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The steganography theory of computing security has been proposed for a long time, but it has not been widely adopted for mainstream steganography using multimedia data as a carrier.The reason is that the prerequisite for calculating secure steganography is to obtain the accurate distribution of the carrier or to accurately sample according to the carrier distribution.However, naturally collected images and audio/video cannot meet this prerequisite.With the development of deep learning technology, various machine-generated media such as image generation and synthesized speech, have become more and more common on the Internet and then generated media has become a reasonable steganography carrier.Steganography can use normal generated media to cover up secret communications, and pursue in distinguishability from normal generated media.The distribution learned by some generative models is known or controllable, which provides an opportunity to push computational security steganography for practical use.Taking the widely used synthetic speech model as an example, a computationally secure symmetric key steganography algorithm was designed and implemented.The message was decompressed into the synthetic audio according to the decoding process of arithmetic coding based on the conditional probability of sample points, and the message receiver had the same generation model to complete the message extraction by reproducing the audio synthesis process.The public key steganography algorithm was additionally designed based on this algorithm, which provided algorithmic support for the realization of full-flow steganographic communication.Steganographic key exchange ensured the security of steganographic content and the security of steganographic behavior was also achieved.The theoretical analysis showed that the security of the proposed algorithm is determined by the randomness of the embedded message.And the steganography analysis experiment further verified that the attacker cannot distinguish the synthesized carrier audio from the encrypted audio.

Background: Metagenomic next-generation sequencing (mNGS) excels in diagnosis of infection pathogens. We aimed to evaluate the performance of mNGS for the diagnosis of Pneumocystis jirovecii pneumonia (PJP) in non-HIV infected children. Methods: Totally 36 PJP children and 61 non-PJP children admitted to the pediatric intensive care unit from March 2018 to December 2021 were retrospectively enrolled. Clinical features of PJP children were summarized. 1,3-β-D glucan (BDG) test and bronchoalveolar lavage fluid (BALF) mNGS were used for evaluation of PJP diagnostic performance. Antimicrobial management modifications for PJP children after the mNGS results were also reviewed. Results: Pneumocystis jirovecii was detected in all PJP children by mNGS (36/36), and the sensitivity of mNGS was 100% (95% confidence interval [CI]: 90.26–100%). The sensitivity of BDG was 57.58% (95% CI: 39.22–74.52%). Of the 26 (72.2%) PJP patients with mixed infection, twenty-four (66.7%) were detected by BALF-mNGS. Thirteen patients (36.1%) had their antimicrobial management adjusted according to the mNGS results. Thirty-six PJP children included 17 (47.2%) primary immunodeficiency and 19 (52.8%) secondary immunodeficiency, of whom 19 (52.8%) survived and 17 (47.2%) died. Compared to survival subgroup, non-survival subgroup had a higher rate of primary immunodeficiency (64.7% vs. 31.6%, P = 0.047), younger age (7 months vs. 39 months, P = 0.011), lower body weight (8.0 kg vs. 12.0 kg, P = 0.022), and lower T lymphocyte counts. Conclusions: The mortality rate of PJP in immunosuppressed children without HIV infection is high and early diagnosis is challenging. BALF-mNGS could help identify PJP and guide clinical management. [ABSTRACT FROM AUTHOR]

To mitigate the decrease in mechanical performance of Sn58Bi/Cu solder joints resulting from electromigration-induced damage. The CeO2 nanoparticles were incorporated into Sn58Bi solder by a melt-casting method, and their effects on the microstructure and properties of Sn58Bi/Cu solder joints under electromigration were investigated. The study results demonstrate that the addition of 0.125 ~ 0.5 wt% CeO2 nanoparticles refines the eutectic microstructure of Sn58Bi solder alloy. At an addition amount of 0.5 wt%, the composite solder alloy exhibits the maximum tensile strength of 68.9 MPa, which is 37% higher than that of the base solder. CeO2 nanoparticle-reinforced Sn58Bi solder can achieve excellent solderbility with Cu substrates and the joints can significantly inhibit the growth of the anodic Bi-rich layer, which is responsible for electromigration. With the extension of current stressing time, Bi-rich and Sn-rich layer are respectively formed on the anode and cathode in the joints. The intermetallic compound (IMC) layer grows asymmetrically, transitioning from a fan-shaped morphology to a flattened structure at the anode and to a thickened mountain-like morphology at the cathode. Adding the CeO2 nanoparticles helps to mitigate the decrease in mechanical performance caused by electromigration damage during current application to some extent. Over the current stressing period of 288 ~ 480 h, the fracture position shifts from the anodic IMC/Bi-rich interface to the cathodic Sn-rich/IMC interface. The fracture mechanism transitions from a brittle fracture characterized by plate-like cleavage to a ductile–brittle mixed fracture with fine dimples and cleavage. [ABSTRACT FROM AUTHOR]