MARS Algorithm Research Articles

M5' and Mars Based Prediction Models for Properties of Self-compacting Concrete Containing Fly Ash

The main purpose of this paper is to predict the properties (mechanical and rheological) of the self-compacting concrete (SCC) containing fly ash as cement replacement by using two decision tree algorithms: M5′ and Multivariate adaptive regression splines (Mars). The M5′ algorithm as a rule based method is used to develop new practical equations while the MARS algorithm besides its high predictive ability is used to determine the most important parameters. To achieve this purpose, a data set containing 114 data points related to effective parameters affect on SSC properties is used. A gamma test is employed to determine the most effective parameters in prediction of the compressive strength at 28 days, the V-funnel time, the slump flow, and the L-box ratio of SCC. The results from this study suggests that tree based models perform remarkably well in predicting the properties of the self-compacting concrete containing fly ash as cement replacement.

Approaches to reducing photon dose calculation errors near metal implants.

Dose calculation errors near metal implants are caused by limitations of the dose calculation algorithm in modeling tissue/metal interface effects as well as density assignment errors caused by imaging artifacts. The purpose of this study was to investigate two strategies for reducing dose calculation errors near metal implants: implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) dose calculation method and use of metal artifact reduction methods for computed tomography (CT) imaging. Both error reduction strategies were investigated using a simple geometric slab phantom with a rectangular metal insert (composed of titanium or Cerrobend), as well as two anthropomorphic phantoms (one with spinal hardware and one with dental fillings), designed to mimic relevant clinical scenarios. To assess the dosimetric impact of metal kernels, the authors implemented titanium and silver kernels in a commercial collapsed cone C/S algorithm. To assess the impact of CT metal artifact reduction methods, the authors performed dose calculations using baseline imaging techniques (uncorrected 120 kVp imaging) and three commercial metal artifact reduction methods: Philips Healthcare's o-mar, GE Healthcare's monochromatic gemstone spectral imaging (gsi) using dual-energy CT, and gsi with metal artifact reduction software (mars) applied. For the simple geometric phantom, radiochromic film was used to measure dose upstream and downstream of metal inserts. For the anthropomorphic phantoms, ion chambers and radiochromic film were used to quantify the benefit of the error reduction strategies. Metal kernels did not universally improve accuracy but rather resulted in better accuracy upstream of metal implants and decreased accuracy directly downstream. For the clinical cases (spinal hardware and dental fillings), metal kernels had very little impact on the dose calculation accuracy (<1.0%). Of the commercial CT artifact reduction methods investigated, the authors found that o-mar was the most consistent method, resulting in either improved dose calculation accuracy (dental case) or little impact on calculation accuracy (spine case). gsi was unsuccessful at reducing the severe artifacts caused by dental fillings and had very little impact on calculation accuracy. gsi with mars on the other hand gave mixed results, sometimes introducing metal distortion and increasing calculation errors (titanium rectangular implant and titanium spinal hardware) but other times very successfully reducing artifacts (Cerrobend rectangular implant and dental fillings). Though successful at improving dose calculation accuracy upstream of metal implants, metal kernels were not found to substantially improve accuracy for clinical cases. Of the commercial artifact reduction methods investigated, o-mar was found to be the most consistent candidate for all-purpose CT simulation imaging. The mars algorithm for gsi should be used with caution for titanium implants, larger implants, and implants located near heterogeneities as it can distort the size and shape of implants and increase calculation errors.

PENGEMBANGAN PERANGKAT LUNAK PENYEMBUNYIAN PESAN TERENKRIPSI MENGGUNAKAN ALGORITMA MARS PADA CITRA DIGITAL DENGAN METODE ADAPTIF

Steganography and cryptography are security method of secret message from third party who curious to see it. Steganography secures the message by hiding it on digital media. While for cryptography, it secures the message by coding it so the message will be understandable only by the message sender and the receiver. In this research, the secret text message shall be encrypted first by using MARS cryptography algorithm, then it’ll be embedded on bitmap extended grayscale image digital media with adaptive steganography method. Adaptive steganography method consisted of three phases: capacity assessment, minimum error replacement (MER) and false contouring. While MARS cryptography algorithm also has three phases: forward mixing, cryptographic core and backward mixing. After being implemented into the program, the resulted stego-images were assessed subjectively by sharing questionnaires, where out of 33 respondents 72% said that before and after embedded, the images don’t have any differences from its brightness, 48% from its noise, and 67% couldn’t choose which one stego-image is. The objective assessment was done by calculating the value of Root Mean Square Error (RMSE) and Peak Signal to Noise Rational (PSNR), where out of 8 tested stego-images had low RMSE and the PSNR were above 30dB. From both assessment, it’s concluded that the resulted stego-images had good quality (no changement significantly). Keywords : Cryptography, Steganography, MARS algorithm, adaptive method

SU‐E‐T‐329: Dosimetric Impact of Implementing Metal Artifact Reduction Methods and Metal Energy Deposition Kernels for Photon Dose Calculations

