved TB case detection or surveillance bias caused by spatial heterogeneity in the co-infection dynamics. Focused interventions and continuous TB-HIV surveillance will ensure adequate resource allocation and significant reduction of HIV burden amongst TB patients.

Principal varying coefficient estimator for high-dimensional models

ABSTRACTWe consider principal varying coefficient models in the high-dimensional setting, combined with variable selection, to reduce the effective number of parameters in semiparametric modelling. The estimation is based on B-splines approach. For the unpenalized estimator, we establish non-asymptotic bounds of the estimator and then establish the (asymptotic) local oracle property of the penalized estimator, as well as non-asymptotic error bounds. Monte Carlo studies reveal the favourable performance of the estimator and an application on a real dataset is presented.

Deviance information criteria for mixtures of distributions

As a Bayesian criterion for model comparison, Spiegelhalter et al. proposed the deviance information criterion (DIC) which consists of two parts: a classical estimate of fit and an effective number of parameters. This model comparison method is based on generalized linear models, and it may be inappropriate to be used for comparison in the case of mixture of distributions mainly due to the label switching and multimodality issues. For this purpose, Celeux et al. proposed several modified DIC constructions and assessed their behaviors under a mixture of distributions, however they did not fully explore the properties of alternative DICs. Here, we study and provide the properties of DIC3, one of the variations Celeux et al. proposed, and propose our modified DIC to lessen the issue raised by using the dataset twice. We compare our proposed criterion to other model selection criteria based on two numerical examples, the Galaxy dataset and the simulated dataset.

Inference in Multiscale Geographically Weighted Regression

A recent paper expands the well‐known geographically weighted regression (GWR) framework significantly by allowing the bandwidth or smoothing factor in GWR to be derived separately for each covariate in the model—a framework referred to as multiscale GWR (MGWR). However, one limitation of the MGWR framework is that, until now, no inference about the local parameter estimates was possible. Formally, the so‐called “hat matrix,” which projects the observed response vector into the predicted response vector, was available in GWR but not in MGWR. This paper addresses this limitation by reframing GWR as a Generalized Additive Model, extending this framework to MGWR and then deriving standard errors for the local parameters in MGWR. In addition, we also demonstrate how the effective number of parameters can be obtained for the overall fit of an MGWR model and for each of the covariates within the model. This statistic is essential for comparing model fit between MGWR, GWR, and traditional global models, as well as for adjusting multiple hypothesis tests. We demonstrate these advances to the MGWR framework with both a simulated data set and a real‐world data set and provide a link to new software for MGWR (MGWR1.0) which includes the novel inferential framework for MGWR described here.

A new method for infill equivalent strut width

Infills are as important members in structural design as beams, columns and braces. They have significant effect on structural behavior. Because of lots of variables in infills like material non-linear behavior, the interaction between frames and infill, etc., the infills performance during an earthquake is complicated, so have led designers do not consider the effect of infills in designing the structure. However, the experimental studies revealed that the infills have the remarkable effect on structure behavior. As if these effects ignored, it might occur soft-story phenomena, torsion or short-column effects on the structures. One simple and appropriate method for considering the infills effects in analyzing, is replacing the infills with diagonal compression strut with the same performance of real infill, instead of designing the whole infill. Because of too many uncertainties, codes and researchers gave many expressions that were not as the same as the others. The major intent of this paper is calculation the width of this diagonal strut, which has the most characteristics of infill. This paper by comprehensive on different parameters like the modulus of young or moment of inertia of columns presents a new formula for achieving the equivalent strut width. In fact, this new formula is extracted from about 60 FEM analyses models. It can be said that this formula is very efficient and accurate in estimating the equivalent strut width, considering the large number of effective parameters relative to similar relationships provided by other researchers. In most cases, the results are so close to the values obtained by the FEM. In this formula, the effect of out of plane buckling is neglected and this formula is used just in steel structures. Also, the thickness of infill panel, and the lateral force applied to frame are constant. In addition, this new formula is just for modeling the lateral stiffness. Obtaining the nearest response in analyzing is important to the designers, so this new formula can help them to reach more accurate response among a lot of experimental equations proposed by researchers.

