Land – Atmosphere – Meteorological Coupling Associated with the 2015 Gorkha (M 7.8) and Dolakha (M 7.3) Nepal Earthquakes

Multiple parameters (brightness temperature, soil moisture, surface latent heat flux, surface air temperature and carbon monoxide) before and after the 2015 Nepal M7.8 Gorkha main earthquake and M7.3 Dolakha aftershock were analysed using satellite observation data. The thermal anomalies from optical and microwave data appear about two months prior to the 2015 Gorkha earthquake. Some of the parameters show anomalous changes at different altitudes about 20 days prior to the main earthquake event and 10 days prior to the strong aftershock. Our results show that pre-earthquake anomalous signals propagate from the in situ to the top of atmosphere, and the anomalies in the atmosphere often observed prior to an impending earthquake. The changes on the land surface and corresponding changes in meteorological and atmospheric parameters show existence of strong coupling during the seismogenic period, although the transfer mechanism of seismic/electromagnetic is still has to be investigated and understood

Blow-up problems for quasilinear reaction diffusion equations with weighted nonlocal source

In this paper, we investigate the following quasilinear reaction diffusion equations $$\begin{cases} \left(b(u)\right)_t =\nabla\cdot\left(\rho\left(|\nabla u|^2\right)\nabla u\right)+c(x)f(u) &\hbox{ in } \Omega\times(0,t^{*}),\\ \frac{\partial u}{\partial \nu}=0 &\hbox{ on } \partial\Omega\times(0,t^{*}), \\ u(x,0)=u_{0}(x)\geq0 & \hbox{ in } \overline{\Omega}. \end{cases}$$ Here $\Omega$ is a bounded domain in $\mathbb{R}^{n}\ (n\geq2)$ with smooth boundary $\partial\Omega$. Weighted nonlocal source satisfies $$c(x)f(u(x,t))\leq a_1+a_2\left(u(x,t)\right)^{p}\left(\int_{\Omega}\left(u(x,t)\right)^{\alpha}{\rm d}x\right)^{m},$$ where $a_2,p,\alpha$ are some positive constants and $a_1, m$ are some nonnegative constants. We make use of a differential inequality technique and Sobolev inequality to obtain a lower bound for the blow-up time of the solution. In addition, an upper bound for the blow-up time is also derived

Blow-up problems for quasilinear reaction diffusion equations with weighted nonlocal source

In this paper, we investigate the following quasilinear reaction diffusion equations $$\begin{cases} \left(b(u)\right)_t =\nabla\cdot\left(\rho\left(|\nabla u|^2\right)\nabla u\right)+c(x)f(u) &\hbox{ in } \Omega\times(0,t^{*}),\\ \frac{\partial u}{\partial \nu}=0 &\hbox{ on } \partial\Omega\times(0,t^{*}), \\ u(x,0)=u_{0}(x)\geq0 & \hbox{ in } \overline{\Omega}. \end{cases}$$ Here $\Omega$ is a bounded domain in $\mathbb{R}^{n}\ (n\geq2)$ with smooth boundary $\partial\Omega$. Weighted nonlocal source satisfies $$c(x)f(u(x,t))\leq a_1+a_2\left(u(x,t)\right)^{p}\left(\int_{\Omega}\left(u(x,t)\right)^{\alpha}{\rm d}x\right)^{m},$$ where $a_2,p,\alpha$ are some positive constants and $a_1, m$ are some nonnegative constants. We make use of a differential inequality technique and Sobolev inequality to obtain a lower bound for the blow-up time of the solution. In addition, an upper bound for the blow-up time is also derived

Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method

The development of knowledge graph (KG) applications has led to a rising need for entity alignment (EA) between heterogeneous KGs that are extracted from various sources. Recently, graph neural networks (GNNs) have been widely adopted in EA tasks due to GNNs' impressive ability to capture structure information. However, we have observed that the oversimplified settings of the existing common EA datasets are distant from real-world scenarios, which obstructs a full understanding of the advancements achieved by recent methods. This phenomenon makes us ponder: Do existing GNN-based EA methods really make great progress? In this paper, to study the performance of EA methods in realistic settings, we focus on the alignment of highly heterogeneous KGs (HHKGs) (e.g., event KGs and general KGs) which are different with regard to the scale and structure, and share fewer overlapping entities. First, we sweep the unreasonable settings, and propose two new HHKG datasets that closely mimic real-world EA scenarios. Then, based on the proposed datasets, we conduct extensive experiments to evaluate previous representative EA methods, and reveal interesting findings about the progress of GNN-based EA methods. We find that the structural information becomes difficult to exploit but still valuable in aligning HHKGs. This phenomenon leads to inferior performance of existing EA methods, especially GNN-based methods. Our findings shed light on the potential problems resulting from an impulsive application of GNN-based methods as a panacea for all EA datasets. Finally, we introduce a simple but effective method: Simple-HHEA, which comprehensively utilizes entity name, structure, and temporal information. Experiment results show Simple-HHEA outperforms previous models on HHKG datasets.Comment: 11 pages, 6 figure

Soil microbiome manipulation triggers direct and possible indirect suppression against <i>Ralstonia solanacearum</i> and <i>Fusarium oxysporum</i>

Soil microbiome manipulation can potentially reduce the use of pesticides by improving the ability of soils to resist or recover from pathogen infestation, thus generating natural suppressiveness. We simulated disturbance through soil fumigation and investigated how the subsequent application of bio-organic and organic amendments reshapes the taxonomic and functional potential of the soil microbiome to suppress the pathogens Ralstonia solanacearum and Fusarium oxysporum in tomato monocultures. The use of organic amendment alone generated smaller shifts in bacterial and fungal community composition and no suppressiveness. Fumigation directly decreased F. oxysporum and induced drastic changes in the soil microbiome. This was further converted from a disease conducive to a suppressive soil microbiome due to the application of organic amendment, which affected the way the bacterial and fungal communities were reassembled. These direct and possibly indirect effects resulted in a highly efficient disease control rate, providing a promising strategy for the control of the diseases caused by multiple pathogens

Study of outgoing longwave radiation anomalies associated with Haiti earthquake

The paper presents an analysis by using the methods of Eddy field calculation mean and wavelet maxima to detect seismic anomalies within the outgoing longwave radiation (OLR) data based on time and space. The distinguishing feature of the method of Eddy field calculation mean is that we can calculate "the total sum of the difference value" of "the measured value" between adjacent points, which could highlight the singularity within data. The identified singularities are further validated by wavelet maxima, which using wavelet transformations as data mining tools by computing the maxima that can be used to identify obvious anomalies within OLR data. The two methods has been applied to carry out a comparative analysis of OLR data associated with the earthquake recently occurred in Haiti on 12 January 2010. Combining with the tectonic explanation of spatial and temporal continuity of the abnormal phenomena, the analyzed results have indicated a number of singularities associated with the possible seismic anomalies of the earthquake and from the comparative experiments and analyses by using the two methods, which follow the same time and space, we conclude that the singularities observed from 19 to 24 December 2009 could be the earthquake precursor of Haiti earthquake