The network intrusion detection system (NIDS) plays a crucial role as a security measure in addressing the increasing number of network threats. The majority of current research relies on ...feature-ready datasets that heavily depend on feature engineering. Conversely, the increasing complexity of network traffic and the ongoing evolution of attack techniques lead to a diminishing distinction between benign and malicious network behaviors. In this paper, we propose a novel end-to-end intrusion detection framework based on a contrastive learning approach. We design a hierarchical Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model to facilitate the automated extraction of spatiotemporal features from raw traffic data. The integration of contrastive learning amplifies the distinction between benign and malicious network traffic in the representation space. The proposed method exhibits enhanced detection capabilities for unknown attacks in comparison to the approaches trained using the cross-entropy loss function. Experiments are carried out on the public datasets CIC-IDS2017 and CSE-CIC-IDS2018, demonstrating that our method can attain a detection accuracy of 99.9% for known attacks, thus achieving state-of-the-art performance. For unknown attacks, a weighted recall rate of 95% can be achieved.
Metal oxide nanoparticles have been widely utilized for the fabrication of functional gas sensors to determine various flammable, explosive, toxic, and harmful gases due to their advantages of low ...cost, fast response, and high sensitivity. However, metal oxide-based gas sensors reveal the shortcomings of high operating temperature, high power requirement, and low selectivity, which limited their rapid development in the fabrication of high-performance gas sensors. The combination of metal oxides with two-dimensional (2D) nanomaterials to construct a heterostructure can hybridize the advantages of each other and overcome their respective shortcomings, thereby improving the sensing performance of the fabricated gas sensors. In this review, we present recent advances in the fabrication of metal oxide-, 2D nanomaterials-, as well as 2D material/metal oxide composite-based gas sensors with highly sensitive and selective functions. To achieve this aim, we firstly introduce the working principles of various gas sensors, and then discuss the factors that could affect the sensitivity of gas sensors. After that, a lot of cases on the fabrication of gas sensors by using metal oxides, 2D materials, and 2D material/metal oxide composites are demonstrated. Finally, we summarize the current development and discuss potential research directions in this promising topic. We believe in this work is helpful for the readers in multidiscipline research fields like materials science, nanotechnology, chemical engineering, environmental science, and other related aspects.
Two-dimensional materials (2DMs) exhibited great potential for applications in materials science, energy storage, environmental science, biomedicine, sensors/biosensors, and others due to their ...unique physical, chemical, and biological properties. In this review, we present recent advances in the fabrication of 2DM-based electrochemical sensors and biosensors for applications in food safety and biomolecular detection that are related to human health. For this aim, firstly, we introduced the bottom-up and top-down synthesis methods of various 2DMs, such as graphene, transition metal oxides, transition metal dichalcogenides, MXenes, and several other graphene-like materials, and then we demonstrated the structure and surface chemistry of these 2DMs, which play a crucial role in the functionalization of 2DMs and subsequent composition with other nanoscale building blocks such as nanoparticles, biomolecules, and polymers. Then, the 2DM-based electrochemical sensors/biosensors for the detection of nitrite, heavy metal ions, antibiotics, and pesticides in foods and drinks are introduced. Meanwhile, the 2DM-based sensors for the determination and monitoring of key small molecules that are related to diseases and human health are presented and commented on. We believe that this review will be helpful for promoting 2DMs to construct novel electronic sensors and nanodevices for food safety and health monitoring.
► Lower N rate is possible for cotton high yielding by decreasing pre-plant application ratio and increasing peak-bloom application ratio. ► The alteration of N split ratio has a longer reproductive ...period, a higher speed of biomass accumulation during the fast accumulation period. ► Cotton biomass is correlated to the accumulation speed more than the duration of fast accumulation period. ► Cotton yield is related to the biomass positively after peak bloom, but negatively before peak bloom.
The conventional nitrogen (N) rate is 300
kg/ha in cotton production in the field with middle level fertility in the Yangtze River Valley in China. Applications of N are usually split into a pre-plant application (PPA), a first bloom application (FBA), and a peak bloom application (PBA), at percentages of 30%, 40%, and 30%, respectively. However, the facts of little positive yield response with a higher N rate, water nutrition enrichment, and cotton early senescence are universal. The purposes of this study were to understand and determine the cotton yield using a lower N rate but allocating more N to the late growing period (PBA) and to specify the characteristics of cotton growth and biomass accumulation by the alteration. Fixing the N rate at 225
kg/ha and FBA ratio at 40%, the effects of different balances for the remaining 60% N between PPA and PBA on cotton (
G. hirsutum L. var. Huazamian H318) growth, yield, and biomass were studied in field trials (2008 and 2009) and a pot trial (2009). The results showed that the balance of 0% PPA and 60% PBA ratio had a shorter seedling period and a longer flowering and boll setting period, compared to the balance of a higher PPA and lower PBA ratio. Both field and pot trials showed the same trend but significant differences existed among treatments in the pot trial. Cotton lint yield was as high as 1200
kg/ha for PA treatment (0% PPA
+
60% PBA), which was significantly higher than any other treatments since it had a higher biomass accumulation speed in both the average (1.3
g/d) and the highest (4.51
g/d) during the fast biomass accumulation period, a higher total biomass (189.7
g/plant) and a higher harvest index (16.82%). These results suggest that allocating more N to PBA should increase cotton biomass, harvest index, and yield.
