Generalized matching networks and their properties

In this paper, we introduce two kinds of graphs: the generalized matching networks (GMNs) and the recursive generalized matching networks (RGMNs). The former generalize the hypercube-like networks (HLNs), while the latter include the generalized cubes and the star graphs. We prove that a GMN on a family of k-connected building graphs is -connected. We then prove that a GMN on a family of Hamiltonian-connected building graphs having at least three vertices each is Hamiltonian-connected. Our conclusions generalize some previously known results.

Minimum neighborhood in a generalized cube

Generalized cubes are a subclass of hypercube-like networks, which include some hypercube variants as special cases. Let theta(G)(k) denote the minimum number of nodes adjacent to a set of k vertices of a graph G. In this paper, we prove theta(G)(k) >= -1/2k(2) + (2n - 3/2)k - (n(2) - 2) for each n-dimensional generalized cube and each integer k satisfying n + 2 <= k <= 2n. Our result is an extension of a result presented by Fan and Lin [J. Fan, X. Lin, The t/k-diagnosability of the BC graphs, IEEE Trans. Comput. 54 (2) (2005) 176-184]. (c) 2005 Elsevier B.V. All rights reserved.

Generalized honeycomb torus is Hamiltonian

Generalized honeycomb torus is a candidate for interconnection network architectures, which includes honeycomb torus, honeycomb rectangular torus, and honeycomb parallelogramic torus as special cases. Existence of Hamiltonian cycle is a basic requirement for interconnection networks since it helps map a "token ring" parallel algorithm onto the associated network in an efficient way. Cho and Hsu [Inform. Process. Lett. 86 (4) (2003) 185-190] speculated that every generalized honeycomb torus is Hamiltonian. In this paper, we have proved this conjecture. (C) 2004 Elsevier B.V. All rights reserved.

A lower bound on the size of k-neighborhood in generalized cubes

The determination of the minimum size of a k-neighborhood (i.e., a neighborhood of a set of k nodes) in a given graph is essential in the analysis of diagnosability and fault tolerance of multicomputer systems. The generalized cubes include the hypercube and most hypercube variants as special cases. In this paper, we present a lower bound on the size of a k-neighborhood in n-dimensional generalized cubes, where 2n + 1 <= k <= 3n - 2. This lower bound is tight in that it is met by the n-dimensional hypercube. Our result is an extension of two previously known results. (c) 2005 Elsevier Inc. All rights reserved.

A novel radix-3/9 algorithm for type-III generalized discrete Hartley transform

A novel radix-3/9 algorithm for type-III generalized discrete Hartley transform (GDHT) is proposed, which applies to length-3(P) sequences. This algorithm is especially efficient in the case that multiplication is much more time-consuming than addition. A comparison analysis shows that the proposed algorithm outperforms a known algorithm when one multiplication is more time-consuming than five additions. When combined with any known radix-2 type-III GDHT algorithm, the new algorithm also applies to length-2(q)3(P) sequences.

Asymmetric phase-transfer-catalyzed synthesis of five-membered cyclic gamma-Amino acid precursors

The first example of an intramolecular enantioselective Michael addition of nitronates onto conjugated systems utilizing a chiral phase-transfer catalyst is described. A range of five-membered gamma-nitro esters with up to three stereocentres have been prepared and the relative and absolute configurations proven by chemical and crystallographic methods. The products are rapidly obtained and are precursors to five-membered cyclic gamma-amino acids.

Thermodynamics of climate change: generalized sensitivities

Using a recent theoretical approach, we study how global warming impacts the thermodynamics of the climate system by performing experiments with a simplified yet Earth-like climate model. The intensity of the Lorenz energy cycle, the Carnot efficiency, the material entropy production, and the degree of irreversibility of the system change monotonically with the CO2 concentration. Moreover, these quantities feature an approximately linear behaviour with respect to the logarithm of the CO2 concentration in a relatively wide range. These generalized sensitivities suggest that the climate becomes less efficient, more irreversible, and features higher entropy production as it becomes warmer, with changes in the latent heat fluxes playing a predominant role. These results may be of help for explaining recent findings obtained with state of the art climate models regarding how increases in CO2 concentration impact the vertical stratification of the tropical and extratropical atmosphere and the position of the storm tracks.

Probing the effect of allylic substitution on cyclic ammonium ylid rearrangements

The [2,3]-sigmatropic rearrangement of tetrahydropyridine-derived ammonium ylids is a valuable method for the preparation of substituted pyrrolidine carboxylates. The presence of an allylic substituent does not intrinsically reduce the yield of rearrangements, and the diastereoselectivity of rearrangement is related to the structure of the diazo reactant. The method represents a very rapid means of accessing complex pyrrolidines, as shown by preparation of a precursor to the core of lactacystin.

Asymmetric synthesis of cyclic β-amino acids and cyclic amines via sequential diastereoselective conjugate addition and ring closing metathesis

Diastereoselective conjugate addition of lithium (S)-N-allyl-N-alpha-methylbenzylamide to a range of alpha,beta-unsaturated esters followed by ring closing metathesis is used to afford efficiently a range of substituted cyclic beta-amino esters in high d.e. Alternatively, conjugate addition to alpha,beta-unsaturated Weinreb amides, functional group conversion and ring closing metathesis affords cyclic amines in high d.e. The further application of this methodology to the synthesis of a range of carbocyclic beta-amino esters via conjugate addition, enolate alkylation and ring closing metathesis is also described. Application of this methodology affords, after deprotection, (S)-homoproline, (S)-homopipecolic acid, (S)-coniine and (1S,2S)-trans-pentacin.

