Power law distributions of seismic rates

We report an empirical determination of the probability density functions P_data(r) of the number r of earthquakes in finite space–time windows for the California catalog for different space (5 × 5 to 50 × 50 km²) and time intervals (0.1 to 1000 days). The data can be represented by asymptotic power law tails together with several cross-overs reasonably explained by one of the most used reference model in seismology (ETAS), which assumes that each earthquake can trigger other earthquakes according to complex cascades. These results are useful to constrain the physics of earthquakes and to estimate the performance of forecasting models of seismicity.     

A tool for the ages: The Probabilistic Cosmogenic Age Analysis Tool (P-CAAT)

While revolutionary to the geomorphic community, the application of terrestrial cosmogenic nuclide (TCN) dating is complicated by geological uncertainties, which often lead to skewed or poorly clustered TCN age distributions. Although a range of statistical approaches are typically used to detect and remove outliers, few are optimized for analysis of TCN datasets. Many are mean- or median-based and therefore explicitly assume a single probability distribution (e.g., Mean Squared Weighted Deviates, Chauvenet's Criterion, etc.). Given the ubiquity of pre- and post-depositional modification of rock surfaces, which occur at different rates in different geomorphic settings, these approaches struggle with multimodal distributions which often characterize TCN datasets. In addition, most statistical approaches do not propagate measurement or production rate uncertainties, which become increasingly important as dataset size or clustering increases. Finally, most approaches provide arithmetic single solutions, irrespective of geologic context.To address these limitations, we present the Probabilistic Cosmogenic Age Analysis Tool (P-CAAT), a new approach for outlier detection and landform age analysis. This tool incorporates both sample age and geologic uncertainties and uses Monte Carlo simulations to eliminate dataset skewness by isolating component normal distributions from a cumulative probability density estimate for datasets with three or more samples. This approach allows geologic context to inform post-analysis interpretations, as researchers can assign landform ages based upon statistically distinct subpopulations, informed by the characteristics of geomorphic systems (e.g., exhumation of boulders as moraines degrade through time). To evaluate the effectiveness of P-CAAT, we analyzed a range of synthetic TCN datasets and compared the results to commonly used statistical approaches for outlier detection. Irrespective of dataset size or clustering, P-CAAT outperformed other approaches and returned accurate solutions that improve in precision as sample size increases. To enable more comprehensive utilization of our approach, P-CAAT is packaged with a GUI interface and is available for download at kgs. uky.edu/anorthite/PCAAT.     

贸易距离与“出口企业生产率之谜”——基于贸易方式和所有权的分析

在中国的出口部门中,低生产率企业的自发出口行为与异质企业贸易理论不符。以贸易距离作为企业出口成本的代理变量,将所有权结构和贸易方式引入标准的异质企业贸易模型研究该"悖论"。大量"纯出口企业"是造成该悖论的主要原因,该类企业贸易距离低于一般出口企业,且大量存在于加工贸易部门和外资企业。从事加工贸易的企业通常只负责产品的生产、加工或组装,存在事前的销售渠道,而外资企业同样存在事前的销售经历,即这2类企业的出口贸易距离均由于自身的异质性特征而被缩短,在此条件下,出口成为低生产率企业的自发选择。提高企业自主创新能力、加大研发投入是中国加工贸易企业转型的必然途径。     

GIS analysis of depression among Twitter users

Depression is a common chronic disorder. It often goes undetected due to limited diagnosis methods and brings serious results to public and personal health. Former research detected geographic pattern for depression using questionnaires or self-reported measures of mental health, this may induce same-source bias. Recent studies use social media for depression detection but none of them examines the geographic patterns. In this paper, we apply GIS methods to social media data to provide new perspectives for public health research. We design a procedure to automatically detect depressed users in Twitter and analyze their spatial patterns using GIS technology. This method can improve diagnosis techniques for depression. It is faster at collecting data and more promptly at analyzing and providing results. Also, this method can be expanded to detect other major events in real-time, such as disease outbreaks and earthquakes.     

Recovery from imposex by a population of the dogwhelk, Nucella lapillus (Gastropoda: Caenogastropoda), on the southeastern coast of England since May 2004: A 52-month study

Over a 52-month period beginning in May 2004 and concluding in August 2008, and coinciding with the period over which TBT was banned as a ship anti-foulant globally, a population of the dogwhelk, Nucella lapillus, was studied for changes in population size and structure, and reproduction. During the study period, the size of the population of N. lapillus on the Mewsbrook Groyne at Littlehampton on the southeastern coast of England grew from ∼25 individuals to >500, i.e., a 20-fold increase. Similarly, population structure normalised to reveal a maximum age of up to ∼3 years. The numbers of egg capsules produced by the N. lapillus population also grew over the study, again by a factor of 20, and the length of the breeding season increased from 7 months in 2004-2005 and 2005-2006 to 11 months in 2006-2007 and 2007-2008. Such changes were reflected in the incidence of imposex from Vas Deferens Index Stages 3 and 4 in 2003 to zero commencing in 2008 and continuing into 2009.Due to a lack of confirmatory chemical data, the changes in population size, structure and reproduction herein reported upon for N. lapillus cannot be correlated positively with changes in ambient TBT levels, but they can and are correlated with freedom from imposex. This is the first time such a dramatic recovery from imposex, following the banning of TBT, has been documented.     

