Digital Commons Network^™

The Sodium-Glutamate Antagonist Riluzole Improves Outcome After Acute Spinal Cord Injury: Results From The Riscis Randomised Controlled Trial Analysed Using A Global Statistical Analytic Technique, Michael G. Fehlings, Karlo M. Pedro, Mohammed Ali Alvi, Ali Moghaddamjou, James S. Harrop, Ralph Stanford, Jonathon Ball, Bizhan Aarabi, Paul M. Arnold, James D. Guest, Shekar N. Kurpad, James M. Schuster, Ahmad N. Nassr, Karl M. Schmitt, Jefferson R. Wilson, Darrel S. Brodke, Faiz U. Ahmad, Albert Yee, Wilson Z. Ray, Nathaniel P. Brooks, Jason Wilson, Diana S.L. Chow, Elizabeth G. Toups, Kevin E. Thorpe, Jiaxin Huang, Peng Huang

School of Medicine Faculty Publications

Background: Spinal cord injury (SCI) clinical trials typically rely on a single primary endpoint to assess drug efficacy. This strategy fails to adequately capture the full impact of treatment in heterogenous neurological conditions like SCI. A more patient-centric analysis requires assessment of neurological function, functional capacity, and quality of life, incorporating meaningful patient-reported outcomes. The global statistical test (GST) addresses this challenge using a unified statistical conclusion regarding the superiority of a treatment strategy over another by evaluating multiple trial endpoints simultaneously. Methods: The RISCIS trial (Safety and Efficacy of Riluzole in Acute Spinal Cord Injury Study) data was analysed …

Three-Stage Latent Dynamics Forecasting (T-Ldf) Framework For Shenzhen Metro Passenger Flow Prediction, Tianze Zhang

Lingnan Theses (MPhil & PhD)

Accurate forecasting of metro passenger flow is vital for efficient urban transportation management and optimal resource allocation in modern cities. Traditional ARIMA-based models effectively capture regular, cyclical patterns but struggle with sudden, nonlinear fluctuations caused by random events such as weather disruptions, special events, or service interruptions. Moreover, existing research predominantly focuses on individual stations, overlooking the complex cross-station interactions inherent in networked metro systems where passenger flows are interconnected across the entire network.

To address these critical limitations, we propose the Three-Stage Latent Dynamics Forecasting (T-LDF) Framework, a novel approach that systematically integrates temporal decomposition, latent dynamics extraction, and …

Secondary Malignancies In Patients With Meningioma: A Surveillance, Epidemiology, And End Results Data Analysis, Maxwell W. Pickles, Thomas Z. Rohan, Shreya Vinjamuri, Nikolaos Mouchtouris, Roger Murayi, David P. Bray, James J. Evans

Department of Neurosurgery Faculty Papers

BACKGROUND: The risk of secondary primary malignancies (SPMs) in meningioma patients is not well understood. In this unidirectional analysis, we evaluated the risk of SPMs occurring following a primary diagnosis of meningioma.

METHODS: The Surveillance, Epidemiology, and End Results (SEER-17) database (2000-2020) was used to identify 124,769 meningioma patients from a total of 9,208,295 cancer cases. Standardized incidence ratios (SIRs) were calculated using SEER's statistical analysis package to evaluate SPM risk. Basic demographic and treatment information was collected as well.

RESULTS: Of the 124,769 patients, 11,411 (9.2%) received diagnoses of an SPM, which correlates to a higher risk than the …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Exact Sampling Of The Six-Vertex Model Using Coupling From The Past, Malaeka Amir

DePaul Discoveries

This paper aims to explore the six-vertex model through simulations designed to investigate the behavior of configurations under specific domain wall boundary conditions. To generate random configurations, we employ the Markov Chain Monte Carlo method while addressing the challenge of mixing times by utilizing the Coupling from the Past (CFTP) algorithm. Implemented in Python, our approach leverages CFTP to ensure exact sampling, avoiding the uncertainty of convergence in traditional Monte Carlo methods. We explore the monotonicity property within this framework and prove that it is only maintained by the steps of this algorithm for very particular values of the parameters.

