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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.

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]

Metformin/glipizide tablets are a compound formulation composed of metformin hydrochloride and glipizide. This study aimed to assess the pharmacokinetics and bioequivalence of two fixed-dose combination (FDC) tablets of metformin/glipizide (500 mg/2.5 mg) in healthy Chinese subjects. We conducted a single-center, open-label, randomized, two-way crossover study with a total of 48 subjects enrolled (24 in each dietary group). The test or reference formulations were given to the subjects at random at a ratio of 1 : 1, with a seven-day washout period. Blood samples, collected at prearranged intervals before and up to 24 hours after dosage, were analyzed using validated LC-MS/MS technology to ascertain plasma concentrations of metformin and glipizide. Finally, 23 subjects completed the fasting and fed studies, respectively. In both studies, the 90% confidence intervals for the geometric mean ratios (test/reference) of the C m ax , AUC0-t, and AUC0-∞ were all found to fall within the acceptable range for bioequivalence (80%–125%). The exposure of metformin/glipizide FDC tablets in vivo was not significantly affected by food. No serious adverse events were observed. In conclusion, this study demonstrated that both the test and reference metformin/glipizide tablets exhibited bioequivalence and were well tolerated under both fasting and fed conditions. This trial is registered with CTR202686. [ABSTRACT FROM AUTHOR]

Aims: Status epilepticus (SE) is the most common neurological emergency in pediatric patients. This study aimed to screen for prognostic biomarkers of SE in the cerebrospinal fluid (CSF) using metabolomics. Methods: Ultra‐performance liquid chromatography quadrupole time‐of‐flight tandem mass spectrometry (UPLC‐QTOF‐MS) was conducted to identify prognostic biomarkers in CSF metabolomics by comparing the poor outcome group (N = 13) with the good outcome group (N = 15) of children with SE. Differentially expressed metabolites were identified using Mann–Whitney U test corrected by Benjamini‐Hochberg and partial least squares discriminant analysis (PLS‐DA). Results: The PLS‐DA model identified and validated significant metabolic differences between the poor and good outcome groups of children with SE (PLS‐DA with R2Y = 0.992 and Q2 = 0.798). A total of 49 prognosis‐related metabolites were identified. Of these metabolites, 20 including glutamyl‐glutamine, 3‐iodothyronamine, and L‐fucose had an area under the curve (AUC) ≥ 80% in prognostic prediction of SE. The logistic regression model combining glutamyl‐glutamine and 3‐iodothyronamine produced an AUC value of 0.976, with a sensitivity of 0.863 and specificity of 0.956. Pathway analysis revealed that dysregulation of the citrate cycle (TCA) and arginine biosynthesis may contribute to poor SE prognosis. Conclusions: This study highlighted the prognosis‐related metabolomic disturbances in the CSF of children with SE and identified potential prognostic biomarkers. A prognostic prediction model combining glutamyl‐glutamine and 3‐iodothyronamine with high predictive value was established. [ABSTRACT FROM AUTHOR]