利用测井资料确定页岩储层有机碳含量的方法优选——以焦石坝页岩气田为例

国内外利用测井资料计算页岩储层有机碳含量的方法众多,但各种计算方法都有其相应的适用性及计算精度的优劣。因此,探讨各种计算方法的适用性并最终确定页岩储层有机碳含量的区域性经验计算模型就显得尤为重要。为此,以四川盆地焦石坝地区上奥陶统五峰组—下志留统龙马溪组页岩储层为例,在继承和发展前人研究成果的基础上,结合区域地质特征,利用改进的ΔlgR技术、体积密度法、自然伽马能谱法、多元拟合法、体积模型法等分别建立了有机碳含量的计算模型,并利用岩心分析资料对模型进行对比优选,最终确立了体积密度法为区域性经验计算模型。实践证明:改进的ΔlgR技术和自然伽马能谱法计算精度相对较差;多元拟合法和体积密度法的计算成果均能满足精度要求,但体积密度法方法更为简单且适用范围更广。为进一步验证经验模型的普适性,应用该方法对焦石坝页岩气田重点井的总有机碳含量开展测井精细解释评价,并利用岩心实测资料进行精度分析,其有机碳含量测井解释符合率介于90.5%~91.0%。     

Water‐soluble ionic polythiophenes for biological and analytical applications

Ionic polythiophenes are important conjugated polymers because of their excellent optical properties and water solubility. They are classified as cationic, anionic and zwitterionic conjugated polyelectrolytes. This review article describes concisely their biological and analytical applications. The specific detection of different negatively charged biomolecules such as DNA and adenosine triphosphate, anions like halides and toxic pseudo‐halide (CN ^?) and environmental pollutants, e.g. surfactants, is discussed. The conformational changes of cationic polythiophenes (CPTs ) induced by various analytes due to formation of ionic conjugates and the cooperative responses of all segments cause dominant signal amplification even in the presence of a small perturbation. In addition, reactive oxygen scavenging, antimicrobial photosensitizing and cell imaging applications of CPTs are documented. Use of anionic polythiophenes for sensing of protamine and cations like Cu²⁺ and Ca²⁺ is also discussed. Finally, sensing of DNA , peptides and surfactants by zwitterionic polythiophenes is included. The concluding part discusses future prospects. © 2016 Society of Chemical Industry     

Hierarchical Spatially Varying Coefficient Process Model

The spatially varying coefficient process model is a nonstationary approach to explaining spatial heterogen-eity by allowing coefficients to vary across space. In this article, we develop a methodology for generalizing this model to accommodate geographically hierarchical data. This article considers two-level hierarchical structures and allow for the coefficients of both low-level and high-level units to vary over space. We assume that the spatially varying low-level coefficients follow the multivariate Gaussian process, and the spatially varying high-level coefficients follow the multivariate simultaneous autoregressive model that we develop by extending the standard simultaneous autoregressive model to incorporate multivariate data. We apply the proposed model to transaction data of houses sold in 2014 in a part of the city of Los Angeles. The results show that the proposed model predicts housing prices and fits the data effectively.     

Data-driven approaches for emissions-minimized paths in urban areas

Concerns about air quality and global warming have led to numerous initiatives to reduce emissions. In general, emissions are proportional to the amount of fuel consumed, and the amount of fuel consumed is a function of speed, distance, acceleration, and weight of the vehicle. In urban areas, vehicles must often travel at the speed of traffic, and congestion can impact this speed particularly at certain times of day. Further, for any given time of day, the observations of speeds on an arc can exhibit significant variability. Because of the nonlinearity of emissions curves, optimizing emissions in an urban area requires explicit consideration of the variability in the speed of traffic on arcs in the network. We introduce a shortest path algorithm that incorporates sampling to both account for variability in travel speeds and to estimate arrival time distributions at nodes on a path. We also suggest a method for transforming speed data into time-dependent emissions values thus converting the problem into a time-dependent, but deterministic shortest path problem. Our results demonstrate the effectiveness of the proposed approaches in reducing emissions relative to the use of minimum distance and time-dependent paths. In this paper, we also identify some of the challenges associated with using large data sets.     