Unified Hybrid Censoring Samples From Power Pratibha Distribution And Its Applications, Mahmoud Mansour, Hebatalla H. Mohammad Dr, Khalaf S. Sultan Prof.

Basic Science Engineering

This paper suggests an extensive inferential method for the Power Pratibha Distribution (PPD) under Unified Hybrid Censoring Schemes (UHCSs), since there is a growing interest in flexible models in both reliability and service operations. This work studies the PPD model using standard Maximum Likelihood Estimation methods and modern Bayesian approaches too. Using a complex architecture, UHCS simulates tests more closely to what is done in practice than by using more basic censoring schemes. Using analysis, the probability and statistical ranges are carefully calculated for the parameters. Tests demonstrate that Bayesian estimation gives better results than many other methods for estimation, …

Effects Of Fair Workweek Laws On Labor Market Outcomes, Joseph Pickens, Aaron Sojourner

Upjohn Institute Working Papers

This paper models fair workweek regulations that require employers to provide employees with (1) schedule predictability via advance notice of their work schedule and premium payments for short-notice changes, and (2) access to hours meaning they must offer open hours to existing employees before hiring new workers. We develop a theoretical model of employers’ responses to these provisions and their implications for employment. Guided by the model, we estimate the effects of recently-adopted fair workweek regulation in New York City’s fast-food sector using a synthetic difference-in-differences design. We find a null employment effect.

Dice Math And Probability, Warren Campbell

SEAS Faculty Publications

The manufacture of dice for tabletop games is a billion-dollar industry. In gaming circles and online forums, the concept of “cursed dice” is a popular topic. Dice are inherently unfair due to the difficulty of manufacturing them with perfect geometric tolerances and uniform material densities. A common method for testing dice fairness is the chi-square statistic, which is typically assumed to follow the chi-square distribution. However, this assumption is only asymptotically valid.

Exact distributions of the statistic can be computed for dice with few sides and a limited number of rolls—such as 2-sided (D2) and 4-sided (D4) dice—but the computational …

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …

Advancing Statistical Methods For Multivariate And Network Meta-Analysis, Yifei Wang

Statistical Science Theses and Dissertations

Multivariate meta-analysis (MMA) and network meta-analysis (NMA) are essential tools for synthesizing evidence across multiple correlated outcomes and treatments. However, these tools face practical challenges, including outcome reporting bias (ORB), unreported within-study correlations, and computational burden. ORB can distort effect estimates in MMA, while missing within-study correlations in multivariate NMA may lead to biased conclusions. To address these challenges, this dissertation introduces two novel statistical methods. For MMA, we propose SemiMMA, a semiparametric and scalable approach that treats ORB as a missing-not-at-random problem and combines inverse propensity weighting (IPW) with the generalized method of moments (GMM). For multivariate NMA, we …

Sequential Data Modeling Of Influenza A Via Traditional, Dwt-Gpr Hybrid, And Deep Learning Architectures, Edmund Fosu Agyemang

Theses and Dissertations

Influenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improved sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional, deep-learning and discrete wavelet (DWT)-Gaussian Process (GP) hybrid models to predict Influenza A outbreaks. Using historical data from January 2009 to December 2023, we compared the performance of traditional ARIMA and ETS models, four variants of DWT-GPR models and six distinct deep learning architectures: Simple RNN, LSTM, GRU, BiLSTM, BiGRU and Transformer. The results reveal a clear superiority of all …

Spatio-Temporal Modeling & Goodness Of Fit Testing For Ecological Fire Data, Jedidiah Olof Lindborg

Theses and Dissertations

An analysis of fire data sets resulting from controlled burns was performed. Spatio-temporal models were applied to the data sets to determine which covariates are significant in predicting fire temperature. The data sets were censored and only contain temperatures above 300C, due to a limitation in the measuring device. To handle the censoring, an extrapolation was used to reconstruct the temperatures below 300C. Models were created and run for a data set with the extrapolated temperatures and a data set with all censored temperatures removed. Several aspects of the model were evaluated, such as the chosen hyperparameters, the spatial covariance …

Functional Data Analysis On Life Expectancy And Healthcare Expenditure, Hagen Sanchez