Purpose:To investigate two strategies for reducing dose calculation errors near metal implants: use of CT metal artifact reduction methods and implementation of metal‐based energy deposition kernels in the convolution/superposition (C/S) method.Methods:Radiochromic film was used to measure the dose upstream and downstream of titanium and Cerrobend implants. To assess the dosimetric impact of metal artifact reduction methods, dose calculations were performed using baseline, uncorrected images and metal artifact reductionMethods:Philips O‐MAR, GE's monochromatic gemstone spectral imaging (GSI) using dual‐energy CT, and GSI imaging with metal artifact reduction software applied (MARs).To assess the impact of metal kernels, titanium and silver kernels were implemented into a commercial collapsed cone C/S algorithm.Results:The CT artifact reduction methods were more successful for titanium than Cerrobend. Interestingly, for beams traversing the metal implant, we found that errors in the dimensions of the metal in the CT images were more important for dose calculation accuracy than reduction of imaging artifacts. The MARs algorithm caused a distortion in the shape of the titanium implant that substantially worsened the calculation accuracy. In comparison to water kernel dose calculations, metal kernels resulted in better modeling of the increased backscatter dose at the upstream interface but decreased accuracy directly downstream of the metal. We also found that the success of metal kernels was dependent on dose grid size, with smaller calculation voxels giving better accuracy.Conclusion:Our study yielded mixed results, with neither the metal artifact reduction methods nor the metal kernels being globally effective at improving dose calculation accuracy. However, some successes were observed. The MARs algorithm decreased errors downstream of Cerrobend by a factor of two, and metal kernels resulted in more accurate backscatter dose upstream of metals. Thus, these two strategies do have the potential to improve accuracy for patients with metal implants in certain scenarios.This work was supported by Public Health Service grants CA 180803 and CA 10953 awarded by the National Cancer Institute, United States of Health and Human Services, and in part by Mobius Medical Systems.

Quantitative structure–activity relationship study on BTK inhibitors by modified multivariate adaptive regression spline and CoMSIA methods

Bruton’s tyrosine kinase (BTK) plays a crucial role in B-cell activation and development, and has emerged as a new molecular target for the treatment of autoimmune diseases and B-cell malignancies. In this study, two- and three-dimensional quantitative structure–activity relationship (2D and 3D-QSAR) analyses were performed on a series of pyridine and pyrimidine-based BTK inhibitors by means of genetic algorithm optimized multivariate adaptive regression spline (GA-MARS) and comparative molecular similarity index analysis (CoMSIA) methods. Here, we propose a modified MARS algorithm to develop 2D-QSAR models. The top ranked models showed satisfactory statistical results (2D-QSAR: Q2 = 0.884, r2 = 0.929, r2pred = 0.878; 3D-QSAR: q2 = 0.616, r2 = 0.987, r2pred = 0.905). Key descriptors selected by 2D-QSAR were in good agreement with the conclusions of 3D-QSAR, and the 3D-CoMSIA contour maps facilitated interpretation of the structure–activity relationship. A new molecular database was generated by molecular fragment replacement (MFR) and further evaluated with GA-MARS and CoMSIA prediction. Twenty-five pyridine and pyrimidine derivatives as novel potential BTK inhibitors were finally selected for further study. These results also demonstrated that our method can be a very efficient tool for the discovery of novel potent BTK inhibitors.

An evaluation of three commercially available metal artifact reduction methods for CT imaging

Three commercial metal artifact reduction methods were evaluated for use in computed tomography (CT) imaging in the presence of clinically realistic metal implants: Philips O-MAR, GE’s monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI monochromatic imaging with metal artifact reduction software applied (MARs). Each method was evaluated according to CT number accuracy, metal size accuracy, and streak artifact severity reduction by using several phantoms, including three anthropomorphic phantoms containing metal implants (hip prosthesis, dental fillings and spinal fixation rods). All three methods showed varying degrees of success for the hip prosthesis and spinal fixation rod cases, while none were particularly beneficial for dental artifacts. Limitations of the methods were also observed. MARs underestimated the size of metal implants and introduced new artifacts in imaging planes beyond the metal implant when applied to dental artifacts, and both the O-MAR and MARs algorithms induced artifacts for spinal fixation rods in a thoracic phantom. Our findings suggest that all three artifact mitigation methods may benefit patients with metal implants, though they should be used with caution in certain scenarios.

Application of principal component analysis–multivariate adaptive regression splines for the simultaneous spectrofluorimetric determination of dialkyltins in micellar media

A new multicomponent analysis method, based on principal component analysis–multivariate adaptive regression splines (PC–MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC–MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC–MS method for determination of dimethyltin and/or dibutyltin.

The Method of Manufactured Universes for validating uncertainty quantification methods

The Method of Manufactured Universes is presented as a validation framework for uncertainty quantification (UQ) methodologies and as a tool for exploring the effects of statistical and modeling assumptions embedded in these methods. The framework calls for a manufactured reality from which “experimental” data are created (possibly with experimental error), an imperfect model (with uncertain inputs) from which simulation results are created (possibly with numerical error), the application of a system for quantifying uncertainties in model predictions, and an assessment of how accurately those uncertainties are quantified. The application presented in this paper manufactures a particle-transport “universe”, models it using diffusion theory with uncertain material parameters, and applies both Gaussian process and Bayesian MARS algorithms to make quantitative predictions about new “experiments” within the manufactured reality. The results of this preliminary study indicate that, even in a simple problem, the improper application of a specific UQ method or unrealized effects of a modeling assumption may produce inaccurate predictions. We conclude that the validation framework presented in this paper is a powerful and flexible tool for the investigation and understanding of UQ methodologies.

