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When designing and operating Earth observation satellites, trade-offs must often be made between different hardware components, operational considerations, etc... For example, considerations include how much of the mass budget should be spent on imaging lenses?, or how long should image exposure times be?. Resulting limitations in image resolution and quality can be partially compensated for using super-resolution (SR) techniques. However, deep SR networks recently applied to satellite imagery are often trained and tested on data that lacks the typical image degrading noise present in real satellite images. In this work, we combine a method for generating realistically degraded satellite images with a deep SR network, in the context of different satellite hardware configurations and geographical types. We use this framework to assess deep SR performance given realistic remote sensing payloads across different terrain types, by evaluating payload- and terrain-dependent SR performance in reconstructing realistically degraded images. The framework allows us to model the effect of alternative satellite hardware configurations on resulting SR image quality, providing insight into optimal satellite operations and payload design in the context of SR-based image quality enhancements. [ABSTRACT FROM AUTHOR]

Objective To establish a risk score model of LncRNA for the prognosis of glioblastoma patients using TCGA database. Methods The gene expression profiles and clinical data of glioblastoma and normal nerve tissues in TCGA database were downloaded to screen differentially-expressed LncRNA. The risk score model of LncRNA was screened and established by univariate and multivariate Cox regression models. Results The expression profiles of glioblastoma genes were obtained from TCGA database, including 169 glioblastoma tissues and 5 normal nerve tissues. The R software edgeR package was used for differentially- expressed gene analysis (logFC≥2 or ≤-2, FDR < 0.05, FDR < 0.05). A total of 7978 differential expressed genes were obtained, of which 1643 were differential expressed lncRNAs. By univariate and multivariate Cox regression analyses, the prognostic risk model was obtained: Risk score=0.59×NDUFB2-AS1-0.41×ZEB1-AS1+0.31×AL139385.1+0.21×AGAP2-AS1. The area under ROC curve(AUC) of the model was 0.864. Risk scores results indicated that the prognosis of patients with high score was worse than that of patients with low score. Conclusion The risk prediction models of NDUFB2-AS1, ZEB1-AS1, AL139385.1 and AGAP2-AS1 mentioned above could effectively predict the prognosis of glioblastoma patients and are expected to be used for clinical treatment guidance.

Under the good situation that the national coal mine safety production situation has basically improved, the coal bump disaster has become the most headache affecting the coal mine safety production. How to prevent the occurrence of coal bump and curb the disastrous coal bump accident has become an urgent problem to be solved in front of coal mine production enterprises and safety supervision departments. Based on the analysis of the current situation of coal bump in China, this paper briefly introduced the key problems in the process of coal bump prevention and control in recent years, such as the cognition problems, the enforcement of laws and regulations, the emphasis on monitoring and the neglect of prevention and control, and the lack of pertinence in the prevention and control of coal bump in China. The specific forms of manifestation were given and the specific countermeasures to solve these problems were put forward, which will provide reference for the prevention and control of coal bump in China in the future.

Background: In countries where air pollution stations are unavailable or scarce, station measurements from other countries and atmospheric remote sensing could jointly provide information to estimate ambient air quality at a sufficiently fine resolution to study the relationship between air pollution exposure and health. Predicting NO2 concentration globally with sufficient spatial and temporal resolution and accuracy for health studies is, however, not a trivial task. Challenges are data deficiency, in terms of NO2 measurements and NO2 predictors, and the development of a statistical model that can typify the regional and continental differences, such as traffic regulations, energy sources, and local weather. Objective: We investigated the feasibility of mapping daytime and nighttime NO2 globally at a high spatial resolution (25 m), by including TROPOMI (TROPOspheric Monitoring Instrument) data and comparing various statistical learning techniques. Method: We separated daytime (7:00 am - 9:59 pm) and nighttime (10:00 pm - 6:59 am) based on the local times. To study if one should build models for each country separately, national models in 4 selected countries (the US, China, Germany, Spain) were developed. We build the models for 2017 and used 3636 stations. Seven statistical learning techniques were applied and the impact of the predictors, model fitting, and predicting accuracy was compared between different techniques, national models, national and global models, and models with and without including the NO2 vertical column density retrieved from TROPOMI. Result and conclusion: The ensemble tree-based methods obtained higher accuracy compared to the linear regression-based methods in national and global models. The global tree-based methods obtained similar accuracy to national models. Different spatial prediction patterns are observed even when the prediction accuracy is very similar. Separating between day and night can be important for more accurate air pollution exposure assessment. The TROPOMI variable is ranked as one of the most important variables in the statistical learning techniques but adding it to global models that contain other precedent remote sensing products does not improve the prediction accuracy.

In view of the innovative construction of combined output plate of real-time non-clearance precision ball transmission,the mechanical model of engagement pair contact point is established based on size error of balls and guide groove. The actual contact angle and normal force are deduced by using the torque balance equation and the contact point stiffness coefficient,and result is exact calculated. The normal force is converted to a distributed force on contact zone. The deformation,pressure and dimensionless stress of contact center zone are analyzed and the law of change that effected by error is analyzed. The results show that the mechanical properties of combined output plate are impacted by the two kinds of errors. The distribution of normal force has an obvious change as the varying of the ball size errors,and it is the main factor for mechanical properties of combined output plate. The research result provides theoretical basis for the design and analysis of combined output plate of precision ball transmission.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s13046-019-1114-2.

The large-span, relaxed antenna network is a large deformation flexible structure due to its low pre-tension level of the wires. Its dynamic analysis under a wind load belongs to dynamic and geometric nonlinear problems, which is very complex to accurately calculate and solve. This paper explores the possibility of the finite particle method (FPM) to the aeolian vibration analysis of a large-span, low stress-tensioned antenna cable net. In the FPM, the antenna network structure is discretized into a group of finite particles, where the motions of all particles follow Newton's second law and can be solved dynamically using a central difference scheme. The effectiveness and applicability of the FPM were verified by comparing the calculation results of the finite element method and FPM. The FPM was used to study the effects of wind speed and the distribution of vibration on the aeolian vibration of antenna cable nets. The results showed that this method is suitable for studying the aeolian vibration of a large-span, low stress-tensioned antenna network and has high computational efficiency and accuracy. [ABSTRACT FROM AUTHOR]