The filter cartridge dust collector has been widely used in industry, but the influence of its internal structure on its operation effects is rarely studied. FLUENT software was used to simulate the ...influence of different air volume and permeability values on the gas–solid two-phase flow of dust removal characteristics for a filter cartridge. The results show that when the air volume of the fan was greater than 1600 m
3
/h, the increase in the dust reduction rate was not obvious, and the high-velocity airflow filled the entire dust removal chamber, which was conducive to the filter using the largest effective filtration area to remove dust; the optimal air volume was 1600 m
3
/h. Furthermore, the dust removal effect gradually became worse when the porosity was higher than 0.65, but the fluidity of the internal air was poor when it was lower than 0.65. The optimum porosity was 0.65. A simulated validation analysis was conducted using the above optimal parameters. As the proportion of particles below 2 μm increased, the dust removal effect worsened.
Droplet networks stabilized by lipid interfacial bilayers or colloidal particles have been extensively investigated in recent years and are of great interest for compartmentalized reactions and ...biological functions. However, current design strategies are disadvantaged by complex preparations and limited droplet size. Here, by using the assembly and jamming of cucurbit8uril surfactants at the oil-water interface, we show a novel means of preparing droplet networks that are multi-responsive, reconfigurable, and internally connected over macroscopic distances. Openings between the droplets enable the exchange of matter, affording a platform for chemical reactions and material synthesis. Our work requires only a manual compression to construct complex patterns of droplet networks, underscoring the simplicity of this strategy and the range of potential applications.
Semantic segmentation of remote sensing images plays a critical role in areas such as urban change detection, environmental protection, and geohazard identification. Convolutional Neural Networks ...(CNN) have been excessively employed for semantic segmentation over the past few years; however, a limitation of the CNN is that there exists a challenge in extracting the global context of remote sensing images, which is vital for semantic segmentation, due to the locality of the convolution operation. It is informed that the recently developed Transformer is equipped with powerful global modeling capabilities. A network called TCNet is proposed in this study, and a parallel-in-branch architecture of the Transformer and the CNN is adopted in the TCNet. As such, the TCNet takes advantage of both Transformer and CNN, both global context and low-level spatial details could be captured in a much shallower manner. In addition, a novel fusion technique called Interactive Self-attention (ISa) is advanced to fuse the multi-level features extracted from both branches. To bridge the semantic gap between regions, a skip connection module called Windowed Self-attention Gating (WSaG) is further developed and added to the progressive upsampling network. Experiments on three public datasets (i.e., Bijie Landslide Dataset, WHU Building Dataset, and Massachusetts Buildings Dataset) depict that TCNet yields superior performance over state-of-the-art models. The IoU values obtained by TCNet for these three datasets are 75.34% (ranked first among ten models compared), 91.16% (ranked first among thirteen models compared), and 76.21% (ranked first among thirteen models compared), respectively.
Single-modal images carry limited information for features representation, and RGB images fail to detect grass weeds in wheat fields because of their similarity to wheat in shape. We propose a ...framework based on multi-modal information fusion for accurate detection of weeds in wheat fields in a natural environment, overcoming the limitation of single modality in weeds detection. Firstly, we recode the single-channel depth image into a new three-channel image like the structure of RGB image, which is suitable for feature extraction of convolutional neural network (CNN). Secondly, the multi-scale object detection is realized by fusing the feature maps output by different convolutional layers. The three-channel network structure is designed to take into account the independence of RGB and depth information, respectively, and the complementarity of multi-modal information, and the integrated learning is carried out by weight allocation at the decision level to realize the effective fusion of multi-modal information. The experimental results show that compared with the weed detection method based on RGB image, the accuracy of our method is significantly improved. Experiments with integrated learning shows that mean average precision (
mAP
) of 36.1% for grass weeds and 42.9% for broad-leaf weeds, and the overall detection precision, as indicated by intersection over ground truth (
IoG
), is 89.3%, with weights of RGB and depth images at α = 0.4 and β = 0.3. The results suggest that our methods can accurately detect the dominant species of weeds in wheat fields, and that multi-modal fusion can effectively improve object detection performance.
Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in ...indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last.
Hydroformylation of 1,2-disubstituted alkenes usually occurs at the α position of the directing heteroatom such as oxygen atom and nitrogen atom. By contrast, to achieve hydroformylation on the β ...position of the heteroatom is a tough task. Herein, we report the asymmetric rhodium-catalyzed hydroformylation of 1,2-disubstituted alkenylsilanes with excellent regioselectivity at the β position (relative to the silicon heteroatom) and enantioselectivity. In a synthetic sense, we achieve the asymmetric hydroformylation on the β position of the oxygen atom indirectly by using the silicon group as a surrogate for the hydroxyl. Density functional theory (DFT) calculations are carried out to examine energetics of the whole reaction path for Rh/YanPhos-catalyzed asymmetric hydroformylation and understand its regioselectivity and enantioselectivity. Our computational study suggests that the silicon group can activate the substrate and is critical for the regioselectivity.
