Web Log Data Analysis: Converting Unstructured Web Log Data into Structured Data Using Apache Pig

Data extraction and analysis have recently received significant attention due to the evolution of social media and large volume of data available in an unstructured form. Hadoop and MapReduce have been continuously implementing and analyzing large amount of data. In this paper Apache Pig, which is one of the high-level platform for analyzing large volume of data and runs on the top of Hadoop is used to analyze unstructured log files and extract information. In this paper, weblog server files are used to analyze and extract meaningful information in an unstructured form to a structured form in Apache Pig framework The main purpose of this paper is to extract, transform and load unstructured data in an Apache Pig framework and analyze the data and its performance on local mode as well as MapReduce mode. This paper further explains in brief about the different steps required to analyze unstructured web server log files in Apache Pig. This paper also compares the efficiency when a large volume of data is processed on MapReduce mode and local mode

Modeling the outbreak and spread of infectious diseases using a bayesian machine learning approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe modeling of infectious diseases and their predictions on space and time is very important as it helps in devising the policies for preventive measures. These predictions should be generated from a probabilistic model to provide the uncertainties and thus the confidence. The phenomenon of spread of infectious diseases is so complex that there are lots of uncertainties in the data and in the process itself. Machine learning methods like neural networks are useful in modeling this complex problem, however, these approaches lack handling of uncertainties. Similarly, it is seen in literature that a combined approach of neural networks and Bayesian inferences have not been explored much. Thus to fill these gaps this thesis aims to develop a combined model containing neural network method and Bayesian inference for modeling and predicting the number of cases of infectious diseases in areal units such as municipalities or health-zones. To introduce the impact of human movement on the spread of infectious disease, the movement data has been used combined with the daily infection data to form a spatial factor and used as a covariate in this study. In addition to this, the spatial correlation due to spatial neighborhood as well as the mobility is taken into account in the model along with the temporal dependencies. The model was evaluated on the COVID-19 dataset for 245 healthzones of the autonomous community of Castilla-Leon, Spain. The results show that the model is generally able to predict the number of cases of infectious diseases with good accuracy. Similarly, the mobility factor was also found to have an influence on the model. However, the flexibility of the model still needs to be evaluated by applying the model to different scenarios

Use of health services in Hill villages in Central Nepal

This paper reports the use and non-use of health care facilities in the Hill villages in central Nepal. The health behaviour model (HBM) is applied to test the significance of socio-economic variables on the use of the modern health care system. The study finds that all three characteristics of the HBM model, predisposing, enabling and need, are significantly related to use and non-use of the modern health care system. The analysis shows that number of living children, respondent’s education, nearness to the road and service centre, value of land, knowledge about health workers and experience of child loss are some of the variables that are positively and significantly related to the use of modern health care. Age of the respondents and household size were found to be negatively associated with health-care use. Contrary to expectation, caste is unimportant. Making use of the qualitative data, this paper argues that the health care system is unnecessarily bureaucratic and patriarchal, which favours the socio-economically well-off

Density Functional Theory Study of Two-Dimensional Boron Nitride Films

Since graphene was isolated in 2004, the number of two-dimensional (2D) materials and their scientific relevance have grown exponentially. Besides graphene, one of the most important and technolocially promizing 2D materials that has emerged in recent years is hexagonal boron nitride, in its monolayer or multilayer form. In my thesis work, I used density functional theory (DFT) calculations to investigate the properties of boron nitride films. In particular, I first studied the properties (i.e. formation energy, defect states, and structure) of point charged defects in monolayer and bilayer hexagonal boron nitride, and subsequently, I focused on the linear and nonlinear mechanical properties of boron nitride films with sp2 and sp3 bonding structures. Experimental detection and characterization of defects in 2D materials are challenging tasks. My research work has been focused on points defects in monolayer (1L-) and bilayer hexagonal boron nitride (2L-hBN). I carried out technical developments and DFT calculations, and I have investigated the structural, formation energy, and electronic properties of both neutral and charged nitrogen and boron vacancy defects, as well as carbon substitutions for nitrogen and boron sites in 1L-hBN and 2L-hBN. These studies have shown that, due to an electrostatic polarization effect, the formation energies of charged defects in 2L-hBN are, in all cases, about 0.5 eV lower than in monolayer 1L-hBN. Moreover, I found that, under the assumption that vacancies and carbon substitutions defects are all present in a 2D h-BN film, there is at least one point defect species that is in a charged state, regardless the value of the Fermi energy. Carbon and boron nitride form a variety of solid allotropes, including layered materials such as graphite and h-BN, and sp3-bonded materials such as diamond and cubic BN. Similarly to the case of graphene, which can be considered a single layer of graphite, in recent years experimental investigations have shown that a diamond or sp3-rich phase can exist in the ultrathin down to the monolayer form. In particular, these experimental studies agree that under pressure and/or upon surface passivation (with hydrogen or hydroxyl groups), a few-layer graphene can transform into a sp3-rich C film, exhibiting a stiffness comparable to that one of diamond. As for BN, although high-purity polycrystalline h-BN is known to form a sp3-bonded phase upon compression, to the best of our knowledge only one very recent experimental study has shown that upon compression, nanosheets of h-BN transform into films rich in sp3 bonds. In this work, the sp3-bonded BN film was proposed to have a surface in contact with the substrate, and hydroxyl groups terminating the surface exposed to air. The aforementioned experimental works underline the importance of studying the mechanical properties of BN ultrathin membranes having both a sp2 and sp3 bonding structure. In my thesis work, I have used a DFT approach to calculate linear and nonlinear elastic constants of BN membranes. These elastic constants were used to estimate the ideal breaking strength under biaxial strain, and this theoretical parameter was then used to quantify the effects of film thickness, surface passivants, and structure on the mechanical strength of the membranes. In my work, I found that compression of a few-layer h-BN film can lead to the formation of various plausible conformations of an ultrathin BN film with a sp3 bonding structure. Most of these sp3-bonded 2D films are energetically stable upon passivation of at least one surface. Nonetheless, I found that three-layer BN films can form stable ultra- thin films with a sp3 bonding structure and clean surfaces. Although the BN sp3-bonded membranes exhibit longitudinal mechanical properties comparable to those of the layered BN film, the benefits of a sp3-bonded membrane include the enhanced mechanical strength in the transverse direction of the film, and potentially, the possibility to form conformations of the films with anisotropic and tunable mechanical and electrical properties