Quantifying the chemical composition of weathering products of Hainan basalts with reflectance spectroscopy and its implications for Mars

With the development of the hyperspectral remote sensing technique,extensive chemical weathering profiles have been identified on Mars.These weathering sequences,formed through precipitation-driven leaching processes,can reflect the paleoenvironments and paleoclimates during pedogenic processes.The specific composition and stratigraphic profiles mirror the mineralogical and chemical trends observed in weathered basalts on Hainan Island in south China.In this study,we investigated the laboratory reflectance spectra of a 53-m-long drilling core of a thick basaltic weathering profile collected from Hainan Island.We established a quantitative spectral model by combining the genetic algorithm and partial least squares regression(GA-PLSR)to predict the chemical properties(SiO2,Al2O3,Fe2O3)and index of laterization(IOL).The entire sample set was divided into a calibration set of 25 samples and a validation set of 12 samples.Specifically,the GA was used to select the spectral subsets for each composition,which were then input into the PLSR model to derive the chemical concentration.The coefficient of determination(R2)values on the validation set for SiO2,Al2O3,Fe2O3,and the IOL were greater than 0.9.In addition,the effects of various spectral preprocessing techniques on the model accuracy were evaluated.We found that the spectral derivative treatment boosted the prediction accuracy of the GA-PLSR model.The improvement achieved with the second derivative was more pronounced than when using the first derivative.The quantitative model developed in this work has the potential to estimate the contents of similar weathering basalt products,and thus infer the degree of alteration and provide insights into paleoclimatic conditions.Moreover,the informative bands selected by the GA can serve as a guideline for designing spectral channels for the next generation of spectrometers.

PTHR1调控VEGF干预骨肉瘤组织血管形成及增殖的机制研究

目的:通过分析临床样本构建细胞转染调控模型,分析甲状旁腺激素1型受体(parathyroid hormone type 1 receptor,PTHR1)调控骨肉瘤血管生成和增殖的分子机制,为骨肉瘤的治疗提供新的临床思路。方法:分析2017年1月至2019年12月就诊于大连理工大学附属肿瘤医院(辽宁省肿瘤医院)与深圳市第二人民医院(深圳大学附属第一医院),经组织病理学确认的四肢经典型骨肉瘤患者临床样本共50例,构建PTHR1沉默的骨肉瘤细胞模型,分析PTHR1在肿瘤组织样本中的差异表达,探索PTHR1表达水平与肿瘤增殖及患者生存率之间的相关性。通过体外实验下调PTHR1表达,分析其对血管生成的调控作用。结果:在骨肉瘤患者的临床样本中,PTHR1在肿瘤组织中的表达量显著高于瘤旁组织,且随着PTHR1表达量显著增高,患者肿瘤体积的增大,远期生存率下降。术后36个月生存率,PTHR1高表达组患者显著低于低表达组。在体外实验中,通过沉默PTHR1表达可以有效降低血管生成素血管内皮生长因子(vascular endothelial growth factor,VEGF)表达,抑制肿瘤细胞增殖,在鸡胚胎卵黄囊模型中,抑制骨肉瘤细胞的血管生成能力。结论:PTHR1在肿瘤组织中显著高表达,且与肿瘤增殖及远期生存具有显著相关性,抑制PTHR1可降低骨肉瘤组织血管生成作用。

Deep Learning Based Data Fusion for Sensor Fault Diagnosis and Tolerance in Autonomous Vehicles

Environmental perception is one of the key technologies to realize autonomous vehicles.Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system.Those sensors are very sensitive to light or background conditions,which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running.In this paper,a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed.By introducing prior features to realize the lightweight network,the features of the input data can be extracted in real time.A new sensor reliability evaluation method is proposed by calculating the global and local confidence of sensors.Through the temporal and spatial correlation between sensor data,the sensor redundancy is utilized to diagnose the local and global confidence level of sensor data in real time,eliminate the fault data,and ensure the accuracy and reliability of data fusion.Experiments show that the network achieves state-of-the-art results in speed and accuracy,and can accurately detect the location of the target when some sensors are out of focus or out of order.The fusion framework proposed in this paper is proved to be effective for intelligent vehicles in terms of real-time performance and reliability.

Superior performance porous carbon nitride nanosheets for helium separation from natural gas:Insights from MD and DFT simulations

Increasing helium(He)demand in fundamental research,medical,and industrial processes necessitates efficient He purification from natural gas.However,most theoretically available membranes focus on the separation of two or three kinds of gas molecules with He and the underlying separation mechanism is not yet well understood.Using molecular dynamic(MD)and first-principle density function theory(DFT)simulations,we systematically demonstrated a novel porous carbon nitride membrane(g-C_(9)N_(7))with superior performance for He separation from natural gas.The structure of g-C_(9)N_(7) monolayer was optimized first,and the calculated cohesive energy confirmed its structural stability.Increasing temperature from 200 to 500 K,the g-C_(9)N_(7)membrane revealed high He permeability,as high as 1.48×10^(7) GPU(gas permeation unit,1 GPU=3.35×10^(-10) mol·s^(-1)·Pa^(-1)·m^(-2))at 298 K,and also exhibited high selectivity for He over other gases(Ar,N_(2),CO_(2),CH_(4),and H_(2)S).Then,the selectivity of He over Ne was found to decrease with increasing the total number of He and Ne molecules,and to increase with increasing He to Ne ratio.More interestingly,a tunable He separation performance can be achieved by introducing strain during membrane separation.Under the condition of 7.5%compressive strain,the g-C_(9)(N_(7)(membrane reached the highest He over Ne selectivity of 9.41×10^(2).It can be attributed to the low energy barrier for He,but increased energy barrier for other gases passing through the membrane,which was subject to a compressive strain.These results offer important insights into He purification using g-C_(9)N_(7)membrane and opened a promising avenue for the screening of industrial grade gas separation with strain engineering.

Adaptive step-size forward advection method for aerosol process simulation

Outdoor aerosol processes are often associated with disasters and diseases,which threaten human life and health.Outdoor aerosols are afluid system affected by meteorological conditions and three-dimensional complex terrain.Their variable wind speed and direction and complex terrain boundary conditions make simulating advection processes difficult.Based on incompressibleflow conditions,we designed an adaptive time step algorithm for forward advection for the rapid simulation of aerosol processes.The method is based on thefirst-order forward semi-Lagrangian advection method with unconditional mass conservation.Thefirst-order truncated error coefficient function theory generates an adaptive time step to control the accuracy of forward advection.Smoke aerosol simulation experiments in two small outdoor scenes were designed,and the effects of the traditional backward advection and forwardfixed step methods were compared with the algorithm in this study.The proposed simulation method showed improved accuracy compared with the other two methods in experimental scenarios;moreover,compared with those of the traditional backward method,the computation time was significantly reduced and the conservation of mass was significantly improved.Thus,the proposed method is a fast simulation method for outdoor aerosol numerical prediction.KEY POLICY HIGHLIGHTS.The first-order forward semi-Lagrangian method,which requires no iteration and less computation and offers unconditional conservation,was used..The law of truncation error coefficient of thefirst-order forward method was studied and an adaptive step algorithm was designed..Full-size real aerosol experiments in small-scale complex outdoor scenes were conducted for verification and comparison of simulation effects.