Engineering Commons^™

Failure Of Ultra-High Molecular Weight Polyethylene Yarns (Uhmpwe) Under Transverse Loading Using Different Indenter Geometries, Karan Shah

Theses and Dissertations

Ultra-high molecular weight polyethylene (UHMWPE) Dyneema® SK-76 fibers are widely used in personnel protection systems. Transverse ballistic impact onto these fibers results in complex multiaxial deformation modes such as axial tension, axial compression, transverse compression, and transverse shear. Previous impact studies on high performance yarns and quasi-static transverse loading of single fibers using different indenter geometries show premature failure of yarns and single fiber caused by the degradation of tensile failure strain due to the presence of such multi-axial deformation modes. However, there is a dearth of failure criterion in the literature for ballistic applications that considers the contribution of …

Nano-Engineered High-Performance Copper-Water Heat Pipes, Ahmed A. Abdulshaheed

Theses and Dissertations

Recently, heat pipes have gained an exceptional reputation as passive systems. They have become an attractive choice for many engineering applications due to their simple design, high rate of heat transfer, low weight, and low cost of maintenance [1]. A Heat pipe consists of a vacuumed shell filled partially with a compatible working fluid. Three main sections comprise a heat pipe: evaporator, adiabatic, and condenser section. The heat pipe utilizes the principle of evaporation and condensation for operations, i.e., the phase change of working fluid. Heat pipe allows high heat rates to transfer over considerable lengths with minimum temperature differences …

"When They Say Weed Causes Depression, But It's Your Fav Antidepressant": Knowledge-Aware Attention Framework For Relationship Extraction, Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth

Publications

With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology (DAO)) to jointly extract …

“When They Say Weed Causes Depression, But It’S Your Fav Antidepressant”: Knowledge-Aware Attention Framework For Relationship Extraction, Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit P. Sheth

Faculty Publications

With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology) to jointly extract entities …

Impact Damage Ascertainment In Composite Plates Using In-Situ Acoustic Emission Signal Signature Identification, Robin James, Roshan Prakash Joseph, Victor Giurgiutiu

Faculty Publications

Barely visible impact damage (BVID) due to low velocity impact events in composite aircraft structures are becoming prevalent. BVID can have an adverse effect on the strength and safety of the structure. During aircraft inspections it can be extremely difficult to visually detect BVID. Moreover, it is also a challenge to ascertain if the BVID has in-fact caused internal damage to the structure or not. This paper describes a method to ascertain whether or not internal damage happened during the impact event by analyzing the high-frequency information contained in the recorded acoustic emission signal signature. Multiple 2 mm quasi-isotropic carbon …

"Is Depression Related To Cannabis?": A Knowledge-Infused Model For Entity And Relation Extraction With Limited Supervision, Kaushik Roy, Usha Lokala, Vedant Khandelwal, Amit P. Sheth

Publications

With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators. However, preliminary scientific research does not conclusively associate cannabis with improved mental health. In this study, we explore the relationship between depression and consumption of cannabis in a targeted social media corpus involving personal use of cannabis with the intent to derive its potential mental health benefit. We use tweets that contain an association among three categories annotated by domain experts - Reason, Effect, and Addiction. The state-of-the-art Natural Langauge Processing techniques fall short in extracting these relationships between …

Knowledge Infused Policy Gradients For Adaptive Pandemic Control, Kaushik Roy, Qi Zhang, Manas Gaur, Amit P. Sheth

Publications

COVID-19 has impacted nations differently based on their policy implementations. The effective policy requires taking into account public information and adaptability to new knowledge. Epidemiological models built to understand COVID-19 seldom provide the policymaker with the capability for adaptive pandemic control (APC). Among the core challenges to be overcome include (a) inability to handle a high degree of non-homogeneity in different contributing features across the pandemic timeline, (b) lack of an approach that enables adaptive incorporation of public health expert knowledge, and (c) transparent models that enable understanding of the decision-making process in suggesting policy. In this work, we take …

