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Coal water slurry is an advanced and efficient coal utilization technology, prepare coal water slurry by gasification wastewater can realize the recycling of wastewater and improve the combustion or gasification efficiency of the slurry. Stabilizer is an important component to ensure fluidity and stability in industrial applications of coal water slurry, and the complex substances in wastewater for coal water slurry preparation have a great influence on the stabilizer. Therefore, in this paper, the influence of the major substances in gasification wastewater on the stabilizer is studied. Results show that (a) Ammonia nitrogen substances have adverse effects on stabilizers, because the ionized NH4+ in the solution will bond with the polar groups on the stabilizer, which will affect the stabilizer to lose its effectiveness and reduce the stability of the slurry. (b) Small organic molecules have little effect on stabilizers, this is because the binding strength and quantity of stabilizer to water molecules is much larger than that of organic matters such as phenolic substances. (c) Highvalent metal ions have a greater impact on stabilizers, the metal ions easily form gels or precipitates with the stabilizer, causing it to lose its effectiveness, and also have an electrostatic effect with the carboxyl group on the side chain of the stabilizer, which makes the stabilizer easy to aggregate together and lose the steric hindrance effect, finally cause the stability of the slurry reduced. The conclusion of the influence of substances in wastewater on stabilizer can provide a technical reference for the selection of suitable wastewater and stabilizer to prepare coal water slurry, then promoting the efficient use of energy.

The vibration of reactors not only produces noise pollution, but also affects the safe operation of reactors. Giant magnetostrictive materials can generate huge expansion and shrinkage deformation in a magnetic field. With the principle of mutual offset between the giant magnetostrictive force produced by the giant magnetostrictive material and the original vibration force of the reactor, the vibration of the reactor can be reduced. In this paper, magnetization and magnetostriction characteristics in silicon steel and the giant magnetostrictive material are measured, respectively. According to the presented magneto-mechanical coupling model including the electromagnetic force and the magnetostrictive force, reactor vibration is calculated. By comparing the vibration of the reactor with different inserted materials in the air gaps between the reactor cores, the vibration reduction effectiveness of the giant magnetostrictive material is validated.

Semantic labelling of remote sensing images is crucial for various remote sensing applications. However, the dense distribution of man‐made and natural objects with similar colours and geographic proximity poses challenges for achieving consistent and accurate labelling results. To address this issue, a novel deep learning model incorporating an octave convolutional neural networks (CNNs) within an end‐to‐end U‐shaped architecture is presented. The approach differs from conventional CNNs in that it employs octave convolutions instead of standard convolutions. This strategy serves to minimize low‐frequency information redundancy while maintaining segmentation accuracy. Furthermore, coordination attention is introduced in the encoder module to enhance the network's ability to extract useful features, focusing on spatial and channel dependencies within the feature maps. This attention mechanism enables the network to better capture channel, direction, and location information. In conclusion, the U‐shaped network is engineered with a completely symmetric structure that employs skip connections to merge low‐resolution information, used for object class recognition, with high‐resolution information to enable precise localization. This configuration ultimately improves segmentation accuracy. Experimental results on two public datasets demonstrate that our U‐ONet achieves state‐of‐the‐art performance, making it a compelling choice for remote sensing image semantic labelling applications. [ABSTRACT FROM AUTHOR]

Summary: CRISPR‐Cas9 is widely used for genome editing, but its PAM sequence requirements limit its efficiency. In this study, we explore Faecalibaculum rodentium Cas9 (FrCas9) for plant genome editing, especially in rice. FrCas9 recognizes a concise 5′‐NNTA‐3′ PAM, targeting more abundant palindromic TA sites in plant genomes than the 5′‐NGG‐3′ PAM sites of the most popular SpCas9. FrCas9 shows cleavage activities at all tested 5′‐NNTA‐3′ PAM sites with editing outcomes sharing the same characteristics of a typical CRISPR‐Cas9 system. FrCas9 induces high‐efficiency targeted mutagenesis in stable rice lines, readily generating biallelic mutants with expected phenotypes. We augment FrCas9's ability to generate larger deletions through fusion with the exonuclease, TREX2. TREX2‐FrCas9 generates much larger deletions than FrCas9 without compromise in editing efficiency. We demonstrate TREX2‐FrCas9 as an efficient tool for genetic knockout of a microRNA gene. Furthermore, FrCas9‐derived cytosine base editors (CBEs) and adenine base editors (ABE) are developed to produce targeted C‐to‐T and A‐to‐G base edits in rice plants. Whole‐genome sequencing‐based off‐target analysis suggests that FrCas9 is a highly specific nuclease. Expression of TREX2‐FrCas9 in plants, however, causes detectable guide RNA‐independent off‐target mutations, mostly as single nucleotide variants (SNVs). Together, we have established an efficient CRISPR‐FrCas9 system for targeted mutagenesis, large deletions, C‐to‐T base editing, and A‐to‐G base editing in plants. The simple palindromic TA motif in the PAM makes the CRISPR‐FrCas9 system a promising tool for genome editing in plants with an expanded targeting scope. [ABSTRACT FROM AUTHOR]