The Clinical Application of Hydrogen as a Medical Treatment

In recent years, it has become evident that molecular hydrogen is a particularyl effective treatment for various disease models such as ischemia-reperfusion injury; as a result, research on hydrogen has progressed rapidly. Hydrogen has been shown to be effective not only through intake as a gas, but also as a liquid medication taken orally, intravenously, or locally. Hydrogenʼs effectiveness is thus multifaceted. Herein we review the recent research on hydrogen-rich water, and we examine the possibilities for its clinical application. Now that hydrogen is in the limelight as a gaseous signaling molecule due to its potential ability to inhibit oxidative stress signaling, new research developments are highly anticipated

Pengaruh Komunikasi Terapeutik Perawat Terhadap Kepuasan Pasien Di Rawat Jalan RSUD Jogja

The Objective of this study is to know influence of nurse therapeutic communication to satisfaction of patients satisfaction in RSUD Yogyakarta. The study was a quantitative research methods such as surveys of descriptive inferential research with cross sectional approach. Number of samples in this research is 285 sample in inpatient and 140 in emergency room. The instrument used a questionnaire. Analysis of data using multiple linear regression. This study show that there is the influence of therapeutic communication nurse to satisfaction of outpatients and Emergency room in RSUD Yogyakarta, and orientation phase is a phase that most influence on patient satisfaction. The most influential to therapeutic communication is termination stage

Efficient searching for grain storage container by combine robot

Partly presented at the 6th International Symposium on Machinery and Mechatronics for Agricultural and Biosystems Engineering ISMAB 2012.In this study, a combine robot was equipped with an autonomous grain container searching function. In order to realize automated grain unloading, the combine robot has to search and identify the grain storage container in an outdoor environment. A planar board was attached to the container. The marker was searched for using a camera mounted on the unloading auger of the combine. An efficient marker searching procedure was proposed on the basis of a numerical analysis of the camera's field of view and was verified experimentally. The results showed that the combine robot efficiently searched for and detected the marker and positioned its spout at the target point over the container to unload the grain

Integrating remote sensing and GIS for prediction of rice protein contents

In this study, protein content (PC) of brown rice before harvest was established by remote sensing (RS) and analyzed to select the key management factors that cause variation of PC using a GIS database. The possibility of finding out the key management factors using GreenNDVI was tested by combining RS and a GIS database. The study site was located at Yagi basin (Japan) and PC for seven districts (85 fields) in 2006 and nine districts (73 fields) in 2007 was investigated by a rice grain taste analyzer. There was spatial variability between districts and temporal variability within the same fields. PC was predicted by the average of GreenNDVI at sampling points (Point GreenNDVI) and in the field (Field GreenNDVI). The accuracy of the Point GreenNDVI model (r 2 > 0.424, RMSE 0.250, RMSE < 0.298%). A general-purpose model (r 2 = 0.392, RMSE = 0.255%) was established using 2 years data. In the GIS database, PC was separated into two parts to compare the difference in PC between the upper (mean + 0.5SD) and lower (mean − 0.5SD) parts. Differences in PC were significant depending on the effective cumulative temperature (ECT) from transplanting to harvest (Factor 4) in 2007 but not in 2006. Because of the difference in ECT depending on vegetation term (from transplanting to sampling), PC was separated into two groups based on the mean value of ECT as the upper (UMECT) and lower (LMECT) groups. In 2007, there were significant differences in PC at LMECT group between upper and lower parts depending on the ECT from transplanting to last top-dressing (Factor 2), the amount of nitrogen fertilizer at top-dressing (Factor 3) and Factor 4. When the farmers would have changed their field management, it would have been possible to decrease protein contents. Using the combination of RS and GIS in 2006, it was possible to select the key management factor by the difference in the Field GreenNDVI

Vision-based uncut crop edge detection for automated guidance of head-feeding combine

This study proposes a vision-based uncut crop edge detection method to be utilized as a part of an automated guidance system for a head-feeding combine harvester, which is widely used in Japan for the harvesting of rice and wheat. The proposed method removes the perspective effects of the acquired images by inverse perspective mapping and recovers the crop rows to their actual parallel states. Then, the uncut crop edges are detected by applying color transformation and the edge detection method. The proposed method has shown outstanding detection performance on the images acquired under various conditions of the paddy field with an average accuracy of 97% and a processing speed of 33 ms per frame

Using multiple sensors to detect uncut crop edges for autonomous guidance systems of head-feeding combine harvesters

This study proposes a method for detection of uncut crop edges using multiple sensors to provide accurate data for the autonomous guidance systems of head-feeding combine harvesters widely used in the paddy fields of Japan for harvesting rice. The proposed method utilizes navigation sensors, such as a real-time kinematic global positioning system (RTK-GPS), GPS compass, and laser range finder (LRF), to generate a three-dimensional map of the terrain to be harvested at a processing speed of 35 ms and obtain the crop height. Furthermore, it can simultaneously detect the uncut crop edges by RANdom SAmple Consensus (RANSAC). The average of the lateral offset value and crop height of the uncut crop edge detected by the proposed method were 0.154 m and 0.537 m, respectively

Path Analysis of Tiller Density of Winter Wheat Demonstrates the Importance of Practices that Manipulate Clod Size Based on Soil Moisture at Seeding in the Rice–Wheat Cropping System

The moisture of paddy soil after rice cropping is a major impediment to the establishment, tillering and yield of winter wheat in the rice−wheat (R−W) cropping system. We examined the seedling establishment ratio, based on soilmoisture at seeding by path analysis of nine soil/plant traits in the farmer's fields in Western Japan where the R−W cropping system was being used, to establish a strategy for improving tiller density by optimizing the seedling establishment ratio. The clod size of surface soil, which showed a significant positive correlation with soil moisture at seeding, had a significant negative direct effect on the seedling establishment ratio. The reduction in seedling establishment ratio, together with fewer tillers per plant, resulted in a significant decrease in tiller density. The sum total of contribution of soil moisture contents to tiller density via clod size was smaller than that of seeding rate, and similar to that of the amount of nitrogen (N) basal dressing. This indicates that manipulating clod size based on soil moisture at seedingprovides an opportunity for maintaining tiller density, as well as changing the amount of N basal dressing with the soil moisture conditions after rice cropping

Application of Color Featuring and Deep Learning in Maize Plant Detection

Maize plant detection was conducted in this study with the goals of target fertilization and reduction of fertilization waste in weed spots and gaps between maize plants. The methods used included two types of color featuring and deep learning (DL). The four color indices used were excess green (ExG), excess red (ExR), ExG minus ExR, and the hue value from the HSV (hue, saturation, and value) color space, while the DL methods used were YOLOv3 and YOLOv3_tiny. For practical application, this study focused on performance comparison in detection accuracy, robustness to complex field conditions, and detection speed. Detection accuracy was evaluated by the resulting images, which were divided into three categories: true positive, false positive, and false negative. The robustness evaluation was performed by comparing the average intersection over union of each detection method across different sub&ndash;datasets&mdash;namely original subset, blur processing subset, increased brightness subset, and reduced brightness subset. The detection speed was evaluated by the indicator of frames per second. Results demonstrated that the DL methods outperformed the color index&ndash;based methods in detection accuracy and robustness to complex conditions, while they were inferior to color feature&ndash;based methods in detection speed. This research shows the application potential of deep learning technology in maize plant detection. Future efforts are needed to improve the detection speed for practical applications