Physical Sciences and Mathematics Commons^™

Chemical And Physical Studies Of Charge Transfer Excited States Of Ruthenium Complexes With Aromatic Ligands, Marim Khalifa Alnaed

Wayne State University Dissertations

The 3MLCT excited states generated from tris-bipyridineruthenium(II), [Ru(bpy)3]2+ and related complexes was ionized by 405 nm irradiation in ambient, acidic aqueous solutions. The photoionization product was Ru(bpy)3]3+ and an electron with a quantum yield of about 0.040.02 in 0.5 M acid. However, 532 nm radiation does not induce photoionization, and it generates the 3MLCT excited state that can be photoionized by 405 nm irradiation. The proton is a very good electron scavenger, and the rate of phoionization of the 3MLCT excited state was determined to be acid dependent and it increased about ten-fold for acid concentrations beween 0.001 and 4 …

Harnessing Radical Reactivity For The Construction Of Natural Product Frameworks And Transition Metal Catalyst Stability In A Modified Kochi Coupling Reaction, Mathes Hewage Hansamali Sakuntala Sirinimal

Wayne State University Dissertations

Polycyclic N-containing heterocycles play an important role in therapeutic

applications and as molecular tools in studying biological processes, making them

important synthetic targets. Current synthetic approaches are too lengthy, use toxic

reagents or are limited to accessing a particular framework and hence, their synthesis

warrant further study. We have recently developed conditions to avoid both reduction and

slow addition in aminyl radical cyclizations in our venture to access the constrained ABC

core of calyciphilline A, a Daphniphyllum alkaloid. An electron deficient enone with a

pendent internal alkyne as the neutral aminyl radical precursor was used for radical

cyclization. Based on …

The Impact Of Machine Learning Algorithms On Benchmarking Process In Healthcare Service Delivery, Egbe-Etu Emmanuel Etu

Wayne State University Theses

Currently, organizations have adopted and implemented a variety of innovative quality management philosophies, approaches, and techniques to stay competitive in an ever-changing global economy. Benchmarking is one of such techniques deployed by organizations to stay competitive. The motivation for this research stems from a real-world problem being faced by hospitals in the healthcare industry who have amassed a ton of data and want to embark on benchmarking project to assess the performance of the emergency departments due to challenges faced with poor management of operations which has led to high patient boarding rates, high patient wait-times, poor quality service, low …

Uncertainty In Combustion Reaction Rates And Its Effects On Combustion Simulations, Joshua Piehl

Wayne State University Theses

This work investigates the uncertainties in reaction rates of an n-dodecane model on turbulent spray combustion simulations. Six major reactions were found to significantly impact the ignition delay of the mechanism in a 0-D batch reactor model. These reactions’ rates were independently modified and placed into individual mechanisms. These newly developed mechanisms were simulated in a 3-D turbulent spray simulation and a 0-D batch reactor at a pressure of 60 bar and temperatures from 900 to 1100 K. The combustion characteristics (e.g. ignition delays, flame lift-off length, liquid and vapor penetration) of the modified mechanisms were compared to those produced …

Deep Learning Methods For Visual Object Recognition, Zeyad Hailat

Wayne State University Dissertations

Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive, time-consuming, and susceptible to noisy labels. Semi-supervised learning methods can alleviate the aforementioned problems by employing one of two techniques. First, utilizing a limited number of labeled data in conjunction with sufficiently large unlabeled data to construct a classification model. Second, exploiting sufficiently large noisy label training data to learn a classification model. In this dissertation, we proposed a few new methods to mitigate the aforementioned problems. …

A Comparison Of Discriminant Function Analysis And Logistic Regression By Categorizing The Incarcerated Mentally Ill, Mona King

Wayne State University Dissertations

Both discriminant function analysis (DFA) and logistic regression (LR) are used to classify subjects into a category/group based upon several explanatory variables (Liong & Foo, 2013). Although the two procedures are generally related, there is no clear advice in the statistical literature on when to use DFA vs. LR, although LR appears to be preferred due to the claim that its underlying assumptions are more easily met (Liong & Foo, 2013). Although DFA and LR use different methods to accomplish their objectives, they can answer the same research questions (Antonogeorgos et al., 2009). This facilitates a practical comparison of their …

Carbazole Based Multifunctional Dopamine Agonists And Related Molecules As Potential Symptomatic And Disease Modifying Therapeutic Agents For Parkinson’S Disease, Asma S.Mohamed Elmabruk

