Background: Diabetes or hypertension contributes to erectile dysfunction (ED). We hypothesized that excess reactive oxygen species (ROS) production evoked by diabetes combined with hypertension may further suppress endothelial nitric oxide (NO) expression/activity and promote oxidative stress in the ED penis. Methods: Twenty-four adult male Wistar Kyoto rats (WKY) and spontaneously hypertensive rats (SHR) were divided into four groups: normal WKY, diabetic WKY, normal SHR and diabetic SHR. Intraperitoneal streptozotocin (65 mg/kg) was applied to induce type I diabetes. After 4-week diabetes and/or hypertension induction, we determined the intra-cavernous pressure (ICP) using electrical stimulation of cavernous nerves, intra-cavernosum NO amount using an electrochemical NO probe, and blood ROS using an ultrasensitive chemiluminescence-amplified analyzer. Western blot analysis and immunohistochemistry were used to explore the pathophysiologic mechanisms of inflammation, apoptosis and autophagy in the penis. A novel NO donor, CysaCysd Lu-5 (CCL5, (RCH2CH2S)(R’R”CHCH2S)Fe(NO)2, 1-4 µg), was intravenously administered to these ED rats for evaluating their ICP responses. Results: In the baseline status, the lucigenin- and luminol-amplified blood ROS were significantly enhanced in the diabetic SHR rats vs normal WKY rats. Significantly decreased ICP, eNOS expression and NO amount were found in the normal SHR, diabetic WKY, and diabetic SHR vs normal WKY rats. Intravenous NO donor L-Arginine markedly increased ICP and NO amount, whereas eNOS inhibitor, Nω-Nitro-L-Arginine methyl ester hydrochloride depressed ICP in all four groups. Diabetes and/or hypertension alone increased fibrosis, proinflammatory NF-kB/ICAM-1 expression, mast cell numbers, CD68 expression and infiltration, Caspase 3-mediated apoptosis, Beclin-1/LC3-II–mediated autophagy and mild Nrf-2/HO-1 expression and depressed eNOS expression in the ED penis. The novel NO donor, CCL5, was more efficient than L-arginine to improve diabetes and/or hypertension–induced ED by the significant increase of ICP. Conclusion: Diabetes combined with hypertension synergistically exacerbated ED through enhanced oxidative stress, inflammation, apoptosis and autophagy and depressed eNOS activity and NO production.

This paper aims to design a one-wheeled robot as regards its pitch freedom and balance control on the one hand and to assess the application feasibility of the GM (1,1) swing estimation controller on the other. System control focuses mainly on one-wheeled robot stability, body swings in position, and speed control. Mathematical modeling and GM (1,1) prediction control are under investigation. The mathematical modeling is firstly conducted through referencing to the Newtonian mechanics and the Lagrange equation, from which the robot transfer function and state-space differential equation are derived. Next, the linear quadratic regulator is applied as the control rule at the balance point. Applying GM (1,1) to assess the robot gyro signal at a dynamic state is a discussion. Next, model reference estimation control is processed, and a mathematical model of the balance control method is completed. Finally, a simulation is conducted to verify the feasibility of the GM (1,1) estimation reference model. The linear quadratic regulator, which is credited with tenacity, can provide pitch swing and balance control of the one-wheeled robot.

The rivers in Taiwan are steep, the surface runoff is rushed into ocean quickly with high speeds and large discharges. When the typhoons hit Taiwan with heavy rain, how to predict correct peak time and peak stage of rivers is the most important aim in this research. Taiwan Typhoon and Flood Research Institute will produce a rainfall forecasting every six hours for disaster warning, according to different physical parameters setting. The research site, Xiuguluan River is steepest one of Taiwan central rivers. By cross section data 、 land use 、 slope 、 soils and the rainfall forecasting, we can get results of each member by integrating the physically based on model HEC-HMS and WASH123D.The research reveals that ensemble numerical modeling can predict precise peak stage of the river by analysis and correction by machine learning system TensorFlow. As for peak time forecasting, it becomes accurate by making use of the open social network information such as facebook、network news、PTT discussion to improve. Moreover, no matter peak time or peak stage, it has highly variation in members. In other words, no member is always the best of typhoons. But we can use the probability flood forecasting to predict and get the best results.