Anticipatory activation in the amygdala and anterior cingulate in generalized anxiety disorder and prediction of treatment response

OBJECTIVE: The anticipation of adverse outcomes, or worry, is a cardinal symptom of generalized anxiety disorder. Prior work with healthy subjects has shown that anticipating aversive events recruits a network of brain regions, including the amygdala and anterior cingulate cortex. This study tested whether patients with generalized anxiety disorder have alterations in anticipatory amygdala function and whether anticipatory activity in the anterior cingulate cortex predicts treatment response. METHOD: Functional magnetic resonance imaging (fMRI) was employed with 14 generalized anxiety disorder patients and 12 healthy comparison subjects matched for age, sex, and education. The event-related fMRI paradigm was composed of one warning cue that preceded aversive pictures and a second cue that preceded neutral pictures. Following the fMRI session, patients received 8 weeks of treatment with extended-release venlafaxine. RESULTS: Patients with generalized anxiety disorder showed greater anticipatory activity than healthy comparison subjects in the bilateral dorsal amygdala preceding both aversive and neutral pictures. Building on prior reports of pretreatment anterior cingulate cortex activity predicting treatment response, anticipatory activity in that area was associated with clinical outcome 8 weeks later following treatment with venlafaxine. Higher levels of pretreatment anterior cingulate cortex activity in anticipation of both aversive and neutral pictures were associated with greater reductions in anxiety and worry symptoms. CONCLUSIONS: These findings of heightened and indiscriminate amygdala responses to anticipatory signals in generalized anxiety disorder and of anterior cingulate cortex associations with treatment response provide neurobiological support for the role of anticipatory processes in the pathophysiology of generalized anxiety disorder.

Time-resolved gas-phase kinetic, quantum chemical and RRKM studies of reactions of silylene with cyclic ethers

Time-resolved kinetic studies of silylene, SiH2, generated by laser flash photolysis of phenylsilane, have been carried out to obtain rate constants for its bimolecular reactions with oxirane, oxetane, and tetrahydrofuran (THF). The reactions were studied in the gas phase over the pressure range 1-100 Torr in SF6 bath gas, at four or five temperatures in the range 294-605 K. All three reactions showed pressure dependences characteristic of third-body-assisted association reactions with, surprisingly, SiH2 + oxirane showing the least and SiH2 + THF showing the most pressure dependence. The second-order rate constants obtained by extrapolation to the high-pressure limits at each temperature fitted the Arrhenius equations where the error limits are single standard deviations: log(k(oxirane)(infinity)/cm(3) molecule(-1) s(-1)) = (-11.03 +/- 0.07) + (5.70 +/- 0.51) kJ mol(-1)/RT In 10 log(k(oxetane)(infinity)/cm(3) molecule(-1) s(-1)) = (-11.17 +/- 0.11) + (9.04 +/- 0.78) kJ mol(-1)/RT In 10 log(k(THF)(infinity)/cm(3) molecule(-1) s(-1)) = (-10.59 +/- 0.10) + (5.76 +/- 0.65) kJ mol(-1)/RT In 10 Binding-energy values of 77, 97, and 92 kJ mol(-1) have been obtained for the donor-acceptor complexes of SiH2 with oxirane, oxetane, and THF, respectively, by means of quantum chemical (ab initio) calculations carried Out at the G3 level. The use of these values to model the pressure dependences of these reactions, via RRKM theory, provided a good fit only in the case of SiH2 + THF. The lack of fit in the other two cases is attributed to further reaction pathways for the association complexes of SiH2 with oxirane and oxetane. The finding of ethene as a product of the SiH2 + oxirane reaction supports a pathway leading to H2Si=O + C2H4 predicted by the theoretical calculations of Apeloig and Sklenak.

A geometric approach to generalized Stokes conjectures

We consider the Stokes conjecture concerning the shape of extreme two-dimensional water waves. By new geometric methods including a nonlinear frequency formula, we prove the Stokes conjecture in the original variables. Our results do not rely on structural assumptions needed in previous results such as isolated singularities, symmetry and monotonicity. Part of our results extends to the mathematical problem in higher dimensions.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Neural networks for identification of nonlinear systems under random piecewise polynomial disturbances

The problem of identification of a nonlinear dynamic system is considered. A two-layer neural network is used for the solution of the problem. Systems disturbed with unmeasurable noise are considered, although it is known that the disturbance is a random piecewise polynomial process. Absorption polynomials and nonquadratic loss functions are used to reduce the effect of this disturbance on the estimates of the optimal memory of the neural-network model.

Identification of linear systems in the presence of piecewise polynomial disturbances

The paper proposes a method of performing system identification of a linear system in the presence of bounded disturbances. The disturbances may be piecewise parabolic or periodic functions. The method is demonstrated effectively on two example systems with a range of disturbances.