An empirical evaluation of multivariate spatial crash frequency models

Many studies have employed spatial, temporal, or a combination of both specifications for analysis of roadway crashes at different spatial levels. However, there is lack of a comprehensive study which compares the crash estimation performance of different spatial weight matrices and their combination with various temporal treatments. The current study fills the research gap by comparing different Full Bayesian (FB) multivariate spatiotemporal crash models. The pedestrian and bicyclist crash data across an eight-year period for 58 counties in California were used as a case study. Three groups of models were developed based on temporal treatment, where each group comprised of 17 models differing on the basis of different adjacency- and distance-based spatial weight matrices. The first group of multivariate models incorporated only unstructured random error term and spatially structured conditional autoregressive (CAR) term. The second group built upon the former and introduced a linear time trend to develop a spatiotemporal model, while the third group allowed the interaction of space and time. The predictive performance of the alternate models across and within groups was assessed by employing several evaluation criteria.The modeling results demonstrated the robustness of models based on the similar signs and closeness of coefficients for the posterior estimates of parameters. For overall model comparison, the pure-distance model D0.5 demonstrated the best performance for different evaluation criteria based on training and test errors across three groups. The variability in performance of other distance models suggested that caution must be exercised for the choice of exponents. The correlation analysis revealed the presence of positive correlations among the criteria based on training errors, as well as with cross-validation. However, a very strong positive correlation was observed between the criteria based on effective number of parameters and posterior deviance, indicating that an increased number of parameters may not lead to improved model fit. This finding reinforced the importance of selecting the optimum weight matrix for spatial correlation as a more complex structure may not lead to expected advantages at model performance. For comparison among three groups of different temporal treatments, the third group demonstrated the best performance and conveyed the benefits of incorporating the spatial and temporal interaction. The results from ANOVA (analysis of variance) and HSD (Honest Significant Differences) tests also established the existence of statistical differences for the superiority of space-time interactions models. However, the box and whisker plots demonstrated high variability among the models of the third group, suggesting that some models may not benefit from interaction term. For comparison among adjacency- and distance-based models, the distance-based models were mostly observed to be superior. However, the greater variability of model performance associated with distance-based models suggested for careful consideration during their selection. Additionally, it is important to note that the results observed in this study are specific to the county-level crash data of California. As such, the study does not recommend generalization of the results for extension to other spatial levels of roadway network, and readers and future research studies are advised to exercise caution before implementing the models.

Multimodal crash frequency modeling: Multivariate space-time models with alternate spatiotemporal interactions

Enhancement of safety for all transportation mode users plays an essential role in the implementation of multimodal transportation systems. Compared with crash frequency models dedicated to motorized mode users, the use of these models has been considerably scarce in the multimodal literature. To fill this research gap, the authors aimed to develop and evaluate three multivariate space-time models with different temporal trends and spatiotemporal interactions. The model estimates justified the use of mode-varying coefficients for explanatory variables as the impact of these factors varied across different crash modes. Largely, a similar set of influential covariates was generated by the three models which indicate their robustness. However, notable differences were observed from the assessment of evaluation criteria pertaining to predictive accuracy based on criteria assessing the training and test errors. The model with time-varying spatial random effects demonstrated superior performance for training and test errors. However, due to the significant increase in number of effective parameters that were utilized for model development, this model appeared to have the largest value of deviance information criterion (DIC). In terms of the comparison between models based on site ranking performance, the time-varying spatial random effects model demonstrated the best performance in both site consistency and method consistency. In other words, the superiority of the model’s predictive performance could be transferred to yield more accurate result at site ranking.

