Switching of both local ferroelectric and magnetic domains in multiferroic Bi0.9La0.1FeO3 thin film by mechanical force

Cross-coupling of ordering parameters in multiferroic materials by multiple external stimuli other than electric field and magnetic field is highly desirable from both practical application and fundamental study points of view. Recently, mechanical force has attracted great attention in switching of ferroic ordering parameters via electro-elastic coupling in ferroelectric materials. In this work, mechanical force induced polarization and magnetization switching were investigated in a polycrystalline multiferroic Bi0.9La0.1FeO3 thin film using a scanning probe microscopy system. The piezoresponse force microscopy and magnetic force microscopy responses suggest that both the ferroelectric domains and the magnetic domains in Bi0.9La0.1FeO3 film could be switched by mechanical force as well as electric field. High strain gradient created by mechanical force is demonstrated as able to induce ferroelastic switching and thus induce both ferroelectric dipole and magnetic spin flipping in our thin film, as a consequence of electro-elastic coupling and magneto-electric coupling. The demonstration of mechanical force control of both the ferroelectric and the magnetic domains at room temperature provides a new freedom for manipulation of multiferroics and could result in devices with novel functionalities

Time-varying skills (versus luck) in U.S. active mutual funds and hedge funds

In this paper, we develop a nonparametric methodology for estimating and testing time-varying fund alphas and betas as well as their long-run counterparts (i.e., their time-series averages). Traditional linear factor model arises as a special case without time variation in coefficients. Monte Carlo simulation evidence suggests that our methodology performs well in finite samples. Applying our methodology to U.S. mutual funds and hedge funds, we find most fund alphas decrease with time. Combining our methodology with the bootstrap method which controls for ‘luck’, positive long-run alphas of mutual funds but hedge funds disappear, while negative long-run alphas of both mutual and hedge funds remain. We further check the robustness of our results by altering benchmarks, fund skill indicators and samples

Dietary Blueberry and Bifidobacteria Attenuate Nonalcoholic Fatty Liver Disease in Rats by Affecting SIRT1-Mediated Signaling Pathway

NAFLD model rats were established and divided into NAFLD model (MG group), SIRT1 RNAi (SI group), blueberry juice (BJ group), blueberry juice + bifidobacteria (BJB group), blueberry juice + SIRT1 RNAi (BJSI group), and blueberry juice + bifidobacteria + SIRT1 RNAi groups (BJBSI group). A group with normal rats was a control group (CG). BJB group ameliorated NAFLD, which was better than BJ group (P<0.05). The lipid accumulation was lower in CG, BJ, and BJB groups than that in MG, SI, BJSI, and BJBSI groups (P<0.05). The levels of SIRT1 and PPAR-α were higher in CG, BJ, and BJB groups than those in MG, SI, BJSI, and BJBSI groups (P<0.05). The levels of SREBP-1c were lower in CG, BJ, and BJB groups than those in MG, SI, BJSI, and BJBSI groups (P<0.05). The biochemical indexes SOD, GSH, and HDL-c were improved from CG to BJB group (P<0.05). Inversely, the levels of AST and ALT, TG, TC, LDL-c, and MDA were decreased from CG to BJB group (P<0.05). These changes enhance antioxidative capability and biochemical index of rats. Blueberry juice and bifidobacteria improve NAFLD by activating SIRTI-mediating signaling pathway

Multi-level anomaly detection in industrial control systems via package signatures and LSTM networks

We outline an anomaly detection method for industrial control systems (ICS) that combines the analysis of network package contents that are transacted between ICS nodes and their time-series structure. Specifically, we take advantage of the predictable and regular nature of communication patterns that exist between so-called field devices in ICS networks. By observing a system for a period of time without the presence of anomalies we develop a base-line signature database for general packages. A Bloom filter is used to store the signature database which is then used for package content level anomaly detection. Furthermore, we approach time-series anomaly detection by proposing a stacked Long Short Term Memory (LSTM) network-based softmax classifier which learns to predict the most likely package signatures that are likely to occur given previously seen package traffic. Finally, by the inspection of a real dataset created from a gas pipeline SCADA system, we show that an anomaly detection scheme combining both approaches can achieve higher performance compared to various current state-of-the-art techniques

In search of the optimal number of fund subgroups

The idea of determining the number of fund subgroups is of central importance in the popular academic field of risk parity portfolio theory, and especially for practitioners’ direct use of fund-of-funds managers. Can the Gaussian Mixture Distributions plug-in approach via traditional procedures select the correct number of fund subgroups? Probably not. According to our in-sample/out-of-sample likelihood score analysis, the actual locations of subgroups in real data (of both U.S. mutual funds and hedge funds) are too close to each other. The information loss incurred by parameter uncertainty outweighs that incurred by misspecification, and can only be slightly alleviated using the nonparametric density estimators. An arbitrary choice of two subgroups only causes affordable information loss relative to more fund subgroups. These findings challenge the reliability of the Gaussian Mixture Distributions plug-in approach via traditional procedures (e.g., Bayesian Information Criterion, Likelihood Ratio and Chi-squared statistics) in selecting the correct number of subgroups

In search of the optimal number of fund subgroups

The idea of determining the number of fund subgroups is of central importance in the popular academic field of risk parity portfolio theory, and especially for practitioners’ direct use of fund-of-funds managers. Can the Gaussian Mixture Distributions plug-in approach via traditional procedures select the correct number of fund subgroups? Probably not. According to our in-sample/out-of-sample likelihood score analysis, the actual locations of subgroups in real data (of both U.S. mutual funds and hedge funds) are too close to each other. The information loss incurred by parameter uncertainty outweighs that incurred by misspecification, and can only be slightly alleviated using the nonparametric density estimators. An arbitrary choice of two subgroups only causes affordable information loss relative to more fund subgroups. These findings challenge the reliability of the Gaussian Mixture Distributions plug-in approach via traditional procedures (e.g., Bayesian Information Criterion, Likelihood Ratio and Chi-squared statistics) in selecting the correct number of subgroups