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Real-time vehicle route and speed co-optimization play a vital role in improving its energy consumption with rapidly shifting traffic and environmental conditions (e.g., traffic jams, temperature, gust wind, etc.). In this article, a novel vehicle eco motion planning (VEMP) method is proposed to optimize the vehicle route and speed simultaneously for minimizing its energy usage with a given origin-destination pair and a travel time limit. The proposed VEMP method is based on the modified Dijkstra algorithm and gradient descent speed optimization to find and update the optimal route and corresponding speed profile in real-time based on changing traffic and driving environment information. Cosimulation studies are conducted for the developed VEMP method in MATLAB with the SUMO traffic model using a real-world map. The simulation results show that for the studied driving environment, the speed optimization in the VEMP method is able to reduce the energy usage by 2.27% over the case of only using speed limits on a fixed route, and the VEMP can reduce the total energy consumption by 23.83% over the fastest route created by the Dijkstra algorithm. The simulation of a sudden traffic jam demonstrates the ability of real-time updating for the proposed VEMP method to handle sudden traffic situations such as vehicle cut-in.

Vehicle energy consumption model, as a function of its operational environment, plays a significant role in real-time optimization of vehicle route and speed for a given pair of origin and destination with a desired arrival time for minimal energy consumption. In this article, a gray-box vehicle energy consumption model is developed based on a high-fidelity vehicle dynamic model with environmental influence based on the kriging modeling method, which includes rolling resistance, aerodynamics, gravity, and energy consumption of air conditioning and heater (HVAC), along with environmental conditions, such as temperature, wind speed, etc. The data-driven model, trained based on Gaussian process assumption, ensures the accuracy of the resulting model with a modeling error below 2.5%. The real-time model updating is based on recursive least-squares optimization using current driving data so that the model used for route and speed optimization represents the current vehicle status. The proposed gray-box model is validated in computer-in-the-loop simulations using SUMO and MATLAB with less than 2% error of energy consumption, which is a significant improvement over the vehicle dynamic model with up to 35% error in certain cases. A case study also indicates energy consumption reduction for vehicle route-speed optimization.

Reducing experimental calibration cost to achieve optimal system performance is challenging, especially for highly nonlinear engine systems. With the advancement and widespread adoption of machine learning methods for control applications, it is now possible to use an intelligent black-box model to efficiently optimize nonlinear control systems without the detailed knowledge of system dynamics. The surrogate-assisted optimization approach has been recently proven to work effectively to reduce the overall experimental budget (cost). However, its application to calibrate and optimize a practical control system experimentally is a challenge due to stochastic system and measurement noises. This article experimentally calibrates and optimizes the control parameters of a 6.7L Ford diesel engine using the stochastic surrogate-assisted optimization automatically based on a stochastic Kriging model, which is the first step toward automating the engine calibration process. Two engine control variables, namely, exhaust gas recirculation valve and variable geometry turbocharger vane positions, are calibrated to obtain a tradeoff between engine efficiency, in terms of brake-specific fuel consumption, and $\text{NO}_{x}$ emissions, utilizing both the deterministic and stochastic surrogate-assisted learning algorithms. Experimental results show a much better representation of the output response surfaces with the stochastic surrogate-assisted optimization than that from the deterministic approach. Note that the stochastic approach helps direct the search toward the global optimal region without wasting the evaluation budget in exploring the local optima, and the deterministic approach cannot effectively handle stochastic system and measurement noises, resulting in overfitting the data. Therefore, to solve a practical optimization problem with noises, stochastic surrogate-assisted optimization is recommended.

This paper presents the development of novel transition flight controllers for a class of urban air mobility aircrafts configured with a fixed-wing and six distributed electric rotor assemblies. Only the two tilt-rotors are utilized for thrust vectoring during transition flight from hovering to steady-level flight, while the four lift-rotors are modulated with aerodynamic lift induced by fixed-wing to maintain stable altitude-hold. Three tractable tilt-rotor articulation profiles are proposed by taking into account of various aircraft and hardware constraints. Given a predefined nominal tilting profile, a family of linear models is obtained by linearizing the nonlinear aircraft model at multiple tilt-rotor angular positions along the tilting profile. Using tilt-rotor angular position as a scheduling parameter, a discrete-time linear parameter-varying model can be constructed, which is then used to develop a novel transition flight control architecture that integrates the adaptive model predictive control law with feedforward effect of the dynamic reference compensation. The simulation results demonstrate the effectiveness of proposed transition flight controllers and its robustness subject to external disturbance.

In this article, motor failure control of a six-rotor electric vertical take-off and landing (eVTOL) urban air mobility aircraft is investigated using adaptive model predictive control (MPC) based on the linear parameter-varying (LPV) model developed using the nonlinear rigid-body aircraft model. For capturing the aircraft dynamics under motor failure conditions, a family of linearized models are obtained by trimming the nonlinear aircraft model at multiple equilibrium conditions and the LPV model is obtained by linking the linear models using the failed rotor speed, where the system transition from healthy to failure is modeled by a scheduling parameter calculated based on failed rotor speed caused by available motor peak power after failure. The proposed adaptive MPC is developed to optimize the system output performance, including the rigid-body aircraft velocity and altitude, by using quadratic programming optimization with reference compensation subject to a set of time-varying constraints representing the current available propeller acceleration calculated based on the motor power. Simulation study is conducted based on the developed LPV control design and original nonlinear rigid-body model, and the simulation results demonstrate that the designed adaptive MPC controller is able to recover and maintain the aircraft at desired stable condition after motor failure.

