Preventing network intrusion is the essential requirement of network security. In recent years, people have conducted a lot of research on network intrusion detection systems. However, with the ...increasing number of advanced threat attacks, traditional intrusion detection mechanisms have defects and it is still indispensable to design a powerful intrusion detection system. This paper researches the NSL-KDD data set and analyzes the latest developments and existing problems in the field of intrusion detection technology. For unbalanced distribution and feature redundancy of the data set used for training, some training samples are under-sampling and feature selection processing. To improve the detection effect, a Deep Stacking Network model is proposed, which combines the classification results of multiple basic classifiers to improve the classification accuracy. In the experiment, we screened and compared the performance of various mainstream classifiers and found that the four models of the decision tree, k-nearest neighbors, deep neural network and random forests have outstanding detection performance and meet the needs of different classification effects. Among them, the classification accuracy of the decision tree reaches 86.1%. The classification effect of the Deeping Stacking Network, a fusion model composed of four classifiers, has been further improved and the accuracy reaches 86.8%. Compared with the intrusion detection system of other research papers, the proposed model effectively improves the detection performance and has made significant improvements in network intrusion detection.
An order of addition experiment is an experiment to study how the order of addition of components affect the results, with the objective of predicting and determining the optimal order of addition of ...components. Order of addition experiment are also commonly used in the drug combination therapy, where experimenting with all drugs combinations is unaffordable. To solve this problem, we constructed a new design table, two-level component factorial design table (TLCF), which combine the component orthogonal array design table and the two-level partial factorial design table by matrix product. TLCF can explore the order and dosage effect of components on the results and can greatly reduce the number of experiments. We also prove that the relative D-efficiency of the TLCF can reach 100% and solve an explicit expression for the D-efficiency of the full design. In the simulation experiment, we compare the D-efficiency of the TLCF with the random design table to prove the superiority of TLCF. Finally, we give a treatment plan for the combination of three drugs for glioblastoma based on the TLCF, which provides a new perspective for the precision treatment of patients.
The electrochemical activity of stacked nitrogen-doped carbon nanotube cups (NCNCs) has been explored in comparison to commercial Pt-decorated carbon nanotubes. The nanocup catalyst has demonstrated ...comparable performance to that of Pt catalyst in oxygen reduction reaction. In addition to effectively catalyzing O2 reduction, the NCNC electrodes have been used for H2O2 oxidation and consequently for glucose detection when NCNCs were functionalized with glucose oxidase (GOx). Creating the catalysts entirely free of precious metals is of great importance for low-cost fuel cells and biosensors.
In treating highly infectious coronavirus disease-19 (COVID-19) pneumonia, intensive care unit (ICU) nurses face a high risk of developing somatic symptom disorder (SSD).The symptom clusters in one ...population may show overlaps and involvements, a phenomenon that should be deliberately resolved to improve the management efficiency.
The present study aims to investigate the symptoms and causes of SSD of ICU nurses treating COVID-19 pneumonia. The research results are expected to provide evidence for the establishment of a better management strategy.
This study enrolled a total of 140 ICU nurses who were selected by Jiangsu Province Hospital to work in Wuhan (the epicenter of the COVID-19 epidemic in China) on February 3, 2020. A questionnaire, Somatic symptom disorders for ICU nurses in Wuhan No. 1 Hospital, was designed based on the International Classification of Functioning, Disability and Health. Exploratory factor analysis was performed to cluster the symptoms and logistic regression analysis to find the risk factors of the symptoms.
Five major symptoms were chest discomfort and palpitation (31.4%), dyspnea (30.7%), nausea (21.4%), headache (19.3%), and dizziness (17.9%). In exploratory factor analysis, the symptoms were classified into three clusters: Cluster A of breathing and sleep disturbances (dizziness, sleepiness, and dyspnea); Cluster B of gastrointestinal complaints and pain (nausea and headache), and Cluster C of general symptoms (xerostomia, fatigue, as well as chest discomfort and palpitation). In Cluster A, urine/feces splash, sex, and sputum splash were independent predictive factors. In Cluster B, fall of protective glasses and urine/feces splash were independent predictive factors. In Cluster C, urine/feces splash and urine/feces clearance were independent predictive factors.
