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Objective: Respiratory diseases are the world’s third leading cause of mortality. Early detection is critical in dealing with respiratory diseases as it improves the effectiveness of intervention, including treatment and reducing the spread. The main aim of this paper is to propose a novel lightweight inception network to classify wide spectrum of respiratory diseases using lung sound signals. Methods: The proposed framework consists of three stages: (a) preprocessing, (b) melspectrogram extraction and conversion into a 3-channel image, lastly (c) classification of the melspectrogram images into different pathological classes using proposed lightweight inception network, namely RDsLINet. Results: Utilizing the proposed architecture, we have achieved high classification accuracy of 96%, 99.5%, 91.6% for seven class classification, six class classification and healthy vs. asthma classification. To the best of our knowledge this is the first work on seven class respiratory disease classification using lung sounds. Whereas, our proposed network outperforms all the existing published works for six class and binary classification. Conclusion: The suggested framework makes use of deep learning methods and offers a standardized evaluation with strong categorization capabilities. In order to distinguish between a wide range of respiratory diseases, our study is a pioneering one that focuses exclusively on lung sounds. Significance: The proposed framework can be translated to real time clinical application which will facilitate the prospect of automated respiratory health screening using lung sounds.

A Matlab‐based optimization algorithm is introduced for inverting fault structures from observed gravity anomalies. A convenient graphical user interface is also presented for incorporating the input parameters without any technical complexity to any users. The inversion code uses particle swarm optimization, and all control parameters are tuned initially for faster convergence. There is no requirement of prior choice of an initial model, that is the advantage of using global optimization. The optimization technique is versatile enough to handle any depth‐varying density distributions. The maximum number of iterations and stopping criterion is fixed initially for getting the best optimized solution. The inverted model's output in terms of fault structure, observed and inverted gravity anomalies and dip, and vertex location of fault plane can be viewed in the graphical user interface at the end of the optimization process. The optimization algorithm is applied to different synthetic models with fixed and depth‐varying density contrasts. All synthetic models are further contaminated with white Gaussian noise for sensitivity analysis, and detailed uncertainty appraisal was also performed for the reliability estimation. Finally, the optimization is implemented for fault structure inversion of the Aswaraopet boundary fault, India, and found that the optimized solution provides a good agreement with the previously published literature. Optimized results indicate that this novel optimization approach demonstrates a robust implementation of fault inversion for any depth‐varying density distributions.

We have developed a MATLAB-based inversion program, b-spline polynomial approximation using the differential evolution algorithm (SPODEA), to recover the concealed basement geometry under heterogeneous sedimentary basins. Earlier inversion techniques used the discretized subsurface interface topography into a grid of juxtaposed elementary prisms to estimate the basement depth of a basin. Such discretization leads to the failure of the depth profile continuity and requires a higher number of inversion parameters for achieving the desired accuracy. The novel approach of SPODEA overcomes such limitations of earlier inversion techniques. SPODEA is based on the segment-wise b-spline optimization technique to estimate the basement depth by using high-order polynomials. Moreover, it can achieve an optimal misfit with minimal parametric information, which reduces the computational expense. Our inversion approach uses the differential evolution algorithm, which provides real parametric optimization and uses b-splines for accurate estimation of continuous depth profiles. The efficiency of our algorithm was determined with two complex synthetic sedimentary basin models comprised of constant and depth-varying density distributions. Furthermore, the uncertainty analysis of our inversion technique is evaluated by incorporating white Gaussian noise into the synthetic models. Finally, the utility of SPODEA is evaluated by inverting gravity anomalies for two different real sedimentary basins. It produces geologically reasonable outcomes that are in close agreement with basement structures from previously reported results.

Endogamy or intra-caste marriage is one of the most resilient of all the caste based practices in India. Even in 2011, the rate of inter caste marriages in India was as low as 5.82%. In this paper we explore whether education has any relationship with this age-old practice of marrying within one’s own caste. Using a nationally representative data set, the Indian Human Development Survey, we find that, in sharp contrast with the findings in the existing literature on out-marriages in the Western countries, education levels of the spouses themselves do not have any association with the likelihood of their own marriage being an inter caste one. However, couples with a more educated mother of the husband have a significantly higher probability of being in an inter caste marriage. One standard deviation increase in the years of education of the husband’s mother is associated with a 10.16% increase in the probability of inter caste marriage over the sample mean. Our analysis highlights the importance of recognizing the institution of arranged marriages in any analysis of Indian marriage markets.

This paper proposes new composite measures of relative and dynamic state performance to improve governance and delivery of public programs in developing countries with a federal structure. We rank the performance of 19 major Indian states on two large development programs launched by the Indian government over the last two decades using publicly available data. Although we find volatility in performance over time, there exists a positive correlation between measures of state capacity, development and accountability with program outcomes. Our findings have important implications for both the design and implementation of public service programs of such large scale.

In this paper we survey the recent literature on top down audits and community monitoring. Whilein the case of top down audits the efficacy of audits depends on the type of punishment- electoral punishmentvs legal punishment, the literature finds that electoral punishment is less effective than legalpunishment. Moreover, electoral punishment depends on whether voters are aware of audit reports.The type of service being audited - the opportunity for corruption and the clarity of rules and regulationseffect the success of audits. However, even when top down audits succeed in cutting down corruption,they may not always lead to better outcomes. Social audits are effective when collective actionproblems are less salient e.g. when groups are homogeneous, when the design is such that communityparticipants feel empowered e.g. through elections to the monitoring committee, information is notuseful by itself unless accompanied by tools to influence outcomes, and finally there is not a clear mapbetween finding corruption and improving ultimate outcomes. We suggest that a fertile area for futureresearch is the design of optimal audit schemes that take account of the opportunities for corruptionand the motivations of both the providers and the auditors.

Misgovernance in India ranks among its toughest problems to solve. Despite decades of seemingly progressive legislation, there is a simmering dissatisfaction even among those for whose benefits the legislation is designed. The electoral debacle of the UPA government in the parliamentary election of 2014 can be partly attributed to the disenchantment that a vast majority of people felt toward the governance issues. We argue that dynastic politics is the hallmark of many political parties in India and it prevents the emergence of meritorious leadership. Just as we need the process of creative destruction in the economic arena for sustained growth, we need a similar process in the political arena so that local leaders are chosen on the basis of their performance. The success of the new government at the center will depend on whether they succeed in jettisoning centralized decision making and moving the political process in this direction.