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The suspension system is a significant part of a vehicle because it transmits the torque and force between the wheels and the frame, meeting the requirements of ride comfort. In this paper, a novel interconnected air suspension was introduced and a cooperative control between interconnected mode activation (IMA) and outsourced mode activation (OMA) was designed. To improve ride comfort in a bus at a minimal energy consumption, this cooperative control based on model predictive control (MPC) for computing the best force and the rule was defined to distribute the best suspension force generated by IMA and OMA. The simulation and experimental results showed that the proposed control strategy significantly improved ride comfort in different conditions. Compared with traditional control in the test vehicle, the RMS of the front-left sprung mass and unsprung vertical acceleration decreased by 24.2% and 14.5%, respectively, under a straight condition; the RMS of the sprung (unsprung) mass vertical acceleration reduced by 22.38% and 15.43%, respectively, under a bump condition. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources, especially the residential photovoltaic (PV) systems, in the distribution networks (DNs) aggravates the ramp-events in transmission system. To address this issue, we propose a novel look ahead dispatch model for the ramp minimization in DNs using distributed energy storage systems (ESSs). The dispatch problem considering the scheduling of ESSs is modeled as a finite-horizon optimization problem and is carried out using model predictive control (MPC) method that takes both current and future information into account. In addition, the optimal power flow in DN is formulated as a second-order cone programing problem to guarantee the global optimality. Numerical results on IEEE 37-bus distribution network show that our proposed model not only brings about 74% reduction of maximum ramp but also yields the near-minimum system operating cost. [ABSTRACT FROM AUTHOR]

Stepwise heating method was used to determine the hydrate phase equilibrium in the water containing graphite nanoparticles. The results show that, compared with deionized water at a given temperature, a significant upward shift of the formation pressure of the CO 2 hydrate formed in graphite nanoparticles suspensions was observed, but the phase equilibrium curves of CO 2 hydrate formation in the different graphite nanoparticles concentrations were basically consistent. In this regard, graphite nanoparticles have a negative effect on the gas hydrate formation. In addition, an improved hydrate thermodynamic model was proposed to predict the hydrate equilibrium conditions under the system of graphite nanoparticles suspensions. The results show that the maximum average deviation of pressure predicted by model is 3.39, but the minimum average deviation is 2.21. It indicates that the data of the model prediction was good agreements with the data obtained from experiments. [ABSTRACT FROM AUTHOR]

Objectives: To assess the independent impact of new genetic variants on conversion to advanced stages of AMD, controlling for established risk factors, and to determine the contribution of genes in predictive models. Methods: In this prospective longitudinal study of 2765 individuals, 777 subjects progressed to neovascular disease (NV) or geographic atrophy (GA) in either eye over 12 years. Recently reported genetic loci were assessed for their independent effects on incident advanced AMD after controlling for 6 established loci in 5 genes, and demographic, behavioral, and macular characteristics. New variants which remained significantly related to progression were then added to a final multivariate model to assess their independent effects. The contribution of genes to risk models was assessed using reclassification tables by determining risk within cross-classified quintiles for alternative models. Results: Three new genetic variants were significantly related to progression: rare variant R1210C in CFH (hazard ratio (HR) 2.5, 95% confidence interval [CI] 1.2–5.3, P = 0.01), and common variants in genes COL8A1 (HR 2.0, 95% CI 1.1–3.5, P = 0.02) and RAD51B (HR 0.8, 95% CI 0.60–0.97, P = 0.03). The area under the curve statistic (AUC) was significantly higher for the 9 gene model (.884) vs the 0 gene model (.873), P = .01. AUC’s for the 9 vs 6 gene models were not significantly different, but reclassification analyses indicated significant added information for more genes, with adjusted odds ratios (OR) for progression within 5 years per one quintile increase in risk score of 2.7, P<0.001 for the 9 vs 6 loci model, and OR 3.5, P<0.001 for the 9 vs. 0 gene model. Similar results were seen for NV and GA. Conclusions: Rare variant CFH R1210C and common variants in COL8A1 and RAD51B plus six genes in previous models contribute additional predictive information for advanced AMD beyond macular and behavioral phenotypes. [ABSTRACT FROM AUTHOR]

