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Based on the NTRU trapdoor used in NIST's Falcon, a signcryption scheme following the sign-then-encrypt paradigm is constructed. The existing partitioning technique based on Waters hash over the lattice can not complete the security reduction in the standard model for the signature part due to the "partiality" of the pre-image generated with the NTRU trapdoor. To address this, a variant of Waters hash over small integers is proposed and, the probability of the successful reduction is analyzed. The resulting signcryption achieves existential unforgeability under the adaptive chosen-message attacks. By utilizing the uniqueness of the secret and the noise in an NTRU instance, the tag used in encryption is eliminated. Furthermore, a method to construct tamper-sensitive lattice public key encryption is proposed. This approach implants the ciphertext-sensitive information into the lattice public key encryption and binds it to the encrypted information. The malleability to the public key ciphertext triggers the change of the message–signature pair so that the IND-CCA2 security of the entire ciphertext can be guaranteed by the signature for the message. Thanks to the rational design and the efficiency of the NTRU trapdoor, the computational overhead of the proposed scheme is reduced significantly compared to the existing lattice-based signcryption scheme, reaching orders of magnitude improvement in efficiency. The experiment shows that the proposed scheme is efficient. [ABSTRACT FROM AUTHOR]

Federated learning has been popular for its ability to train centralized models while protecting clients' data privacy. However, federated learning is highly susceptible to poisoning attacks, which can result in a decrease in model performance or even make it unusable. Most existing defense methods against poisoning attacks cannot achieve a good trade-off between robustness and training efficiency, especially on non-IID data. Therefore, this paper proposes an adaptive model filtering algorithm based on the Grubbs test in federated learning (FedGaf), which can achieve great trade-offs between robustness and efficiency against poisoning attacks. To achieve a trade-off between system robustness and efficiency, multiple child adaptive model filtering algorithms have been designed. Meanwhile, a dynamic decision mechanism based on global model accuracy is proposed to reduce additional computational costs. Finally, a global model weighted aggregation method is incorporated, which improves the convergence speed of the model. Experimental results on both IID and non-IID data show that FedGaf outperforms other Byzantine-robust aggregation rules in defending against various attack methods. [ABSTRACT FROM AUTHOR]

Network intrusion detection technology is key to cybersecurity regarding the Internet of Things (IoT). The traditional intrusion detection system targeting Binary or Multi-Classification can detect known attacks, but it is difficult to resist unknown attacks (such as zero-day attacks). Unknown attacks require security experts to confirm and retrain the model, but new models do not keep up to date. This paper proposes a Lightweight Intelligent NIDS using a One-Class Bidirectional GRU Autoencoder and Ensemble Learning. It can not only accurately identify normal and abnormal data, but also identify unknown attacks as the type most similar to known attacks. First, a One-Class Classification model based on a Bidirectional GRU Autoencoder is introduced. This model is trained with normal data, and has high prediction accuracy in the case of abnormal data and unknown attack data. Second, a multi-classification recognition method based on ensemble learning is proposed. It uses Soft Voting to evaluate the results of various base classifiers, and identify unknown attacks (novelty data) as the type most similar to known attacks, so that exception classification becomes more accurate. Experiments are conducted on WSN-DS, UNSW-NB15, and KDD CUP99 datasets, and the recognition rates of the proposed models in the three datasets are raised to 97.91%, 98.92%, and 98.23% respectively. The results verify the feasibility, efficiency, and portability of the algorithm proposed in the paper. [ABSTRACT FROM AUTHOR]

In the field of genetic code expansion (GCE), improvements in the efficiency of noncanonical amino acid (ncAA) incorporation have received continuous attention. By analyzing the reported gene sequences of giant virus species, we noticed some sequence differences at the tRNA binding interface. On the basis of the structural and activity differences between Methanococcus jannaschii Tyrosyl-tRNA Synthetase (MjTyrRS) and mimivirus Tyrosyl-tRNA Synthetase (MVTyrRS), we found that the size of the anticodon-recognized loop of MjTyrRS influences its suppression activity regarding triplet and specific quadruplet codons. Therefore, three MjTyrRS mutants with loop minimization were designed. The suppression of wild-type MjTyrRS loop-minimized mutants increased by 1.8–4.3-fold, and the MjTyrRS variants enhanced the activity of the incorporation of ncAAs by 15–150% through loop minimization. In addition, for specific quadruplet codons, the loop minimization of MjTyrRS also improves the suppression efficiency. These results suggest that loop minimization of MjTyrRS may provide a general strategy for the efficient synthesis of ncAAs-containing proteins. [ABSTRACT FROM AUTHOR]

