Conformations and fluorescence of encapsulated stilbene

Absorption and emission spectra of free and encapsulated stilbene in two different capsules were calculated using the DFT and the TDDFT methodology at the B3LYP, CAM-B3LYP, M06-2X, PBE0, and ωB97X-D/6-31G(d,p) levels of theory. The present work is directed toward the theoretical interpretation of recent experimental results on control of stilbene conformation and fluorescence in capsules [Ams, M. R.; et al. Beilstein J. Org. Chem. 2009, 5, 79]. The results of the calculations are in agreement with experiment and show that fluorescence of trans-stilbene persists in the large cage while it is quenched in the small one. It is found that the geometry of trans-stilbene in the ground as well as in the first excited singlet state is unaffected by encapsulation in the large cage, and consequently the absorption and emission spectra are similarly unaffected. In the small cage, the ground state of encapsulated trans-stilbene is distorted, with the two phenyl groups twisted, while the geometry of the excited state, after relaxation, lies at the conical intersection with the ground state. Consequently, there is no emission similar to that of free trans-stilbene, and the state decays nonradiatively to the ground state.     

Chimeras in random non-complete networks of phase oscillators

We consider the simplest network of coupled non-identical phase oscillators capable of displaying a "chimera" state (namely, two subnetworks with strong coupling within the subnetworks and weaker coupling between them) and systematically investigate the effects of gradually removing connections within the network, in a random but systematically specified way. We average over ensembles of networks with the same random connectivity but different intrinsic oscillator frequencies and derive ordinary differential equations (ODEs), whose fixed points describe a typical chimera state in a representative network of phase oscillators. Following these fixed points as parameters are varied we find that chimera states are quite sensitive to such random removals of connections, and that oscillations of chimera states can be either created or suppressed in apparent bifurcation points, depending on exactly how the connections are gradually removed.     

Elimination of motion and pulsation artifacts using BLADE sequences in knee MR imaging

The purpose of this study is to evaluate the ability of proton density (PD)-BLADE sequences in reducing or even eliminating motion and pulsatile flow artifacts in knee magnetic resonance imaging examinations. Eighty consecutive patients, who had been routinely scanned for knee examination, participated in the study. The following pairs of sequences with and without BLADE were compared: (a) PD turbo spin echo (TSE) sagittal (SAG) fat saturation (FS) in 35 patients, (b) PD TSE coronal (COR) FS in 19 patients, (c) T2 TSE axial in 13 patients and (d) PD TSE SAG in 13 patients. Both qualitative and quantitative analyses were performed based on the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and relative contrast (ReCon) measures of normal anatomic structures. The qualitative analysis was performed by experienced radiologists. Also, the presence of image motion and pulsation artifacts was evaluated. Based on the results of the SNR, CRN and ReCon for the different sequences and anatomical structures, the BLADE sequences were significantly superior in 19 cases, whereas the corresponding conventional sequences were significantly superior in only 6 cases. BLADE sequences eliminated motion artifacts in all the cases. However, motion artifacts were shown in (a) six PD TSE SAG FS, (b) three PD TSE COR FS, (c) three PD TSE SAG and (d) two T2 TSE axial conventional sequences. In our results, it was found that, in PD FS sequences (sagittal and coronal), the differences between the BLADE and conventional sequences regarding the elimination of motion and pulsatile flow artifacts were statistically significant. In all the comparisons, the PD FS BLADE sequences (coronal and sagittal) were significantly superior to the corresponding conventional sequences regarding the classification of their image quality. In conclusion, this technique appears to be capable to potentially eliminate motion and pulsatile flow artifacts in MR images.     

Neural network solution of pantograph type differential equations

We investigate the approximate solution of pantograph type functional differential equations using neural networks. The methodology is based on the ideas of Lagaris et al, and itis applied to various problems with a proportional delay term subject to initial or boundary conditions. The proposed methodology proves to be very efficient.     

Wilcoxon‐type rank‐sum statistics for selecting the best population: Some advances

In this article, we introduce three new nonparametric procedures for testing the equality of two lifetime distributions. The proposed testing processes are based on appropriately modified Wilcoxon‐type rank‐sum statistics. The exact null distribution of these statistics is studied and closed formulae for the corresponding exact probability of correct selection of the best population are derived for the class of Lehmann alternatives. A detailed numerical study is carried out to elucidate the performance of the proposed testing schemes. For illustration purposes, a real data application is presented in some detail.     

Enhancing PSO methods for global optimization

The Particle Swarm Optimization (PSO) method is a well-established technique for global optimization. During the past years several variations of the original PSO have been proposed in the relevant literature. Because of the increasing necessity in global optimization methods in almost all fields of science there is a great demand for efficient and fast implementations of relative algorithms. In this work we propose three modifications of the original PSO method in order to increase the speed and its efficiency that can be applied independently in almost every PSO variant. These modifications are: (a) a new stopping rule, (b) a similarity check and (c) a conditional application of some local search method. The proposed were tested using three popular PSO variants and a variety test functions. We have found that the application of these modifications resulted in significant gain in speed and efficiency.     

On a topological closeness of perturbed Julia sets

In the present work we expand our previous work in [1] by introducing the Julia Deviation Distance and the Julia Deviation Plot in order to study the stability of the Julia sets of noise-perturbed Mandelbrot maps. We observe a power-law behaviour of the Julia Deviation Distance of the Julia sets of a family of additive dynamic noise Mandelbrot maps from the Julia set of the Mandelbrot map as a function of the noise level. Additionally, using the above tools, we support the invariance of the Julia set of a noise-perturbed Mandelbrot map under different noise realizations.     

Extending the Newton-Kantorovich hypothesis for solving equations

The famous Newton-Kantorovich hypothesis (Kantorovich and Akilov, 1982 [3], Argyros, 2007 [2], Argyros and Hilout, 2009 [7]) has been used for a long time as a sufficient condition for the convergence of Newton’s method to a solution of an equation in connection with the Lipschitz continuity of the Fréchet-derivative of the operator involved. Here, using Lipschitz and center-Lipschitz conditions, and our new idea of recurrent functions, we show that the Newton-Kantorovich hypothesis can be weakened, under the same information. Moreover, the error bounds are tighter than the corresponding ones given by the dominating Newton-Kantorovich theorem (Argyros, 1998 [1]; [2] and [7]; Ezquerro and Hernández, 2002 [11]; [3]; Proinov 2009, 2010 [16] and [17]).Numerical examples including a nonlinear integral equation of Chandrasekhar-type (Chandrasekhar, 1960 [9]), as well as a two boundary value problem with a Green’s kernel (Argyros, 2007 [2]) are also provided in this study.     

Parsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study

Nonlinear manifold learning algorithms, such as diffusion maps, have been fruitfully applied in recent years to the analysis of large and complex data sets. However, such algorithms still encounter challenges when faced with real data. One such challenge is the existence of “repeated eigendirections,” which obscures the detection of the true dimensionality of the underlying manifold and arises when several embedding coordinates parametrize the same direction in the intrinsic geometry of the data set. We propose an algorithm, based on local linear regression, to automatically detect coordinates corresponding to repeated eigendirections. We construct a more parsimonious embedding using only the eigenvectors corresponding to unique eigendirections, and we show that this reduced diffusion maps embedding induces a metric which is equivalent to the standard diffusion distance. We first demonstrate the utility and flexibility of our approach on synthetic data sets. We then apply our algorithm to data collected from a stochastic model of cellular chemotaxis, where our approach for factoring out repeated eigendirections allows us to detect changes in dynamical behavior and the underlying intrinsic system dimensionality directly from data.