Non-Classical Crystallization of Thin Films and Nanostructures in CVD Process

Non-classical crystallization, where crystals grow by the building blocks of nanoparticles, has become a significant issue not only in solution but also in the gas phase synthesis such as chemical vapor deposition (CVD). Recently, non-classical crystallization was observed in solution in-situ by transmission electron microscope (TEM) using a liquid cell technique. In various CVD processes, the generation of charged nanoparticles (CNPs) in the gas phase has been persistently reported. Many evidences supporting these CNPs to be the building blocks of thin films and nanostructures were reported. According to non-classical crystallization, many thin films and nanostructures which had been believed to grow by individual atoms or molecules turned out to grow by the building blocks of CNPs. The purpose of this paper is to review the development and the main results of non-classical crystallization in the CVD process. The concept of non-classical crystallization is briefly described. Further, it will be shown that the puzzling phenomenon of simultaneous diamond deposition and graphite etching, which violates the second law of thermodynamics when approached by classical crystallization, can be approached successfully by non-classical crystallization. Then, various aspects of non-classical crystallization in the growth of thin films and nanostructures by CVD will be described

Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.Comment: 10 pages, 9 figure

PCR for Diagnosis of Male Trichomonas vaginalis Infection with Chronic Prostatitis and Urethritis

The aim of this study was to assess the usefulness of PCR for diagnosis of Trichomonas vaginalis infection among male patients with chronic recurrent prostatitis and urethritis. Between June 2001 and December 2003, a total of 33 patients visited the Department of Urology, Hanyang University Guri Hospital and were examined for T. vaginalis infection by PCR and culture in TYM medium. For the PCR, we used primers based on a repetitive sequence cloned from T. vaginalis (TV-E650). Voided bladder urine (VB1 and VB3) was sampled from 33 men with symptoms of lower urinary tract infection (urethral charge, residual urine sensation, and frequency). Culture failed to detect any T. vaginalis infection whereas PCR identified 7 cases of trichomoniasis (21.2%). Five of the 7 cases had been diagnosed with prostatitis and 2 with urethritis. PCR for the 5 prostatitis cases yielded a positive 330 bp band from bothVB1 and VB3, whereas positive results were only obtained from VB1 for the 2 urethritis patients. We showed that the PCR method could detect T. vaginalis when there was only 1 T. vaginalis cell per PCR mixture. Our results strongly support the usefulness of PCR on urine samples for detecting T. vaginalis in chronic prostatitis and urethritis patients

Algorithm to estimate daily PAR at the ocean surface from GOCI data: description and evaluation

Photosynthetically available radiation (PAR) reaching the ocean surface controls phytoplankton growth, primary productivity, and evolution within marine ecosystems. Therefore, accurate daily PAR estimates are important for a broad range of marine biology and biogeochemistry applications. In this study, hourly data from Geostationary Ocean Color Imager (GOCI), the world’s first geostationary ocean color sensor, was employed to estimate daily mean PAR at the ocean surface around the Korean Peninsula using a budget model based on plane-parallel theory. In situ PAR data collected from two ocean research stations (Socheong-cho and Ieodo) were used to evaluate the accuracy of the GOCI PAR estimates. First, the instantaneous in situ measurements were checked for calibration and exposure errors against Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer calculations under the clearest sky conditions and adjusted to eliminate biases. After adjustment, the root-means-square error (RMSE) between 6S and in situ PAR data was reduced from 6.08 (4.81%) and 3.82 (3.93%) mol/m2/day to 2.85 (2.26%) and 1.74 (1.21%) mol/m2/day at the Socheong-cho and Ieodo stations, respectively, and the coefficient of determination R2 was 0.99. Then, the GOCI daily mean PAR estimated by the initial algorithm were corrected using the 2015 adjusted in situ daily PAR measurements collected under clear-sky conditions. The daily mean PAR values derived from GOCI data in all conditions were improved after the correction, with RMSE reduced from 4.58 (8.30%) to 2.57 (4.65%) mol/m2/day and R2 = 0.97. The comparison statistics were similar for 2015 and 2016 combined, with RMSE of 2.52 (4.38%) and mean bias error (MBE) of –0.40 (–0.70%), indicating that the correction was also effective in cloudy conditions. On the other hand, daily PAR estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Himawari Imager (AHI) yielded larger RMSE of 6.24 (10.40%) mol/m2/day and MBE of –2.49 (–4.15%) mol/m2/day (MODIS) and RMSE of 3.71 (6.51%) mol/m2/day and MBE of –2.65 (–4.65%) mol/m2/day (AHI) against in situ measurements. The GOCI-based daily PAR model developed in this study is reliable and suitable for investigating the marine environment around the Korean Peninsula

Automatic Control on Dosing Coagulant as to Stream Current

Abstract: As recently raw water quality has been polluted as well as its quality has been remarkably varied according to season and region, the precise control of coagulant dosage is being keenly required in water treatment plants. The amount of coagulant is closely related to raw water quality such as turbidity, alkalinity, water temperature, pH, electrical conductivity, etc. Since the optimum quantity of chemicals is not yet finalized, so dosage rate must be decided by using jar test that takes one or two hours. Hereupon, the output signal of stream current and multi-regression on historical data were proposed to be applied to the coagulant dosing control. In consequence of applying the scheme to automatic determination of the dosage rate, it was testified that the determination of dosage rate was very effective in case it is performed as to real-time sensing of water quality and the output signal of stream current