Micromanipulation and cloning studies on buffalo oocytes and embryos using nucleus transfer

1. Nine male volunteers were exposed to the pyrethroid insecticide cyfluthrin. The study was performed in an exposure room, where an aerosol containing cyfluthrin was sprayed to obtain atmospheres with mean cyfluthrin concentrations of 160 and 40 micrograms/m3. Four volunteers were exposed for 10, 30 and 60 min at 160 micrograms/m3 and another five volunteers were exposed for 60 min at 40 micrograms/m3. For 160 micrograms/m3 exposure urine samples were collected before and immediately after exposure as well as for the periods 1-2, 2-3, 3-4, 4-5, 5-6, 6-12 and 12-24 h after exposure. For 40 micrograms/m3 exposure urine samples were collected before and 2 h after exposure. 2. The main urinary cyfluthrin metabolites, cis-/trans-3-(2,2-dichlorovinyl)-2,2-dimethylycyclopropane carboxylic acid (DCCA) and 4-fluoro-3-phenoxybenzoic acid (FPBA), were determined. The limit of detection (LOD) for all metabolites was 0.0025 microgram in an urine sample of 5 ml (0.5 microgram/l). After inhalative exposure of 40 micrograms cyfluthrin/m3 air for 60 min, the amount of metabolites in urine collected in the first 2 h after exposure was less than the LOD, namely 0.14 microgram for cis-DCCA, 0.15-0.28 microgram for trans-DCCA and 0.12-0.23 microgram for FPBA. 3. Of the metabolites, 93% was excreted within the first 24 h (peak excretion rates between 0.5 and 3 h) after inhalative exposure of 160 micrograms/m3. The mean half-lives were 6.9 h for cis-DCCA, 6.2 h for trans-DCCA and 5.3 h for FPBA. 4. The mean trans-:cis-DCCA ratio was 1.9 for the time course as well as for each subject. 5. The amount of metabolites in urine depends on the applied dose, on the exposure time and shows interindividual differences.     

Direction-Dependent Learning Approach for Radial Basis Function Networks

Direction-dependent scaling, shaping, and rotation of Gaussian basis functions are introduced for maximal trend sensing with minimal parameter representations for input output approximation. It is shown that shaping and rotation of the radial basis functions helps in reducing the total number of function units required to approximate any given input-output data, while improving accuracy. Several alternate formulations that enforce minimal parameterization of the most general radial basis functions are presented. A novel "directed graph" based algorithm is introduced to facilitate intelligent direction based learning and adaptation of the parameters appearing in the radial basis function network. Further, a parameter estimation algorithm is incorporated to establish starting estimates for the model parameters using multiple windows of the input-output data. The efficacy of direction-dependent shaping and rotation in function approximation is evaluated by modifying the minimal resource allocating network and considering different test examples. The examples are drawn from recent literature to benchmark the new algorithm versus existing methods     

Differential pixel-based low-power and high-speed implementation of DCT for on-board satellite image processing

Low-power and high-speed discrete cosine transform (DCT) implementation of the images captured by the satellites presents a hardware design problem. The cost of the DCT implementation is dominated by the complexity of the multiplication of input data (image) with the DCT matrix. The techniques for minimising the complexity of multiplication by employing a differential pixel method are presented. In the proposed method 8times8 blocks of input image matrix are considered, the difference between the adjacent pixels is calculated and those differential pixels are used in DCT transformation. Synthesis results on 0.18 mum CMOS technology show that the proposed method gives an average of 13.2% reduction in power consumption and 10.9% improvement in speed over the conventional method. The proposed method can also be combined with the common subexpression elimination method for further reduction.     

Viscometric studies of starch-g-polyacrylamide composites

Composites of starch-g-polyacrylamide, starch, and polyacrylamide have been prepared by gamma radiation-initiated polymerization of acrylamide in the presence of starch in aqueous medium. The polymerization was studied as a function of the (i) amount of water (ii) monomer concentration, and (iii) total dose. The optimum conditions for maximum conversion of the monomer to homopolymer and graft copolymer have been evaluated. Percentage of grafting of polyacrylamide could not be determined precisely as both the homopolymer and the graft are soluble in common solvents and all attempts to separate the graft from the homopolymer were unsuccessful. However, the formation of the starch-g-polyacrylamide copolymer was confirmed by the turbidimetric studies using acetone as a nonsolvent. The products of polymerization of acrylamide in the presence of starch consisted of unreacted starch, starch-g-polyacrylamide, and polyacrylamide and is referred to as the composite. Acrylamide was also polymerized in the absence of starch using γ-rays as means of initiation and the optimum conditions for maximum conversion of acrylamide to polyacrylamide have been evaluated. Viscosity behavior of the composite and polyacrylamide was studied in aqueous medium at 30±0.04°C. The reduced specific viscosity of the aqueous solution of acrylamide and the composite as well was found to increase with increasing dilution, the effect being more pronounced in the composite. This tends to indicate that both the homopolymer and the composite behave as polyelectrolytes. An attempt has been made to explain the polyelectrolytic behavior of the homopolymer and the composite. © 1993 John Wiley & Sons, Inc.     