Sudden vehicle propulsion torque change under tip-in/out maneuver often leads to low-frequency longitudinal vibration due to the flexibility in the half-shaft and tire slip, which greatly affects vehicle drivability. Note that the vibration frequency is between 1 and 10 Hz and is difficult to be absorbed by the vehicle mechanical system. To optimize the vehicle drivability under tip-in maneuver, an Adaptive Linear Quadratic Tracking (ALQT) anti-jerk traction controller is proposed in this paper. Based on the experimental data, a Carsim-Simulink co-simulation model is developed for assessing control performance. A control-oriented model, considering the nonlinear characteristics of the tire-road friction coefficient and slip ratio, is then proposed. A reference model with rigid axle is used to provide the equilibrium points and reference velocity trajectory. Jacobi linearization method is then used to linearize the model along the desired trajectory and a linear deviation model based on equilibrium points is obtained. Finally, the deviation compensation receding horizon LQT controller is designed along with the Kalman state estimation. The effectiveness of the designed controller is assessed via simulation studies under different road surfaces and compared with PID and LQR controllers. The LQT controller is able to track the desired velocity profile with minimum jerk while increasing road safety. Furthermore, the effect of LQT weighting coefficients under different road surfaces are discussed. Simulation results show that the ALQT controller is able to optimize vehicle drivability under different road surfaces and the weighting matrices shall be selected based on the road condition for optimal drivability.

This paper proposes a fault detection algorithm to identify online sensor performance degradation and failure, where the sensor faults are characterised by variations of the sensor measurement noise covariance matrix. That is, the proposed algorithm estimates the slowly-varying sensor measurement noise covariance and detects the abrupt and/or intermittent change of sensor measurement noise covariance. A memory-based technique is used to detect the abrupt (or intermittent) change of sensor noise covariance matrix. The memory-based technique is adopted due to its simplicity and online applicability. The proposed algorithm originally is designed for discrete linear time-varying (DLTV) systems and applied to discrete linear parameter-varying (DLPV) systems. Simulation results show that the proposed algorithm is capable of estimating the slowly-varying and detecting the abrupt (or intermittent) change of sensor measurement noise covariance for multiple-input and multiple-output discrete linear parameter-varying systems, where the scheduling parameters lie within a compact set. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence.

This paper proposes a fault detection algorithm to detect online sensor performance degradation and failure due to sensor ageing, where the sensor faults due to ageing are characterised by slow variations of the sensor measurement noise covariance matrix. The proposed algorithm utilises the information about the quality of weighted innovation sequence to estimate the slowly-varying sensor noise covariance. The iterative manner of the proposed algorithm leads to significant reduction of the computational load, reduced sensitivity to initial conditions and improved estimation accuracy, making it suitable for online applications. Simulation results show that the proposed algorithm is capable of estimating the slowly-varying sensor noise covariance for multiple-input and multiple-output systems with noise covariance varying linearly, exponentially, or linearly with sinusoid fluctuation. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence, better estimation accuracy and less computation load in contrast to published literature.

In this article, two novel linear parameter–varying modeling and control techniques are proposed for active flutter suppression of a smart airfoil model. The smart airfoil model is instrumented with a moving mass that can be used to actively control the airfoil pitching and plunging motions. The first linear parameter–varying modeling approach makes use of the moving mass position as a scheduling parameter, and the hard constraint at the boundaries is imposed by proper selection of the parameter-varying function. The second modeling technique utilizes nonlinear springs and dampers, which are added to both ends of the airfoil groove to confine the motion of the moving mass. A state-feedback-based linear parameter–varying gain-scheduling controller with the guaranteed H∞performance is proposed by utilizing the dynamics of the moving mass. In this study, both the position of the moving mass and the free-stream airspeed are considered as the scheduling parameters. The numerical simulations demonstrate the effectiveness of the proposed linear parameter–varying control architectures by significantly improving the performance, while increasing the flutter speed and reducing the control effort.

New approach is developed in this paper for mode matching of LTI models at different grid points such that a Linear Parameter Varying model can be obtained through interpolation. The approach is based on matching eigenvector associated with each eigenvalue such that state consistency can be guaranteed for local LTI models. The novelty of the developed approach is the ability to handle mode veering phenomena, where two distinct real poles converge into two repeated ones then separate into complex conjugate poles and vise versa. Linear interpolation procedure based on Least-Squares errors is also presented to interpolate local LTI models. The proposed approach is demonstrated through numerical example.

