Distribution of suspended sediment in the coastal sea off the Ganges–Brahmaputra River mouth: observation from TM data

Remote sensing technique was applied to estimate suspended sediment concentration (SSC) and to understand transportation, distribution and deposition of suspended sediment in the estuary and throughout the coastal sea, off the Ganges–Brahmaputra River mouth. During low river discharge period, zone of turbidity maximum is inferred in the estuary near the shore. SSC map shows that maximum SSC reaches 1050 mg/l in this period. Magnitude of SSC is mainly owing to resuspension of the bottom surface sediments induced by tidal currents flowing over shallow water depths. The influence of depth on resuspension is farther revealed from the distribution and magnitude of SSC along the head of Swatch of No Ground (SNG) submarine canyon. During high river discharge period, huge river outflow pushed the salt wedge and flashes away the suspended sediments in the coastal sea off the river mouth. Zone of turbidity maximum is inferred in the coastal water approximately within 5–10 m depth of water, where the maximum SSC reaches 1700 mg/l. In this period, huge fluvial input of the suspended sediments including the resuspended bottom sediments and the particles remaining in suspension for longer period of time since their initial entry control mainly the magnitude of SSC. In the estuary near the shore, seasonal variation in the magnitude of SSC is not evident. In the coastal sea (>5 m water depth), seasonal influence in the magnitude of SSC could be concluded from the discrepancy between SSC values of two different seasons. Transportation and deposition of suspended sediments also experiences seasonal variations. At present, suspended sediments are being accumulated on the shallow shelf (between 5 and 10 m water depth) in low discharge period and on the mid-shelf (between 10 and 75 m water depth) during high discharge period. An empirical (exponential) relationship was found between gradual settle down of suspended sediments in the coastal sea and its lateral distance from the turbidity maximum.     

相依性条件下滨海软土隧道盾构施工渗漏水风险评价

滨海地区多为海相和湖相沉积的软土,施工难度大,渗漏水病害多发,因此,应对盾构施工过程的渗漏水风险进行评价来保证隧道安全施工。构建渗漏水风险评价三级指标体系,将云模型与Copula函数结合,基于Copula函数对于风险指标中相关风险因素的相依性处理,云理论对于定性与定量概念的处理与转换优势,构建相依性条件下的二维和三维云-Copula模型。以宁波市轨道交通1号线为例,利用云-Copula模型计算二级指标对于各个风险等级的隶属度,确定危险性较大的二级指标为注浆质量、止水条、衬砌混凝土自防水。利用D-S证据理论进行证据融合确定该检测区段渗漏水病害的风险状态为安全,但有向基本安全状态发展的趋势。通过对危险性较大的指标加强监控,使渗漏水得到有效控制,为软土盾构隧道施工过程中渗漏水病害的风险评价与管理提供了一种新的方法。     

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SSRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.     

带延迟的双险种复合Poisson风险模型

由于保险公司风险经营规模的不断扩大,考虑到单险种风险模型的局限性,研究了带延迟的双险种复合Poisson风险模型,给出了该模型破产概率的表达式及有限时间生存概率等重要指标.     

集值渐近准非扩张映象多步迭代的收敛性

在严格凸Banach空间中,用集值映象点值化方法,证明了集值渐近准非扩张映象带误差的三步迭代列收敛于耦合不动点的充要条件.     

模拟海洋环境条件下碳纤维复合材料力学性能试验研究

本文主要进行了CFRP材料浸渍于碱溶液、浸渍于盐溶液及经人工老化后的力学性能试验。试验证实,上述几种长期环境条件作用对CFRP的极限强度影响不大,但能显著降低CFRP的极限延伸率,碱溶液浸渍、盐溶液浸渍显著降低了CFRP的弹性模量,而人工老化则几乎没什么影响。试验结果分析证明,应用于加固结构分析的CFRP的应力-应变关系,可应用线弹性关系。     

Why is the southwest the most productive region of the East Sea/Sea of Japan?

The East Sea/Sea of Japan is a moderately productive sea that supports a wealth of living marine resources. Of the East Sea subregions, the southwest has the highest productivity. Various authors have proposed coastal upwelling, the Tsushima Current, the Changjiang Dilute Water, eddies, or discharge from the Nagdong River as potential sources of additional nutrients. In this paper, we propose, using satellite data from 1998 to 2006, that the biological productivity of the southwestern region is enhanced mainly by wind-driven upwelling along the Korean coast. Firstly, the climatology of seasonal patterns suggests that the enhanced chlorophyll a along the Korean coast is of local origin. Secondly, coastal upwelling is frequent in all seasons except winter. For example, along the coast of the Ulgi region, enhanced chlorophyll a due to coastal upwelling was observed for 25–92% of the time between Jun and Sep in the period 1998–2006. Thirdly, the advection of upwelled water through various pathways to the deeper basin was observed. Fourthly, there appeared to be a strong correlation between the interannual chlorophyll a variations of the coastal upwelling regions and the Ulleung Basin. The chlorophyll a patterns of both regions were closely related to the wind pattern in the upwelling regions, but not to that in the Ulleung Basin. Finally, changes in advection pathways also appeared to affect the productivity of the Ulleung Basin. Since 2004, there has been a shift in the pathways of upwelled water, and consequent increases in chlorophyll a in the Ulleung Basin were observed. This last observation requires further investigation.     

Shaping the Coast with Permits: Making the State Regulatory Permitting Process Transparent with Text Mining

This paper examines the California Coastal Commission's permitting process. Using several text mining techniques, including web scraping, information extraction, and supervised classification, I demonstrate how to retrieve empirical data from unstructured texts, namely public meeting agendas and staff reports. Contrary to the concern that the Commission routinely delays or rejects permitting requests, the data reveal that outright rejection of permit applications is rare. On average, eight of ten applications were approved. Single-family homes and commercial development projects were approved about 80% of the time; the rates were about 70% for seawalls and retaining walls, and 60% for land-use changes. Most applications were processed swiftly, with a median application length of 3 months. The agency's influence comes primarily from negotiating each application. Qualitative study of 50 cases pertaining to single-family home construction reveals that the agency adopts a “managed development” approach, that is, allowing development but scrupulously managing various aspects of development. These case studies illustrate how the agency interprets the broad, abstract state laws and translates the mandates into enforceable actions as permitting conditions. In areas where the state mandates conflict, particularly over development in receding shorelines, the agency has the largest leverage in creating and implementing its preferred policies. The text mining techniques demonstrated in this paper can be applied to study any governmental agency. These techniques help to extract information from a massive volume of papers and organize them into a database for analyses. The empirical data extracted from texts can significantly increase bureaucratic transparency.     

A stochastic model of traffic flow: Theoretical foundations

In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.     

经验过程的一般加权大数定律

研究了经验过程中加权系数具有某些较弱性质的加权和收敛问题.利用经验过程中已有的几个概率不等式与一般加权和的对称化不等式,得到了经验过程中的独立同分布随机元序列的这类加权和的强、弱大数定律成立的充分条件(E‖f(X0)‖1/αG<∞).同时,对经验过程中的Cesaro大数定律和欧拉弱大数定律进行了推广.