Sagar Kamarthi - Academia.edu (original) (raw)

Papers by Sagar Kamarthi

Dynamical complex systems such as ecosystems, biological systems, economic systems and technologi... more Dynamical complex systems such as ecosystems, biological systems, economic systems and technological infrastructures thrive on their networked components. Their resilience depends on their ability to recover from the impact of perturbations. Due to complex interactions among components in the networked systems, perturbations could cause cascading failures, consequently instability, and eventually collapse of the systems. Theoretical studies have been far from successful in predicting these events because of the high-dimensional structure of interacting components. The studies on complex systems have yet to fully explore the occurrences of state transition, systemic collapse, and impact of structural properties on resilience. In this work, we address these longstanding theoretical issues. We derive a set of formulations that help us understand the mechanism of high-dimensional interactions among components and uncover the principles that control the dynamics of interacting components. Our formulation reduces the system's high dimensional dynamics to a parsimonious resilience function of a parameter that solely depends on the topology but effectively captures the complex interaction structure. The experimental results demonstrate that the formulation can accurately predict the resilience loss brought about by perturbations and identify the tipping point where the systemic collapse occurs. These predictive results can be used for enhancing a complex system's ability to withstand perturbations and avert catastrophic collapses. The i study highlights the topological properties that can be used as principles for improving the resilience of ecosystems, biological systems, economic systems and technological infrastructures.

It is well established that lack of physical activity is detrimental to overall health of an indi... more It is well established that lack of physical activity is detrimental to overall health of an individual. Modern day activity trackers enable individuals to monitor their daily activity to meet and maintain targets and to promote activity encouraging behavior. However, the benefits of activity trackers are attenuated over time due to waning adherence. One of the key methods to improve adherence to goals is to motivate individuals to improve on their historic performance metrics. In this work we developed a machine learning model to dynamically adjust the activity target for the forthcoming week that can be realistically achieved by the activity-tracker users. This model prescribes activity target for the forthcoming week. We considered individual user-specific personal, social, and environmental factors, daily step count through the current week (7 days). In addition, we computed an entropy measure that characterizes the pattern of daily step count for the current week. Data for trai...

International Journal of Prognostics and Health Management

The increasing complexity of a vehicle's digital architecture has created new opportunities t... more The increasing complexity of a vehicle's digital architecture has created new opportunities to revolutionize the maintenance paradigm. The Artificial Intelligence (AI) assisted maintenance system is a promising solution to enhance efficiency and reduce costs. This review paper studies the research trends in AI-assisted vehicle maintenance via keyword co-occurrence network (KCN) analysis. The KCN methodology is applied to systematically analyze the keywords extracted from 3153 peer-reviewed papers published between 2011 and 2022. The network metrics and trend analysis uncovered important knowledge components and structure of the research field covering AI applications for vehicle maintenance. The emerging and declining research trends in AI models and vehicle maintenance application scenarios were identified through trend visualizations. In summary, this review paper provides a comprehensive high-level overview of AI-assisted vehicle maintenance. It serves as a valuable resource ...

The International Journal of Advanced Manufacturing Technology, Nov 30, 2016

Magnesium oxide (MgO) is a good candidate for an interface layer in multifunctional metal-oxide n... more Magnesium oxide (MgO) is a good candidate for an interface layer in multifunctional metal-oxide nanoscale thinfilm heterostructures due to its high breakdown field and compatibility with complex oxides through O bonding. In this research, molecular beam epitaxy (MBE) is used to deposit 10 nm to 15 nm MgO single-crystal films on silicon carbide with hexagonal polytype 6H (6H-SiC) to serve as an interface layer for effective integration of functional oxides. In this work, the effect of MBE process control variables on the growth rate of the MgO film measured in nanometers per minute is investigated. Experiments are conducted at various process conditions and the resulting MgO film growth rate at each combination of process conditions is measured. The process control variables studied are the substrate temperature (100°C-300°C), magnesium source temperature (328°C-350°C), plasma intensity (0 mV-550 mV), and percentage oxygen on the starting surface of 6H-SiC substrate (9 %-13 %) after the substrate is prepared by high-temperature hydrogen etching. The film thickness is computed using the effective attenuation length (EAL) of silicon photoelectron peak intensity as measured by x-ray photoelectron spectroscopy (XPS). The film thickness is converted to growth rate by dividing it with the duration of film growth. Using the experimental data, a neural network model is developed to estimate growth rate for any given process variable combination. From this neural network model, multiple replications of data were generated to conduct a 3-level full factorial design of experiments and response surface-based analysis. The study reveals that the plasma intensity has the most significant influence on growth rate. The results indicate that growth rate is relatively low on high-quality substrates with √3 × √3 R30°reconstructed 6H-SiC (0001) surface with optimum oxygen content (approximately 10 %); in contrast, the growth rate is relatively high on substrates with high surface roughness and excessive oxygen on the starting substrate surface.