This paper presents a control-oriented, reaction-based diesel combustion model that predicts the time-based rate of combustion, in-cylinder gas temperature, and pressure over one engine cycle. The model, based on the assumption of a homogeneous thermodynamic combustion process, uses a two-step chemical reaction mechanism that consists of six species: diesel fuel (C10.8H18.7), oxygen (O2), carbon dioxide (CO2), water (H2O), nitrogen (N2), and carbon monoxide (CO). The temperature variation rate is calculated based on the rate of change of species concentrations; the heat loss correlation is also used to study the model performance. The accuracy of the model is evaluated using test data from a GM 6.6 L, eight-cylinder Duramax engine. The main contribution is the model calibration under different key operational conditions over a large engine speed and load range as well as different injection timings and exhaust gas recirculation rates by solving the optimal calibration problem. The calibrated reaction-based model accurately predicts the indicated mean effective pressure, while keeping the errors of in-cylinder pressure and temperature small, and, at the same time, significantly reduces the calibration effort, especially when the engine is operated under multiple fuel injection operations compared with Wiebe-based combustion models. The calibrated model parameters have a strong correlation to engine speed, load, and injection timings, and, as a result, a universal parameter calibration structure is proposed for entire operational conditions.

This paper develops synthesis conditions for Static Output-Feedback (SOF) Gain-Scheduling (GS) control with guaranteed upper bound of H 2 performance for continuous-time Linear Parameter Varying (LPV) systems, where measurements of scheduling parameters are affected by uncertainties or measurement noise. The control problem is solved through an iterative two-stage design procedure. In the first stage, parameter-dependent state-feedback controller is obtained to minimize upper-bound of the H 2 performance. Then, this controller is used as input to the second stage to synthesize Robust Gain-Scheduling (RGS) static output-feedback gain with minimal H 2 performance. In both stages, the synthesis conditions are given in terms of Parametrized Linear Matrix Inequalities (PLMIs). Robust SOF (parameter-independent) controller can be synthesized as special case of the developed synthesis conditions. Two examples have been presented to illustrate the benefit of the proposed approach. One is an academic numerical example from literature and the other one is a realistic LPV model of Electric Variable Valve Timing (EVVT) actuator for automotive engines that utilizes engine speed and vehicle battery voltage as time-varying scheduling parameters. [ABSTRACT FROM AUTHOR]

A control-oriented model for the variable-geometry turbocharger is critical for model-based variable-geometry turbocharger control design. Typically, the variable-geometry turbocharger turbine power is modeled with a fixed mechanical efficiency of the turbocharger on the assumption of an isentropic process. The fixed-efficiency approach is an oversimplification and may lead to modeling errors because of an overpredicted or underpredicted compressor power. This leads to the use of lookup-table-based approaches for defining the mechanical efficiency of the turbocharger. Unfortunately, since the vane position of a variable-geometry turbocharger introduces a third dimension into these maps, real-time implementation requires three-dimensional interpolations with increased complexity. Map-based approaches offer greater fidelity in comparison with the fixed-efficiency approach but may introduce additional errors due to interpolation between the maps and extrapolation to extend the operational range outside the map. Interpolation errors can be managed by using dense maps with extensive flow bench testing; smooth extrapolation is necessary when the turbine is operated outside the mapped region, e.g. in low-flow and low-speed conditions. Extending the map to this region requires very precise flow control and measurement using a motor-driven compressor, which currently is not a standard test procedure. In this paper, a physics-based control-oriented model of the turbine power and the associated power loss is proposed and developed, where the turbine efficiency is modeled as a function of both the vane position of the variable-geometry turbocharger and the speed of the turbine shaft. As a result, the proposed model eliminates the interpolation errors with smooth extension to operational conditions outside typically mapped regions.

Traditionally the transmission gear-shifting schedule is based upon the throttle position and the vehicle (or engine) speed. This paper proposes to add a third parameter, called the terrain coefficient, to form a three-parameter gear-shifting schedule for improving the fuel economy of a vehicle. The terrain coefficient is a compound parameter consisting of the road grade and the rolling resistance coefficient. It can be estimated in real time by the proposed multi-step recursive least-squares method. The dynamic programming and the moving least-squares method are adopted to optimize the gear sequences and to generate the three-parameter gear-shifting schedule. The proposed gear-shifting schedule is evaluated against the traditional two-parameter gear-shifting schedule via Simulink simulations and on-road experiments using a heavy-duty vehicle. The simulation results for the Urban Dynamometer Driving Schedule and the US06 Supplemental Federal Test Procedure driving cycles show that the fuel economies of the proposed gear-shifting schedule are improved by 3.3% and 2.7% respectively over that of the traditional two-parameter schedule. The experimental results indicate that the three-parameter gear-shifting schedule improves the fuel economy by 3.5% over the traditional schedule with a satisfactory acceleration performance.

Turbulent jet ignition combustion is a promising concept for achieving high thermal efficiency and low NOx(nitrogen oxides) emissions. A control-oriented turbulent jet ignition combustion model with satisfactory accuracy and low computational effort is usually a necessity for optimizing the turbulent jet ignition combustion system and developing the associated model-based turbulent jet ignition control strategies. This article presents a control-oriented turbulent jet ignition combustion model developed for a rapid compression machine configured for turbulent jet ignition combustion. A one-zone gas exchange model is developed to simulate the gas exchange process in both pre- and main-combustion chambers. The combustion process is modeled by a two-zone combustion model, where the ratio of the burned and unburned gases flowing between the two combustion chambers is variable. To simulate the influence of the turbulent jets on the rate of combustion in the main-combustion chamber, a new parameter-varying Wiebe function is proposed and used for the mass fraction burned calculation in the main-combustion chamber. The developed model is calibrated using the least-squares fitting and optimization procedures. Experimental data sets with different air-to-fuel ratios in both combustion chambers and different pre-combustion chamber orifice areas are used to calibrate and validate the model. The simulation results show good agreement with the experimental data for all the experimental data sets. This indicates that the developed combustion model is accurate for developing and validating turbulent jet ignition combustion control strategies. Future work will extend the rapid compression machine combustion model to engine applications.

