R&D in clean technology: A project choice model with learning

In this study, we investigate the qualitative and quantitative effects of an R&D subsidy for a clean technology and a Pigouvian tax on a dirty technology on environmental R&D when it is uncertain how long the research takes to complete. The model is formulated as an optimal stopping problem, in which the number of successes required to complete the R&D project is finite and learning about the probability of success is incorporated. We show that the optimal R&D subsidy with the consideration of learning is higher than that without it. We also find that an R&D subsidy performs better than a Pigouvian tax unless suppliers have sufficient incentives to continue cost-reduction efforts after the new technology success-fully replaces the old one. Moreover, by using a two-project model, we show that a uniform subsidy is better than a selective subsidy.

A silicon micromachined photonic band gap cell, characteristics and an application

A novel PBG cell based on micromachining of Silicon using wet anisotropic etching has been considered. Since this is based on etching of the Silicon substrate, it is amenable to fabrication with standard Silicon processes and integration with millimeter wave circuits. We characterize this kind of PBG cell by full wave simulations using a time domain code. For the purpose of characterization, the scenario of a 50 ohm microstrip line placed on a Silicon substrate which is anisotropically etched to create patterns with sloping walls is considered. This is shown to produce the well known PBG response of stop bands in certain frequency bands. We look at the variation in the transmission coefficient (S-21) response as the number of periods, length based average fill factor and depth of micromachining are varied. One application of a low pass filter has been proposed and simulated results are given.

Effect of substituent on the thermodynamics of D-glucopyranoside binding to concanavalin A, pea (Pisum sativum) lectin and lentil (Lens culinaris) lectin

Titration calorimetry measurements of the binding of phenyl-alpha (alpha PhOGlu), 3-methoxy (3MeOGlu), fluorodeoxy and deoxy derivatives of alpha-D-glucopyranose (Glu) to concanavalin A (conA), pea lectin and lentil lectin were performed at approx. 10 and 25 degrees C in 0.01 M dimethylglutaric acid/NaOH buffer, pH 6.9, containing 0.15 M NaCl and Mn2+ and Ca2+ ions. Apparently the 3-deoxy, 4-deoxy and 6-deoxy as well as the 4-fluorodeoxy and 6-fluorodeoxy derivatives of Glu do not bind to the lectins because no heat release was observed on the addition of aliquots of solutions of these derivatives to the lectin solutions. The binding enthalpies, delta H0b, and entropies, delta S0b, determined from the measurements were compared with the same thermodynamic binding parameters for Glu, D-mannopyranoside and methyl-alpha- D-glucopyranoside (alpha MeOGlu). The binding reactions are enthalpically driven with little change in the heat capacity on binding, and exhibit enthalpy-entropy compensation. Differences between the thermodynamic binding parameters can be rationalized in terms of the interactions apparent in the known crystal structures of the methyl-alpha-D-mannopyranoside-conA [Derewenda, Yariv, Helliwell, Kalb (Gilboa), Dodson, Papiz, Wan and Campbell (1989) EMBO J. 8, 2189-2193] and pea lectin-trimanno-pyranoside [Rini, Hardman, Einspahr, Suddath and Carber (1993) J. Biol. Chem. 268, 10126-10132] complexes. Increases in the entropy change on binding are observed for alpha MeOGlu binding to pea and lentil lectin, for alpha PhOGlu binding to conA and pea lectin, and for 3MeOGlu binding to pea lectin relative to the entropy change for Glu binding, and imply that the phenoxy and methoxy substituents provide additional hydrophobic interactions in the complex. Increases in the binding enthalpy relative to that of Glu are observed for deoxy and fluoro derivatives in the C-1 and C-2 positions and imply that these substituents weaken the interaction with the surrounding water, thereby strengthening the interaction with the binding site.

Measurement of $d\sigma/dy$ of Drell-Yan $e^+e^-$ pairs in the $Z$ Mass Region from $p\bar{p}$ Collisions at $\sqrt{s}=1.96$ TeV

We report on a CDF measurement of the total cross section and rapidity distribution, $d\sigma/dy$, for $q\bar{q}\to \gamma^{*}/Z\to e^{+}e^{-}$ events in the $Z$ boson mass region ($66M_{ee}

Detection of a periodic structure hidden in random background: the role of signal amplitude in the matched filter detection method

The matched filter method for detecting a periodic structure on a surface hidden behind randomness is known to detect up to (r(0)/Lambda) gt;= 0.11, where r(0) is the coherence length of light on scattering from the rough part and 3 is the wavelength of the periodic part of the surface-the above limit being much lower than what is allowed by conventional detection methods. The primary goal of this technique is the detection and characterization of the periodic structure hidden behind randomness without the use of any complicated experimental or computational procedures. This paper examines this detection procedure for various values of the amplitude a of the periodic part beginning from a = 0 to small finite values of a. We thus address the importance of the following quantities: `(a)lambda) `, which scales the amplitude of the periodic part with the wavelength of light, and (r(0))Lambda),in determining the detectability of the intensity peaks.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time,recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through a pseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets of measurements involving various load cases, we expedite the speed of thePD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time, recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through apseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets ofmeasurements involving various load cases, we expedite the speed of the PD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small. Copyright (C) 2009 John Wiley & Sons, Ltd.

