Carrier transport and bias stress stability of IGZO TFT with heterojunction channel

An InGaZnOx (IGZO) thin-film transistor (TFT) has been received considerable attention for use in next-generation displays owing to their excellent electrical properties. Although a field effect mobility (mFE) of the IGZO TFT (10~15 cm2V–1s–1) is over ten times larger than that of an amorphous silicon TFT, further enhancement of the mFE is desired to expand their applications. Several approaches have been proposed to improve the mFE of oxide TFT. Among them, it is known in the IGZO material system that an increase of In content is effective to enhance the mFE of the IGZO TFT since a conduction band of the IGZO is mainly composed of an In 5s orbitals. However, high In composition leads to an increase carrier concentration (oxygen vacancy) in the film, result in a degradation of TFT properties such as negative shift of threshold voltage and hump in transfer characteristics. Please click Additional Files below to see the full abstract

Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability

Consistent map building in petrochemical complexes for frefghter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of frefghter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle flters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The diference between the original FastSLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was signifcant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refnery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability

Modeling and Prediction of Driving Behaviors Using a Nonparametric Bayesian Method with AR Models

To develop a new generation advanced driver assistance system that avoids a dangerous condition in advance, we need to predict driving behaviors. Since a nonparametric Bayesian method with a two-level structure successfully predicted the symbolized behaviors only, we applied a nonparametric Bayesian method with linear dynamical systems to predicting the driving behavior. The method called the beta process autoregressive hidden Markov model (BP-AR-HMM) segments driving behaviors into states each of which corresponds to an AR model and it predicts future behaviors using the estimated future state sequence and the dynamical systems therein. Here, the segmentation as well as the parameters of the dynamical systems are determined using given training data in an unsupervised way. We carried out experiments with real driving data and found that the BP-AR-HMM predicted driving behaviors better than other methods

Multicenter retrospective and comparative study of 5-minute versus 15-second endoscopic papillary balloon dilation for removal of bile duct stones

Background and study aims Endoscopic papillary balloon dilation (EPBD) is a method of bile duct stone removal that has a better long-term outcome but a high risk of post-ERCP pancreatitis (PEP). Recent studies have suggested that 5-minute EPBD can reduce the incidence of PEP. This study aimed to examine the safety and effectiveness of longer duration EPBD compared with shorter duration EPBD (5 minutes vs. 15 seconds after disappearance of the waist of a dilation catheter). Patients and methods Patients without a history of endoscopic sphincterotomy or EPBD who underwent EPBD to remove bile duct stones were selected retrospectively from five centers. The incidence of PEP, other early adverse events, and outcomes of EPBD were compared between the groups. A multivariable analysis of risk factors for PEP was performed. Results: A total of 607 patients (157 and 450 in the 5-minute and 15-second EPBD groups, respectively) were included. There were no statistically significant differences between the groups in terms of the incidence of PEP (8.3 % and 8.9 % in the 5-minute and 15-second EPBD groups, respectively; P = 0.871) and the incidence of overall early adverse events ( P = 0.999). Although 5-minute EPBD elongated the procedure time (45 vs. 37 minutes, P < 0.001), it increased the rate of complete stone removal during a single session ( P < 0.001) and decreased the use of lithotripsy ( P < 0.001). Conclusions: Compared with 15-second EPBD, 5-minute EPBD did not reduce the incidence of PEP

Regulation of the MDM2-P53 pathway and tumor growth by PICT1 via nucleolar RPL11

PICT1 (also known as GLTSCR2) is considered a tumor suppressor because it stabilizes phosphatase and tensin homolog (PTEN), but individuals with oligodendrogliomas lacking chromosome 19q13, where PICT1 is located, have better prognoses than other oligodendroglioma patients. To clarify the function of PICT1, we generated Pict1-deficient mice and embryonic stem (ES) cells. Pict1 is a nucleolar protein essential for embryogenesis and ES cell survival. Even without DNA damage, Pict1 loss led to p53-dependent arrest of cell cycle phase G1 and apoptosis. Pict1-deficient cells accumulated p53, owing to impaired Mdm2 function. Pict1 binds Rpl11, and Rpl11 is released from nucleoli in the absence of Pict1. In Pict1-deficient cells, increased binding of Rpl11 to Mdm2 blocks Mdm2-mediated ubiquitination of p53. In human cancer, individuals whose tumors express less PICT1 have better prognoses. When PICT1 is depleted in tumor cells with intact P53 signaling, the cells grow more slowly and accumulate P53. Thus, PICT1 is a potent regulator of the MDM2-P53 pathway and promotes tumor progression by retaining RPL11 in the nucleolu

ノンパラメトリック ベイズ ヒ テイジョウ ジケイレツ モデリング ホウ ニ ヨル コウドウ カイセキ

博第1384号甲第1384号博士(工学)奈良先端科学技術大学院大

HYDROLYSIS OF ISOLATED SOIL POLYSACCHARIDE WITH ACIDIC ION EXCHANGE RESINAND APPLICATION OF GEL FILTRATION TO THE HYDROLYZATES

Abstract not availabl