The ICU nurses in Wuhan showed varying and overlapping SSDs. These SSDs could be classified into three symptom clusters. Based on the characteristics of their SSDs, specific interventions could be implemented to safeguard the health of ICU nurses.
•Dual-frequency ultrasound combined with PMS had a synergistic effect.•Three new degradation pathways for tetracycline degradation were proposed.•O2∙-, •OH and SO4∙- were the main contributors to the ...oxidation process.•Dual-frequency ultrasound provided a higher number of active bubbles.
Tetracycline has received a great deal of interest for the harmful effects of substance abuse on ecosystems and humanity. The effects of different processes on the degradation of tetracycline were compared, with dual-frequency ultrasound (DFUS) in combination with peroxymonosulfate (PMS) being the most effective for the tetracycline degradation. Free radical scavenging experiments showed that O2∙-,SO4∙- and •OH were the main reactive radicals in the degradation of tetracycline. According to the major intermediates of tetracycline degradation identified, three possible degradation pathways were proposed, which are of significance for translational studies of tetracycline degradation. Notably, these intermediates were found to be significantly less toxicity. The number of active bubbles in the degradation vessel was calculated using a semi-empirical formula, and a higher value of 1.44 × 108 L-1s−1 of bubbles was obtained when using dual-frequency ultrasound at 20 kHz (210 W/L) and 80 kHz (85.4 W/L). Therefore, compared to 20 kHz, although the yield of strong oxidizing substances from individual active bubbles decreased slightly, a significant increment of the number of active bubbles still resulted in a higher synergistic effect, and the combination of DFUS and PMS should be effective in promoting the generation of reactive free radicals and mass transfer processes within the degradation vessel, which provides a method for efficient removal of tetracycline from wastewater.
With the development of deepfake technology, deepfake detection has received widespread attention. Although some deepfake forensics techniques have been proposed, they are still very difficult to ...implement in real-world scenarios. This is due to the differences in different deepfake technologies and the compression or editing of videos during the propagation process. Considering the issue of sample imbalance with few-shot scenarios in deepfake detection, we propose a multi-feature channel domain-weighted framework based on meta-learning (MCW). In order to obtain outstanding detection performance of a cross-database, the proposed framework improves a meta-learning network in two ways: it enhances the model’s feature extraction ability for detecting targets by combining the RGB domain and frequency domain information of the image and enhances the model’s generalization ability for detecting targets by assigning meta weights to channels on the feature map. The proposed MCW framework solves the problems of poor detection performance and insufficient data compression resistance of the algorithm for samples generated by unknown algorithms. The experiment was set in a zero-shot scenario and few-shot scenario, simulating the deepfake detection environment in real situations. We selected nine detection algorithms as comparative algorithms. The experimental results show that the MCW framework outperforms other algorithms in cross-algorithm detection and cross-dataset detection. The MCW framework demonstrates its ability to generalize and resist compression with low-quality training images and across different generation algorithm scenarios, and it has better fine-tuning potential in few-shot learning scenarios.
Metal–graphitic interfaces formed between metal nanoparticles (MNPs) and carbon nanotubes (CNTs) or graphene play an important role in the properties of such hybrid nanostructures. This Perspective ...summarizes different types of interfaces that exist within the metal–carbon nanoassemblies and discusses current efforts on understanding and modeling the interfacial conditions and interactions. Characterization of the metal–graphitic interfaces is described here, including microscopy, spectroscopy, electrochemical techniques, and electrical measurements. Recent studies on these nanohybrids have shown that the metal–graphitic interfaces play critical roles in both controlled assembly of nanoparticles and practical applications of nanohybrids in chemical sensors and fuel cells. Better understanding, design, and manipulation of metal–graphitic interfaces could therefore become the new frontier in the research of MNP/CNT or MNP/graphene hybrid systems.
