Photocatalytic Reforming of Glycerol over Gold and Palladium as an Alternative Fuel Source

The photocatalytic reforming of glycerol to hydrogen over palladium and gold modified TiO₂ catalysts is reported. The rate of hydrogen production exceeds that measured for the photoreforming of methanol over both catalysts with the palladium catalyst performing approximately four times better than the gold. The reaction occurs under ambient conditions and neither system suffered from poisoning after extensive testing. This therefore offers a potentially attractive route by which one of the major waste products from the manufacture of biodiesel can be converted into a useful product.     

Evaluation of colloidal silica suspension as efficient additive for improving physicochemical and in vitro biological properties of calcium sulfate-based nanocomposite bone cement

In the present study new calcium sulfate-based nanocomposite bone cement with improved physicochemical and biological properties was developed. The powder component of the cement consists of 60 wt% α-calcium sulfate hemihydrate and 40 wt% biomimetically synthesized apatite, while the liquid component consists of an aqueous colloidal silica suspension (20 wt%). In this study, the above mentioned powder phase was mixed with distilled water to prepare a calcium sulfate/nanoapatite composite without any additive. Structural properties, setting time, compressive strength, in vitro bioactivity and cellular properties of the cements were investigated by appropriate techniques. From X-ray diffractometer analysis, except gypsum and apatite, no further phases were found in both silica-containing and silica-free cements. The results showed that both setting time and compressive strength of the calcium sulfate/nanoapatite cement improved by using colloidal silica suspension as cement liquid. Meanwhile, the condensed phase produced from the polymerization process of colloidal silica filled the micropores of the microstructure and covered rodlike gypsum crystals and thus controlled cement disintegration in simulated body fluid. Additionally, formation of apatite layer was favored on the surfaces of the new cement while no apatite precipitation was observed for the cement prepared by distilled water. In this study, it was also revealed that the number of viable osteosarcoma cells cultured with extracts of both cements were comparable, while silica-containing cement increased alkaline phosphatase activity of the cells. These results suggest that the developed cement may be a suitable bone filling material after well passing of the corresponding in vivo tests.     

Anionic membrane and ionomer based on poly(2,6-dimethyl-1,4-phenylene oxide) for alkaline membrane fuel cells

Hydroxyl-ion conductive poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) membranes with different characteristics were prepared via relatively simple bromination/amination serial reactions with reduced number of involved chemicals and shorter reaction time. The effects of reactants ratio, reaction atmosphere, polymer concentration, casting solvent, and hydroxylation treatment on reaction were investigated in details. The microstructure, water uptake, swelling ratio, ion-exchange capacity and ionic conductivity of the membranes were also studied. The obtained results demonstrate that, the ionic conductivity of the membrane is dependent on casting solvent. The N-methyl-2-pyrrolidonecast membrane exhibits the highest conductivity with the thinnest film. Although the membrane was prepared via a relatively simple preparation route with least toxic chemicals, a competitive ionic conductivity value of 1.64 × 10⁻² S cm⁻¹ was achieved at 60 °C. A power density of 19.5 mW cm⁻² has been demonstrated from the alkaline membrane fuel cell operated at 70 °C, assembled from the entirely homemade membrane electrode assembly without any hot-pressing.     

Rainfall-runoff Modeling in a Watershed Scale Using an Object Oriented Approach Based on the Concepts of System Dynamics

Relying on a linear causal thinking, most of the hydrological models fail to incorporate socio-economic characteristics of a watershed with hydrological and environmental attributes. Based on a systems thinking philosophy, the paper aims to adopt an Object-Oriented (OO) approach based on the concepts of System Dynamics (SD) such as stocks and flows to analyze the dynamics in a hydrological system in a watershed scale. Object-Oriented modeling is a way to organize data into discrete, recognizable entities called objects. These objects could be concrete (such as a river reach) or conceptual (such as a policy decision). In the present paper, VENSIM PLE has been used for the modeling purpose. The application was illustrated in an Iranian watershed. The model was examined using validity and verifying tests. The results showed that the model is capable of generating the monthly runoff quite well. The values of R² are 0.69 and 0.61 for generated discharge values at Polchehr and Doab stations respectively. Also the values of NSE are 0.66 and 0.64 for estimated discharge values at the same stations. The capability of model was more clarified comparing R² and NSE coefficients obtained by a SWAT model with those obtained by Watershed Hydrological Model developed in this study.     

A novel approach to accelerate the convergence speed of a stochastic multi-agent system using recurrent neural nets

One problem in the design of multi-agent systems is the difficulty of predicting the occurrences that one agent might face, also to recognize and to predict their optimum behavior in these situations. Therefore, one of the most important characteristic of the agent is their ability during adoption, to learn, and correct their behavior. With consideration of the continuously changing environment, the back and forth learning of the agents, the inability to see the agent’s action first hand, and their chosen strategies, learning in a multi-agent environment can be very complex. On the one hand, with recognition to the current learning models that are used in deterministic environment that behaves linearly, which contain weaknesses; therefore, the current learning models are unproductive in complex environments that the actions of agents are stochastic. Therefore, it is necessary for the creation of learning models that are effective in stochastic environments. Purpose of this research is the creation of such a learning model. For this reason, the Hopfield and Boltzmann learning algorithms are used. In order to demonstrate the performance of their algorithms, first, an unlearned multi-agent model is created. During the interactions of the agents, they try to increase their knowledge to reach a specific value. The predicated index is the number of changed states needed to reach the convergence. Then, the learned multi-agent model is created with the Hopfield learning algorithm, and in the end, the learned multi-agent model is created with the Boltzmann learning algorithm. After analyzing the obtained figures, a conclusion can be made that when learning impose to multi-agent environment the average number of changed states needed to reach the convergence decreased and the use of Boltzmann learning algorithm decreased the average number of changed states even further in comparison with Hopfield learning algorithm due to the increase in the number of choices in each situation. Therefore, it is possible to say that the multi-agent systems behave stochastically, the more closer they behave to their true character, the speed of reaching the global solution increases.     