Using several pair-wise informant sequences for de novo prediction of alternatively spliced transcripts

BackgroundAs part of the ENCODE Genome Annotation Assessment Project (EGASP), we developed the MARS extension to the Twinscan algorithm. MARS is designed to find human alternatively spliced transcripts that are conserved in only one or a limited number of extant species. MARS is able to use an arbitrary number of informant sequences and predicts a number of alternative transcripts at each gene locus.ResultsMARS uses the mouse, rat, dog, opossum, chicken, and frog genome sequences as pairwise informant sources for Twinscan and combines the resulting transcript predictions into genes based on coding (CDS) region overlap. Based on the EGASP assessment, MARS is one of the more accurate dual-genome prediction programs. Compared to the GENCODE annotation, we find that predictive sensitivity increases, while specificity decreases, as more informant species are used. MARS correctly predicts alternatively spliced transcripts for 11 of the 236 multi-exon GENCODE genes that are alternatively spliced in the coding region of their transcripts. For these genes a total of 24 correct transcripts are predicted.ConclusionThe MARS algorithm is able to predict alternatively spliced transcripts without the use of expressed sequence information, although the number of loci in which multiple predicted transcripts match multiple alternatively spliced transcripts in the GENCODE annotation is relatively small.

Efficient sampling schemes for Bayesian MARS models with many predictors

Multivariate adaptive regression spline fitting or MARS (Friedman 1991) provides a useful methodology for flexible adaptive regression with many predictors. The MARS methodology produces an estimate of the mean response that is a linear combination of adaptively chosen basis functions. Recently, a Bayesian version of MARS has been proposed (Denison, Mallick and Smith 1998a, Holmes and Denison, 2002) combining the MARS methodology with the benefits of Bayesian methods for accounting for model uncertainty to achieve improvements in predictive performance. In implementation of the Bayesian MARS approach, Markov chain Monte Carlo methods are used for computations, in which at each iteration of the algorithm it is proposed to change the current model by either (a) Adding a basis function (birth step) (b) Deleting a basis function (death step) or (c) Altering an existing basis function (change step). In the algorithm of Denison, Mallick and Smith (1998a), when a birth step is proposed, the type of basis function is determined by simulation from the prior. This works well in problems with a small number of predictors, is simple to program, and leads to a simple form for Metropolis-Hastings acceptance probabilities. However, in problems with very large numbers of predictors where many of the predictors are useless it may be difficult to find interesting interactions with such an approach. In the original MARS algorithm of Friedman (1991) a heuristic is used of building up higher order interactions from lower order ones, which greatly reduces the complexity of the search for good basis functions to add to the model. While we do not exactly follow the intuition of the original MARS algorithm in this paper, we nevertheless suggest a similar idea in which the Metropolis-Hastings proposals of Denison, Mallick and Smith (1998a) are altered to allow dependence on the current model. Our modification allows more rapid identification and exploration of important interactions, especially in problems with very large numbers of predictor variables and many useless predictors. Performance of the algorithms is compared in simulation studies.

Maximal Correlation and Adaptive Splines

Friedman and Silverman proposed the TURBO algorithm for univariate additive modeling and also introduced the concept of minimum span as an important ingredient of the algorithm. Recently, Friedman adopted the idea of minimum span to develop his MARS algorithm for multivariate additive modeling. Based on the observation that the correlation between basis functions on a single variable increases as their knots get closer, this article presents a maximal correlation constraint to replace the minimum-span condition in these algorithms. Limiting the maximal correlation between a new basis function and the set already in the model is a more effective way of avoiding degeneracies involved in the multivariate regression. For a univariate model, we show by example that MARS version 3.5 and the modified algorithm proposed herein are competitive and that the latter is sometimes superior. We also show that the running time of the new algorithm is linear in the sample size even though we locate the optimal knot size without restricting knots to the set of observed data sites.

Maximal Correlation and Adaptive Splines

Friedman and Silverman proposed the TURBO algorithm for univariate additive modeling and also introduced the concept of minimum span as an important ingredient of the algorithm. Recently, Friedman adopted the idea of minimum span to develop his MARS algorithm for multivariate additive modeling. Based on the observation that the correlation between basis functions on a single variable increases as their knots get closer, this article presents a maximal correlation constraint to replace the minimum-span condition in these algorithms. Limiting the maximal correlation between a new basis function and the set already in the model is a more effective way of avoiding degeneracies involved in the multivariate regression. For a univariate model, we show by example that MARS version 3.5 and the modified algorithm proposed herein are competitive and that the latter is sometimes superior. We also show that the running time of the new algorithm is linear in the sample size even though we locate the optimal knot size without restricting knots to the set of observed data sites.