In this study we focused on the emotional facet of nurses' stress, which is a significant contributing factor in nursing accidents, and through analyzing medical near-miss cases based on a mathematical model, we aim to prevent nursing accidents before they occur. From the standpoint of nursing science and engineering, based on a non-linear curve function similar to the sigmoid curve, using the elements of a transformation matrix for mapping, with nurses' 'emotions' as the parameter, we defined a four-dimensional matrix with a double structure, the function of which was to assign weightings. Further, research conducted through simulation under the parameters of the functions and weightings revealed realistic and meaningful elements, and incidents of medical near-miss cases were analyzed. The results indicate that, even utilizing unrefined function settings, it is possible to build a system that helps prevent nursing accidents before they occur.

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We propose an approach to the determination of class borders on a SOM with non-equal class distributions. Our approach treats the class distribution as a variance-covariance matrix. The class distribution is expressed by a variance-covariance matrix and its decision border between the classes is determined from input data by using the eigenvalues and the corresponding eigenvectors of the matrices. Using the iris dataset of Fisher, it is shown that our approach allows the effect of non-equal class distributions on the decision borders to be successfully visualized in a qualitative and comprehensible manner.

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This paper describes the correlation between the subjective evaluation by using Visual Analog Scale (VAS) for the effects of the unpleasant sound, and Heart Rate Variability (HRV) analysis. The experiment employed 16 healthy subjects. HRVs of the subjects are recorded when they listen to the unpleasant sound and when they listen to nothing. Furthermore, in order to investigate the psychological state change, the subjects are required to answer VAS questionnaire for both cases (listening to the unpleasant sound, and not). The HRV data are analyzed by using FFT. The experiment result shows that there exists relationship between VAS data and coefficient of covariance in HRV.

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The microscopic information of ion track is essential in the field of the heavy-ion therapy for cancer treatment. The knowledge of effect on human tissue by heavy particle beam is used to develop a reliable medical system of treatment planning. For the deep understanding of the ion track structure in water target, we are developing a 3D Monte Carlo program. As the demonstration of the code for secondary electrons, we calculated electron track and examined the reliability of electron code in terms of electron ranges in water. And we have expanded this electron code to a the case of heavy ion incidence. The calculated ion track may imply a different nature of energy dependence from that interpreted by the macroscopic picture.

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We examined the validity of the evaluation of competitors in application of the fuzzy reasoning for university students' archery players, from the relation with the evaluation values calculated by the fuzzy reasoning (Product-Sum-Gravity Method), based on the competition results during the contest and the physiological (blood pressures and heart rates) and psychological (mood) indicators before the contest. As a result, the values of evaluation based on the competition results (ranking) and the fuzzy reasoning before the contest clearly showed a higher tendency in the groups of high and middle performers, compared with the Group of low performers, in the competition results (ranking). Also, on the correlation with the values of evaluation in the competition results (scores) and the fuzzy reasoning, a positive correlation was confirmed, and a negative correlation was not found. Therefore, it was suggested that the values of evaluation obtained by the fuzzy reasoning on the physiological and psychological states of the competitors before a contest can be applied for a possible and valid evaluation of university students' archery players.

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In order to realize intelligent agents such as autonomous mobile robots, Reinforcement Learning is one of the necessary techniques in control systems. However there exist many problems to apply reinforcement learning to real-world tasks. The most severe problem is a huge number of iterations in the learning phase. In order to deal with the problem, the instruction approach for reinforcement learning agents based on sub-rewards and forgetting mechanisms were proposed and shown to be effective. However the relationship between the instruction and the learning performance of reinforcement learning has not been adequately clarified. In this study, in order to clarify the instruction performance in the reinforcement learning, we propose an instruction knowledge acquisition method for the reinforcement learning scheme by the particle swarm optimization (PSO) algorithm. Through numerical experiments of the mountain car task and the Acrobat task, we show the validness of the proposed approach in terms of learning speed and accuracy.

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We have tried the visibility prediction in consideration of Web accessibility. In our previous studies, visibility of young adults, the elderly, and dichromat's visibility (Protanope and Deuteranope) has been predicted respectively. However, it is necessary to group together formula of visibility prediction. In this report, visibility of dichromat's visibility was predicted using transformation of color vision model and linear discriminant function of young adults. As the results, there was no difference in visibilities between protanope, deuteranope, and observed value. However, when a font color has many red component, the result of deuteranope visibility prediction was a tendency judged low. Therefore, it was shown that visibility prediction can be performed using our proposed method.