First Measurement of the tt̅ Differential Cross Section dσ/dMtt̅ in pp̅ Collisions at √s=1.96 TeV

We present a measurement of the $\ttbar$ differential cross section with respect to the $\ttbar$ invariant mass, dSigma/dMttbar, in $\ppbar$ collisions at $\sqrt{s}=1.96$ TeV using an integrated luminosity of $2.7\invfb$ collected by the CDF II experiment. The $\ttbar$ invariant mass spectrum is sensitive to a variety of exotic particles decaying into $\ttbar$ pairs. The result is consistent with the standard model expectation, as modeled by \texttt{PYTHIA} with \texttt{CTEQ5L} parton distribution functions.

Search for standard model Higgs boson production in association with a W boson using a neural network discriminant at CDF

We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 inverse fb. We select events consistent with a signature of a single charged lepton, missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to $150 GeV/c^2, respectively.

Application of D transforms in the analysis of nonlinear distributed parameter systems

The scope of application of Laplace transforms presently limited to the study of linear partial differential equations, is extended to the nonlinear domain by this study. This has been achieved by modifying the definition of D transforms, put forth recently for the study of classes of nonlinear lumped parameter systems. The appropriate properties of the new D transforms are presented to bring out their applicability in the analysis of nonlinear distributed parameter systems.

Synthesis and identification of a new class of (S)-2,6-diamino-4,5,6,7-tetrahydrobenzo[d]thiazole derivatives as potent antileukemic agents

Benzothiazoles are multitarget agents with broad spectrum of biological activity. Among the antitumor agents discovered in recent years, the identification of various 2-(4-aminophenyl) benzothiazoles as potent and selective antitumor drugs against different cancer cell lines has stimulated remarkable interest. Some of the benzothiazoles are known to induce cell cycle arrest, activation of caspases and interaction with DNA molecule. Based on these interesting properties of benzothiazoles and to obtain new biologically active agents, a series of novel 4,5,6,7-tetrahydrobenzo[d]thiazole derivatives 5(a-i) were synthesized and evaluated for their efficacy as antileukemic agents in human leukemia cells (K562 and Reh). The chemical structures of the synthesized compounds were confirmed by H-1 NMR, LCMS and IR analysis. The cytotoxicity of these compounds were determined using trypan blue exclusion, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and lactate dehydrogenase (LDH) assays. Results showed that, these compounds mediate a significant cytotoxic response to cancer cell lines tested. We found that the compounds having electron withdrawing groups at different positions of the phenyl ring of the thiourea moiety displayed significant cytotoxic effect with IC50 value less than 60 mu M. To rationalize the role of electron withdrawing group in the induction of cytotoxicity, we have chosen molecule 5g (IC50 similar to 15 mu M) which is having chloro substitution at ortho and para positions. Flow cytometric analysis of annexin V-FITC/ propidium iodide (PI) double staining and DNA fragmentation suggest that 5g can induce apoptosis.

Comparative study of filter-bank mean-energy distance for automated segmentation of speech signals

This paper describes a method of automated segmentation of speech assuming the signal is continuously time varying rather than the traditional short time stationary model. It has been shown that this representation gives comparable if not marginally better results than the other techniques for automated segmentation. A formulation of the 'Bach' (music semitonal) frequency scale filter-bank is proposed. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks considering this model. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. 'Bach' filters are seen to marginally outperform the other filters.

A mixed-split scheme for 2-D DPCM based LSF quantization

In this paper a mixed-split scheme is proposed in the context of 2-D DPCM based LSF quantization scheme employing split vector product VQ mechanism. Experimental evaluation shows that the new scheme is successfully being able to show better distortion performance than existing safety-net scheme for noisy channel even at considerably lower search complexity, by efficiently exploiting LSF trajectory behavior across the consecutive speech frames.

Design of a FIR filter for image restoration using principal component neural networks

The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design a finite impulse response filter based on principal component neural network (PCNN) is proposed in this study for image filtering, optimized in the sense of visual inspection and error metric. This algorithm exploits the inter-pixel correlation by iteratively updating the filter coefficients using PCNN. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions. Further, the number of unknown parameters is very few and most of these parameters are adaptively obtained from the processed image.

Language independent automated segmentation of speech using Bach scale filter-banks

This correspondence describes a method for automated segmentation of speech. The method proposed in this paper uses a specially designed filter-bank called Bach filter-bank which makes use of 'music' related perception criteria. The speech signal is treated as continuously time varying signal as against a short time stationary model. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. The Bach filters are seen to marginally outperform the other filters.