The dynamic-scheduling problem of transmission tasks (DSTT) is an important problem in the daily work of radio and television transmission stations. The transmission effect obtained by the greedy algorithm for task allocation is poor. In the case of more tasks and equipment and smaller time division, the precise algorithm cannot complete the calculation within an effective timeframe. In order to solve this problem, this paper proposes a discrete particle swarm optimization algorithm (DPSO), builds a DSTT mathematical model suitable for the DPSO, solves the problem that particle swarm operations are not easy to describe in discrete problems, and redefines the particle motion strategy and adds random disturbance operation in its probabilistic selection model to ensure the effectiveness of the algorithm. In the comparison experiment, the DPSO achieved much higher success rates than the greedy algorithm (GR) and the improved genetic algorithm (IGA). Finally, in the simulation experiment, the result data show that the accuracy of the DPSO outperforms that of the GR and IGA by up to 3.012295% and 0.11115%, respectively, and the efficiency of the DPSO outperforms that of the IGA by up to 69.246%. [ABSTRACT FROM AUTHOR]

Transmission task static allocation (TTSA) is one of the most important issues in the automatic management of radio and television stations. Different transmission tasks are allocated to the most suitable transmission equipment to achieve the overall optimal transmission effect. This study proposes a TTSA mathematical model suitable for solving multiple intelligent algorithms, with the goal of achieving the highest comprehensive evaluation value, and conducts comparative testing of multiple intelligent algorithms. An improved crossover operator is proposed to solve the problem of chromosome conflicts. The operator is applied to improved genetic algorithm (IGA) and hybrid intelligent algorithms. A discrete particle swarm optimization (DPSO) algorithm is proposed, which redefines the particle position, particle movement direction, and particle movement speed for the problem itself. A particle movement update strategy based on a probability selection model is designed to ensure the search range of the DPSO, and random perturbation is designed to improve the diversity of the population. Based on simulation, comparative experiments were conducted on the proposed intelligent algorithms and the results of three aspects were compared: the success rate, convergence speed, and accuracy of the algorithm. The DPSO has the greatest advantage in solving TTSA. [ABSTRACT FROM AUTHOR]

The interaction of amino acids with toxic heavy metals influences their immobilization and bioavailability in soils. However, the complexation ability of amino acids with Cd has not been well studied. The complexes of amino acids and cadmium were investigated by density functional theory (DFT) calculations and Fourier transform infrared spectrometry (FTIR) analyses. The complex structures were found to be [COc, COc] for fatty amino-cadmium and PheCd2+, [COc, COc, COs] for GluCd2+ and ThrCd2+, respectively. The complex energy of these conformers followed the order PheCd2+> AlaCd2+ > LeuCd2+ > GluCd2+ > GlyCd2+ > ThrCd2+. Importantly, all of the complex energy values were less than zero, indicating that these complexes could be easily dissolved in water. The Cd2+ concentration decreased with increasing amino acid concentration in aqueous solution. The complex stability constants (logβ) followed the order PheCd2+> AlaCd2+ > LeuCd2+ > GluCd2+ > GlyCd2+ > ThrCd2+, consistent with the order of the calculated complex energy values. The Cd removal efficiencies by Thr, Glu, Gly, Ala, Leu, and Phe were 38.88%, 37.47%, 35.5%, 34.72%, 34.04%, and 31.99%, respectively. In soil batch tests, the total Cd concentration in soil decreased in the presence of amino acids, while the Cd concentration in water increased from 231.97 μg/L to 652.94~793.51 μg/L. The results of sequential extraction showed that the acid-extractable fraction and the reducible fraction of Cd sharply decreased. Consequently, the significant features of amino acids along with their biocompatibility make them potentially applicable chelators in Cd-contaminated soil remediation processes. [ABSTRACT FROM AUTHOR]

As a stressful and sensitive task, driving can be disturbed by various factors from the health condition of the driver to the environmental variables of the vehicle. Continuous monitoring of driving hazards and providing the most appropriate business services to meet actual needs can guarantee safe driving and make great use of the existing information resources and business services. However, there is no in-depth research on the perception of a driver's health status or the provision of customized business services in case of various hazardous situations. In order to constantly monitor the health status of the drivers and react to abnormal situations, this paper proposes a context-aware service system providing a configurable architecture for the design and implementation of the smart health service system for safe driving, which can perceive a driver's health status and provide helpful services to the driver. With the context-aware technology to construct a smart health services system for safe driving, this is the first time that such a service system has been implemented in practice. Additionally, an assessment model is proposed to mitigate the impact of the acceptable abnormal status and, thus, reduce the unnecessary invocation of the services. With regard to different assessed situations, the business services can be invoked for the driver to adapt to hazardous situations according to the services configuration model, which can take full advantage of the existing information resources and business services. The evaluation results indicate that the alteration of the observed status in a valid time range T can be tolerated and the frequency of the service invocation can be reduced.