Synthesis of Janus/non-Janus hollow graphene oxide micro- and nanoparticles and the effects of their localization on the thermal conductivity of blend-based polymer composites

Journal of Materials Science - In this study, the effects of Janus, hydrophilic and hydrophobic hollow graphene oxide (HO) particles on the thermal conductivity of binary polymer blends were...     

Computer graphics generated from the iteration of algebraic transformations in the complex plane

Algorithms for the generation of intricate shapes resulting from the iteration of algebraic transformations are presented. A special convergence test makes possible the production of a visually striking class of displayable objects. Several illustrations are chosen as examples of the diversity of forms which result from this technique.     

Prediction and simulation of monthly groundwater levels by genetic programming

Groundwater level is an effective parameter in the determination of accuracy in groundwater modeling. Thus, application of simple tools to predict future groundwater levels and fill-in gaps in data sets are important issues in groundwater hydrology. Prediction and simulation are two approaches that use previous and previous-current data sets to complete time series. Artificial intelligence is a computing method that is capable to predict and simulate different system states without using complex relations. This paper investigates the capability of an adaptive neural fuzzy inference system (ANFIS) and genetic programming (GP) as two artificial intelligence tools to predict and simulate groundwater levels in three observation wells in the Karaj plain of Iran. Precipitation and evaporation from a surface water body and water levels in observation wells penetrating an aquifer system are used to fill-in gaps in data sets and estimate monthly groundwater level series. Results show that GP decreases the average value of root mean squared error (RMSE) as the error criterion for the observation wells in the training and testing data sets 8.35 and 11.33 percent, respectively, compared to the average of RMSE by ANFIS in prediction. Similarly, the average value of RMSE for different observation wells used in simulation improves the accuracy of prediction 9.89 and 8.40 percent in the training and testing data sets, respectively. These results indicate that the proposed prediction and simulation approach, based on GP, is an effective tool in determining groundwater levels.     

A computationally scalable fast intra coding scheme for HEVC video encoder

Multimedia Tools and Applications - The High Efficiency Video Coding (HEVC) adopts 35 intra prediction modes to provide a more precise intra prediction. As a result, HEVC intra coding is much more...     

Decision‐tree‐based methods for differential diagnosis of β‐thalassemia trait from iron deficiency anemia

Several discrimination indices have been proposed to distinct between β‐thalassemia trait (βTT) and iron deficiency anemia (IDA). This study is the first application of tree‐based methods for differential diagnosis of βTT from IDA. One hundred forty‐four patients with hypochromic microcytic anemia aged 18–40 years old from Ayat Hospital of Tehran were recruited. Classification and Regression tree, CHi‐squared Automatic Interaction Detector (CHAID), Exhaustive CHi‐squared Automatic Interaction Detector (E‐CHAID), Quick, Unbiased, Efficient Statistical Tree (QUEST), Classification Rule with Unbiased Interaction Selection and Estimation (CRUISE), and Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) have been used to discriminate the diagnosis. Mean corpuscular volume (MCV) was found as the main predictor in discrimination. All the mentioned tree‐based methods showed acceptable sensitivity, specificity, accuracy, Youden's index, false positive and negative rate, positive and negative predictive values and AUC in differential diagnosis of βTT from IDA. However, Classification Rule with Unbiased Interaction Selection and Estimation revealed more precise classification with an area under the curve value of 0.99. Decision‐tree‐based methods can be used to develop sensitive and accurate diagnostic methods for differentiating βTT from IDA.     

Generating test as a web service (TaaWS) through a method-based attribute grammar

International Journal on Software Tools for Technology Transfer - Service-Oriented Architecture (SOA) is a technology for designing software systems. By using this architecture, new challenges...     

Applying reinforcement learning for web pages ranking algorithms

Ranking web pages for presenting the most relevant web pages to user's queries is one of the main issues in any search engine. In this paper, two new ranking algorithms are offered, using Reinforcement Learning (RL) concepts. RL is a powerful technique of modern artificial intelligence that tunes agent's parameters, interactively. In the first step, with formulation of ranking as an RL problem, a new connectivity-based ranking algorithm, called RL_Rank, is proposed. In RL_Rank, agent is considered as a surfer who travels between web pages by clicking randomly on a link in the current page. Each web page is considered as a state and value function of state is used to determine the score of that state (page). Reward is corresponded to number of out links from the current page. Rank scores in RL_Rank are computed in a recursive way. Convergence of these scores is proved. In the next step, we introduce a new hybrid approach using combination of BM25 as a content-based algorithm and RL_Rank. Both proposed algorithms are evaluated by well known benchmark datasets and analyzed according to concerning criteria. Experimental results show using RL concepts leads significant improvements in raking algorithms.     

Integration of distributed energy resources into low voltage grid: A market-based multiperiod optimization model

This paper develops a multiperiod optimization model for an interconnected micro grid with hierarchical control that participates in wholesale energy market to maximize its benefit (i.e. revenues-costs). In addition to the operational constraints of distributed energy resources (DER) including both inter-temporal and non-inter-temporal types, the adequacy and steady-state security constraints of micro grid and its power losses are incorporated in the optimization model. In the presented model, DER are integrated into low voltage grid considering both technical and economical aspects. This integration as a micro grid can participate in wholesale energy market as an entity with dual role including producer and consumer based on the direction of exchanged power. The developed model is evaluated by testing on a micro grid considering different cases and the results are analyzed.     

Electrospun core-shell nanofibers based on polyethylene oxide reinforced by multiwalled carbon nanotube and silicon dioxide nanofillers: A novel and effective solvent-free electrolyte for lithium ion batteries

Insertion of conductive fillers into solvent-free polymer electrolytes enhances electrochemical behavior of the electrolyte membranes leading to higher ionic conductivity, lower capacity fading, and so on. Although, the presence of the conductive fillers in the polymer matrixes increases the risk of electrical shorting, herein, polyethylene oxide (PEO)-based core-shell nanofibers were prepared via a simple electrospinning method. In the core-shell electrospun fibers, ethylene carbonate (EC) and lithium perchlorate (LiClO₄) were used as a plasticizer and as a lithium salt, respectively. The core component was enwrapped by the PEO/EC/LiClO₄ shell part incorporated with SiO₂ nanoparticles. Various properties of the fabricated membranes were evaluated by changing the ratio of multiwall carbon nanotubes (MWCNTs) in the core part of the nanofibers. The morphology and core-shell structure of the electrospun fibers were studied by FESEM and TEM images. According to FTIR and XRD results, addition of the EC plasticizer and the fillers into the as-spun fibers increased the fraction of free ions and the amorphous regions. From electrochemical impedance spectroscopy studies, the ionic conductivity enhanced by insertion of the plasticizer molecules and the filler particles into the core-shell structures. The highest ionic conductivities of 0.09 and 0.21 mS.cm⁻¹ were obtained for the free-filler and the filler-loaded nanofibrous membranes, respectively. The prepared mats obeyed the Arrhenius behavior ( R²~1 ). Dielectric studies confirmed the obtained data from the ionic conductivities. Furthermore, the capacity residual was enhanced from 69% to 85% by incorporation of the MWCNTs filler into the core component of the electrospun nanofibers. The presented results may facilitate development of versatile nanofibrous membranes embedded with the conductive fillers as solvent-free electrolytes applicable in lithium-ion batteries.