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Peripheral arterial disease (PAD) is caused to the lower extremity atherosclerotic disease. Its diagnosis is needed to obtain much kind of the information of vascular morphology as well as the blood flow information based on hemodynamics. The diagnosis of the PAD using magnetic resonance imaging equipment without contrast medium is available as a useful method on visual screening in clinical practice. In this paper, we propose a novel method for visualizing hemodynamics to arterial images obtained by a non-contrast enhanced magnetic resonance angiography based on the optical flow with the image pyramid processing on successive slice, and satisfied experimental results are obtained.

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Pre- and post- operative changes of patients undergoing either an open cholecystectomy or a laparoscopic cholecystectomy under general or general plus epidural anesthesia at Koga hospital 21 were evaluated by determination of salivary amylase levels on the day of the operation and on the first postoperative day. The laparoscopic group (13 cases: seven males, six females; eight general, five general + epidural anesthesia) displayed lower levels of salivary amylase than the open group (seven cases: four males, three females; three general, four general + epidural anesthesia). Postoperative amylase level of the laparoscopic group decreased from 74.8 KU/L to 69.5 KU/L. The amylase level in the open group was elevated from 119.7 KU/L to 150.1 KU/L. The results indicate that the open cholecystectomy, with the post-, and the pre-operative increase in the amylase levels perhaps being an indicator of an anxiety for the surgery and of the wound pain, respectively.

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We rewrote the famous Bayes' theory into quantum Bayes' Form. In order to practice that process, all of classical probabilities were necessary that they were reinterpreted into operators, state vectors, probability amplitude, and expectation values. The difference between classical version and quantum form was whether interference's term exists or not. Quantum Bayes' form had the additional term Re(Int). At junctions, all of pure states were mixed. Some attenuated states were generated instead of all pure states at junction. In neurons, examples of those areas meant neuro-synaptic junctions, ephapse of axons, and endplate of muscles, and so on. Final state vector \B> had the various information of environmental state at the conditions of the mixed and reduced purities. So, if we would like to obtain the pure state and the other quantities, we needed take a series of mathematical operations as making expectation values.

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We considered quantum Bayes' form which had many convergence's points. And each neuron interacts through axons and synapses. Various noises affected on signal conductions of those axons and communications of synapses, which caused the many mistakes through ephapse and synaptic interferences. However, the quantum states of our communication channels were described as the same form in spite of those noise disturbing communication's signals. The difference between quantum Bayes' form and classical Bayes' theory is whether interference's term existed in or not. The communication channels, having ephapse and synaptic interferences, whose structures were similar to quantum amida lots.

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We proposed that polaritons are approximately governed by Schrodinger equation, and one of the powerful method are perturbation series expansion when our quantum neural nets. And an explicit expression of solution for quantum neural nets is given by applying the convergence type of both initial and final state vectors. Then I attempted to rewrite the classical neural network into quantum neural net, and we noticed the similarities with both classical network and quantum one as for network construction and the method of weight calculation. However, quantum neural net belongs to the world of complex numbers, and its results of calculations do not mean probability but probability amplitude. So, the most different between quantum net and classical one exists in whether those neural nets have quantum interferences or not. We clearly obtained the concrete method of calculations and quantum Bayes' form by using state vectors.

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I proposed the positive hypotheses of neuo-interferences, ephapse as engineering models. The nero-interferences and synaptic ones as ephapse are propagated by polaritons which are a kind of quasi particles, i.e. quantized polarization waves. Polaritons, which has spin 1, are massive vector particles and massive photon. polaritons are connecting between many ionic current, Na^+, K^+, Cl^- etc when neurons, fibers (axons) are conducting their excitations. The Na^+ currents, into insides of membranes of axons, cause the K^+ currents to outside of axons through charged or non-charged quantized polarization wave, i.e. polaritons. Various interferences, ephapse, synaptic etc., are intermediated by polaritons. Those quantum interferences are commonly adjusting our neural and brain's conditions by interacting with each neurone. One of my purposes is to study effect of quantum neuro-interferences, and proposed new concepts of neural network accompanied with quantum interferences.

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Recently, image fusion technique is used on medical field which is obtained various different modalities. Also the technique is performed as a useful application as the visual screening. The improvement and efficiency of the diagnostic function come to be attempted by overlapping the two images, the involved area where the interpretation of radiogram was difficult from the one modality in the other modality. To make a fusion image, registration is important task. However, because many registration techniques need manual operation, increased burden to the doctors and the difference of result becomes a problem. In this paper, we propose a method for image fusion on head CT and MR image to assist surgical plan for the Cyber Knife. Our registration technique is performed two main steps; 1) global registration by using center of gravity, and outline information of image, 2) final registration by use of the mutual information which is selected some VOIs. In this paper, we report some experimental results with registration accuracy and computational processing time.

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