Proceedings of SPIE, Dec 29, 2000

ABSTRACT

Functional properties of thin film metal oxides depend upon their stoichiometric and structural u... more Functional properties of thin film metal oxides depend upon their stoichiometric and structural uniformity. Controlling the film deposition process can help tune the functionality of these films by ensuring the control over chemistry and structure of the films. The high volume manufacturing of functional devices will benefit from the development of reliable control models developed from research efforts in designing robust manufacturing processes. The use of neural networks as computer models to simulate the molecular beam epitaxy (MBE) of iron oxide thin films is presented in this work. Monte Carlo experiments are used to study the sensitivities and significances of process control variables to the stoichiometric performance indicators. Moreover, we also explore the relationship between growth dynamics of iron oxide (Fe2O3, Fe3O4, and mixed FexOy) and magnesium oxide (MgO) thin films. The common metal adsorption controlled growth mechanism of two films with different structural and stoichiometric complexities were observed and the similarities among the trends of analogous stoichiometric indicators at comparable metal arrival rates of the two films are presented. The dependence of undesirable bonding states of iron and magnesium metals with the film thicknesses was also observed in both processes. The commonalities suggest the potential to use of neural network assisted Monte Carlo analysis to link common atomic-level mechanisms to processing variables in one nano-scale system and use them to predict some level of behavior in other nanoscale processes with similar atomic-level mechanisms.

Page 1. A Generic IDEFO Model of a Production System for Mass Customization Thomas P. Cullinane&#... more Page 1. A Generic IDEFO Model of a Production System for Mass Customization Thomas P. Cullinane', Pratap S. S. Chinnaiah, Naken Wongvasu and S aga V. Kamarthi Dept. of Mechanical, Industrial and Manufacturing Engineering ...

2021 ASEE Virtual Annual Conference Content Access, Jul 26, 2021

Sustainability

With the potential of Industry 4.0 technologies to enable sustainable manufacturing, asset life c... more With the potential of Industry 4.0 technologies to enable sustainable manufacturing, asset life cycle management (ALCM) has been gaining increasing attention in recent years. This study explores the evolution of Industry 4.0 technology applications to sustainable ALCM from 2002 to 2021. This study is based on keywords collected from 3896 ALCM-related scientific articles published in the Web of Science, IEEE Xplore and Engineering Village between 2002 and 2021. We conducted a review analysis of these keywords using a network science-based methodology, which unlike the tedious traditional literature review methods, gives the capability to analyze a huge number of scientific articles efficiently. We built keyword co-occurrence networks (KCNs) from the keywords and explored the network characteristics to uncover meaningful knowledge patterns, knowledge components, knowledge structure, and research trends in the body of literature at the intersection of ALCM and Industry 4.0. The network...

Journal of Intelligent Manufacturing, 2022

In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engin... more In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber manufacturing. Using a network science and data mining-based Keyword Co-occurrence Network (KCN) methodology, this work analyzes the trends in data science topics in the manufacturing literature over the past two decades to inform the researchers, educators, industry leaders of knowledge trends in intelligent manufacturing. It studies the evolution of research topics and methods in data science, Internet of Things (IoT), cloud computing, and cyber manufacturing. The KCN methodology is applied to systematically analyze the keywords collected from 84,041 papers published in top-tier manufacturing journals between 2000 and 2020. It is not practically feasible to review this large body of literature through tradition manual approaches like systematic review and scoping review to discover insights. The results of network modeling and...

