Physical Sciences and Mathematics Commons^™

Parallel Algorithms For Steiner Forest, Laleh Ghalami

Wayne State University Dissertations

The Steiner Forest Problem is one of the fundamental combinatorial optimization problemsin operations research and computer science. Its applications range from network design to computational biology. Given an undirected graph with non-negative weights for edges and a set of pairs of vertices called terminals, the Steiner Forest Problem is to find the minimum cost subgraph that connects each of the terminal pairs together. The Steiner Forest Problem is APX-hard and NP-hard to approximate within 96/95. Several heuristic and approximation algorithms, with different approximation guarantees, have been proposed for the Steiner Forest Problem. Despite the several research in designing sequential and …

Deep Learning As Native Scientific Workflows In The Modern Swfms - Dataview, Junwen Liu

Wayne State University Dissertations

Scientific workflow has become a common practice for scientists to effectively formalize and structure complex scientific processes, which in turn has accelerated scientific discoveries in numerous research fields. With the recent thriving of deep learning in broad scientific projects, there is a rising need for deep learning support in scientific workflow infrastructures SWFMSs. However, current GPU-enabled deep learning frameworks are developed separately, not suitable for direct exploitation in SWFMSs, which forces scientists to handle deep learning outside of SWFMSs and then integrate in workflows in an ad-hoc manner. What workflow users pressingly need today is a user-friendly and well-integrated SWFMS …

Adversarial Machine Learning For Advanced Medical Imaging Systems, Xin Li

Wayne State University Dissertations

Although deep neural networks (DNNs) have achieved significant advancement in various challenging tasks of computer vision, they are also known to be vulnerable to so-called adversarial attacks. With only imperceptibly small perturbations added to a clean image, adversarial samples can drastically change models’ prediction, resulting in a significant drop in DNN’s performance. This phenomenon poses a serious threat to security-critical applications of DNNs, such as medical imaging, autonomous driving, and surveillance systems. In this dissertation, we present adversarial machine learning approaches for natural image classification and advanced medical imaging systems.

We start by describing our advanced medical imaging systems to …

Optical Signal Processing With Discrete-Space Metamaterials, Mohammad Moein Moeini

Wayne State University Dissertations

As digital circuits are approaching the limits of Moore’s law, a great deal of efforthas been directed to alternative computing approaches. Among them, the old concept of optical signal processing (OSP) has attracted attention, revisited in the light of metamaterials and nano-photonics. This approach has been successful in realizing basic mathematical operations, such as derivatives and integrals, but it is difficult to be applied to more complex ones. Inspired by digital filters, we propose a radically new OSP approach, able to realize arbitrary mathematical operations over a nano-photonic platform. We demonstrate this concept for the case of spatial differentiation, image …

Assessment Of Small-Scale Mechanical Damage On Safety Performance Of Lithium-Ion Batteries, Maher A. Almohammedali

Wayne State University Dissertations

Battery safety studies of Li-ion batteries (LIBs) related to mechanical abusive loadinginvolving contact compression of hard objects, bending and nail penetration usually show that LIBs can withstand some mechanical deformation prior to internal short circuit (ISC). Stress or strain-based criteria has been suggested to understand local conditions between the anode, cathode and the separator due such abusive mechanical loading. A strain-based criterion has also been developed at Wayne State and reported in open literature (Newaz et al.) Focus of such work has been to determine strain levels that may rupture the separator putting in contact the anode and cathode leading …

Non-Parametric Tests For Testing Of Scale Parameters, Manish Goyal, Narinder Kumar

Journal of Modern Applied Statistical Methods

One of the fundamental problems in testing of equality of populations is of testing the equality of scale parameters. The subsequent usages for scale are dispersion, spread and variability. In this paper, we proposed non-parametric tests based on U-Statistics for the testing of equality of scale parameters. The null distribution of proposed tests is developed and its Pitman efficiency is worked out to compare proposed tests with respect to some existing tests. Simulation study is carried out to compute the asymptotic power of proposed tests. An illustrative example is also provided.

Rhizobium Symbiotic Capacity Shapes Root-Associated Microbiomes In Soybean, Yuanhui Lu, Bin Ma, Wenfeng Chen, Klaus Schlaeppi, Matthias Erb, Erinne Stirling, Lingfei Hu, Entao Wang, Yunzeng Zhang, Kankan Zhao, Zhijiang Lu, Shudi Ye, Jianming Xu

