High-intensity multi-bunch beam generation by a photo-cathode RF gun

A multi-bunch photo-cathode RF gun system has been developed as an electron source for the production of intense quasi-monochromatic X-rays based on inverse Compton scattering. The desired multi-bunch beam is 100 bunches/pulse with a total charge of 500 nC and a bunch spacing of 2.8 ns. We modified the gun cavity of a ‘BNL-type IV’ RF gun to allow a CsTe cathode plug in the end plate. The system uses a four-dipole chicane beam line to allow the injection of laser light normal to the cathode surface. We compensate the gun cavity beam loading caused by the high-intensity multi-bunch electron beam by injecting the laser pulse before RF power has filled the cavity. We have achieved a total intensity of 220 nC in 100 bunches with a bunch-to-bunch energy spread under 1.3% (peak-to-peak). This paper concentrates on experiments to generate the high-intensity multi-bunch beam with compensation of the bunch-to-bunch energy spread due to heavy beam loading.     

A novel chromatographic separation technique using tertiary pyridine resin for the partitioning of trivalent actinides and lanthanides

A novel chromatographic separation technique using a tertiary pyridine type resin has been applied to the partitioning of the trivalent actinides (An) and lanthanides (Ln) and several successful results have been shown. In an alcoholic hydrochloric acid system, the trivalent An were clearly separated from the Ln, while no such group separation was achieved in an alcoholic nitric acid system. On the other hand, the nitric acid system was more effective for the intragroup (i.e. individual) separation of the trivalent An and the Ln than the hydrochloric acid system. On the basis of these results, a novel concept for the partitioning of the trivalent An and Ln using the present separation technique and its flowchart have been proposed with its advantages and disadvantages.     

Neural network approach for multicriteria solid transportation problem

In this paper, we propose neural network approach for multicriteria solid transportation problems(STP). First we suggest a neural network architecture to solve single-objective STP according to augmented Lagrange multiplier method. Due to the massive computing unit-neurons and parallel mechanism of neural network approach can solve the large scale problem efficiently and optimal solution can be got. Then we transform the original multicriteria problem into a single objective problem by global criteria method and adopt the neural network approach to solve it. By this way we can get the satisfactory solution of the original problem. The procedure and efficiency of this approach are shown with numerical simulations.     

Spanning tree-based genetic algorithm for bicriteria transportation problem

In this paper, we present a new approach which is spanning tree-based genetic algorithm for bicriteria transportation problem. The transportation problem have the special data structure in solution characterized as a spanning tree. In encoding transportation problem, we introduce one of node encoding which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation was designed based on this encoding. And we designed the criterion that chromosome always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy and roulette wheel selection is used. Numerical experiments will be shown the effectiveness and efficiency of the proposed algorithm.     

Neural network technique for fuzzy multiobjective linear programming

Neural Network(NN) is well-known as one of powerful computing tools to solve optimization problems. Due to the massive computing unit-neurons and parallel mechanism of neural network approach we can solve the large-scale problem efficiently and optimal solution can be gotten. In this paper, we intoroduce improvement of the two-phase approach for solving fuzzy multiobjectve linear programming problem with both fuzzy objectives and constraints and we propose a new neural network technique for solving fuzzy multiobjective linear programming problems. The procedure and efficiency of this approach are shown with numerical simulations.     

Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then rules selected by a GA-based multi-objective rule selection method. This rule selection method can be applied to high-dimensional pattern classification problems with many continuous attributes by restricting the number of antecedent conditions of each candidate fuzzy if-then rule. As candidate rules, we only use fuzzy if-then rules with a small number of antecedent conditions. Thus it is easy for human users to understand each rule selected by our method. Our rule selection method has two objectives: to minimize the number of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called “non-dominated solutions” because two conflicting objectives are considered. In this paper, we examine the performance of our GA-based rule selection method by computer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine the generalization ability for test patterns. We show that a fuzzy rule-based classification system with an appropriate number of rules has high generalization ability.     

