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As the primary link in cybersecurity, the intrusion detection system (IDS) is of indispensable importance. Many studies have proposed sophisticated artificial intelligence (AI) models to detect intrusion behavior from a large amount of data, yet they have ignored the fact that poor data quality has a direct impact on the performance of IDS. The poor data quality is mainly attributed to the interference and damage, such as data tampering, poisoning, and corruption, which leads to decision-making deviations, triggering a serious trust crisis of model application. This paper proposes a multi-indicator comprehensive evaluation method (MICEM) to ensure the reliability of AI decision-making from data perspective. First, several evaluation indicators are established to analyze the potential risks that intrusion detection data may face from the different dimensions, and specific quantitative methods are provided. Second, a comprehensive evaluation is conducted based on the results of each indicator to determine the quality of the intrusion detection data as a whole, thus guaranteeing the usability and reliability of AI-enabled IDS. Finally, the effectiveness and practicality of the proposed MICEM are fully verified by evaluating the benchmark-CICIDS2017 dataset and the real intrusion detection dataset.

Quantum walks are powerful kernels in quantum computing protocols, and possess strong capabilities in speeding up various simulation and optimization tasks. One striking example is provided by quantum walkers evolving on glued trees1, which demonstrate faster hitting performances than classical random walks. However, their experimental implementation is challenging, as this involves highly complex arrangements of an exponentially increasing number of nodes. Here, we propose an alternative structure with a polynomially increasing number of nodes. We successfully map such graphs on quantum photonic chips using femtosecond-laser direct writing techniques in a geometrically scalable fashion. We experimentally demonstrate quantum fast hitting by implementing two-dimensional quantum walks on graphs with up to 160 nodes and a depth of eight layers, achieving a linear relationship between the optimal hitting time and the network depth. Our results open up a scalable path towards quantum speed-up in classically intractable complex problems.

The pandemic of novel coronavirus disease (COVID-19) has developed as a tremendous threat to global health. Although most COVID-19 patients present with respiratory symptoms, some present with gastrointestinal (GI) symptoms like diarrhoea, loss of appetite, nausea/vomiting and abdominal pain as the major complaints. These features may be attributable to the following facts: (a) COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and its receptor angiotensin converting enzyme 2 (ACE2) was found to be highly expressed in GI epithelial cells, providing a prerequisite for SARS-CoV-2 infection; (b) SARS-CoV-2 viral RNA has been found in stool specimens of infected patients, and 20% of patients showed prolonged presence of SARS-CoV-2 RNA in faecal samples after the virus converting to negative in the respiratory system. These findings suggest that SARS-CoV-2 may be able to actively infect and replicate in the GI tract. Moreover, GI infection could be the first manifestation antedating respiratory symptoms; patients suffering only digestive symptoms but no respiratory symptoms as clinical manifestation have also been reported. Thus, the implications of digestive symptoms in patients with COVID-19 is of great importance. In this review, we summarise recent findings on the epidemiology of GI tract involvement, potential mechanisms of faecal–oral transmission, GI and liver manifestation, pathological/histological features in patients with COVID-19 and the diagnosis, management of patients with pre-existing GI and liver diseases as well as precautions for preventing SARS-CoV-2 infection during GI endoscopy procedures.

At approximately 3:50 a.m. (UTC + 8) on August 21, 2020, a massive rainfall-induced landslide occurred in Zhonghaicun, Fuquan town, Hanyuan County, Sichuan Province, China, forming an approximately 40.85 × 104 m3 landslide accumulation, burying eight houses and approximately 100 m of roads, and causing long-term traffic interruptions. The landslide was comprehensively evaluated through field investigation, UAV photography, borehole drilling, and laboratory tests. According to movement and accumulation characteristics, the landslide is divided into the main sliding zones (the source area, impact sliding area, shoveling-accumulation area, and accumulation area) and landslide-affected zones. The deformation and failure of the Zhonghaicun landslide are related to the lithology (existence of a weak interlayer), geomorphology (microrelief changes), and antecedent rainfall. However, the main trigger of the landslide is continuous rainfall, which increases the landslide saturation and pore water pressure and reduces the mechanical strength of the weak layer. The landslide failure mode is complex. The upper slope is affected by rainfall and loses stability first. Under the impact of the sliding mass, sliding of the lower slope is triggered. This study of the Zhonghaicun landslide characterizes the evolution process of a complex rainfall-induced landslide and provides ways to mitigate landslide disasters.

