Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Carbon Dioxide Appropriation Using Alkanolamine Blends: Vapor-Liquid Equilibrium Modelling Approach

Design of sour-gas treating processes with alkanolamine solvents requires knowledge of vapor liquid equilibrium(VLE)of the aqueous acid gas–alkanolamine systems.An approximate thermodynamic model is developed to correlate and predict the vapor-liquid equilibrium(VLE)of CO2 in aqueous N-Ethyl Ethanolamine (EAE) solution in the temperature range (303.1-323.1 K).The values of deprotonation constant(K4)and carbamate reversion constant(K5)are determined by using the model derived from the VLE data of the ternary system(CO2 + EAE+ H2O).The model predictions are in good agreement with the experimental data of CO2 solubility in aqueous EAE solution available in the open literature.Similarly modified Kent Eisenberg model is validated for the quaternary(CO2 + AMP+PZ+ H2O)system.To consider the phase non-ideality in the(CO2+AMP+PZ+H2O)system we assumed the equilibrium constants are a function of temperature,CO2 partial pressure and amine concentration.The adjustable equilibrium constants Ki’ are then estimated.Rigorous thermodynamic model i.e.NRTL model is developed and VLE data of(CO2 + MDEA+ H2O)is correlated to find out the interaction parameters.The model predictions are in good agreement with the experimental data of CO2 solubility in aqueous MDEA solution available in the open literature.Density and viscosity of two novel tertiary alkanolamines including1-(2-hydroxyethyl)piperidine(1-(2-HE)PP)and 2-diethylaminoethanol(DEAE)in their aqueous blends with Piperazine(PZ)have been measured over a temperature range of(303.1, 308.1, 313.1, 318.1, 323.1)K and total amine mass fraction in all the blends was kept constant at 30 %.The mass % ratios of(PZ)/ (1-(2-HE)PP or DEAE)considered for measurements were 3/27, 6/24, 9/21 and 12/18.Density and viscosity of the ternary mixtures were correlated as functions of temperature and amine composition using thermodynamic framework.Modeling and simulation is done in MATLAB platform

Reductive Biotransformation of Ethyl Acetoacetate: A Comparative Studies using Free and Immobilized Whole Yeast Cells

Bioreduction of ethyl acetoacetate with free and immobilized yeast whole cell was achieved by using water and sucrose combination. After detachment from immobilized beads under basic condition, the corresponding ethyl(S)-(+)-3-hydroxybutanoate was isolated with 98 to 100% yield. Immobilized beads of yeast whole cell were prepared at different temperature which affects the morphology and physiology of the beads for the diffusion of the enzyme, which shown the maximum conversion of the substrate to products as compared to the free yeast whole cell

Using Fuzzy Matching of Queries to optimize Database workloads

Directed Acyclic Graphs (DAGs) are commonly used in Databases and Big Data computational engines like Apache Spark for representing the execution plan of queries. We refer to such graphs as Query Directed Acyclic Graphs (QDAGs). This paper uses similarity hashing to arrive at a fingerprint such that the fingerprint embodies the compute requirements of the query for QDAGs. The fingerprint, thus obtained, can be used to predict the runtime behaviour of a query based on queries executed in the past having similar QDAGs. We discuss two approaches to arrive at a fingerprint, their pros and cons and how aspects of both approaches can be combined to improve the predictions. Using a hybrid approach, we demonstrate that we are able to predict runtime behaviour of a QDAG with more than 80% accuracy.Comment: 9 pages, 5 figure

Transformer-based Flood Scene Segmentation for Developing Countries

Floods are large-scale natural disasters that often induce a massive number of deaths, extensive material damage, and economic turmoil. The effects are more extensive and longer-lasting in high-population and low-resource developing countries. Early Warning Systems (EWS) constantly assess water levels and other factors to forecast floods, to help minimize damage. Post-disaster, disaster response teams undertake a Post Disaster Needs Assessment (PDSA) to assess structural damage and determine optimal strategies to respond to highly affected neighbourhoods. However, even today in developing countries, EWS and PDSA analysis of large volumes of image and video data is largely a manual process undertaken by first responders and volunteers. We propose FloodTransformer, which to the best of our knowledge, is the first visual transformer-based model to detect and segment flooded areas from aerial images at disaster sites. We also propose a custom metric, Flood Capacity (FC) to measure the spatial extent of water coverage and quantify the segmented flooded area for EWS and PDSA analyses. We use the SWOC Flood segmentation dataset and achieve 0.93 mIoU, outperforming all other methods. We further show the robustness of this approach by validating across unseen flood images from other flood data sources.Comment: Presented at NeurIPS 2021 Workshop on Machine Learning for the Developing Worl

Observational study of scalpel versus electrocautery for subcutaneous incision in elective gynaecological surgeries

Background: Considering higher rate of postoperative wound complications in Government set up hospitals, this study was an attempt to compare incision time, incisional blood loss, hospital stay, post-operative pain and postoperative wound complications when subcutaneous tissue is opened with either scalpel or electrocautery in elective gynaecological surgeries after keeping all other clinical and surgical variables same i.e. age, BMI, haemoglobin, incision depth and hospital stay.Methods: This was a prospective observational comparative study conducted in one of the tertiary teaching hospital in Western Maharashtra, India over 12 months. All patients (n=100) were divided into 2 groups. Group A in which skin and subcutaneous tissue was dissected by using scalpel and Group B in which after skin, anterior abdominal wall was opened by using electrocautery. Data analyzed for indication, incisional blood loss, incision time, postoperative pain, wound complications and hospital stay.Results: There were no significant association between preoperative diagnosis and the development of a post-operative wound complications. Mean incision blood loss was found to be significantly higher in group A compared to group B patients. Postoperative pain was significantly higher in group A (P value <0.05). Among wound complications, no statistically significant differences were seen regarding wound complications for the two groups.Conclusions: Electrosurgical dissection for abdominal incision is safe, less time consuming and with less blood loss during subcutaneous incision and produces less postoperative pain. We conclude that the method of subcutaneous tissue incision was unrelated to the development of postoperative abdominal incision problems

Multi-level Scalable Textual-Graphical Password Authentication Scheme for Web based Applications

Nowadays, user authentication is one of the important topics in information security. Authentication is necessary in multi-user systems. User name and password are used to authenticate a user. Text-based strong password scheme can provide security to a certain degree. Users tend to pick short passwords or passwords that are easy to remember, which makes the passwords vulnerable for attackers to break. Furthermore, textual password is vulnerable to shoulder-surfing, hidden camera and spy-ware attacks. Graphical authentication has been proposed as a possible alternative solution to text-based authentication, motivated particularly by the fact that humans can remember images better than text. However, they are mostly vulnerable to shoulder surfing. In this paper, we propose a Multi-level Scalable Textual-Graphical Password Authentication Scheme for web based applications. This scheme integrates both graphical and textual password schemes, and provides multi-level authentication scheme as compared to previously proposed single level scheme. In this scheme multi-level authentication is obtained by making use of SMS service, hence provides more secure service. This scheme shows significant potential bridging the gap between conventional textual password and graphical password. Further enhancements of this scheme are proposed and briefly discussed