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Abstract Objective To delineate the metabolomic differences in plasma samples between patients with coronary artery disease (CAD) and those with concomitant CAD and type 2 diabetes mellitus (T2DM), and to pinpoint distinctive metabolites indicative of T2DM risk. Method Plasma samples from CAD and CAD-T2DM patients across three centers underwent comprehensive metabolomic and lipidomic analyses. Multivariate logistic regression was employed to discern the relationship between the identified metabolites and T2DM risk. Characteristic metabolites' metabolic impacts were further probed through hepatocyte cellular experiments. Subsequent transcriptomic analyses elucidated the potential target sites explaining the metabolic actions of these metabolites. Results Metabolomic analysis revealed 192 and 95 significantly altered profiles in the discovery (FDR

Lithium-ion batteries are widely used in modern society as important energy storage devices due to their high energy density, rechargeable performance, and light weight. However, the capacity and performance of lithium-ion batteries gradually degrade with the number of charge or discharge cycles and environmental conditions, which can affect the reliability and lifetime of the batteries, so it is necessary to accurately evaluate their health. The belief rule base (BRB) model is an evaluation model constructed based on rules that can handle uncertainties in the operation of lithium-ion batteries. However, lithium-ion batteries may be affected by disturbances from internal or external sources during operation, which may affect the evaluation results. To prevent this problem, this paper proposes a disturbance-considering BRB modeling approach that considers the possible effects of disturbances on the battery in the operating environment and quantifies the disturbance-considering capability of the assessment model in combination with expert knowledge. Second, robustness and interpretability constraints are added in this paper, and an improved optimization algorithm is constructed that maintains or possibly improves the resistance of the model to disturbance. Finally, using the lithium-ion batteries provided by the National Aeronautics and Space Administration (NASA) Prediction Centre of Excellence and the University of Maryland as a case study, this paper verifies that the proposed modeling approach is capable of constructing robust models and demonstrates the effectiveness of the improved optimization algorithm.

Ship target detection using radar has important applications in the military and civilian fields. As a decoy, the corner reflector (CR) can successfully deceive a radar by its strong radar cross-section (RCS) to protect a ship. In order to discriminate between a CR and ship, this paper proposes a discrimination method based on three-dimensional characteristics. First, we deduce the basic scattering of CR by the high-frequency electromagnetic theory, and propose a CR decomposition which can solve the problem that the Krogager decomposition has terrible errors in clutter. Then, we introduce the definition of the main scattering polarization and give the multi-dimensional characteristic of CR. Subsequently, we analyze the spatial-time characteristic of radar based on the three-dimensional proportional guidance. With the CR mean square error (MSE), a CR discrimination method is proposed based on the time-spatial-polarization (TSP) joint domains. Finally, the proposed method is analyzed and compared using the fully polarimetric data of Feko software, which can achieve 95% discrimination probability and 4.1% false alarm probability.

This paper is concerned with the problem of gain-scheduled H2 controller synthesis for continuous-time linear parameter-varying systems. In this problem, the system matrices in the state-space form are polytopic and patameterized and the admissible values of the parameters are assumed to be measurable on-line in a polytope space. By employing a basis-parameter-dependent Lyapunov function and introducing some slack variables to the well-established performance conditions, sufficient conditions for the existence of the desired gain-scheduled H2 state feedback and dynamic output feedback controllers are established in terms of parameterized linear matrix inequalities. Based on the polytopic characteristic of the dependent parameters and a convexification method, the corresponding controller synthesis problem is then cast into finite-dimensional convex optimization problem which can be efficiently solved by using standard numerical softwares. Numerical examples are given to illustrate the effectiveness and advantage of the proposed methods. [ABSTRACT FROM AUTHOR]

We propose an adaptive total generalized variation (TGV) based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising. The variable splitting (VS) and the classical augmented Lagrangian method (ALM) are used to solve the proposed model. With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference. Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment. [ABSTRACT FROM AUTHOR]

The extended fuzzy Kalman filter (EFKF) of non-linear systems which can deal with fuzzy uncertainty effectively has been developed recently. But it seems to be inapplicable to the cases where the states change abruptly or there exist model mismatches in non-linear systems. Therefore, based on the EFKF, a new concept of the improved fuzzy Kalman filter (IFKF) is proposed in this article. Due to the introduction of the extension orthogonality principle given as a criterion to design the new algorithm, the IFKF can track the abrupt changes of the states and has definite robustness against the model mismatches. Finally, computer simulations with a MIMO non-linear model are presented, which illustrate that the proposed IFKF has the strong tracking ability and robustness against the model mismatches. [ABSTRACT FROM AUTHOR]

As the processing control and sensory evaluation of green tea are highly subjective and the tea industry is highly professionalized, it is desirable that to find a more objective way of evaluating the quality of tea is found. In this paper, two models were set up using the BP-MLP and RBF neural networks, a sensory quality prediction model, using eleven parameters measured during processing as variables, such as leaf temperature, moisture content, etc., and a sensory quality evaluation model using fourteen parameters related to green tea as variables, such as image information were set up. The overall results suggested that leaf temperature, moisture content measured during production could effectively predict the sensory quality of green tea, with parameters as image information of green tea able to effectively evaluate its sensory quality. Compared with the BP-MLP neural network, the RBF model displayed much greater accuracy as a prediction model, reducing the relative error from 0.204 to 0.006. [ABSTRACT FROM AUTHOR]