XHQE: A hybrid system for scalable selectivity estimation of XML queries

With the increasing popularity of XML applications in enterprise and big data systems, the use of efficient query optimizers is becoming very essential. The performance of an XML query optimizer depends heavily on the query selectivity estimators it uses to find the best possible query execution plan. In this work, we propose a novel selectivity estimator which is a hybrid of structural synopsis and statistics, called XHQE. The structural synopsis enhances the accuracy of estimation and the structural statistics makes it scalable to the allocated memory space. The structural synopsis is generated by labeling the nodes of the source XML dataset using a fingerprint function and merging subtrees with similar fingerprints (i.e. having similar structures). The generated structural synopsis and structural statistics are then used to estimate the selectivity of given queries. We studied the performance of the proposed approach using different types of queries and four benchmark datasets with different structural characteristics. We compared XHQE with existing algorithms such as Sampling, TreeSketch and one histogram-based algorithm. The experimental results showed that the XHQE is significantly better than other algorithms in terms of estimation accuracy and scalability for semi-uniform datasets. For non-uniform datasets, the proposed algorithm has comparable estimation accuracy to TreeSketch as the allocated memory size is highly reduced, yet the estimation data generation time of the proposed approach is much lower (e.g., TreeSketch took more than 50 times longer than that of the proposed approach for XMark dataset). Comparing to the histogram-based algorithm, our approach supports regular twig quires in addition to having higher accuracy when both run under similar memory constraints.     

Connected dominating set construction in cognitive radio networks

Cognitive radio networks (CRNs) are drawing more and more attention along with the increasingly scarce spectrum resource. A CRN can be easily invalid due to stochastic activities of primary users. How to sustain the connectivity of CRNs and prolong the lifetime of CRNs become challenging issues. Inspired by the success of constructing a connected dominating set (CDS) as a virtual backbone in traditional wireless networks to prolong the lifetime of the network, we study the CDS construction in CRNs in this paper. We propose a three-phase centralized algorithm and a distributed algorithm. Theoretical analysis shows that our algorithms have better performance than that of existing results.     

Pseudo Almost Automorphic Solutions of Recurrent Neural Networks with Time-Varying Coefficients and Mixed Delays

In this paper, existence, uniqueness and global exponential stability of pseudo almost automorphic solutions for a class of recurrent neural networks with time-varying coefficients and mixed delays are established by employing the fixed point theorem and differential inequality. Numerical example with graphical illustration is given to illuminate our main results.     

Stabilizing periodic orbits of a class of mechanical systems with impulse effects: A Lyapunov constraint approach

This paper study the stabilization of mechanical system with impulse effects around a hybrid limit cycle, the proposed control approach is based on LaSalle’s invariance principle for hybrid systems and Layounov constraint based method. Theorem 2 shows necessary and sufficient condition of the existence and the uniqueness of the developed controller which leads to a system of partial differential equations (PDE) whose solutions are the kinetic and potential energy of smooth Lyapunov function, furthermore Theorem 3 gave an alternative existence condition which states that the largest positively invariant set should be nowhere dense and closed and it is none other than the hybrid limit cycle itself.

     

Adaptive green traffic signal controlling using vehicular communication

The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today’s metropolitan life cannot be overemphasized. The vehicular ad hoc network (VANET), as an integral component of intelligent transportation systems (ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems (TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses: (1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and (2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service (QoS). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is used. The V2V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network QoS criteria.     

Intelligent cooperation management of multi-radio access technology towards the green cellular networks for the twenty-twenty information society

An unprecedented increase in subscribers and demand for high-speed data are considered a critical step towards the new era of mobile wireless networks, i.e., Fifth Generation (5G), where the legacy mobile communication system will still be operational for a long time in the future. This has subsequently increased the overall energy consumption, operational costs and carbon footprint of cellular networks, due to increase the number of base stations (BSs), which consume the most energy. Switching BSs off/on in accordance with the traffic pattern variations is considered an effective method for improving energy efficiency. However, the main concerns from the network operators are the requirements to switched on/off the BSs, coverage issues and secured the radio service for the affected area. Hence, the main focus of this study is to develop an intelligent cooperation management (switch BSs on/off) within a multi-radio access technology (RAT) environment between a future generation 5G into the existing LTE and UMTS cellular network towards green cellular networks, while guaranteeing maximum cells coverage area during a switch off session. Particle swarm optimisation has been adopted in this study to maximize the cell coverage area under the constraints of the transmission power of the BS \((P_{tx})\), the total antenna gain (G), the bandwidth (BW), the signal-to-interference-plus-noise ratio (SINR), and shadow fading \((\sigma )\). Moreover, the modulation and coding scheme, the data rate, and the energy efficiency are considered. The results have shown that by applying the proposed a dynamic multi-RAT BSs switching off\(\backslash \)on strategy according to the traffic load variations, the daily energy savings of up to 42.3% can be achieved, with guaranteed maximum cells coverage area.     

