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The isoquinoline alkaloids have attracted widespread attention from chemists and biologists due to their complex structures and prominent bioactivities. With the aims of screening for antiviral compounds produced by fungi, in this study, three new (1 - 3), together with four known (4–7) isoquinolines were isolated from the cigar tobacco-derived endophytic fungus Aspergillus felis. Their structures were determined by means of HRESIMS and NMR spectroscopic studies. Interestingly, an anti-TMV test revealed that compounds 2 and 4 showed high activity with inhibition rates of 43.2% and 38.4%, respectively. These rates are higher than those of the positive control ningnanmycin (33.2%). The isolation of the above isoquinolines may provide materials for the study of more potent anti-TMV inhibitors and contribute to an improved utilization of cigar tobacco-derived endophytic fungi. [Display omitted] • Seven isoquinolines were obtained from the Nicotiana endophytic fungus Aspergillus. felis. • Compounds 1 - 3 are new isoquinoline alkaloids. • Compounds 2 and 4 showed noteworthy anti-TMV activities. [ABSTRACT FROM AUTHOR]

This work demonstrates for the first time the formation of nanogratings inside Barium Gallo-Germanate (BGG) glasses using femtosecond laser direct writing. These heavy oxide glasses, with a characterized transparency window up to 6 μ m, along with the polarization sensitive birefringent nanostructures induced by femtosecond laser, offer an excellent platform for the development of mid-Infrared photonic applications. The fabricated glasses, containing both rare earth elements (Gd, Y) and transition metals (Zn, Ta), are directly compared to amorphous GeO 2 and commercial alumino-germanate (Corning-9754) glasses commonly used in this field. The nanogratings processing window in each glass was investigated by varying laser parameters (pulse duration, energy, repetition rate), and electron microscopy imaging directly evidenced the porous nanostructures. Among the results, a strong form birefringence in 8% ZnO containing BGG glass, e.g., up to −0.029 ± 0.001, was reported. Finally, birefringent embedded Fresnel GRIN plates were directly written in a BGG glass, to demonstrate the functionalization potential of these materials. [ABSTRACT FROM AUTHOR]

How to identify the screen-shot image is an important branch of image source forensics. Although the physical feature of moiré patterns may be left after LCD recapture operation, this feature may also be well concealed with the development of deep learning-based techniques for demoiréing operation. In this paper, to solve the forensic problem of screen-shot and demoiréd image operation, we select several typical demoiréing algorithms to generate datasets for training and detecting demoiréd images. Meanwhile, we also perform the task of identifying specific demoiréing algorithms. DenseNet can extract fine demoiréd image features because of its enhanced feature reuse and improved model performance. The re-attention mechanism is also added to our designed network to extract the image's global features and avoid the self-attention collapse problem in ViT. Therefore, we design a network based on DenseNet cascaded with DeepViT structure to detect the demoiréd image and identify its corresponding demoiréing algorithm. In a large number of comparison experiments of various typical image recapture detection methods, our method achieves the best detection results and network performance. • Improve the generality of the screen-shot image detection task. • Propose a network dedicated to the demoiréing algorithm identification. • Analyze the features of DenseNet and DeepViT in demoiréd image forensics. • Has a detection accuracy of 0.9126 in the generated demoiréd image dataset. [Display omitted] [ABSTRACT FROM AUTHOR]

20 years ago, volume nanogratings were discovered in silica glass upon ultrafast laser irradiation. Since then, these extraordinary self-organised and birefringent structures have been observed in a wide range of transparent materials, including crystals and glasses. In the latter, the role of glass composition and chemistry drastically impact the overall nanostructuring, properties, and morphologies of the fabricated nanogratings, but there still is no global understanding of the origin of various observed discrepancies. Consequently, this work aims to provide a materials roadmap to stimulate the development and selection of glass materials for nanogratings imprinting, depending on the final application, and intends to comprehensively pinpoint the differences existing between glasses. A critical overview of the discovery of nanogratings in multicomponent glasses is presented from 2003 to 2023, and general aspects of the nanogratings formation mechanisms are provided. Then, principal nanograting characteristics are collected, summarized, and discussed (e.g., periodicity, nanopore size) along with useful "technical performances" (e.g., nanogratings energy window, maximum retardance, nanogratings power consumption) that are discussed in the light of chemical composition effects. Finally, a summary of the main optical applications based on nanogratings is given, such as birefringent or geometric phase optics, optical data storage, micro/nanofluidic devices, third harmonic generation and optical fiber sensors. [ABSTRACT FROM AUTHOR]

Barium germano-gallate glasses are attractive glass hosts for photonic applications in the mid-infrared region up to 6 μm. In this work, we investigate the photosensitivity of such glasses under femtosecond laser, with an emphasize on the formation of refractive index changes. Six glasses with varying compositions (including addition of K, Na, Y, and La) were studied. We observed several transformation regimes in the pulse energy – repetition rate landscape: Type I (isotropic refractive index change) and a spatial broadening regime with a phase shift Δϕ > 2π rad at 550 nm. This translates into refractive index changes Δn > 10−2 and is comparable to values obtained in most chalcogenide glasses. The effect of glass composition on Δϕ appears correlated to the number of non-bridging oxygen presented in the glass and is brought to evidence by monitoring the Cations/GaO 3/2 ratio. This provides a way to design a range of germano-gallate glasses suitable to imprinting high refractive index contrast. [ABSTRACT FROM AUTHOR]

