Showing papers by "Missouri University of Science and Technology published in 2009"

Toxicity of nano- and micro-sized ZnO particles in human lung epithelial cells

Abstract: This is the first comprehensive study to evaluate the cytotoxicity, biochemical mechanisms of toxicity, and oxidative DNA damage caused by exposing human bronchoalveolar carcinoma-derived cells (A549) to 70 and 420 nm ZnO particles. Particles of either size significantly reduced cell viability in a dose- and time-dependent manner within a rather narrow dosage range. Particle mass-based dosimetry and particle-specific surface area-based dosimetry yielded two distinct patterns of cytotoxicity in both 70 and 420 nm ZnO particles. Elevated levels of reactive oxygen species (ROS) resulted in intracellular oxidative stress, lipid peroxidation, cell membrane leakage, and oxidative DNA damage. The protective effect of N-acetylcysteine on ZnO-induced cytotoxicity further implicated oxidative stress in the cytotoxicity. Free Zn2+ and metal impurities were not major contributors of ROS induction as indicated by limited free Zn2+ cytotoxicity, extent of Zn2+ dissociation in the cell culture medium, and inductively-coupled plasma-mass spectrometry metal analysis. We conclude that (1) exposure to both sizes of ZnO particles leads to dose- and time-dependent cytotoxicity reflected in oxidative stress, lipid peroxidation, cell membrane damage, and oxidative DNA damage, (2) ZnO particles exhibit a much steeper dose–response pattern unseen in other metal oxides, and (3) neither free Zn2+ nor metal impurity in the ZnO particle samples is the cause of cytotoxicity.

The water footprint of biofuels: a drink or drive issue?

Abstract: The water consumption and agrochemical use during biofuel production could adversely impact both availability and quality of a precious resource.

Reinforcement Learning: A Tutorial Survey and Recent Advances

Abstract: In the last few years, reinforcement learning (RL), also called adaptive (or approximate) dynamic programming, has emerged as a powerful tool for solving complex sequential decision-making problems in control theory. Although seminal research in this area was performed in the artificial intelligence (AI) community, more recently it has attracted the attention of optimization theorists because of several noteworthy success stories from operations management. It is on large-scale and complex problems of dynamic optimization, in particular the Markov decision problem (MDP) and its variants, that the power of RL becomes more obvious. It has been known for many years that on large-scale MDPs, the curse of dimensionality and the curse of modeling render classical dynamic programming (DP) ineffective. The excitement in RL stems from its direct attack on these curses, which allows it to solve problems that were considered intractable via classical DP in the past. The success of RL is due to its strong mathematical roots in the principles of DP, Monte Carlo simulation, function approximation, and AI. Topics treated in some detail in this survey are temporal differences, Q-learning, semi-MDPs, and stochastic games. Several recent advances in RL, e.g., policy gradients and hierarchical RL, are covered along with references. Pointers to numerous examples of applications are provided. This overview is aimed at uncovering the mathematical roots of this science so that readers gain a clear understanding of the core concepts and are able to use them in their own research. The survey points to more than 100 references from the literature.

Oxidative Stress in Diabetes and Alzheimer's Disease

Abstract: Oxidative stress plays a major role in diabetes as well as in Alzheimer's disease and other related neurological diseases. Intracellular oxidative stress arises due to the imbalance in the production of reactive oxygen/reactive nitrogen species and cellular antioxidant defense mechanisms. In turn, the excess reactive oxygen/reactive nitrogen species mediate the damage of proteins and nucleic acids, which have been shown to have direct and deleterious consequences in diabetes and Alzheimer's disease. Oxidative stress also contributes to the production of advanced glycation end products through glycoxidation and lipid peroxidation. The advanced glycation end products and lipid peroxidation products are ubiquitous to diabetes and Alzheimer's disease and serve as markers of disease progression in both disorders. Antioxidants and advanced glycation end products inhibitors, either induced endogenously or exogenously introduced, may counteract with the deleterious effects of the reactive oxygen/reactive nitrogen species and thereby, in prevention or treatment paradigms, attenuate or substantially delay the onset of these devastating pathologies.