Learning low-rank kernel matrices for constrained clustering

Constrained clustering methods (that usually use must-link and/or cannot-link constraints) have been received much attention in the last decade. Recently, kernel adaptation or kernel learning has been considered as a powerful approach for constrained clustering. However, these methods usually either allow only special forms of kernels or learn non-parametric kernel matrices and scale very poorly. Therefore, they either learn a metric that has low flexibility or are applicable only on small data sets due to their high computational complexity. In this paper, we propose a more efficient non-linear metric learning method that learns a low-rank kernel matrix from must-link and cannot-link constraints and the topological structure of data. We formulate the proposed method as a trace ratio optimization problem and learn appropriate distance metrics through finding optimal low-rank kernel matrices. We solve the proposed optimization problem much more efficiently than SDP solvers. Additionally, we show that the spectral clustering methods can be considered as a special form of low-rank kernel learning methods. Extensive experiments have demonstrated the superiority of the proposed method compared to recently introduced kernel learning methods.     

Dynamic clustering using combinatorial particle swarm optimization

Combinatorial Particle Swarm Optimization (CPSO) is a relatively recent technique for solving combinatorial optimization problems. CPSO has been used in different applications, e.g., partitional clustering and project scheduling problems, and it has shown a very good performance. In partitional clustering problem, CPSO needs to determine the number of clusters in advance. However, in many clustering problems, the correct number of clusters is unknown, and it is usually impossible to estimate. In this paper, an improved version, called CPSOII, is proposed as a dynamic clustering algorithm, which automatically finds the best number of clusters and simultaneously categorizes data objects. CPSOII uses a renumbering procedure as a preprocessing step and several extended PSO operators to increase population diversity and remove redundant particles. Using the renumbering procedure increases the diversity of population, speed of convergence and quality of solutions. For performance evaluation, we have examined CPSOII using both artificial and real data. Experimental results show that CPSOII is very effective, robust and can solve clustering problems successfully with both known and unknown number of clusters. Comparing the obtained results from CPSOII with CPSO and other clustering techniques such as KCPSO, CGA and K-means reveals that CPSOII yields promising results. For example, it improves 9.26 % of the value of DBI criterion for Hepato data set.     

Optimal design of structures subjected to time history loading by swarm intelligence and an advanced metamodel

This paper proposes a new metamodeling framework that reduces the computational burden of the structural optimization against the time history loading. In order to achieve this, two strategies are adopted. In the first strategy, a novel metamodel consisting of adaptive neuro-fuzzy inference system (ANFIS), subtractive algorithm (SA), self organizing map (SOM) and a set of radial basis function (RBF) networks is proposed to accurately predict the time history responses of structures. The metamodel proposed is called fuzzy self-organizing radial basis function (FSORBF) networks. In this study, the most influential natural periods on the dynamic behavior of structures are treated as the inputs of the neural networks. In order to find the most influential natural periods from all the involved ones, ANFIS is employed. To train the FSORBF, the input–output samples are classified by a hybrid algorithm consisting of SA and SOM clusterings, and then a RBF network is trained for each cluster by using the data located. In the second strategy, particle swarm optimization (PSO) is employed to find the optimum design. Two building frame examples are presented to illustrate the effectiveness and practicality of the proposed methodology. A plane steel shear frame and a realistic steel space frame are designed for optimal weight using exact and approximate time history analyses. The numerical results demonstrate the efficiency and computational advantages of the proposed methodology.     

Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification

In video coding, research is focused on the development of fast motion estimation (ME) algorithms while keeping the coding distortion as small as possible. It has been observed that the real world video sequences exhibit a wide range of motion content, from uniform to random, therefore if the motion characteristics of video sequences are taken into account before hand, it is possible to develop a robust motion estimation algorithm that is suitable for all kinds of video sequences. This is the basis of the proposed algorithm. The proposed algorithm involves a multistage approach that includes motion vector prediction and motion classification using the characteristics of video sequences. In the first step, spatio-temporal correlation has been used for initial search centre prediction. This strategy decreases the effect of unimodal error surface assumption and it also moves the search closer to the global minimum hence increasing the computation speed. Secondly, the homogeneity analysis helps to identify smooth and random motion. Thirdly, global minimum prediction based on unimodal error surface assumption helps to identify the proximity of global minimum. Fourthly, adaptive search pattern selection takes into account various types of motion content by dynamically switching between stationary, center biased and, uniform search patterns. Finally, the early termination of the search process is adaptive and is based on the homogeneity between the neighboring blocks.Extensive simulation results for several video sequences affirm the effectiveness of the proposed algorithm. The self-tuning property enables the algorithm to perform well for several types of benchmark sequences, yielding better video quality and less complexity as compared to other ME algorithms. Implementation of proposed algorithm in JM12.2 of H.264/AVC shows reduction in computational complexity measured in terms of encoding time while maintaining almost same bit rate and PSNR as compared to Full Search algorithm.