The tire-road interaction generates vehicle driving forces, which affect vehicle performance such as maximum acceleration and stability. Sequential extended Kalman filter (S-EKF) integrated with a slope method has been used for tire-road friction coefficient estimation with its own limitations, along with several “cause-based” and “effect-based” methods. This research proposes a new stochastic-based evaluation criterion using existing vehicle sensor signals with the help of the data-driven Kriging model. The proposed estimation method is validated by both CarSim™ simulation and experimental studies, respectively, under different road conditions. The results show that the proposed novel criterion has a strong correlation with the road friction coefficient and provide an improved tire-road friction coefficient estimation. A signal fusion estimation scheme based on both S-EKF and proposed evaluations is developed to improve estimation robustness.

An advanced rheomolder, equipped with a thin-walled phone cover mold, was introduced and used to manufacture the AZ91D magnesium alloy. Effects of different processing parameters including pouring temperature, cylinder temperature and injection velocity on the microstructure and mechanical properties were investigated. The results showed that, with the decrease of pouring temperature or the increase of injection velocity, the size of α1-Mg decreases while the sphericity increases, and the porosity fraction reduces, which raise mechanical properties of rheomolded AZ91D. As the cylinder temperature decreases, the size of α1-Mg decreases and the sphericity increases initially and then remains relatively unchanged while the solid fraction increases continuously. Also, the cylinder temperature has a significant effect on mechanical properties of rheomolded AZ91D and the highest mechanical property is obtained at a cylinder temperature of 555 °C. The optimal rheomolded AZ91D with tensile strength of 273 MPa and elongation of 7.2 % were obtained at the pouring temperature of 600 °C, injection velocity of 2.1 m/s and cylinder temperature of 555 °C. Compared with high pressure die-casting, the tensile strength and elongation were increased by 22.4 and 213 %, respectively.

It is fairly challenging to achieve a smooth mode transition between spark ignition combustion and homogeneous charge compression ignition combustion for a spark ignition engine capable of homogeneous charge compression ignition, because their in-cylinder thermal and charge mixture properties are quite different owing to the distinct combustion characteristics. In this paper, a mode transition strategy between spark ignition combustion and homogeneous charge compression ignition combustion was developed over the entire transition boundary between the spark ignition mode and the homogeneous charge compression ignition mode, where the engine speed is between 1100 r/min and 2000 r/min and the corresponding load (the indicated mean effective pressure) is between 3.8 bar and 5.0 bar. During the combustion mode transition and steady-state homogeneous charge compression ignition operation the normalized air-to-fuel ratio of the engine was maintained between 1.0 and 1.3; the charge air was heated by a manifold charge cooler heated by the engine coolant, and a test gasoline fuel with a research octane number of 85 was used for all the experiments conducted. The mode transition strategy was experimentally validated on a spark ignition engine capable of homogeneous charge compression ignition equipped with electric variable-valve-timing systems, dual-lift valves and an electronic throttle control system. Because of the limitation on the electric variable-valve-timing response time, it takes 5–10 cycles of the engine, depending on the engine speed, to complete the mode transition. The throttle was opened step by step to its wide-open position for the transition from spark ignition to homogeneous charge compression ignition or closed directly to the target position for the transition from homogeneous charge compression ignition to spark ignition, where the hybrid combustion mode was employed with spark assistance to change gradually to the desired homogeneous charge compression ignition mode or spark ignition combustion mode respectively, by increasing or decreasing the percentage of homogeneous charge compression ignition combustion during the hybrid combustion operation. The experimental results show that the developed strategy is able to achieve a smooth combustion mode transition with the net mean effective pressure and the combustion phase fluctuations at the level of stable spark ignition and homogeneous charge compression ignition combustion.

Robust Gain-Scheduling (RGS) control synthesis conditions are developed in this paper. Differently from most existing literature in gain-scheduling control, the derived conditions assume scheduling parameters are inexactly measured. This is a realistic assumption for practical control applications. Sufficient conditions in terms of Parametrized Bilinear Matrix Inequalities (PBMIs) for synthesizing dynamic output-feedback controllers with guaranteed H∞performance are given. The resulting controller not only guarantees robust stability and H∞performance but also ensures robustness against scheduling parameter uncertainties. An numerical example is presented to demonstrate the effectiveness of the developed approach and compare with existing approaches from literature.