Small-Signal Impedance Measurement of Power-Electronics-Based AC Power Systems Using Line-to-Line Current Injection

Abstract: Naval ships as well as aerospace power systems are incorporating a greater degree of power electronic switching sources and loads. Although these components provide exceptional performance, they are prone to instability due to their high efficiency and constant power characteristics that can exhibit negative impedance nature at certain frequencies. When designing these systems, integrators must consider the impedance versus frequency at an interface (which designates source and load). Stability criteria have been developed in terms of source and load impedances for both dc and ac systems, and it is often helpful to have techniques for impedance measurement. For dc systems, the measurement techniques have been well established. This paper introduces a new method of impedance measurement for three-phase ac systems. By injecting an unbalanced line-to-line current between two lines of the ac system, all impedance information in the traditional synchronous reference frame d-q model can be determined. For medium-voltage systems, the proposed technique is simpler and less costly than having an injection circuit for each phase. Since the current injection is between only two phase lines, the proposed measurement device can be used for both ac and dc interfaces. Simulation and laboratory measurements demonstrate the effectiveness of this new technique.

Effect of tool geometry on static strength of friction stir spot-welded aluminum alloy

Abstract: Friction stir spot welding is performed on 5083 Al alloy using tools with a conventional cylindrical pin and the proposed triangular pin. Partial metallurgical bond (called as ‘hook’) is formed in the weld region between the overlapped metal sheets. The tool-pin geometry significantly affects the hook shape. Under the same process condition, welds made with the cylindrical pin have a continuous hook which bypasses the stir zone and points downward towards the weld bottom. By contrast, for welds made with the triangular pin, the hook is directed upwards and then arrested at the periphery of the stir zone. The difference in the hook shape could be attributed to the asymmetric rotation of the triangular pin that may cause the material in the vicinity of the pin to move back and forth in the radial direction resulting in the hook being broken-up (dispersed) in the stir zone. In addition, the triangular pin results in a finer grain structure in the stir zone compared to the cylindrical pin. Static strength of welds made with the triangular pin is twice that of welds made with the cylindrical pin, which is attributed to the finer grain size as well as tensile failure mode as a result of the arrested hook.

Coordinated Reactive Power Control of a Large Wind Farm and a STATCOM Using Heuristic Dynamic Programming

Abstract: A novel interface neurocontroller (INC) is proposed for the coordinated reactive power control between a large wind farm equipped with doubly fed induction generators (DFIGs) and a static synchronous compensator (STATCOM). The heuristic dynamic programming (HDP) technique and radial basis function neural networks (RBFNNs) are used to design this INC. It effectively reduces the level of voltage sags as well as the over-currents in the DFIG rotor circuit during grid faults, and therefore, significantly enhances the fault ride-through capability of the wind farm. The INC also acts as a coordinated external damping controller for the wind farm and the STATCOM, and therefore, improves power oscillation damping of the system after grid faults. Simulation studies are carried out in PSCAD/EMTDC and the results are presented to verify the proposed INC.

Effect of borate glass composition on its conversion to hydroxyapatite and on the proliferation of MC3T3-E1 cells.

Abstract: Glasses containing varying amounts of B(2)O(3) were prepared by partially or fully replacing the SiO(2) in silicate 45S5 bioactive glass with B(2)O(3). The effects of the B(2)O(3) content of the glass on its conversion to hydroxyapatite (HA) and on the proliferation of MC3T3-E1 cells were investigated in vitro. Conversion of the glasses to HA in dilute (20 mM) K(2)HPO(4) solution was monitored using weight loss and pH measurements. Proliferation of MC3T3-E1 cells was determined qualitatively by assay of cell density at the glass interface after incubation for 1 day and 3 days, and quantitatively by fluorescent measurements of total DNA in cultures incubated for 4 days. Higher B(2)O(3) content of the glass increased the conversion rate to HA, but also resulted in a greater inhibition of cell proliferation under static culture conditions. For a given mass of glass in the culture medium, the inhibition of cell proliferation was alleviated by using glasses with lower B(2)O(3) content, by incubating the cell cultures under dynamic rather than static conditions, or by partially converting the glass to HA prior to cell culture.