State Consistence Of Data-Driven Reduced Order Models For Parametric Aeroelastic Analysis, William C. Krolick, Jung I. Shu, Yi Wang, Kapil Pant

Faculty Publications

This paper investigates the state consistence of parametric data-driven reduced order models (ROMs) in a state-space form obtained by various system identification methods, including autoregressive exogenous (ARX) and subspace identification (N4SID), for aeroelastic analysis in varying flight conditions. The target flight envelop is first partitioned into discrete grid points, on each of which an aerodynamic ROM is constructed using system identification to capture the dependence of the generalized aerodynamic force on the generalized displacement of structural modes. High-fidelity aeroelastic modal perturbation simulations are used to generate the ROM training and verification data. Aerodynamic ROMs not on the grid point are …

Editors’ Choice-Power-Generating Electrochemical Co2 Scrubbing From Air Enabling Practical Aemfc Application, Yiwei Zheng, Garrett Huang, Mrinmay Mandal, John R. Varcoe, Paul A. Kohl, William E. Mustain

Faculty Publications

Anion exchange membrane fuel cells (AEMFCs) have been widely touted as a low-cost alternative to existing proton exchange membrane fuel cells. However, AEMFCs operating on air suffer from a severe performance penalty caused by carbonation from exposure to CO2. Many approaches to removing CO2 from the cathode inlet would consume valuable energy and complicate the systems-level balance-of-plant. Therefore, this work focuses on an electrochemical solution where CO2 removal would still generate power, but not expose an entire AEMFC stack to carbonation conditions. Such a system consists of two AEMFCs in series. The first AEMFC, which acts as an anion exchange …

Effect Of Membrane Properties On The Carbonation Of Anion Exchange Membrane Fuel Cells, Yiwei Zheng, Lyzmarie Nicole Irizarry, Noor Ui Hassan, Eric R. Williams, Morgan Stefik, Jacob M. Lamanna, Daniel S. Hussey, Mustain E William

Faculty Publications

Anion exchange membrane fuel cells (AEMFC) are potentially very low-cost replacements for proton exchange membrane fuel cells. However, AEMFCs suffer from one very serious drawback: significant performance loss when CO2 is present in the reacting oxidant gas (e.g., air) due to carbonation. Although the chemical mechanisms for how carbonation leads to voltage loss in operating AEMFCs are known, the way those mechanisms are affected by the properties of the anion exchange membrane (AEM) has not been elucidated. Therefore, this work studies AEMFC carbonation using numerous high-functioning AEMs from the literature and it was found that the ionic conductivity of the …

Identify Rna-Associated Subcellular Localizations Based On Multi-Label Learning Using Chou’S 5-Steps Rule, Hao Wang, Yijie Ding, Jijun Tang Ph.D., Quan Zou, Fei Guo

Faculty Publications

Background: Biological functions of biomolecules rely on the cellular compartments where they are located in cells. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency. However, lots of existing RNA subcellular localization classifiers only solve the problem of single-label classification. It is of great practical significance to expand RNA subcellular localization into multi-label classification problem.

Results: In this study, we extract multi-label classification datasets about RNA-associated subcellular localizations on various types of RNAs, and then construct subcellular localization datasets on four RNA categories. In order to …

First-Order Comprehensive Adjoint Sensitivity Analysis Methodology For Critical Points In Coupled Nonlinear Systems. I: Mathematical Framework, Dan Gabriel Cacuci

Faculty Publications

This work presents the novel first-order comprehensive adjoint sensitivity analysis methodology for critical points (1st-CASAM-CP), which enables the exact and efficient computation of the first-order sensitivities of responses defined at critical points (maxima, minima, saddle points) of coupled nonlinear models of physical systems characterized by imprecisely known parameters underlying the models, boundaries, and interfaces between the coupled systems. Responses defined at critical points are important in many applications, including system optimization, safety analyses and licensing. For the design and licensing of nuclear reactors, such essentially important responses include the maximum temperatures of the fuel and cladding in hot channels. The …