Most practical automotive problems require the design of experiments (DoEs) over a number of different operating conditions to deliver optimal calibration parameters. DoE is especially crucial for automotive engine calibration problems due to its increasing complexity and nonlinearity. As the complexity of the system increases, the DoE applications require a significant amount of expensive testing. However, only a limited number of testings are available and desired. The current work addresses this issue by presenting an adaptive DoE method based on Bayesian optimization to find optimal parameter settings with a significantly reduced number of physical testings (or function evaluations). To further improve optimization efficiency, this work presents a new approach: concurrent Bayesian optimization, which searches for optimal DoE under multiple operating conditions simultaneously. The method utilizes a surrogate model and a novel concurrent evolutionary multi-objective optimization method: concurrent non-dominated sorting genetic algorithm-II, to solve adaptive DoE in multiple operating conditions with a limited number of function evaluations. The experimental study is carried out on a gasoline engine calibration problem using a high-fidelity GT-SUITE™ engine model. The experimental results demonstrate the effectiveness of the algorithm by optimizing engine performance with a significantly reduced number of expensive testings to achieve accurate optimal solutions. The method simultaneously performs engine calibration at eight different operating conditions using only 500–600 testings, compared to the traditional approach, where each operating condition requires 300–500 testings independently to achieve optimal results.

Existing methods of detecting the combustion phase of a diesel engine are mainly based upon high-cost in-cylinder pressure sensors. This paper presents a method of estimating the location of 75% of the mass fraction burned by using the traditional knock sensor signal. It is observed through experimental data that the knock signal has a close correlation to the location of 75% of the mass fraction burned. In this study, the knock integration is used to represent the knock intensity, while the piecewise knock integration (integration over a crank angle of 1°) is used to detect the location of 75% of the mass fraction burned. The proposed estimation method was validated using the experimental data and provided an accurate estimation of the location of 75% of the mass fraction burned. In addition, the feasibility of using the knock sensor signal to detect the biodiesel content is demonstrated using experimental data.

Transfer case clutch is crucial in determining traction torque distribution between front and rear tires for four-wheel-drive (4WD) vehicles. Estimating time-varying clutch surface friction coefficient is critical for traction torque control since it is proportional to the clutch output torque. As a result, this paper proposes a real-time adaptive lookup table strategy to provide the time-varying clutch surface friction coefficient. Specifically, the clutch-parameter-dependent (such as clutch output torque and clutch touchpoint distance) friction coefficient is first estimated with available low-cost vehicle sensors (such as wheel speed and vehicle acceleration); and then a clutch-parameter-independent approach is developed for clutch friction coefficient through a one-dimensional lookup table. The table nodes are adaptively updated based on a fast recursive least-squares (RLS) algorithm. Furthermore, the effectiveness of adaptive lookup table is demonstrated by comparing the estimated clutch torque from adaptive lookup table with that estimated from vehicle dynamics, which achieves 14.8 Nm absolute mean squared error (AMSE) and 2.66% relative mean squared error (RMSE).

This paper studies the potential fuel economy improvement that can be achieved by optimising powertrain control parameters without modifying its hardware. A real-time powertrain model was developed and implemented in Simulink. It consists of a simple diesel engine model, an automatic transmission (AT) model with a torque converter, a vehicle dynamic model, and an integrated controller for both the engine and transmission. In particular, a dynamic gearshift clutch model was developed for the AT gearbox. A hardware-in-the-loop (HIL) simulation environment was also established to simulate the developed real-time powertrain model, along with a simplified vehicle model under the federal test procedure (FTP), US06, and urban driving cycles. To evaluate the proposed control parameter optimisation process for a heavy-duty vehicle, the fuel consumptions of the FTP, US06, and urban driving cycles were used as the evaluation criterion, based upon different gear shifting control parameters and throttle slope angle. The HIL simulation results show that about 2% fuel economy can be gained by optimising the throttle slope angle; and simulation results also demonstrated that the optimised gearshift schedule provides the fuel economy improvement between 2.11% and 7.6% over the traditional gearshift schedule, where the most significant improvement was obtained for the urban driving cycle.

To implement the homogeneous charge compression ignition combustion mode in a spark ignition engine, it is necessary to have smooth mode transition between the spark ignition and homogeneous charge compression ignition combustions. The spark ignition–homogeneous charge compression ignition hybrid combustion mode modeled in this paper describes the combustion mode that starts with the spark ignition combustion and ends with the homogeneous charge compression ignition combustion. The main motivation of studying the hybrid combustion mode is that the percentage of the homogeneous charge compression ignition combustion is a good parameter for combustion mode transition control when the hybrid combustion mode is used during the transition. This paper presents a control oriented model of the spark ignition–homogeneous charge compression ignition hybrid combustion mode, where the spark ignition combustion phase is modeled under the two-zone assumption and the homogeneous charge compression ignition combustion phase under the one-zone assumption. Note that the spark ignition and homogeneous charge compression ignition combustions are special cases in this combustion model. The developed model is capable of simulating engine combustion over the entire operating range, and it was implemented in a real-time hardware-in-the-loop simulation environment. The simulation results were compared with those of the corresponding GT-Power model, and good correlations were found for both spark ignition and homogeneous charge compression ignition combustions.