Microglia are phagocytosis-competent CNS cells comprising a spectrum of subtypes with beneficial and/or detrimental functions in acute and chronic neurodegenerative disorders. The heterogeneity of ...microglia suggests differences in phagocytic activity and phenotype plasticity between microglia subtypes. To study these issues, primary murine glial cultures were cultivated in the presence of serum, different growth factors and cytokines to obtain M0-like, M1-like, and M2-like microglia as confirmed by morphology, M1/M2 gene marker expression, and nitric oxide assay. Single-cell analysis after 3 hours of phagocytosis of
E.coli
particles or IgG-opsonized beads showed equal internalization by M0-like microglia, whereas M1-like microglia preferably internalized
E.coli
particles and M2-like microglia preferably internalized IgG beads, suggesting subtype-specific preferences for different phagocytosis substrates. Time-lapse live-cells imaging over 16 hours revealed further differences between microglia subtypes in phagocytosis preference and internalization dynamics. M0- and, more efficiently, M1-like microglia continuously internalized
E.coli
particles for 16 hours, whereas M2-like microglia discontinued internalization after approximately 8 hours. IgG beads were continuously internalized by M0- and M1-like microglia but strikingly less by M2-like microglia. M2-like microglia initially showed continuous internalization similar to M0-like microglia but again discontinuation of internalization after 8 hours suggesting that the time of substrate exposure differently affect microglia subtypes. After prolonged exposure to
E.coli
particles or IgG beads for 5 days all microglia subtypes showed increased internalization of
E.coli
particles compared to IgG beads, increased nitric oxide release and up-regulation of M1 gene markers, irrespectively of the phagocytosis substrate, suggesting phenotype plasticity. In summary, microglia subtypes show substrate- and time-dependent phagocytosis preferences and phenotype plasticity. The results suggest that prolonged phagocytosis substrate exposure enhances M1-like profiles and M2-M1 repolarization of microglia. Similar processes may also take place in conditions of acute and chronic brain insults when microglia encounter different types of phagocytic substrates.
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This study aims to make full use of the agricultural waste peanut shells to lower material costs and achieve cleaner production at the same time. Cellulose nanofibrils (CNF) extracted ...from peanut shells were mixed with acrylic acid (AA) and dimethyl diallyl ammonium chloride (DMDAAC) to prepare a new type of capsule core (dust suppressant). Then, the self-adaptive AA-DM-CNF/CA microcapsules were prepared under the action of calcium alginate. The infrared spectroscopy and X-ray diffraction analysis results suggest that AA, DMDAAC and CNF have experienced graft copolymerization which leads to the formation of an amorphous structure. The scanning electron microscopy analysis results demonstrate that the internal dust suppressant can expand and break the wall after absorbing water, featuring a self-adaptive function. Meanwhile, the laser particle size analysis results show that the microcapsules, inside which the encapsulated dust suppressant can be observed clearly, maintain a good shape. The product performance experimental results reveal that the capsule core and the capsule wall achieve synergistic dust suppression, thus lengthening the dust suppression time. The product boasts good dust suppression, weather resistance, degradation and synergistic combustion performances. Moreover, this study, as the first report on the development and analysis of dust-suppressing microcapsules, fills in the research gap on the reaction mechanism between dust-suppressing microcapsules and coal by MS simulation. The proposed AA-DM-CNF/CA dust-suppressing microcapsules can effectively lower the dust concentration in the space and protect the physical and mental health of coal workers. In general, this research provides a new insight into the structure control and performance enhancement of dust suppressants. Expanding the application range of microcapsules is of crucial economic and social benefits.
Environmental information plays an important role in deep reinforcement learning (DRL). However, many algorithms do not pay much attention to environmental information. In multi-agent reinforcement ...learning decision-making, because agents need to make decisions combined with the information of other agents in the environment, this makes the environmental information more important. To prove the importance of environmental information, we added environmental information to the algorithm. We evaluated many algorithms on a challenging set of StarCraft II micromanagement tasks. Compared with the original algorithm, the standard deviation (except for the VDN algorithm) was smaller than that of the original algorithm, which shows that our algorithm has better stability. The average score of our algorithm was higher than that of the original algorithm (except for VDN and COMA), which shows that our work significantly outperforms existing multi-agent RL methods.
Coal seam water injection technology plays an important role in the reduction of dust in the working face. To improve the wetting effect of low-permeability and difficult-to-wet coal seams and ...achieve dust control at the source, it is necessary to carry out quantitative research on the relationship between the wettability and physical chemistry properties in coal and to clarify the factors and mechanisms that affect the wettability of coal. In this study, six samples with different ranks were adopted to experimentally acquire the coal parameters, including the proximate compositions, ultimate contents, chemical bonds, pore-fracture structures, and nuclear magnetic resonance (NMR) T
2
-spectra peaks. Moreover, the molecular dynamic behavior of six coals with different metamorphic degrees was simulated to reveal the wettability from a microscopic view. A quantification coefficient was constructed to comprehensively represent the wettability of coal dust and was further verified by experimental data. Results demonstrated that a proposed coefficient was presented as the product among square root of moisture contents, square root of ratios of carbon contents, hydroxyl ratios, half to the third power of pore diameters, and reciprocal of the square of oxygen content ratios. This coefficient appeared a good correlation (
R
2
= 0.74) with the normalized T
2g
(P
3
), which signified that moisture contents, ratios of carbon contents, hydroxyl ratios, and pore diameters posed the positive relationships with T
2g
(P
3
). However, the larger T
2g
(P
3
) corresponded to the lower ratios of oxygen contents. Especially, the linear correlations between T
2g
(P
3
) and moisture contents/pore sizes presented higher accuracies of 0.86 and 0.84, respectively. The contents of ash, volatile matter, hydrogen/nitrogen, carboxyl/carbonyl groups, and surface area play weak roles in wettability, appearing the less than 0.5 fitting coefficients.
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