Palaniappan Ramu

In this study, an analysis is performed to evaluate the geometric differences between the right and left lungs associated with bacterial pneumonia using chest radiographs. For this, the chest radiographic images of pediatric patients are considered. Segmentation of the right and left lungs are performed using the Reaction-Diffusion Level Set (RDLS) method. Feature determinants such as area, minor axis length, circularity, and orientation are extracted from both the right and left lungs. An asymmetry index between the right and the left lung is proposed and is derived from the extracted geometric features. The contribution of individual right and left lungs to the asymmetry index is analyzed in detail. The results demonstrate that the RDLS method is able to precisely segment the right and left lungs by preserving the structural alterations. Variations in the asymmetry indices are observed among the considered images indicating the effect of localized abnormalities. Left lung features are found to be better correlated with the asymmetry indices indicating a higher contribution of the left lung features as compared to the right lung features. This study demonstrates the possibility that structural alterations in bacterial pneumonia can be characterized using the lung asymmetry indices. It can be used as a potential non-invasive biomarker for the diagnosis of pulmonary abnormalities.

The Farmer Producer Company (FPC), a subset of the Farmer Producer Organization (FPO), is an important institutional form designed to organize farmer groups towards better coordinated farming and marketing. In the Indian context, as FPCs have emerged as new forms of members-led agribusiness, their ability to identify prevailing social ties and tap them effectively towards business growth needs to be better understood. Although social capital is studied broadly for its potential to drive organizational performance, it has been poorly researched in farmer collectives such as FPCs. The current work examines the effect of social capital on benefits and business performance at the level of member groups in FPCs. An empirical analysis was conducted in which two FPCs, which differed significantly in their mobilization strategies, farming methods, and supply chain linkages, were surveyed. Data collected from the surveys were visualized and clustering analysis was carried out using Self Organizing Maps (SOM), an unsupervised Artificial Neural Network (ANN) tool. Insights from clustering reveal the importance of pre-existing social ties, leadership, participation in group activities and the geographical affinity of groups in benefits realization and business performance of FPCs. The importance of bottom-up approaches in establishing robust supply chain linkages in emerging FPCs was keyed out through this work. The inferences through SOM, distilled strategies for FPCs' stakeholders in prioritizing interventions for member groups and in generating broader implications for policy makers accounting social capital in new institutional models.

Robust Design architectures permit identifying designs in the input space that minimize the mean as well as the spread of the response in the performance space, when the input variables are uncertain. Often, information about uncertainties is not readily available and are usually characterized by scarce samples that might contain extremes. Since extremes are part of the data, they cannot be excluded but including extremes alter the measures of spread such as standard deviation. Hence, it is imperative to develop a robust design architecture where the measure of spread estimations are less sensitive or insensitive to extremes. We propose using L-moments to estimate the measure of spread, the second L-moment (l2) and use it in the robust design formulation. We consider the cases of design variables which can be deterministic or random, and random variables. Hence, we use a dual surrogate framework where a design surrogate is built first. At each point in the DoE, scarce samples that might include extremes of the random variables are propagated through the design surrogate. Mean and measure of spread are computed by the L-moments approach at each point in the DoE, from the responses computed upon propagation, and used to build analysis surrogate which is used for identifying the robust design. The proposed approach is demonstrated on 2D Aspenberg function, 5D truss and 17D rotor disk design examples. The results reveal the superiority of the proposed approach over the conventional formulation.

Normal transformations are often used in reliability analysis. A Third order Polynomial Normal Transformation (TPNT) approach is used in this work. The underlying idea is to approximate the Cumulative Distribution Function (CDF) of the response in probit space using a third order polynomial while imposing monotonicity constraints. The current work proposes to apply log transformation to the ordinate of the transformed CDF and hence names the approach Log-TPNT. The log transformed data assists in improved fitting to the tails of the distribution resulting in better predictions of extreme values. Log-TPNT is demonstrated on a suite of statistical distributions covering all types of tails and analytical examples that cover aspects of high dimensions, non-linearity and system reliability. Results reveal that Log-TPNT can predict the response values corresponding to high reliability, with samples as scarce as 9. Finally, the variations associated with the response estimates are quantified using bootstrap.