A new convolutional perfectly matched layer (CPML) is proposed and implemented in one-step leapfrog alternately direction-implicit finite-difference time-domain (ADI-FDTD) method. Different from the previous developed perfectly matched layers (PMLs) used in the ADI-FDTD methods, the proposed PML has the same form as that used for the conventional FDTD method, requiring only one-step computations of the CPML auxiliary quantities. Stability of the proposed CPML is verified semianalytically and its high absorption efficiency is proven numerically. The proposed CPML is then applied to a number of applications; they include radiation generated by a current source in free space, propagating and evanescent modes in a rectangular waveguide, and transient current prediction on an aircraft that is modeled with a numerical grid of 402 × 48 × 262. Accurate results have been obtained in comparison with the results obtained with the other methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper demonstrates an approach to predict the chaotic time series with outliers using annealing robust fuzzy neural networks (ARFNNs). A combination model that merges support vector regression (SVR), radial basis function networks (RBFNs) and simplified fuzzy inference system is used. The SVR has the good performances to determine the number of rules in the simplified fuzzy inference system and initial weights for the fuzzy neural networks (FNNs). Based on these initial structures, and then annealing robust learning algorithm (ARLA) can be used effectively to overcome outliers and adjust the parameters of structures. Simulation results show the superiority of the proposed method with different SVR for training and prediction of chaotic time series with outliers. [Copyright &y& Elsevier]

The Model for Prediction Across Scales (MPAS) is used to simulate the East Asian winter monsoon (EAWM) over the 2011–2020 winter. The 45 day hindcasts are made with 30 km horizontal resolution and constructed to a time-lagged ensemble system. The climatology, the major modes of EAWM variability, and the blocking activities are examined. The evaluation results reveal that MPAS can simulate the climatologic characteristics of EAWM reasonably, with a surface cold bias of 4% and a positive rainfall bias of 9% over East Asia. MPAS can perform skillfully in the forecasts of surface temperature probability of East Asia and is more reliable in detecting below normal and above normal events. The features that influence the EAWM variability are also analyzed. MPAS simulates reasonably in the occurrence frequency of blocking high in both locations and duration time. The empirical orthogonal function analysis also shows that MPAS can capture the two major modes of the surface temperature of EAWM. On the other hand, it is also found that a biased sea surface temperature may modify the circulations over the Western Pacific and affect the simulated occurrence frequency of cold events near Taiwan during winter. [ABSTRACT FROM AUTHOR]

The strategies of the extrapolation adjusted by model prediction (ExAMP) blending scheme, which trusts the field pattern predicted by extrapolation and allows the field intensity to be adjusted by numerical weather prediction (NWP), for rainfall nowcasting are analyzed in this study. The McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) and the Weather Research and Forecasting (WRF) model serve as the extrapolation and NWP models, respectively. Seven 150-min rainfall nowcasting experiments with different strategies are carried out for 37 sampled periods from seven heavy rainfall events in Taiwan in 2019. The results of the overall statistics indicate that, for the extrapolation component, extrapolating the current rainfall rate estimated from the lowest dual-polarimetric radar observations is a superior strategy. The ExAMP scheme that blends the MAPLE and WRF forecasts can surpass both components in 150-min rainfall nowcasting, and an empirical limitation on the innovation of intensity during the blending procedure is found unnecessary in this study. Moreover, the spatial performance for two contrasting events reveals the ability of ExAMP in grasping the rainfall strengthening and weakening in different areas. The skill statistics separately at rainfall strengthening gauges and weakening gauges further prove the effectiveness of ExAMP even though it is effective in intensity correction instead of pattern correction. [ABSTRACT FROM AUTHOR]

