Application of mainstream object relational database to real time database applications in industrial automation

This thesis examines the proposition that because of recent huge increases in processing power, disk and memory capacities the commercial mainstream object relational databases may now be a viable option to replace dedicated real-time databases in industrial automation. The benefits are lower product cost, greater availability of trained manpower for development and maintenance and lower risks due to larger installed base and larger number of platforms supported. The issues considered in testing this proposition were performance, ability to mimic critical real-time database features, replication of the real-time database application development and administration tools and finally the low overhead high speed, real-time data compression facility available in real-time databases. An efficient yet simple real-time compression algorithm was developed for use with relational databases and benchmarked. Extensive comparative benchmarking has been done to convincingly prove the proposition. The results overwhelmingly show, that for a majority of industrial real-time database applications, the performance offered by a commercial object relational database on a current platform are more than adequate

For whom the phone does (not) ring? Discrimination in the rental housing market in Delhi, India

Using an audit experiment carried out on of India’s largest real estate websites, we document striking variations between landlords’ treatment of upper-caste Hindus, Other Backward Castes, Scheduled Castes, and Muslims. We find strong evidence of discrimination against Muslim applicants, both in terms of probability of being contacted and the number of contacts, relative to upper-caste Hindu (UC) applicants, in the rental housing market in Delhi and its largest suburbs. While the probability that a landlord responds to an upper-caste applicant is 0.35, this is only 0.22 for a Muslim applicant. We also find suggestive evidence that when landlords respond to both UC and Muslim applicants, they call back the UC applicant sooner. Muslim applicants are especially disadvantaged when applying to rent one-bedroom houses; there is an additional 20 percentage points reduction in the probability of a callback. In contrast, we find no clear evidence that landlords are less likely to respond to Scheduled Castes and Other Backward Classes. However, our estimates may understate the true differentials in callback ratios as a result of our failure to perfectly link all callbacks to a listing

Essays in development economics and political economy

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2007.Includes bibliographical references.This thesis is a collection of three empirical essays on issues in economic development, with a focus ,on political economy and the labor market in India. Chapter 1] analyzes the effect of television coverage on political voice by examining the functioning of Question Hour, a forum for political discussion in India's Parliament, which is intended to foster government accountability to the people by allowing Members of Parliament (MPs) to raise questions about issues of concern to the electorate which the Government must answer. I use an unusual source of variation in the telecast status of Question Hour, made possible by the fact that it was only shown on television every other week, to assess what effect television had. I find that MPs did not become more likely to represent the concerns of the voters in their constituency. I argue that the evidence is consistent with party establishments exercising greater control on the participation of their MPs when Question Hour was televised than they otherwise did. Chapter 2 studies caste and religion in India's new economy sectors - IT (software) and IT-enabled services (call-centers) - by sending fictitious resumes in response to job openings in and around Delhi, India advertised in major city papers and online job sites. We find evidence of discrimination against Other Backward Classes (OBCs) and Scheduled Castes (SCs) in the call-center industry but no corresponding results for these or any other groups (including Muslims) in software jobs. We do however find that having a higher-quality resume helps SC applicants in software jobs the most, and OBCs not at all. We argue that the evidence for SCs is consistent with predictions from theories of statistical discrimination.(cont.) Chapter 3 asks whether there is empirical evidence of differential treatment by gender in India's Civil Service by following the careers of 1457 civil servants in India from the time they were recruited to the time they reached the fifth of seven levels within the Civil Service hierarchy. I compile and use a newly-collected data set made up of employment records for all entrants into the Indian Administrative Service, or IAS, between the years 1971 and 1984. Using the individual's rank at entry as a measure of initial quality, I compare the career progress of men and women in each quartile of the rank distribution for each entering cohort, and find that women in the lowest rank quartile take significantly longer than similarly-ranked men (as well as than higher-ranked men and women) to be promoted to level 5 of the civil service hierarchy, which I argue is evidence of statistical discrimination against women in the Indian civil service.by Saugato Datta.Ph.D

Recursive model for dose-time responses in pharmacological studies

Background: Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage Results: In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time response with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. Conclusion: We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharamcological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds

Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks

Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated with CNN-based deep learning, can boost the predictive accuracy. We illustrate the superior predictive capabilities of the proposed fra- mework as compared to state-of-the-art methodologies in drug sensitivity prediction scenarios using synthetic datasets, drug chemical descriptors as predictors from NCI60, and both transcriptomic information and drug descriptors as predictors from GDSC

Energy storage for renewable energy

Presented at All Energy Australia 2012 International Conference, Melbourn

Pumped Hydro Using the Downstream River Channel for Time Shifting Storage

The surge of Renewable Energy (RE) and particularly Solar PV in the journey to replace fossil fuels, is producing excess energy that is way more than the demand on sunny days. The visible network demand too is getting reduced by behind the meter installed residential PV. This is leading to energy prices approaching close to zero and even negative in sunny parts of the day.The lack of demand is leading to widespread curtailment of RE. This near free energy is creating opportunities for lower round trip efficiency storage options. Pumped hydro energy storage(PHES) operates between two reservoirs by pumping from lower to the higher reservoir when energy is cheap, to store gravitational energy and allows water to flow down from higher to lower through a turbine to dispatch the stored energy during peak demand. Freshwater PHES traditionally offered 80 to 90% efficiency, though seawater PHES can be as low as 72% if the sea is 3 to 4 km away from the dam. Market share of PHES is still around 95% of energy storage in 2019. However there are not enough sites available to scale PHES by 10 times to meet the projected demand to 2040. Building new dams or boring long tunnels is expensive and the low cost sites are already developed and water is scarce globally especially inland. All of which makes it very difficult to use conventional PHES to meet the urgent new large (over 1000GW) global energy storage requirements necessary to firm Renewable Energy sources. This paper examines the existing river channel downstream of existing dams, as the potential free lower reservoir. Water from the dam is allowed to dispatch energy by installing hydro turbines or using existing hydro turbines. The water flows down stream at rates around 0.5 to 1m/s causing it to take several hours to flow down 10km to 30km. A suitable point is found where the river meanders back towards the dam and the straight-line distance is shorter. Water is pumped back from such downstream location(s) back into the dam using close to zero or negative cost( a revenue opportunity) RE. The low cost of the energy used for pumping up say between -1000 and 10$/MWH makes it viable to operate such river channel based pumped hydro even if the round trip percentage efficiency is only in the high 50s. We modelled the Burrinjuck Dam and the Murrumbidgee river down stream of the dam, to find a 1210MWH daily storage opportunity offering a round trip efficiency of 58% using only 7GL (7%) of the 1026GL capacity of the dam. This means the PHES operation can continue even, if in the dry years the dam is only 30% full(300GL). We believe such a river channel based PHES is possible in most existing dams all over the world unlocking a new massive potential for PHES, to ease the transition to renewables at an affordable cost. This could be the missing piece of the puzzle

Recursive model for dose-time responses in pharmacological studies

Background: Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage Results: In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time response with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. Conclusion: We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharamcological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds

Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks

Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated with CNN-based deep learning, can boost the predictive accuracy. We illustrate the superior predictive capabilities of the proposed fra- mework as compared to state-of-the-art methodologies in drug sensitivity prediction scenarios using synthetic datasets, drug chemical descriptors as predictors from NCI60, and both transcriptomic information and drug descriptors as predictors from GDSC