The Pore Confinement Effect of FDU-12 Mesochannels on MoS 2

FDU-12 silica with highly ordered face-centered cubic mesoporous structure is developed as support to prepare Mo/FDU-12 catalysts for hydrodesulfurization (HDS) of dibenzothiophene (DBT). A series of Mo/FDU-12 catalysts are synthesized by using incipient wetness impregnation method with different MoO3 loadings (6, 8, 10, 12, and 15 wt.%). The objective of this work is to explore the pore confinement effect of FDU-12 mesochannels on the MoS2 morphology with various metal loadings. It is found that, as increasing MoO3 loadings from 6 to 15 wt.%, the MoS2 nanocrystallites transform from monolayer to multilayer and the morphology changes from straight layered to curved and then to ring-like and finally to spherical-like morphology due to the restriction of cage-like pore channels of FDU-12 support. The HDS results show that the catalytic activity increases first and then decreases with the best HDS performance at the MoO3 loading of 10 wt.%. In addition, we compared the HDS activity of Mo catalyst supported on FDU-12 with that on the commercial γ-Al2O3 and SBA-15; the result exhibits that FDU-12 is superior to the other two supports due to its large pore size and ordered three-dimensional open pore channels

Metallic hydrophobic surface fabrication and wettability study

Hydrophobic surfaces can be designed to have useful properties such as self-cleaning, anti-icing, and flow drag reduction. Research interests in this area have been growing with rising demands from various industries. Hydrophobic surfaces can be fabricated by coating, micro or nano-scale texturing, or a combination of the two. For industrial applications, methods for mass production of hydrophobic surfaces are desired. This thesis investigated two hydrophobic surface fabrication methods, laser machining and sandblasting, and conducted wettability analysis of the fabricated surfaces. In the laser machining, four microscale surface structures including channel, pillar, varied channel and varied pillar, are designed and fabricated. The static contact angles of all laser-machined samples are close to 130° without any coating. In sandblasting fabrication, three standoff distances (10 mm, 20 mm and 30 mm) between the spray nozzle and target surfaces are tested. For stainless steel, lower standoff distance leads to increased water contact angle on the sandblasted surfaces. For carbon steel, sandblasting increases wettability of the carbon steel, with lower contact angle from lower standoff distance. A low energy coating (Aculon) is applied on the samples from both fabrication methods. In the analysis, samples are divided into two groups, one for coated samples, and the other for the uncoated ones. Overall, the coating increases static contact angle and decreases hysteresis in all laser-machined samples and sandblasted ones. The difference in wettability of the samples from the two fabrication methods is analyzed in details. Sandblasted samples can reach 113°±4° without any coating, compared with static contact angle of 128°±5° from the laser-machined sample with pillar. After coating, the water contact angle of sandblasted samples increases to 137°±3° compared with 142°±4° on laser machined samples with pillar. The results of contact angle hysteresis are nearly same for the two methods before coating. After coating, contact angle hysteresis on sandblasted samples is overall lower than that on laser-machined samples

Label Enhanced Event Detection with Heterogeneous Graph Attention Networks

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Different from English ED, Chinese ED suffers from the problem of word-trigger mismatch due to the uncertain word boundaries. Existing approaches injecting word information into character-level models have achieved promising progress to alleviate this problem, but they are limited by two issues. First, the interaction between characters and lexicon words is not fully exploited. Second, they ignore the semantic information provided by event labels. We thus propose a novel architecture named Label enhanced Heterogeneous Graph Attention Networks (L-HGAT). Specifically, we transform each sentence into a graph, where character nodes and word nodes are connected with different types of edges, so that the interaction between words and characters is fully reserved. A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction. Furthermore, we convert each label into a trigger-prototype-based embedding, and design a margin loss to guide the model distinguish confusing event labels. Experiments on two benchmark datasets show that our model achieves significant improvement over a range of competitive baseline methods

CBSeq: A Channel-level Behavior Sequence For Encrypted Malware Traffic Detection

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic. Compared with signature-based methods, they have higher scalability and flexibility. However, affected by the frequent variants and updates of malware, current methods suffer from a high false positive rate and do not work well for unknown malware traffic detection. It remains a critical task to achieve effective malware traffic detection. In this paper, we introduce CBSeq to address the above problems. CBSeq is a method that constructs a stable traffic representation, behavior sequence, to characterize attacking intent and achieve malware traffic detection. We novelly propose the channels with similar behavior as the detection object and extract side-channel content to construct behavior sequence. Unlike benign activities, the behavior sequences of malware and its variant's traffic exhibit solid internal correlations. Moreover, we design the MSFormer, a powerful Transformer-based multi-sequence fusion classifier. It captures the internal similarity of behavior sequence, thereby distinguishing malware traffic from benign traffic. Our evaluations demonstrate that CBSeq performs effectively in various known malware traffic detection and exhibits superior performance in unknown malware traffic detection, outperforming state-of-the-art methods.Comment: Submitted to IEEE TIF

1-(Benzyl­ideneamino)pyridinum iodide

In the title compound, C12H11N2 +·I−, the aromatic rings are oriented at a dihedral angle of 73.40 (3)°. In the crystal structure, π–π contacts between the pyridine rings and the benzene and pyridine rings [centroid–centroid distances = 3.548 (3) and 4.211 (3) Å] may stabilize the structure