The Atmospheric Scanning Electron Microscope with open sample space observes dynamic phenomena in liquid or gas

Although conventional electron microscopy (EM) requires samples to be in vacuum, most chemical and physical reactions occur in liquid or gas. The Atmospheric Scanning Electron Microscope (ASEM) can observe dynamic phenomena in liquid or gas under atmospheric pressure in real time. An electron-permeable window made of pressure-resistant 100 nm-thick silicon nitride (SiN) film, set into the bottom of the open ASEM sample dish, allows an electron beam to be projected from underneath the sample. A detector positioned below captures backscattered electrons. Using the ASEM, we observed the radiation-induced self-organization process of particles, as well as phenomena accompanying volume change, including evaporation-induced crystallization. Using the electrochemical ASEM dish, we observed tree-like electrochemical depositions on the cathode. In silver nitrate solution, we observed silver depositions near the cathode forming incidental internal voids. The heated ASEM dish allowed observation of patterns of contrast in melting and solidifying solder. Finally, to demonstrate its applicability for monitoring and control of industrial processes, silver paste and solder paste were examined at high throughput. High resolution, imaging speed, flexibility, adaptability, and ease of use facilitate the observation of previously difficult-to-image phenomena, and make the ASEM applicable to various fields.     

Soft computing approach for reliability optimization: State-of-the-art survey

In the broadest sense, reliability is a measure of performance of systems. As systems have grown more complex, the consequences of their unreliable behavior have become severe in terms of cost, effort, lives, etc., and the interest in assessing system reliability and the need for improving the reliability of products and systems have become very important. Most solution methods for reliability optimization assume that systems have redundancy components in series and/or parallel systems and alternative designs are available. Reliability optimization problems concentrate on optimal allocation of redundancy components and optimal selection of alternative designs to meet system requirement. In the past two decades, numerous reliability optimization techniques have been proposed. Generally, these techniques can be classified as linear programming, dynamic programming, integer programming, geometric programming, heuristic method, Lagrangean multiplier method and so on. A Genetic Algorithm (GA), as a soft computing approach, is a powerful tool for solving various reliability optimization problems. In this paper, we briefly survey GA-based approach for various reliability optimization problems, such as reliability optimization of redundant system, reliability optimization with alternative design, reliability optimization with time-dependent reliability, reliability optimization with interval coefficients, bicriteria reliability optimization, and reliability optimization with fuzzy goals. We also introduce the hybrid approaches for combining GA with fuzzy logic, neural network and other conventional search techniques. Finally, we have some experiments with an example of various reliability optimization problems using hybrid GA approach.     

Lower Irritation Potential of Laureth‐3 Carboxylate Amino Acid Salt

Sodium laureth sulfate and amino acid type surfactants have been categorized as low‐skin irritation chemicals based on results of previous skin irritation tests. However, detergent‐induced skin irritation still occurs. Detergents with a low‐skin irritation effect are required, since the number of cosmetics for sensitive skin has increased. Therefore, an in vitro method of testing the safety of cosmetics for sensitive skin is required. Skin irritation by anionic surfactants was investigated to determine the effect of laureth‐3 carboxylate (polyoxyethylene lauryl carboxymethyl ether) amino acid salt on skin irritation. In addition, sodium laureth sulfate and amino acid type surfactants, regarded as low‐irritation surfactants, were also tested for skin irritation. The skin irritation effect of laureth‐3 carboxylate lysine salt (Surfactant 1 ), sodium laureth sulfate (Surfactant 2 ), and sodium N‐lauroyl glutamate (Surfactant 3 ) were investigated using a reconstructed human cultured epidermal model, LabCyte EPI‐MODEL24 6D. Cell viabilities of cultured epidermal cells exposed to Surfactant 1 (5.0 % aq.), Surfactant 2 , and Surfactant 3 were 82.0, 45.0 and 19.1 %, respectively. There were significant differences in cell viability upon exposure to 5.0 % aqueous test solutions of the three test chemicals. The results of the current investigation indicate that Surfactant 1 has a low skin‐irritation effect.     

Cationic polymerization of β-pinene with the AlCl3/SbCl3 binary catalyst: Comparison with α-pinene polymerization

The polymerization of β-pinene with the AlCl₃/SbCl₃ binary catalyst was investigated in toluene at −40°C and was compared with that of α-pinene. The polymerization of β-pinene with AlCl₃ alone was very rapid and retarded on addition of SbCl₃, in sharp contrast to that of α-pinene where added SbCl₃ remarkably accelerated it to give relatively high molecular weight oligomers. Attempted copolymerization of the two isomers with the binary catalyst, in turn, induced their parallel homopolymerizations, indicating that the copolymerization was difficult due to the large difference in reactivity. The homopolymerizations with AlCl₃/SbCl₃ were not seriously affected by a sterically hindered base, 2,6-di-tert-butyl-4-methylpyridine (DTBMP); the initiating species, therefore, would be different in nature from a proton. © 1996 John Wiley & Sons, Inc.