Physics-Based Via and Trace Models for Efficient Link Simulation on Multilayer Structures Up to 40 GHz

Abstract: Analytical models for vias and traces are presented for simulation of multilayer interconnects at the package and printed circuit board levels. Vias are modeled using an analytical formulation for the parallel-plate impedance and capacitive elements, whereas the trace-via transitions are described by modal decomposition. It is shown that the models can be applied to efficiently simulate a wide range of structures. Different scenarios are analyzed including thru-hole and buried vias, power vias, and coupled traces routed into different layers. By virtue of the modal decomposition, the proposed method is general enough to handle structures with mixed reference planes. For the first time, these models have been validated against full-wave methods and measurements up to 40 GHz. An improvement on the computation speed of at least two orders of magnitude has been observed with respect to full-wave simulations.

Reliability sensitivity analysis with random and interval variables

Abstract: In reliability analysis and reliability-based design, sensitivity analysis identifies the relationship between the change in reliability and the change in the characteristics of uncertain variables. Sensitivity analysis is also used to identify the most significant uncertain variables that have the highest contributions to reliability. Most of the current sensitivity analysis methods are applicable for only random variables. In many engineering applications, however, some of uncertain variables are intervals. In this work, a sensitivity analysis method is proposed for the mixture of random and interval variables. Six sensitivity indices are defined for the sensitivity of the average reliability and reliability bounds with respect to the averages and widths of intervals, as well as with respect to the distribution parameters of random variables. The equations of these sensitivity indices are derived based on the first-order reliability method (FORM). The proposed reliability sensitivity analysis is a byproduct of FORM without any extra function calls after reliability is found. Once FORM is performed, the sensitivity information is obtained automatically. Two examples are used for demonstration. Copyright © 2009 John Wiley & Sons, Ltd.

Optimal control of unknown affine nonlinear discrete-time systems using offline-trained neural networks with proof of convergence

Abstract: The optimal control of linear systems accompanied by quadratic cost functions can be achieved by solving the well-known Riccati equation. However, the optimal control of nonlinear discrete-time systems is a much more challenging task that often requires solving the nonlinear Hamilton―Jacobi―Bellman (HJB) equation. In the recent literature, discrete-time approximate dynamic programming (ADP) techniques have been widely used to determine the optimal or near optimal control policies for affine nonlinear discrete-time systems. However, an inherent assumption of ADP requires the value of the controlled system one step ahead and at least partial knowledge of the system dynamics to be known. In this work, the need of the partial knowledge of the nonlinear system dynamics is relaxed in the development of a novel approach to ADP using a two part process: online system identification and offline optimal control training. First, in the system identification process, a neural network (NN) is tuned online using novel tuning laws to learn the complete plant dynamics so that a local asymptotic stability of the identification error can be shown. Then, using only the learned NN system model, offline ADP is attempted resulting in a novel optimal control law. The proposed scheme does not require explicit knowledge of the system dynamics as only the learned NN model is needed. The proof of convergence is demonstrated. Simulation results verify theoretical conjecture.

Optimization of vehicle-to-grid scheduling in constrained parking lots

Abstract: An automatic Vehicle-to-Grid (V2G) technology can contribute to the utility grid. V2G technology has drawn great interest in the recent years. Success of the sophisticated automatic V2G research depends on efficient scheduling of gridable vehicles in constrained parking lots. Parking lots have constraints of space and current limits for V2G. However, V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning reserves and reliability. As number of gridable vehicles in V2G is much higher than small units of existing systems, unit commitment (UC) with V2G is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to solve the V2G, as PSO has been demonstrated to reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit. In the proposed model, binary PSO optimizes the on/off states of power generating units easily. Vehicles are presented by signed integer number instead of 0/1 to reduce the dimension of the problem. Typical discrete version of PSO has less balance between local and global searching abilities to optimize the number of charging/discharging gridable vehicles in the constrained system. In the same model, balanced PSO is proposed to optimize the V2G part in the constrained parking lots. Finally, results show a considerable amount of profit for using proper scheduling of gridable vehicles in constrained parking lots.

