We show that if
V
is a vertex operator algebra such that all the irreducible ordinary
V
-modules are
C
1
-cofinite and all the grading-restricted generalized Verma modules for
V
are of finite length, ...then the category of finite length generalized
V
-modules has a braided tensor category structure. By applying the general theorem to the simple affine vertex operator algebra (resp. superalgebra) associated to a finite simple Lie algebra (resp. Lie superalgebra)
g
at level
k
and the category
K
L
k
(
g
)
of its finite length generalized modules, we discover several families of
K
L
k
(
g
)
at non-admissible levels
k
, having braided tensor category structures. In particular,
K
L
k
(
g
)
has a braided tensor category structure if the category of ordinary modules is semisimple or more generally if the category of ordinary modules is of finite length. We also prove the rigidity and determine the fusion rules of some categories
K
L
k
(
g
)
, including the category
K
L
-
1
(
sl
n
)
. Using these results, we construct a rigid tensor category structure on a full subcategory of
K
L
1
(
sl
(
n
|
m
)
)
consisting of objects with semisimple Cartan subalgebra actions.
Using the tensor category theory developed by Lepowsky, Zhang and the second author, we construct a braided tensor category structure with a twist on a semisimple category of modules for an affine ...Lie algebra at an admissible level. We conjecture that this braided tensor category is rigid and thus is a ribbon category. We also give conjectures on the modularity of this category and on the equivalence with a suitable quantum group tensor category. In the special case that the affine Lie algebra is
sl
^
2
, we prove the rigidity and modularity conjectures.
Natural flavonoids have powerful antioxidant activity and have been reported to show promising protective effects against cataracts. The plant Kaempferia parviflora (K. parviflora) is indigenous to ...southeast Asia, including Thailand, and typically contains polymethoxylated flavones. The flavones in K. parviflora are reported to have various biological properties. Recently, polymethoxylated flavones of K. parviflora (KPMFs) were shown to have potent Sirtuin 1 enzyme-stimulating and anti-glycation activities that led to the suppression of cataract formation. Matrix metalloproteinases (MMPs) are upregulated in several pathologic ocular diseases, including cataracts, and have been established as an attractive target for the prevention and/or treatment of specific cataract phenotypes, such as anterior subcapsular cataract (ASC) and posterior capsular opacification (PCO). In the present study, we investigated the effect of KPMFs on MMP (gelatinase) activity in the human lens epithelial cell line, SRA01/04. We demonstrated that KPMFs inhibited the phorbol ester-induced MMP-9 activity and the mRNA expression through the suppression of mitogen-activated protein kinases (MAPKs) phosphorylation in human lens epithelial cells; 5,7-dimethoxyflavone was found to exert the most potent inhibition, but 3,5,7,4′-tetramethoxyflavone and 3,5,7,3′,4′-pentamethoxyflavone also resulted in considerable inhibition. Our results suggested that the consumption of PMFs isolated from K. parviflora, may be an effective strategy to delay the development of cataracts, such as ASC and PCO.
In recent years, the Internet has shown rapid development, and network security issue has gradually become the focus of research by scholars and enterprises. Network security time series is a ...reliable source to obtain future network security situation, so as to develop network security defense strategy by exploring the correlation of time series. The network security time series is a reliable source to obtain the future network security situation, and it is the main direction of current network security defense by exploring the correlation of time series, and analyzing the future network security situation so as to formulate network security defense strategies. This is the main direction of network security defense. The existing research focuses on the short-term prediction of network attacks, and the robustness and accuracy of long-term prediction still have big problems. To fuse the information from different data sources and capture the correlation between sequences, we design a data source selection module based on the similarity of measurement curves. We then model the network security situation prediction based on deep learning models and propose a situation prediction model based on Temporal Convolutional Network (TCN)-combined Transformer, which focuses on the time series long-term prediction problem, combining the network condition and attack situation to obtain the future network security situation. Our proposed model is divided into three parts, which are the information encoding module, the information synthesis module, and situation value calculation and prediction accuracy evaluation module. The selected multi-dimensional situations element data are used as model input, and the TCN-combined Transformer is employed as the network security situational data processing unit to complete the information fusion and prediction tasks. Finally, the role of data source selection on prediction accuracy is evaluated using an ablation study. We experimented and evaluated the model at different prediction horizon lengths using five existing baseline models and three performance metrics. The experimental results show that our proposed prediction model has better robustness and accuracy in most of the metrics.
