Thermal and mechanical properties of plasticized poly(L‐lactide) nanocomposites with organo‐modified montmorillonites

Nanocomposites of poly(lactide) (PLA) and the PLA plasticized with diglycerine tetraacetate (PL‐710) and ethylene glycol oligomer containing organo‐modified montmorillonites (ODA‐M and PGS‐M) by the protonated ammonium cations of octadecylamine and poly(ethylene glycol) stearylamine were prepared by melt intercalation method. In the X‐ray diffraction analysis, the PLA/ODA‐M and plasticized PLA/ODA‐M composites showed a clear enlargement of the difference of interlayer spacing between the composite and clay itself, indicating the formation of intercalated nanocomposite. However, a little enlargement of the interlayer spacing was observed for the PLA/PGS‐M and plasticized PLA/PGS‐M composites. From morphological studies using transmission electron microscopy, a finer dispersion of clay was observed for PLA/ODA‐M composite than PLA/PGS‐M composite and all the composites using the plasticized PLA. The PLA and PLA/PL‐710 composites containing ODA‐M showed a higher tensile strength and modulus than the corresponding composites with PGS‐M. The PLA/PL‐710 (10 wt %) composite containing ODA‐M showed considerably higher elongation at break than the pristine plasticized PLA, and had a comparable tensile modulus to pure PLA. The glass transition temperature (T_g) of the composites decreased with increasing plasticizer. The addition of the clays did not cause a significant increase of T_g. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci, 2006     

Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals

Evolutionary computation generally aims to create the optimal individual which represents optimal action rules when it is applied to agent systems. Genetic Network Programming (GNP) has been proposed as one of the graph-based evolutionary computations in order to create optimal individuals. GNP with rule accumulation is an extended algorithm of GNP, which extracts a large number of rules throughout the generations and stores them in rule pools, which is different from general evolutionary computations. Concretely, the individuals of GNP with rule accumulation are regarded as evolving rule generators in the training phase and the generated rules in the rule pools are actually used for decision making. In this paper, GNP with rule accumulation is enhanced in terms of its rule extraction and classification abilities for generating stock trading signals considering up and down trends and occurrence frequency of specific buying/selling timing. A large number of buying and selling rules are extracted by the individuals evolved in the training period. Then, a unique classification mechanism is used to appropriately determine whether to buy or sell stocks based on the extracted rules. In the testing simulations, the stock trading is carried out using the extracted rules and it is confirmed that the rule-based trading model shows higher profits than the conventional individual-based trading model.     

A random-forests-based classifier using class association rules and its application to an intrusion detection system

With the rapid developments of network technology, devices connected to the network in a variety of fields have increased, and then, network security has become more important. Rule-based classification for intrusion detection is useful, because it is not only easily understood by humans, but also accurate for the classification of new patterns. Genetic network programming (GNP) is one of the rule-mining techniques as well as the evolutionary-optimization techniques. It can extract rules efficiently even from an enormous database, but still needs more accuracy and stability for practical use. This paper describes a classification system with random forests, employing weighted majority vote in the classification to enhance its performance. For the performance evaluation, NSL-KDD (Network Security Laboratory-Knowledge Discovery and Data Mining) data set is used and the proposed method is compared with the conventional methods, including other machine-learning techniques (Random forests, SVM, J4.8) in terms of the accuracy and false positive rate.     

纳米技术在元器件领域的应用与发展对细胞工学的影响

作为生物纳米技术之一而引人注目的再生学，对再生物的培养一直是人们期待解决的课题之一。但器官是由有规则且有着复杂构造的细胞组成，并维持在适合细胞的生存环境下提供氧和营养。在目前还没有找到适合培养器官整体的单元的情况下，以培养为目的，适合细胞配置的显微组织体被认为是非常的重要。     

Perfect computational equivalence between quantum Turing machines and finitely generated uniform quantum circuit families

In order to establish the computational equivalence between quantum Turing machines (QTMs) and quantum circuit families (QCFs) using Yao’s quantum circuit simulation of QTMs, we previously introduced the class of uniform QCFs based on an infinite set of elementary gates, which has been shown to be computationally equivalent to the polynomial-time QTMs (with appropriate restriction of amplitudes) up to bounded error simulation. This result implies that the complexity class BQP introduced by Bernstein and Vazirani for QTMs equals its counterpart for uniform QCFs. However, the complexity classes ZQP and EQP for QTMs do not appear to equal their counterparts for uniform QCFs. In this paper, we introduce a subclass of uniform QCFs, the finitely generated uniform QCFs, based on finite number of elementary gates and show that the class of finitely generated uniform QCFs is perfectly equivalent to the class of polynomial-time QTMs; they can exactly simulate each other. This naturally implies that BQP as well as ZQP and EQP equal the corresponding complexity classes of the finitely generated uniform QCFs.     