Comparative evaluation of temporal correlation treatment in crash frequency modelling

ABSTRACTThere is relatively little research dedicated to the evaluation of different temporal treatments on modelling performance. This study proposed two new methods which combined the strengths of linear trend and time-varying coefficients with the autoregressive process and compared their performance with seven other temporal models used in the past. All models generated a similar number of statistically significant variables and close variable coefficients, but different modelling performance. For prediction accuracy, the model which accounts only for autoregressive effect illustrated superior performance in terms of cross-validation and typical assessment, which was based on same data used to develop models. Nonetheless, if the penalized criterion was used, both proposed models outperformed other competing models, indicating their capability to yield similar prediction accuracy with relatively smaller effective number of parameters. This suggests further exploration of models that combine various temporal treatments. Finally, the correlations were also observed among the various modelling assessment criteria.

A systematic study on numerical simulation of electrified jet printing

In this paper, a numerical model based on a systematic study of electrified jet printing is presented. The Volume of Fluid (VOF) method which suits for modeling multiphase flows with a continuous interface is used. The surface tension force is calculated with the Continuum Surface Force (CSF) method and the electric forces are added to the momentum equation by taking the divergence of the Maxwell stress tensor. A systematic study is carried out by introducing three dimensionless numbers, namely Reynolds, Electro-Weber and Weber numbers. Employing these dimensionless numbers, the number of effective parameters is reduced, and a relative comparison of the importance of competing forces on the process becomes possible. It is observed that the electric forces contribute to the formation of the jet by acting on its tip, and by pulling the jet towards the deposition surface. The results show that an increase in Reynolds and Electro-Weber numbers both lead to form a thinner jet. It is also observed that further increase in Electro-Weber and Reynolds numbers leads to the formation of an unstable jet.

PERFORMANCE EVALUATION OF DYNAMIC LOAD BALANCING SYSTEM BY USING NUMBER OF EFFECTIVE PARAMETERS

Today, the World Wide Web is growing at an increasing rate and occupied a big percentage of the traffic in the Internet. These systems lead to overloaded and congested proxy servers. Load Balancing and Clustering of web servers helps in balancing this load among various web servers. In this paper we have given solution for load balancing among web servers and evaluated their performance using number of effective parameters. This paper explains the main objectives of proposed algorithm. It shows architecture diagram for load balancing in web server clusters. Proposed algorithm is explained using Pseudocode. Time complexity of proposed algorithm is also shown. Finally, it is concluded with experimental results andanalysis.

LA-Based Approach for IoT Security

The most important aspect in IoT is security. The provision of security in IoT systems is the responsibility of a trust management mechanism. However a trust management mechanism comprises a number of components, of which secure routing is vital among them. There are a number of effective parameters in secure routing which have been considered in the presented multi-objective optimization model. In this paper, Multi-Objective Learning Automata (MOLA) was used to solve secure routing problem which can simultaneously optimize all parameters. There exist three methods of training LA, and the results of the different methods were compared in this study. The proposed approach can be used with both administrator and users because their requirements are considered in a model and it is quite easy for administrator and users to comprehend.

Improving Performance of Dynamic Load Balancing among Web Servers by Using Number of Effective Parameters

Improving Performance of Dynamic Load Balancing among Web Servers by Using Number of Effective Parameters

Injectivity and quantification of capillary trapping for CO2 storage: A review of influencing parameters

CO2 injection for storage in subsurface geologic medium is one of the techniques developed in the past years to mitigate anthropological CO2. Prior to CO2 injection, it is essential to predict the feasibility of medium in terms of storage capacity, injectivity, trapping mechanisms, and containment. There have been many studies regarding techniques which can be applied to ensure the safety of CO2 injection. However, there are few studies indicating the importance of capillary trapping during and after CO2 injection. The aim of this study is to review the fundamentals of injectivity and its relationship with capillary trapping for CO2 storage in depleted oil and gas reservoirs. Considering the number of effective parameters which are associated with the injectivity and capillary trapping, it is recommended to perform a comprehensive analysis to determine the optimum injection rate and safe storage medium before operation.