Prediction Models for Estimation of Soil Moisture Content

This thesis introduces the implementation of different supervised learning techniques for producing accurate estimates of soil moisture content using empirical information, including meteorological and remotely sensed data. The models thus developed can be extended to be used by the personal remote sensing systems developed in the Center for Self-Organizing Intelligent Systems (CSOIS). The dfferent models employed extend over a wide range of machine-learning techniques starting from basic linear regression models through models based on Bayesian framework. Also, ensembling methods such as bagging and boosting are implemented on all models for considerable improvements in accuracy. The main research objective is to understand, compare, and analyze the mathematical backgrounds underlying and results obtained from dfferent models and the respective improvisation techniques employed

Development of a carbon-based polymer composite product for efficient recovery of crude oil in oil spill environments: report of researcher exchange March 2019

The India-UK Water Centre (IUKWC) promotes cooperation and collaboration between the complementary priorities of NERC-MoES water security research. This document reports on the Junior Researcher Exchange program conducted at the Indian Institute of Science (IISc) in Bangalore, India, during the month of March 2019. The theme of the Research Exchange; transforming science into management catchment solutions, brought into sharp focus the issues surrounding laboratory based results and scaled up solutions to catchment management. Awareness of this unfortunate reality inspired this exchange to attempt to produce output that works on simple, scalable principles for removing crude oil from water. The lead researcher Mr Jonathan Bloor from the University of Plymouth in the UK worked with host researcher, Dr Sai Siva Gorthi and his lead postdoctoral researcher Dr Vikram S. to develop polymer based composite products for the recovery of crude oil. The outcome of the exchange resulted in a prototype Graphene Oxide (GO) Aerogel foam that can separate crude oil and water via simple gravity method. Additional output also involved the rapid prototyping of electrospinning nanofibre membranes to enhance the selectivity and mechanical strength of the foam. This report is intended for members of the IUKWC Open Network and water security stakeholders

Influence of Twitter on Hydroxychloroquine Medication Prescriptions for COVID-19 Patients

Social media is proposed to play a crucial role in healthcare providers\u27 care decisions. However, social media information may not always be reliable as false information is prone to virality, and emotional cues may drive information sharing. Hence, in our study, we strive to understand the influence of social media on healthcare providers\u27 care decisions by empirically examining the influence of Twitter discourse regarding Hydroxychloroquine (HCQ) on the actual prescribing rates of the drug in the USA for treating COVID-19 patients. We assembled panel data by collecting tweets from Twitter API v2 and Hydroxychloroquine prescriptions from the Symphony Health dataset on the COVID-19 research database to achieve our research objectives. Econometric analysis of our panel data indicates that Twitter discourse positively influences the proportion of Hydroxychloroquine prescriptions prescribed to COVID-19 patients. Our study has implications for research and practice

Structured light techniques for 3D surface reconstruction in robotic tasks

Robotic tasks such as navigation and path planning can be greatly enhanced by a vision system capable of providing depth perception from fast and accurate 3D surface reconstruction. Focused on robotic welding tasks we present a comparative analysis of a novel mathematical formulation for 3D surface reconstruction and discuss image processing requirements for reliable detection of patterns in the image. Models are presented for a parallel and angled configurations of light source and image sensor. It is shown that the parallel arrangement requires 35\% fewer arithmetic operations to compute a point cloud in 3D being thus more appropriate for real-time applications. Experiments show that the technique is appropriate to scan a variety of surfaces and, in particular, the intended metallic parts for robotic welding tasks

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic

Fluid flow estimation with multiscale ensemble filters based on motion measurements under location uncertainty

International audienceThis paper proposes a novel multi-scale fluid flow data assimilation approach, which integrates and complements the advantages of a Bayesian sequential assimilation technique, the Weighted Ensemble Kalman filter (WEnKF). The data assimilation proposed in this work incorporates measurement brought by an efficient multiscale stochastic formulation of the well-known Lucas-Kanade (LK) estimator. This estimator has the great advantage to provide uncertainties associated to the motion measurements at different scales. The proposed assimilation scheme benefits from this multiscale uncertainty information and enables to enforce a physically plausible dynamical consistency of the estimated motion fields along the image sequence. Experimental evaluations are presented on synthetic and real fluid flow sequences

Characterization of MEMS Electrostatic Actuators Beyond Pull-In

The operational range of MEMS electrostatic parallel plate actuators can be extended beyond pull-in with the presence of an intermediate dielectric layer, which has a significant effect on the behaviour of such actuators. Here we study the behaviour of cantilever beam electrostatic actuators beyond pull-in using a beam model along with a dielectric layer. Three possible static configurations of the beam are identified over the operational voltage range. We call them floating, pinned and flat: the latter two are also called arc-type and S-type in the literature. We compute the voltage ranges over which the three configurations can exist, and the points where transitions occur between these configurations. Voltage ranges are identified where bi-stable and tri-stable states exist. A classification of all possible transitions (pull-in and pull-out as well as transitions we term pull-down and pull-up) is presented based on the dielectric layer parameters. A scaling law is found in the flat configuration. Dynamic stability analyses are presented for the floating and pinned configurations. For high dielectric layer thickness, discontinuous transitions between configurations disappear and the actuator has smooth predictable behaviour, but at the expense of lower tunability. Hence, designs with variable dielectric layer thickness can be studied in future to obtain both regularity/predictability as well as high tunability