Background: Different automated systems have been developed to improve the maintenance of target range of arterial Oxygen saturation (SPO2) in premature infants with respiratory distress. Objectives: This study aimed to develop a Remote Closed-Loop Automatic Oxygen Control (RCLAC) as an efficient monitoring device. Then the means of the fraction of inspired Oxygen (FIO2) and SPO2 by routine manual Control (RMC) and RCLAC were compared. Methods: A developmental-descriptive study was carried out in an Iranian hospital (Tehran, Iran,2015-2017). Twenty-two preterm infants with gestational age 24-28 weeks entered the study. A database was prepared based on pulse oximeter parameters. A Wi-Fi module was implemented to receive data from a pulse oximeter and send inputs to the user’, s mobile. Vibrate alarm was implemented for high or low FIO2. After receiving notifications associated with an increase or decrease of FIO2 levels and user’, s confirmation,the alterations were applied on the ventilator. Results: The mean FIO2 in the RMC system was significantly higher than the RCLAC system (98. 1, 2. 67 vs 79. 5, 16. 03,P = 0. 0001). According to the results, when the SPO2 reached close to target SPO2 range and consequently FIO2 changed (decreased or increased based on target SPO2), heart rate showed a regular beating with a decrease in the numbers. Conclusions: Remote Closed-Loop Automatic Oxygen Control system as a simple device could prevent preterm neonates from sustained hypo-hyperoxemic and arrhythmia episodes. Moreover, by using RCLAC, there was no need for continuous monitoring that may reduce the workload of NICU medical staff. Collecting reliable data and recording information in digital forms were also other benefits. Further studies with larger sample size are strongly suggested.

In this paper a new method based on multistage fuzzy Control using intelligent ‎optimization techniques is proposed to optimize the Automatic train Control system. In ‎this method the energy function which elaborates the train power consumption is first ‎minimized by applying Kuhn Tucker conditions. As a result the switching points at ‎which the Control strategies are changed between acceleration coasting constant speed ‎and braking are determined considering the track specifications including gradients ‎and curvatures as well as the practical train model. Since in constant speed strategy ‎the energy optimization leads to infinite changes in Control inputs which is not ‎practically possible a multistage fuzzy Control based on fuzzy dynamic programming ‎with fuzzy termination time is incorporated to overcome this difficulty. Multistage ‎fuzzy Control will optimize other significant goals involving safety traceability in ‎speed and accuracy in distance and limit the number of switching among different ‎strategies. Hence the method will satisfy the practical requirements. This method ‎considers the running time between two stations as independent and Controllable ‎variables therefore the calculated strategies are very useful and appropriate for traffic ‎Control which the time regulations make a vital and important role. The simulation ‎results will demonstrate the efficiency of proposed method.‎

Heavy, long-term precipitations can lead to severe floods in most drainage basins, thus causing financial losses and casualties (Fotakis and Sidiropoulos, 2014, Avila-Melgar, et al. 2017). Low precipitation, lack of surface water and groundwater, and increased demand for water calls for optimized use of reservoirs and floods. Inevitably, critical points should be identified and simple non-structural and structural methods or a combination of both, which are environmentally friendly and feasible, should be employed for flood prediction and Control. In addition to imposing considerable costs, construction of flood Control installations cannot fully eliminate the flood risks and, therefore, more severe floods may still lead to disasters. Construction of large flood Control structures such as dykes is not only financially unjustifiable, but also inappropriate as solution both environmentally and socioeconomically. Flood prediction and alarm systems are among the non-structural criteria the importance of which are further realized over time. Installation and implementation of smart flood prediction and alarm systems can lead to reduced casualties and damages from both perspectives of flood risk management and nonstructural management.

Shadow detection provides worthwhile information for Remote sensing applications, e. g. building height estimation. Shadow areas are formed in the opposite side of the sunlight radiation to tall objects, and thus solar illumination angle is required to find probable shadow areas. In the recent years, Very High Resolution (VHR) imagery provides more detailed data from the objects including shadow areas. In this regard, the motivation of this paper is to propose a reliable feature, Shadow Low Gradient Direction (SLGD), to Automatically determine shadow and solar illumination direction in the VHR data. The proposed feature is based on the inherent spatial feature of fine-resolution shadow areas. Therefore, it can facilitate shadow-based operations, especially when the solar illumination information is not available in Remote sensing metadata. Shadow intensity is supposed to be dependent on two factors including the surface material and sunlight illumination, which is analyzed by directional gradient values in low gradient magnitude areas. This feature considers the sunlight illumination, and ignores the material differences. The method is fully implemented on the Google Earth Engine cloud computing platform, and is evaluated on the VHR data with 0. 3 m resolution. Finally, the SLGD performance is evaluated in determining shadow direction and compared in refining shadow maps.