城镇黑臭河道治理:多水塘活水链人工湿地的应用

随着现代工业的发展,城市黑臭河道问题日趋严重,严重影响了城市形象、生态环境和居民身心健康。利用多水塘活水链人工湿地技术净化江苏常州永胜河河水水质。监测结果显示:该技术使水体总氮浓度削减了92.0%、总磷浓度削减了82.9%,处理后的水体总氮、总磷含量达到地表IV类水标准。项目年处理水量约30万t,每年以较低的成本(平均水处理费用约为0.03元/t),削减总氮、总磷和氨氮量分别为1496.6 kg、176.0 kg和1408.6 kg。通过引植多种浮叶植物、挺水植物、沉水植物,该湿地修复工程为当地营造了优美的湿地景观。多水塘活水链人工湿地是集经济效益、生态效益、环境效益于一体的新型水体生态治理技术,为我国城镇黑臭河道治理提供了一条新的途径。     

传统村落景观基因信息链与自动识别模型构建——以陕西省为例

传统村落作为活的文化遗产,承载了大量历史记忆,是地域文化景观基因识别与模型构建研究的重要切入点。以陕西省71个国家级传统村落为例,基于景观基因理论建立陕西省传统村落景观基因识别体系,识别出陕西省传统村落景观基因特征;运用类型学原理和N级编码理论对景观基因进行编码,构建陕西省传统村落景观基因信息链并生成基因谱系;借鉴生物学中“胞-链-形”DNA碱基序列模型,提取出环境基因、建筑基因、农耕文化基因和宗族文化基因四个公共基因作为景观基因元（胞）,以村内道路系统作为基因链,构建传统村落景观基因DNA模型与自动识别模型,以此对传统村落的区位、类型、特征和文化基因进行自动识别。为传统村落景观基因信息有效传承与存储,以及乡村建设动态发展提供理论借鉴。     

潜流带生物地球化学特征研究进展

潜流带(即地表水-地下水交互作用带)是位于地表水和地下水之间发生物质和能量交换的,具有物理、化学、生物梯度的饱和含水带。潜流带内发生着强烈的生物地球化学过程,在改善水环境质量和维持水生态系统稳定中发挥着重要作用。潜流带生物地球化学特征主要受河床沉积物结构特征、水文特征、环境特征、生物群落格局、物质输入等因素的影响,潜流带内C、N、P元素以及有毒金属元素(Fe、Mn、As、Hg等)会发生氧化-还原、吸附与解吸、沉淀与溶解、生物催化等生物地球化学过程。原位监测、数值模拟、原位培养和地球物理探测等技术是潜流带中生物地球化学过程研究中常用的研究手段。未来针对潜流带的研究可以从监测和数值模拟技术、河床沉积物变化特征以及微生物生态系统的结构和功能等方面进一步开展。     

一种基于GA-BP-MC神经网络的高铁桥墩沉降预测模型

提出一种基于马尔科夫链修正的遗传BP神经网络预测模型（GA-BP-MC）,利用遗传算法的全局寻优能力初始化BP神经网络权值和阈值,初步建立GA-BP神经网络预测模型,结合马尔科夫链的无后效性修正模型预测值,形成高精度GA-BP-MC神经网络变形预测模型。结合高铁桥墩沉降数据,分别与BP神经网络、GA-BP神经网络预测模型进行对比,结果表明,该预测模型精度最高。     

苹果手机零部件全球价值链的价值分配与中国角色演变

产品内分工已成为支配当代企业分工与产品生产的重要模式。研究智能手机生产网络价值分配格局,对辨析中国大陆企业在移动通讯产业价值链中所处地位与演进具有重要意义。论文利用2012—2017年苹果公司供应商数据,以其零部件价值含量与技术要求为测度标准,分析了苹果手机零部件全球价值链的分配特征以及中国大陆企业在其中的地位变化。发现：高价值环节的供应商主要以美国、日本、韩国为主,而中低价值环节则以中国台湾、日本和中国大陆供应商为主,且中国大陆与中国台湾在中间环节占据重要地位。处于价值链高端的供应商分支数量在中国大陆和美国缩减明显,日本、韩国较为稳定,中国台湾增加明显。在价值链的中低端环节,供应商分支趋向中国大陆集中。印度、巴西以及越南等地成为新的中低端价值环节转移承接地。近年来,中国大陆在苹果手机价值链的高端环节得到了提升,长三角、珠三角、京津冀、成渝、中原地区等地逐渐形成了苹果供应商分支的集聚区域,呈现出零部件生产的区域分工特征。长三角(上海-苏州为核心)与珠三角(深圳-东莞为核心),承接了价值链多个生产环节,是中国大陆嵌入苹果手机生产网络的主要阵地。成渝地区既承接了芯片类企业布局,也承接了价值链低端的组装代工和包装服务环节,呈现出在价值链两端双向承接的特征。环渤海、福建及湖南等地主要承接苹果手机价值链的中间环节,而中原地区主要以苹果手机组装与包装服务为主,承接价值链的低端环节。最后,论文从创新驱动、政府作用与外资等方面探讨了影响中国大陆地位提升的因素。     

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.     

