An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Non-intrusive load monitoring (NILM) analyzes the terminal voltage and total current to offer comprehensive appliance-specific consumption data. Many state-of-the-art NILM models make a crucial assumption that recognized devices within the training set are responsible for triggering switching events. However, continuous addition of new devices diminishes the practical efficacy of existing methods for load monitoring. In this manuscript, we introduce a novel approach based on the Conditional Variational Auto-Encoder (CVAE) to address the challenge of classifying familiar appliances and identifying unfamiliar ones by leveraging V-I trajectory characteristics. In our proposed approach, during the training phase, we promote the alignment of capsule features belonging to the same familiar class with a predefined Gaussian distribution, where each class has its own distribution. To achieve this, we adopt the variational autoencoder framework and utilize a collection of Gaussian priors as an estimate for the following distribution. By employing this approach, we can regulate the compactness of features belonging to the same class around the mean of the corresponding Gaussian distributions. This enables us to control the classifier's capability to identify samples from unfamiliar classes. Testing findings on the public dataset illustrate the efficiency of our approach.
Compounds (2R*,3S*)-1-(3,4-dimethoxyphenyl)-3-{3-methoxy-2-[(2R*)-tetrahydropyran-2-yloxy]phenyl}-2,3-epoxy-1-propanone, C23H26O7, (I), and trans-1-(3,4-dimethoxyphenyl)-3-[3-methoxy-2-(methoxymethoxy)phenyl]-2,3-epoxy-1-propanone, C20H22O7, (II), were obtained on epoxidation of chalcones. The stereochemistries of (I) and (II) were elucidated. In both compounds, the substituents on the oxirane ring are trans-oriented. Compound (I) was obtained together with a diastereometric form that differs from (I) with respect to the configuration of the asymmetric C atom in the tetrahydropyran group. The geometries of the substituted oxirane rings of (I) and (II) are very similar. The hydrogen-bonding patterns, mediated via weak C—H⋯O interactions, differ considerably. The crystal structures of (I) and (II) are compared with those of related chalcone epoxides. The conversion of (I) and (II) into lignin-related phenylcoumarans is discussed.
Herein, we report the first example of one-time password (OTP) generation and two-factor authentication (2FA) using a molecular approach. OTPs are passwords that are valid for one entry only. For the next login session, a new, different password is generated. This brings the advantage that any undesired recording of a password will not risk the security of the authentication process. Our molecular realization of the OTP generator is based on a photochromic molecular triad where the optical input required to set the triad to the fluorescent form differs depending on the initial isomeric state.
1H NMR spectral data for lignin model compounds are of interest in connection with the interpretation of NMR spectra of lignins recorded by 1D (1H NMR) and certain 2D spectroscopic techniques. A database comprising such spectral data is being created. Derivatization influences the peak positions in the 1H NMR spectra of lignin models to a large extent. Similarly, an exchange of solvent often results in dramatic shifts of peak positions. Solvent and derivatization effects can be employed for the interpretation of lignin spectra in structural terms. Stereochemistry strongly influences the position of signals in 1H NMR spectra. This offers a possibility to elucidate the stereochemistry of the structural elements in lignins based on 1H NMR spectral data of model compounds.
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