Cuproptosis-related genes (CRGs) are important for tumor development. However, the functions of CRGs across cancers remain obscure. We performed a pan-cancer investigation to reveal the roles of CRGs ...across cancers. In an analysis of 26 cancers, 12 CRGs were differentially expressed, and those CRGs were found to have prognostic value across different cancer types. The expression of CRGs exhibited varied among tumors of 6 immune subtypes and were significantly correlated with the 16 sensitivities of drugs. The expression of CRGs were highly correlated with immunological subtype and tumor microenvironment (TME) of prostate cancer. We also established CRGs-related prognostic signatures that closely correlated with prognosis and drug sensitivity of prostate cancer patients. Single-cell RNA-seq revealed that several CRGs were enriched in the cancer cells. Finally, an in vitro experiment showed that elesclomol, a cuproptosis inducer, targets ferredoxin 1 and suppress cell viability in prostate cancer cells. In conclusion, we carried out a comprehensive investigation for determining CRGs in differential expression, prognosis, immunological subtype, TME, and cancer treatment sensitivity across 26 malignancies; and validated the results in prostate cancer. Our research improves pan-cancer knowledge of CRGs and identifies more effective immunotherapy strategies.
A new parameter optimization and uncertainty assessment procedure using the Bayesian inference with an adaptive Metropolis-Hastings (AM-H) algorithm is presented for extreme rainfall frequency ...modeling. An efficient Markov chain Monte Carlo sampler is adopted to explore the posterior distribution of parameters and calculate their uncertainty intervals associated with the magnitude of estimated rainfall depth quantiles. Also, the efficiency of AM-H and conventional maximum likelihood estimation (MLE) in parameter estimation and uncertainty quantification are compared. And the procedure was implemented and discussed for the case of Chaohu city, China. Results of our work reveal that: (i) the adaptive Bayesian method, especially for return level associated to large return period, shows better estimated effect when compared with MLE; it should be noted that the implementation of MLE often produces overy optimistic results in the case of Chaohu city; (ii) AM-H algorithm is more reliable than MLE in terms of uncertainty quantification, and yields relatively narrow credible intervals for the quantile estimates to be instrumental in risk assessment of urban storm drainage planning.
Flexible electronics with highly thermal stability and mechanical strength are highly needed in advanced transportation systems. Semiconducting single‐walled carbon nanotubes are one of the leading ...active materials for such thin film transistors because they are printable, flexible, thermally stable, and mechanically strong. Dielectrics with large capacitance are another major component, and polymer electrolytes are printed for flexible electronics, but they suffer from poor mechanical strength and low operating temperature. Here, a transparent, mechanically flexible, and thermally stable polyfluorinated electrolyte (PFE) is developed with high capacitance by curing printed polyfluorinated resin (PFR) and ionic liquid composite at high temperature. PFE inherits the mechanical flexibility and thermal stability from PFR. The immobilized ionic liquid inside the porous structures of PFE accounts for the high capacitance. With top‐gated PFE, fully printed electronically pure single‐chirality (6,5) single‐walled carbon nanotube (SWCNT) thin‐film transistors (TFTs) exhibit air stable, consistent, and reliable ambipolar characteristics with high transconductance (1 mS) and small subthreshold swing (<0.15 V dec−1) at low voltage in ambient air for p‐type and n‐type carriers, and >105 ON/OFF current ratio for both carriers under low operation voltage.
An air stable (6,5) single chirality single‐walled carbon nanotube (SWCNT) thin‐film transistor (TFT) backplane is fully printed on a light‐emitting diode (LED) array to drive LED pixels under 3 V. High‐performance fully printed (6,5) SWCNT TFTs exhibit ambipolar properties with high transconductance (1 mS) and small subthreshold swing (<0.15 V dec−1) at low voltage in ambient air.
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