Influence of boundary conditions and geometric imperfections on lateral–torsional buckling resistance of a pultruded FRP I-beam by FEA

Presented are results from geometric non-linear finite element analyses to examine the lateral torsional buckling (LTB) resistance of a Pultruded fibre reinforced polymer (FRP) I-beam when initial geometric imperfections associated with the LTB mode shape are introduced. A data reduction method is proposed to define the limiting buckling load and the method is used to present strength results for a range of beam slendernesses and geometric imperfections. Prior to reporting on these non-linear analyses, Eigenvalue FE analyses are used to establish the influence on resistance of changing load height or displacement boundary conditions. By comparing predictions for the beam with either FRP or steel elastic constants it is found that the former has a relatively larger effect on buckling strength with changes in load height and end warping fixity. The developed finite element modelling methodology will enable parametric studies to be performed for the development of closed form formulae that will be reliable for the design of FRP beams against LTB failure

The Information Content of Implied Volatility in the Hong Kong and Singapore Covered Warrants Markets

This paper examines the informational content and predictive power of implied volatility over different forecasting horizons in a sample of European covered warrants traded in the Hong Kong and Singapore markets. The empirical results show that time-series-based volatility forecasts outperform implied volatility forecast as a predictor of future volatility. The finding also suggests that implied volatility is biased and informationally inefficient. The results are due to the fact in Hong Kong and Singapore the covered warrants markets are dominated by retail investors, who tend to use covered warrants’ leverage to speculate on the price movements of the underlying rather than to express their view on volatility.

Asymmetric Inflation Hedge of Housing Return: A Non-linear Vector Error Correction Approach

Conclusions of past works on the inflation hedging ability of real estate investment are not consistent. The reason for this perplexity might be the neglect of separation between high and low state of inflation, which has a great influence on empirical results. In order to examine the inflation hedging effectiveness of real estate with Taiwanese monthly housing returns and inflation, this paper uses the inflation as the threshold variable to create the nonlinear vector correction model that divides the inflation rates into high and low regime. We find robust evidence that when inflation rates are higher than 0.83% threshold value, housing returns are able to hedge against inflation, and, otherwise, they are unable. Using new methodology to discover new implications is main contribution of this study.Housing prices; Inflation; Nonlinear VECM; Taiwan

Securing Downlink Massive MIMO-NOMA Networks with Artificial Noise

In this paper, we focus on securing the confidential information of massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks by exploiting artificial noise (AN). An uplink training scheme is first proposed with minimum mean squared error estimation at the base station. Based on the estimated channel state information, the base station precodes the confidential information and injects the AN. Following this, the ergodic secrecy rate is derived for downlink transmission. An asymptotic secrecy performance analysis is also carried out for a large number of transmit antennas and high transmit power at the base station, respectively, to highlight the effects of key parameters on the secrecy performance of the considered system. Based on the derived ergodic secrecy rate, we propose the joint power allocation of the uplink training phase and downlink transmission phase to maximize the sum secrecy rates of the system. Besides, from the perspective of security, another optimization algorithm is proposed to maximize the energy efficiency. The results show that the combination of massive MIMO technique and AN greatly benefits NOMA networks in term of the secrecy performance. In addition, the effects of the uplink training phase and clustering process on the secrecy performance are revealed. Besides, the proposed optimization algorithms are compared with other baseline algorithms through simulations, and their superiority is validated. Finally, it is shown that the proposed system outperforms the conventional massive MIMO orthogonal multiple access in terms of the secrecy performance

Variance-Optimal Offline and Streaming Stratified Random Sampling

Stratified random sampling (SRS) is a fundamental sampling technique that provides accurate estimates for aggregate queries using a small size sample, and has been used widely for approximate query processing. A key question in SRS is how to partition a target sample size among different strata. While Neyman allocation provides a solution that minimizes the variance of an estimate using this sample, it works under the assumption that each stratum is abundant, i.e., has a large number of data points to choose from. This assumption may not hold in general: one or more strata may be bounded, and may not contain a large number of data points, even though the total data size may be large. We first present VOILA, an offline method for allocating sample sizes to strata in a variance-optimal manner, even for the case when one or more strata may be bounded. We next consider SRS on streaming data that are continuously arriving. We show a lower bound, that any streaming algorithm for SRS must have (in the worst case) a variance that is {\Omega}(r) factor away from the optimal, where r is the number of strata. We present S-VOILA, a practical streaming algorithm for SRS that is locally variance-optimal in its allocation of sample sizes to different strata. Our result from experiments on real and synthetic data show that VOILA can have significantly (1.4 to 50.0 times) smaller variance than Neyman allocation. The streaming algorithm S-VOILA results in a variance that is typically close to VOILA, which was given the entire input beforehand