Abstract Physical Layer Security (PLS) in Cognitive Radio Networks (CRN) improves the confidentiality, availability, and integrity of the external communication between the devices/ users. The security models for sensing and beamforming reduce the impact of adversaries such as eavesdroppers in the signal processing layer. To such an extent, this article introduces a Secure Channel Estimation Model (SCEM) using Channel State Information (CSI) and Deep Learning (DL) to improve the PLS. In this proposed model, the CSI is exploited to evaluate the channel utilization and actual capacity availability throughout the allocation intervals. The change in channel capacity and utilization augments the need for security through 2-level key shared authentication. The deep learning algorithm verifies the authentication completeness for maximum channel capacity utilization irrespective of adversary interference. This verification follows mutual authentication between the primary and secondary users sharing the maximum capacity channel with high secrecy. The learning monitors the outage secrecy rates to verify failed allocations such that the replacement for allocation is pursued. Thus, the physical layer security between different user categories is administered through maximum CSI exploitation with high beamforming abilities. The proposed model leverages the secrecy rate by 10.77% and the probability of detection by 15.01% and reduces the interference rate by 11.07% for the varying transmit powers.

Abstract Misoprostol was originally used to treat gastric ulcers, and has been widely used in abortion, cervical maturation, induced labour and postpartum hemorrhage. But there are still many undetected adverse events (AEs). The purpose of this study was to provide a comprehensive overview of the safety of misoprostol. Adverse events related to misoprostol were collected from the FDA Adverse Event Reporting System (FAERS) database from the first quarter of 2004 to the second quarter of 2024. This study used proportional disequilibrium methods such as reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM) to detect AEs. After analyzing 17,427,762 adverse event reports, a total of 2032 adverse events reports related to misoprostol were identified, involving 23 system organ classes and 30 preferred terms. The most common AEs were foetal exposure during delivery(n = 201), uterine tachysystole(n = 95), uterine rupture (n = 95), and heart rate decreased (n = 93). Although most AEs complied with the drug instruction, new AEs signals such as congenital aqueductal stenosis and congenital brain damage were also identified. Clinicians should make appropriate evaluation when using misoprostol, closely monitor the indicators of patients, and have appropriate countermeasures for possible adverse events.

Abstract The anisotropic behavior of fiber-reinforced polymer composites, coupled with their susceptibility to various failure modes, poses challenges for their structural health monitoring (SHM) during service life. To address this, non-destructive testing techniques have been employed, but they often suffer from drawbacks such as high costs and suboptimal resolutions. Moreover, routine inspections fail to disclose incidents or failures occurring between successive assessments. As a result, there is a growing emphasis on SHM methods that enable continuous monitoring without grounding the aircraft. Our research focuses on advancing aerospace SHM through the utilization of piezoresistive MXene fibers. MXene, characterized by its 2D nanofiber architecture and exceptional properties, offers unique advantages for strain sensing applications. We successfully fabricate piezoresistive MXene fibers using wet spinning and integrate them into carbon fiber-reinforced epoxy laminates for in-situ strain sensing. Unlike previous studies focused on high strain levels, we adjust the strain levels to be comparable to those encountered in practical aerospace applications. Our results demonstrate remarkable sensitivity of MXene fibers within low strain ranges, with a maximum sensitivity of 0.9 at 0.13% strain. Additionally, MXene fibers exhibited high reliability for repetitive tensile deformations and low-velocity impact loading scenarios. This research contributes to the development of self-sensing composites, offering enhanced capabilities for early detection of damage and defects in aerospace structures, thereby improving safety and reducing maintenance expenses.

Abstract Given the large volumes of sensitive information transmitted over the Internet, digital signatures are essential for verifying message authenticity and integrity. A key challenge is minimizing computationally intensive operations, such as modular inverses, without compromising security. In this research, we propose the DSADH $$\pi$$ algorithm, which introduces a confusion step directly into the signature itself, rather than only applying it to the message, using a dynamic substitution box. It is generated with the number pi and changes with each signing. In addition, to enhance security, this work uses a 2048-bit prime, double the length frequently used. This proposal induces chaotic behavior in the signature, making it highly sensitive to any changes in the signer’s private key or message content, thereby enhancing authentication and integrity verification. Moreover, the proposed algorithm computes a single multiplicative modular inverse during verification and none during signing, unlike other approaches that require inverse computation in both stages. Since the required inverse is for the Diffie-Hellman session key, it always exists and can be precomputed per communication rather than per message. Consequently, DSADH $$\pi$$ is on average 45 times faster than DSA. Additionally, we introduce a method to assess signature security by constructing images from signature bytes generated by slight changes to the signer’s private key and message. Then, their chaotic behavior is evaluated with cryptographic metrics.

Abstract Trust and rapport are essential abilities for human–robot interaction. Producing emotional expressions in the robots’ faces is an effective way for that purpose. Androids can show human-like facial expressions of basic emotions. However, whether androids can show the facial expression of complex emotions remains unknown. In this experiment, we investigated the android Nikola’s ability to produce 22 dynamic facial expressions of complex emotions. For each video, 240 international participants (120 Japanese, 120 German) rated the emotions expressed by Nikola. For 13 complex emotions (i.e., amusement, appal, awe, boredom, contentment, coyness, hatred, hesitation, moral disgust, not face, pain, sleepiness, suspicion), participants of both samples rated the target emotion above the mean of other non-target emotions. Four emotions (bitterness, confusion, pride, relief) were rated above mean by one sample. For twelve of these emotions, target emotions were among the highest ranked. The results suggest that androids can produce the facial expressions of a wide range of complex emotions, which can facilitate human–robot interactions.