146,589 research outputs found
Proportional sampling strategy: A compendium and some insights
There have been numerous studies on the effectiveness of partition and random testing. In particular, the proportional sampling (PS) strategy has been proved, under certain conditions, to be the only form of partition testing that outperforms random testing regardless of where the failure-causing inputs are. This paper provides an integrated synthesis and overview of our recent studies on the PS strategy and its related work. Through this synthesis, we offer a perspective that properly interprets the results obtained so far, and present some of the interesting issues involved and new insights obtained during the course of this research. © 2001 Elsevier Science Inc. All rights reserved.postprin
The Effect of Entrepreneurial Orientation to Low Cost Strategy, Differentiation Strategy, Sustainable Innovation and Performance of Small and Medium Enterprises (Studies at Batik Small and Medium Enterprises in East Java Province, Indonesia)
This study aims to examine, analyze and explain the Effect of Entrepreneurial Orientation to Low Cost Strategy, Differentiation Strategies, Sustainable Innovation and Performance of Batik Small and Medium Enterprises in East Java Province, Indonesia.This research is explanatory research and sampling was done by using multi- stage area sampling,which is done through three stages, first, the determination of purposive sampling area, with a population of 2685 business units. Second, the sample set that batik Small Medium Enterprises owners are determined based on Slovin formula by 190 people. Third, calculate the proportion of samples in the four study areas with proportional random sampling. Data collection done through questionnaires which distributed through enumerators services, email and sent through the post. in addition, the researchers also conducted interviews with respondents directly. Furthermore, the data collected were processed using analysis of variance -based SEM with PLS software .The findings of this study is the first, entrepreneurial orientation significantly influence low cost strategy and differentiation strategy, but the effect is not significant to the sustainable innovation Second, low cost strategies significantly influence the sustainable innovation. Third, differentiation strategy significantly influence to the sustainable innovation. Fourth , sustainable innovation significantly influence the performance of Small Medium Enterprises . Dominant variable that affecting the sustainable innovation is differentiation strategy , thus it can be said that in implementing sustainable innovation and improve the performance of Small Medium Enterprises , especially in batik industry is more suitable with differentiation strategy , because batik is an Indonesian cultural heritage artworks that highlight characteristic area of origin . However, for batik industry, batik Small Medium Enterprises can implement a hybrid strategy which is differentiation strategy and low cost strategies.Contribution of this study to scientific development is enriched theory of strategic management in based resources perspective, market-based and knowledge-based. Sustainable innovation is part of the evolution to be executed by the company's organization. Innovation come from creativity which is a collection of good ideas of knowledge, and experience that are in the human mind and then mixed into useful innovations on an ongoing basis. But innovation also identify customer needs of the present, and the future as well as develop new solutions to customer needs. in order to make the sustainable innovation more complete, it is recommended for further researcher, to add a field of study related to market-based and knowledge-based view. Keywords: Entrepreneurial Orientation, Low Cost Strategy, Differentiation Strategies, Sustainable Innovation, Performance Batik Small and Medium Enterprise
Sequential Design for Ranking Response Surfaces
We propose and analyze sequential design methods for the problem of ranking
several response surfaces. Namely, given response surfaces over a
continuous input space , the aim is to efficiently find the index of
the minimal response across the entire . The response surfaces are not
known and have to be noisily sampled one-at-a-time. This setting is motivated
by stochastic control applications and requires joint experimental design both
in space and response-index dimensions. To generate sequential design
heuristics we investigate stepwise uncertainty reduction approaches, as well as
sampling based on posterior classification complexity. We also make connections
between our continuous-input formulation and the discrete framework of pure
regret in multi-armed bandits. To model the response surfaces we utilize
kriging surrogates. Several numerical examples using both synthetic data and an
epidemics control problem are provided to illustrate our approach and the
efficacy of respective adaptive designs.Comment: 26 pages, 7 figures (updated several sections and figures
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Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.
MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online
An Iterative Scheme for Leverage-based Approximate Aggregation
The current data explosion poses great challenges to the approximate
aggregation with an efficiency and accuracy. To address this problem, we
propose a novel approach to calculate the aggregation answers with a high
accuracy using only a small portion of the data. We introduce leverages to
reflect individual differences in the samples from a statistical perspective.
Two kinds of estimators, the leverage-based estimator, and the sketch estimator
(a "rough picture" of the aggregation answer), are in constraint relations and
iteratively improved according to the actual conditions until their difference
is below a threshold. Due to the iteration mechanism and the leverages, our
approach achieves a high accuracy. Moreover, some features, such as not
requiring recording the sampled data and easy to extend to various execution
modes (e.g., the online mode), make our approach well suited to deal with big
data. Experiments show that our approach has an extraordinary performance, and
when compared with the uniform sampling, our approach can achieve high-quality
answers with only 1/3 of the same sample size.Comment: 17 pages, 9 figure
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