A probabilistic bridge safety evaluation against floods

To further capture the influences of uncertain factors on river bridge safety evaluation, a probabilistic approach is adopted. Because this is a systematic and nonlinear problem, MPP-based reliability analyses are not suitable. A sampling approach such as a Monte Carlo simulation (MCS) or importance sampling is often adopted. To enhance the efficiency of the sampling approach, this study utilizes Bayesian least squares support vector machines to construct a response surface followed by an MCS, providing a more precise safety index. Although there are several factors impacting the flood-resistant reliability of a bridge, previous experiences and studies show that the reliability of the bridge itself plays a key role. Thus, the goal of this study is to analyze the system reliability of a selected bridge that includes five limit states. The random variables considered here include the water surface elevation, water velocity, local scour depth, soil property and wind load. Because the first three variables are deeply affected by river hydraulics, a probabilistic HEC-RAS-based simulation is performed to capture the uncertainties in those random variables. The accuracy and variation of our solutions are confirmed by a direct MCS to ensure the applicability of the proposed approach. The results of a numerical example indicate that the proposed approach can efficiently provide an accurate bridge safety evaluation and maintain satisfactory variation

A web-based architecture for implementing electronic procurement in military organisations."

Abstract In recent years, the vital development of the Internet offers increasing opportunity for electronic commerce. E-commerce attracts much attention from enterprises, not only to get connection with others and make a profit from their product/service, but also to reduce the costs of internal and external operational procedures. Procurement is a very critical task because it is a matter not only of making a profit, but also of staying in business in a highly competitive environment. In the government sector, procurement is sometimes the source of corruption, scandal and abuse of public resources. Besides inadequately qualified personnel, "transparency" of the procurement environment becomes another source of problems in procurement procedure. This paper investigates a case study of e-commerce in the Taiwanese military organization by diagnosing and preventing procurement faults, constructing a transparent procurement environment, and enhancing military procurement efficiency, and is an attempt to establish an e-market environment via web-based architecture on e-procurement procedure. The design of a relational database is introduced and system implementation is presented. Also, efficiency and benefits of the proposed system are discussed

Deep Learning for Spin-Orbit Torque Characterizations with a Projected Vector Field Magnet

Spin-orbit torque characterizations on magnetic heterostructures with perpendicular anisotropy are demonstrated on a projected vector field magnet via hysteresis loop shift measurement and harmonic Hall measurement with planar Hall correction. Accurate magnetic field calibration of the vector magnet is realized with the help of deep learning models, which are able to capture the nonlinear behavior between the generated magnetic field and the currents applied to the magnet. The trained models can successfully predict the applied current combinations under the circumstances of magnetic field scans, angle scans, and hysteresis loop shift measurements. The validity of the models is further verified, complemented by the comparison of the spin-orbit torque characterization results obtained from the deep-learning-trained vector magnet system with those obtained from a conventional setup comprised of two separated electromagnets. The damping-like spin-orbit torque (DL-SOT) efficiencies (|$\xi_{DL}$|) extracted from the vector magnet and the traditional measurement configuration are consistent, where |$\xi_{DL}$| $\approx$ 0.22 for amorphous W and |$\xi_{DL}$| $\approx$ 0.02 for $\alpha$-W. Our work provides an advanced method to meticulously control a vector magnet and to conveniently perform various spin-orbit torque characterizations

Current-Induced magnetization switching by the high spin Hall conductivity α\alpha-W

The spin Hall effect originating from 5d heavy transition metal thin films such as Pt, Ta, and W is able to generate efficient spin-orbit torques that can switch adjacent magnetic layers. This mechanism can serve as an alternative to conventional spin-transfer torque for controlling next-generation magnetic memories. Among all 5d transition metals, W in its resistive amorphous phase typically shows the largest spin-orbit torque efficiency ~ 0.20-0.50. In contrast, its conductive and crystalline $\alpha$ phase possesses a significantly smaller efficiency ~ 0.03 and no spin-orbit torque switching has yet been realized using $\alpha$-W thin films as the spin Hall source. In this work, through a comprehensive study of high quality W/CoFeB/MgO and the reversed MgO/CoFeB/W magnetic heterostructures, we show that although amorphous-W has a greater spin-orbit torque efficiency, the spin Hall conductivity of $\alpha$-W ($|\sigma_{\operatorname{SH}}^{\alpha\operatorname{-W}}|=3.71\times10^{5}\operatorname{\Omega}^{-1}\operatorname{m}^{-1}$) is ~3.5 times larger than that of amorphous-W ($|\sigma_{\operatorname{SH}}^{\operatorname{amorphous-W}}|=1.05\times10^{5}\operatorname{\Omega}^{-1}\operatorname{m}^{-1}$). Moreover, we demonstrate spin-orbit torque driven magnetization switching using a MgO/CoFeB/$\alpha$-W heterostructure. Our findings suggest that the conductive and high spin Hall conductivity $\alpha$-W can be a potential candidate for future low power consumption spin-orbit torque memory applications