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Articles 121 - 150 of 1260
Full-Text Articles in Engineering
Stabilization Of Expansive Soil Using Geopolymers From Locally Available Resources, Anand Puppala, Miladin Radovic, Nripojyoti Biswas, Sopharith Chou, Oscar Huang
Stabilization Of Expansive Soil Using Geopolymers From Locally Available Resources, Anand Puppala, Miladin Radovic, Nripojyoti Biswas, Sopharith Chou, Oscar Huang
Publications
Traditional calcium-based stabilizers are generally used to stabilize native expansive soils that are often found throughout Region 6. Due to their high carbon footprint, many studies are being conducted to identify a suitable green alternative for stabilizing expansive soils. Geopolymer (GP) received a lot of attention in the past few decades owing to its similar properties to that of ordinary Portland cement (OPC) yet with a lower carbon footprint. Previous studies have shown that metakaolin-based GP is an effective stabilizer to stabilize expansive soils. This study investigated the feasibility of stabilizing native expansive soils with GP synthesized with locally sourced …
Framework Of Internal Damage Identification In Inhomogeneous Medium Interweaving Wave Scattering Model And Deep Learning, Suyun Ham, Stefan Romanoschi, Yin Chao Wu, Dafnik Saril Kumar David
Framework Of Internal Damage Identification In Inhomogeneous Medium Interweaving Wave Scattering Model And Deep Learning, Suyun Ham, Stefan Romanoschi, Yin Chao Wu, Dafnik Saril Kumar David
Publications
The primary objective of this project is to investigate the framework of internal damage identification in inhomogeneous media (IHM) using deep learning techniques and wave scattering. The study focuses on two main steps: 1) analyzing the wave response pattern in homogeneous media (HM) and 2) characterizing the wave response variation (WRV) in IHM with different aggregate sizes and random distributions. The WRV patterns are then classified and used to predict internal damage using a convolutional neural network (CNN). The accuracy of the finite element (FE) model is verified using laboratory tests with artificial internal crack models of varying depths. The …
Identifying Wrong-Way Driving Entry Points On Interstate Highways Using Land-Use Impact Assessment And 911 Calls, Farman Mia, Samer Dessouky
Identifying Wrong-Way Driving Entry Points On Interstate Highways Using Land-Use Impact Assessment And 911 Calls, Farman Mia, Samer Dessouky
Publications
The research aims to provide insight into the initiation event of Wrong-way Driving (WWD) on highways. The intense severity of a WWD accident is encouraging the authorities to install mitigation systems that will ensure safety for motorists on highways. The WWD events are mostly resulting of intoxicated driving, where the unconscious driver takes the wrong way to enter the highway and place other motorists into life-threatening danger. This research attempted to explore from basic knowledge of WWD occurrence to examine the origin and driving philosophy of intoxicated drivers from land-use impact and mathematical modeling point of views. To identify the …
Automated Vehicle Impacts On Underserved Populations, Kate Kyung Hyun, Stephen Mattingly, Katie Kam
Automated Vehicle Impacts On Underserved Populations, Kate Kyung Hyun, Stephen Mattingly, Katie Kam
Publications
This research report describes the scenario framework created to determine the potential role automated vehicles (AVs) have in benefiting and adversely impacting the following underserved populations: people with disabilities and impairments (visually, hearing, physically, intellectually) and with autism spectrum disorder, younger and older populations, immigrants, low income, indigenous, rural, female and LGBTQ+, vulnerable road users (cyclists, pedestrians, low speed riders and drivers), and minority races. After presenting a literature review, three chapters explain the 3 modules of the framework: (1) scenario construction, (2) scenario factors, and (3) GIS methodology for evaluating community level AV impacts. Module 1 describes the use …
Monotonic And Cyclic Behavior Of High Strength Reinforcing Steel (Hsrs) After High Temperature Exposure, Himan Hojat Jalali, Moein Ebrahimi, Khaled Abuhishmeh, Mohammadamin Soltanianfard
Monotonic And Cyclic Behavior Of High Strength Reinforcing Steel (Hsrs) After High Temperature Exposure, Himan Hojat Jalali, Moein Ebrahimi, Khaled Abuhishmeh, Mohammadamin Soltanianfard
Publications
Conventional reinforced concrete transportation structures in North America often use Grade 40 ksi-60 ksi reinforcing steel. The introduction of high strength reinforcing steel (HSRS) with higher yield strength in the reinforced concrete industry has led to many economic and workability advantages such as construction time saving, reduced labor cost and steel consumption which in turn alleviates congestion of reinforcement, savings in transportation and deliveries that finally results in lower carbon emission. Despite these benefits, many states, including those in Region 6, have not widely adopted the use of higher strength reinforcing steels in practice due to lack of knowledge and …
