Deep Space 1 at comet 19P/Borrelly: Magnetic field and plasma observations

On September 22, 2001 the Deep Space 1 spacecraft performed a flyby at comet 19P/Borrelly at a solar distance of 1.36 AU leading the Earth by 74^° in longitude. The spacecraft-comet distance at closest approach was 2171 km. The bow shock had a magnetic compression ratio of 2.5 at a distance of 147 100 km from the nucleus. Deep Space 1 first entered the sheath region essentially from the north polar region. Fluctuations from the cometary ion pickup were present throughout the sheath region and even well upstream of the shock, as expected. The magnetic field pileup region had a peak field strength of 83 nT and was shown to be consistent with a pressure equal to the solar wind ram pressure. The peak field location was offset from the time of closest approach. It is uncertain whether this is a spatial or temporal variation. Draping of magnetic fields around the nucleus was sought, but evidence for this was not apparent in the data. A possible explanation is that the interplanetary solar wind was composed of turbulent short-scale fields, and thus the fields were not symmetric about the point of closest approach. During the flyby phase there were in general few intervals of ACE data where there were large scale Parker spiral fields. With the addition of plasma data, the shock properties are investigated. The characteristics of magnetic draping, pileup and fluctuations are explored. These comet 19P/Borrelly results are contrasted with other cometary flyby results.     

Dependence of the Characteristics of Solar Wind Discontinuities and Shocks with Heliolatitude and Radial Distance

The distribution of the shocks in the heliosphere and their characteristic variations are investigated using Ulysses observations. The jumps in solar wind velocity, IMF magnitude, and proton density across the shocks and discontinuities are evaluated and used to characterize them. The distribution of these discontinuities with respect to heliolatitude ± 80° and with radial distance 1 to 5 AU are analyzed during solar minimum and solar maximum to understand their global behavior. It is noticed that the jumps in solar wind parameters associated with shocks and discontinuities are more prominent during the second orbit of Ulysses, which coincided with the maximum phase of solar activity.     

Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling

In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit estimate of the associated uncertainty. This uncertainty arises from incomplete process representation, uncertainty in initial conditions, input, output and parameter error. The generalized likelihood uncertainty estimation (GLUE) framework was one of the first attempts to represent prediction uncertainty within the context of Monte Carlo (MC) analysis coupled with Bayesian estimation and propagation of uncertainty. Because of its flexibility, ease of implementation and its suitability for parallel implementation on distributed computer systems, the GLUE method has been used in a wide variety of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models that require significant computational time to run and produce the desired output. In this paper we improve the computational efficiency of GLUE by sampling the prior parameter space using an adaptive Markov Chain Monte Carlo scheme (the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm). Moreover, we propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic and real-world streamflow data from two catchments with different hydrologic regimes.     

基于GIS的西平县土地利用变化分析

在遥感和地理信息系统软件支持下,通过2002年的ETM＋和2007年的CBERS遥感图像分析,利用马尔科夫链的元胞自动机模型与构建土地利用适宜性图层,模拟西平县2012年土地利用变化。结果表明：到2012年,西平县城镇及工矿用地将在原有基础上向四周辐射发展,交通运输用地将在原来基础上拓宽,村镇居民点及公路周围的耕地将减少,林地、其他用地也将减少。     

Scientific field training for human planetary exploration

Forthcoming human planetary exploration will require increased scientific return (both in real time and post-mission), longer surface stays, greater geographical coverage, longer and more frequent EVAs, and more operational complexities than during the Apollo missions. As such, there is a need to shift the nature of astronauts’ scientific capabilities to something akin to an experienced terrestrial field scientist. To achieve this aim, the authors present a case that astronaut training should include an Apollo-style curriculum based on traditional field school experiences, as well as full immersion in field science programs. Herein we propose four Learning Design Principles (LDPs) focused on optimizing astronaut learning in field science settings. The LDPs are as follows:

(1)

LDP#1: Provide multiple experiences: varied field science activities will hone astronauts’ abilities to adapt to novel scientific opportunities

(2)

LDP#2: Focus on the learner: fostering intrinsic motivation will orient astronauts towards continuous informal learning and a quest for mastery

(3)

LDP#3: Provide a relevant experience—the field site: field sites that share features with future planetary missions will increase the likelihood that astronauts will successfully transfer learning

(4)

LDP#4: Provide a social learning experience—the field science team and their activities: ensuring the field team includes members of varying levels of experience engaged in opportunities for discourse and joint problem solving will facilitate astronauts’ abilities to think and perform like a field scientist.

The proposed training program focuses on the intellectual and technical aspects of field science, as well as the cognitive manner in which field scientists experience, observe and synthesize their environment. The goal of the latter is to help astronauts develop the thought patterns and mechanics of an effective field scientist, thereby providing a broader base of experience and expertise than could be achieved from field school alone. This will enhance their ability to execute, explore and adapt as in-field situations require.     

热带风暴过程中珊瑚礁坪示踪砂运移的现场实验研究

１９９５年１０月在小东海珊瑚礁海岸的礁坪上进行了示踪砂运动的现场实验。本次实验对浪,流以及示踪砂采样作了同步观测。根据所获的实验数据,分析在９５１６号热带风暴天气过程礁坪上示踪砂运移的基本规律。在一级近似下,测定并计算了泥沙在波生流作用下的漂移速度与流速比因子０．００２,混合深度小于２ｃｍ。这一结果与其它已往结果相比明显小得多。     

塔里木巴什托油气田巴楚组顶部油气层录井特征分析

详细分析了巴什托油气田BK2、BK3H、BK4H以及BK5H等6口井的地质录井资料,显示巴楚组顶部油气层自上而下见到的含油气级别一般为油斑、油迹、气测异常等,上好下差,油气主要集中于顶部油气层之顶上,反映出油气向上运移储集比较好。巴楚组及以上地层纵、横向上的稳定性较好,深度、厚度均变化不大,且以M4井为中心向各方向均呈现增厚—减薄有规律的交替变化。巴什托油气田巴楚组油藏具有较好的开采价值,但还应不断钻探寻找新的含油气构造,发现新的油气田。     

Reliability bearing capacity analysis of footings on cohesive soil slopes using RFEM

Reliability analysis of bearing capacity of a strip footing at the crest of a simple slope with cohesive soil was carried out using the random finite element method (RFEM). Analyses showed that the coefficient of variation and the spatial correlation length of soil cohesion can have a large influence on footing bearing capacity, particularly for slopes with large height to footing width ratios. The paper demonstrates cases where a footing satisfies a deterministic design factor of safety of 3 but the probability of design failure is unacceptably high. Isotropic and anisotropic spatial variability of the soil strength was also considered.     

The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.     

青岛市大气污染时间序列分析预报方法研究

根据青岛市大气污染监测资料 ,采用时间序列分析方法 ,建立多种预报模型 ,有原序列周期外延法、均生函数周期外延法、均生函数逐步回归法以及自回归预报法等 ,最后提出一种综合预报模型。连续预报试验表明 ,综合预报模型优于任何个别预报模型 ,有较好的预报能力。利用马尔可夫概型对污染状态 (轻、中、重 )进行了分级预报试验 ,也获得良好的效果。