Environmental Science and Geology Faculty Research Publications

Root-microbiome interactions are of central importance for plant performance and yield. A distinctive feature of legumes is that they engage in symbiosis with N₂-fixing rhizobia. If and how the rhizobial symbiotic capacity modulates root-associated microbiomes are still not yet well understood. We determined root-associated microbiomes of soybean inoculated with wild type (WT) or a noeI mutant of Bradyrhizobium diazoefficiens USDA 110 by amplicon sequencing. UPLC-MS/MS was used to analyze root exudates. The noeI gene is responsible for fucose-methylation of Nod factor secreted by USDA 110 WT strain. Soybean roots inoculated with the noeI mutant showed a significant decrease …

ℂ-Motivic Modular Forms, Bogdan Gheorghe, Daniel C. Isaksen, Achim Krause, Nicolas Ricka

Mathematics Faculty Research Publications

We construct a topological model for cellular, 2-complete, stable C-motivic homotopy theory that uses no algebro-geometric foundations.We compute the Steenrod algebra in this context, and we construct a “motivic modular forms” spectrum over ℂ.

Comparative Study Of New And Traditional Estimators Of A New Lifetime Model, Sandeep Kumar Maurya, Sanjay Kumar Singh, Umesh Singh

Journal of Modern Applied Statistical Methods

In this article, we have studied the behavior of estimators of parameter of a new lifetime model, suggested by Maurya et al. (2016), obtained by using methods of moments, maximum likelihood, maximum product spacing, least squares, weighted least squares, percentile, Cramer-von-Mises, Anderson-Darling and Right-tailed Anderson-Darling. Comparison of the estimators has been done on the basis of their mean square errors, biases, absolute and maximum absolute differences between empirical and estimated distribution function and a newly proposed criterion. We have also obtained the asymptomatic confidence interval and associated coverage probability for the parameter.

On The Extension Of Exponentiated Pareto Distribution, Amal S. Hassan, Saeed Elsayed Hemeda, Said G. Nassr

Journal of Modern Applied Statistical Methods

In this study, an extended exponentiated Pareto distribution is proposed. Some statistical properties are derived. We consider maximum likelihood, least squares, weighted least squares and Bayesian estimators. A simulation study is implemented for investigating the accuracy of different estimators. An application of the proposed distribution to a real data is presented.

A New Generating Family Of Distributions: Properties And Applications To The Weibull Exponential Model, El-Sayed A. El-Sherpieny, Salwa Assar, Tamer Helal

Journal of Modern Applied Statistical Methods

A new method for generating family of distributions was proposed. Some fundamental properties of the new proposed family include the quantile, survival function, hazard rate function, reversed hazard and cumulative hazard rate functions are provided. This family contains several new models as sub models, such as the Weibull exponential model which was defined and discussed its properties. The maximum likelihood method of estimation is using to estimate the model parameters of the new proposed family. The flexibility and the importance of the Weibull-exponential model is assessed by applying it to a real data set and comparing it with other known …

Jmasm 55: Matlab Algorithms And Source Codes Of 'Cbnet' Function For Univariate Time Series Modeling With Neural Networks (Matlab), Cagatay Bal, Serdar Demir

Journal of Modern Applied Statistical Methods

Artificial Neural Networks (ANN) can be designed as a nonparametric tool for time series modeling. MATLAB serves as a powerful environment for ANN modeling. Although Neural Network Time Series Tool (ntstool) is useful for modeling time series, more detailed functions could be more useful in order to get more detailed and comprehensive analysis results. For these purposes, cbnet function with properties such as input lag generator, step-ahead forecaster, trial-error based network selection strategy, alternative network selection with various performance measure and global repetition feature to obtain more alternative network has been developed, and MATLAB algorithms and source codes has been …

Combination Of High Specific Activity Carbon-14 Labeling And High Resolution Mass Spectrometry To Study Pesticide Metabolism In Crops: Metabolism Of Cycloxaprid In Rice, Dahang Shen, Zhijiang Lu, Jiayin Zhong, Sufen Zhang, Qingfu Ye, Wei Wang, Jay Gan

Environmental Science and Geology Faculty Research Publications

The study of pesticide metabolism in crops is critical for assessing the mode of action and environmental risks of pesticides. However, the study of pesticide metabolism in crops is usually complicated and it is often a daunting challenge to accurately screen the metabolites of novel pesticides in complex matrices. This study demonstrated a combined use of high-specific activity carbon-14 labeling and high-resolution mass spectrometry (HSA-¹⁴C-HRMS) for metabolism profiling of a novel neonicotinoid cycloxaprid in rice. By generating the characteristic radioactive peaks on the liquid chromatogram, the use of ¹⁴C can eliminate the severe interference of complex matrices …

Bayesian Sensitivity-Specificity And Roc Analysis For Finding Key Drivers, Stan Lipovetsky, Michael W. Conklin

Journal of Modern Applied Statistical Methods

Finding key drivers in regression modeling via Bayesian Sensitivity-Specificity and Receiver Operating Characteristic is suggested, and clearly interpretable results are obtained. Numerical comparisons with other techniques show that this methodology can be useful in practical statistical modeling and analysis helping to researchers and managers in making meaningful decisions.