Green Mobility In Texas: Comparative Environmental Impacts And Lifecycle Cost Analysis Of Hybrid, Electric, And Hydrogen Fuel Cell Cars, Raghava R. Kommalapati, Hongbo Du, Md Jobair Bin Alam
Green Mobility In Texas: Comparative Environmental Impacts And Lifecycle Cost Analysis Of Hybrid, Electric, And Hydrogen Fuel Cell Cars, Raghava R. Kommalapati, Hongbo Du, Md Jobair Bin Alam
Publications
Compared to gasoline/diesel cars and battery electric cars (BECs), hydrogen fuel cell cars (HFCCs) are relatively new. Two main advantages of HFCCs over electric vehicles are no special requirements for heavy batteries and long charging time. There is no HFCC in Texas; thus, exploring the use of electricity or hydrogen produced from renewable energies for personal cars and mitigating vehicle emissions is critical for sustaining a long-term decarburization strategy for megacities like Houston and Dallas. This project addressed this critical gap and developed environmental impacts and cost assessments for hybrid gasoline cars, BECs, and HFCCs in Texas under several possible …
Smart3pm: Smart Pavement Monitoring, Management, And Maintenance, Damien Stephens, Mena Souliman, Mukul Shirvaikar, Samer Dessouky
Smart3pm: Smart Pavement Monitoring, Management, And Maintenance, Damien Stephens, Mena Souliman, Mukul Shirvaikar, Samer Dessouky
Publications
An application specific multi-platform smartphone application can utilize on-board accelerometer, gyroscope, and GPS sensors, along with software derived signals from the same sensors, to sample vibrational and geolocation datasets to capture pavement distresses such as potholes when mounted in a standardized configuration in a vehicle. Several observations were made with regard to the signals obtained from the accelerometer, gyroscope, and GPS sensors, and it was determined that the raw sensor outputs are capable of sampling statistically significant datasets which can be used to distinguish pavement distress from normal driving conditions. Furthermore, an approximate sensor noise margin is established, and a …
Design Of 3d Printable Eco-Concrete By Utilizing Rheology Modifiers For Sustainable Infrastructure, Maryam Hojati, Muhammad Saeed Zafar
Design Of 3d Printable Eco-Concrete By Utilizing Rheology Modifiers For Sustainable Infrastructure, Maryam Hojati, Muhammad Saeed Zafar
Publications
In recent years, 3D printing technology has gained significant momentum across various industries within the construction sector, 3D printing has emerged as a promising technique; however, there are challenges associated with designing suitable construction materials and adjusting their properties for the 3D printing process. One of the key considerations in this regard is the rheological properties, encompassing parameters such as viscosity, yield stress, and thixotropy. These properties play a pivotal role in shaping the characteristics of 3D-printed concrete, both in its fresh and hardened states, including buildability, extrudability, and mechanical performance. Researchers have traditionally relied on chemical admixtures, such as …
Development Of A Web Application For Traffic Accident Mapping And Analysis, Susan Bogus Halter, Su Zhang, Arian Golrokh Amin
Development Of A Web Application For Traffic Accident Mapping And Analysis, Susan Bogus Halter, Su Zhang, Arian Golrokh Amin
Publications
It is widely accepted that road traffic safety is a significant public health issue. One of the effective ways to improve road traffic safety is analyzing crash data to understand where traffic accidents occur, identify associated spatial and temporal patterns, and determine causation. In the State of New Mexico, locations of traffic accidents are currently visualized using a variety of static maps. Although these statics maps are easier to create and producers can control how users view the data, users cannot customize these maps to meet their special needs. That being said, new maps need to be created for any …
Motorcycle Crash Analysis, Hatim Sharif, Samer Dessouky
Motorcycle Crash Analysis, Hatim Sharif, Samer Dessouky
Publications
The Fatality Analysis Reporting System database shows that, in stark contrast to the 34 percent decline in non-motorcyclist crash–related fatalities, motorcyclist crash–related fatalities were up 86 percent with only three year-to-year declines since 1997, while non-motorcyclist crash–related fatalities had 13 year-to-year declines since 1997. At the national level, the rate of motorcyclist fatalities per vehicle mile traveled is 29 times higher than the rate among passenger car occupants, with overall injury rates approximately five times higher among motorcyclists than passenger car occupants. Given the frequency of motorcycle crashes and their staggering toll in terms of loss of life and economic …
