Computational Science and Engineering

17-hydroexemestane is the major metabolite of exemestane in vivo. Previous studies have shown that 17-hydroexemestane is androgenic and bone protective. Due to structure similarities, we hypothesized that, like exemestane, 17-hydroexemestane is an inhibitor of aromatase (CYP19). Our aim was to assess the potency (IC 50 ) of 17-hydroexemestane toward CYP19 inhibition, and to determine the specific CYPs responsible for 17-hydroexemestane metabolism. Using recombinant human CYP19, we investigated the ability of exemestane and 17-hydroexemestane to block the formation of estradiol from testosterone. We found that 17-hydroexemestane potently inhibited aromatase. IC 50 values for the inhibition of CYP19 by exemestane and 17-hydroexemestane were 1.5 μM and 3 μM, respectively. Furthermore, using recombinant human P450s, human liver microsomes, and HPLC analytical techniques, we identified one major metabolite (MIII) of 17-hydroexemestane in the human liver microsomal incubate. In a bank of 15 well-characterized HLMs, MIII formation rate was significantly correlated with the activity of CYP3A (r s = 0.78, p=0.001).In a panel of baculovirus-expressed CYP enzymes, only CYP3A4 and CYP3A5 catalyzed MIII formation at the highest rate. In sum, these in vitro data suggest that 17-hydroexemestane is a potent inhibitor of CYP19 and that CYP3A plays a major role in its metabolism. Whether genetic polymorphisms and drug interactions involving these enzymes may contribute to the disposition and action of 17-hydroexemestane in breast cancer patients remains to be elucidated.

This paper proposes an approach to implement optimal control laws of nonlinear systems in real time. Our methodology does not require solving two-point boundary-value problems on-line and may not require it o -line either. The optimal control law is learned using a generalized Sugeno controller (GSC) from a family of optimal trajectories. We compare the trajectories generated by the GSC and the trajectories yielded by the optimal feedback control law when applied to Zermelo's ship steering problem.

A linearized dynamical model of shipboard crane with the Maryland Rigging is derived by using Lagrange's equations. Based on the linearized model, numerical resonant frequencies of Maryland Rigging are obtained and verified for complete nonlinear model. One disturbance, the ship roll motion angle, and three control authorities, changing the length of pulley cable, pulley-brake mechanism control option and load control torque are included in the linearized model for analysis. Controllability and observability are confirmed for each control variable. An active control law to cancel the effect of ship roll motion on load pendulation is achieved by changing the cable length of the pulley and proved to be valid for small ship roll motion. Also, a controller based on linear quadratic regulator (LQR) is designed to reduce pendulation

We propose a hybrid fuzzy-linear controller for a nonlinearterminal voltage-controlled DC motor. The nonlinearity includes theeffect of magnetization saturation and a nonlinear fan load. The inputto the proposed Fuzzy Logic Controller (FLC) is an 'aggregate' errorbetween the required and the actual rpm and its time derivatives. TheFLC is designed to approximate a bang-bang controller and itscoefficients are chosen as a tradeoff between short rise time, smallsteady-state errors, and control chattering. The FLC performance wascompared to that of a well-known design; namely a sliding modecontroller with a boundary layer. Although the FLC design procedure iseasier, the performance of the two controllers are quite similar.Unfortunately, the FLC design was not robust enough to counter theeffect of large time delays introduced by the power electronicsinterface.

A state-space model of the shipboard crane based on implicit description of the crane with no simplifying assumptions is developed. By choosing appropriate states, full control authorities for changing the length of the rope is achieved. Three control actions are considered: changing the luffing angle and changing the length of the rope from two points on the boom. A chaotic rolling moment, with a dominant frequency of the same order as the resonance frequency of the shipboard crane, is applied to the ship as an external disturbance. The effect of this disturbance is studied. Feedforward and feedback controllers are then designed and tested for this shipboard crane to suppress the load pendulation caused by ship rolling. The friction in the pulley is assumed negligible. The simulation results based on these controllers show more than 98% decrease in the pendulation magnitude due to control

“The Nation's health, wealth, and security rely on the production and distribution of certain goods and services. The array of physical assets, processes, and organizations across which these goods and services move are called critical infrastructures.” 1 This ...

We designed a feedforward control law that greatly decreases the load sway of a shipboard crane due to ship rolling. This feedforward control uses measurements of ship rolling angle at each instant. At different operating points the optimal feedforward gain changes while is numerically computable. Here, we endeavor to bring forth the utility of the use of a Genetic Algorithm (GA) based approach to optimize the mapping of feedforward gain in four dimensional space. The process is based on the numerical calculation of the optimal feedforward gain for any rolling angle (ρ), and length of the rope (L), and luffing angle (δ0). The optimal gain is calculated for a group of points in the working space and then fit a function of order n to these points in a four dimensional space. Our choice for this problem includes real value GA with a combination of different crossover methods. The cost function is the sum of squared errors at selected points and we aim to minimize it. Since moving the load to another location also changes the optimal gain, the new improved gain scheduling further reduces the swinging within the whole working space. GA is a directed serach method and is capable of searching for variables of functions with any desired structure. The major advantages of using GA for function mappings is that the function does not have to be linear or in any specific form.

