Search results for: Jung Hyun Kim

Authors: Ho-Won Jung, Donghun Lee, Hyung-Jin Kim

Abstract:

Predictive analytics (data analysis) uses a subset of measurements (the features, predictor, or independent variable) to predict another measurement (the outcome, target, or dependent variable) on a single person or unit. It applies empirical methods in statistics, operations research, and machine learning to predict the future, or otherwise unknown events or outcome on a single or person or unit, based on patterns in data. Most analyses of metabolic syndrome are not predictive analytics but statistical explanatory studies that build a proposed model (theory building) and then validate metabolic syndrome predictors hypothesized (theory testing). A proposed theoretical model forms with causal hypotheses that specify how and why certain empirical phenomena occur. Predictive analytics and explanatory modeling have their own territories in analysis. However, predictive analytics can perform vital roles in explanatory studies, i.e., scientific activities such as theory building, theory testing, and relevance assessment. In the context, this study is to demonstrate how to use our predictive analytics to support theory building (i.e., hypothesis generation). For the purpose, this study utilized a big data predictive analytics platform TM based on a co-occurrence graph. The co-occurrence graph is depicted with nodes (e.g., items in a basket) and arcs (direct connections between two nodes), where items in a basket are fully connected. A cluster is a collection of fully connected items, where the specific group of items has co-occurred in several rows in a data set. Clusters can be ranked using importance metrics, such as node size (number of items), frequency, surprise (observed frequency vs. expected), among others. The size of a graph can be represented by the numbers of nodes and arcs. Since the size of a co-occurrence graph does not depend directly on the number of observations (transactions), huge amounts of transactions can be represented and processed efficiently. For a demonstration, a total of 13,254 metabolic syndrome training data is plugged into the analytics platform to generate rules (potential hypotheses). Each observation includes 31 predictors, for example, associated with sociodemographic, habits, and activities. Some are intentionally included to get predictive analytics insights on variable selection such as cancer examination, house type, and vaccination. The platform automatically generates plausible hypotheses (rules) without statistical modeling. Then the rules are validated with an external testing dataset including 4,090 observations. Results as a kind of inductive reasoning show potential hypotheses extracted as a set of association rules. Most statistical models generate just one estimated equation. On the other hand, a set of rules (many estimated equations from a statistical perspective) in this study may imply heterogeneity in a population (i.e., different subpopulations with unique features are aggregated). Next step of theory development, i.e., theory testing, statistically tests whether a proposed theoretical model is a plausible explanation of a phenomenon interested in. If hypotheses generated are tested statistically with several thousand observations, most of the variables will become significant as the p-values approach zero. Thus, theory validation needs statistical methods utilizing a part of observations such as bootstrap resampling with an appropriate sample size.

Keywords: explanatory modeling, metabolic syndrome, predictive analytics, theory building

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Authors: Ching-Sung Lee, Wei-Chou Hsu, Han-Yin Liu, Hung-Hsi Huang, Si-Fu Chen, Yun-Jung Yang, Bo-Chun Chiang, Yu-Chuang Chen, Shen-Tin Yang

Abstract:

AlGaN/GaN high electron mobility transistors (HEMTs) have been intensively studied due to their intrinsic advantages of high breakdown electric field, high electron saturation velocity, and excellent chemical stability. They are also suitable for ultra-violet (UV) photodetection due to the corresponding wavelengths of GaN bandgap. To improve the optical responsivity by decreasing the dark current due to gate leakage problems and limited Schottky barrier heights in GaN-based HEMT devices, various metal-oxide-semiconductor HEMTs (MOS-HEMTs) have been devised by using atomic layer deposition (ALD), molecular beam epitaxy (MBE), metal-organic chemical vapor deposition (MOCVD), liquid phase deposition (LPD), and RF sputtering. The gate dielectrics include MgO, HfO2, Al2O3, La2O3, and TiO2. In order to provide complementary circuit operation, enhancement-mode (E-mode) devices have been lately studied using techniques of fluorine treatment, p-type capper, piezoneutralization layer, and MOS-gate structure. This work reports an Al2O3-dielectric Al0.25Ga0.75N/GaN E-mode MOS-HEMT design by using a cost-effective ozone water oxidization technique. The present ozone oxidization method advantages of low cost processing facility, processing simplicity, compatibility to device fabrication, and room-temperature operation under atmospheric pressure. It can further reduce the gate-to-channel distance and improve the transocnductance (gm) gain for a specific oxide thickness, since the formation of the Al2O3 will consume part of the AlGaN barrier at the same time. The epitaxial structure of the studied devices was grown by using the MOCVD technique. On a Si substrate, the layer structures include a 3.9 m C-doped GaN buffer, a 300 nm GaN channel layer, and a 5 nm Al0.25Ga0.75N barrier layer. Mesa etching was performed to provide electrical isolation by using an inductively coupled-plasma reactive ion etcher (ICP-RIE). Ti/Al/Au were thermally evaporated and annealed to form the source and drain ohmic contacts. The device was immersed into the H2O2 solution pumped with ozone gas generated by using an OW-K2 ozone generator. Ni/Au were deposited as the gate electrode to complete device fabrication of MOS-HEMT. The formed Al2O3 oxide thickness 7 nm and the remained AlGaN barrier thickness is 2 nm. A reference HEMT device has also been fabricated in comparison on the same epitaxial structure. The gate dimensions are 1.2 × 100 µm 2 with a source-to-drain spacing of 5 μm for both devices. The dielectric constant (k) of Al2O3 was characterized to be 9.2 by using C-V measurement. Reduced interface state density after oxidization has been verified by the low-frequency noise spectra, Hooge coefficients, and pulse I-V measurement. Improved device characteristics at temperatures of 300 K-450 K have been achieved for the present MOS-HEMT design. Consequently, Al2O3-dielectric Al0.25Ga0.75N/GaN E-mode MOS-HEMTs by using the ozone water oxidization method are reported. In comparison with a conventional Schottky-gate HEMT, the MOS-HEMT design has demonstrated excellent enhancements of 138% (176%) in gm, max, 118% (139%) in IDS, max, 53% (62%) in BVGD, 3 (2)-order reduction in IG leakage at VGD = -60 V at 300 (450) K. This work is promising for millimeter-wave integrated circuit (MMIC) and three-terminal active UV photodetector applications.