Urban navigation beyond shortest route: The case of safe paths

Advancements in mobile technology and computing have fostered the collection of a large number of civic datasets that capture the pulse of urban life. Furthermore, the open government and data initiative has led many local authorities to make these datasets publicly available, hoping to drive innovation that will further improve the quality of life for the city-dwellers. In this paper, we develop a novel application that utilizes crime data to provide safe urban navigation. Specifically, using crime data from Chicago and Philadelphia we develop a risk model for their street urban network, which allows us to estimate the relative probability of a crime on any road segment. Given such model we define two variants of the SafePaths problem where the goal is to find a short and low-risk path between a source and a destination location. Since both the length and the risk of the path are equally important but cannot be combined into a single objective, we approach the urban-navigation problem as a biobjective shortest path problem. Our algorithms aim to output a small set of paths that provide tradeoffs between distance and safety. Our experiments demonstrate the efficacy of our algorithms and their practical applicability.     

1∶2000成图比例尺数字航空摄影测量质量控制与保障

通过《太原市1∶2 000成图比例尺数字航空摄影测量》项目实践,以该项目中1∶2 000数字线划图制作为例,结合数据分层详细介绍了数字摄影测量中的质量控制与保障的措施,以供类似测量工作借鉴。     

Privacy-preserving computation of participatory noise maps in the cloud

This paper presents a privacy-preserving system for participatory sensing, which relies on cryptographic techniques and distributed computations in the cloud. Each individual user is represented by a personal software agent, deployed in the cloud, where it collaborates on distributed computations without loss of privacy, including with respect to the cloud service providers. We present a generic system architecture involving a cryptographic protocol based on a homomorphic encryption scheme for aggregating sensing data into maps, and demonstrate security in the Honest-But-Curious model both for the users and the cloud service providers. We validate our system in the context of NoiseTube, a participatory sensing framework for noise pollution, presenting experiments with real and artificially generated data sets, and a demo on a heterogeneous set of commercial cloud providers. To the best of our knowledge our system is the first operational privacy-preserving system for participatory sensing. While our validation pertains to the noise domain, the approach used is applicable in any crowd-sourcing application relying on location-based contributions of citizens where maps are produced by aggregating data – also beyond the domain of environmental monitoring.     

Semiparametric analysis of clustered interval-censored survival data with a cure fraction

A generalization of the semiparametric Cox’s proportional hazards model by means of a random effect or frailty approach to accommodate clustered survival data with a cure fraction is considered. The frailty serves as a quantification of the health condition of the subjects under study and may depend on some observed covariates like age. One single individual-specific frailty that acts on the hazard function is adopted to determine the cure status of an individual and the heterogeneity on the time to event if the individual is not cured. Under this formulation, an individual who has a high propensity to be cured would tend to have a longer time to event if he is not cured. Within a cluster, both the cure statuses and the times to event of the individuals would be correlated. In contrast to some models proposed in the literature, the model accommodates the correlations among the observations in a more natural way. A multiple imputation estimation method is proposed for both right-censored and interval-censored data. Simulation studies show that the performance of the proposed estimation method is highly satisfactory. The proposed model and method are applied to the National Aeronautics and Space Administration’s hypobaric decompression sickness data to investigate the factors associated with the occurrence and the time to onset of grade IV venous gas emboli under hypobaric environments.     

论述大板基础典型施工方案

介绍了大板基础施工的特点、应用范围及工艺原理,并从前期工作、基坑开挖、底板基础施工等方面入手,详细分析了大板基础施工过程中应注意的操作要点,指出大板基础施工工艺先进、实用性强,具有良好的经济效益和社会效益。