Peroxisomal regulation of redox homeostasis and adipocyte metabolism

Peroxisomes are ubiquitous cellular organelles required for specific pathways of fatty acid oxidation and lipid synthesis, and until recently their functions in adipocytes have not been well appreciated. Importantly, peroxisomes host many oxygen-consumption reactions and play a major role in generation and detoxification of reactive oxygen species (ROS) and reactive nitrogen species (RNS), influencing whole cell redox status. Here, we review recent progress in peroxisomal functions in lipid metabolism as related to ROS/RNS metabolism and discuss the roles of peroxisomal redox homeostasis in adipogenesis and adipocyte metabolism. We provide a framework for understanding redox regulation of peroxisomal functions in adipocytes together with testable hypotheses for developing therapies for obesity and the related metabolic diseases

Monitoring Mechanisms, Managerial Incentives, Investment Distortion Costs, and Derivatives Usage

We relate derivatives usage to the level of corporate governance/monitoring mechanisms, managerial incentives and investment decisions of UK firms. We find evidence to suggest that the monitoring environment, e.g., board size, influences the use of both currency and interest rate derivatives usage. Managerial compensation also influences derivatives usage. Investment decisions are affected by the governance and managerial compensation of firms, which in turn impact on derivatives usage. We find a strong tendency for UK firms to reduce derivatives usage in situations where derivatives usage should be increased. There is limited evidence that firms use hedging substitutes to avoid monitoring from external capital markets

Fault detection and diagnosis of rotating machinery using modified particle filter

In order to effectively monitor condition and detect fault types of high nonlinear system, and extract the features of system state under strong noise background, this paper proposes a novel fault detection and diagnosis (FDD) method based on modified particle filter (PF). The artificial neural network is incorporated in PF for adaptively adjusting weight of particle. In the modified PF, the large weight particles are split into several small weight particles, the particles with smaller weight is adjusted by using artificial neural network. By which the particles in the low probability density region are adjusted to the high probability density region, and the problem of particle leanness is solved effectively. Moreover, this paper also uses time-varying auto regressive (TVAR) and Akaike information criterion (AIC) methods to establish state space model for state estimation. Finally, the proposed method is implemented for fault diagnosis on a roller bearing. Good results are obtained, and the bearing faults, such as the outer race, the inner race and the roller element defects, have been effectively discriminated