Determination of diffusion parameters and biosorption of cadmium in aqueous solution using algae biomass

The objective behind this study is to investigate the phenomenon and kinetics of Cd(II) ion biosorption by the algae fibers, as well as the development of mathematical model that would describe the phenomenon of ions transport through porous algae. The results obtained have shown an inter-particle rate constant of diffusion K_int = 0.188 (mg·g^?1·min^?1/2), R² = 0.999, and adsorption amount at equilibrium Q_Seq = 1.87 mg·g^?1. These results indicated that the diffusion of ions existed in the porous algae. The proposed mathematical model is used to optimize the biosorption of heavy metal ions by connecting the model parameters with the fiber performances.     

Fabrication of cation exchange membrane from polyvinyl alcohol using lignin sulfonic acid: Applications in diffusion dialysis process for alkali recovery

We report polyvinyl alcohol (PVA)-based hybrid membranes composed of salt of lignin sulfonic acid (LSA) and tetraethyl orthosilicate. The concentration of LSA with respect to PVA varied from 10% to 40%. The hybrid membranes showed water uptake (W_U) in the range of 122–210%, ion exchange capacities in the range of 0.32–0.75 mmol g^?1, dialysis coefficient (U_OH) from 0.0068 to 0.0119 m h^?1, and selectivity (S) from 15 to 26. The hybrid membranes also showed thermal and mechanical stability.     

Relaxation dynamic and electrical mobility for poly(methyl methacrylate)‐polyaniline composites

Composites of emeraldine form of polyaniline (PAni) and poly(methyl methacrylate) (PMMA) are prepared by emulsion polymerization method in definite ratios. The chemical structure of the samples and their morphologies have been investigated by different techniques including FTIR, UV–vis, XRD, SEM, and TGA. Enhancement in thermal stability of the obtained composites by PAni additions has been confirmed. Alpha analyzer, in frequency range 0.1 Hz to 20 MHz, was employed to investigate the molecular dynamics of the prepared samples and the accompanied electrical conductivity at temperatures ranging from 223 to 423 K. Conductivity investigations showed that mobility has the more dominant effect on the charge transportation. The dynamic peak at lower temperatures of the PMMA has high activation energy (83.8 kJ/mol). While, Maxwill‐Wagner‐Siller process due to the interfacial polarization in the composites has only 7.5 kJ/mol activation energy. This is a clear indication of the ease of the investigated dynamic. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45415.     

Maize-nutrient response information applied across Sub-Saharan Africa

The profit potential for a given investment in fertilizer use can be estimated using representative crop nutrient response functions. Where response data is scarce, determination of representative response functions can be strengthened by using results from homologous crop growing conditions. Maize (Zea mays L.) nutrient response functions were selected from the Optimization of Fertilizer Recommendations in Africa (OFRA) database of 5500 georeferenced response functions determined from field research conducted in Sub-Saharan Africa. Three methods for defining inference domains for selection of response functions were compared. Use of the OFRA Inference Tool (OFRA-IT; http://agronomy.unl.edu/OFRA) resulted in greater specificity of maize N, P, and K response functions with higher R² values indicating superiority compared with using the Harvest Choice Agroecological Zones (HC-AEZ) and the recommendation domains of the Global Yield Gap Atlas project (GYGA-RD). The OFRA-IT queries three soil properties in addition to climate-related properties while the latter two options use climate properties only. The OFRA-IT was generally insensitive to changes in criteria ranges of 20–25% used in queries suggesting value in using wider criteria ranges compared with the default for information scarce crop nutrient response functions.