University of Tabriz

About: University of Tabriz is a education organization based out in Tabriz, Iran. It is known for research contribution in the topics: Population & Nanocomposite. The organization has 12141 authors who have published 20976 publications receiving 313982 citations.

Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting

Abstract: Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.

Tunable phase control for subluminal to superluminal light propagation

Abstract: We demonstrate tunable control of the group velocity of a weak probe pulse from subluminal to superluminal. The model is an extended $\ensuremath{\Lambda}$-type system with two extra control fields and an extra energy level. Phase variation of one of the control fields imparts the tunability in the group velocity along with other interesting spectral behavior in the absorption spectrum.

Cross-species amplification of microsatellite loci in aphids: assessment and application

Abstract: Despite the relative ease of isolating microsatellites, their development still requires substantial inputs of time, money and expertise For this reason there is considerable interest in using existing microsatellites on species from which markers were not cloned We tested cross-species amplification of 48 existing aphid loci in species of the following genera: Aphidinae: Aphidini: Aphis and Rhopalosiphum ; Aphidinae: Macrosiphini: Acyrthosiphum , Brevicoryne , Diuraphis , Illinoia , Macrosiphoniella , Macrosiphum , Metopeurum , Metapolophium , Myzus , Phorodon , Sitobion and Uroleucon and Neuquenaphidinae: Neuquenaphis Our results show cross-species application of known microsatellite loci is a highly promising source of codominant markers for population genetic and evolutionary studies in aphids