63 research outputs found
Investigating MapReduce framework extensions for efficient processing of geographically scattered datasets
No full textWe observe two important trends brought about by the evolution of Internet in recent years. Firstly to improve end-to-end application performance in presence of bottlenecks in the wide-area Internet communication, modern day Internet services are designed in a decentralized fashion involving geographically distributed data-centers connected through the Internet. Secondly the pervasive nature of Internet services has resulted into an exponential growth in the size of digital information created, captured or replicated. Organizations are keenly interested in mining this information to uncover trends, statistics and other actionable information which can give them competitive advantage. These two trends necessitate the design of a large-scale data processing system which can operate efficiently in a distributed environment involving multiple datacenters connected through the Internet. In recent years, MapReduce programming model and specifically its open source implementation Hadoop is gaining a lot of traction for performing large-scale data processing in a centralized environment. Our evaluation of different real-world usage scenarios of Hadoop deployments revealed that the organizations with the distributed datasets are required to copy the entire dataset to a centralized location so that it can be efficiently processed by the Hadoop MapReduce framework. As the Internet evolves growth in the size of distributed datasets would outpace the improvements in the network bandwidth available in the Internet. At that point the approach of copying the entire dataset to a single location using Internet would become infeasible. In this thesis, we have investigated the possibility of extending the MapReduce and specifically Hadoop framework to operate in a distributed environment involving multiple datacenters connected through the Internet. We also have proposed policies to improve the performance of Hadoop MapReduce framework in a distributed environment. We have observed that our policies improve the performance of Hadoop framework substantially.M.S.Includes bibliographical referencesby Hrishikesh Gadr
Framework for crawling and local event detection using twitter data
No full textTwitter is a popular social media service, with millions of registered users as of December 2010. Twitter hosts substantial amounts of user-contributed data of real-world events. Twitter‟s core functions represent a simple social awareness stream model. Twitter users share information about upcoming events, the events the users are attending and events being broadcasted. The users also specify their location in their profile on Twitter. We can programmatically collect data from Twitter using their API and detect top terms and events in the data. Researchers can use this program to collect any kind of data from social networks easily. Journalists can get a real time the list of events detected by this method. In this thesis, we propose a solution to tackle the problem above. We wrote scripts that collected Twitter data through Twitter API. The scripts collect data according to user location and by search keywords. We built a web interface that provides mechanism to manage the collection of data. The web interface allows addition of new locations and keywords to the data collection. We collected Twitter data for important locations across the United States of America and the world using these tools. We use two approaches to detect trends in the data. In the first approach, we detected spikes in data by looking at overall rate of tweets at each location over a period of time. In the second approach, .we indexed the data according to location and time of the day. Then, we identified trends in the indexed data by ranking the terms according to spikes in term frequency. Using our framework, we can detect the top events and trends for a given time period and location according to Twitter data.M.S.Includes bibliographical referencesby Hrishikesh Baksh
Surfactant protein D inhibits HIV-1 infection of target cells via interference with gp120-CD4 interaction and modulates pro-inflammatory cytokine production
No full text© 2014 Pandit et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Surfactant Protein SP-D, a member of the collectin family, is a pattern recognition protein, secreted by mucosal epithelial cells and has an important role in innate immunity against various pathogens. In this study, we confirm that native human SP-D and a recombinant fragment of human SP-D (rhSP-D) bind to gp120 of HIV-1 and significantly inhibit viral replication in vitro in a calcium and dose-dependent manner. We show, for the first time, that SP-D and rhSP-D act as potent inhibitors of HIV-1 entry in to target cells and block the interaction between CD4 and gp120 in a dose-dependent manner. The rhSP-D-mediated inhibition of viral replication was examined using three clinical isolates of HIV-1 and three target cells: Jurkat T cells, U937 monocytic cells and PBMCs. HIV-1 induced cytokine storm in the three target cells was significantly suppressed by rhSP-D. Phosphorylation of key kinases p38, Erk1/2 and AKT, which contribute to HIV-1 induced immune activation, was significantly reduced in vitro in the presence of rhSP-D. Notably, anti-HIV-1 activity of rhSP-D was retained in the presence of biological fluids such as cervico-vaginal lavage and seminal plasma. Our study illustrates the multi-faceted role of human SPD against HIV-1 and potential of rhSP-D for immunotherapy to inhibit viral entry and immune activation in acute HIV infection. © 2014 Pandit et al.The work (Project no. 2011-16850) was supported by Medical Innovation Fund of Indian Council of Medical Research, New Delhi, India (www.icmr.nic.in/)
Structural and optical behavior of thin films of protein (BSA)-Polyelectrolyte (PAA, PSS) complexes
No full textNumerical Study On The Impact Of Self Induced Gravity Waves On Offshore Wind Farms
