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.     

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.     

Spectroscopic imaging and the characterization of the autofluorescence properties of human bronchus tissues using UV laser diodes

Ultraviolet laser diodes (UV-LD) were used for the excitation source of autofluorescence (AF) measurements and spectroscopic imaging of the AF originating from the human bronchus was obtained. The AF spectra from normal bronchus tissues were measured and a clear AF spectrum was obtained by using a short wavelength (400 nm) laser diode; the overlap of the AF signal and excitation source could be substantially eliminated. In order to study the origin of AF intensity deterioration from bronchus tissue due to the formation of tumor tissues, the fluorescence spectrum was measured for various AF substances under various conditions. The blue AF signal of elastin and NADH solutions, which could not be easily studied by conventional excitation light sources, as well as the green AF became weak by adding lactic acid. The AF spectrum was measured for 512/spl times/512 pixels and the intensity mapping as a function of emission wavelength was obtained. Two-dimensional information of the AF signal intensity distribution for a certain wavelength component was measured. The feature originating from a region as small as about 100 /spl mu/m could be recognized. Numerical calculations of the data were performed and precise features of the AF were revealed.     

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.     

Thermal behaviour and particle size evaluation of primary clusters in a water-based magnetic fluid

This paper reports the experimental research on thermal behaviour and particle size evaluation of primary clusters of ferromagnetic nano-particles in a water-based magnetic fluid. The magnetic fluids are suspensions of ultra fine particles coated with a molecular layer of dispersant in a liquid carrier such as water or kerosene. The particles are coated with single- or double-layer of surfactant to achieve stable dispersion. Numerous experimental studies have indicated the existence of the primary cluster of ferromagnetic nano-particles in a water-based magnetic fluid. The purpose of this research is to evaluate the particle size of the primary clusters by applying the Einstein's equation for Brownian motion assuming that the primary cluster has a spherical-shape. The thermal behaviour of ferromagnetic nano-particles in magnetic fluids is investigated through the micro visualization using the optical darkfield microscope system and particle tracking velocimetry data processing system. Real-time visualization of the Brownian motion of primary clusters in a water-based magnetic fluid was carried out. The experimental results clarified that the primary cluster size depends upon the concentration of the ferromagnetic nano-particle in the magnetic fluid.     

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.