Effect of Gas Presence On Erosive Wear of Split-Vane Electrical Submersible Pump

Lectur

Automatic arc discharge-induced helical long period fiber gratings and its sensing applications

We experimentally demonstrate an automatic arc discharge technology for inscribing high-quality helically twisted long period fiber gratings (H-LPFGs) with greatly improved inscription efficiency for single mode fibers. The proposed technology has been developed by implementing an embedded program in a commercial fusion splicer, which employs an ultraprecision motorized translation stage while the tensioning mass required by conventional inscribing technology is eliminated. More significantly, the arc-induced H-LPFGs have been reported to have potential usage as sensors in temperature, refractive index, twist stress, and strain

Undamaged measurement of the sub-micron diaphragm and gap by tri-beam interference

A simple, high-accuracy and non-destructive method for the measurement of diaphragm thickness and microgap width based on modulated tri-beam interference is demonstrated. With this method, a theoretical estimation error less than 0.5% for a diaphragm thickness of ~1 μm is achievable. Several fiber-tip air bubbles with different diaphragm thicknesses (6.25, 5.0, 2.5 and 1.25 μm) were fabricated to verify our proposed measurement method. Furthermore, an improved technique was introduced by immersing the measured object into a liquid environment to simplify a four-beam interference into tri-beam one. By applying this improved technique, the diaphragm thickness of a fabricated in-fiber rectangular air bubble is measured to be about 1.47 μm, and the averaged microgap width of a standard silica capillary is measured to be about 10.07 μm, giving a corresponding measurement error only 1.27% compared with actual scanning electron microscope (SEM) results

Design and Fabrication of High Activity Retention Al-Based Composite Powders for Mild Hydrogen Generation.

Al-Bi-Sn-Cu composite powders for hydrogen generation were designed from the calculated phase diagram and prepared by the gas atomization process. The morphologies and structures of the composite powders were investigated using X-ray diffraction (XRD) and a scanning electron microscope (SEM) equipped with energy-dispersive X-ray (EDX) spectroscopy, and the results indicate that the Cu additive enhanced the phase separation between the Al-rich phase and the (Bi, Sn)-rich phase. The hydrogen generation performances were investigated by reacting the materials with distilled water. The Al-Bi-Sn-Cu powders reveal a stable hydrogen generation rate, and the Al-10Bi-7Sn-3Cu (wt%) powder exhibits the best hydrogen generation performance in 50 °C distilled water which reaches 856 mL/g in 800 min. In addition, the antioxidation properties of the powders were also studied. The Al-10Bi-7Sn-3Cu (wt%) powder has a good resistance to oxidation and moisture, which shows great potential for being the hydrogen source for fuel cell applications

k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

In this paper, a computational method based on machine learning technique for identifying Alzheimer's disease genes is proposed. Compared with most existing machine learning based methods, existing methods predict Alzheimer's disease genes by using structural magnetic resonance imaging (MRI) technique. Most methods have attained acceptable results, but the cost is expensive and time consuming. Thus, we proposed a computational method for identifying Alzheimer disease genes by use of the sequence information of proteins, and classify the feature vectors by random forest. In the proposed method, the gene protein information is extracted by adaptive k-skip-n-gram features. The proposed method can attain the accuracy to 85.5% on the selected UniProt dataset, which has been demonstrated by the experimental results