Soil compaction causes substantial reduction in agriculture productivity and has always been of great distress for farmers. Intensive agriculture seems to be more crucial in causing compaction. High ...mechanical load, less crop diversification, intensive grazing, and irrigation methods lead to soil compaction. It is further exasperated when these factors are accompanied with low organic matter, animal trampling, engine vibrations, and tillage at high moisture contents. Soil compaction increases soil bulk density and soil strength, while decreases porosity, aggregate stability index, soil hydraulic conductivity, and nutrient availability, thus reduces soil health. Consequently, it lowers crop performance via stunted aboveground growth coupled with reduced root growth. This paper reviews the potential causes of compaction and its consequences that have been published in last two decades. Various morphological and physiological alterations in plant as result of soil compaction have also been discussed in this review.
In large-scale network topology discovery, due to the complex network structure and dynamic change characteristics, it is always the focus of network topology measurement to obtain as many network ...paths as possible in a short time. In this paper, we propose a large-scale network path probing approach in order to solve the problems of low probing efficiency and high probing redundancy commonly found in current research. By improving the packet delivery order and the update strategy of time-to-live field values, we redesigned and implemented an efficient large-scale network path probing tool. The experimental results show that the method-derived tool can complete path probing for a sample of 12 million/24 network address segments worldwide within 1 hour, which greatly improves the efficiency of network path probing. Meanwhile, compared to existing methods, the proposed method can reduce the number of packets sent by about 10% with the same number of network addresses found, which effectively reduces probing redundancy and alleviates the network load.
Medicinal plants produce important substrates for their adaptation and defenses against environmental factors and, at the same time, are used for traditional medicine and industrial additives. Plants ...have relatively little in the way of secondary metabolites via biosynthesis. Recently, the whole-genome sequencing of medicinal plants and the identification of secondary metabolite production were revolutionized by the rapid development and cheap cost of sequencing technology. Advances in functional genomics, such as transcriptomics, proteomics, and metabolomics, pave the way for discoveries in secondary metabolites and related key genes. The multi-omics approaches can offer tremendous insight into the variety, distribution, and development of biosynthetic gene clusters (BGCs). Although many reviews have reported on the plant and medicinal plant genome, chemistry, and pharmacology, there is no review giving a comprehensive report about the medicinal plant genome and multi-omics approaches to study the biosynthesis pathway of secondary metabolites. Here, we introduce the medicinal plant genome and the application of multi-omics tools for identifying genes related to the biosynthesis pathway of secondary metabolites. Moreover, we explore comparative genomics and polyploidy for gene family analysis in medicinal plants. This study promotes medicinal plant genomics, which contributes to the biosynthesis and screening of plant substrates and plant-based drugs and prompts the research efficiency of traditional medicine.
Patients with subarachnoid hemorrhage (SAH) often suffer from cognitive function impairments even when they have received proper treatment, such as the clipping or coiling of aneurysms, and this ...causes problems with returning to work and burdens the family. Increasing attention has been paid to mesenchymal stem cell (MSC)-derived extracellular vesicle (MSC-EV) as promising therapeutic vesicles for stroke management. In this study, we explored the potential role of MSC-EV in a rat model of SAH. We observed that MSC-EV ameliorated early brain injury (EBI) after SAH by reducing the apoptosis of neurons and that SAH induced an increase in the expression level of miR-21 in the prefrontal cortex and hippocampus. In addition, using miRNA profiling and CSF sequencing data from the exRNA Atlas, we demonstrated that EV-derived miR-21 protected neurons from apoptosis and alleviated SAH-induced cognitive dysfunction. The neuroprotective role of MSC-EV was abrogated by miR-21 knockdown or the administration of MK2206, a PTEN/Akt inhibitor. Overall, our results suggest that MSC-EV promotes neuronal survival and alleviates EBI after SAH through transferring miR-21 to recipient neurons.
Network security situational awareness (NSSA) aims to capture, understand, and display security elements in large-scale network environments in order to predict security trends in the relevant ...network environment. With the internet's increasingly large scale, increasingly complex structure, and gradual diversification of components, the traditional single-layer network topology model can no longer meet the needs of network security analysis. Therefore, we conduct research based on a multi-layer network model for network security situational awareness, which is characterized by the three-layer network structure of a physical device network, a business application network, and a user role network. Its network characteristics require new assessment methods, so we propose a multi-layer network link importance assessment metric: the multi-layer-dependent link entropy (MDLE). On the one hand, the MDLE comprehensively evaluates the connectivity importance of links by fitting the link-local betweenness centrality and mapping entropy. On the other hand, it relies on the link-dependent mechanism to better aggregate the link importance contributions in each network layer. The experimental results show that the MDLE has better ordering monotonicity during critical link discovery and a higher destruction efficacy in destruction simulations compared to classical link importance metrics, thus better adapting to the critical link discovery requirements of a multi-layer network topology.
Studying the interactions between biomolecules and material interfaces play a crucial role in the designing and synthesizing of functional bionanomaterials with tailored structure and function. ...Previously, a lot of studies were performed on the self-assembly of peptides in solution through internal and external stimulations, which mediated the creation of peptide nanostructures from zero-dimension to three-dimension. In this study, we demonstrate the self-assembly behavior of the GNNQQNY peptide on the surface of mica and highly oriented pyrolytic graphite through tailoring the self-assembly conditions. Various factors, such as the type of dissolvent, peptide concentration, pH value, and evaporation period on the formation of peptide nanofibers and nanoribbons with single- and bi-directional arrays are investigated. It is found that the creation of peptide nanoribbons on both mica and HOPG can be achieved effectively through adjusting and optimizing the experimental parameters. Based on the obtained results, the self-assembly and formation mechanisms of peptide nanoribbons on both material interfaces are discussed. It is expected that the findings obtained in this study will inspire the design of motif-specific peptides with high binding affinity towards materials and mediate the green synthesis of peptide-based bionanomaterials with unique function and application potential.
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