First-Order Comprehensive Adjoint Sensitivity Analysis Methodology For Critical Points In Coupled Nonlinear Systems. Ii: Application To A Nuclear Reactor Thermal-Hydraulics Safety Benchmark, Dan Gabriel Cacuci

Faculty Publications

Responses defined at critical points are particularly important for reactor safety analyses and licensing (e.g., the maximum fuel and/or clad temperature). The novel mathematical framework of the first-order comprehensive adjoint sensitivity analysis methodology for critical points (1st-CASAM-CP) is applied in this work to develop a reactor safety thermal-hydraulics benchmark model which admits exact closed-form expressions for the adjoint functions and for the first-order sensitivities of responses defined at critical points (maxima, minima, saddle points) in physical systems characterized by imprecisely known parameters, external and internal boundaries. This benchmark model is designed for verifying the capabilities and accuracies of computational tools …

Opioid Medication Use And Education Following Sports Medicine Procedures: An Evidence-To-Practice Review, Caitlin S. O’Mara, Michael G. Ward, Zachary K. Winkelmann

Clinical Practice in Athletic Training

According to the Centers for Disease Control (CDC), illicit and prescription drug overdoses are responsible for 128 deaths every day in the United States. In 2018, 70% of all overdose related deaths involved opioids. Efforts to minimize the opioid epidemic focus on community education, research, partnership, and healthcare support. Under the CDC guidelines, current practices include monitoring trends of drug use and drug related deaths, conducting research to recognize areas in need of improvement and to analyze effectiveness of current treatments, partner with community organizations and healthcare systems that deal firsthand with opioid users, and educate the public on drug …

Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth

Publications

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to …

Nlp Is Not Enough - Contextualization Of User Input In Chatbots, Nathan Dolbir, Triyasha Dastidar, Kaushik Roy

Publications

AI chatbots have made vast strides in technology improvement in recent years and are already operational in many industries. Advanced Natural Language Processing techniques, based on deep networks, efficiently process user requests to carry out their functions. As chatbots gain traction, their applicability in healthcare is an attractive proposition due to the reduced economic and people costs of an overburdened system. However, healthcare bots require safe and medically accurate information capture, which deep networks aren’t yet capable of due to user text and speech variations. Knowledge in symbolic structures is more suited for accurate reasoning but cannot handle natural language …

Kinetics Study Of The Hydrodeoxygenation Of Xylitol Over A ReoX-Pd/Ceo2 Catalyst, Blake Macqueen, Michael Royko, Bradie S. Crandall, Andreas Heyden, Yomaira J. Pagán-Torres, Jochen A. Lauterbach

Faculty Publications

In this study, we elucidate the reaction kinetics for the simultaneous hydrodeoxygenation of xylitol to 1,2-dideoxypentitol and 1,2,5-pentanetriol over a ReOx-Pd/CeO2 (2.0 weight% Re, 0.30 weight% Pd) catalyst. The reaction was determined to be a zero-order reaction with respect to xylitol. The activation energy was elucidated through an Arrhenius relationship as well as non-Arrhenius kinetics. The Arrhenius relationship was investigated at 150–170◦ C and a constant H2 pressure of 10 bar resulting in an activation energy of 48.7 ± 10.5 kJ/mol. The investigation of non-Arrhenius kinetics was conducted at 120–170◦ C and a sub-Arrhenius relation was elucidated with activation energy …

Model And Data, Equilibrium Of Self-Formed, Single-Thread, Sand-Bed Rivers, Enrica Viparelli, Esther C. Eke

Faculty Publications

This submission contains the following files, also uploaded as Supplementary Information for the manuscript Equilibrium of self-formed, single-thread, sand-bed rivers submitted after major revisions to the AGU journal Geophysical Research Letters.

Excel file with embedded macro Equilibrium_calculator.xlxm, contains the solver of the analytical model presented in the manuscript.

Excel file Equilibrium_results.xlxs contains model data used to generate the plots.