No full textThe present work studies the impact of self-induced Atmospheric Gravity Waves (AGWs) excited by an moderately sized offshore wind farm immersed in a Conventionally Neutral Boundary Layer (CNBL). A wind farm of finite span-wise length, consisting of 25 NREL 15MW wind turbines laid out in (5 X 5 Aligned manner) is considered. In order to achieve the same, two customized open source CFD RANS solvers are used, based on the OpenFOAM and SOWFA environments. The study primarily focuses on the impact of thermal stratification in the Free Atmosphere, the strength of the capping inversion and height of the same on the characteristics of the AGWs excited. Further, the work also evaluates the impact of the excited AGWs on the wind farm in the form of velocity deficits at each individual turbine column and also power down the analysis.The study finds that for a higher capping inversion strength, the AGWs excited by the wind farms are trapped and propagate horizontally along the capping inversion along with its vertically propagating counterpart. These cause velocity fluctuations at the hub height of the turbine, which results in AGW induced wind farm blockage that lowers the amount of incoming kinetic energy at the first turbine row and an increased velocity at the last two rows. Furthermore, the trapped AGWs cause the collective wake of the wind farm to recover faster. Moreover, the study proves that the height of the capping inversion determines the extent of AGW excitation by the wind farm. It was found lower capping inversion heights led to a higher AGW excitation and higher AGW induced flow blockage and wake recovery. Lastly, the study also finds that the thermal stratification in the Free Atmosphere determines the vertical wavelength of the AGWs propagating in the free atmosphere. Furthermore, the study also reports that for a higher Free Atmosphere stratification, a lower AGW induced blockage is observed.Aerospace Engineerin
Balanced-force numerical method for two phase flow at the onset of instability
No full textThe Interface Capturing method, which is a finite volume method as formulated by Queutey and Vissoneau for free surface immiscible, incompressible multiphase flows employs a collocated arrangement of unknownsand achieves a discrete force balance for the case when the interface coincides with the faces of the control volumes in the computational domain. This constraint limits the applicability of the method. Furthermore,the authors do not provide the exact formulation of the operators involved in the pressure velocity coupling. In the present research, a balanced-force numerical method is formulated, applicable for an interface thatneither has to coincide nor be aligned with the faces of the control volumes. The approach consists of the reconstruction of the values of the flow variables at the interface based on the interface jump conditions, with which the limit values of the normal derivatives at the interface are calculated. Furthermore, the construction of the operators of the discrete system is delineated to achieve a discrete force balance, by incorporating the reconstructed flow variables and employing a discretization which complies with the interface jump conditions. It is sufficient for a stationary discrete formulation to comply with the differential equation and the interface jump conditions. However, to apply this approach to solve unsteady flow problems the influence of the reformulated operators on the stability properties of the system should also be investigated.The properties of the individual operators are analyzed as well as their behaviour when they are embedded in the complete solver algorithm. Results are shown for both steady and unsteady test cases and compared with numerical results obtained with OpenFOAM. The resulting framework avoids the occurrence ofspurious velocities as it discretely complies with the interface conditions.<br/
REVIEW OF SOCIO DEMOGRAPHIC FACTORS AND OBSTETRIC CAUSES OF STILLBIRTHS AT TERTIARY CARE HOSPITAL
No full textABSTRACT Background: -Occurrence of stillbirth pose difficult situation for the obstetrician and cause great psychological and emotional trauma to the couple and the family. It reflects the suboptimum quality of maternal and child health services. Stillbirth rates are unacceptably high in developing countries .Study was carried out to find out the causes of stillbirths and the socio demographic profile of women ,who had stillbirth. Material and Methods: -A clinical observational study was undertaken at tertiary care teaching hospital over a period of 10 months from January 2011 to October 2011. Results:-Illiteracy, rural residence, lack of antenatal care ,low socioeconomic status were commonly associated with stillbirths. Pregnancy induced hypertension, ante partum hemorrhage ,cord related accidents and preterm labour were mainly responsible for stillbirths .Low birth weight and extreme prematurity were directly related to stillbirths. Conclusion:-High rate of ante partum stillbirths due to hypertension ,ante partum hemorrhage and preterm labour can be reduced by early recognition of the problem ,regular antenatal check ,color Doppler study to diagnose fetal growth restriction, and timely obstetric intervention .Ultrasonograpy to diagnose cord abnormalities ,use of intrapartum electronic fetal monitoring, partograph and prevention of prolongation of second stage of labour will help in reduction of stillbirths
Kernel Regression Coefficients for Practical Significance
No full textQuantitative researchers often use Student’s t-test (and its p-values) to claim that a particular regressor is important (statistically significantly) for explaining the variation in a response variable. A study is subject to the p-hacking problem when its author relies too much on formal statistical significance while ignoring the size of what is at stake. We suggest reporting estimates using nonlinear kernel regressions and the standardization of all variables to avoid p-hacking. We are filling an essential gap in the literature because p-hacking-related papers do not even mention kernel regressions or standardization. Although our methods have general applicability in all sciences, our illustrations refer to risk management for a cross-section of firms and financial management in macroeconomic time series. We estimate nonlinear, nonparametric kernel regressions for both examples to illustrate the computation of scale-free generalized partial correlation coefficients (GPCCs). We suggest supplementing the usual p-values by “practical significance” revealed by scale-free GPCCs. We show that GPCCs also yield new pseudo regression coefficients to measure each regressor’s relative (nonlinear) contribution in a kernel regression
Bidirectional expansion for keyword search on graph databases
No full textRelational, XML and HTML data can be represented as graphs with entities as nodes and relationships as edges. Text is associated with nodes and possibly edges. Keyword search on such graphs has received much attention lately. A central problem in this scenario is to efficiently extract from the data graph a small number of the "best" answer trees. A Backward Expanding search, starting at nodes matching keywords and working up toward confluent roots, is commonly used for predominantly text-driven queries. But it can perform poorly if some keywords match many nodes, or some node has very large degree.In this paper we propose a new search algorithm, Bidirectional Search, which improves on Backward Expanding search by allowing forward search from potential roots towards leaves. To exploit this flexibility, we devise a novel search frontier prioritization technique based on spreading activation. We present a performance study on real data, establishing that Bidirectional Search significantly outperforms Backward Expanding search.© AC
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