The role of the transmission network in the Power System is to transmit the power generated in the power plants to the load centers and the interconnected power systems. The transmission of electric power has to take place in the most efficient way in addition to providing flexibility in the process. Flexible A.C. Transmission System (FACTS) promotes the use of static controllers to enhance the controllability and increase the power transfer capability. Providing reactive shunt compensation with shunt-connected capacitors and reactors is a well established technique to get a better voltage profile in a power system. Shunt capacitors are inexpensive but lack dynamic capabilities, thus some form of dynamically controlled reactive power compensation becomes essential. This feature is provided by Static VAR Compensator (SVC). The work presented here also compares SVC with fixed capacitor compensation and documents the superiority of SVC using Computer Simulation and its performance for reactive power management and better voltage control.

Automated stripping of skulls from infantile brain MR Images is the fundamental work to visualize cerebral surface and to measure cerebral volumes. They are important to evaluate cerebral diseases because most cerebral diseases cause morphometric changes in cerebrum. This study proposes a novel image segmentation method based on fuzzy rule-based active surface model. The proposed method was validated by applying it to two neonatal (3W and 4W) and six infantile (5W to 4Y2M) subjects. The mean sensitivity was 98.84 %, and false-positive rate was 1.21 %, and the cerebral surface was visualized well.

We consider the analysis and filtering of a deterministic signal with slowly time-varying spectra (or, simply, a slowly time-varying signal) using the optimally smoothed Wigner distribution (OSWD). We compare this mixed time-frequency representation (MTFR) to other MTFR's such as the spectrogram, the short-time Fourier transform (STFT), and the Wigner and pseudo-Wigner distributions. We show that the normalized average frequency of the OSWD of a slowly timevarying signal whose phase can be locally expanded in a quadratic function of time yields the exact instantaneous frequency even if the signal is amplitude modulated. Using concepts from modulation theory, we show that the bandwidth (BW) of the OSWD is almost independent of the length of the analysis window, unlike the BW of the spectrogram. We also consider the decomposition of a slowly time-varying signal into its components in the presence of noise by masking the optimal STFT, which is the STFT computed with the window of the OSWD. We propose a new approach to designing linear time-varying filters for slowly time-varying signals which is based on the concept of local nonstationarity cancellation, and we show that it is equivalent to masking the optimal STFT. The performance of the filter in suppressing white noise, and in decomposing a slowly time-varying signal into its components is studied and compared to the performance of the techniques based on the STFT.

DC motors are widely used in industry such as mechanics, robotics, and aerospace engineering. In this paper, we present a high performance control method for position control of DC motors. Fault-tolerant control model are also addressed to combine with neuro-robust control approach. It is shown that with the proposed control algorithms, external disturbances and coupled dynamics inherent in the system are effectively compensated using neural network unit in which no analytical estimation on the upper bound of the reconstruction error and uncertainties is needed. Simulations results of the position control also confirm the effectiveness of the proposed methods.

A Novel or Methodological approach to comprehensively analyze the vulnerability of interdependent infrastructures is introduced in our paper; two types of vulnerability are considered: structural vulnerability and functional vulnerability. For structural vulnerability, infrastructures topologies are the only information while operating regimes of different infrastructures are further taken into consideration to analyze functional vulnerability. In this paper, we propose a flexible and extensible framework for network survivability testing and evaluation based interdependency modeling of Smart Grid and SCADA network. We developed novel approach for vulnerability reduction considering both structural and functional vulnerability. We computed interdependent effect between these networks under different conditions. We used MATLAB for simulation and analysis of methodology developed by us for interdependency analysis. The contribution of this paper provides a feasible research approach for the normalization of network survivability testing and evaluation and the promotion of network survivability research.

The role of the transmission network in the Power System is to transmit the power generated in the power plants to the load centers and the interconnected power systems. The transmission of electric power has to take place in the most efficient way in addition to providing flexibility in the process. Flexible A.C. Transmission System (FACTS) promotes the use of static controllers to enhance the controllability and increase the power transfer capability. Providing reactive shunt compensation with shunt-connected capacitors and reactors is a well established technique to get a better voltage profile in a power system. Shunt capacitors are inexpensive but lack dynamic capabilities, thus some form of dynamically controlled reactive power compensation becomes essential. This feature is provided by Static VAR Compensator (SVC). The work presented here also compares SVC with fixed capacitor compensation and documents the superiority of SVC using Computer Simulation and its performance for reactive power management and better voltage control.