Performance Of The Beta-Binomial Model For Clustered Binary Responses: Comparison With Generalized Estimating Equations, Seongah Im

Journal of Modern Applied Statistical Methods

This study examined performance of the beta-binomial model in comparison with GEE using clustered binary responses resulting in non-normal outcomes. Monte Carlo simulations were performed under varying intracluster correlations and sample sizes. The results showed that the beta-binomial model performed better for small sample, while GEE performed well under large sample.

From Mathematics To Medicine: A Practical Primer On Topological Data Analysis (Tda) And The Development Of Related Analytic Tools For The Functional Discovery Of Latent Structure In Fmri Data, Andrew Salch, Adam Regalski, Hassan Abdallah, Raviteja Suryadevara, Michael J. Catanzaro, Vaibhav A. Diwadkar

Mathematics Faculty Research Publications

fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. …

Novel Application Of 210po-210pb Disequilibria To Date Snow, Melt Pond, Ice Core, And Ice-Rafted Sediments In The Arctic Ocean, Mark Baskaran, Katherine Krupp, Mark Baskaran

Environmental Science and Geology Faculty Research Publications

We collected surface ocean water, snow, grab ice, ice core, melt pond and ice-rafted sediment (IRS) from 5 ice stations during the Western Arctic US GEOTRACES cruise (USGCG Healy; August 10 – October 7, 2015) and analyzed for 210Po (T1/2 = 138.4 days) and 210Pb (T1/2 = 22.3 years) in dissolved and particulate phases (snow, grab ice, ice core, surface seawater) to investigate the 210Po:210Pb disequilibria in these matrices. Thirteen aerosol samples, using a large-volume aerosol sampler (PM10), from Dutch Harbor, AK to North Pole, were also collected and analyzed for 210Po/210Pb to quantify the atmospheric depositional input to the …

210po And 210pb As Tracers Of Particle Cycling And Export In The Western Arctic Ocean, Wokil Bam, Kanchan Maiti

Environmental Science and Geology Faculty Research Publications

The distribution and vertical fluxes of particulate organic carbon and other key elements in the Arctic Ocean are primarily governed by the spatial and seasonal changes in primary productivity, areal extent of ice cover, and lateral exchange between the shelves and interior basins. The Arctic Ocean has undergone rapid increase in primary productivity and drastic decrease in the areal extent of seasonal sea ice in the last two decades. These changes can greatly influence the biological pump as well as associated carbon export and key element fluxes. Here, we report the export of particulate organic and inorganic carbon, particulate nitrogen …

Pareto Distribution Under Hybrid Censoring: Some Estimation, Gyan Prakash

Journal of Modern Applied Statistical Methods

In the present study, the Pareto model is considered as the model from which observations are to be estimated using a Bayesian approach. Properties of the Bayes estimators for the unknown parameters have studied by using different asymmetric loss functions on hybrid censoring pattern and their risks have compared. The properties of maximum likelihood estimation and approximate confidence length have also been investigated under hybrid censoring. The performances of the procedures are illustrated based on simulated data obtained under the Metropolis-Hastings algorithm and a real data set.

Calibration-Based Estimators Using Different Distance Measures Under Two Auxiliary Variables: A Comparative Study, Piyush Kant Rai, Alka Singh, Muhammad Qasim

Journal of Modern Applied Statistical Methods

This article introduces calibration estimators under different distance measures based on two auxiliary variables in stratified sampling. The theory of the calibration estimator is presented. The calibrated weights based on different distance functions are also derived. A simulation study has been carried out to judge the performance of the proposed estimators based on the minimum relative root mean squared error criterion. A real-life data set is also used to confirm the supremacy of the proposed method.

A Global Dataset Of Atmospheric 7be And 210pb Measurements: Annual Air Concentration And Depositional Flux, Fule Zhang, Jinlong Wang, Mark Baskaran, Qiangqiang Zhong, Yali Wang, Jussi Paatero, Jinzhou Du

Environmental Science and Geology Faculty Research Publications

7Be and 210Pb air concentration and depositional flux data provide key information on the origins and movements of air masses, as well as atmospheric deposition processes and residence time of aerosols. After their deposition onto the Earth's surface, they are utilized for tracing soil redistribution processes on land, particle dynamics in aquatic systems, and mixing processes in open ocean. Here we present a global dataset of air concentration and depositional flux measurements of atmospheric 7Be and 210Pb made by a large number of global research communities. Data were collected from published papers between 1955 and early 2020. It includes the …

Robust Lag Weighted Lasso For Time Series Model, Tahir R. Dikheel, Alaa Q. Yaseen

Journal of Modern Applied Statistical Methods

The lag-weighted lasso was introduced to deal with lag effects when identifying the true model in time series. This method depends on weights to reflect both the coefficient size and the lag effects. However, the lag weighted lasso is not robust. To overcome this problem, we propose robust lag weighted lasso methods. Both the simulation study and the real data example show that the proposed methods outperform the other existing methods.