Type 2 diabetes mellitus (T2DM) is one of the most common chronic disease and the seventh leading... more Type 2 diabetes mellitus (T2DM) is one of the most common chronic disease and the seventh leading cause of death in the United States. Casting T2DM in a product platform framework, this research aims to establish the relationships between contributing factors and complications. Once the tree structure is created by analyzing the historical patient data, patients are clustered based on their complication characteristics and the contributing factors, such as age, race, blood pressure, glycemic levels, and hemoglobin levels. Along with patient clustering, treatment plans are also clustered simultaneously. This creates a mapping between patient groups and treatment groups, with one optimal treatment plan for each cluster of patients. When an individual patient’s membership is determined, the association between the patient and the optimal treatment plan is automatically identified. The healthcare providers can tailor the treatment plan based on the individual’s unique needs. The propose...

The need for STEM (science, technology, engineering, and math) workforce is well documented in th... more The need for STEM (science, technology, engineering, and math) workforce is well documented in the literature. The lack of interest among school-age students in STEM careers and the reason for such lack of interest are also well documented. Pedagogical research suggests that K-12 students learn best by engaging them in activities that relate to their daily lives and that reinforce principles through hands-on tasks. Research also suggests that the engineering design process (EDP) offers the best platform to implement these activities because it typically involves critical thinking combined with hands-on tasks to motivate the students. While many variations of using the EDP in student teaching exist, we introduce an innovative methodology of using and implementing the concept of “capstone experience” at the high school level; the EDP encourages open-ended problem solving and multiple solutions. The capstone experience is rooted in the capstone design project course that is typically r...

PLOS ONE, 2021

In current clinical settings, typically pain is measured by a patient’s self-reported information... more In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pai...

BACKGROUND It is well established that lack of physical activity is detrimental to the overall he... more BACKGROUND It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. OBJECTIVE The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. METHODS ...

2011 ASEE Annual Conference & Exposition Proceedings

is a Ph.D. Candidate in the Department of Mechanical and Industrial Engineering at Northeastern U... more is a Ph.D. Candidate in the Department of Mechanical and Industrial Engineering at Northeastern University. Her research focuses on the implementation of engineering design processes using traditional and virtual methods. Her Ph.D. focus is on product design, development and commercialization incorporating environmental impact and human factors design. She received her B.S. in Mechanical and Biomedical Engineering from Rensselaer Polytechnic Institute and her M.S. in Technological Entrepreneurship from Northeastern University.

PLOS ONE, 2018

Background & aims A common limiting factor in the throughput of gastrointestinal endoscopy units ... more Background & aims A common limiting factor in the throughput of gastrointestinal endoscopy units is the availability of space for patients to recover post-procedure. This study sought to identify predictors of abnormally long recovery time after colonoscopy performed with procedural sedation. In clinical research, this type of study would be performed using only one regression modeling approach. A goal of this study was to apply various "machine learning" techniques to see if better prediction could be achieved. Methods Procedural data for 31,442 colonoscopies performed on 29,905 adult patients at Massachusetts General Hospital from 2011 to 2015 were analyzed to identify potential predictors of long recovery times. These data included the identities of hospital personnel, and the initial statistical analysis focused on the impact of these personnel on recovery time via multivariate logistic regression. Secondary analyses included more information on patient vitals both to identify secondary predictors and to predict long recoveries using more complex techniques. Results In univariate analysis, the endoscopist, procedure room nurse, recovery room nurse, and surgical technician all showed a statistically significant relationship to long recovery times, with p-value below 0.0001 in all cases. In the multivariate logistic regression, the most significant predictor of a long recovery time was the identity of the recovery room nurse, with the endoscopist also showing a statistically significant relationship with a weaker effect. Complex techniques led to a negligible improvement over simple techniques in prediction of long recovery periods.

Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2019

In the current work, the effects of design (groove depth and groove width) and operational (tempe... more In the current work, the effects of design (groove depth and groove width) and operational (temperature and velocity) parameters on aerodynamic performance parameters (coefficient of drag and coefficient of lift) of an isolated passenger car tire have been investigated. The study is conducted by using neural network-based Monte-Carlo analysis on computational fluid dynamics (CFD). The computer experiments are designed to obtain the causal relationship between tire design, operational, and aerodynamic performance parameters. The Reynolds-averaged Navier–Stokes equations-based RealizableK-εmodel has been employed to analyze the variations in flow patterns around an isolated tire. The design parameters are varied over wide range and full factorial design, while considering temperature and velocity is completely explored to draw conclusive results. The multi-layer perceptron type neural network with the back-propagation algorithm is trained to map any non-linearity in causal relationshi...