Enabling Machine Learning On The Edge Using Sram Conserving Efficient Neural Networks Execution Approach, Bharath Sudharsan, Pankesh Patel, John G. Breslin, Muhammad Intizar Ali

Publications

Edge analytics refers to the application of data analytics and Machine Learning (ML) algorithms on IoT devices. The concept of edge analytics is gaining popularity due to its ability to perform AI-based analytics at the device level, enabling autonomous decision-making, without depending on the cloud. However, the majority of Internet of Things (IoT) devices are embedded systems with a low-cost microcontroller unit (MCU) or a small CPU as its brain, which often are incapable of handling complex ML algorithms.

In this paper, we propose an approach for the ecient execution of already deeply compressed, large neural networks (NNs) on tiny …

Knowledge Infused Policy Gradients With Upper Confidence Bound For Relational Bandits, Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth

Publications

Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc. However, most of the algorithms use at feature vectors to represent context whereas, in the real world, there is a varying number of objects and relations among them to model in the context. For example, in a music recommendation system, the user context contains what music they listen to, which artists create this music, the artist albums, etc. Adding richer relational context representations also introduces a much larger context space making exploration-exploitation harder. To improve the efficiency of exploration-exploitation knowledge about the …

A Meta-Gradient Approach To Learning Cooperative Multi-Agent Communication Topology, Qi Zhang, Dingyang Chen

Publications

In cooperative multi-agent reinforcement learning (MARL), agents often can only partially observe the environment state, and thus communication is crucial to achieving coordination. Communicating agents must simultaneously learn to whom to communicate (i.e., communication topology) and how to interpret the received message for decision-making. Although agents can efficiently learn communication interpretation by end-to-end backpropagation, learning communication topology is much trickier since the binary decisions of whether to communicate impede end-to-end differentiation. As evidenced in our experiments, existing solutions, such as reparameterization tricks and reformulating topology learning as reinforcement learning, often fall short. This paper introduces a meta-learning framework that aims …

Kinetics Study Of The Hydrodeoxygenation Of Xylitol Over A ReoX-Pd/Ceo2 Catalyst, Blake Macqueen, Michael Royko, Bradie S. Crandall, Andreas Heyden, Yomaira J. Pagán-Torres, Jochen A. Lauterbach

Faculty Publications

In this study, we elucidate the reaction kinetics for the simultaneous hydrodeoxygenation of xylitol to 1,2-dideoxypentitol and 1,2,5-pentanetriol over a ReOx-Pd/CeO2 (2.0 weight% Re, 0.30 weight% Pd) catalyst. The reaction was determined to be a zero-order reaction with respect to xylitol. The activation energy was elucidated through an Arrhenius relationship as well as non-Arrhenius kinetics. The Arrhenius relationship was investigated at 150–170◦ C and a constant H2 pressure of 10 bar resulting in an activation energy of 48.7 ± 10.5 kJ/mol. The investigation of non-Arrhenius kinetics was conducted at 120–170◦ C and a sub-Arrhenius relation was elucidated with activation energy …

Redeployment Of Athletic Trainers During A Pandemic, James Caiola

Clinical Practice in Athletic Training

Before COVID-19 was recognized as a global pandemic involving the United States of America in March of 2020, MedStar Health employed 40 Athletic Trainers (ATs) in multiple settings. While ATs worked in the traditional setting at 17 high schools, 2 universities, and with 2 professional teams in the Baltimore, MD and Washington, DC area, they also have been used in nontraditional ways. They are used in the physician practice setting to manage clinic flow, evaluate patients and present to the physician, provide pre- and post-surgical patient education, and being an overall patient point of contact in a busy clinic. They …

Telehealth Optimization – Role Of Clinical Athletic Trainers In Orthopedic/Sports Medicine Practice, Katie Harbacheck, Laura Grambo, Shaun Keefer, Eric Klein, Christine Boyd, Carolyn Ford-Hemann, Cameron D’Alpe, Steve Frick, Kevin Shea

Clinical Practice in Athletic Training

Background: Prior to COVID-19, telehealth (TH) implementation in most health systems was minimal. The transition to high rates of TH was dramatic in response to COVID-19, and many health systems struggled to develop optimal, efficient TH workflows. Hypothesis/Purpose: The primary purpose of this study was to evaluate the role of clinically integrated athletic trainers (ATs) into the TH transition of a major orthopedic academic center through growth of TH visits as well as physician satisfaction. Methods: Workflows and tip sheets were designed to include considerations of TH visit increases, adoption of appropriate technology platforms and optimal staff models. The rapid …

