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RISS 인기검색어
- 검색키워드
- 저자 : Donald D. Kim(Donald D. Kim) (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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A Scalable Smart Greenhouse Design For Mongolian Families
Donald D. Kim(Donald D. Kim),Doug Young Suh(Doug Young Suh) 적정기술학회 2022 적정기술학회지(Journal of Appropriate Technology) Vol.8 No.3
Life expectancy in Mongolia is male 63.8 and female 72.8. Total life expectancy is 68.1, which gives Mongolia a World Life Expectancy ranking of 132 in the WHO 2022 report. The number one reason for the mission of Mongolians is cardiovascular disease, which is closely related to their diet. The staple food of Mongolians is meat. The annual vegetable consumption of Mongolians announced by the UN in 2017 was 47.31 kg, far below the average value of 250 kg in developed countries. This paper proposes a smart greenhouse for families where Mongolian families can grow vegetables at home. This paper presents a smart home greenhouse that meets the environmental characteristics and requirements of the Mongolian region. We manage sensors and actuators through the Raspberry Pi platform to provide an environment necessary for cultivation. It describes the architecture of an integrated management system that stores sensor data in the cloud system's storage, apply reinforcement learning, and controls the operating point of the smart greenhouse. It describes how to manage the system internally using three layers, the physical layer, the embedded OS layer, and the application layer, and to communicate with the cloud through the IoT edge manager.
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A Verified Formal Specification of A Secured Communication Method For Smart Card Applications
Donald D. Kim 적정기술학회 2021 적정기술학회지(Journal of Appropriate Technology) Vol.7 No.2
In remote villages without access to modern IT technology, simple devices such as smartcards can be used to carry out business transactions. These devices typically store multiple business applications from multiple vendors. Although devices must prevent malicious or accidental security breaches among the applications, a secure communication channel between two applications from different vendors is often required. In this paper, first, we propose a method of establishing secure communication channels between applications in embedded operating systems that run on multi-applet smart cards. Second, we enforce the high assurance using an intransitive noninterference security policy. Thirdly, we formalize the method through the Z language and create the formal specification of the proposed secure system. Finally, we verify its correctness using Rushby s unwinding theorem.
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Multi-study Integration of Brain Cancer Transcriptomes Reveals Organ-Level Molecular Signatures
Sung, Jaeyun,Kim, Pan-Jun,Ma, Shuyi,Funk, Cory C.,Magis, Andrew T.,Wang, Yuliang,Hood, Leroy,Geman, Donald,Price, Nathan D. Public Library of Science 2013 PLoS computational biology Vol.9 No.7
<▼1><P>We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein – Identification of Structured Signatures and Classifiers (ISSAC) – that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.</P></▼1><▼2><P><B>Author Summary</B></P><P>From a multi-study, integrated transcriptomic dataset, we identified a marker panel for differentiating major human brain cancers at the gene-expression level. The ISSAC molecular signatures for brain cancers, composed of 44 unique genes, are based on comparing expression levels of pairs of genes, and phenotype prediction follows a diagnostic hierarchy. We found that sufficient dataset integration across multiple studies greatly enhanced diagnostic performance on truly independent validation sets, whereas signatures learned from only one dataset typically led to high error rate. Molecular signatures of brain cancers, when obtained using all currently available gene-expression data, achieved 90% phenotype prediction accuracy. Thus, our integrative approach holds significant promise for developing organ-level, comprehensive, molecular signatures of disease.</P></▼2>
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