Wayne State University Dissertations

Parkinson’s disease (PD) is a progressive neurodegenerative disease that develops from gradual depletion of dopamine (DA) and dopaminergic neurons in the substantia nigra pars compacta (SNc) with the accumulation of intraneuronal proteinaceous matter named as Lewy bodies. The four cardinal symptoms associated with PD are tremor, rigidity, bradykinesia, and postural instability. Although the exact mechanism and etiology of PD are not fully understood, several factors have been implicated in the pathogenesis and progression of PD including protein aggregation, oxidative stress, mitochondrial dysfunction, environmental, and genetic factors.

The current therapy of Parkinson’s disease is categorized into four classes: levodopa, DA agonists, …

Identification Of Streptococcus Pyogenes Using Raman Spectroscopy, Ehsan Majidi

Wayne State University Dissertations

Despite the attention that Raman Spectroscopy has gained recently in the area of pathogen identification, the spectra analyses techniques are not well developed. In most scenarios, they rely on expert intervention to detect and assign the peaks of the spectra to specific molecular vibration. Although some investigators have used machine-learning techniques to classify pathogens, these studies are usually limited to a specific application, and the generalization of these techniques is not clear. Also, a wide range of algorithms have been developed for classification problems, however, there is less insight to applying such methods on Raman spectra. Furthermore, analyzing the Raman …

Sofie: Smart Operating System For Internet Of Everything, Jie Cao

Wayne State University Dissertations

The proliferation of Internet of Things and the success of rich cloud services have pushed the

horizon of a new computing paradigm, Edge computing, which calls for processing the data at

the edge of the network. Applications such as cloud offloading, smart home, and smart city

are idea area for Edge computing to achieve better performance than cloud computing. Edge

computing has the potential to address the concerns of response time requirement, battery life

constraint, bandwidth cost saving, as well as data safety and privacy.

However, there are still some challenges for applying Edge computing in our daily life. The …

Representation Learning With Convolutional Neural Networks, Haotian Xu

Wayne State University Dissertations

Deep learning methods have achieved great success in the areas of Computer Vision and Natural Language Processing. Recently, the rapidly developing field of deep learning is concerned with questions surrounding how we can learn meaningful and effective representations of data. This is because the performance of machine learning approaches is heavily dependent on the choice and quality of data representation, and different kinds of representation entangle and hide the different explanatory factors of variation behind the data.

In this dissertation, we focus on representation learning with deep neural networks for different data formats including text, 3D polygon shapes, and brain …

Qualitative Change Detection Approach For Preventive Therapies, Cristina Mitrea

Wayne State University Dissertations

Currently, most diseases are diagnosed only after disease-associated changes have occurred. In this PhD dissertation, we propose a paradigm shift from treating the disease to maintaining the healthy state. The proposed approach is able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility of therapeutic interventions before the occurrence of symptoms. The change detection method exploits knowledge from biological networks and longitudinal data using a system impact analysis approach. This approach is validated on eight datasets, for seven different model organisms and eight biological phenomena. On these data, our proposed method performs well, consistently identifying …

Real-Time Guarantees For Wireless Networked Sensing And Control, Yu Chen

Wayne State University Dissertations

Wireless networks are increasingly being explored for mission-critical sensing and control in emerging domains such as connected and automated vehicles, Industrial 4.0, and smart city. In wireless networked sensing and control (WSC) systems, reliable and real- time delivery of sensed data plays a crucial role for the control decision since out-of-date information will often be irrelevant and even leads to negative effects to the system. Since WSC differs dramatically from the traditional real-time (RT) systems due to its wireless nature, new design objective and perspective are necessary to achieve real-time guarantees.