Bridge Inspection Training And Enhanced Operations Using Augmented Reality, Fernando Moreu, Ali Mohammadkhorasani, Kaveh Malek
Bridge Inspection Training And Enhanced Operations Using Augmented Reality, Fernando Moreu, Ali Mohammadkhorasani, Kaveh Malek
Publications
This research project focused on the innovative application of Augmented Reality (AR) headsets to enhance bridge inspection processes and inspection training. The study encompassed the visualization of 3D point cloud data, utilized the Eye-Tracking features of AR headsets for inspection quantification, evaluate the applicability of AR in the bridge inspection training and instruction of an AR virtual menu for the in-field bridge inspection. The primary tasks included establishing a database connection for AR headset, designing an AR menu tailored for bridge inspection, devising an evaluation method based on Eye-Tracking capabilities, and integrating AR into bridge inspection training. Once the technical …
Deep Reinforcement Learning-Based Digital Twin For Risk Improved Decision Making In Transportation Construction, Yong-Cheol Lee, Anisha Deria
Deep Reinforcement Learning-Based Digital Twin For Risk Improved Decision Making In Transportation Construction, Yong-Cheol Lee, Anisha Deria
Publications
Organizing and overseeing a large number of transportation construction and maintenance projects that generally entail several miles of a worksite are a critical burden for each DOT. In addition, it requires manual monitoring of a project or construction managers to identify a progress status, a work activity, and a safety issue in a job site. Because of the projected huge volume, complexity, significant impacts of future transportation infrastructure projects, it is evident that we are now facing a critical need to create a means of improving the results of work zone management and evaluating their impacts on our society. In …
The Effect Of Covid-19 Lockdown On Traffic Crashes In Texas., Hatim Sharif, Samer Dessouky
The Effect Of Covid-19 Lockdown On Traffic Crashes In Texas., Hatim Sharif, Samer Dessouky
Publications
An analysis of traffic patterns during COVID-19 has identified that reduction in the miles traveled has a significant negative correlation with COVID-19 cases and deaths across the USA. Preliminary statistics in the US suggest an increase in fatal crashes over the period of the lockdown in comparison to the same period in previous years. Moreover, while total crashes are down, motor vehicle crashes involving non-motorists became more prevalent. At the same time, non-motorist traffic has increased while motor vehicle traffic and crashes have shifted to the local systems. The main goal of this research is to perform a comprehensive evaluation …
Calcined Clays As Alternative Supplementary Cementitious Material And Precursor For Geopolymer Binders In Transportation Infrastructure, Hassan Noorvand, Miladin Radovic, Marwa Hassan, Adriana A. Alvarado, Oscar Huang
Calcined Clays As Alternative Supplementary Cementitious Material And Precursor For Geopolymer Binders In Transportation Infrastructure, Hassan Noorvand, Miladin Radovic, Marwa Hassan, Adriana A. Alvarado, Oscar Huang
Publications
This project aimed to assess the feasibility of using locally sourced calcined kaolinitic clays both as supplementary cementitious materials (SCMs) and as precursors for synthesizing geopolymers (GPs). For SCMs, a comprehensive set of characterizations was conducted, including X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), particle size analysis, and loss on ignition (LOI), along with evaluations of concrete properties such as strength activity index (SAI), slump, air content, compressive strength, and surface resistivity. For GPs, compressive strength was tested. The study yielded several key findings: (1) Three types of calcined clays (CCs) were analyzed based …
Prediction Of Moisture Resistance Of Polymeric Asphalt Binders Through The Atomic Force Microscopy (Afm) Technique, Zahid Hossain, Ashraf Elsayed, Khaja Sameer Sadat
Prediction Of Moisture Resistance Of Polymeric Asphalt Binders Through The Atomic Force Microscopy (Afm) Technique, Zahid Hossain, Ashraf Elsayed, Khaja Sameer Sadat
Publications
Moisture damage is a significant concern for state Departments of Transportation (DOTs) and other agencies due to its adverse impact on pavement performance as well as longevity. The main focus of the current study project is to quantify, at the molecular level, the interaction between binder and aggregate at the interface level in the form of adhesion forces between bitumen samples and minerals of various chemical compositions. First of all, A thorough review of the literature on moisture degradation in asphalts was conducted. Based on the literature review, a comprehensive project plan and test matrix were created. Binder samples from …
