• The Korean Journal of Ecology
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• v.23 no.2
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• pp.101-106
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• 2000

The grand challenge for the 21$^{st$ century is to harness knowledge of the earth`s biological and ecological diversity to understand how they shape global environmental systems. This insight benefits both science and society. Biological and ecological data are among the most diverse and complex in the scientific realm. spanning vast temporal and spatial scales, distant localities. and multiple disciplines. Environmental informatics is an emerging discipline applying information science, ecology, and biodiversity to the understanding and solution of environmental problems. In this paper we give an overview of the experiences of the San Diego Supercomputer Center (SDSC) with this new multidisciplinary science, discuss the application of computing resources to the study of environmental systems, and outline strategic partnership activities in environmental iformatics that are underway, We hope to foster interactions between ecology, biodiversity, and conservation researchers in East Asia-Pacific Rim and those at SDSC and the Partnership for Biodiversity Informatics.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.353-359
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• 1998

Factorial designs with two-level factors represent the smallest factorial experiments. The system of notation and confounding and fractional factorial schemes developed for the $2^N$system are found in standard textbooks of experimental designs. Just as in the $2^N$ system, the general confounding and fractional factorial schemes are possible in $3^N,5^N$, .... , and $\rho^N$ where $\rho$ is a prime number. Hence, we have the $\rho^N$ system. In this article, the author proposes a new algorithm for constructing fractional factorial plans in the $\rho^N$ system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.56-63
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• 2015

Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used. In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.7
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• pp.3035-3039
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• 2014

Background: Lung cancer is one of the most prevalent malignancies in the world and both incidence and mortality rates are continuing to rise in Pakistan. However, epidemiological studies to identify common lung cancer determinants in the Pakistani population have been limited. Materials and Methods: In this retrospective case-control study, 400 cases and 800 controls were enrolled from different hospitals of all provinces of Pakistan. Information about socio-demographic, occupational, lifestyle and dietary variables was extracted by questionnaire from all subjects. Odd ratios (ORs) and 95% confidence intervals (CIs) were calculated. and dose-response associations were also assessed for suitable factors. Results: Strong associations were observed for smoking (OR=9.4, 95%CI=6.9-12.8), pesticide exposure (OR=5.1, 95%CI=3.1-8.3), exposure to diesel exhaust (OR=3.1, 95%CI=2.1-4.5), red meat consumption (OR=2.9, 95%CI=1.8-4.7) and chicken consumption (OR=2.8, 95%CI=1.7-49). Other associated factors observed were welding fumes (OR=2.5, 95%CI=1.0-6.5), sedentary living (OR=2.0, 95%CI=1.6-2.6), family history (OR=2.0, 95%CI=0.8-4.9), wood dust (OR=1.9, 95%CI=1.2-3.1), tea consumption (OR=1.8, 95%CI=1.2-2.6), coffee consumption (OR=1.8, 95%CI=1.1-2.8), alcoholism (OR=1.7, 95%CI=1.1-2.5) and asbestos exposure(OR=1.5, 95%CI=0.5-4.4). Consumption of vegetables (OR=0.3, 95%CI=0.2-0.4), juices (OR=0.3, 95%CI=0.3-0.4), fruits (OR=0.7, 95%CI=0.5-0.9) and milk (OR=0.6, 95%CI=0.5-0.8) showed reduction in risk of lung cancer. Strongest dose-response relationships were observed for smoking ($X^2=333.8$, $p{\leq}0.0000001$), pesticide exposure ($X^2=50.9$, $p{\leq}0.0000001$) and exposure to diesel exhaust ($X^2=51.8$, $p{\leq}0.0000001$). Conclusions: Smoking, pesticide exposure, diesel exhaust and meat consumption are main lung cancer determinants in Pakistan. Consuming vegetables, fruits, milk and juices can reduce the risk of lung cancer risk, as in other countries.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9C
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• pp.691-696
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• 2008

A new vocoder of Code Excited Linear Predictive (CELP) based on Adaptive Multi Rate (AMR) 7.4kbit/s mode is proposed in this paper. The proposed vocoder achieves a better compression rate in an environment of Speaker Dependent Coding System (SDSC) and is efficiently used for systems, such as OGM(Outgoing message) and TTS(Text To Speech), which needs only one person's speech. In order to enhance the compression rate of a coder, a new Line Spectral Pairs(LSP) code-book is employed by using Centroid Neural Network (CNN) algorithm. In comparison with original(traditional) AMR 7.4 Kbit/s coder, the new coder shows 27% higher compression rate while preserving synthesized speech quality in terms of Mean Opinion Score(MOS).