A flat 2.0 μm ultra broadband emission with a full width at half maximum (FWHM) of 329 nm is achieved in 1 mol.% Tm2O3 and 0.05 mol.% Ho2O3 co-doped gallium tellurite glasses upon the excitation of an 808 nm laser diode. The influence of Tm3+ and Ho3+ contents on 2.0 μm spectroscopic properties of gallium tellurite glasses is minutely investigated by absorption spectra, emission spectra, and lifetime measurement. In addition, emission cross section and gain coefficient of Ho3+ ions at 2.0 μm are calculated, and the maximum values reach 8.2×10−21 cm2 and 1.54 cm-1, respectively. Moreover, forward and backward energy transfer probability between Tm3+ and Ho3+ ions are qualitatively evaluated by the extended spectral overlap method. Large ratio of the forward energy transfer from Tm3+ to Ho3+ to the backward one (19.7) and high forward energy transfer coefficient (6.22×1039 cm6/s) are responsible for effective 2.0 μm emission from Ho3+ ions. These results manifest that Tm3+/Ho3+ co-doped gallium tellurite glass is suitable for potential applications of broadband light sources and tunable fiber lasers operating in eye-safe 2.0 µm spectral region.

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Automatic malware detection was aimed at determining whether the application is malicious or not with automated systems. Android malware attacks have gained tremendous pace owing to the widespread use of mobile devices. Although significant progress has been made in antimalware techniques, these methods mainly rely on the program features, ignoring the importance of source code analysis. Furthermore, the dynamic analysis is low code coverage and poor efficiency. Hence, we propose an automatic Android malware detection approach, named HyGNN-Mal. It analyzes the Android applications at source code level by exploiting the sequence and structure information. Meanwhile, we combine the typical static features, permissions, and APIs. In HyGNN-Mal, we utilize a deep traversal tree neural network (Deep-TNN) to process the code structure information. Particularly, we add position information to code sequence information before putting in self-attention mechanism. The evaluations conducted on multiple public datasets indicate that our method can accurately identify and classify the malicious software, and their best accuracy is 99.62% and 99.2%, respectively.

Pomegranate flowers as row materials were used for extraction of polysaccharides by water-extraction and alcohol-precipitation method. After purification, the physical and chemical properties, structure, monosaccharide composition and molecular weight were studied. The results showed that the polysaccharides from pomegranate flowers mainly contained two kinds of water soluble acidic polysaccharides, and monosaccharide composition were arabinose and galactose, both contained hydroxyl, carboxyl, amino, hydroxyl radical, sulfate, beta glycosidic bond and alpha glycosidic bond structure. The molecular weight of PP1 and PP2 were 6.16 × 10 4 (±6.6%) and 9.01 × 10 4 (±3.2%), respectively. The results of this study laid the foundation for further development and application of polysaccharides from pomegranate flowers.

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