Uncertainty propagation in puff-based dispersion models using polynomial chaos

Atmospheric dispersion is a complex nonlinear physical process with numerous uncertainties in model parameters, inputs, source parameters, initial and boundary conditions. Accurate propagation of these uncertainties through the dispersion models is crucial for a reliable prediction of the probability distribution of the states and assessment of risk. A simple three-dimensional Gaussian puff-based dispersion model is used as a test case to study the effect of uncertainties in the model parameters and initial conditions on the output concentration. A polynomial chaos based approach is used to numerically investigate the evolution of the model output uncertainties due to initial condition and parametric uncertainties. The polynomial chaos solution is found to be an accurate approximation to ground truth, established by Monte Carlo simulation, while offering an efficient computational approach for large nonlinear systems with a relatively small number of uncertainties.     

Analysis and characterization of comparison shopping behavior in the mobile handset domain

In this work we characterize the session-level behavior of users on an Indian mobile phone comparison shopping website. We also correlate the popularity of handset on various news sources to its popularity on the shopping website. There are three aspects to our study: data analysis, correlation between news sources of product information and popularity of a handset, and behavior prediction. We have used KL divergence to show that a time-homogeneous Markov chain is observed when the number of clicks varies from 5 to 30. Our results depict that Markov chain model does not hold in entirety for comparison shopping setting but tells us how far the Markov chain model holds for this setting. Our analysis corroborates intuition that increasing price leads to decrease in popularity. After the strong correlation between various variables and user behavior was found, we predict the users macro (the overall sales of handset) and micro behavior (whether a user will convert or exit the site) using Markov logic networks. Our predictive model validates the intuition that past browsing behavior is an important predictor for future behavior. Methodology of combining data analysis with machine learning is, in our opinion, a new approach to the empirical study of such data sets.     

A fast automatic optimal threshold selection technique for image segmentation

In this article, a fast context-sensitive threshold selection technique is presented to solve the image segmentation problems. In lieu of histogram, the proposed technique employs recently defined energy curve of the image. First, the initial thresholds are selected in the middle of two consecutive peaks on the energy curve. Then based on the cluster validity measure, the optimal number of potential thresholds and the bounds where the optimal value of each potential threshold may exist are determined. Finally, genetic algorithm (GA) is employed to detect the optimal value of each potential threshold from their respective defined bounds. The proposed technique incorporates spatial contextual information of the image in threshold selection process without loosing the benefits of histogram-based techniques. Computationally it is very efficient. Moreover, it is able to determine the optimal number of segments in the input image. To assess the effectiveness of the proposed technique, the results obtained are compared with four state-of-the-art methods cited in the literature. Experimental results on large number of images confirmed the effectiveness of the proposed technique.     

Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures

Health monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous inspection robots, which can replace current manual inspections, are examples of edge devices. Incorporation of pretrained deep learning algorithms into these robots for autonomous damage detection is a challenging problem since these devices are typically limited in computing and memory resources. This study introduces a solution based on network pruning using Taylor expansion to utilize pretrained deep convolutional neural networks for efficient edge computing and incorporation into inspection robots. Results from comprehensive experiments on two pretrained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in detail with respect to performance, memory demands, and the inference time for damage detection. It is shown that the proposed approach significantly enhances resource efficiency without decreasing damage detection performance.     

A variational approach to path planning for hyper-redundant manipulators

Motion planning for hyper-redundant manipulators in a complicated and cluttered workspace is a challenging problem. Many of the path planning algorithms, based on cell decomposition or potential field, fail due to the high dimensionality and complex nature of the C-space. Probabilistic roadmap methods (PRM) which have been proven to be successful in high dimensional C-spaces suffer from the drawback of generating paths which involve a lot of redundant motion. In this paper, we propose a path optimizing method to improve a given path in terms of path length and the safety against the collisions, using a variational approach. The capability of variational calculus to optimize a path is demonstrated on a variety of examples. The approach succeeds in providing a good quality path even in high dimensional C-spaces.