Effect of various steps of purification on ash content and ash constitution of cellulose. I. Cellulose prepared from cotton linters

The effect of the various steps of purification on the ash content and ash constitution of cellulose prepared from cotton linters has been examined. After each step of purification, samples were analysed for total ash, SiO₂, Ca, Mg, Fe, Al, Cu and Mn. Two methods of bleaching were investigated. The first involves double treatment of cellulose with sodium hypochlorite solution, and the second one treatment with hypochlorite followed by another treatment with sodium chlorite solution. The second method has a better effect in diminishing the ash content.     

Nonlinear modelling of f.e.t.-input operational amplifiers

A square-law model for f.e.t.-input operational amplifiers is applied, by means of describing functions, to the evaluation of slew-induced distortion in a simple active filter. The computed frequency responses are validated by measurements at large signal levels.     

Performance analysis of coated plutonia particle fuel compact for radioisotope heater units

Coated plutonia particle fuel has been proposed recently for use in radioisotope power systems and radioisotope heater units for a variety of space missions requiring power levels from milliwatts to tens or even hundreds of watts. The ²³⁸PuO₂ fuel kernels are coated with a strong layer of ZrC designed to fully retain the helium gas generated by the radioactive decay of ²³⁸Pu. A recent investigation has concluded that helium retention in large-grain (200 μm) granular and polycrystalline fuel kernels is possible even at high-temperatures (>1700 K). Results of performance analysis showed that this fuel form could increase by 2.3–2.4 times the thermal power output of a light weight radioisotope heater unit. These figures are for a single-size (500 μm) particles compact, assuming 10% and 5% helium gas release respectively, and a fuel temperature of 1723 K, following 10 years of storage. A binary-size (300 and 1200 μm) particles compact increases the thermal power output of the RHU by an additional 15%.     

Cm-Pu (Curium-Plutonium)

Supplemental Literature Review

Cm-Pu (Curium-Plutonium)     

Cost-sensitive boosting for classification of imbalanced data

Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. The significant difficulty and frequent occurrence of the class imbalance problem indicate the need for extra research efforts. The objective of this paper is to investigate meta-techniques applicable to most classifier learning algorithms, with the aim to advance the classification of imbalanced data. The AdaBoost algorithm is reported as a successful meta-technique for improving classification accuracy. The insight gained from a comprehensive analysis of the AdaBoost algorithm in terms of its advantages and shortcomings in tacking the class imbalance problem leads to the exploration of three cost-sensitive boosting algorithms, which are developed by introducing cost items into the learning framework of AdaBoost. Further analysis shows that one of the proposed algorithms tallies with the stagewise additive modelling in statistics to minimize the cost exponential loss. These boosting algorithms are also studied with respect to their weighting strategies towards different types of samples, and their effectiveness in identifying rare cases through experiments on several real world medical data sets, where the class imbalance problem prevails.     

Sentence alignment using P-NNT and GMM

Parallel corpora have become an essential resource for work in multilingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross-language information retrieval and machine translation applications. In this paper, we present two new approaches to align English–Arabic sentences in bilingual parallel corpora based on probabilistic neural network (P-NNT) and Gaussian mixture model (GMM) classifiers. A feature vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the probabilistic neural network and Gaussian mixture model. Another set of data was used for testing. Using the probabilistic neural network and Gaussian mixture model approaches, we could achieve error reduction of 27% and 50%, respectively, over the length based approach when applied on a set of parallel English–Arabic documents. In addition, the results of (P-NNT) and (GMM) outperform the results of the combined model which exploits length, punctuation and cognates in a dynamic framework. The GMM approach outperforms Melamed and Moore’s approaches too. Moreover these new approaches are valid for any languages pair and are quite flexible since the feature vector may contain more, less or different features, such as a lexical matching feature and Hanzi characters in Japanese–Chinese texts, than the ones used in the current research.     

