Quantum chemical computational studies on bis-thiourea zinc acetate

In this study, quantum chemical calculations of vibrational spectra, Raman spectra, electronic properties (total energy, dipole moment, electronegativity, chemical hardness and softness), Mulliken atomic charges and thermodynamic parameters of bis-thiourea zinc acetate (BTZA) have been performed using Gaussian 09 program. Additionally, nonlinear optical (NLO), conformational, natural bond orbital (NBO) analyses of BTZA have been carried out using the same program. The structural and spectroscopic data of the molecule in the ground state have been calculated using Hartree-Fock (HF) and density functional method (DFT/B3LYP) with the 6-311++G(d,p) basis set. In addition, the molecular frontier orbital energies (HOMO, HOMO-1, LUMO and LUMO+1) of the title compound have been calculated at the HF and B3LYP levels. The calculated HOMO and LUMO energies show that charge transfer occurs within the molecule. Finally, the calculated results were applied to simulate infrared and Raman spectra of the title compound which showed good agreement with the experimental ones.     

Gamma random waypoint mobility model for wireless ad hoc networks

Random waypoint (RWP) mobility model is widely used in ad hoc network simulation. The model suffers from speed decay as the simulation progresses and may not reach the steady state in terms of instantaneous average node speed. Furthermore, the convergence of the average speed to its steady state value is delayed. This usually leads to inaccurate results in protocol validation of mobile ad hoc networks modeling. Moreover, the probability distributions of speed vary over the simulation time, such that the node speed distribution at the initial state is different from the corresponding distribution at the end of the simulation. In order to overcome these problems, this paper proposes a modified RWP mobility model with a more precise distribution of the nodes' speed. In the modified model, the speeds of nodes are sampled from gamma distribution. The results obtained from both analysis and simulation experiments of the average speed and the density of nodes' speed indicate that the proposed gamma random waypoint mobility model outperforms the existing RWP mobility models. It is shown that a significant performance improvement in achieving higher steady state speed values that closely model the pre‐assumed average speeds are possible with the proposed model. Additionally, the model allows faster convergence to the steady state, and probability distribution of speed is steady over the simulation time. Copyright © 2012 John Wiley & Sons, Ltd.     

Developing an ecologically friendly isothermal bath to obtain a new class high-tenacity and high-modulus polypropylene fibers

A number of production methods have been developed for high-performance fibers; however, most processes use toxic solvents or generate a lot of unwanted by-products. Our research resulted in the development of a new family of high-performance polypropylene (PP) fibers by utilizing a simple, ecologically friendly bath (ECOB). Various commodity polymers can be used with ECOB melt spinning system at high throughputs and performance benefits. Our treated as-spun PP fibers had a highly oriented, but not crystalline precursor morphology with f _a up to 0.6 generating superior mechanical properties. After drawing at draw ratios of 1.49 at 120 °C, highly oriented crystalline and amorphous phases were achieved for the drawn fibers with f _c and f _a values of 0.95 and 0.87, respectively. This fine structure for ECOB-treated fibers resulted in tenacity close to 12 g/d, initial modulus higher than 150 g/d, and ultimate elongation at break of 20 %. The polymer melting point of the new fibrillar PP fibers increased by 9 °C.     

A problem space algorithm for single machine weighted tardiness problems

We propose a problem space genetic algorithm to solve single machine total weighted tardiness scheduling problems. The proposed algorithm utilizes global and time-dependent local dominance rules to improve the neighborhood structure of the search space. They are also a powerful exploitation (intensifying) tool since the global optimum is one of the local optimum solutions. Furthermore, the problem space search method significantly enhances the exploration (diversification) capability of the genetic algorithm. In summary, we can improve both solution quality and robustness over the other local search algorithms reported in the literature.     

A new automatic target recognition system based on wavelet extreme learning machine

In this paper, an automatic system is presented for target recognition using target echo signals of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms by using X-band pulse radar. The past studies in the field of radar target recognition have shown that the learning speed of feedforward neural networks is in general much slower than required and it has been a major disadvantage. There are two key reasons forth is status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms (Feng et al., 2009, Huang and Siew, 2004, Huang and Chen, 2007, Huang and Chen, 2008, Huang et al., 2006, Huang et al., 2010, Huang et al., 2004, Huang et al., 2005, Huang et al., 2012, Huang et al., 2008, Huang and Siew, 2005, Huang et al., 2011, Huang et al., 2006, Huang et al., 2006a, Huang et al., 2006b, Lan et al., 2009, Li et al., 2005, Liang et al., 2006, Liang et al., 2006, Rong et al., 2009, Wang and Huang, 2005, Wang et al., 2011, Yeu et al., 2006, Zhang et al., 2007, Zhu et al., 2005). To resolve these disadvantages of feedforward neural networks for automatic target recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) (Feng et al., 2009, Huang and Siew, 2004, Huang and Chen, 2007, Huang and Chen, 2008, Huang et al., 2006, Huang et al., 2010, Huang et al., 2004, Huang et al., 2005, Huang et al., 2012, Huang et al., 2008, Huang and Siew, 2005, Huang et al., 2011, Huang et al., 2006, Huang et al., 2006a, Huang et al., 2006b, Lan et al., 2009, Li et al., 2005, Liang et al., 2006, Liang et al., 2006, Rong et al., 2009, Wang and Huang, 2005, Wang et al., 2011, Yeu et al., 2006, Zhang et al., 2007, Zhu et al., 2005) which randomly choose hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. Moreover, the Discrete Wavelet Transform (DWT) and wavelet entropy is used for adaptive feature extraction in the time-frequency domain in feature extraction stage to strengthen the premium features of the ELM in this study. The correct recognition performance of this new system is compared with feedforward neural networks. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.     

