11 research outputs found
Development of Hybrid Adaptive Neuro Fuzzy Inference System - Harris Hawks Optimizer (ANFIS-HHO) for Monthly Inlet Flow to Dam Reservoirs Prediction
Nowadays, machine learning models are able to make good predictions based on pattern extraction between data. In this study, a neural-fuzzy network (ANFIS) was used to predict the inflow to the reservoirs of a dam namely, the Mahabad dam located in the northwestern part of Iran. A new Harris Hawk (HHO) optimization algorithm was also used to improve the ANFIS (HHO-ANFIS) structure. Monthly precipitation and temperature and inlet flow data to the reservoir one to three months ago were used as input parameters as 6 different input patterns. About 70% of the data was used for training and 30% to test the models. The results showed that the ANFIS model has good accuracy in training data although, for test data, its accuracy was greatly reduced. The development of the HHO-ANFIS model improved the accuracy of the prediction. The patterns with all input parameters had the highest prediction accuracy. In this pattern, values ​​of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash Sutcliffe Efficiency coefficient (NSE) for test data were 3.9 MCM, 2.41 MCM, and 0.86, respectively. Due to the good performance of the model used, it can be recommended for time series predictions
Reduction of the filler network interaction in novel inner liner compound based on SBR/rectorite nanocomposite by glycerin
Optimization and effect of 3-aminopropyltriethoxysilane content on the properties of bentonite-filled ethylene propylene diene monomer composites
Thermo-oxidative decomposition kinetics of elastomeric composites based on styrene-(ethylene-butylene)-styrene triblock copolymer and organomontmorillonite
Molecular identification and subtype distribution of Blastocystis sp. in farm and pet animals in Turkey
Maintenance workflow management in hospitals: An automated multi-agent facility management system
Moderate treadmill exercise ameliorates amyloid-β-induced learning and memory impairment, possibly via increasing AMPK activity and up-regulation of the PGC-1α/FNDC5/BDNF pathway
Optimized ultrasonic-assisted oil extraction and biodiesel production from the seeds of Maesopsis eminii
Diabetic complications in the cornea
Diabetic corneal alterations, such as delayed epithelial wound healing, edema, recurrent erosions, neuropathy/loss of sensitivity, and tear film changes are frequent but underdiagnosed complications of both type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus. The disease affects corneal epithelium, corneal nerves, tear film, and to a lesser extent, endothelium, and also conjunctiva. These abnormalities may appear or become exacerbated following trauma, as well as various surgeries including retinal, cataract or refractive. The focus of the review is on mechanisms of diabetic corneal abnormalities, available animal, tissue and organ culture models, and emerging treatments. Changes of basement membrane structure and wound healing rates, the role of various proteinases, advanced glycation end products (AGEs), abnormal growth and motility factors (including opioid, epidermal, and hepatocyte growth factors) are analyzed. Experimental therapeutics under development, including topical naltrexone, insulin, inhibitors of aldose reductase, and AGEs, as well as emerging gene and cell therapies are discussed in detail