5,982 research outputs found
Static & dynamic approaches testing eclectic paradigm: evidence of EU firms’ investment decisions in China
Diabetes awareness among children: A comparison study of diabetes knowledge in school-age children between U.S. and Kuwait
Astronomy in the Cloud: Using MapReduce for Image Coaddition
In the coming decade, astronomical surveys of the sky will generate tens of
terabytes of images and detect hundreds of millions of sources every night. The
study of these sources will involve computation challenges such as anomaly
detection and classification, and moving object tracking. Since such studies
benefit from the highest quality data, methods such as image coaddition
(stacking) will be a critical preprocessing step prior to scientific
investigation. With a requirement that these images be analyzed on a nightly
basis to identify moving sources or transient objects, these data streams
present many computational challenges. Given the quantity of data involved, the
computational load of these problems can only be addressed by distributing the
workload over a large number of nodes. However, the high data throughput
demanded by these applications may present scalability challenges for certain
storage architectures. One scalable data-processing method that has emerged in
recent years is MapReduce, and in this paper we focus on its popular
open-source implementation called Hadoop. In the Hadoop framework, the data is
partitioned among storage attached directly to worker nodes, and the processing
workload is scheduled in parallel on the nodes that contain the required input
data. A further motivation for using Hadoop is that it allows us to exploit
cloud computing resources, e.g., Amazon's EC2. We report on our experience
implementing a scalable image-processing pipeline for the SDSS imaging database
using Hadoop. This multi-terabyte imaging dataset provides a good testbed for
algorithm development since its scope and structure approximate future surveys.
First, we describe MapReduce and how we adapted image coaddition to the
MapReduce framework. Then we describe a number of optimizations to our basic
approach and report experimental results comparing their performance.Comment: 31 pages, 11 figures, 2 table
A deformation transformer for real-time cloth animation
Achieving interactive performance in cloth animation has significant implications in computer games and other interactive graphics applications. Although much progress has been made, it is still much desired to have real-time high-quality results that well preserve dynamic folds and wrinkles. In this paper, we introduce a hybrid method for real-time cloth animation. It relies on datadriven models to capture the relationship between cloth deformations at two resolutions. Such data-driven models are responsible for transforming low-quality simulated deformations at the low resolution into high-resolution cloth deformations with dynamically introduced fine details. Our data-driven transformation is trained using rotation invariant quantities extracted from the cloth models, and is independent of the simulation technique chosen for the lower resolution model. We have also developed a fast collision detection and handling scheme based on dynamically transformed bounding volumes. All the components in our algorithm can be efficiently implemented on programmable graphics hardware to achieve an overall real-time performance on high-resolution cloth models. © 2010 ACM.postprin
Simulation of the electrohydrodynamic instability process used in the fabrication of hierarchic and hollow micro/nanostructures
Exemplar-Based Image and Video Stylization Using Fully Convolutional Semantic Features
postprin
The Design and Construction of Public Service Building in Developing Rural Regions During the Post COVID-19 Period: Cased on a Chinese Village Centre
The spread of COVID-19 has caused an increasing demand for public medical room. Cases of Chinese Huoshenshan Hospital and mobile cabin hospitals proved the effectiveness of constructing emergent medical buildings. However, these cases, usually with strict requirements on technology and infrastructure, are hard to implement in developing rural regions. Therefore, there is an urgent need for adapting industrial construction to the rural situation. This research introduced an adaptive approach for rural projects delivery during COVID-19. It is based on a longitudinal case study, recording and analysing the construction process of a village centre in Jiangsu, China, from 2019 to 2020. By comparing the construction process of actual operation and traditional method, the advantages in a shorter building period and lower labour density were verified. This research pointed out neglected risks in developing countries and provided a practical construction approach in these areas. It supported the prevention of COVID-19 global wide
Hubungan antara Perilaku Belajar Siswa dalam Pembelajaran Ekonomi dengan Hasil Belajar Siswa di SMA
: "This study intend to dig up information about correlation between student learning behavior in the learning economy with learning outcomes at grade 10 SMAS taman Mulia at Kubu raya district. Population in this research aggregate 105 student with samples a total of 30 student were determined by random sampling technique. Collecting data using the technique of direct communication, indirect communication techniques and techniques of documentary studies with data collection tools such as observation sheets, interview, questionnaire and learning outcomes that come from school. Based on the analysis of product moment correlation, obtained, rhitungan < rtabel 0,138 dan rtabel < 0,361, pada db N = 30. Apparently the price r xy = 0.138 r smaller than the table ,This means giving the consequences of rejecting Ha , which reads : " There is a positive and significant relationship between behavioral study with the results of student learning in the learning economy in SMAs Taman Mulia Kubu Raya " and accept Ho , which reads : " There is a positive and significant relationship behavior learning with student learning outcomes in the learning economy in SMAs Taman Mulia Kubu Raya
Effects of different factors on the forward extraction of soy protein in reverse micelle systems
Reverse micelle extraction is a new technology for the extraction of protein. In this research, three kinds of reverse micelle systems, anionic surfactant sodium bis(2-ethylhexyl) sulfosuccinate (AOT) reverse micelle system, sodium dodecyl sulfate (SDS) reverse micelle system, and cationic surfactant cetyltrimethyl ammonium bromide (CTAB) reverse micelle system, were used to extract soy protein respectively. Effects of soy flour concentration, Wo ([H2O]/[AOT]), temperature, time, pH, ionic strength and ultrasonic power on forward extraction efficiency of soy protein were investigated. The effect of AOT reverse micelle diameter was studied as well. AOT reverse micelle system had higher extraction efficiency than SDS and CTAB systems. The main factors that affected the forward extraction were soy flour concentration, temperature and pH. The optimal conditions in AOT system were soy flour concentration being 0.007 g/ml, Wo 16, pH 6.5, temperature 34°C, time 20 min, KCl concentration of 0.1 mol/L and ultrasound power of 240 W. Under these conditions, the extraction efficiency of soy protein was 85.5%. The forward extraction efficiency of soy protein in AOT reverse micelle system increased with the increase of the reverse micelle diameter. Reverse micelle extraction is an effective way to extract soy protein.Key words: Soy protein, reverse micelle, forward extraction, sodium bis(2-ethylhexyl) sulfosuccinate (AOT), sodium dodecyl sulfate (SDS), cetyltrimethyl ammonium bromide (CTAB)
A hybrid recursive multilevel incomplete factorization preconditioner for solving general linear systems
In this paper we introduce an algebraic recursive multilevel incomplete factorization preconditioner, based on a distributed Schur complement formulation, for solving general linear systems. The novelty of the proposed method is to combine factorization techniques of both implicit and explicit type, recursive combinatorial algorithms, multilevel mechanisms and overlapping strategies to maximize sparsity in the inverse factors and consequently reduce the factorization costs. Numerical experiments demonstrate the good potential of the proposed solver to precondition effectively general linear systems, also against other state-of-the-art iterative solvers of both implicit and explicit form
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