Early-Age Strength Monitoring Of Sensor-Embedded 3d Printed Structures, Ali Kazemian, Marwa Hassan, Hassan Noorvand, Gabriel Arce, Kasra Banijamali
Early-Age Strength Monitoring Of Sensor-Embedded 3d Printed Structures, Ali Kazemian, Marwa Hassan, Hassan Noorvand, Gabriel Arce, Kasra Banijamali
Publications
Construction 3D printing (C3DP) is an automated technology that has the potential to reduce the construction time and cost by utilizing different types of robotic systems. Due to the absence of formwork in C3DP, potential excessive water evaporation can cause issues to the freshly-printed structures that are exposed to the ambient curing condition. Moreover, the 3D printed elements should carry the service loads in a short time after printing to provide a load-bearing capacity. Destructive and non-destructive methods are the two types of methods that are being used in the construction industry for quality monitoring. Due to the time-consuming nature …
Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth
Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth
Publications
Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or “state” across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a framework for temporal alignment with several configurable toggles. We present four alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world applications. This paper discusses the potential benefits and …
Causal Neuro-Symbolic Ai: A Synergy Between Causality And Neuro-Symbolic Methods, Utkarshani Jaimini, Cory Henson, Amit Sheth
Causal Neuro-Symbolic Ai: A Synergy Between Causality And Neuro-Symbolic Methods, Utkarshani Jaimini, Cory Henson, Amit Sheth
Publications
Causal Neuro-Symbolic AI combines the benefits of causality with Neuro-Symbolic Artificial Intelligence (NeSyAI). More specifically, it (1) enriches NeSyAI systems with explicit representations of causality, (2) integrates causal knowledge with domain knowledge, and (3) enables the use of NeSyAI techniques for causal AI tasks. The explicit causal representation yields insights that predictive models may fail to analyze from observational data. It can also assist people in decision-making scenarios where discerning the cause of an outcome is necessary to choose among various interventions.
Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy
Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy
Publications
Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance1.
Neurosymbolic Customized And Compact Copilots, Kaushik Roy, Megha Chakraborty, Yuxin Zi, Manas Gaur, Amit Sheth
Neurosymbolic Customized And Compact Copilots, Kaushik Roy, Megha Chakraborty, Yuxin Zi, Manas Gaur, Amit Sheth
Publications
Large Language Models (LLMs) are credible with open-domain interactions such as question answering, summarization, and explanation generation [1]. LLM reasoning is based on parametrized knowledge, and as a consequence, the models often produce absurdities and inconsistencies in outputs (e.g., hallucinations and confirmation biases) [2]. In essence, they are fundamentally hard to control to prevent off-the-rails behaviors, are hard to fine-tune, customize for tailored needs, prompt effectively (due to the “tug-of-war” between external and parametric memory), and extremely resource-hungry due to the enormous size of their extensive parametric configurations [3,4]. Thus, significant challenges arise when these models are required to perform …
A Comprehensive Survey On Rare Event Prediction, Chathurangi Shyalika Jayakody Kankanamalage, Ruwan Wickramarachchi, Amit Sheth
A Comprehensive Survey On Rare Event Prediction, Chathurangi Shyalika Jayakody Kankanamalage, Ruwan Wickramarachchi, Amit Sheth
Publications
Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistics and ML. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, …
Fundamentals Of Sustainability, Eva Maleviti
Fundamentals Of Sustainability, Eva Maleviti
Publications
Fundamentals of Sustainable Aviation is the frst textbook to survey the critical feld of sustainability within the aviation industry. Taking a systems thinking approach, it presents the foundational principles of sustainability and methodically applies them to diferent aviation sectors. Opening with the basics of sustainability, emphasising the Sustainable Development Goals, the book then considers the environmental, economic and social dimensions of aviation. The following chapters apply these insights to aviation design, supply chains, operations, maintenance and facilities. The fnal chapter examines the concept of resilience in sustainable aviation. Overall, the textbook shows how future sustainability can be achieved by making …
Personalized Bayesian Inference For Explainable Healthcare Management And Intervention, Utkarshani Jaimini, Krishnaprasad Thirunaravan, Maninder Kalra, Robin Dawson, Amit Sheth
Personalized Bayesian Inference For Explainable Healthcare Management And Intervention, Utkarshani Jaimini, Krishnaprasad Thirunaravan, Maninder Kalra, Robin Dawson, Amit Sheth