K nearest neighbor reinforced expectation maximization method

K nearest neighbor and Bayesian methods are effective methods of machine learning. Expectation maximization is an effective Bayesian classifier. In this work a data elimination approach is proposed to improve data clustering. The proposed method is based on hybridization of k nearest neighbor and expectation maximization algorithms. The k nearest neighbor algorithm is considered as the preprocessor for expectation maximization algorithm to reduce the amount of training data making it difficult to learn. The suggested method is tested on well-known machine learning data sets iris, wine, breast cancer, glass and yeast. Simulations are done in MATLAB environment and performance results are concluded.     

The State of the Art in HDR Deghosting: A Survey and Evaluation

Obtaining a high quality high dynamic range (HDR) image in the presence of camera and object movement has been a long‐standing challenge. Many methods, known as HDR deghosting algorithms, have been developed over the past ten years to undertake this challenge. Each of these algorithms approaches the deghosting problem from a different perspective, providing solutions with different degrees of complexity, solutions that range from rudimentary heuristics to advanced computer vision techniques. The proposed solutions generally differ in two ways: (1) how to detect ghost regions and (2) what to do to eliminate ghosts. Some algorithms choose to completely discard moving objects giving rise to HDR images which only contain the static regions. Some other algorithms try to find the best image to use for each dynamic region. Yet others try to register moving objects from different images in the spirit of maximizing dynamic range in dynamic regions. Furthermore, each algorithm may introduce different types of artifacts as they aim to eliminate ghosts. These artifacts may come in the form of noise, broken objects, under‐ and over‐exposed regions, and residual ghosting. Given the high volume of studies conducted in this field over the recent years, a comprehensive survey of the state of the art is required. Thus, the first goal of this paper is to provide this survey. Secondly, the large number of algorithms brings about the need to classify them. Thus the second goal of this paper is to propose a taxonomy of deghosting algorithms which can be used to group existing and future algorithms into meaningful classes. Thirdly, the existence of a large number of algorithms brings about the need to evaluate their effectiveness, as each new algorithm claims to outperform its precedents. Therefore, the last goal of this paper is to share the results of a subjective experiment which aims to evaluate various state‐of‐the‐art deghosting algorithms.     

An Efficient SMO Algorithm for Solving Non-smooth Problem Arising in $$varepsilon $$ ε -Insensitive Support Vector Regression

Neural Processing Letters - Classical support vector regression (C-SVR) is a powerful function approximation method, which is robust against noise and performs a good generalization, since it is...     

Synthesis,characterization, and voltammetric hydrogen peroxide sensing on novel monometallic (Ag,Co/MWCNT) and bimetallic (AgCo/MWCNT) alloy nanoparticles

Multiwall carbon nanotube supported (MWCNT) Ag, Co, and Ag-Co alloy nanocatalysts were synthesized at varying metal loadings by borohydride reduction methods without stabilizers to obtain enhanced hydrogen peroxide sensitivity. The resulting materials were characterized employing Scanning electron microscopy (SEM), Energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). For electrochemical measurements carried out cyclic voltammetry (CV) and differential pulse voltammetry (DPV), glassy carbon electrode (GCE) was modified with Ag/MWCNT, Co/MWCNT, and Ag-Co/MWCNT alloy nanoparticles. Ag-Co/MWCNT/GCE exhibited the highest performance toward electrochemical oxidation of H₂O₂ in 0.1 M phosphate buffered solution (PBS). Furthermore, the sensitivity and the limit of detection values for Ag-Co/MWCNT/GCE were obtained as 57.14 µA cm^?2 mM^?1and 0.74 µM, respectively. However, the sensitivity values for Ag/MWCNT/GCE, and Co/MWCNT/GCE are 41.66 and 13.88 µA cm^?2 mM^?1, respectively. The LOD values were predicted as 1.84 µM for Ag/MWCNT/GCE and 3.3 µM for Co/MWCNT/GCE.

In addition, the interference experiment indicated that the Ag-Co/MWCNT alloy nanoparticles have good selectivity toward H₂O₂.