Keywords: MOS-HEMT, enhancement mode, AlGaN/GaN, passivation, ozone water oxidation, gate leakage

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Authors: Chia-Jung Chen, Yu-Chi Hsu, June-Dong Lin, Kun-Chen Chan, Chieh-Tien Wang, Li-Ching Wu, Chung-Feng Liu

Abstract:

With fast advances in healthcare technology, various total laboratory automation (TLA) processes have been proposed. However, adopting TLA needs quite high funding. This study explores an early adoption experience by Taiwan’s large-scale hospital group, the Chimei Hospital Group (CMG), which owns three branch hospitals (Yongkang, Liouying and Chiali, in order by service scale), based on the five stages of Everett Rogers’ Diffusion Decision Process. 1.Knowledge stage: Over the years, two weaknesses exists in laboratory department of CMG: 1) only a few examination categories (e.g., sugar testing and HbA1c) can now be completed and reported within a day during an outpatient clinical visit; 2) the Yongkang Hospital laboratory space is dispersed across three buildings, resulting in duplicated investment in analysis instruments and inconvenient artificial specimen transportation. Thus, the senior management of the department raised a crucial question, was it time to process the redesign of the laboratory department? 2.Persuasion stage: At the end of 2013, Yongkang Hospital’s new building and restructuring project created a great opportunity for the redesign of the laboratory department. However, not all laboratory colleagues had the consensus for change. Thus, the top managers arranged a series of benchmark visits to stimulate colleagues into being aware of and accepting TLA. Later, the director of the department proposed a formal report to the top management of CMG with the results of the benchmark visits, preliminary feasibility analysis, potential benefits and so on. 3.Decision stage: This TLA suggestion was well-supported by the top management of CMG and, finally, they made a decision to carry out the project with an instrument-leasing strategy. After the announcement of a request for proposal and several vendor briefings, CMG confirmed their laboratory automation architecture and finally completed the contracts. At the same time, a cross-department project team was formed and the laboratory department assigned a section leader to the National Taiwan University Hospital for one month of relevant training. 4.Implementation stage: During the implementation, the project team called for regular meetings to review the results of the operations and to offer an immediate response to the adjustment. The main project tasks included: 1) completion of the preparatory work for beginning the automation procedures; 2) ensuring information security and privacy protection; 3) formulating automated examination process protocols; 4) evaluating the performance of new instruments and the instrument connectivity; 5)ensuring good integration with hospital information systems (HIS)/laboratory information systems (LIS); and 6) ensuring continued compliance with ISO 15189 certification. 5.Confirmation stage: In short, the core process changes include: 1) cancellation of signature seals on the specimen tubes; 2) transfer of daily examination reports to a data warehouse; 3) routine pre-admission blood drawing and formal inpatient morning blood drawing can be incorporated into an automatically-prepared tube mechanism. The study summarizes below the continuous improvement orientations: (1) Flexible reference range set-up for new instruments in LIS. (2) Restructure of the specimen category. (3) Continuous review and improvements to the examination process. (4) Whether installing the tube (specimen) delivery tracks need further evaluation.

Keywords: innovation decision process, total laboratory automation, health care

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