Publications
Chronic healthcare conditions such as Asthma re- quires constant monitoring and managing of symptoms and their triggers for better quality of life. Each asthma patient reacts very differently to potential triggers. Hence, there is a need to develop a explainable personalized framework for each patient to capture susceptibility to asthma triggers. We developed a personalized knowledge-based probabilistic model to predict asthma exacerbation for different environmental factors utilizing patient generated health data from pediatric asthma patients. Further, the personalized model provides a metric, called Health Coefficient, to quantify the health of a patient for varying environmental factors. We demonstrate the predictive …
Neurosymbolic Value-Inspired Ai (Why, What, And How), Amit Sheth, Kaushik Roy
Neurosymbolic Value-Inspired Ai (Why, What, And How), Amit Sheth, Kaushik Roy
Publications
The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large Language Models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as part of human society, sharing human values, especially as these systems are deployed in high-stakes settings (e.g., healthcare, autonomous driving, etc.). Towards this end, neurosymbolic AI systems are attractive due to their potential to enable easy-tounderstand and interpretable interfaces for facilitating valuebased decision-making, by leveraging explicit representations of shared values. …
K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur
K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur
Publications
Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to tend to a user’s persona appropriately. This is particularly crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. …
Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth
Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth
Publications
Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a fullbodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by …
Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth
Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth
Publications
Despite their wide applications to language understanding tasks, large language models (LLMs) still face challenges such as hallucinations - the occasional fabrication of information, and alignment issues - the lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Additionally, the black-box nature of LLMs makes it highly challenging to train them meaningfully in order to achieve a desired behavior. Specifically, the attempt to adjust LLMs’ concept embedding spaces can be highly intractable, which involves analyzing the implicit impact on LLMs’ numerous parameters and the resulting inductive biases. This paper proposes a novel architecture that wraps powerful …
Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy
Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy
Publications
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …
Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth
Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth
Publications
Current Virtual Mental Health Assistants (VMHAs) primarily offer counseling and suggestive care but do not assist with patient diagnosis due to their lack of training in safety-constrained and specialized clinical process knowledge, referred to as ProKnow. In this work, we define ProKnow as an ordered set of information aligned with evidence-based guidelines or categories of conceptual understanding used by domain experts. We also introduce a new dataset of diagnostic conversations guided by safety constraints and Pro- Know, known as ProKnow-data. We develop a method for natural language question generation (NLG) designed to interactively gather diagnostic information from patients, termed ProKnow-algo. …
Dynamics Of A 3-D Inlet/Isolator Measured With Fast Pressure-Sensitive Paint, Andrew N. Bustard, Mark E. Noftz, Mitsugu Hasegawa, Hirotaka Sakaue, Joseph S. Jewell, Nicholas J. Bisek, Thomas J. Juliano
Dynamics Of A 3-D Inlet/Isolator Measured With Fast Pressure-Sensitive Paint, Andrew N. Bustard, Mark E. Noftz, Mitsugu Hasegawa, Hirotaka Sakaue, Joseph S. Jewell, Nicholas J. Bisek, Thomas J. Juliano
Publications
Fast pressure-sensitive paint (PSP) was applied to an inlet/isolator designed using the Osculating Internal Waverider Inlet with Parallel Streamlines (OIWPS) method. The dorsal isolator surface pressure was measured using anodized-aluminum PSP through transparent cast acrylic that makes up the ventral portion of the isolator. Temperature-sensitive paint was utilized to correct for the PSP’s temperature sensitivity. The model was tested under Mach 5.7 flow at Re = 8.5 ×106 /m and 10.2 ×106 /m in the AFOSR–Notre Dame Large Mach-6 Quiet Tunnel (ANDLM6QT) under conventional noise conditions. Flow phenomena, such as shocks originating in the inlet and flow separation at the …