First, we proposed Optimal Node Activation Multiple Access (ONAMA) …

3d Face Reconstruction And Emotion Analytics With Part-Based Morphable Models, Hai Jin

Wayne State University Dissertations

3D face reconstruction and facial expression analytics using 3D facial data are new

and hot research topics in computer graphics and computer vision. In this proposal, we first

review the background knowledge for emotion analytics using 3D morphable face model, including

geometry feature-based methods, statistic model-based methods and more advanced

deep learning-bade methods. Then, we introduce a novel 3D face modeling and reconstruction

solution that robustly and accurately acquires 3D face models from a couple of images

captured by a single smartphone camera. Two selfie photos of a subject taken from the

front and side are used to guide our …

Predictable Reliability In Inter-Vehicle Communications, Chuan Li

Wayne State University Dissertations

Predictably reliable communication in wireless networked sensing and control systems (WSC) is a basic enabler for performance guarantee. Yet current research efforts are either focus on maximizing throughput or based on inaccurate interference modelling methods, which yield unsatisfactory results in terms of communication reliability. In this dissertation, we discuss techniques that enable reliable communication in both traditional wireless sensor networks and highly mobile inter-vehicle communication networks. We focus our discussion on traditional wireless sensor networks in Chapter 2 where we discuss mechanisms that enable predictable and reliable communications with no centralized infrastructures. With the promising results in Chapter 2, we …

System Support For Stream Processing In Collaborative Cloud-Edge Environment, Quan Zhang

Wayne State University Dissertations

Stream processing is a critical technique to process huge amount of data in real-time manner.

Cloud computing has been used for stream processing due to its unlimited computation

resources. At the same time, we are entering the era of Internet of Everything (IoE). The emerging

edge computing benefits low-latency applications by leveraging computation resources at

the proximity of data sources. Billions of sensors and actuators are being deployed worldwide

and huge amount of data generated by things are immersed in our daily life. It has become

essential for organizations to be able to stream and analyze data, and provide low-latency …

Addressing The Proton Radius Puzzle Using Qed-Nrqed Effective Field Theory, Steven Patrick Dye

Wayne State University Dissertations

In 2010 the first extraction of the proton charge radius from muonic hydrogen was found to be five standard deviations away from the regular hydrogen value. Seven years later, this proton radius puzzle still persists, and challenges our understanding of the structure of the proton. An effective field theory analysis using Non-Relativistic Quantum Electrody- namics (NRQED) indicates that the muonic hydrogen result can be interpreted as a large, compared to some model estimates, muon-proton spin-independent contact interaction. The muonic hydrogen result can be tested by a muon-proton scattering experiment, MUSE, that is planned at the Paul Scherrer Institute in Switzerland. …

Approximating The Distribution Of Indefinite Quadratic Forms In Normal Variables By Maximum Entropy Density Estimation, Ghasem Rekabdar, Rahim Chinipardaz

Journal of Modern Applied Statistical Methods

The quadratic form of non-central normal variables is presented based on a sum of weighted independent non-central chi-square variables. This presentation provides moments of quadratic form. The maximum entropy method is used to estimate the density function because distribution moments of quadratic forms are known. A Euclidean distance is proposed to select an appropriate maximum entropy density function. In order to compare with other methods some numerical examples were evaluated. Also, for discrimination between two groups by the Euclidean distances, we obtained a stochastic representation for the linear discriminant function using the quadratic form. The maximum entropy estimation was an …

Semi-Parametric Method To Estimate The Time-To-Failure Distribution And Its Percentiles For Simple Linear Degradation Model, Laila Naji Ba Dakhn, Mohammed Al-Haj Ebrahem, Omar Eidous

Journal of Modern Applied Statistical Methods

Most reliability studies obtained reliability information by using degradation measurements over time, which contains useful data about the product reliability. Parametric methods like the maximum likelihood (ML) estimator and the ordinary least square (OLS) estimator are used widely to estimate the time-to-failure distribution and its percentiles. In this article, we estimate the time-to-failure distribution and its percentiles by using a semi-parametric estimator that assumes the parametric function to have a half- normal distribution or an exponential distribution. The performance of the semi-parametric estimator is compared via simulation study with the ML and OLS estimators by using the mean square error …

Jmasm 48: The Pearson Product-Moment Correlation Coefficient And Adjustment Indices: The Fisher Approximate Unbiased Estimator And The Olkin-Pratt Adjustment (Spss), David A. Walker

Journal of Modern Applied Statistical Methods

This syntax program is intended to provide an application, not readily available, for users in SPSS who are interested in the Pearson product–moment correlation coefficient (r) and r biased adjustment indices such as the Fisher Approximate Unbiased estimator and the Olkin and Pratt adjustment.

