A Shaft Pillar Mining Subsidence Calculation Using Both Probability Integral Method and Numerical Simulation

In order to prolong the life cycle of the coal mine, Jinggezhuang (‘JGZ’) coal mine decided to excavate the shaft pillar. The first panel 0091 was designed near the pillar boundary as an experiment in shaft pillar mining. Both probability integral method (PIM) and FLAC3D were used to evaluate the influence on the shaft safety. PIM parameters were obtained from previous surface subsidence station. The rock property is based on the lab mechanical test. A simulated FLAC3D model containing shafts and a panel was built based on stratigraphic information. Surface subsidence results of PIM show that the 0091-panel excavation has no influence on the shafts. The simulated results show that the subsidence of the main shaft and air shaft is small and can be ignored, but it could cause the auxiliary shaft 220 mm horizontal displacement. So, the stress and displacement of the underground part of shaft were analyzed, it shows that the stress changes, subsidence and displacement are mainly located at the top part of the shafts. According to the stress and movement of the simulated shafts, 0091 was decided to be excavated and a surface monitor line was built and measured. In comparison of PIM, FLAC3D, and measured data, the PIM results fit the surface subsidence better. And the FLAC3D results have smaller maximum subsidence and greater influence area than measured. But FLAC3D can provide more details such as displacement, subsidence, stress and strain of both surface and underground. So, for a planned mining excavation, both methods should be used especially for the evaluation of deformation of underground constructions. In the future, with the development of the rock numerical computation technology, the numerical simulation method will be recommended first. The research shows compare of two methods of the coal mine subsidence calculation and provides a solution method for shaft pillar mining

Intermediate intraseasonal variability in the western tropical Pacific Ocean: meridional distribution of equatorial Rossby waves influenced by a tilted boundary

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 50(4),(2020): 921-933, doi:10.1175/JPO-D-19-0184.1.Intermediate-depth intraseasonal variability (ISV) at a 20–90-day period, as detected in velocity measurements from seven subsurface moorings in the tropical western Pacific, is interpreted in terms of equatorial Rossby waves. The moorings were deployed between 0° and 7.5°N along 142°E from September 2014 to October 2015. The strongest ISV energy at 1200 m occurs at 4.5°N. Peak energy at 4.5°N is also seen in an eddy-resolving global circulation model. An analysis of the model output identifies the source of the ISV as short equatorial Rossby waves with westward phase speed but southeastward and downward group velocity. Additionally, it is shown that a superposition of first three baroclinic modes is required to represent the ISV energy propagation. Further analysis using a 1.5-layer shallow water model suggests that the first meridional mode Rossby wave accounts for the specific meridional distribution of ISV in the western Pacific. The same model suggests that the tilted coastlines of Irian Jaya and Papua New Guinea, which lie to the south of the moorings, shift the location of the northern peak of meridional velocity oscillation from 3°N to near 4.5°N. The tilt of this boundary with respect to a purely zonal alignment therefore needs to be taken into account to explain this meridional shift of the peak. Calculation of the barotropic conversion rate indicates that the intraseasonal kinetic energy below 1000 m can be transferred into the mean flows, suggesting a possible forcing mechanism for intermediate-depth zonal jets.This study is supported by the National Natural Science Foundation of China (Grants 91958204 and 41776022), the China Ocean Mineral Resources Research and Development Association Program (DY135-E2-3-02), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA22000000). L. Pratt was supported by the U.S. National Science Foundation Grant OCE-1657870. F. Wang thanks the support from the Scientific and Technological Innovation Project by Qingdao National Laboratory for Marine Science and Technology (Grant 2016ASKJ12), the National Program on Global Change and Air-Sea Interaction (Grant GASI-IPOVAI-01-01), and the National Natural Science Foundation of China (Grants 41730534, 41421005, and U1406401)

Charging load prediction method for expressway electric vehicles considering dynamic battery state-of-charge and user decision

Accurate prediction of electric vehicle (EV) charging loads is a foundational step in the establishment of expressway charging infrastructures. This study introduces an approach to enhance the precision of expressway EV charging load predictions. The method considers both the battery dynamic state-of-charge (SOC) and user charging decisions. Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow features. A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow. An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values. Based on this foundation, an EV charging decision model was developed which considers expressway node distinctions. EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model. Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions. Finally, the proposed method is applied to a case study of the Lian-Huo expressway. An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways, thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios

Single-cell transcriptomics reveals aberrant skin-resident cell populations and identifies fibroblasts as a determinant in rosacea.