Elevated LDL-C is a major risk factor for atherosclerotic cardiovascular disease, the leading cause of death worldwide. Proprotein convertase subtilisin/kexin type 9 (PCSK9) has pronounced effects on LDL-C levels via its modulation of hepatic LDL receptors (LDLR), the main pathway for cholesterol removal from the circulation. The epidermal growth factor precursor homology domain A (EGF-A) of the LDLR serves as a primary contact with PCSK9 via a flat interface, presenting a challenge for identifying small molecule PCSK9-LDLR disruptors. We employed an affinity-based screen of 1013in vitro-translated macrocyclic peptides to identify high affinity PCSK9 ligands that utilize a novel, induced-fit pocket and partially disrupt the PCSK9-LDLR interaction (Hit 1 - PCSK9-LDLR FRET, IC502 nM, Amax41%). Structure-based design led to 13PCSK9i, a molecule with enhanced in vitrofunction (PCSK9-LDLR FRET, IC502 nM, Amax78%) and pharmacokinetic properties suitable for in vivoevaluation. To determine if 13PCSK9i’s functional activity in vitrowould translate in vivo, C57BL/6 mice were dosed with vehicle or 13PCSK9iviasubcutaneous injection twice-daily for 3 days, and blood and liver were collected for the measurement of plasma total cholesterol (TC) and hepatic LDLR density. A PCSK9 antibody was included as a positive control. As illustrated in the figure, 13PCSK9iincreased hepatic LDLR density in a dose-dependent manner. Despite being 1/100thits size, 13PCSK9i(MW = 1.65 kDa) increased hepatic LDLR density on par with a PCSK9 antibody (MW = 144 kDa). Plasma TC levels were significantly reduced versus vehicle in all 13PCSK9idose groups, with similar reductions noted in the 13PCSK9i30 mg/kg and PCSK9 antibody groups (44% and 48%, respectively). In summary, 13PCSK9ibreaks new ground with its previously undescribed allosteric mechanism and, in turn, as the smallest molecule identified to date with in vivoPCSK9-LDLR disruptor function.

Most nonpeptide antagonists for CC-chemokine receptors share a common pharmacophore with a centrally located, positively charged amine that interacts with the highly conserved glutamic acid (Glu) located in position 6 of transmembrane helix VII (VII:06). We present a novel CCR8 nonpeptide agonist, 8-[3-(2-methoxyphenoxy)benzyl]-1-phenethyl-1,3,8-triaza-spiro[4.5]decan-4-one (LMD-009), that also contains a centrally located, positively charged amine. LMD-009 selectively stimulated CCR8 among the 20 identified human chemokine receptors. It mediated chemotaxis, inositol phosphate accumulation, and calcium release with high potencies (EC50from 11 to 87 nM) and with efficacies similar to that of the endogenous agonist CCL1, and it competed for 125I-CCL1 binding with an affinity of 66 nM. A series of 29 mutations targeting 25 amino acids broadly distributed in the minor and major ligand-binding pockets of CCR8 uncovered that the binding of LMD-009 and of four analogs [2-(1-(3-(2-methoxyphenoxy)benzyl)-4-hydroxypiperidin-4-yl)benzoic acid (LMD-584), N-ethyl-2-4-methoxybenzenesulfonamide (LMD-902), N-(1-(3-(2-methoxyphenoxy)benzyl)piperidin-4-yl)-2-phenyl-4-(pyrrolidin-1yl)butanamide (LMD-268), and N-(1-(3-(2-methoxyphenoxy)benzyl)piperidin-4-yl)-1,2,3,4-tetrahydro-2-oxoquinoline-4-carboxamide (LMD-174)] included several key-residues for nonpeptide antagonists targeting CCR1, -2, and -5. It is noteworthy that a decrease in potency of nearly 1000-fold was observed for all five compounds for the Ala substitution of the anchor-point GluVII:06 (Glu286) and a gain-of-function of 19-fold was observed for LMD-009 (but not the four other analogs) for the Ala substitution of PheVI:16 (Phe254). These structural hallmarks were particularly important in the generation of a model of the molecular mechanism of action for LMD-009. In conclusion, we present the first molecular mapping of the interaction of a nonpeptide agonist with a chemokine receptor and show that the binding pocket of LMD-009 and of analogs overlaps considerably with the binding pockets of CC-chemokine receptor nonpeptide antagonists in general.

Recombinant adenovirus has been used widely as an in vivo gene transfer vector, although its transfection efficiency in bladder tissue is limited. Several studies have indicated that the bladder surface glycosaminoglycan (GAG) layer functions as a nonspecific anti-adherence factor and possibly as a first line anti-infection defense mechanism. We determined whether recombinant adenovirus mediated gene transfer could be enhanced in intact bladders by HCl pretreatment and by alterations in the GAG layer.

