The aggregation-diffusion equation with the intermediate exponent

We consider a Keller-Segel model with non-linear porous medium type diffusion and nonlocal attractive power law interaction, focusing on potentials that are less singular than Newtonian interaction. Here, the nonlinear diffusion is chosen to be $\frac{2d}{d+2s}<m<2-\frac{2s}{d}$ in which case the steady states are compactly supported. We analyse under which conditions on the initial data the regime that attractive forces are stronger than diffusion occurs and classify the global existence and finite time blow-up of solutions. It is shown that there is a threshold value which is characterized by the optimal constant of a variant of Hardy-Littlewood-Sobolev inequality such that the solution will exist globally if the initial data is below the threshold, while the solution blows up in finite time when the initial data is above the threshold

Preparation of Attapulgite Loaded Nano Zero Valent Iron Material and Its Adsorption of Silver

In the extensive industrial production process, a large amount of silver containing wastewater has been produced, and the veil of its potential harm has gradually been unveiled. The emission standards of various pollutants formulated and issued by the Ministry of Ecology and Environment of China have already strictly limited the emission limits of silver. In this paper, a simple, environment-friendly and inexpensive method was used to synthesize a composite material with reducing and adsorbing effects on silver by using purified attapulgite and ferrous salt (FeSO4·7H2O) as raw materials, potassium borohydride (KBH4) as reducing agent, and chemical liquid phase reduction method. The experimental results showed that under the conditions of 1:1 ratio of iron to soil, 0.25 mol·L-1 concentration of KBH4, 25 C temperature and 120 min time, the synthesized attapulgite loaded nano-zero-valent iron composite (nZVI/ATP) had good adsorption performance for Ag(I)

Functional miR-142a-3p induces apoptosis and macrophage polarization by targeting tnfaip2 and glut3 in grass carp (Ctenopharyngodon idella)

In the process of microbial invasion, the inflammation reaction is induced to eliminate the pathogen. However, un-controlled or un-resolved inflammation can lead to tissue damage and death of the host. MicroRNAs (miRNAs) are the signaling regulators that prevent the uncontrolled progress of an inflammatory response. Our previous work strongly indicated that miR-142a-3p is related to the immune regulation in grass carp. In the present study, we found that the expression of miR-142a-3p was down-regulated after infection by Aeromonas hydrophila. tnfaip2 and glut3 were confirmed as be the target genes of miR-142a-3p, which were confirmed by expression correlation analysis, gene overexpression, and dual luciferase reporter assay. The miR-142a-3p can reduce cell viability and stimulate cell apoptosis by targeting tnfaip2 and glut3. In addition, miR-142a-3p also regulates macrophage polarization induced by A. hydrophila. Our results suggest that miR-142a-3p has multiple functions in host antibacterial immune response. Our research provides further understanding of the molecular mechanisms between miRNAs and their target genes, and provides a new insights for the development of pro-resolution strategies for the treatment of complex inflammatory diseases in fish.31802285, CARROS-45-03info:eu-repo/semantics/publishedVersio

Analysis of the Electrical and Thermal Properties for Magnetic Fe3O4-Coated SiC-Filled Epoxy Composites.

Orderly arranged Silicon carbide (SiC)/epoxy (EP) composites were fabricated. SiC was made magnetically responsive by decorating the surface with iron oxide (Fe3O4) nanoparticles. Three treatment methods, including without magnetization, pre-magnetization and curing magnetization, were used to prepare SiC/EP composites with different filler distributions. Compared with unmodified SiC, magnetic SiC with core-shell structure was conducive to improve the breakdown strength of SiC/EP composites and the maximum enhancement rate was 20.86%. Among the three treatment methods, SiC/EP composites prepared in the curing-magnetization case had better comprehensive properties. Under the action of magnetic field, magnetic SiC were orderly oriented along the direction of an external field, thereby forming SiC chains. The magnetic alignment of SiC restricted the movement of EP macromolecules or polar groups to some extent, resulting in the decrease in the dielectric constant and dielectric loss. The SiC chains are equivalent to heat flow channels, which can improve the heat transfer efficiency, and the maximum improvement rate was 23.6%. The results prove that the orderly arrangement of SiC had a favorable effect on dielectric properties and thermal conductivity of SiC/EP composites. For future applications, the orderly arranged SiC/EP composites have potential for fabricating insulation materials in the power electronic device packaging field

Transcriptome and digital gene expression analysis reveal immune responses of mantle and visceral mass pearl culturing in Hyriopsis cumingii