Influence of primary structure on thermal oxidative degradation of polypropylene impact copolymer

Summary Thermal oxidative degradation was carried out using four kinds of polypropylene impact copolymers, and the relationship between primary structure and degradation behavior was investigated by the results of ¹³C-NMR, TEM, GPC and the determination of hydroperoxide value (HPO) and number of chain scission (NCS). The degradation behavior mainly depended on the ethylene content and on the tacticity. The thermal degradation was found to be controllable by the primary structure.     

Practical application of a sea-water battery in deep-sea basin and its performance

Stable power supply is essential for various long-term sea floor geophysical observations. Due to a simple structure and a large energy capacity, sea-water batteries have been developed and used for such observations. However, the characteristics of sea-water batteries have not been well known in the case of installations at depths more than 5000 m in deep-sea basin. In 2000, a sea floor borehole broadband seismic observatory was installed in the northwestern Pacific basin where the water depth is 5577 m. For electric power supply, a Sea-Water Battery (SWB) system with monitoring and control was developed and used. The SWB system consists of four sea-water battery cells, a DC/DC converter, the Power Control System, the Data Logger, and an accumulator. The conditions of the SWB system were recorded more than 1 year, and the monitoring data was recovered. The SWB system generated enough power for the observation system consuming power of 6 W in average and continued to supply power for at least 5 years. From the monitoring data, the SWB can supply up to the long-term average of at least 13 W. The energy density is estimated to be 318 Wh kg⁻¹. The sea-water battery is useful for application of long-term power consumption even in the deep-sea basin.     

Multicentric Castleman''s disease with lymphoid interstitial pneumonia and polyneuropathy

During emergencies in their natural environments, vertebrates initiate coping mechanisms that redirect behavior away from nonessential activities and towards survival. Reproductive behaviors are suppressed. Evidence from field studies on passerine birds shows that this inhibition may not depend on the suppression of gonadal sex steroids, since during the breeding season they remain elevated despite activation of the stress response. We hypothesize that an alternate, central mechanism mediates the inhibition of reproductive behavior during stress in passerines. In this study, we tested the intracerebroventricular effects of endogenous opioids and corticotropin-releasing factor (CRF), neuropeptides implicated in the stress response, on courtship behavior in wild-caught female white-crowned sparrows. Beta-endorphin (beta-EN) significantly inhibited copulation solicitation, an estrogen-dependent courtship display, 30 min after treatment. Naloxone, an opioid antagonist, enhanced the behavior. CRF caused a suppression of solicitation that was reversible by pretreatment with naloxone, suggesting an intermediary role for endogenous opioids in CRF-induced suppression of courtship. The effects of beta-EN and CRF on solicitation appear to be independent of any general effects on locomotor activity. These results support our hypothesis that stress neuropeptides orchestrate coping behaviors in wild birds.     

Evaluation of geometric efficiency and radiation exposure in z-axis for volume scan

The International Electrotechnical Commission (IEC) recommended that a display be provided on the operating console of medical computed tomography (CT) scanners when the geometric efficiency (GE) in the z-axis is 70% or less. Taking into account the increase in the number of detector rows and the changes in the scanning method, the present study was conducted to review the GE and to evaluate the exposure dose in the z-axis direction. The GE for a single-rotation scan was calculated in accordance with IEC-606024 Ed. 2. The findings of the present study suggest that 320-row area detector computed tomography volume scan (single axial scan with no table movement in which it just happened to use a very large beam width) extends 18.8 mm beyond the nominal beam collimation, even when the GE is >70%. However, compared with the helical scan, which is the present mainstream of CT examinations, the dose beyond the scan length is narrower in volume scan for a limited scan length of 160 mm or less.     

Nonlinear control system using learning Petri network

According to recent understanding of brain science, it is suggested that there is a distribution of functions in the brain, which means that different neurons are activated depending on which sort of sensory information the brain receives. We have already developed a learning network with a function distribution which is called the Learning Petri Network (LPN) and have shown that this network could learn nonlinear and discontinuous mappings which the Neural Network (NN) cannot. In this paper, a more realistic application which has dynamic characteristics is studied. From simulation results of a nonlinear crane control system using LPN controller, it is clarified that the control performance of LPN controller is superior to that of NN controller. © 2000 Scripta Technica, Electr Eng Jpn, 131(3): 58‐69, 2000