Purpose: Product as a service (PaaS) is a method of business innovation for traditional product companies to make their offerings more attractive, move up the value chain and find more customers. However, the customer acceptance of PaaS depends on the market in which it operates. The purpose of this study is to understand, through an automotive lease PaaS evaluation and from an outside-in user perspective, the effects of the main intrinsic and extrinsic factors of a product, its design and brand, on the lease versus buy decision of automobiles by consumers in an emerging market, to help companies transition to a PaaS business model. Design/methodology/approach: A survey of actual car owners and lease car users was conducted. Confirmatory factor analysis was used to test the measurement model, followed by logistic regression for statistical modeling. As a practice evaluation of an existing PaaS example, this study further explored the expectations and experience of automotive lease users to link practical insights to analytical results and help businesses make a successful transition to PaaS. Findings: The results of the study showed the effect of the brand as significant in lease versus buy decisions. Brand loyalty is more important when leasing than when buying a car. However, brand awareness/association is less important for leasing than buying. There was no significant difference in consumer expectations of product design in automotive lease PaaS. Customers who buy and lease cars have the same expectations of the overall product in terms of its key design attributes. Originality/value: The literature on PaaS has mostly focused on strategies, frameworks and guidelines for implementation and empirical studies are limited. Comparative analyses between PaaS and traditional ownership models are also limited. It is unclear how consumers’ expectations differ between PaaS and traditional ownership models, especially in emerging markets, because PaaS’s success depends on the market in which it operates. This study addresses this gap in the literature.

In this work, an attempt has been made to analyze the shape variations in mediastinum for differentiation of Coronavirus Disease-2019 (COVID-19) and normal conditions in chest X-ray images. For this, the images are obtained from a publicly available dataset. Segmentation of mediastinum from the raw images is performed using Reaction Diffusion Level Set (RDLS) method. Shape-based features are extracted from the delineated mediastinum masks and are statistically analyzed. Further, the features are fed to two classifiers, namely, multi-layer perceptron and support vector machine for differentiation of normal and COVID-19 images. From the results, it is observed that the employed RDLS method is able to delineate mediastinum from the raw chest X-ray images. Eight shape features are observed to be statistically significant. The mean values of these features are found to be distinctly higher for COVID-19 images as compared to normal images. Area under the curve of greater than 76.9% is achieved for both the classifiers. It appears that mediastinum could be used as a region of interest for computerized detection and mass screening of the disease.

Bayesian inference is a powerful technique for damage/flaw detection in critical structures. This paper explores the application of Bayesian inference to identify the flaws (voids/inclusions) and its parameters, assuming no information about the flaws is known a priori. Multiple flaws are represented by a parameter vector that contains the locations and the geometric information. In the inverse problem framework, the Scaled Boundary Finite Element Method (SBFEM) with quadtree decomposition is used to solve the forward problem. The flaw parameters are statistically quantified using the Bayesian inference. The likelihood function and prior information update the joint distribution of the number of flaws and their corresponding flaw parameters. The sampling to estimate the posterior distribution of the flaw parameters is based on the trans-dimensional Reversible Jump Markov Chain Monte Carlo (RJMCMC). Also, the impact of additive Gaussian noise on the observed data is investigated. Numerical examples include identifying multiple voids of different shapes such as circle and ellipse and identifying voids and inclusions, using the proposed approach with input sensor data at different noise levels.

Graphical optimization allows solving one or two dimensional optimization problems visually by merely plotting the objective function and constraint function contours. In addition to the discovery of optima, such a visualization-based approach enables understanding and interpretation of design variable and objective behavior with respect to feasibility and optimality, permitting intuitive decision making for designers. However, visualization of optimization problems in higher dimensions is challenging, though it is desirable. Interpretable self-organizing map (iSOM) is an artificial neural network that enables visualization of many dimensions via two-dimensional representations. We introduce iSOM to solve multidimensional optimization problems graphically. In the current work, a novel graphical representation of the n-dimensional feasible region, called B-matrix is constructed using iSOM. B-matrix is used to represent feasible range of design variables and objective function on separate plots. Consequently, dimension-wise shrinkage in the search space is also obtained. The proposed approach is demonstrated on various benchmark analytical examples and engineering examples with dimensions ranging from 2 to 30.

In the present study, a multi-fidelity, multi-objective and multi-disciplinary design problem statement for aeroelastic optimization of an aircraft wing has been posed. The problem minimizes the twin objectives of transonic drag and the weight of the structure during cruise flight at Mach 0.8 at 10 km altitude. The wing geometry was parametrized using 11 design variables. A multi-fidelity-based co-kriging metamodel was used to replace the multi-disciplinary analysis routine in the optimization problem. Reynolds-averaged Navier–Stokes (RANS) and Euler solvers were used as high and low fidelity aerodynamic solvers while refined and coarse meshes were used with a default Finite Element (FEM) solver for multi-fidelity structural analysis. Latin hypercube sampling was used to generate 100 design points, out of which 30 were used to perform high fidelity simulations and the rest were used for low fidelity simulations. A high fidelity MDA routine run had a computational time of 4 h while the low fidelity runs were of 30 min. A successful and computationally inexpensive coupled aeroelastic optimization methodology has been demonstrated using MDF and co-kriging. The aerodynamic coefficients Cl and Cd showed improvement of 4.69% and 17.9%, respectively, compared to the baseline values. The structural weight of the optimized geometry was reduced by 355.7 Kg, and there was 14.54% reduction in the maximum von-Mises stress in the optimized structure.