Red light-emitting Carborane-BODIPY dyes: Synthesis and properties of visible-light tuned fluorophores with enhanced boron content

A small library of 2,6- and 3,5-distyrenyl-substituted carborane-BODIPY dyes was efficiently synthesized by means of a Pd-catalyzed Heck coupling reaction. Styrenyl-carborane derivatives were exploited as molecular tools to insert two carborane clusters into the fluorophore core and to extend the π-conjugation of the final molecule in a single synthetic step. The synthetic approach allows to increase the molecular diversity of this class of fluorescent dyes by the synthesis of symmetric or asymmetric units with enhanced boron content. The structural characterization and the photoluminescence (PL) properties of synthesized dyes were evaluated, and the structure/properties relationships have been investigated by theoretical calculations. The developed compounds exhibit a significant bathochromic shift compared to their parent fluorophore scaffolds, and absorption and emission patterns were practically unaffected by the different substituents (Me or Ph) on the Ccluster atom (Cc) of the carborane cage or the cluster isomer (ortho- or meta-carborane). Remarkably, the presence of carborane units at 2,6-positions of the fluorophore produced a significant increase of the emission fluorescent quantum yields, which could be slightly tuned by changing the Cc-substituent and the carborane isomer, as well as introducing ethylene glycol groups at the meso-position of the BODIPY

Deep convolutional neural network with 2D spectral energy maps for fault diagnosis of gearboxes under variable speed.

For industrial safety, correct classification of gearbox fault conditions is necessary. One of the most crucial tasks in data-driven fault diagnosis is determining the best set of features by analyzing the statistical parameters of the signals. However, under variable speed conditions, these statistical parameters are incapable of uncovering the dynamic characteristics of different fault conditions of gearboxes. Later, several deep learning algorithms are used to improve the performance of the feature selection process, but domain knowledge expertise is still necessary. In this paper, a combination domain knowledge analysis and a deep neural network is proposed. By using the input acoustic emission (AE) signal, a two-dimensional spectrum energy map (2D AE-SEM) is created to form an identical fault pattern for various speed conditions of gearboxes. Then, a deep convolutional neural network (DCNN) is proposed to investigate the detailed structure of the 2D input for final fault classification. This 2D AE-SEM offers a graphical depiction of acoustic emission spectral characteristics. Our proposed system offers vigorous and dynamic classification performance through the proposed DCNN with a high diagnostic fault classification accuracy of 96.37% in all considered scenarios

Hemodynamic-informed parcellation of fMRI data in a Joint Detection Estimation framework

International audienceIdentifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called Hemodynamic Response Function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contributions commit to pre-specified spatial supports for the hemodynamic territories by defining these supports either as individual voxels or a priori fixed brain parcels. In this paper, we introduce a Joint Parcellation-Detection-Estimation (JPDE) procedure that incorporates an adaptive parcel identification step based upon local hemodynamic properties. Efficient inference of both evoked activity, HRF shapes and supports is then achieved using variational approximations. Validation on synthetic and real fMRI data demonstrate the JPDE performance over standard detection estimation schemes and suggest it as a new brain exploration tool

Coupling dynamics of a geared multibody system supported by Elastohydrodynamic lubricated cylindrical joints

A comprehensive computational methodology to study the coupling dynamics of a geared multibody system supported by ElastoHydroDynamic (EHD) lubricated cylindrical joints is proposed throughout this work. The geared multibody system is described by using the Absolute-Coordinate-Based (ACB) method that combines the Natural Coordinate Formulation (NCF) describing rigid bodies and the Absolute Nodal Coordinate Formulation (ANCF) characterizing the flexible bodies. Based on the finite-short bearing approach, the EHD lubrication condition for the cylindrical joints supporting the geared system is considered here. The lubrication forces developed at the cylindrical joints are obtained by solving the Reynolds’ equation via the finite difference method. For the evaluation of the normal contact forces of gear pair along the Line Of Action (LOA), the time-varying mesh stiffness, mesh damping and Static Transmission Error (STE) are utilized. The time-varying mesh stiffness is calculated by using the Chaari’s methodology. The forces of sliding friction along the Off-Line-Of-Action (OLOA) are computed by using the Coulomb friction models with a time-varying coefficient of friction under the EHD lubrication condition of gear teeth. Finally, two numerical examples of application are presented to demonstrate and validate the proposed methodology.National Natural Science Foundations of China under Grant 11290151, 11221202 and 11002022, Beijing Higher Education Young Elite Teacher Project under Grant YETP1201

Variational Bayesian causal connectivity analysis for fMRI

The ability to accurately estimate effective connectivity among brain regions from neuroimaging data could help answering many open questions in neuroscience. We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data. The method uses a vector autoregressive model for the latent variables describing neuronal activity in combination with a linear observation model based on a convolution with a hemodynamic response function. Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm. The computational efficiency of the method enables us to apply it to large scale problems with high sampling rates and several hundred regions of interest. We use a comprehensive empirical evaluation with synthetic and real fMRI data to evaluate the performance of our method under various conditions.This work was partially supported by the National Institute of Child Health and Human Development (R01 HD042049). Martin Luessi was partially supported by the Swiss National Science Foundation Early Postdoc Mobility fellowship 148485. This work was supported in part by the Department of Energy under Contract DE-NA0000457, the “Ministerio de Ciencia e Innovación” under Contract TIN2010-15137, and the CEI BioTic with the Universidad de Granada Data were provided (in part) by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University