Rosacea is a chronic inflammatory skin disorder, whose underlying cellular and molecular mechanisms remain obscure. Here, we generate a single-cell atlas of facial skin from female rosacea patients and healthy individuals. Among keratinocytes, a subpopulation characterized by IFNγ-mediated barrier function damage is found to be unique to rosacea lesions. Blocking IFNγ signaling alleviates rosacea-like phenotypes and skin barrier damage in mice. The papulopustular rosacea is featured by expansion of pro-inflammatory fibroblasts, Schwann, endothelial and macrophage/dendritic cells. The frequencies of type 1/17 and skin-resident memory T cells are increased, and vascular mural cells are characterized by activation of inflammatory pathways and impaired muscle contraction function in rosacea. Most importantly, fibroblasts are identified as the leading cell type producing pro-inflammatory and vasodilative signals in rosacea. Depletion of fibroblasts or knockdown of PTGDS, a gene specifically upregulated in fibroblasts, blocks rosacea development in mice. Our study provides a comprehensive understanding of the aberrant alterations of skin-resident cell populations and identifies fibroblasts as a key determinant in rosacea development

Accelerating wound healing by biomineralizing crystallization formed from ZIF-8/PLA nanofibers with enhanced revascularization and inflammation reduction

IntroductionPolylactic acid (PLA) is a synthetic polymer material with good biodegradability, biocompatibility, and bioabsorbability, electrospinning is a convenient and efficient method for preparing PLA nanofibers as wound dressing. However, PLA nanofibers as wound dressings lack biological functions, including promoting angiogenesis, extracellular matrix secretion and regulating inflammation, which are crucial for skin regeneration. Herein, we aimed to develop an effectively methods to enhance biological activity of PLA nanofibers through biomimetic mineralized induced by Zeolite imidazolate framework-8 (ZIF-8) for promoting wound healing.MethodsThe ZIF-8/PLA nanofibers were prepared by electrospinning and immersed in simulated body fluids (SBF) to obtain mineralized PLA nanofibers (mZIF-8/PLA). The physicochemical and mechanical properties, Ions releases, and biocompatibility of the mZIF-8/PLA nanofibers were evaluated in vitro. The regeneration capability of the nanofibers was systemically investigated in vivo using the excisional wound-splinting model in Rats.ResultsHydroxyapatite-like crystals was observed on the surface of nanofibers, EDS-mapping confirmed that the crystal deposits in mZIF-8/PLA nanofibers are composed of calcium, phosphorus, and zinc elements. The mineralized crystallization increased the roughness of PLA nanofibers by altering its surface topography, and significantly improved its mechanical property and hydrophilicity. Biomimetic mineralized mZIF-8/PLA nanofibers significantly improve the biological activity for promoting fibroblast proliferations. The Zinc and calcium ions released from hydroxyapatite-like crystals induced by ZIF-8 also promotes angiogenesis, enhances extracellular matrix deposition and reduces inflammatory infiltration in wound healing model.ConclusionsIn summary, this study demonstrates that mineralized ZIF-8/PLA nanofibers could promote wound healing through regulating angiogenesis and reducing inflammatory response

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification

This paper proposes a novel spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification. Current deep learning-based methods have limitations in spectral and spatial feature representation of hyperspectral images, and these shortcomings will severely restrict the hyperspectral image classification performance. The proposed spectral-spatial multi-layer perceptron network exclusively utilizes multi-layer perceptron to represent and classify hyperspectral images. Specifically, the spectral multi-layer perceptron is investigated to model the long-range dependencies along the spectral dimension, because all diagnostic spectral bands contribute to classification performance. Then, we exploit the spatial multi-layer perceptron to extract local spatial features from hyperspectral data, which are also crucial for land cover classification. Furthermore, global spectral characteristics and local spatial features are integrated to perform the hyperspectral image spectral-spatial classification. Three benchmark hyperspectral datasets are employed for comparative classification experiments and ablation study, and experimental results certify the effectiveness and advancement of the proposed model in terms of collaborative classification accuracy