The αv integrin is present in vascular endothelium, including that of the kidney. It is also upregulated in the presence of inflammatory cytokines and in some neoplasms. In an effort to transduce vascular endothelial cells, we compare in vitro and in vivo adenoviral gene transfer of a vector with a high affinity peptide ligand to the αv integrin incorporated into the fiber coat protein AdZ.F(RGD) to an unmodified vector, AdZ. Cell transduction assays were performed on human umbilical vein endothelial cells (HuVEC) and human pulmonary epithelial cells (A549). In vitro competition assays were performed in the presence of either wild type (F5K) or chimeric (F5K(RGD)) soluble recombinant fiber protein. Transduction efficiency was determined by quantitative β-galactosidase activity. In vivo gene transfer was compared infusing either AdZ or AdZ.F(RGD) into the left renal artery of the rat and assaying β-galactosidase staining of the kidney. Gene transfer was also evaluated in the presence of a competitive RGD or control RGE peptide. There was a marked increase in transgene expression in HuVEC cells with AdZ.F(RGD) as compared to AdZ. The increased expression with AdZ.F(RGD) was more prominent in the endothelial as opposed to the epithelial cell line. Furthermore pre-incubation of these cells with either F5K or F5K(RGD) soluble fiber protein markedly decreased β-galactosidase activity of AdZ, whereas only the F5K(RGD) decreased β-galactosidase activity of AdZ.F(RGD). AdZ.F(RGD) also resulted in significantly enhanced β-galactosidase expression in the vascular endothelium of the kidney (for comparable amounts of virus injected) as well as significantly higher gene transfer to cortical vasculature. Coinfusion of an RGD peptide with AdZ.F(RGD) blocked gene transfer whereas a control RGE peptide did not. We conclude that incorporating a high affinity peptide ligand into the adenoviral fiber protein can preferentially enhance in vitro and in vivo adenoviral transfection efficiency in endothelial cells. Enhancing transfection efficiency will not only broaden the scope of disease processes addressed with adenoviral vectors but also allow the use of lower titer, thereby limiting toxicity. This vector has additional potential to transduce endothelial cells within tumors or ischemic tissue where αv integrins are upregulated. Copyright © 1999 John Wiley & Sons, Ltd.

Poly(DL-lactic-co-glycolic acid) (PLGA) foams are an osteoconductive support that holds promise for the development of bone tissue in vitro and implantation into orthopedic defects. Because it is desirable that foams maintain their shape and size, we examined a variety of foams cultured in vitro with osteoblastic cells. Foams were prepared with different porosities and pore sizes by the method of solvent casting/porogen leaching using 80, 85, and 90 wt% NaCl sieved with particle sizes of 150–300 and 300–500 µm and characterized by mercury intrusion porosimetry. Foams seeded with cells were found to have volumes after 7 days in static culture that decreased with increasing porosity: the least porous exhibited no change in volume while the most porous foams decreased by 39 ± 10%. In addition, a correlation was observed between decreasing foam volume after 7 days in culture and decreasing internal surface area of the foams prior to seeding. Furthermore, foams prepared with the 300–500 µm porogen had lower porosities, greater mean wall thicknesses between adjacent pores, and larger volumes after 7 days in culture than those prepared with the smaller porogen. Two culture conditions for maintaining cells, static and agitated (in a rotary vessel), were found to have similar influences on foam size, cell density, and osteoblastic function for 7 and 14 days in culture. Finally, we examined unseeded foams in aqueous solutions of pH 3.0, 5.0, and 7.4 and found no significant decrease in foam size with degradation. This study demonstrates that adherent osteoblastic cells may collapse very porous PLGA foams prepared by solvent casting/particulate leaching: a potentially undesirable property for repair of orthopedic defects.

The original q-Markov COVariance Equivalent Realization (q-Markov Cover) method for identification required white noise test signals, which cannot be generated exactly. This paper replaces the unrealizable white-noise signal with a realizable signal (the pseudo-random binary signal, PRBS), and proves that when the period of the PRBS approaches infinity the q-Markov Cover algorithm, operating with PRBS, matches the first q Markov and covariance parameters (as in the original theory with white-noise test signals). The existing q-Markov Cover algorithm will fail to match covariance and Markov parameters exactly due to non-white test signals. The new algorithm will fail to match covariance and Markov parameters exactly due to the finite period of PRBSs. We demonstrate however that the results with PRBSs are far superior to results with 'white' noise signals. Apparently, the 'non-whiteness' of the test signal degrades the identification performance worse than the 'non-infinite' period of the PRBS.

Four kinds of ℒ2 to ℒ∞ gains for sampled-data systems are defined and derived in this paper. It turns out that these gains can be expressed by analytical formulae which are easy to compute (only a discrete Lyapunov equation is involved).

In the presence of all disturbances whose outer products are bounded by a known matrix N, this paper develops controllers to minimize the control L∞ norm bound subject to L∞ constraints on each output, the resulting controller is an LQG controller with a special choice of the output weight matrix and input noise intensity matrix, that are both iterated. The design algorithm turns out to be feasible if the algorithm converges.

The expression of the apolipoprotein (apo) E and C-l genes in different tissues is determined by distinct regulatory elements that are distributed upstream and downstream of these genes, including a common hepatic control region that is located 18 kb downstream of the apoE gene promoter. The hepatic control region contains all of the sequences needed to direct expression to the liver, but it requires the presence of a non-specific regulatory element in the proximal promoter of the apoE gene for its action.