The analgesic effect of Ephedra Herb (EH) is believed to be derived from the anti-inflammatory action of pseudoephedrine (Pse). We recently reported that ephedrine alkaloids–free EH extract (EFE) ...attenuates formalin-induced pain to the same level as that achieved by EH extract (EHE), which suggests that the analgesic effect of EH may not be due to ephedrine alkaloids (EAs). To examine the contribution of EAs to the analgesic effect of EH, mice were injected with formalin to induce a biphasic pain reaction (first phase, 0–5 min; second phase, 10–45 min) at various time points after oral administration of the following test drugs: ephedrine (Eph), Pse, “authentic” EHE from Tsumura & Co. (EHE-Ts), EFE, and EHE that was used as the source of EFE (EHE-To). Biphasic pain was suppressed at 30 min after administration of Eph, EHE-Ts, and EHE-To. At 6 h after administration of EFE, EHE-To, and Pse—and at 4 to 6 h after administration of EHE-Ts—only second-phase pain was suppressed; however, the effect of Pse at 6 h was not significant. These results suggested that EHE has a biphasic analgesic effect against biphasic formalin-induced pain: in the first phase of analgesia (30 min after administration), biphasic pain is suppressed by Eph; in the second phase of analgesia (4–6 h after administration), second-phase pain is alleviated by constituents other than EAs, although Pse may partially contribute to the relief of second-phase pain.
With the popularity of wireless networks, wireless sensor networks (WSNs) have advanced rapidly, and their flexibility and ease of deployment have resulted in more security concerns, making it ...critical to research network intrusion prevention for WSNs. Denial of service (DoS) is a common network attack, achieving its goal by bringing down the target network. A DoS attack on WSNs devices with limited resources would be fatal. This paper proposes a method based on principal component analysis (PCA) and a deep convolution neural network (DCNN) for DoS traffic anomaly detection in WSNs, based on the vulnerability of WSNs to attacks and the limited storage space of their devices. Compared with the conventional deep learning structure, the proposed model has a lightweight structure and more effective feature extraction capability, which can effectively detect network abnormal traffic in WSNs devices with limited storage capacity. To assure the effectiveness of the proposed model, receiver operating characteristic (ROC) curves, various classification metrics, and confusion matrices are used to verify the classification results of the model. Through experimental comparison, the proposed model, with small model size, outperforms other mainstream abnormal traffic detection models in terms of classification effect.
Aiming at the problem of low visibility of underwater environment, which leads to the leakage of small target detection and low accuracy, this paper proposes an improved algorithm USSTD-YOLOv8n ...(Underwater small-size target detection YOLOv8n) based on YOLOv8n. First, CARAFE is adopted as anew up-sampling method to achieve more correct feature reconstruction under low underwater visibility. Second, Context Guided Block (CG block) is introduced to replace part of the convolutional structure, which makes USSTD-YOLOv8n have stronger feature extraction capability. Finally, Inner-CIoU is adopted as the loss function to improve the generalization ability of USSTD-YOLOv8n, to obtain more correct detection results. To verify the robustness and accuracy of the model, a new experimental strategy is used to perform one set of ablation experiments and three sets of comparison experiments on the URPC2018 and URPC2020 datasets, the mAP @ 0.5 was 0.7670,0.7910 and 0.7044, compared to the YOLOv8n algorithm, map@0.5 increased 0.0260, 0.008 and 0.007. It is proved through four sets of experiments that USSTD-YOLOv8n has better detection performance in underwater small target detection task.
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