On the basis of the massive amount of published literature and the long-term practice of our research group in the field of prevention and control of rockburst, the research progress and shortcomings in understanding the rockburst phenomenon have been comprehensively investigated. This study focuses on the occurrence mechanism and monitoring and early warning technology for rockburst in coal mines. Results showed that the prevention and control of rockburst had made significant progress. However, with the increasing mining depth, several unresolved concerns remain challenging. From the in-depth research and analysis, it can be inferred that rockburst disasters involve three main problems, i.e., the induction factors are complicated, the mechanism is still unclear, and the accuracy of the monitoring equipment and multi-source stereo monitoring technology is insufficient. The monitoring and warning standards of rockburst need to be further clarified and improved. Combined with the Internet of Things, cloud computing, and big data, a study of the trend of rockburst needs to be conducted. Furthermore, the mechanism of multiphase and multi-field coupling induced by rockburst on a large scale needs to be explored. A multisystem and multiparameter integrated monitoring and early warning system and remote monitoring cloud platform for rockburst should be explored and developed. High-reliability sensing technology and equipment and perfect monitoring and early warning standards are considered to be the development direction of rockburst in the future. This research will help experts and technicians adopt effective measures for controlling rockburst disasters.

Pyroptosis is recently identified as an inflammatory form of programmed cell death. However, the roles of pyroptosis-related genes (PS genes) in major depressive disorder (MDD) remain unclear. This study developed a novel diagnostic model for MDD based on PS genes and explored the pathological mechanisms associated with pyroptosis. First, we obtained 23 PS genes that were differentially expressed between healthy controls and MDD cases from GSE98793 dataset. There were obvious variation in immune cell infiltration profiles and immune-related pathway enrichment between healthy controls and MDD cases. Then, a novel diagnostic model consisting of eight PS genes ( GPER1 , GZMA, HMGB1 , IL1RN , NLRC4 , NLRP3 , UTS2 , and CAPN1 ) for MDD was constructed by random forest (RF) and least absolute shrinkage and selection operator (LASSO) analyses. ROC analysis revealed that our model has good diagnostic performance, AUC = 0.795 (95% CI 0.721–0.868). Subsequently, the consensus clustering method based on 23 differentially expressed PS genes was constructed to divide all MDD cases into two distinct pyroptosis subtypes (cluster A and B) with different immune and biological characteristics. Principal component analysis (PCA) algorithm was performed to calculate the pyroptosis scores (“PS-scores”) for each sample to quantify the pyroptosis regulation subtypes. The MDD patients in cluster B had higher “PS-scores” than those in cluster A. Furthermore, we also found that MDD patients in cluster B showed lower expression levels of 11 interferon (IFN)-α isoforms. In conclusion, pyroptosis may play an important role in MDD and can provide new insights into the diagnosis and underlying mechanisms of MDD.

With the development of cloud computing, users are more inclined to outsource complex computing tasks to cloud servers with strong computing capacity, and the cloud returns the final calculation results. However, the cloud is not completely trustworthy, which may leak the data of user and even return incorrect calculations on purpose. Therefore, it is important to verify the results of computing tasks without revealing the privacy of the users. Among all the computing tasks, the polynomial calculation is widely used in information security, linear algebra, signal processing and other fields. Most existing polynomial-based verifiable computation schemes require that the input of the polynomial function must come from a single data source, which means that the data must be signed by a single user. However, the input of the polynomial may come from multiple users in the practical application. In order to solve this problem, the researchers have proposed some schemes for multi-source outsourced data, but these schemes have the common problem of low efficiency. To improve the efficiency, this paper proposes an efficient polynomial-based verifiable computation scheme on multi-source outsourced data. We optimize the polynomials using Horner’s method to increase the speed of verification, in which the addition gate and the multiplication gate can be interleaved to represent the polynomial function. In order to adapt to this structure, we design the corresponding homomorphic verification tag, so that the input of the polynomial can come from multiple data sources. We prove the correctness and rationality of the scheme, and carry out numerical analysis and evaluation research to verify the efficiency of the scheme. The experimental indicate that data contributors can sign 1000 new data in merely 2 s, while the verification of a delegated polynomial function with a power of 100 requires only 18 ms. These results confirm that the proposed scheme is better than the existing scheme.

