Cooperative Energy Management of Hybrid DC Renewable Grid Using Decentralized Control Strategies

This paper attempted to control a hybrid DC microgrid in islanded operation mode using decentralized power management strategies. Proposed adaptive I/V characteristic for hybrid photovoltaic (PV) and battery energy storage system (BESS) and wind turbine generator (WTG) adapts the distributed energy resources (DER) behavior independently in accordance with the load demand. Hence, the PV module can spend its maximum power on load demand and spend the extra power for charging the BESS, which will regulate DC bus voltage and maintain the power balance within the microgrid. When load demand is beyond the maximum generation power of PV unit, WTG will supply the energy shortage. The proposed control system was applied on the DC microgrid in order to achieve control objectives through a decentralized procedure, without telecommunication links. In order to validate the proposed strategies, the control system was implemented on a DC microgrid within MATLAB/SIMULINK, where the simulation results were analyzed and validated

Using High-Resolution Climate Models to Identify Climate Change Hotspots in the Middle East: A Case Study of Iran

The adverse effects of climate change will impact all regions around the world, especially Middle Eastern countries, which have prioritized economic growth over environmental protection. However, these impacts are not evenly distributed spatially, and some locations, namely climate change hotspots, will suffer more from climate change consequences. In this study, we identified climate change hotspots over Iran—a developing country in the Middle East that is facing dire economic situations—in order to suggest pragmatic solutions for vulnerable regions. We used a statistical index as a representative of the differences in climatic parameters for the RCP8.5 and RCP4.5 forcing pathways between historical data (1975–2005), near-future data (2030–2060) and far-future data (2070–2100). More specifically, we used downscaled high-resolution (0.25°) meteorological data from five GCMs of the CMIP5 database to calculate the statistical metric. Results indicate that for the far-future period and RCP4.5, regions stretching from the northwest to southeast of Iran, namely the Hotspot Belt, are the most vulnerable areas, while, for RCP8.5, almost the whole country is vulnerable to climate change. The highest and lowest differences in temperature for RCP8.5 in 2070–2100 are observed during summer in the northwestern and central parts and during winter in the northern and northeastern parts. Moreover, the maximum increase and decrease in precipitation are identified over the western parts of Iran during fall and winter, respectively. Overall, western provinces (e.g., Lorestan and Kermanshah), which are mostly reliant on rainfed agriculture and other climate-dependent sectors, will face the highest change in climate in the future. As these regions have less adaptive capacity, they should be prioritized through upstream policy change and special budget allocation from the government to increase their resiliency against climate change

A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with RMSE=0.0292 (cm3/cm3) and R2=0.92 for the Sentinel-2 derived SSM and RMSE=0.0317 (cm3/cm3) and R2=0.84 for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different (pvalue=0.035 in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%

Predictive power of Pediatric Trauma Score (PTS) in predicting of child’s mortality : Predictive power of Pediatric Trauma Score (PTS)

Introduction: Trauma is a serious global health issue, and children are among the world's most vulnerable victims. Pediatric Trauma Score (PTS) is a rating for the prediction of death in pediatric with trauma .This study aimed to evaluate the predictive power of PTS in predicting death in children with trauma. Methods: This prospective study was part of a national study to develop a primary model for estimating mortality by adjusting the severity of injury in Iran. Which was performed on 92 pediatric trauma participants. To predict the predictive power of PTS, the Area under the Curve (AUC), 95 % confidence interval (95 % CI), sensitivity, specificity, coefficient of determination and, odds ratio were utilized. All tests were carried out with a significance level of 0.05. Results: The mean age of patients participating in this study was 11.86 ± 4.94 years and 68 (73.91%) of them were male. The most common injury type was head and face (53.26%) trauma and the most common cause of trauma was motorcycle accidents (27.17%), respectively. The AUC value for PTS score was 0.911 and its coefficient of determination (R2) was 38%. Conclusion: PTS is a good score for predicting trauma death in children in Iran. PTS can be used especially for triage of children with trauma in hospitals