Thermal, Spectroscopic, Cyclic Voltammetric, and Biological Activity Studies of Cobalt(II), Nickel(II), and Copper(II) Complexes of Dicarboxylic Amino Acids and 8-Hydroxyquinoline

Mixed ligand complexes of Co^II, Ni^II and Cu^II with dicarboxylic amino acids (aspartic, glutamic or H₂ADA) as primary ligands and 8-hydroxyquinoline as a secondary ligand were prepared and characterized by elemental analysis, conductivity measurements, thermal analysis, cyclic voltammetry, and electronic and infrared (IR) spectroscopy. Cyclic voltammetry reveals a reversibility of the Cu^II/Cu^I couple, while the reactions of the Co^II and Ni^II complexes are irreversible. All complexes have a metal-to-ligand ratios of 1:1:1 and octahedral structures are suggested to be attained by interactions among both of the amino acids and 8-hydroxyquinoline anions with the metal ions. The reaction orders and activation energies have been computed by means of the Coats–Redfern and Horowitz–Metzger equations. The biological activity of selected complexes as anti-fungal agents has been tested.     

Phenomena Detection in Mobile Wireless Sensor Networks

Environmental phenomena, such as fires, poisonous gases, and oil spills, can be detected by wireless sensor networks (WSNs) that cover the geographical area of the phenomena. These sensors collaboratively monitor the area to detect the sensors’ readings that deviate from normal reading patterns after which a phenomena is declared. This research proposes a distributed algorithm to detect dynamic phenomena using mobile WSNs under the assumption that there is no centralized server to collect and aggregate the sensors data. Therefore, the sensors self-organize into disjoint groups by first electing a few sensors to be group heads (GHs) and then the rest of the sensors group themselves with the nearest GH. Each group of sensors detect phenomena locally. Then, the GHs communicate the detected local phenomena information among themselves to aggregate the information and detect the global phenomena. Moreover, the paper proposes two GH election algorithms, namely the Last Group Head election algorithm and the Distributed Group Head election algorithm. The experimental results show that the proposed election algorithms reduce the energy costs of the mobile WSN by 54–66 % as compared with the straightforward election algorithm. In addition, this paper proposes an optimization technique to further reduce the energy costs of reporting the global phenomena to about 33 % by reducing the size of the reported phenomena information. The proposed algorithms are validated through a comprehensive set of experiments conducted using the NS2 network simulator.     

An adaptive memetic algorithm for feature selection using proximity graphs

We propose a multivariate feature selection method that uses proximity graphs for assessing the quality of feature subsets. Initially, a complete graph is built, where nodes are the samples, and edge weights are calculated considering only the selected features. Next, a proximity graph is constructed on the basis of these weights and different fitness functions, calculated over the proximity graph, to evaluate the quality of the selected feature set. We propose an iterative methodology on the basis of a memetic algorithm for exploring the space of possible feature subsets aimed at maximizing a quality score. We designed multiple local search strategies, and we used an adaptive strategy for automatic balancing between the global and local search components of the memetic algorithm. The computational experiments were carried out using four well‐known data sets. We investigate the suitability of three different proximity graphs (minimum spanning tree, k‐nearest neighbors, and relative neighborhood graph) for the proposed approach. The selected features have been evaluated using a total of 49 classification methods from an open‐source data mining and machine learning package (WEKA). The computational results show that the proposed adaptive memetic algorithm can perform better than traditional genetic algorithms in finding more useful feature sets. Finally, we establish the competitiveness of our approach by comparing it with other well‐known feature selection methods.     

What Is the Potential of Integrating Phase Space Reconstruction with SVM-FFA Data-Intelligence Model? Application of Rainfall Forecasting over Regional Scale