This study was designed to investigate the in vivobiodegration and biocompatibility of a poly(propylene fumarate) (PPF)-based orthopedic biomaterial. The effects of varying the PPF to N-vinyl pyrrolidinone ratio and PPF to ß-tricalcium phosphate content were studied. The composite mechanical properties and local tissue interactions were analyzed over 12 weeks. An initial increase in both compressive modulus and strength was seen for composite formulations that incorporated ß-tricalcium phosphate. The samples incorporating a higher PPF to N-vinyl pyrrolidinone ratio reached a maximal compressive strength of 7.7 MPa and a maximal compressive modulus of 191.4 MPa at 3 weeks. The lower PPF to N-vinyl pyrrolidinone ratio samples gained a maximum compressive strength of 7.5 MPa initially and a compressive modulus of 134.0 MPa at 1 week. At 6 weeks, all samples for formulations incorporating ß-tricalcium phosphate crumbled upon removal and were not mechanically tested. Samples that did not incorporate ß-tricalcium phosphate were very weak and insufficient for bone replacement at the 4-day time point and beyond. Tissue interactions resulted in a mild inflammatory response at the initial time points and mature fibrous encapsulation by 12 weeks. © 1998 John Wiley & Sons, Inc. J. Biomed Mater Res, 41, 1–7, 1998.

If a certain realizability condition is satisfied, the Q-Markov COVER identification algorithm provides a state-space realization which matches the first Q Markov parameters and the first Q covariance parameters of the identified plant. However, due to the presence of nonlinearities, often this realizability test fails and the measured Markov and covariance parameters cannot be realized by a linear time-invariant model. In this note, alternating projection techniques are used to compute the closest set of covariance parameters such that the realizability constraints are satisfied. In addition, fixed-order constraints and output covariance constraints can be enforced for the identified realization using the above techniques. This algorithm is used in an iterative closed-loop identification and control redesign scheme.

We performed deletion analysis of WT1-reporter constructs containing up to 24 kilobases of 5′-flanking and first intron WT1sequence in stably transfected cultured cells as an unbiased approach to identify cis elements critical for WT1transcription. Although not a tissue-specific element, a proximate 9-base pair CTC repeat accounted for ~80% of WT1transcription in this assay. Enhancer activity of the element and mutated versions correlated completely with their ability to form a DNA-protein complex in gel shifts. Antibody supershift, oligonucleotide competition, and Southwestern studies indicated that the CTC-binding factor is the transcriptional activator Sp1. Sp1 binds the CTC repeat with an affinity, KD= 0.37 nM, at least as high as the consensus GC box. Similar CTC repeats are found in promoters of other growth-related genes. Because Sp1 is important for WT1expression, we examined Sp1 immunohistochemistry in fetal and adult kidney. In a pattern that precedes that of WT1message, Sp1 immunostaining was highest in uninduced mesenchyme, early tubules, developing podocytes, and mature glomeruli, but was minimal in mature proximal tubules. This work suggests abundant Sp1 may be a prerequisite for WT1expression, and that Sp1 may have a wider role in nephrogenesis.

No Abstract.

A multi-objective optimization of a coupled casing treatment (CCT) for an axial transonic compressor is performed in this study. The coupled casing treatment is the basis axial slots with a circumferential groove located at various positions along the slots. During the optimization stage, five important parameters to control the geometry are used as the optimal variables. The stall margin and the peak efficiency are selected as the optimal objectives. Non-dominated sorting genetic algorithm II coupled with radial basis function (RBF) approximation is used to search for Pareto-optimal solutions. Then, four optimal configurations are selected from Pareto-front for further analysis. As shown in the simulation results with and without the coupled casing treatments, the leakage flow is reenergized and the blocking region near the blade leading edge at rotor tip is decreased by the use of these structures under the low flowrate condition, which is the main reason for stability enhancement. Besides, a coupled casing treatment with the groove settled near the end of the basis slots have the potential to generate more injection flow and extend the operating range of compressor further.

The objective of this study is to discuss the effect and mechanism of lithium chloride on the rehabilitation of locomotion post spinal cord injury (SCI) by observing the effect of lithium chloride on the expression of the brain-derived neurotrophic factor (BDNF)/tropomyosin receptor kinase B (TrkB) pathway. In total, 36 Sprague-Dawley (SD) rats were randomly divided into the sham operation group (n = 12), model group (n = 12), and lithium chloride group (n = 12). The sham operation group underwent laminectomy, while for the model group and the lithium chloride group with the NYU spinal cord impactor the SCI model was established. Basso, Beattie, and Bresnahan (BBB) score was used to evaluate locomotion after administration for 1, 3, 5, and 7 days, and the tissues were gathered for Nissl staining, transmission electron microscopy, immunofluorescence, and Western blot. With a statistical difference (P< 0.05) on the 3rd day and significant difference (P< 0.01) on the 5th day post administration, a higher BBB score was observed in the lithium chloride group indicating that lithium chloride improved the locomotion function after SCI. A better structure and morphology of neuron were observed by Nissl staining in the lithium chloride group. Lithium chloride promoted BDNF secretion from neurons in the spinal cord anterior horn with a significant difference compared to the model group (P< 0.01). Compared with the model group, lithium chloride significantly promoted the expression of BDNF protein and phosphorylated TrkB protein (P< 0.05), but no difference in the expression of TrkB was detected. Lithium chloride can alleviate the locomotion function after SCI with a mechanism that it can promote BDNF secretion from neurons in the spinal cord anterior horn and phosphorylation of TrkB to upregulate the BDNF/TrkB pathway supporting survival of neurons and regeneration and remyelination of axons.