Effect of chemical modification on FTIR spectra. I. Physical and chemical behavior of coir

Coir fibers were chemically modified through alkali treatment, and crosslinking with formaldehyde, para-phenylene diamine, phthalic anhydride, and combined crosslinking-cyanoethylation reactions in appropriate solvent and catalyst. The parent and chemically modified coir were characterized by FTIR spectra. The percent moisture regain, tensile strength, and behavior toward some chemical reagents (solubility %) of parent and chemically modified fibers have also been evaluated. The modified fibers showed significant hydrophobicity, improved tensile strength, and good chemical resistance. © 1995 John Wiley & Sons, Inc.     

Synthesis and characterization of solid-phase super acid catalysts and their application for isomerization of n-alkanes

In the present work, first, the reference catalyst super acidic nanostructured sulfated zirconia (SZ) and super acidic nanostructured aluminum chloride impregnated sulfated zirconium oxides in mole ratios of Zr⁴⁺:Al³⁺ as 2:1 (ACSZ-1), 1:1 (ACSZ-2), and 1:2 (ACSZ-3) were synthesized by a simple precipitation method. The catalytic performance of these four catalysts were evaluated during the isomerization of n-hexane, n-heptane, and n-octane to their corresponding branched chain isomers at low temperature and pressure conditions. ACSZ-2 shows high activity toward isomerization of n-hexane, n-heptane, and n-octane into their corresponding branched chain isomers. The reference catalyst SZ was proved to be less effective compare to the other three synthesized ACSZ catalysts. Ammonia-temperature-programmed desorption of these two materials ensures that the super acidity of ACSZ-2 is higher than that of SZ. Atomic force microscopic and scanning electron microscopic pictures predict the nature of the surface of the catalysts. Transmission electron micrographic analysis indicates the presence of particle-bulks having average size 12–20?nm, presenting an amorphous nature and having no definite surface morphology of ACSZ-2. Fourier transform infrared provides an outline regarding different linkages and bond connectivities between atoms and groups in ACSZ-2 and SZ. After catalyst evaluation and characterization a probable reaction mechanism has been proposed theoretically. The reactivity and selectivity of ACSZ-2 and SZ as well as the order and activation energy of the isomerization reactions in presence of ACSZ-2 have been calculated. The use of ACSZ-2 is beneficial from the point of cost efficiency as well as its use is energy saving.     

Multi-objective evolutionary algorithm for SSSC-based controller design

In this paper, an evolutionary multi-objective optimization approach is employed to design a static synchronous series compensator (SSSC)-based controller. The design objective is to improve the transient performance of a power system subjected to a severe disturbance by damping the multi-modal oscillations namely; local mode, inter-area mode and inter-plant mode. A genetic algorithm (GA)-based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented and compared with a PI controller under various disturbances namely; three-phase fault, line outage, loss of load and unbalanced faults to show the effectiveness and robustness of the proposed approach.     

Computer-aided design of chiral ligands. Part I. Database search methods to identify chiral ligand types for asymmetric reactions

The utility of database searching to identify chiral ligand motifs is outlined. The key elements of three known chiral ligands have been described as bond vectors. The CAVEAT program was then used to screen the Cambridge Structural Database (CSD), portions of the Chemical Abstracts Services three-dimensional database (CAS-3D), and the TRIAD tricyclic structure database for scaffolds containing these elements. Scaffolds corresponding to the known starting points were identified indicating that this method can be used to identify chiral ligand structural motifs. In addition, alternate structural motifs were found that suggested alternative possible ligands.     

Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models

Multi-objective clustering algorithms are preferred over its conventional single objective counterparts as they incorporate additional knowledge on properties of data in the from of objectives to extract the underlying clusters present in many datasets. Researchers have recently proposed some standardized multi-objective evolutionary clustering algorithms based on genetic operations, particle swarm optimization, clonal selection principles, differential evolution and simulated annealing, etc. In many cases it is observed that hybrid evolutionary algorithms provide improved performance compared to that of individual algorithm. In this paper an automatic clustering algorithm MOIMPSO (Multi-objective Immunized Particle Swarm Optimization) is proposed, which is based on a recently developed hybrid evolutionary algorithm Immunized PSO. The proposed algorithm provides suitable Pareto optimal archive for unsupervised problems by automatically evolving the cluster centers and simultaneously optimizing two objective functions. In addition the algorithm provides a single best solution from the Pareto optimal archive which mostly satisfy the users' requirement. Rigorous simulation studies on 11 benchmark datasets demonstrate the superior performance of the proposed algorithm compared to that of the standardized automatic clustering algorithms such as MOCK, MOPSO and MOCLONAL. An interesting application of the proposed algorithm has also been demonstrated to classify the normal and aggressive actions of 3D human models.     

Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach

Early detection and diagnosis of faults in industrial machines would reduce the maintenance cost and also increase the overall equipment effectiveness by increasing the availability of the machinery systems. In this paper, a semi-nonparametric approach based on hidden Markov model is introduced for fault detection and diagnosis in synchronous motors. In this approach, after training the hidden Markov model classifiers (parametric stage), two matrices named probabilistic transition frequency profile and average probabilistic emission are computed based on the hidden Markov models for each signature (nonparametric stage) using probabilistic inference. These matrices are later used in forming a similarity scoring function, which is the basis of the classification in this approach. Moreover, a preprocessing method, named squeezing and stretching is proposed which rectifies the difficulty of dealing with various operating speeds in the classification process. Finally, the experimental results are provided and compared. Further investigations are carried out, providing sensitivity analysis on the length of signatures, the number of hidden state values, as well as statistical performance evaluation and comparison with conventional hidden Markov model-based fault diagnosis approach. Results indicate that implementation of the proposed preprocessing, which unifies the signatures from various operating speeds, increases the classification accuracy by nearly 21% and moreover utilization of the proposed semi-nonparametric approach improves the accuracy further by nearly 6%.     

FSE2R: An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocol in USN

The 3D Underwater Sensor Network (USNs) has become the most optimistic medium for tracking and monitoring underwater environment. Energy and collision are two most critical factors in USNs for both sparse and dense regions. Due to harsh ocean environment, it is a challenge to design a reliable energy efficient with collision free protocol. Diversity in link qualities may cause collision and frequent communication lead to energy loss; that effects the network performance. To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing (FSE2R) is proposed. Our proposal’s key idea is based on computation of node distance from the sink, Residual Energy (RE) of each node and Signal to Interference Noise Ratio (SINR). The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision. The novel proposal compares with existing protocols like H2AB, DEEP, and E2LR to achieve Quality of Service (QoS) in terms of throughput, packet delivery ratio and energy consumption. The comparative analysis shows that FSE2R gives on an average 30% less energy consumption, 24.62% better PDR and 48.31% less end-to-end delay compared to other protocols.     

Fabrication of Food Grade Vitamin E Nanoemulsion by Low Energy Approach,Characterization and Its Application

The present study was carried out to fabricate the food grade vitamin E acetate nanoemulsion using edible mustard oil and to evaluate its improved bioactivities. A food-grade vitamin E acetate nanoemulsion was fabricated using the edible mustard oil and surfactant Tween-80. Flocculation was not observed for 15 days. The nanoemulsion was characterized for droplet morphology and size distribution using atomic force microscope and zetasizer, respectively. We observe a stable nanoemulsion of spherical morphology and a size distribution of 86.45 ± 3.61 nm. Further, the high-performance liquid chromatography method was used to determine the vitamin E acetate concentration and encapsulation efficiency for the stable nanoemulsion. These nanoemulsions showed improved bioactivity, antioxidant, and antimicrobial activity and could be potentially used to increase the shelf life of fruit juice.     

Development and performance evaluation of an improved complex valued radar pulse compressor

Pulse compression is an important and burning issue in radar signal processing. In the recent past, many adaptive and neural network based methods have been proposed to achieve effective pulse compression performance for real coded transmitted waveforms. Even though the radar signal is complex, it is mostly processed as real-valued in-phase and quadrature components. Hence it is desirable that for processing complex radar signal for pulse compression both the structure as well as the learning algorithm associated with it need to be complex in nature. Accordingly in this paper a novel adaptive method is proposed by employing a complex valued fully connected cascaded (CFCC) neural network. For training this network, a new complex Levenberg–Marquardt (CLM) algorithm is derived and used for imparting effective training of its weights. The new CLM based CFCC (CFCC-CLM) model offers superior convergence performance with the least residual mean squared error during training phase compared to those provided by the multilayer perceptron (MLP) trained with complex domain backpropagation (CDBP) and CLM based methods. Further the comparison of peak signal-to-sidelobe ratio (PSR) under noisy and Doppler shift conditions of the proposed method exhibits best performance compared to those offered by the MLP-CDBP, MLP-CLM and the matched filter (MF) based methods.