Pattern recognition of seismogenic nodes using Kohonen selforganizing map: example in west and south west of Alborz region

Pattern recognition of seismic and morphostructural nodes plays an important role in seismic hazard assessment. This is a known fact in seismology that tectonic nodes are prone areas to large earthquake and have this potential. They are identified by morphostructural analysis. In this study, the Alborz region has considered as studied case and locations of future events are forecast based on Kohonen Self-Organized Neural Network. It has been shown how it can predict the location of earthquake, and identifies seismogenic nodes which are prone to earthquake of M5.5+ at the West of Alborz in Iran by using International Institute Earthquake Engineering and Seismology earthquake catalogs data. First, the main faults and tectonic lineaments have been identified based on MZ (land zoning method) method. After that, by using pattern recognition, we generalized past recorded events to future in order to show the region of probable future earthquakes. In other word, hazardous nodes have determined among all nodes by new catalog generated Self-organizing feature maps (SOFM). Our input data are extracted from catalog, consists longitude and latitude of past event between 1980-2015 with magnitude larger or equal to 4.5. It has concluded node D1 is candidate for big earthquakes in comparison with other nodes and other nodes are in lower levels of this potential.     

中国企业出口动态的空间差异性

对于企业出口动态的研究有助于更好地理解支撑贸易增长、贸易方式结构变化的机制。本文描述近年来中国企业出口动态的空间格局,并从地方出口多样性的角度解释出口动态的空间差异性。研究发现,在2003—2011年,中国企业出口动态在时间上呈现从高速增长到稳定增长的变化特征;空间上表现出地区集聚特征,贸易高进入型地区呈现出从沿海向内陆转移的趋势,且在一般贸易企业中更为明显。中国企业出口动态受到产品出口多样性和市场出口多样性影响,产品出口多样性主要依靠信息发现效应提升地方企业出口市场进入率,并不能阻止企业退出出口市场;市场出口多样性不能促进企业进入出口市场,但可抑制在位企业退出市场,且这种风险分散效应对于一般贸易更强。此外,对于能够在多次或持续出口的企业而言,出口产品相关多样性能够降低地方企业出口退出率,表明出口学习效应的效果可能与企业参与出口的时间有关。     

A novel framework for regional collision risk identification based on AIS data

Identification of regional collision risk in water area is of significance for the safety of navigation. However, traditional risk identification models are subject to the limitations in accuracy, short-term identification and traffic characteristics. Herein, a framework was put forward to identify regional collision risk instantaneously based on AIS data. The vessels were clustered by using the spatial clustering method. Afterwards, the framework was divided into two steps. Firstly, collision risk of each cluster was obtained by collision risk and contribution of the vessels within the cluster. An analytical method was adopted to identify collision risk of each vessel from the perspective of vessel pairs. Contribution of each vessel was determined by using improved Shapley value method in game theory. Secondly, regional collision risk was obtained by collision risk and contribution of each cluster. Case studies were carried out based on the AIS data of Northern Yellow Sea in China to validate the validity of the proposed framework. The results show that the proposed framework can effectively identify collision risk in water area, presenting the potential for collision risk monitoring and collision risk analysis of water area.     

大连市文化企业的空间分布变化与区位影响因素

基于大连市2000年、2008年和2016年文化企业数据,以街道为空间尺度,运用全局自相关、热点区分析、负二项回归模型等方法探究大连市文化企业空间分布变化与区位影响因素。结果发现:① 大连市文化企业空间布局整体呈现明显的“市中心”偏好,2000-2016年文化企业空间扩散现象明显,但核心城区的集聚程度却不断提高,最终呈现出“两心一带”的分布格局。② 大连市的南部地区一直是全市文化企业的热点地区,表现出明显的圈层特性。在研究时段内热点区域面积减少,但是热点集聚中心的位置没有发生变化。③ 生产型文化企业仍高度集聚于传统中心城区,消费型文化企业则向城区边缘和新区扩散,形成多中心集聚的分布格局。④ 在街道空间尺度下,区位要素和集聚要素对文化企业的区位选择具有显著影响,政策要素的影响不显著。与消费型文化企业相比,生产型文化企业对集聚效益、文化资源和商务环境等因素更敏感。     

太湖地区工业绿色化进程研究——以无锡市为例

太湖区域工业化进程的加速,对区域生态环境造成了极大的干扰,积极推进工业绿色化,对保护太湖,推进区域可持续发展具有重要意义．本文以濒临太湖的无锡市为例,通过企业环境统计和问卷调查资料分析,考察该市工业绿色化进程及其与环境压力、企业性质的相关作用,得出以下结论:①无锡市工业绿色化不断发展,工业结构中高污染行业逐步缩减,企业的环境意识不断增强,企业的环境行为有明显改善的趋势;②无锡市目前企业实施绿色化的生产行为主要压力来自政府管制,只有少部分面向国际市场的企业具有较强的市场压力意识;③企业的环境行为与企业的规模、所有制、区位等密切相关,规模大的企业环境行为较好,国有企业和集体企业的环境行为优于外商投资企业和私营企业;④无锡工业绿色化的水平还处于初级阶段．发达地区要推进工业绿色化的进程,仍然要强化政府管制能力,并引导市场和公众发挥更多的作用．