Accuracy of pixel-based classification: application of different algorithms to landscapes of Western Iran.

peer reviewedScenarios for monitoring land cover on a large scale, involving large volumes of data, are becoming more prevalent in remote sensing applications. The accuracy of algorithms is important for environmental monitoring and assessments. Because they performed equally well throughout the various research regions and required little human involvement during the categorization process, they appear to be resilient and accurate for automated, big area change monitoring. Malekshahi City is one of the important and at the same time critical areas in terms of land use change and forest area reduction in Ilam Province. Therefore, this study aimed to compare the accuracy of nine different methods for identifying land use types in Malekshahi City located in Western Iran. Results revealed that the artificial neural network (ANN) algorithm with back-propagation algorithms could reach the highest accuracy and efficiency among the other methods with kappa coefficient and overall accuracy of approximately 0.94 and 96.5, respectively. Then, with an overall accuracy of about 91.35 and 90.0, respectively, the methods of Mahalanobis distance (MD) and minimum distance to mean (MDM) were introduced as the next priority to categorize land use. Further investigation of the classified land use showed that good results can be provided about the area of the land use classes of the region by applying the ANN algorithm due to high accuracy. According to those results, it can be concluded that this method is the best algorithm to extract land use maps in Malekshahi City because of high accuracy

Unsustainable Anthropogenic Activities: A Paired Watershed Approach of Lake Urmia (Iran) and Lake Van (Turkey)

Water availability in lakes must be studied in order to better manage ecosystems within lake basins and meet economic development needs. Despite being Iran’s largest lake, Lake Urmia’s water level and surface area have declined dramatically over the past two decades. During the same period, Lake Van in Turkey maintained a relatively stable water level and surface area. As a result, comparing factors related to water level and surface area in these lakes, which have similar geographical and climate conditions but different management policies, can be an appropriate way to identify the causes of water declines in Lake Urmia. Comparing these variables may help explain observed differences in lake behavior between 2000 and 2016. Hydrometric and climatic parameters, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were used to achieve this goal. Changes in precipitation, temperature, and evapotranspiration in both lakes show essentially identical trends, but this is not a convincing explanation for Lake Urmia’s water surface changes. The results revealed that dam construction and water diversion projects, the expansion of irrigated agriculture, and the lake’s shallow depth in most parts were the primary causes of Lake Urmia’s shrinkage compared to Lake Van

The Effect of Oral Simvastatin on the Clinical Outcome of Patients with Severe Traumatic Brain Injury: A Randomized Clinical Trial

BACKGROUND: Despite recent promising pharmacological and technological advances in neurosurgical intensive care, the overall TBI-related mortality and morbidity remain high and still pose a major clinical problem. The aim of this study was to evaluate the effect of oral simvastatin on the clinical outcome ofpatients with severe TBI.METHODS: In a double-blind placebo-controlled randomized clinical trial a total of 98 patients with severe TBI in Imam Khomeini Hospital in Sari, Iran, were evaluated. Patients who meet the inclusion criteria were randomly allocated into two groups (n=49). In addition to supportive therapies, the intervention group received oral simvastatin (40 mg, daily) for 10 days, and the control group received the placebo (10 days). Patients' Glasgow coma scale (GCS) score, in hospital mortality, duration of mechanical ventilation and length of ICU and neurosurgery ward stay were evaluated during three-time intervals (T1: admission, T2: discharge and T3: one month after discharge).RESULTS: The percentage of conscious patients was 18.9% (7 cases) in the simvastatin group and 3.1% (1 case) in controls (P=0.06) at T2. One month after discharge (T3) the proportion of conscious patients significantly increased in the simvastatin group compared to control group (64.9 % versus 28.1 %; P=0.002). There was no significant difference for the mean of GCS score between the simvastatin group and control group at T1 (6.41 ± 1.30 versus 6.41 ± 1.28, respectively; P = 0.98). However, the mean score of GCS in patients who received simvastatin was significantly greater than controls at T2 and T3 (p<0.05). There was no significant differences between two group in-terms of length of mechanical ventilation, ICU and neurosurgery ward stay.CONCLUSION: According to the results of this study it seems that using simvastatin may be an effective and promising therapeutic modality for improving GCS score during TBI recovery