Raghunathan Rengasamy

Background: Different formulae have been developed globally to estimate gestational age (GA) by ultrasonography in the first trimester of pregnancy. In this study, we develop an Indian population-specific dating formula and compare its performance with published formulae. Finally, we evaluate the implications of the choice of dating method on preterm birth (PTB) rate. This study’s data was from GARBH-Ini, an ongoing pregnancy cohort of North Indian women to study PTB. Methods: Comparisons between ultrasonography-Hadlock and last menstrual period (LMP) based dating methods were made by studying the distribution of their differences by Bland-Altman analysis. Using data-driven approaches, we removed data outliers more efficiently than by applying clinical parameters. We applied advanced machine learning algorithms to identify relevant features for GA estimation and developed an Indian population-specific formula (Garbhini-GA1) for the first trimester. PTB rates of Garbhini-GA1 and other formulae were compared by estimating sensitivity and accuracy. Results: Performance of Garbhini-GA1 formula, a non-linear function of crown-rump length (CRL), was equivalent to published formulae for estimation of first trimester GA (LoA, − 0.46,0.96 weeks). We found that CRL was the most crucial parameter in estimating GA and no other clinical or socioeconomic covariates contributed to GA estimation. The estimated PTB rate across all the formulae including LMP ranged 11.27–16.50% with Garbhini-GA1 estimating the least rate with highest sensitivity and accuracy. While the LMP-based method overestimated GA by 3 days compared to USG-Hadlock formula; at an individual level, these methods had less than 50% agreement in the classification of PTB. Conclusions: An accurate estimation of GA is crucial for the management of PTB. Garbhini-GA1, the first such formula developed in an Indian setting, estimates PTB rates with higher accuracy, especially when compared to commonly used Hadlock formula. Our results reinforce the need to develop population-specific gestational age formulae.

A single coalescence event in a 2D concentrated emulsion in a microchannel can trigger an avalanche of similar events that can destabilize the entire assembly of drops. The sensitive dependence of the process on numerous parameters makes the propagation dynamics appear probabilistic. In this article, a stochastic simulation framework is proposed to understand this collective behavior in a system employing a large number of drops. We discover that the coalescence propagation dynamics exhibit a critical behavior where two outcomes are favored: no avalanche and large avalanches. Our analysis reveals that this behavior is a result of the inherent autocatalytic nature of the process. The effect of the aspect ratio of the drop assembly on the propagation dynamics is studied. We generate a parametric plot that shows the region of the parameter space where the propagation, averaged over the ensemble, is autocatalytic: where the possibility of near destabilization of the drop assembly appears.

Kalman filter and its variants have been used for state estimation of systems described by ordinary differential equation (ODE) models. Moving Horizon Estimation (MHE) has been a popular approach in chemical engineering community for the estimation of both ODE and differential algebraic equation (DAE) systems but is computationally demanding. There has been some work on applying Extended Kalman filter for state estimation of DAE systems with measurements as functions of only the differential states. This work describes the estimation of nonlinear DAE systems with measurements being a function of both the differential and algebraic states. An Unscented Kalman filter (UKF) formulation is also derived for semi-explicit index 1 DAE systems. The utility of these formulations are demonstrated through a case study.

The ability to manipulate drops is essential for integrating multiple processes in droplet microfluidics-based lab-on-a-chip devices. Examples of such droplet manipulation operations include sorting, sequencing, synchronization, and so forth. Microfluidic networks are promising platforms for performing these functionalities. This work explores the design of entry times of drops, a set of operating parameters, in a microfluidic network to achieve a desired functionality. Here, we specifically focus on sorting of drops at the exit of the network. For the first time, droplet microfluidic networks are perceived as hybrid dynamical systems. This viewpoint allows us to develop a methodology for designing the entry times of drops in microfluidic networks to achieve desired functionalities through the notion of constraint satisfaction. The solution to this constraint satisfaction problem provides entry times that result in the desired functionality. The proposed method allows one to establish the existence/nonexistence of entry time solutions for a desired functionality.

Recently we proposed a simple, computationally inexpensive multi-agent simulation strategy to understand drop movement in a microchannel with Hele-Shaw flow geometry (2D). This has helped us understand the collective behavior of the drops in these systems. However, transitioning these systems to practice would require that strategies are developed to combat the inherent fluctuations that are a part of any physical system. In the present work we utilize the simulation strategy for understanding the impact of design choices in minimizing the effect of uncertainties, and the sensitivity of the device behavior to operational variables that can potentially be used as controlled variables. We study a control metric, time of contact, which is defined as the time spent by a drop in the microchannel when its distance from its neighboring drops is less than some critical value. By simplifying the model further we are able to analyze the contact time dynamics as a function of geometric parameters and operating conditions analytically. We also carry out the complete numerical simulation of our models to understand active control of drop clusters.

Li-ion batteries are a popular choice of rechargeable battery for use in many applications like portable electronics, automobiles as well as stationary applications for providing uninterruptable power supply. State of Charge (SoC) and State of Health (SoH) are important metrics of a Li-ion battery that can help in both battery prognostics and diagnostics for ensuring high reliability and prolonged lifetime. The ML algorithms available in the literature for SoC and SoH prediction involves use of various derived features rather than directly measurable features making it difficult for industrial applications. In this work, we use battery data obtained from different batteries to develop supervised models that can be used for the on-line estimation of SoC and SoH. This work involves two parts: a) developing a classifier based on SoH b) dynamic prediction of battery SoC given the past operational data of current, voltage, and temperature of the battery which are easily measurable. Random forest algorithm is used for battery site classification based on the SoH data available from the manufacturer. The battery SoC estimation is performed using a random forest algorithm and Neural network-based NARX model.