Pairwise Balanced Designs From Cyclic Pbib Designs, D. K. Ghosh, N. R. Desai, Shreya Ghosh

Journal of Modern Applied Statistical Methods

A pairwise balanced designs was constructed using cyclic partially balanced incomplete block designs with either (λ1 – λ2) = 1 or (λ2 – λ1) = 1. This method of construction of Pairwise balanced designs is further generalized to construct it using cyclic partially balanced incomplete block design when |(λ1 – λ2)| = p. The methods of construction of pairwise balanced designs was supported with examples. A table consisting parameters of Cyclic PBIB designs and its corresponding constructed pairwise balanced design is also included.

Generalized Ratio-Cum-Product Estimator For Finite Population Mean Under Two-Phase Sampling Scheme, Gajendra Kumar Vishwakarma, Sayed Mohammed Zeeshan

Journal of Modern Applied Statistical Methods

A method to lower the MSE of a proposed estimator relative to the MSE of the linear regression estimator under two-phase sampling scheme is developed. Estimators are developed to estimate the mean of the variate under study with the help of auxiliary variate (which are unknown but it can be accessed conveniently and economically). The mean square errors equations are obtained for the proposed estimators. In addition, optimal sample sizes are obtained under the given cost function. The comparison study has been done to set up conditions for which developed estimators are more effective than other estimators with novelty. The …

Two Different Classes Of Shrinkage Estimators For The Scale Parameter Of The Rayleigh Distribution, Talha Omer, Zawar Hussain, Muhammad Qasim, Said Farooq Shah, Akbar Ali Khan

Journal of Modern Applied Statistical Methods

Shrinkage estimators are introduced for the scale parameter of the Rayleigh distribution by using two different shrinkage techniques. The mean squared error properties of the proposed estimator have been derived. The comparison of proposed classes of the estimators is made with the respective conventional unbiased estimators by means of mean squared error in the simulation study. Simulation results show that the proposed shrinkage estimators yield smaller mean squared error than the existence of unbiased estimators.

Extending Singh-Maddala Distribution, Mohamed Ali Ahmed

Journal of Modern Applied Statistical Methods

A new distribution, the exponentiated transmuted Singh-Maddala distribution (ETSM), is presented, and three important special distributions are illustrated. Some mathematical properties are obtained, and parameters estimation method is applied using maximum likelihood. Illustrations based on random numbers and a real data set are given.

How To Apply Multiple Imputation In Propensity Score Matching With Partially Observed Confounders: A Simulation Study And Practical Recommendations, Albee Ling, Maria Montez-Rath, Maya Mathur, Kris Kapphahn, Manisha Desai

Journal of Modern Applied Statistical Methods

Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. However, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms. We found that commonly applied missing data methods resulted in biased and inefficient estimates, …

A New Right-Skewed Upside Down Bathtub Shaped Heavy-Tailed Distribution And Its Applications, Sandeep Kumar Maurya, Sanjay K. Singh, Umesh Singh

Journal of Modern Applied Statistical Methods

A one parameter right skewed, upside down bathtub type, heavy-tailed distribution is derived. Various statistical properties and maximum likelihood approaches for estimation purpose are studied. Five different real data sets with four different models are considered to illustrate the suitability of the proposed model.

On The Level Of Precision Of A Heterogeneous Transfer Function In A Statistical Neural Network Model, Christopher Godwin Udomboso

Journal of Modern Applied Statistical Methods

A heterogeneous function of the statistical neural network is presented from two transfer functions: symmetric saturated linear and hyperbolic tangent sigmoid. The precision of the derived heterogeneous model over their respective homogeneous forms are established, both at increased sample sizes hidden neurons. Results further show the sensitivity of the heterogeneous model to increase in hidden neurons.

A New Generalized Family Of Distributions For Lifetime Data, Maha A. D. Aldahlan, Mohamed G. Khalil, Ahmed Z. Afify

Journal of Modern Applied Statistical Methods

A new class of continuous distributions called the generalized Burr X-G family is introduced. Some special models of the new family are provided. Some of its mathematical properties including explicit expressions for the quantile and generating functions, ordinary and incomplete moments, order statistics and Rényi entropy are derived. The maximum likelihood is used for estimating the model parameters. The flexibility of the generated family is illustrated by means of two applications to real data sets.