Theses and Dissertations

This study analyzes the life expectancy for 237 countries from 1950-2023 and the healthcare expenditure for 50 countries from 1970-2022, and how life expectancy and healthcare expenditure relate to each other. Functional Principal Components Analysis was used to analyze the life expectancy and healthcare expenditure for each of the countries. Additionally, the regions of the countries were analyzed to identify any regional trends for the life expectancy data. Due to missing data and the structure of the healthcare data, multiple imputation methods and Principal Component Analysis techniques were explored for the healthcare data. Furthermore, a simulation study was conducted to …

Approaches To Enhancing Multiple Hypothesis Testing Methods With Side-Information, Siyu Zheng

Theses and Dissertations

Lesion-symptom mapping (LSM) studies offer insight into the brain areas involved in various aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Additionally, high-dimensional observational data can be grouped according to brain anatomical structure.

In this …

Functional Time Transformation Model With Applications To Digital Health, Rahul Ghosal Ph.D., Marcos Matabuena, Sujit K. Ghosh

Faculty Publications

The advent of wearable and sensor technologies now leads to functional predictors which are intrinsically infinite dimensional. While the existing approaches for functional data and survival outcomes lean on the well-established Cox model, the proportional hazard (PH) assumption might not always be suitable in real-world applications. Motivated by physiological signals encountered in digital medicine, we develop a more general and flexible functional time-transformation model for estimating the conditional survival function with both functional and scalar covariates. A partially functional regression model is used to directly model the survival time on the covariates through an unknown monotone transformation and …

Graph Convolutional Networks Enable Fast Hemorrhagic Stroke Monitoring With Electrical Impedance Tomography, J. Toivanen, V. Kolehmainen, A. Paldanius, A. Hänninen, A. Hauptmann, Sarah J. Hamilton

Mathematical and Statistical Science Faculty Research and Publications

Objective: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to computationally expensive nonlinear model-based methods. Methods: A post-processing approach with graph convolutional networks is employed. Utilizing the flexibility of the graph setting, a graph U-net is trained on linear difference reconstructions from 2D simulated stroke data and applied to fully 3D images from realistic simulated and experimental data. An additional network, trained on 3D vs. 2D images, is also considered for comparison. Results: Post-processing the linear difference reconstructions through the graph U-net significantly improved the image quality, resulting in images …

Post-Selection Inference In Regression Models, Qinyan Shen

Theses and Dissertations

This dissertation develops new methodology for valid statistical inference following model selection in various regression settings. We first consider the logistic regression model for a binary response that can only be observed at the group level, as in group testing, and is subject to testing error. With the true responses only partially observed, we employ the expectation-maximization (EM) algorithm to account for missing information in the response data and simultaneously conduct variable selection using the LASSO-penalized log-likelihood function. After variable selection, we extend an existing post-selection inference method based on the polyhedral lemma \parencite{lee2016exact} to make inferences on selected covariates, …

Methods And Applications For Bayesian Semiparametric Survival Analysis, Zile Zhao

Theses and Dissertations

Survival analysis is a cornerstone of biomedical and clinical research and plays an important role in fields as diverse as engineering, actuarial science, and sociology. In this dissertation, we develop new semiparametric Bayesian methodology for three problems from survival analysis: 1) adjustment for treatment crossover in randomized controlled trials (RCTs), 2) multilevel modeling of clustered survival outcomes when the cluster size is also informative, and 3) divide-and-conquer Bayesian inference for massive survival data. Our methods are semiparametric in the sense that we assume the covariates have a linear effect with regard to the log-hazard or the log- survival time; however, …

Some Likelihood-Based Methods For Clustering Functional Data, Tong Shan

Theses and Dissertations

The analysis of functional data is an increasingly relevant part of statistics. The exploratory data analytic method of cluster analysis plays a very important role in different fields. Over the years, researchers have developed many clustering approaches, striving to achieve more accurate and efficient clustering. In Chapter 1, we aim to improve the accuracy of our outcome when clustering functional data. To achieve this goal, we use a predictive likelihood function which serves as an objective function to optimize in order to determine the most appropriate clusters. To optimize the objective function over the space of clustering partitions, we produce …

A Spatial Scan Statistic For Group Testing Data, Vincent Onyame

Theses and Dissertations

Group testing involves pooling specimens from multiple individuals and offers an efficient means to surveil low-prevalence pathogens, but poses challenges for spatial cluster detection when only pooled results are observed. In this thesis, we develop a spatial scan statistic tailored to group-testing data with variable pool sizes. The statistic compares a null hypothesis of a homogeneous infection rate across all clusters to an alternative hypothesis that infection probabilities differ inside and outside a candidate cluster, with both models fitted by maximum likelihood estimation. We approximate the null distribution of the maximum likelihood ratio test via Monte Carlo simulation.