Snake-like robots have gained popularity in the last three decades for their ability to utilize several gaits in order to navigate through different terrains. They are analogous in morphology to snakes, tentacles, and elephant trunks. We propose a novel method of navigating a snake-like robot based on the Harmonic Field with Optimized Boundary Conditions (HFOBC) and a boundary following algorithm. We apply the HFOBC navigation function using a number of fictitious charges equally spaced on each link. These charges actively follow the potential field towards the target. Futhermore, a generalized mathematical model for an n-link snake-like robot based on Lagrange formulation has also been proposed in this paper.

Zadeh's original motivation for fuzzy logic and the Fuzzy Rule-Based System (FRBS) was linguistic and hence possessed highly interpretable components. But as the complexity of a typical FRBS increases, it often becomes more like an uninterpretable neural network, and the Principle of Incompatibility predicts a degradation in interpretability for the same accuracy. This is particularly true of the so-called Takagi-Sugeno-Kang (TSK), or simply the Sugeno, approximator. We argue that imposing additional structure on the TSK system can significantly improve the tradeoff inherent in the Principle of Incompatibility. A promising structure was proposed recently in which the membership functions are local and sufficiently differentiable, and the consequent polynomials are rule-centered. This structure leads to the general interpretation that the consequent polynomials are Taylor series expansions. On this interpretation, a foundation for an algebra and a calculus of FRBSs can be built. We will illustrate these aspects of the proposed structure and discuss issues of modularity, functionality, and scalability of FRBSs.

The objective is an unmanned vehicle is to complete a given mission, i.e., to arrive at a given destination safely within a prespecified time. As a result, the basic pathplanning problem discussed in this paper is the problem of finding a short (fuel-optimal) and collision-free path between the two states in an environment with static or dynamic obstacles [13]. For a static environment with perfect information, the traditional path-finding algorithms such as A-star [4] can provide an optimal solution. However, for a dynamic environment with uncertain factors or gradually changing topology, the conventional optimization algorithm may not be applicable. Instead, an adaptive and robust algorithm is favored.

Internally symmetric proteins are proteins that have a symmetrical structure in their monomeric single-chain form. Around 10–15% of the protein domains can be regarded as having some sort of internal symmetry. In this regard, we previously published SymD (symmetry detection), an algorithm that determines whether a given protein structure has internal symmetry by attempting to align the protein to its own copy after the copy is circularly permuted by all possible numbers of residues. SymD has proven to be a useful algorithm to detect symmetry. In this paper, we present a new parallelized algorithm called Parallel-SymD for detecting symmetry of proteins on clusters of computers. The achieved speedup of the new Parallel-SymD algorithm scales well with the number of computing processors. Scaling is better for proteins with a larger number of residues. For a protein of 509 residues, a speedup of 63 was achieved on a parallel system with 100 processors.

The growth of mobile devices has provided significant opportunities for developing healthcare apps based on the mobile device ability to collect data. Unfortunately, the data collection is often intermittent. Missing data present significant challenges to trend analysis of time series. Straightforward approaches consisting of supplementing missing data with constant or zero values or with linear trends can severely degrade the quality of the trend analysis. In this paper, we present a robust adaptive approach to discover the trends from fragmented time series. The approach proposed in this paper is based on the hypothesis-testingbased adaptive spline filtering (HASF) trend analysis algorithm, which can accommodate non-uniform sampling and is therefore inherently robust to missing data. HASF adapts the nodes of the spline based on hypothesis testing and variance minimization, which adds to its robustness. Further improvement is obtained by filling gaps by data estimated in an earlier trend analysis, provided by HASF itself. Three variants for filling the gaps of missing data are considered, the best of which seems to consist of filling significantly large gaps with linear splines matched for continuity and smoothness with cubic splines covering datadense regions. Small gaps are ignored and addressed by the underlying cubic spline fitting. Finally, the existing measurements are weighted according to their importance by simply transferring the importance of the missing data to their existing neighbors. The methods are illustrated and evaluated using heart rate data sets, blood pressure data sets, and noisy sine data sets. INDEX TERMS Trend analysis, missing measurements, mobile health, mHealth, adaptive filtering, hypothesis testing, fragmented time series, health behavior change, missing observation, smartphone, digital health, health care.

The aim of this paper is the identification of the most harmful malicious attacks in a smart grid with basis on the removal of buses in a particular sequence. For that, we define the Electrical Most Damaging Element (EMDE) and the Iterated Centrality Measure (ICM). The EMDE is the element that leads to the largest unsatisfied load increase after removed, in the current state of the smart grid. The ICM is a meaningful scaled centrality for iterated attacks. Attack strategies such as the IEMDE (Iterated Electrical Most Damaging Element) and the Iterated Most Central Element (IMCE) are proposed as references for evaluating the impact of failure sequences by comparison. For each fault strategy approach, the vulnerability curves as well as a scalability analysis are presented. It is demonstrated that the IEMDE approximated the $$N-k-\varepsilon$$ N - k - ε algorithm, but with reduced computational expense. Furthermore, the IMCE approach provided an efficient fault profile close to the pe...