Abstract. Objectives: This report presents toxicological profile available on a superparamagnetic iron oxide (SPIO) nanoparticle in vivo. Materials and Methods: Single-and repeat-dose toxicity studies were performed with SPIO given subcutaneously in mice. Results: The SPIO nanoparticle exhibited a low toxicity profile, with no treatment-related deaths and few transient clinical signs. SPIO was taken up and distributed in heart, spleen, liver, lung, kidney, brain decreasingly within 24 hours after injection. After repeated injections for 10days, the accumulation of iron on organs studied is slight, indicating that iron is eliminated fast at 100mg/kg given subcutaneously in mice. At histopathology, no iron-positive pigment was observed in macrophages of multiple organs (mainly heart, liver, spleen, lung, kidney, brain). Conclusion:The results of most of the studies demonstrated low hazard potential in mice following acute injection to the SPIO nanoparticle tested in this program. All effects observed are unlikely to occur in clinical practice because of the single low dosing in humans. IntroductionRecently, medical applications of nanotechnology have attracted growing interest. Until now, a large number of new nanotechnology-based concepts for therapeutic and diagnostic medicines have emerged, and their feasibility has been demonstrated [1] . However, the risk associated with passive

Engineering medical applications are enriched by the fabrication potential of the growing technology of Micro-Electro-Mechanical Systems (MEMS). Within this technological expansion, device manufacturing costs, failure and long-term performance reliability are critical issues that have to be resolved using basic probabilistic design methodologies which are yet largely unexploited by industrial and service companies at the mature innovation level. Modeling and testing of high-performance MEMS is a promising route, based upon these methodologies, to enhancing reliability and preventing surface failure. In this paper, we focus on the modeling of the mechanical properties of MEMS, as exemplified by a capacitive accelerometer, using probabilistic techniques. The accuracy of these techniques is also evaluated for the accelerometer with regard to those parameters that affect mainly reliability and failure. The simulated analysis of the mechanical properties is performed with easy-to-use probabilistic software known as “NESSUS”. It is concluded that probabilistic design methodologies are very effective and balanced for making design decisions that can, with both reliability and ease, ensure component or system efficiency.

In the fault diagnosis of a machine, frequencies of its vibration are important indicators to show conditions of the machine. There are two main categories of methods to estimate frequency. One is based on the fast Fourier transform, and the other is on the signal subspace decomposition. Using FFT directly to estimate frequency may introduce larger estimation error, several approaches are proposed to correct or decrease the error, which comprise phase difference, energy centrobaric, interpolation and search method. The signal subspace decomposition method (SSDM) consists of Pisarenko harmonic decomposition, multiple signal classification. In order to assess the performance of these methods, the Cramer-Rao bound is used to compare with the error variance of difference frequency estimation methods, and simulations are based on Monte Carlo experiments for various record sizes and signal-to-noise ratios (SNR’s). The results show that there is a turning point about 25 dB for FFT based methods, above which FFT based methods are less sensitive to the noise, and SSDM achieves higher precision estimation at higher SNR and for the short time series, but produces poor accuracy at lower SNR’s.

It is critical to understand multiphase flow applications with regard to dynamic behavior. In this paper, a systematic approach to the study of these applications is pursued, leading to separated flows comprising the effects of free surface flows and wetting. For the first time, wetting phenomena (three wetting regimes such as no wetting, 90 º wetting angle and absolute wetting) are added in the separated flow model. Special attention is paid to computational fluid dynamics (CFD) in order to envisage the relationship between complex metallurgical practices such as mass and momentum exchange, turbulence, heat, reaction kinetics and electromagnetic fields. Simulations are performed in order to develop sub-models for studying multiphase flow phenomena at larger scales. The outcomes show that a proper mixture of techniques is valuable for constructing larger-scale models based upon sub-models for recreating the hierarchical structure of a detailed CFD model applicable throughout the process.

Micro/nano outcrops generated on hydrophobic surfaces are vital outcomes of the super-hydrophobic mechanism in the fabrication of miniature batteries, super-capacitors, field effect transistors and electrochemical and biological sensors. In systematized posts positioned on superhydrophobic surfaces, it is critical to alter the contact angle, whilst the retreating one depends upon the post size and spacing. In this paper, it is shown that calculation of the apparent surface free energy concerned with the probe liquid surface tension, and both advancing and receding contact angles (a and r respectively), is useful for bringing special attention to shedding more light on the wetting properties of superhydrophobic surfaces. A simulation is performed, in order to present this interesting phenomenon, which is in reasonable agreement with experiment. It is concluded that the computation supports categorizing the wetting phenomena as well as encouraging further progress in the fabrication of MEMS/NEMS structures with high efficiency, degree of precision, accuracy, uniformity, aspect ratio and through-put.

As an important means to improve the reliability of complex manufacturing systems and to ensure that they can function better, condition monitoring systems (CMS) are usually designed and used in manufacture industries. And reliability is one of the most important characteristics of CMS. Fuzzy set theory based on reliability modeling and analysis methods for CMS are proposed in this paper. The methods include a fuzzy graph modeling technique for analyzing the system reliability of CMS, and fuzzy reliability computing methods to quantify uncertainties in function decomposition of CMS and compute system reliability of CMS by invoking Fuzzy-set theory and approaches. On the other hand, the fuzzy reliability of CMS based on fuzzy failure subset and confidence level is defined, and system reliability indexes of CMS are set up to compute the system reliability of CMS. A case of application is used to illustrate the procedure.

Pressure vessel contained with different nozzles which caused geometric discontinuity of the pressure vessel wall, which resulted in stress concentration around the nozzle. There may be the chances of failure of vessel junction, which was attributed to the high stress concentration. Therefore, detail stress distribution analysis need to be done for pressure vessel with the nozzle. Determination of limit pressure at different location on lateral nozzle by using finite element method. Lateral nozzle was subjected to internal pressure and in-plane bending moment. Results found that plastic hinge occurred in the nozzle-vessel junction area shoulder. Plastic limit loading increased with the increasing of outside diameter and wall thickness of branch pipe when the size of primary piping was constant value, whereby the influence of outside diameter of branch pipe was more remarkable. Moreover, engineering estimation formulas of plastic limit in-plane bending moment was obtained based on plastic limit loading database.

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