A Query Routing Approach Based on Users’ Satisfaction for Resource Discovery in Service-Oriented Networks

Service oriented networks are distributed computing infrastructures that provide widely distributed resources. These networks are dynamic and their size and complexity continue to increase and allow to users a ubiquitous access to available resources and services. Therefore, efficient query routing approaches in large and highly distributed service oriented networks are required and need to be adaptive in order to cope with a dynamically changing environment. In this paper, a query routing approach based on mobile agents and random walks with a reinforcement learning technique is presented. By enhancing random walks with a reinforcement learning mechanism centered on users’ satisfaction, this approach allows dynamic and self-adaptive location of required resources. Peers incorporate knowledge from past and present queries which will be used during next searches by mobile agents to select their next hops. This approach is analyzed through two query routing techniques using the network simulator ns2.     

Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System

It is remarkable that several hydrological parameters have a significant effect on the reservoir operation. Therefore, operating the reservoir system is complex issue due to existing the nonlinearity hydrological variables. Hence, determining modern model has high ability in handling reservoir operation is crucial. The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. The major objective for the proposed model is minimizing the deficit volume between water releases and the irrigation water demand. The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed models have been utilized of finding the optimal policies to operate Timah Tasoh Dam, which is located in Malaysia. The study utilized considerable statistical indicators to explore the efficiency of the models. The simulation period showed that SMLA approach outperforms both of conventional algorithms. The SMLA attained high Reliability and Resilience (Rel. = 0.98%, Res. = 50%) and minimum Vulnerability (Vul. = 21.9 of demand). It is demonstrated that shark machine learning algorithm would be a promising tool in handling the long-term optimization problem in operation a reservoir system.     

Financial time series prediction using polynomial pipelined neural networks

This paper proposes a novel type of higher-order pipelined neural network: the polynomial pipelined neural network. The proposed network is constructed from a number of higher-order neural networks concatenated with each other to predict highly nonlinear and nonstationary signals based on the engineering concept of divide and conquer. The polynomial pipelined neural network is used to predict the exchange rate between the US dollar and three other currencies. In this application, two sets of experiments are carried out. In the first set, the input data are pre-processed between 0 and 1 and passed to the neural networks as nonstationary data. In the second set of experiments, the nonstationary input signals are transformed into one step relative increase in price. The network demonstrates more accurate forecasting and an improvement in the signal to noise ratio over a number of benchmarked neural networks.     

Mild sulfonated polyether ketone ether ketone ketone incorporated polysulfone membranes for microbial fuel cell application

To address the impediments of low power generation of Nafion, which is the main hurdle in the commercialization of microbial fuel cells (MFC), the current study focuses on developing a new PEM for MFC from mild sulfonation of PEKEKK with relatively improved physiochemical properties. In this study, mild post sulfonation of a polyether ketone ether ketone ketone (PEKEKK) has been successfully achieved using 98% H2SO4 at 90°C under reflux. 5%–30% (wt%) of sulfonated PEKEKK (SPEKEKK) loaded polysulfone (PSU) composite membranes were fabricated via a solution casting method. Ingeminating evidence of the sulfonation and structure of sulfonated polymer was proved by ¹H NMR peaks integration data and FTIR, respectively. The addition of SPEKEKK to PSU showed significant improvement in conductivity owing to the availability of more protonated sites ( SO3H) and water mediated pathways for the conduction of protons. The composite membrane containing 30 wt% SPEKEKK exhibits the highest conductivity of 0.12 S/cm at 90°C. The water uptakes and swelling ratio of the composite membranes are all higher than that of the pristine PSU membrane and show an increasing trend with increasing SPEKEKK content, thus validating the availability of water domains. Meanwhile, the lowest initial decomposition temperatures assigned to sulfonic acid groups and main chain degradation of the polysulfone/polyether ketone ether ketone ketone (PSU/SPEKEKK) composite membranes occurred at ~300°C and ~500°C respectively, which reflects an excellent thermal stability property. The experimental results indicate that the PSU/SPEKEKK membrane has the potential to greatly enhance the efficiency of MFCs.     

Development of high strength,porous mullite ceramic hollow fiber membrane for treatment of oily wastewater

Ceramic hollow fiber membranes (CHFMs) are known for their excellent characteristics including high surface area, compact design, and good chemical, thermal, and mechanical stabilities. Despite these interesting attributes, CHFMs are also prone to certain limitations, such as brittleness and high cost that hinder them from being commercialized. To mitigate this drawback, we have developed a high strength, porous ceramic hollow fiber membrane, derived from mullite–kaolinite powder, for efficient oil–wastewater separation. The superhydrophilic, low-cost mullite-based (CHFM) was successfully fabricated through combined phase inversion and sintering techniques. Prior to the fabrication, the as-received mullite–kaolinite was characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and Brunauer–Emmett–Teller (BET) analyses. Subsequently, operational parameters such as the effect of mullite content, sintering temperature, and air gap were optimized during the fabrication of mullite ceramic hollow fiber membrane. The resulting membranes were systematically characterized and evaluated in terms of morphology, porosity, mechanical strength, water flux, and oil–water separation. Increasing the mullite content, air gap, and sintering temperature enhanced the formation of microvoid structure. It is interesting to note that the mechanical strength of 86 MPa was obtained for the membrane containing 60 wt % of mullite sintered at 1450 °C and an air gap of 5 cm. The membrane induced a stable permeate water flux and oil rejection of mullite CHFM of 182 L/m²?h and 97.1%, respectively. As compared to kaolin ceramic counterparts, this porous mullite ceramic hollow fiber membrane can be used in various water treatment applications, including for the separation of oily wastewater due to its mechanical strength and water flux.