Inferential Procedures For Log Logistic Distribution With Doubly Interval Censored Data, Yue Fang Loh, Jayanthi Arasan, Habshah Midi, M. R. Abu Bakar

Journal of Modern Applied Statistical Methods

The log logistic model with doubly interval censored data is examined. Three methods of constructing confidence interval estimates for the parameter of the model were compared and discussed. The results of the coverage probability study indicated that the Wald outperformed the likelihood ratio and jackknife inferential procedures.

On Poisson Quasi-Lindley Distribution And Its Applications, Razika Grine, Halim Zeghdoudi

Journal of Modern Applied Statistical Methods

This paper proposes a recent version of compound Poisson distributions named the Poisson quasi-Lindley (PQL) distribution by compounding Poisson and quasi-Lindley distributions. Some properties of the distributions are given with estimation and some illustrative examples.

Detection Of Outliers In Univariate Circular Data Using Robust Circular Distance, Ehab A. Mahmood, Sohel Rana, Habshah Midi, Abdul Ghapor Hussin

Journal of Modern Applied Statistical Methods

A robust statistic to detect single and multi-outliers in univariate circular data is proposed. The performance of the proposed statistic was tested by applying it to a simulation study and to three real data sets, and was demonstrated to be robust.

Modeling Agreement Between Binary Classifications Of Multiple Raters In R And Sas, Aya A. Mitani, Kerrie P. Nelson

Journal of Modern Applied Statistical Methods

Cancer screening and diagnostic tests often are classified using a binary outcome such as diseased or not diseased. Recently large-scale studies have been conducted to assess agreement between many raters. Measures of agreement using the class of generalized linear mixed models were implemented efficiently in four recently introduced R and SAS packages in large-scale agreement studies incorporating binary classifications. Simulation studies were conducted to compare the performance across the packages and apply the agreement methods to two cancer studies.

Jmasm 49: A Compilation Of Some Popular Goodness Of Fit Tests For Normal Distribution: Their Algorithms And Matlab Codes (Matlab), Metin Öner, İpek Deveci Kocakoç

Journal of Modern Applied Statistical Methods

The main purpose of this study is to review calculation algorithms for some of the most common non-parametric and omnibus tests for normality, and to provide them as a compiled MATLAB function. All tests are coded to provide p-values for those normality tests, and the proposed function gives the results as an output table.

The Impact Of Predictor Variable(S) With Skewed Cell Probabilities On Wald Tests In Binary Logistic Regression, Arwa Alkhalaf, Bruno D. Zumbo

Journal of Modern Applied Statistical Methods

A series of simulation studies are reported that investigated the impact of a skewed predictor(s) on the Type I error rate and power of the Wald test in a logistic regression model. Five simulations were conducted for three different regression models. A detailed description of the impact of skewed cell predictor probabilities and sample size provide guidelines for practitioners wherein to expect the greatest problems.

Jmasm 50: A Web-Based Shiny Application For Conducting A Two Dependent Samples Maximum Test (R), Saverpierre Maggio, Gokul Bhandari, Shlomo S. Sawilowsky

Journal of Modern Applied Statistical Methods

A web-based Shiny application written in R statistical language was developed and deployed online to calculate a new two dependent samples maximum test as presented in Maggio and Sawilowsky (2014b). The maximum test allows researchers to conduct both the dependent samples t-test and Wilcoxon signed-ranks tests on same data without raising concerns associated with Type I error inflation and choice of statistical tests (Maggio and Sawilowsky, 2014a). The maximum test in R statistical language provides a friendly user interface.

Jmasm 47: Anova_Hov: A Sas Macro For Testing Homogeneity Of Variance In One-Factor Anova Models (Sas), Isaac Li, Yi-Hsin Chen, Yan Wang, Patricia RodríGuez De Gil, Thanh Pham, Diep Nguyen, Eun Sook Kim, Jeffrey D. Kromrey

Journal of Modern Applied Statistical Methods

Variance homogeneity (HOV) is a critical assumption for ANOVA whose violation may lead to perturbations in Type I error rates. Minimal consensus exists on selecting an appropriate test. This SAS macro implements 14 different HOV approaches in one-way ANOVA. Examples are given and practical issues discussed.

A Remark For The Admissibility Of Rao’S U-Test, Z. D. Bai, C. R. Rao, M. T. Tsai

Journal of Modern Applied Statistical Methods

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Front Matter, Jmasm Editors

Journal of Modern Applied Statistical Methods

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Vol. 16, No. 2 (Full Issue), Jmasm Editors

Journal of Modern Applied Statistical Methods

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