• Proposes a physics-constrained GP model to predict the hydrodynamic characteristics of array of WECs with any layout. • Embeds prior physical knowledge in the GP model by designing physics-constrained kernels. • Proposes an integrated kernel to explicitly incorporate physical features such as invariance, symmetry, and additivity. • The proposed physics-constrained GP model is data-efficient and requires less training data to achieve desired accuracy. • The proposed model is less vulnerable to the curse of dimensionality and has good scalability for large arrays. To improve the efficiency of wave farms and achieve maximum power generation, the layout of wave energy converters (WECs) in an array needs to be carefully designed so that the hydrodynamic interactions can be positively exploited. For this, the hydrodynamic characteristics of the WEC array in different layouts need to be calculated. However, such calculations using numerical models usually entail significant computational cost, especially for large arrays of WECs. To address the computational challenge, a physics-constrained Gaussian process (GP) model is proposed to replace the original expensive numerical model and predict the hydrodynamic characteristics of the WECs for any array layout. By exploring the relationship between the WEC array (i.e., the input) and different hydrodynamic characteristics (i.e., the output), we summarize a set of physical constraints/features, including invariance, symmetry, and additivity. This prior knowledge about the input-output relationship is then directly embedded in the constructed GP model through the design of physics-constrained kernels. In particular, a double-sum invariant kernel is first developed to incorporate the invariance and symmetry features, and then an additive kernel is developed to incorporate the additive feature of the problem. The invariant kernel and the additive kernel are then integrated to construct the physics-constrained GP model. Compared to the standard GP model, the proposed physics-constrained GP models require less training data to achieve the desired accuracy in predicting the hydrodynamic characteristics and are also less vulnerable to the curse of dimensionality (i.e., good scalability for large arrays) due to the use of an additive kernel. The efficiency, accuracy, and scalability of the proposed approach are demonstrated through an application to predict the hydrodynamic characteristics for WEC arrays of different sizes and layouts. [ABSTRACT FROM AUTHOR]

The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation and pathogenesis. For the present in vitro fermentation experiment, the CSH of ruminal mixed microbes was considered, along with other data records of pH, ammonia-nitrogen concentration, and neutral detergent fibre digestibility, conditions of surface tension and specific surface area in two different time scales. A dataset of 170,707 perturbations of input variables, grouped into two blocks of data, was constructed. Next, Expected Measurement Moving Average – Machine Learning (EMMA-ML) models were developed in order to predict CSH after perturbations of all input variables. EMMA-ML is a Perturbation Theory method that combines the ideas of Expected Measurement, Box-Jenkins Operators/Moving Average, and Time Series Analysis. Seven regression methods have been tested: Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, Elastic Net regression, Neural Networks regression, and Random Forests (RF). The best regression performance has been obtained with RF (EMMA-RF model) with an R-squared of 0.992. The model analysis has shown that CSH values were highly dependent on the in vitro fermentation parameters of detergent fibre digestibility, ammonia – nitrogen concentration, and the expected values of cell surface hydrophobicity in the first time scale. [ABSTRACT FROM AUTHOR]

• Ion separation using composite nanoporous adsorbents. • Neural simulation of adsorption via nanostructure materials. • Machine learning-based simulation of adsorption separation using nanomaterials. A machine learning simulation via artificial neural network was implemented in this work to analyse adsorption removal of cadmium ions from aqueous solutions. The considered solid adsorbent in this work was a nanocomposite of mesoporous silica type and a polymer. To separate ions from water the mesoporous nanocomposite was selected due to its large pores. The simulation runs were carried out to correlate the adsorption parameters including solution pH and adsorbent dosage to the equilibrium concentration of ions in the liquid phase. The training and validation of the neural network indicated agreement with the experimental data for adsorption of cadmium ions, with high accuracy. The statistical evaluation of the data indicated great accuracy with R2 greater than 0.999 for the fitting. The findings of the modelling indicated that the effect of adsorbent dosage is not significant compared to the solution's pH. The adsorption removal was increased with enhancing the solution pH due to the interaction between the ions and the surface of adsorbent. The results revealed that the developed neural network simulation is robust for prediction of ions removal from water using nanocomposite adsorbents. [ABSTRACT FROM AUTHOR]