Medical Knowledge-Enriched Textual Entailment Framework, Shweta Yadav, Vishal Pallagani, Amit P. Sheth

Publications

One of the cardinal tasks in achieving robust medical question answering systems is textual entailment. The existing approaches make use of an ensemble of pre-trained language models or data augmentation, often to clock higher numbers on the validation metrics. However, two major shortcomings impede higher success in identifying entailment: (1) understanding the focus/intent of the question and (2) ability to utilize the real-world background knowledge to capture the context beyond the sentence. In this paper, we present a novel Medical Knowledge-Enriched Textual Entailment framework that allows the model to acquire a semantic and global representation of the input medical text …

Identifying Depressive Symptoms From Tweets: Figurative Language Enabled Multitask Learning Framework, Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit P. Sheth, Jeremiah Schumm

Publications

Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, healthcare workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the …

Covid-19 In Spain And India: Comparing Policy Implications By Analyzing Epidemiological And Social Media Data, Parth Asawa, Manas Gaur, Kaushik Roy, Amit P. Sheth

Publications

The COVID-19 pandemic has forced public health experts to develop contingent policies to stem the spread of infection, including measures such as partial/complete lockdowns. The effectiveness of these policies has varied with geography, population distribution, and effectiveness in implementation. Consequently, some nations (e.g., Taiwan, Haiti) have been more successful than others (e.g., United States) in curbing the outbreak. A data-driven investigation into effective public health policies of a country would allow public health experts in other nations to decide future courses of action to control the outbreaks of disease and epidemics. We chose Spain and India to present our analysis …

Experimental Investigation Of Transverse Loading Behavior Of Ultra-High Molecular Weight Polyethylene Yarns, Karan Deepak Shah, Subramani Sockalingam

Faculty Publications

Ultra-high molecular weight polyethylene (UHMWPE) Dyneema^® SK-76 fibers are widely used in personnel protection systems. Transverse ballistic impact onto these fibers results in complex multiaxial deformation modes such as axial tension, axial compression, transverse compression, and transverse shear. Previous experimental studies on single fibers have shown a degradation of tensile failure strain due to the presence of such multi-axial deformation modes. In this work, we study the presence and effects of such multi-axial stress-states on Dyneema^® SK-76 yarns via transverse loading experiments. Quasi-static transverse loading experiments are conducted on Dyneema^® SK-76 single yarn at different starting angles …

Analytical And Experimental Study Of Fatigue-Crack-Growth Ae Signals In Thin Sheet Metals, Roshan Joseph, Victor Giurgiutiu

Faculty Publications

The acoustic emission (AE) method is a very popular and well-developed method for passive structural health monitoring of metallic and composite structures. AE method has been efficiently used for damage source detection and damage characterization in a large variety of structures over the years, such as thin sheet metals. Piezoelectric wafer active sensors (PWASs) are lightweight and inexpensive transducers, which recently drew the attention of the AE research community for AE sensing. The focus of this paper is on understanding the fatigue crack growth AE signals in thin sheet metals recorded using PWAS sensors on the basis of the Lamb …

Local Eigenvalue Modification Procedure For Real-Time Model Updating Of Structures Experiencing High-Rate Dynamic Events, Claire Rae Drnek

Theses and Dissertations

Estimating the state of structures that experience high-rate dynamics requires real-time model updating capabilities. In this work, high-rate dynamic events are characterized by 1) large uncertainties in the external loads, 2) high levels of non-stationarities and heavy disturbances, and 3) unmodeled dynamics generated from changes in system configurations. To achieve real-time model updating, an algorithm must circumvent any pre-calculations and be able to update the structure’s state on the timescale of 2 ms or less. This can be accomplished in one of two ways: either by creating a simplified model of a complex structure or by simplifying the calculations needed …