Ultrathin buffer layer at organic/organic interface for managing the recombination profile in organic light‐emitting diodes: Metal versus dielectric buffer

We report on the utilization of an ultrathin buffer layer at the organic/organic (O/O) interface to enhance device efficiency in organic light‐emitting diodes. Two different kinds of buffer layers are examined: metal and dielectric. It is shown that employment of an ultrathin Ag layer with a thickness of 1–2 nm enhances the device performance, while a MgF₂ dielectric buffer cannot affect the device properties considerably. In particular, the turn‐on voltage of the device with an appropriate buffer layer is reduced about 3 V, its current efficiency increases by a factor of more than three, and the power efficiency increases by a factor of more than five in comparison to the control device when a Ag buffer layer is introduced at the O/O interface. By employment of the buffer layer at the interface, an accumulation of current carriers appears within the device that redistribute the recombination profile toward the interior part of the emissive layer. Also, morphological examinations reveal that distinguishable phase segregation occurs in the blend of the hole‐transport layer. In particular, the polymer component remains at the surface and facilitates the hole transport into the successive layers. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43894.     

Out-of-Field Hippocampus from Partial-Body Irradiated Mice Displays Changes in Multi-Omics Profile and Defects in Neurogenesis

The brain undergoes ionizing radiation exposure in many clinical situations, particularly during radiotherapy for brain tumors. The critical role of the hippocampus in the pathogenesis of radiation-induced neurocognitive dysfunction is well recognized. The goal of this study is to test the potential contribution of non-targeted effects in the detrimental response of the hippocampus to irradiation and to elucidate the mechanisms involved. C57Bl/6 mice were whole body (WBI) or partial body (PBI) irradiated with 0.1 or 2.0 Gy of X-rays or sham irradiated. PBI consisted of the exposure of the lower third of the mouse body, whilst the upper two thirds were shielded. Hippocampi were collected 15 days or 6 months post-irradiation and a multi-omics approach was adopted to assess the molecular changes in non-coding RNAs, proteins and metabolic levels, as well as histological changes in the rate of hippocampal neurogenesis. Notably, at 2.0 Gy the pattern of early molecular and histopathological changes induced in the hippocampus at 15 days following PBI were similar in quality and quantity to the effects induced by WBI, thus providing a proof of principle of the existence of out-of-target radiation response in the hippocampus of conventional mice. We detected major alterations in DAG/IP3 and TGF-β signaling pathways as well as in the expression of proteins involved in the regulation of long-term neuronal synaptic plasticity and synapse organization, coupled with defects in neural stem cells self-renewal in the hippocampal dentate gyrus. However, compared to the persistence of the WBI effects, most of the PBI effects were only transient and tended to decrease at 6 months post-irradiation, indicating important mechanistic difference. On the contrary, at low dose we identified a progressive accumulation of molecular defects that tended to manifest at later post-irradiation times. These data, indicating that both targeted and non-targeted radiation effects might contribute to the pathogenesis of hippocampal radiation-damage, have general implications for human health.     

A self-tuning ANN model for simulation and forecasting of surface flows

Artificial neural networks (ANN) are applicable for and forecasting without the need to calculate complex nonlinear functions. This paper evaluates the effectiveness of temperature, evapotranspiration, precipitation and inflow factors, and the lag time of those factors, as variables for simulating and forecasting of runoff. The genetic algorithm (GA) is coupled with ANN to determine the optimal set of variables for streamflow forecasting. The minimization of the total mean square error (MSE) is considered as the objective function of the ANN-GA method in this paper. Our results show the effectiveness of the ANN-GA for simulating and forecasting runoff with consistent accuracy compared with using pure ANN for runoff simulation and forecasting.     

Assessing the Accuracy of GIS-Based Analytical Hierarchy Process for Watershed Prioritization; Gorganrood River Basin,Iran

Watershed prioritization based on the natural and anthropogenic factors involves by locating the critical areas of flood hazard, which cause socio-economic and environmental consequences to take up mitigation activities on priority basis. The pair-wise comparisons of natural and anthropogenic factors is a bit problematic, because these two mentioned factors are different from typology view point. In order to assess flood hazard potential by using (1) only natural factors (FHPNF), (2) only anthropogenic factors (FHPAF), and (3) ensemble the obtained sub-watersheds priorities from natural and anthropogenic factors , the coupling of the Analytical Hierarchy Process (AHP) and Geographical Information Systems (GIS) were applied the Gorganrood river basin of Iran. Each effective factor was assigned to appropriate weight based on Saaty’s 9 point scale and the obtained weights were normalized through the Eigenvector method. By using the Weighted Linear Combination (WLC), two flood hazard potential indexes were defined separately for anthropogenic and natural factors. Finally, both indices values were combined to determine sub-watersheds priority. For the validation of the predictions, the Receiver Operating Characteristic (ROC) curve and historical data of flash flood events were used. According to the results of ROC curves, the FHPNF and FHPAF maps showed a reasonable good performance in watershed prioritization with area under ROC curve (AUC) values of 76.1 and 79.5 %, respectively. In addition, these results imply that one and two sub-watersheds fall under very high and high priority, respectively. The results of this study act as guidelines for managers and planners to determine sub-watersheds priority and rational management of watersheds based on both natural and anthropogenic components.     

Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization

Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Their broad applicability, ease of use, and global perspective may be considered as the primary reason for their success. The honey-bees mating process may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bees mating. In this paper, the honey-bees mating optimization algorithm (HBMO) is presented and tested with few benchmark examples consisting of highly non-linear constrained and/or unconstrained real-valued mathematical models. The performance of the algorithm is quite comparable with the results of the well-developed genetic algorithm. The HBMO algorithm is also applied to the operation of a single reservoir with 60 periods with the objective of minimizing the total square deviation from target demands. Results obtained are promising and compare well with the results of other well-known heuristic approaches.     

Developing Strategies for Agricultural Water Management of Large Irrigation and Drainage Networks with Fuzzy MCDM

Sustainable water resources management aims at increasing the efficient use of water and achieving food security. This work proposes a generalized novel spatial fuzzy strategic planning (SFSP) in combination with multi-criteria decision making (MCDM) and a conceptual agricultural water use model for determining sustainable agricultural water management strategies. The proposed framework is applied to an irrigation and drainage network in Iran, which constitutes a large-scale water resource system. A spatial strength, weakness, opportunity, and threat (SWOT) analysis of internal and external factors related to agricultural water management is applied in this work. Possible water management strategies were ranked with the MCDM approach that combines the Analytic Hierarchy Process (AHP) and the Fuzzy technique for order-preference by similarity to ideal solution (TOPSIS). The AHP estimates the criteria weights and the TOPSIS model prioritizes the agricultural water management strategies. The results of SWOT analysis show that the final scores of the internal and external factors are equal to 2.9 and 2.73, respectively. Accordingly, the most attractive strategic type is a SO (aggressive) strategy, and a combination of structural and non-structural strategies (SO, ST, and WO strategies) are the top-ranked ones. Proposed strategies for water supply and demand management are the development and rehabilitation of the physical structure of water resources system of irrigation network, improvement of operation management and maintenance of water resources system, wastewater management, and inter-basin water transfer within the irrigation network. The results indicate that the total annual volume of agricultural water under normal conditions is about 1.8 billion cubic meters, of which about 1707 million cubic meters (95%) issue from surface water sources and 90 million cubic meters (5%) from groundwater sources. The proposed model and the calculated results provide viable and effective solutions for the implementation of sustainable management of water resources and consumption in large-scale water resources systems.

     

Generalized Storage Equations for Flood Routing with Nonlinear Muskingum Models

Water Resources Management - The nonlinear Muskingum model is a leading method for hydrologic routing. The efficiency of the nonlinear Muskingum model for routing of hydrograph outflow has been...     

A comparison of Zayandehrood River water values for agriculture and the environment

River water management is challenging not only since they are open systems with changing physical structures, but also because the water values are mostly unknown over varied sectors. If policymakers grasp water values, water management will be more efficient. This research intends to examine the values of water in agriculture, which receives the most substantial portion of water resources, with the values of water in the environment in Isfahan located in the Zayandehrood River basin of Iran. The consequences of contingent valuation and production function methods revealed that per cubic metre value of water is 13 times higher in the environment than agriculture. The government should reconsider the higher value of the environment despite it is a non‐market value. The contingent valuation model additionally proved that women exhibited 21% more willingness to pay than men in order to protect the environment; however, they are paid less by 36%.     

Extracting Optimal Policies of Hydropower Multi-Reservoir Systems Utilizing Enhanced Differential Evolution Algorithm

Deriving the optimal policies of hydropower multi-reservoir systems is a nonlinear and high-dimensional problem which makes it difficult to achieve the global or near global optimal solution. In order to optimally solve the problem effectively, development of optimization methods with the purpose of optimizing reservoir operation is indispensable as well as inevitable. This paper introduces an enhanced differential evolution (EDE) algorithm to enhance the exploration and exploitation abilities of the original differential evolution (DE) algorithm. The EDE algorithm is first applied to minimize two benchmark functions (Ackley and Shifted Schwefel). In addition, a real world two-reservoir hydropower optimization problem and a large scale benchmark problem, namely ten-reservoir problem, were considered to indicate the effectiveness of the EDE. The performance of the EDE was compared with the original DE to solve the three optimization problems. The results demonstrate that the EDE would have a powerful global ability and faster convergence than the original DE to solve the two benchmark functions. In the 10-reservoir optimization problem, the EDE proved to be much more functional to reach optimal or near optimal solution and to be effective in terms of convergence rate, standard deviation, the best, average and worst values of objective function than the original DE. Also, In the case of two-reservoir system, the best values of the objective function obtained 93.86 and 101.09 for EDE and DE respectively. Based on the results, it can be stated that the most important reason to improve the performance of the EDE algorithm is the promotion of local and global search abilities of the DE algorithm using the number of novel operators. Also, the results of these three problems corroborated the superior performance, the high efficiency and robustness of the EDE to optimize complex and large scale multi-reservoir operation problems.