Multiblock methods have been proposed to capture the complex characteristics of plant-wide monitoring due to the enlargement of process industries. These methods based on automatic sub-block division and copula–correlation, which simultaneously describe the correlation degree and correlation patterns, are designed for sub-block partition. However, the selection of variables for each sub-block through copula–correlation analysis requires a pre-defined cutoff parameter which is difficult to be determined without sufficient prior knowledge, and a "bad" parameter leads to a degraded performance. Therefore, a weighted copula-correlation-based multiblock principal component analysis (WCMBPCA) is proposed. First, the variables in each sub-block are obtained through the copula–correlation analysis-based weighted strategy rather than the cutoff parameter, which highly avoids information loss and prevents "noisy" information. Second, a PCA model is established in each sub-block. Third, a Bayesian inference strategy is used to merge the monitoring results of all sub-blocks. Finally, an online-horizon Bayesian fault diagnosis system is established to identify the fault type of the system based on the statistics of each sub-block. The average detection rate and the average diagnosis rate for numerical example are 77.85% and 98.95%, and that for TE example are 80.63% and 89.50%. Comparing with other candidate methods, the proposed method achieves excellent detection and diagnostic performance. • The copula-correlation, which describes correlation degree and correlation pattern simultaneously, is used to describe the relationship among variables. • The weighted strategy, through assigning different weights to different variables in each sub-block, prevents "noisy" variables involved and reserves the important information. • The PCA model is established in each block and the Bayesian inference strategy is adopted for the comprehensive monitoring result. • An online-horizon based Bayesian fault diagnosis system is established to identify the fault type of the system. [ABSTRACT FROM AUTHOR]

Data hiding is an essential practice for ensuring the security of confidential data and secret communication and thus has been intensively studied. The development of deep learning enables the realization of video hiding, which aims to hide a video into another. Different from image data hiding, stego videos generated by video hiding methods need to undergo video coding in real-world scenarios because the unencoded raw videos are too large to be easily transmitted or stored. However, previous video-hiding works did not consider this, leading to a common vulnerability to information loss caused by video coding on stego videos. In other words, previous works considered videos simply as sequences of temporally continuous images, and thus the image sequences output from the data hiding network were fed directly into the recovery network to obtain secret information without video coding. We propose a module called CompressionSimulator to make stego videos robust to video coding. CompressionSimulator is pre-trained to simulate the distortion caused by video coding, and then trained together with the whole framework to make the data hiding network robust to video coding. Furthermore, an invertible neural network based framework is introduced for our video hiding task, i.e. hiding one video in another, in which we design an auxiliary module to capture the spatio-temporal relationships of videos. In general, we develop a video hiding network (VHNet) that provides video coding robustness. We conduct experiments to prove the necessity of incorporating video coding into the design of video hiding networks. Results of experiments conducted on the UCF 101 dataset show that our proposed work outperforms previous methods, and our framework achieves optimal performance in both visual and quantitative comparisons. To the best of our knowledge, we are the first paper to propose a learning-based scheme to improve the robustness of hiding videos with videos. [Display omitted] • Consider real-world applications and design a module to enhance robustness against video coding. • Use an invertible neural network to realize large-capacity data hiding. • First attempt to realize video hiding with robustness to video coding. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerator (TENG) has been widely researched and used for sustainable energy, sensing and other applications in recent years due to its energy conversion properties. However, in practical applications, TENG is often exposed to complex and harsh environments, so ensuring its stable output performance is a significant challenge. Here, a flexible, superhydrophobic and environmentally resistant TENG for energy harvesting and self-powered sensing is proposed by a cost-effective surface modification and co-blending method. The roughness, hydrophobicity and surface potential of the composites are significantly enhanced by the introduction of surface modified BN (m-BN), resulting in excellent triboelectric output (43 W/m2) of the m-BN/TENG. Moreover, the m-BN/TENG not only maintains 90% initial electrical output at 80% humidity but also its output performance has no noticeable change after treatment with acid and alkali. Apart from energy harvesting, the TENG has been demonstrated as a self-powered humidity sensor with a sensitivity of − 80 nA RH−1 and a self-powered droplet detector with an ultra-fast response time of 0.6 ms. Our work provides a practical approach for stable energy harvesting and self-powered sensing of TENG in harsh environments. [Display omitted] • A flexible, superhydrophobic and environmentally resistant TENG is proposed. • The m-BN/TENG maintains 90% initial electrical output at 80% humidity. • The enhancement mechanism of TENG is analyzed experimentally and theoretically. • A self-powered humidity sensor and droplet detector were built based on the TENG. [ABSTRACT FROM AUTHOR]