Through a …

Regression With Atypical Data: Measurement Error, Periodicity, And Non-Normality, Nicholas W. Woolsey

Theses and Dissertations

Regression is a ubiquitous and fundamental method that can be found in any ele- mentary statistics course. The simplicity and self evidently useful nature of linear regression beguiles a non-negligible portion of researchers into disrespecting assump- tions required by these models, namely in terms of accuracy of covariates and the underlying nature of the data. This disregard can at best lead to meaningless results and at worse cause significant misunderstandings in scientific pursuit.

In this dissertation we strive propose remedies to violations of specific assump- tions. Namely the assumptions that covariates are either observed without measure- ment error or they …

Autoregressive Modeling Of Dna Molecule Shapes Accompanied By An Empirical Assessment Of The Ljung-Box Test, David William Custer

Theses and Dissertations

The assumption of independence rarely holds in real-world data. Correlated observations are ubiquitous, especially in sequential contexts where time series models are essential for capturing temporal dependence. This study analyzed six groups of damaged and undamaged DNA sequences, where an "F" in the middle of a sequence indicates damage. One biological aim is to understand how DNA regenerates with the assistance of proteins that recognize damaged regions. Motivated by empirical support for AR(2) modeling, we fit autoregressive models to the first three principal component scores of each DNA group, capturing the dominant structure in the data. We conducted model diagnostics, …

Chemical And Physical Characterization Of Verbenaceae Essential Oils Using Modern Analytical Methods And Chemometrics, Aleksandra N. Hilliard

Master's Theses

Essential oils have been used for generations to treat various health issues, including mitigating insomnia, reducing stress, inflammation, and preserving foods, to mention a few. Essential oils have complex chemistry, which defines the essence of the host plant. Recently, increased commercial uses of essential oils have prompted attention at the governmental level to monitor the chemical, physical, or biological activity of essential oils. This study is an attempt to add new information for consumers to the understanding of the composition of essential oils.

Modern analytical techniques including FTIR-ATR, GC-MS, LC-MS-MS, multivariate analysis statistics, and physio-chemical techniques (electrochemistry, DPPH, TRC, and …

Variable Importance, Knockoff Filters, And Improving False Discovery And False Negative Rates, Nicholas Ehlman

Dissertations

Tree ensemble methods such as Random Forests and Boosted Trees have introduced a range of variable importance statistics, offering powerful tools for feature selection. The advent of knockoff filters marked a significant advancement by combining the use of these variable importance statistics with the ability to control the False Discovery Rate (FDR). However, achieving a low FDR frequently comes at the cost of a high False Negative Rate (FNR), limiting the power of such approaches. In this work, we propose a novel method for leveraging knockoff variables to keep both FDR and FNR low. While this method does not have …

Study On Reasonable Parameters Of Natural Smoke Exhaust Shafts In Tunnels With Different Longitudinal Slopes, Yang Tao, Zeng Yuekai, Li Peijun, Ren Rui, Deng Min, Zhang Aoyu

Journal of China & Foreign Highway

To obtain the reasonable parameters of a smoke exhaust shaft in a tunnel under different longitudinal slope conditions,the temperature,flow velocity,and smoke exhaust efficiency of the tunnel were taken as indexes,and the numerical model was established by using fire dynamics simulator (FDS ) software to study the influence of the longitudinal slope of the tunnel,shaft width,and shaft length on the ventilation and smoke exhaust in case of a fire.The results show that under the longitudinal slope conditions,with the increase in the shaft width and length,the maximum temperature in the tunnel decreases,and the smoke exhaust efficiency of the shaft increases.However,the influence of …

Study On Salt Release Mechanism Of Self-Melting Ice Asphalt Mixture And Its Impact On Road Performance, Li Tao, Zhang Xiu ’Ang

