Sharp Riesz-Fejér Inequality for Harmonic Hardy Spaces

Potential Analysis - We prove sharp version of Riesz-Fejér inequality for functions in harmonic Hardy space $h^{p}(mathbb {D})$ on the unit disk $mathbb {D}$ , for p >?1, thus...     

Estimating the size of an object captured with error

Central European Journal of Operations Research - In many applications we are faced with the problem of estimating object dimensions from a noisy image. Some devices like a fluorescent microscope,...     

Hierarchical Equations of Motion Simulation of Temperature‐Dependent Two‐Dimensional Electronic Spectroscopy of the Chlorophyll a Manifold in LHCII

We simulated two‐dimensional electronic spectra (2DES) of the chlorophyll a manifold of light‐harvesting complex II (LHCII) at various temperatures (77, 110, 150, 190, 230, 273, and 293 K) using the hierarchical equations of the motion‐phase matching approach. We confirm the main excitation energy transfer pathways assignments within the chlorophyll a manifold of LHCII measured in a recent work (J. Phys. Chem. B 2019, 123, 6765–6775). The calculated transfer rates are also in general agreement with the measured rates. We also provided theoretical confirmation for the experimental assignments, as uphill and downhill energy transfer processes, of 2D spectral features that were reported in recent experimental reports. These temperature‐dependent features were also ascertained to follow the detailed‐balance principle.     

Recent trends in image evaluation of HPTLC chromatograms

Abstract

In the current article, an overview of recent applications and opportunities of image evaluation of high-performance thin-layer chromatograms (HPTLC) in food analysis and natural product research is presented. The article shortly covers the aspects of specialized software packages for image analysis, image post-correction, signal acquisition, and preprocessing. Contemporary aspects of image-based HPTLC fingerprinting, quantification of target compounds, and advanced chemometric modeling were reviewed. Special attention was dedicated to freely available software packages. Advantages and disadvantages of each one were discussed in terms of abilities to obtain chromatographic profiles and perform crucial steps in signal manipulation, such as background subtraction, denoising, and background detrending. The most frequently applied techniques for signal manipulation have been discussed and recommendation provided. In that sense, the article aims to provide a valuable guideline for readers dealing with the application of HPTLC image analysis in food and natural products research, especially in connection with the most frequently used chemometric techniques, in domains of pattern recognition, classification, and regression.     

Design of branched and chiral solvatochromic probes: toward quantifying polarity gradients in dendritic macromolecules

[reaction: see text] A pair of chiral, branched monomer building blocks, consisting of a solvatochromic probe and a spectroscopically inactive volume dummy, has been developed. The probe can selectively be excited, and its fluorescence characteristics provide information about local polarity. Incorporation of these monomers into high-generation polyester dendrimers should enable a detailed investigation of the polarity/density profile in dendritic architectures and ultimately allow for the realization of energy gradients from one chromophore building block only.     

Simplified batch equilibration for D/H determination of non‐exchangeable hydrogen in solid organic material

Hydrogen isotopic analysis of organic materials has been widely applied in studies of paleoclimate, animal migration, forensics, food and flavor authentication, and the origin and diagenesis of organic matter. Hydrogen bound to carbon (C‐H) generally retains isotopic information about the water present during organic matter synthesis and associated biosynthetic fractionations, but hydrogen bound to other elements (O, S, or N) can readily exchange with atmospheric water vapor and reflects recent exposure to water or vapor. These two pools must be separated to obtain meaningful information from isotope ratios of organic materials. Previously published analytical methods either replace exchangeable H chemically or control its isotopic composition, usually by equilibration with water or waters of known isotopic composition. In addition, the fraction of H that is exchangeable can vary among samples and is itself of scientific interest. Here we report an improved and automated double‐equilibration approach. Samples are loaded in a 50‐position autosampler carousel in an air‐tight aluminum equilibration chamber. Water vapor of known isotopic composition is pumped through the chamber at 115°C for at least 6 h. After flushing with dry N₂ and being cooled, the carousel is rapidly transferred from the equilibration chamber to a He‐purged autosampler attached to a pyrolysis elemental analyzer connected to an isotope ratio mass spectrometer. By equilibrating two aliquots of each sample with two isotopically distinct waters, it is possible to calculate both (1) the D/H ratio of non‐exchangeable H, and (2) the fraction of H that is exchangeable. Relative to previous double‐equilibration techniques, this approach offers significant reductions in sample size and labor by allowing simultaneous equilibration of several tens of samples. Copyright © 2009 John Wiley & Sons, Ltd.     

Poly(ethylene oxide)-block-poly(α-cinnamyl-ε-caprolactone-co-ε-caprolactone) diblock copolymer nanocarriers for enhanced solubilization of caffeic acid phenethyl ester

We report novel micellar carriers, comprising pendant cinnamyl moieties in the core-forming block, designed to increase the solubilization of caffeic acid phenethyl ester (CAPE) in aqueous media. Amphiphilic poly(ethylene oxide)-block-poly(α-cinnamyl-ε-caprolactone-co-ε-caprolactone) (PEO-b-P(CyCL-co-CL) diblock copolymers were synthesized by ring-opening copolymerization of α-propargyl-ε-caprolactone and ε-caprolactone from a monofunctional PEO macroinitiator and subsequent attachment of cinnamyl groups via click reaction. In addition, a linear PEO-b-PCL diblock copolymer was synthesized and used in this study for comparison. Next, nanosized micelles from PEO-b-P(CyCL-co-CL) and PEO-b-PCL were formed via the solvent evaporation method and then loaded with CAPE. Dynamic and electrophoretic light scattering, and transmission electron microscopy were used to characterize both blank and loaded carriers. The potential of the micelles comprising pendant cinnamyl group to solubilize CAPE in water was evaluated in a comparative fashion to that of nonmodified PEO-b-PCL diblock copolymer.     

Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions

The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions. When modeling complex sequences, attention mechanisms are regarded as the established approach to support deep neural networks with intrinsic interpretability. This paper focuses on the emerging trend of specifically designing diagnostic datasets for understanding the inner workings of attention mechanism based deep learning models for multivariate forecasting tasks. We design a novel benchmark of synthetically designed datasets with the transparent underlying generating process of multiple time series interactions with increasing complexity. The benchmark enables empirical evaluation of the performance of attention based deep neural networks in three different aspects: (i) prediction performance score, (ii) interpretability correctness, (iii) sensitivity analysis. Our analysis shows that although most models have satisfying and stable prediction performance results, they often fail to give correct interpretability. The only model with both a satisfying performance score and correct interpretability is IMV-LSTM, capturing both autocorrelations and crosscorrelations between multiple time series. Interestingly, while evaluating IMV-LSTM on simulated data from statistical and mechanistic models, the correctness of interpretability increases with more complex datasets.