Impact Time and Angle Guidance with Sliding Mode Control

Abstract: A novel sliding mode-based impact time and angle guidance law for engaging a modern warfare ship is presented in this paper. In order to satisfy the impact time and angle constraints, a line-of-sight rate shaping process is introduced. This shaping process results in a tuning parameter that can be used to create a line-of-sight rate profile to satisfy the final time and heading angle requirements and to yield acceptable normal acceleration values. In order to track the desired line-of-sight rate profile in the presence of uncertainties, a novel robust second-order sliding mode control law is developed using a backstepping concept. Due to the robustness of the control law, it can be applied to many realistic engagement scenarios which include uncertainties such as target motion. Numerical simulations with different warship engagements are presented to illustrate the potential of the developed method.

Bio-inspired node localization in wireless sensor networks

Abstract: Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented.

New Techniques for Measuring Impedance Characteristics of Three-Phase AC Power Systems

Abstract: Naval ships as well as aerospace power systems are incorporating an increasing amount of power electronic switching sources and loads. Although these power-electronics-based components provide exceptional performance, they are prone to instability due to their constant power characteristics that lead to negative impedance. When designing these systems, integrators must consider the impedance versus frequency at a power system interface (which designates source and load). Stability criteria have been developed in terms of source and load impedance for both dc and ac power systems, and it is often helpful to have techniques for impedance measurement. For dc power systems, the measurement techniques have been well established. This paper suggests several methods for measuring ac impedance including utilization of power converters, wound-rotor induction machines, and chopper circuits. Simulation and laboratory results on an example ac power system demonstrate the effectiveness of the proposed methods.

The risk in early design method

Abstract: Risk assessments are necessary to anticipate and prevent accidents from occurring or repeating. Current probabilistic risk assessment methods require mature design proposals to analyse. Since product safety and reliability are affected the most by decisions made during the early design phases, a risk assessment that can be performed with less mature data during these design phases is needed. This study focuses specifically on the relationship between function and risk in early design by presenting a mathematical mapping from product function to risk assessments that can be used in the conceptual design phase. An investigation of a spacecraft orientation subsystem is used to demonstrate the mappings. The results from the study and its spacecraft application yield a preliminary risk assessment method that can be used to identify and assess risks as early as the conceptual phase of design. The preliminary risk assessment presented in this paper is a tool that will aid designers by identifying risks as well a...

Densification and mechanical properties of TaC-based ceramics

Abstract: Densification of TaC was enhanced while the grain growth was suppressed by adding 1 or 2 wt% B4C, which allowed mechanical properties to be analyzed. A relative density of >98% was achieved for TaC with B4C additions by hot pressing at 2100 °C. By comparison, additive-free TaC could be hot pressed to only 94% relative density at 2300 °C. X-ray diffraction analysis identified TaB2 in TaC–B4C ceramics, indicating that a reaction occurred during hot pressing. Mechanical properties including Young's modulus, flexure strength, Vickers’ hardness, and fracture toughness were studied. The Griffith equation was used to calculate critical flaw sizes from measured properties. This analysis combined with observations of fracture surfaces identified surface flaws induced by machining and subsurface impurities picked up during milling as the critical flaws.

One-Pot Synthesis of Interpenetrating Inorganic/Organic Networks of CuO/Resorcinol-Formaldehyde Aerogels: Nanostructured Energetic Materials

Abstract: For many applications ranging from catalysis to sensors to energetic materials, it is desirable to produce intimate mixtures of nanoparticles. For instance, to improve the reaction rates of energetic materials, the oxidizing agent and the fuel need to be mixed as intimately as possible, ideally at the nanoscopic level. In this context, the acidity of a hydrated CuCl(2) solution reacting toward a network of CuO nanoparticles (a good oxidant) is used to induce one-pot cogelation of a nanostructured network of a resorcinol-formaldehyde resin (RF, the fuel). The resulting wet gels are dried to aerogels, and upon pyrolysis under Ar, the interpenetrating CuO/RF network undergoes a smelting reaction toward metallic Cu. Upon ignition in the open air, pure RF aerogels do not burn, while CuO/RF composites, even with substoichiometric CuO, sustain combustion, burning completely leaving only a solid residue of CuO whose role then has been that of a redox mediator through the smelting reaction.