Rainfall modeling is one of the major component process in the meteorological engineering. Hence, exploring an advance and reliable intelligent model for its forecasting is essential for water resources engineering. In this current research, novel integrative intelligence model coupled with phase space reconstruction is proposed to forecast monthly rainfall in Chhattisgarh State, India. The proposed model is a hybridization of support vector machine (SVM) model with firefly optimization algorithm (FFA). The modeling is undertaken based on three stages starting with configuring the delay time and embedding dimension using mutual information and false nearest neighbors to determine the input matrix of the forecasting model. In the second stage, the firefly optimizer is employed to tune the SVM model. Finally, the hybrid model is conducted to forecast the monthly time scale rainfall time series. Monthly time scale rainfall data for sixteen raingauge stations over a century (1901–2002) are utilized and tested. A validation of the capacity of the suggested model is carried out by comparing the accuracy results with classical SVM and hybrid SVM-FFA “without mutual information analysis” models. The three predictive models are trained using 75% of available data set and tested the remaining 25% dataset. The model’s results were statistically verified using mean absolute error and best-good-fitness measurements in addition to Taylor diagram visualization. In conclusion, the proposed model was significantly improved the forecasting accuracy of the modeling. Also, it was exhibited a very robust intelligent model that can be applied for the Indian regional zone for monthly rainfall forecasting.     

Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach

Water Resources Management - Accurate water level forecasting is important to understand and provide an early warning of flood risk and discharge. It is also crucial for many plants and animal...     

Separation of a binary mixture of ethylene and ethane by adsorption on Na-ETS-10

Ethylene produced by steam cracking and thermal decomposition of ethane must be purified prior to use in the production of plastics, rubber and films. Ethane–ethylene separation is generally achieved by cryodistillation. The energy and equipment costs associated with ethylene purification could be significantly reduced by the development of alternative separation methods. Both modelling predictions for the binary adsorption of ethylene and ethane, and recent work investigating the adsorption of ethane and ethylene on the surface of Engelhard Titanosilicate-10 (ETS-10), a large-pored, mixed octahedral/tetrahedral titanium silicate molecular sieve, indicate that this molecular sieve might be an adsorbent capable of ethylene/ethane separations. However, the actual separation of a binary mixture of ethylene and ethane on ETS-10 has yet to be demonstrated. In this work, Na-ETS-10 was used to separate a mixture of ethylene and ethane (59% C₂H₄, 41% C₂H₆) from an industrial process stream with a measured binary bed selectivity for ethylene over ethane of approximately 5 at 25 °C and 1 atm. In a laboratory-scale demonstration, both high purity ethane and significantly enriched ethylene were produced, and the Na-ETS-10 adsorbent was regenerated for further separation cycles by both steam and microwave desorption, without degradation of the adsorbent or products.     

Novel heterocyclic synthesis and investigation on radiation‐grafted low‐density polyethylene with 2‐N‐vinylpyrrolidone

Radiation‐induced graft polymerization of low‐density polyethylene with N‐vinylpyrrolidone, LDPE‐g‐PNVP, was used as a starting material for the synthesis of polyfunctionally substituted heterocyclic products. Thus, LDPE‐g‐PNVP reacts with ylidenemalononitrile derivatives to give the Michael addition products. In multistep reaction, LDPE‐g‐PNVP reacts with N,N‐dimethylformamide dimethyl acetal (DMFDMA), hydrazine hydrate and malononitrile, respectively, to give a hydropyrrolopyridazine derivative. The latter could also be prepared via the reaction of LDPE‐g‐PNVP with DMFDMA, followed by treating with cyanoacetohydrazide. Also, LDPE‐g‐PNVP reacts with malononitrile to give an adduct product, dimer malononitrile derivative 13. The latter reacts with sulfur element to afford the thiophene derivative. Furthermore, this adduct reacts with hydrazine hydrate to isolate the original starting material, LDPE‐g‐PNVP, and aminopyridine derivative. The resulted films were characterized by infrared (IR) spectroscopy, ¹H nuclear magnetic resonance (¹H‐NMR) mass spectroscopy, elemental analysis, swelling behavior, and electron scanning microscope. © 1999 John Wiley & Sons, Inc. J Appl Polym Sci 74: 2963–2970, 1999     

Correction to: Fractional strain energy and its application to the free vibration analysis of a plate

Microsystem Technologies - In the original article, one of the co-author’s (Ghader Rezazadeh) surname has been published incorrectly. The correct surname should be Rezazadeh.     

NodeRank: Finding influential nodes in social networks based on interests

The Journal of Supercomputing - Finding influential members in social networks received a lot of interest in recent literature. Several algorithms have been proposed that provide techniques for...