Reliability of mooring lines and piles for a permanently manned vessel in a tropical cyclone environment

The factors of safety for stationkeeping systems in current standards (ISO, API) are not derived or validated using reliability analyses. As the oil and gas exploration and production is breaking new boundaries, deploying new floating systems and moving into regions with harsher environments, it is of paramount importance to understand what level of reliability these new marine structures are achieving. This paper presents a reliability analysis of the mooring system of a Floating LNG (FLNG) vessel permanently moored and permanently manned offshore North West Australia in a tropical cyclone environment. The reliability analysis addresses both the mooring chain and the pile foundation. The analysis accounts for the long term characteristics of the environment, including the short term variability, in response to a given sea state and the variability and uncertainty in strength of the mooring chain and the pile. The stationkeeping system was analysed using detailed time domain simulations, capturing system non-linearities and low frequency oscillations as well as wave frequency responses and, thereby, reducing modelling uncertainties to a minimum.It is found that for the conditions modelled, neither the chain nor the pile meet a target reliability of 10⁻⁴/annum using the factors of safety commonly used in design following current ISO and API standards. New factors of safety are proposed to achieve this target reliability. For the pile design, one complicating factor is that current design standards do not explicitly define the exceedance probability that should be associated with the characteristic value of the undrained shear strength to be used in the design. It is demonstrated that the required factor of safety is crucially dependent on the definition of this characteristic value and on the level and the type of uncertainty in the soil strength profile. A recommendation is made regarding the definition of this characteristic value and the associated factor of safety. Furthermore, it is found that designing the mooring system to an environmental condition with a return period of 10,000 yr (as an Abnormal Limit State event), and setting the factor of safety to unity, meets the target reliability of 10⁻⁴/annum for the pile, if the characteristic undrained shear strength is a lower bound, defined in this paper by the 10th percentile value. For the chain however, this target reliability is not achieved.     

Method‐dependent influence of environmental variables on reef fish assemblages when comparing trap and video surveys

Despite substantial survey effort and a large body of literature on abiotic and biotic factors in temperate reef ecosystems, knowledge of the complex and interactive effects of environmental variables on those communities is limited. Various survey methods have been developed to study environmental predictors of biodiversity, but there remains a gap in our understanding of how survey results are influenced by environmental factors. Here, we surveyed the fish assemblage associated with southeastern U.S. temperate marine reefs with simultaneous, paired trap, and camera gears throughout a ~50,000 km² area during 2011–2013 and assessed the influence of environmental variables on the trap‐ and video‐surveyed assemblages. Predictor variables in the multivariate general linear models included depth, temperature, month, year, location, substrate relief, percent sessile biota, biota type, and turbidity. Depth and latitude had the greatest influence on the fish assemblage for both gears. The influence of habitat variables differed between methods and percent biota explained more variation in the fish assemblage when assessed by traps, while substrate relief and biota type explained more variation in the fish assemblage when assessed by video. In general, habitat complexity was positively related to the abundance of fishes in the video survey, but there was a negative relationship in the trap survey. Differences between gears were species‐specific and the influences of environmental variables were similar for some species such as Haemulon plumierii and Hyporthodus niveatus. The methods presented here can be used to assess method‐dependent differences in fish assemblages, which is a necessary precursor to assess the effect of environmental variables on the accuracy of surveys.     

一种海洋混合层深度的智能识别方法研究

文章提出了一种识别混合层深度的人工智能方法。该方法在温度(密度)与压强(或深度)间建立线性模型, 并且将其系数和方差做成一组表征廓线特征的统计量。初始时为模型设定一个主观的先验分布, 在一个自海表向下移动的窗口内通过贝叶斯链式法则和最小描述长度原理学习新数据, 得到系数均值的最大后验概率估计。用F-检验识别系数发生突变的位置, 以此确定混合层的存在性及其深度。通过2017年2月太平洋海域的地转海洋学实时观测阵(Array for Real-time Geostrophic Oceanography, ARGO)数据进行测试, 并且以质量因子(Quality Index, QI)值作为判断识别混合层深度结果准确性的依据, 发现该方法相比于梯度法、阈值法、混合法、相对变化法、最大角度法和最优线性插值法在识别结果上具备更大的QI值。表明该方法能够准确识别混合层深度。