New tools for self-organized pattern formation

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Study on temporal and spatial evolution characteristics of water accumulation in coal mining subsidence area with high groundwater level: taking Anhui Province Mining Area as an example

In recent years, with the large-scale and high-intensity mining of coal resources, the problem of water accumulation in mining areas with high groundwater levels has become particularly prominent, which has had a serious impact on the surrounding ecological environment. In order to provide scientific basis for the restoration of the ecological environment, the study on the temporal and spatial evolution characteristics and influencing factors of the coal mining subsidence area with high groundwater level were carried out. Taking the whole mining area of Anhui Province as the research subject, based on Landsat TM/OLI remote sensing data, the NDWI and visual interpretation method were used to conduct surveys on the water accumulation area in the subsidence area from 1995 to 2020 (22 periods ) and 12 months in 2020 (12 periods) and the spatial information of waterlogging in the coal mining subsidence area in Anhui Province in recent 25 years was obtained. Combined with hydrological and rainfall data, the factors affecting the spatio-temporal evolution of waterlogging in the subsidence area were analyzed and discussed. The results show that: ① In the past 25 years, the area of accumulated water in the coal mining subsidence area in Anhui Province has been growing in three stages: slow, fast and stable. During the study period, the average stagnant area increased by about 6 times, from 18.95 km2 to 118.09 km2, with an average annual increase of 3.97 km2. ② From the time scale, the evolution of accumulation area in the subsidence area can be divided into three stages: the first stage (1995—2005), due to the fact that most of the accumulation water has not yet stabilized initially, the growth rate is relatively slow, with an average annual growth rate of 4.65%; In the second stage (2005—2013), based on the rapid growth of coal mining, the area of accumulation water has also entered a period of rapid growth, with an average annual growth rate of 6.64%; In the third stage (2013—2020), the growth rate has begun to decrease, and the accumulation water has gradually stabilized, with an average annual growth rate of 3.42%. From the spatial scale, the accumulation water is mainly concentrated in Huainan and Huaibei cities, accounting for about 70% of the total accumulated water area. ③The long-term factor for the change of the water accumulation is coal mining volume, while the main influencing factor in short time scale is atmospheric rainfall. ④The logistic regression curve was used to establish a prediction model for the water accumulation area of coal mining subsidence in Anhui Province. It is predicted that the coal mining subsidence water area in Anhui Province will still be in a low-speed growth stage in the future. By 2030, the accumulation area in the dry season will reach about 130 km2. The high-precision water accumulation information in the subsidence area was obtained, and its temporal and spatial evolution laws and influencing factors were analyzed, which can provide a scientific basis for the treatment of water accumulation in the coal mining subsidence area with high groundwater level and the ecological restoration of the subsidence area

Research on the Stability Evaluation Model of Composite Support Pillar in Backfill-Strip Mining

Backfill-strip mining, which combines the advantages of strip mining and backfill mining, is proposed to overcome high cost and shortage of filling materials in coal mines at present. The composite support pillar (CSP) is a combined support pillar of the filling body and coal pillar for supporting the overlying strata and achieving subsidence control. The stability of CSP is the key to the success of subsidence control in backfill-strip mining engineering. A stability evaluation model of the CSP mechanical model was proposed. First, the lateral stress between the coal pillar and filling body is calculated in consideration of their interaction relation in CSP based on the earth pressure theory. Then, the width calculation models of the broken and plastic zones of three types of CSPs are established on the basis of limit equilibrium theory. On this basis, the mathematical model of the safety design width of the three types of CSPs is proposed to ensure the stability of CSP. Meanwhile, an engineering case of stability width design of CSP is shown. This study can provide theoretical reference and technical support for the engineering design of backfill-strip mining

Multiple Climate Change Scenarios and Runoff Response in Biliu River

The impacts of temperature and precipitation changes on regional evaporation and runoff characteristics have been investigated for the Biliu River basin, which is located in Liaoning Province, northeast China. Multiple climate change scenarios from phase 3 and phase 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) (21 scenarios in total) were utilized. A calibrated hydrologic model—SWAT model—was used to simulate future discharges based on downscaled climate data through a validated morphing method. Results show that both annual temperature and precipitation increase under most of the CMIP3 and CMIP5 scenarios, and increase more in the far future (2041–2065) than in the near future (2016–2040). These changes in precipitation and temperature lead to an increase in evaporation under 19 scenarios and a decrease in runoff under two-thirds of the selected scenarios. Compared to CMIP3, CMIP5 scenarios show higher temperature and wider ranges of changes in precipitation and runoff. The results provide important information on the impacts of global climate change on water resources availability in the Biliu River basin, which is beneficial for the planning and management of water resources in this region