Journal of China & Foreign Highway

To study the long-term salt release characteri stics of self-melting ice asphalt mixture and the impact of the complete salt release on pavement durability,an accelerated salt release method was developed using a Los Angeles abrasion tester.The salt release mechanism under varying asphalt types,deicing agent contents,and gradation types was revealed.The impact of the complete salt release on road performance was analyzed.Results show that the Los Angeles abrasion tester-based salt release method achieves 91 times and 114 times higher salt release efficiency than the magnetic stirrer and natural immersion methods,respectively.SBS-modified asphalt mixture exhibits a longer salt release duration.The salt release duration is …

Research On Size Effect On Cubic Compressive Strength Of Ground Granulated Blast Furnace Slag-Fly Ash Stabilized Large-Size Macadam, Liu Qunyan, Liu Liping, He Miao, Yuan Lingqing, Zhang Liang

Journal of China & Foreign Highway

To analyze the effect of specimen size on the compressive strength of ground granulated blast furnace slag-fly ash (GGBFS-FA ) stabilized large-size macadam,the 200 mm × 200 mm × 200 mm cubic specimen and 3 d compressive strength were designated as a reference specimen and strength in this study,and numerical simulations of compressive strength were performed by using a discrete element method for GGBFS-FA stabilized large-size macadam specimens with a maximum particle size (dmax) of 73 mm and five side lengths of 2.0dmax,2.5dmax,3.0dmax,3.5dmax,and 4.0dmax.The size effect mechanism on the mixture ’s compressive strength was revealed from the perspective of energy.By …

Effect Of Mineral Admixtures On Properties Of Shotcrete, Ma Kaikai, Zhao Feng, Sun Rongxiao, Han Song, Hu Peng, Wang Kun

Journal of China & Foreign Highway

To study the influence of different mineral admixtures on the properties of shotcrete,the effects of four kinds of mineral admixtures including fly ash,silica fume,metakaolin,and carbon black on the rebound rate,one-shot thickness,and compressive strength of shotcrete were analyzed through tests on concrete rebound rate,one-shot thickness,and compressive strength.The results show that:① as the content of carbon black,silica fume,and metakaolin increases from 5% to 10%,the rebound rate of shotcrete decreases by 25.76%,16.46%,and 16.70%,respectively;② carbon black and silica fume have a good effect on increasing the one-shot thickness of concrete.When the content of carbon black and silica fume is 10%,the one-shot thickness exceeds 35 …

Study On Rheological Properties Of Basalt Fiber Asphalt Mastic Modified With Silane Coupling Agent, Liu Yuande, Zheng Nanxiang, Zhang Xiang, Gao Haiwei, Zhang Bowen

Journal of China & Foreign Highway

To investigate the rheological properties of basalt fiber asphalt mastic,a silane coupling agent was used as a modifier for basalt fibers,and the effects of varying fiber contents were analyzed. Cone penetration tests,bending beam rheological tests,and dynamic shear rheometer tests were conducted to evaluate the rheological properties of asphalt mastic consistency, high-temperature stability,and low-temperature cracking resistance under different temperatures and fiber contents.The findings show that the silane-coupled basalt fibers enhance the shear strength and consistency of asphalt mastic.After modification,the phase angle decreases;elastic response increases,and complex modulus improves.Adsorption capacity between fibers and asphalt increases significantly,enhancing high-temperature deformation resistance and low-temperature cracking resistance …

Analysis Of Mechanical Properti Es Of Integral Jacking Basket Arch Bridge During C Onstruction, Chen Tao, Qiu Jingkui, Wang Ji

Journal of China & Foreign Highway

Taking the Xindian Bridge in Ni ngbo as the background,this paper analyzed the mechanical properties of the integral jacking basket arch bridge during construction.Midas finite element software was used to establish the spatial model of the main bridge,and the deviation of the suspender force and support reaction force of the completed bridge from the design was 1.84% and 1.8%,respectively.The maximum deviation of the displacement calculation of the tie beam at the bridge completion stage was 22 mm,and the maximum deviation of the displacement of the arch rib at the same stage was ‒ 12 mm,which showed that the finite element …