A proposition on memes and meta-memes in computing for higher-order learning

Abstract: In computational intelligence, the term ‘memetic algorithm’ has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a ‘meme’ has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as ‘memetic algorithm’ is too specific, and ultimately a misnomer, as much as a ‘meme’ is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Utilizing two conceptual case studies, we illustrate the power of high-order meme-based learning. With applications ranging from cognitive science to machine learning, memetic computing has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning.

An Improved UPFC Control for Oscillation Damping

Abstract: This paper proposes a new control approach for a unified power flow controller (UPFC) for power system oscillation damping. This control is simple to implement, yet is valid over a wide range of operating conditions. It is also effective in the presence of multiple modes of oscillation. The proposed control is implemented in several test systems and is compared against a traditional PI control.

Access control policy combining: theory meets practice

Abstract: Many access control policy languages, e.g., XACML, allow a policy to contain multiple sub-policies, and the result of the policy on a request is determined by combining the results of the sub-policies according to some policy combining algorithms (PCAs). Existing access control policy languages, however, do not provide a formal language for specifying PCAs. As a result, it is difficult to extend them with new PCAs. While several formal policy combining algebras have been proposed, they did not address important practical issues such as policy evaluation errors and obligations; furthermore, they cannot express PCAs that consider all sub-policies as a whole (e.g., weak majority or strong majority). We propose a policy combining language PCL, which can succinctly and precisely express a variety of PCAs. PCL represents an advancement both in terms of theory and practice. It is based on automata theory and linear constraints, and is more expressive than existing approaches. We have implemented PCL and integrated it with SUN's XACML implementation. With PCL, a policy evaluation engine only needs to understand PCL to evaluate any PCA specified in it.

Fiber Inline Core–Cladding-Mode Mach–Zehnder Interferometer Fabricated by Two-Point CO $_{2}$ Laser Irradiations

Abstract: We report a fiber inline Mach-Zehnder-type core-cladding-mode interferometer fabricated by two-point CO2 laser irradiations on a single-mode fiber. The laser irradiations caused efficient light coupling from the core mode to the lower order cladding modes and vise versa. High-quality interference spectra with a fringe visibility of about 20 dB were observed for four different interferometer lengths (5, 10, 20, and 40 mm). The temperature sensitivity of the device with a length of 5 mm was measured to be 0.0817 nm/degC. The sensitivity for refractive index measurement of the device was comparable with a long-period fiber grating of LP04 cladding mode.

Potentials and promises of computational intelligence for smart grids

Abstract: The electric power grid is a complex adaptive system under semi-autonomous distributed control. It is spatially and temporally complex, non-convex, nonlinear and non-stationary with a lot of uncertainties. The integration of renewable energy such as wind farms, and plug-in hybrid and electric vehicles further adds complexity and challenges to the various controllers at all levels of the power grid. A lot of efforts have gone into the development of a smart grid to align the interests of the electric utilities, consumers and environmentalists. Advanced computational methods are required for planning and optimization, fast control of power system elements, processing of field data and coordination across the grid. Distributed and coordinated intelligence at all levels and across levels of the electric power grid — generation, transmission and distribution is inevitable if a true smart grid is to be reality. Computational intelligence (CI) is the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex, uncertain and changing environments. These adaptive mechanisms include artificial and bio-inspired intelligence paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate. The paradigms of CI mimic nature for solving complex problems. CI is successor of artificial intelligence and is the way of the future computing. This paper presents the potentials and promises of CI to realize an intelligent smart grid.