107 research outputs found
Understanding automated decisions [winner - staff prize]
No full textAlgorithms and automated systems influence important areas of our lives. Is it possible to explain how they work using interface design? Is this a good thing? Acknowledgements: Arnav Joshi, Paul-Marie Carfantan, Nandra Anissa, and collaborators from Projects by IF. Funding from Open Society Foundation
Hydrogel-based active plasmonics
No full textHydrogel-based active plasmonics is an emerging field with extensive scope for innovative research and practical applications in various areas such as biosensing, soft robotics and actuators, and optical displays. Upon integration of plasmonic systems into responsive hydrogel matrices, researchers can fabricate smart nanocomposite hydrogels with tunable optical properties. Considering its potential,
limited research has been conducted on understanding the optical properties of hydrogel-based plasmonic systems. This thesis aimed to fabricate, investigate and modulate the optical properties of a plasmonic nanocomposite hydrogel when subjected to mechanical stretching.
The nanocomposite hydrogel matrix was successfully synthesized using a copolymer system of acrylamide and sodium acrylate, incorporated with DNA-gold nanorod- based (FAuNR-DNAo) plasmonic assemblies through photopolymerization. The stability of the FAuNR-DNAo complexes under different experimental conditions, such as the presence of photosensitive agents and UV exposure, was studied using circular dichroism and UV-visible spectroscopy. α-ketoglutaric acid was identified as an effective photoinitiator for further nanocomposite hydrogel studies.
The optical responses of the nanocomposite hydrogels were evaluated using CD spectroscopy. The challenges associated with measuring complex, chiral, and anisotropic systems are thoroughly discussed. This research opens up exciting possibilities for designing multifunctional materials with tailored optical properties and stimuli-responsive behaviours. It also provides a foundation for future investigations and advancements in this multidisciplinary field, paving the way for technological breakthroughs in the fields of nanophotonics, biosensing, dynamic colour displays, and responsive actuators
Studying the Economic Impact of the Demonetization Across Indian Districts
No full textThis thesis studies the economic impact of the Indian demonetization which was a unique monetary event that made 86.9 percent of the total currency in circulation illegal tender overnight. The decision to demonetize high-value currency notes was taken by the Indian government on November 8th, 2016, leading to a severe shortage of cash. This thesis tries to analyze how the impact of the demonetization differed across districts in India and how the characteristics of those districts pertaining to education, electricity and tap water access, employment, and technology access can help explain these differences. The thesis uses satellite data on human-generated night light activity to quantify the impact of the demonetization on economic activity. It is found that districts that had a higher literacy rate and a higher percentage of households with access to electricity experienced a less severe economic impact of the demonetization. The economic impact due to the demonetization was more severe in districts with a higher percentage of marginal workers in their workforce. Amongst the various sectors of employment, agriculture, manufacturing, and construction were affected less severely by the demonetization compared to wholesale and retail trade. These insights have the potential to help policymakers minimize the negative economic impacts of a policy like the demonetization by understanding which districts or sub-geographical regions are more susceptible to these impacts
Low-Frequency Acoustic Source Detection and Localization
No full textIn aviation, clear-air turbulence (CAT) is a major cause of in-flight injuries. It occurs in cloudless skies and cannot be detected by the onboard weather radar. Studies have predicted the extent of CAT to increase substantially in the next few decades, thus
necessitating a method for detecting CAT.
With CAT known to generate low-frequency and infrasonic acoustic emissions, acoustic-based methods can potentially be deployed for detection and localization. This thesis studies low-frequency acoustic source detection and localization in the context of CAT.
Localizing low-frequency acoustic sources is challenging for acoustic beamforming which suffers from poor resolution at low source frequencies. A deep learning-based method is adopted as an alternative. Deep learning models for two-dimensional and three-dimensional acoustic source localization (ASL) have been built using synthetic data and computationally inexpensive neural network architectures. These models are necessary to prove the viability of deep learning for low-frequency ASL. The thesis then explores the potential of a deep learning-enabled, acoustics-based method for CAT detection in the future through a large-scale, virtual flight case, set up for the detection of a representative CAT source. The flight case tries to predict what an in-flight microphone will detect around a CAT source through a technique known as auralization which simulates the acoustic field of a source by modeling the sound propagation and determining what a receiver would hear.
The deep learning models yield promising qualitative and quantitative results that prove the feasibility of using deep learning for low-frequency ASL. Combined with the results from auralization, it can be concluded that there exists considerable scope for deep learning-enabled, acoustics-based detection and localization of CAT. The future work involves expanding the current scale of research with deeper network architectures to process real, in-flight acoustic data
India's Proposed Data Protection Framework - Recommendations on Individual Participation Rights
No full textSelective Economic Progress: The Growth of Income-Inequality in India
No full textThis thesis quantitatively dissects the trend in income-inequality in India, as affected by various other macroeconomic factors, between 2004 and 2014, with the aim of constructing a viable model to predict future income-inequality levels. Causation between some of the potential affecting factors and income inequality is identified and is in line with the hypothesis that there is a causational relationship between these macroeconomic factors and income inequality.
An analysis of the trends in income inequality allowed for a trend-based prediction model to be constructed and implemented. Statistical linkage between income- inequality and its causes allowed for another more comprehensive income-inequality prediction model to be constructed. The comprehensive model accounted for the effect of the causational factors for income inequality determined by this thesis
Leveraging Predictive Analytics To Improve Prognostic Models For Uterine Cancer
No full textUterine Corpus Endometrial Carcinoma (UCEC) presents notable disparities in incidence and outcomes across racial groups, warranting deeper investigation through transcriptomic and predictive modeling approaches. This thesis presents a comprehensive transcriptomic analysis of RNA-Seq data from 177 individuals, comprising both tumor and control samples, specifically from the UCEC_CN_High molecular subtype. The cohort was stratified by race, focusing on differences between Black and White individuals to explore race-associated gene expression patterns. To uncover genes with prognostic significance, LASSO (Least Absolute Shrinkage and Selection Operator) regression was applied for feature selection, identifying a subset of genes most strongly associated with overall survival. These selected genes were then utilized in a Cox Proportional Hazards Model to estimate patient-specific risk scores. The dataset was divided into a training set (70%) for model development and a testing set (30%) for performance evaluation. The integration of transcriptomic profiling with survival analysis enables a biologically informed risk stratification, providing insight into molecular drivers of UCEC and potential race-specific biomarkers. The findings highlight the potential of using survival modeling in cancer genomics to enhance prognostic accuracy and address health disparities. This study contributes to the growing body of research focused on personalized cancer therapy and racial equity in biomedical outcomes
Investigation of Cavity Flameholders as Flow Artefacts in a Dual-mode, Axisymmetric Scramjet
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