Password plays a vital role in identity authentication. However, password security is facing great challenges. In this paper, we research the password cracking technology based on artificial intelligence, aiming to study the probability of password cracking in common password setting methods, and provide references for the setting of password. First of all, we collected a large amount of user's personal information and passwords, and analysed the correlation between the personal information and passwords. And then, we implemented a password guessing model based on improved Transformer in which information weights were introduced into the data pre-processing and the modified beam search algorithm was used in the model to quickly search the top ranked output results. The percentage of password cracked was 68.63%, and the average guess time was 51.99 seconds. The experiment result shows that artificial intelligence brings great challenges to user password security, and this paper puts forward suggestions on user setting passwords.

Background Increasing evidence has shown that circular RNAs (circRNAs) are involved in neurodegenerative disorders, but their roles in neurological toxoplasmosis are yet to know. This study examined miRNA and circRNA expressions in mouse brain following oral infection with T. gondii Pru strain. Results Total RNA extracted from acutely infected (11 days post infection (DPI)), chronically infected (35 DPI) and uninfected mouse brain samples were subjected to genome-wide small RNA sequencing. In the acutely infected mice, 9 circRNAs and 20 miRNAs were upregulated, whereas 67 circRNAs and 28 miRNAs were downregulated. In the chronically infected mice, 2 circRNAs and 42 miRNAs were upregulated, whereas 1 circRNA and 29 miRNAs were downregulated. Gene ontology analysis predicted that the host genes that produced the dysregulated circRNAs in the acutely infected brain were primarily involved in response to stimulus and ion binding activities. Furthermore, predictive interaction networks of circRNA-miRNA and miRNA-mRNA were constructed based on genome-wide transcriptome sequencing and computational analyses, which might suggest the putative functions of miRNAs and circRNAs as a large class of post-transcriptional regulators. Conclusions These findings will shed light on circRNA-miRNA interactions during the pathogenesis of toxoplasmosis, and they will lay solid foundation for studying the potential regulation roles of miRNAs and circRNAs in T. gondii induced pathogenesis.

In cold and humid regions, ice accretion sometimes develops on the blades of wind turbines. Blade icing reduces the power generation of the wind turbine and affects the safe operation of the wind farm. For this paper, ultrasonic micro-vibration was researched as an effective de-icing method to remove ice from the wind turbine blade surface and improve the efficiency of wind turbine power generation. A blade segment with NACA0018 airfoil and the hollow structure at the leading edge was designed. The modal analysis of the blade was simulated by ANSYS, and the de-icing vibration mode was selected. Based on the simulation results, the blade segment sample with PZT patches was machined, and its natural frequencies were measured with an impedance analyzer. A return-flow icing wind tunnel system, and a device used to measure the adhesive strength of ice covering the airfoil blade, were designed and manufactured. The experiments on the adhesive strength of the ice were carried out under the excitation of the ultrasonic vibration. The experimental results show that the adhesive strength of the ice, which was generated under the dynamic flow field condition, was lower than the ice generated by water under the static flow field condition. Under the excitation of the ultrasonic vibration, the adhesive strength of the ice decreased. When the excitation frequency was 21.228 kHz, the adhesive strength was the lowest, which was 0.084 MPa. These research findings lay the theoretical and experimental foundations for researching in-depth the application of the ultrasonic de-icing technology to wind turbines.