Silicon nanowire AFM tips grown on released scanning probe cantilevers from stencil-deposited catalysts

We present a parallel, full wafer technique for deposition of catalyst on released scanning probe bodies for the growth of individual high aspect-ratio Si nanowire tips. 1-D probes are necessary for imaging high aspect-ratio nano-patterns, such as deep and narrow trench geometries, or nanometer lateral resolution in single cell probing [1]. Together with carbon nanotubes, Si nanowires are excellent candidates for providing such tips. The main challenge remains the efficient integration at full-wafer scale of these 1D nano-objects with the scanning probe bodies, while still maintaining their good mechanical properties as scanning tips [2]. We are integrating here NW growth with a shadow mask technique, a unique approach providing the capability of parallel nanopatterning on top of 3D substrates [3-4]. In this work stencil lithography is used to deposit nano-catalysts for Si nanowire growth at controlled positions on released cantilever bodies. We started by fabricating two wafers: the substrate with tip-less Si scanning probes connected only by a thin Si bridge to the wafer body, and the stencil, with 100 nm thin low-stress SiN membranes containing apertures, as shown in Fig. 1. A customized SUSS MA/BA6 stencil aligner was used to align the two and mechanically clamp them. In the aligned position, each aperture from one membrane corresponds to a position close to the tip of the cantilevers. The clamped set was introduced in an evaporator and 20 nm Au was deposited on the tip of the cantilevers through the 300 nm diameter circular stencil apertures, as illustrated in Fig. 2. Epitaxial Si nanowires were then grown from the Au catalysts by the vapor-liquid-solid method at 530 ºC with a hydrogen diluted SiH4 precursor with a partial pressure of 0.2 mbar for 5 minutes. Fig. 3 shows an example of Si nanowire grown at the tip of the cantilever. The grown nanowires were well suited for tapping mode atomic force microscopy measurements. We scanned Si nanotrenches fabricated by e-beam lithography, 100 nm wide, 275 nm apart, and 600 nm deep. The comparison between scans of this sample done with a super-sharp tip and with a Si nanowire tip is shown in Fig. 4. The high aspect-ratio of the nanowire tips provided a clear advantage for this geometry: the side walls of the trenches are much sharper when scanned with the nanowire tip, while a deconvolution technique has to be used if attempting to obtain the same information from the super-sharp tip image. We thus proved for the first time the use of stencil lithography for the deposition of catalysts at controlled positions on released micro-structures at full-wafer scale. Si nanowires were grown from the deposited catalysts on cantilever tips. These nanowires were used as scanning probes for 1:6 aspect-ratio Si nanotrenches and showed to perform much better than commercially-available super-sharp tips

Disclosing incoherent sparse and low-rank patterns inside homologous GPCR tasks for better modelling of ligand bioactivities

There are many new and potential drug targets in G protein-coupled receptors (GPCRs) without sufficient ligand associations, and accurately predicting and interpreting ligand bioactivities is vital for screening and optimizing hit compounds targeting these GPCRs. To efficiently address the lack of labeled training samples, we proposed a multi-task regression learning with incoherent sparse and low-rank patterns (MTR-ISLR) to model ligand bioactivities and identify their key substructures associated with these GPCRs targets. That is, MTR-ISLR intends to enhance the performance and interpretability of models under a small size of available training data by introducing homologous GPCR tasks. Meanwhile, the low-rank constraint term encourages to catch the underlying relationship among homologous GPCR tasks for greater model generalization, and the entry-wise sparse regularization term ensures to recognize essential discriminative substructures from each task for explanative modeling. We examined MTR-ISLR on a set of 31 important human GPCRs datasets from 9 subfamilies, each with less than 400 ligand associations. The results show that MTR-ISLR reaches better performance when compared with traditional single-task learning, deep multi-task learning and multi-task learning with joint feature learning-based models on most cases, where MTR-ISLR obtains an average improvement of 7% in correlation coefficient (r2) and 12% in root mean square error (RMSE) against the runner-up predictors. The MTR-ISLR web server appends freely all source codes and data for academic usages.1