Biomineralization is a widespread phenomenon in marine mollusks and is responsible for the production of shells and pearls. However, the regulatory mechanisms governing the adaptive immune responses in the mollusk mantle and visceral mass during mineralization remain unclear. In this work, we examined the mantle and visceral mass immune responses of Hyriopsis cumingii during pearl culture using high-throughput sequencing techniques. A mantle transcriptome database was established using transcriptome sequencing technology and reference to the major databases. Digital gene expression profiling was used to identify the differentially expressed genes of mantle and visceral mass at different insertion periods. Moreover, quantitative real-time PCR was used to verify the expression of five immune-related genes. Transcriptome sequencing results showed 257,457 unigenes were identified. Digital gene expression profiles showed 1389, 3572, 1888, and 2613 differentially expressed genes (DEGs) in the mantle and visceral mass at 5, 20, 50, and 90 d after insertion, respectively, with the highest number at 20 d and the lowest at 5 d after insertion (q &lt; 0.05). A cluster analysis of the DEGs showed similar clustering and expression features in the mantle to the control group, and at 5, 50 and 90 d, after mantle insertion. The DEGs in the visceral mass showed similar clustering and expression features to the control group and at 5, 20 and 50 d after insertion. We also screened 22 immune-related DEGs in the mantle and visceral mass during the same pearl culture period, including serine/threonine-protein kinase NLK, C-type lectin, and galectin. The greatest number of DEGs was found 90 d after insertion. Compared with the mantle, more immune-related DEGs were down-regulated than up-regulated in the visceral mass during pearl culture, indicating that the immune regulatory mechanisms in the visceral mass and the mantle differ during pearl culture, and that the visceral mass is liable to higher infection and mortality rates. Quantitative real-time PCR results showed that the expression of five immune-related genes was consistent with DGE results. Our findings will further knowledge of the immune systems that are present in the mantle and visceral mass during pearl culture

Self-Interpretable Graph Learning with Sufficient and Necessary Explanations

Self-interpretable graph learning methods provide insights to unveil the black-box nature of GNNs by providing predictions with built-in explanations. However, current works suffer from performance degradation compared to GNNs trained without built-in explanations. We argue the main reason is that they fail to generate explanations satisfying both sufficiency and necessity, and the biased explanations further hurt GNNs' performance. In this work, we propose a novel framework for generating SUfficient aNd NecessarY explanations (SUNNY-GNN for short) that benefit GNNs' predictions. The key idea is to conduct augmentations by structurally perturbing given explanations and employ a contrastive loss to guide the learning of explanations toward sufficiency and necessity directions. SUNNY-GNN introduces two coefficients to generate hard and reliable contrastive samples. We further extend SUNNY-GNN to heterogeneous graphs. Empirical results on various GNNs and real-world graphs show that SUNNY-GNN yields accurate predictions and faithful explanations, outperforming the state-of-the-art methods by improving 3.5% prediction accuracy and 13.1% explainability fidelity on average. Our code and data are available at https://github.com/SJTU-Quant/SUNNY-GNN

Load balancing scheme research on load lead transfer of heterogeneous wireless network

This article analyzes the lag characteristic of call arrival rate(CAR) of general vertical handoff which is the representative of simple additive weighting(SAW) in multi-attribute decision theory.It aims at the load imbalance phenomenon caused by vertical handoff for heterogeneous wireless network.Firstly,we should build the universal mobile telecommunication system/wireless local areal network(UMTS/WLAN) heterogeneous wireless network.We can use the time series to make the model of seasonal autoregressive intergrated moving average(SARIMA) so that the Call Arrival Rate can be predicted.Then,according to the Call Arrival Rate,we can lead business handoff and transfer business bandwidth in advance.Therefore,the modified time series predict SAW(TSAW) is formed.The result shows that TSAW overcomes the load delay disadvantages of simple additive weighting(SAW) and makes the network Load balancing better

Random pore structure and REV scale flow analysis of engine particulate filter based on LBM

In this article, lattice Boltzmann method (LBM) is used to simulate the multi-scale flow characteristics of the engine particulate filter at the pore scale and the representative elementary volume (REV) scale, respectively. Four kinds of random wall-pore structures are considered, which are circular random structure, square random structure, isotropic quartet structure generation set (QSGS), and anisotropic QSGS, with difference analysis done. In terms of the REV scale, the influence of different inlet flow velocities and wall permeabilities on the flow in single channel is analyzed. The result indicates that the internal seepage laws of random structures constructed in this article and single channel are in accordance with Darcy’s law. Circular random structure has better permeability than square random structure. Isotropic QSGS has better fluidity than anisotropic one. The flow in single channel is similar to Poiseuille flow. The flow lines in the channel are complicated and a large number of vortices appear at the ends of channel with high inlet flow rate. With the increase of inlet velocity, the static pressure in channel gradually increases along the axial direction as well as the seepage velocity. The temperature field in the channel becomes more uniform as the flow velocity increases, and the higher temperature distribution appears on the wall of the porous media

An Empirical Study for Adopting Machine Learning Approaches for Gas Pipeline Flow Prediction

As industrial control technology continues to develop, modern industrial control is undergoing a transformation from manual control to automatic control. In this paper, we show how to evaluate and build machine learning models to predict the flow rate of the gas pipeline accurately. Compared with traditional practice by experts or rules, machine learning models rely little on the expertise of special fields and extensive physical mechanism analysis. Specifically, we devised a method that can automate the process of choosing suitable machine learning algorithms and their hyperparameters by automatically testing different machine learning algorithms on given data. Our proposed methods are used in choosing the appropriate learning algorithm and hyperparameters to build the model of the flow rate of the gas pipeline. Based on this, the model can be further used for control of the gas pipeline system. The experiments conducted on real industrial data show the feasibility of building accurate models with machine learning algorithms. The merits of our approach include (1) little dependence on the expertise of special fields and domain knowledge-based analysis; (2) easy to implement than physical models; (3) more robust to environment changes; (4) requiring much fewer computation resources when it is compared with physical models that call for complex equation solving. Moreover, our experiments also show that some simple yet powerful learning algorithms may outperform industrial control problems than those complex algorithms