Lithium ion batteries are one of the most commercially used batteries. Though they are widely used in mobile phones, laptops and other consumer electronics, concerns related to their safety and efficient operation still persists. One of the major issues in Li-ion batteries is the capacity degradation with aging due to various mechanisms such as solid electrolyte interphase (SEI) formation and dissolution, thermal runaway, and Li-plating. In this work, we describe a capacity fade minimizing model predictive control approach for identification and realization of optimal charge-discharge cycles for Li-ion batteries. Optimum charging profiles are obtained such that the reduction in charge carrying capacity with cycling is minimized, while still obtaining required charging. We expect the proposed strategy to improve battery capacity and prolong lifespan. Examples that demonstrate the significance of the proposed framework by comparing battery performance with and without the presence of controller are discussed. Extensions to this work in terms of addressing various battery failure mechanisms, on-line identification of failure mechanisms, and designs for on-line implementation in real battery systems are also outlined.

Introduction: The integrity of the intestinal epithelium is crucial for human health and is harmed in autism spectrum disorder (ASD). An aberrant gut microbial composition resulting in gut-derived metabolic toxins was found to damage the intestinal epithelium, jeopardizing tissue integrity. These toxins further reach the brain via the gut-brain axis, disrupting the normal function of the brain. A mechanistic understanding of metabolic disturbances in the brain and gut is essential to design effective therapeutics and early intervention to block disease progression. Herein, we present a novel computational framework integrating constraint based tissue specific metabolic (CBM) model and whole-body physiological pharmacokinetics (PBPK) modeling for ASD. Furthermore, the role of gut microbiota, diet, and oxidative stress is analyzed in ASD. Methods: A representative gut model capturing host-bacteria and bacteria-bacteria interaction was developed using CBM techniques and patient data. Simultaneously, a PBPK model of toxin metabolism was assembled, incorporating multi-scale metabolic information. Furthermore, dynamic flux balance analysis was performed to integrate CBM and PBPK. The effectiveness of a probiotic and dietary intervention to improve autism symptoms was tested on the integrated model. Results: The model accurately highlighted critical metabolic pathways of the gut and brain that are associated with ASD. These include central carbon, nucleotide, and vitamin metabolism in the host gut, and mitochondrial energy and amino acid metabolisms in the brain. The proposed dietary intervention revealed that a high-fiber diet is more effective than a western diet in reducing toxins produced inside the gut. The addition of probiotic bacteria Lactobacillus acidophilus, Bifidobacterium longum longum, Akkermansia muciniphila, and Prevotella ruminicola to the diet restores gut microbiota balance, thereby lowering oxidative stress in the gut and brain. Conclusion: The proposed computational framework is novel in its applicability, as demonstrated by the determination of the whole-body distribution of ROS toxins and metabolic association in ASD. In addition, it emphasized the potential for developing novel therapeutic strategies to alleviate autism symptoms. Notably, the presented integrated model validates the importance of combining PBPK modeling with COBRA -specific tissue details for understanding disease pathogenesis.

Energy storage (batteries) and conversion devices (fuel cells) operate based on electrochemical principles. Electrochemical impedance spectroscopy (EIS) is an important experimental technique that can be used to optimize the performance of these devices at the design stage. Further, EIS can also be used for non-invasive, in-situ diagnostics of device performance, while operational. While EIS is a powerful technique, the instrumentation required for implementation can be bulky and the time for analysis could also become unacceptable, particularly for online applications. The proposed work is an investigation and development of a rapid impedance measurement technology using large bandwidth and short duration diagnostic signals. The key concept behind the approach is the use of a particular definition of instantaneous frequency in tandem with chirp signals for testing. The instantaneous frequency definition allows one-to-one time frequency mapping and the chirp signal incorporates a wide bandwidth of frequencies. As a result, a novel impedance plot qualitatively, and largely quantitatively, matching the corresponding EIS plot is generated. Consequently, any diagnostic approach based on EIS can potentially be realized using the proposed approach. The impedance plots are generated in a very short time making the approach amenable for online applications. In-silico validation of the proposed method on few equivalent circuits of electrochemical systems is presented in this work; future work will include experimental validation of the technique on real electrochemical systems.

The quality of process data in a chemical plant significantly affects the performance and benefits gained from activities like performance monitoring, online optimization and control. Since many chemical processes often exhibit nonlinear dynamics, techniques like Extended Kalman Filter (EKF) and Nonlinear Dynamic Data Reconciliation (NDDR) have been developed to improve the data quality. There are various issues that arise with the use of either of these techniques: EKF cannot handle inequality or equality constraints, while the NDDR has high computational cost. Recently a recursive estimation technique for nonlinear dynamic processes has been proposed which combines the merits of EKF and NDDR techniques. This technique, named as Recursive Nonlinear Dynamic Data Reconciliation (RNDDR), provides state and parameter estimates that satisfy bounds and other constraints imposed on them. However, the estimate error covariance matrix in RNDDR is computed in the same manner as in EKF, that is, the effects of both nonlinearity and constraints are neglected in the computation of the estimate error covariance matrix. A relatively new method known as the Unscented Kalman Filter has been developed for nonlinear processes, in which the statistical properties of the estimates are computed without resorting to linearization of the nonlinear equations. This leads to improved accuracy of the estimates. In this paper, we combine the merits of the Unscented Kalman Filter and the RNDDR to obtain the Unscented Recursive Nonlinear Dynamic Data Reconciliation (URNDDR) technique. This technique addresses all concerns arising due to the presence of nonlinearity and constraints within a recursive estimation framework, resulting in an efficient, accurate and stable method for real-time state and parameter estimation for nonlinear dynamic processes. © 2006 Elsevier Ltd. All rights reserved.