Header Detection to Improve Multimedia Quality Over Wireless Networks

Wireless multimedia studies have revealed that forward error correction (FEC) on corrupted packets yields better bandwidth utilization and lower delay than retransmissions. To facilitate FEC-based recovery, corrupted packets should not be dropped so that maximum number of packets is relayed to a wireless receiver's FEC decoder. Previous studies proposed to mitigate wireless packet drops by a partial checksum that ignored payload errors. Such schemes require modifications to both transmitters and receivers, and incur packet-losses due to header errors. In this paper, we introduce a receiver-based scheme which uses the history of active multimedia sessions to detect transmitted values of corrupted packet headers, thereby improving wireless multimedia throughput. Header detection is posed as the decision-theoretic problem of multihypothesis detection of known parameters in noise. Performance of the proposed scheme is evaluated using trace-driven video simulations on an 802.11b local area network. We show that header detection with application layer FEC provides significant throughput and video quality improvements over the conventional UDP/IP/802.11 protocol stack     

Positron annihilation study and computational modeling of defect production in neutron-irradiated reactor pressure vessel steels

Positron annihilation spectroscopy (PAS) and a computer simulation were used to investigate a defect production in reactor pressure vessel (RPV) steels irradiated by neutrons. The RPV steels were irradiated at 250 °C in a high-flux advanced neutron application reactor. The PAS results showed that mainly single vacancies were created to a great extent as a result of a neutron irradiation. Formation of vacancies in the irradiated materials was also confirmed by a coincidence Doppler broadening measurement. For estimating the concentration of the point defects in the RPV steels, we applied computer simulation methods, including molecular dynamics (MD) simulation and point defect kinetics model calculation. MD simulations of displacement cascades in pure Fe were performed with a 4.7 keV primary knock-on atom to obtain the parameters related to displacement cascades. Then, we employed the point defect kinetics model to calculate the concentration of the point defects. By combining the positron trapping rate from the PAS measurement and the calculated vacancy concentrations, the trapping coefficient for the vacancies in the RPV steels was determined, which was about 0.97 × 10¹⁵ s⁻¹. The application of two techniques, PAS and computer simulation, provided complementary information on radiation-induced defect production.     

Thermal–hydraulic modeling of the onset of flow instability in MTR reactors

Prediction of the onset of the flow instability (OFI) in steady and transient sub-cooled flow boiling is an important consideration in the design and operation of nuclear reactors, in particular for materials testing reactors (MTR). In this study, a predictive model for OFI in the MTR has been developed. The model is based on both the heat balance during the bubble generation and condensation processes, and the force balance for the detached bubbles at the onset of significant void (OSV). The only adjustable coefficient involved in the proposed model is quantified by comparison with the experimental data of Whittle and Forgan [Whittle, R.H., Forgan, R., 1967. A correlation for the minima in the pressure drop versus flow-rate curves for sub-cooled water flowing in narrow heated channels. Nucl. Eng. Des. 6, 89–99], which covers the wide range of MTR operating conditions. The model predictions are compared with predictions of some previous models, and it is shown that the present model results in smaller deviation from the experimental data. A correlation for the heat flux at OFI is also developed based on the present model. The developed correlation gives lower deviation from the experimental data than the well-known correlation of Whittle and Forgan. The model is also used to predict the OFI locus during a transient, where it shows good agreement with the short transient data of Lee and Bankoff [Lee, S.C., Bankoff, S.G., 1993. Prediction of the onset of flow instability in transient sub-cooled flow boiling. J. Nucl. Eng. Des. 139, 149–159] as well.     

Optimizing resources in model selection for support vector machine

Tuning support vector machine (SVM) hyperparameters is an important step in achieving a high-performance learning machine. It is usually done by minimizing an estimate of generalization error based on the bounds of the leave-one-out (LOO) such as radius-margin bound and on the performance measures such as generalized approximate cross-validation (GACV), empirical error, etc. These usual automatic methods used to tune the hyperparameters require an inversion of the Gram-Schmidt matrix or a resolution of an extra-quadratic programming problem. In the case of a large data set these methods require the addition of huge amounts of memory and a long CPU time to the already significant resources used in SVM training. In this paper, we propose a fast method based on an approximation of the gradient of the empirical error, along with incremental learning, which reduces the resources required both in terms of processing time and of storage space. We tested our method on several benchmarks, which produced promising results confirming our approach. Furthermore, it is worth noting that the gain time increases when the data set is large.